diff --git "a/env-llmeval/lib/python3.10/site-packages/scipy/special/_ufuncs.pyx" "b/env-llmeval/lib/python3.10/site-packages/scipy/special/_ufuncs.pyx"
new file mode 100644--- /dev/null
+++ "b/env-llmeval/lib/python3.10/site-packages/scipy/special/_ufuncs.pyx"
@@ -0,0 +1,21968 @@
+# This file is automatically generated by _generate_pyx.py.
+# Do not edit manually!
+
+from libc.math cimport NAN
+
+include "_ufuncs_extra_code_common.pxi"
+include "_ufuncs_extra_code.pxi"
+__all__ = ['agm', 'airy', 'airye', 'bdtr', 'bdtrc', 'bdtri', 'bdtrik', 'bdtrin', 'bei', 'beip', 'ber', 'berp', 'besselpoly', 'beta', 'betainc', 'betaincc', 'betainccinv', 'betaincinv', 'betaln', 'binom', 'boxcox', 'boxcox1p', 'btdtr', 'btdtri', 'btdtria', 'btdtrib', 'cbrt', 'chdtr', 'chdtrc', 'chdtri', 'chdtriv', 'chndtr', 'chndtridf', 'chndtrinc', 'chndtrix', 'cosdg', 'cosm1', 'cotdg', 'dawsn', 'ellipe', 'ellipeinc', 'ellipj', 'ellipk', 'ellipkinc', 'ellipkm1', 'elliprc', 'elliprd', 'elliprf', 'elliprg', 'elliprj', 'entr', 'erf', 'erfc', 'erfcinv', 'erfcx', 'erfi', 'erfinv', 'eval_chebyc', 'eval_chebys', 'eval_chebyt', 'eval_chebyu', 'eval_gegenbauer', 'eval_genlaguerre', 'eval_hermite', 'eval_hermitenorm', 'eval_jacobi', 'eval_laguerre', 'eval_legendre', 'eval_sh_chebyt', 'eval_sh_chebyu', 'eval_sh_jacobi', 'eval_sh_legendre', 'exp1', 'exp10', 'exp2', 'expi', 'expit', 'expm1', 'expn', 'exprel', 'fdtr', 'fdtrc', 'fdtri', 'fdtridfd', 'fresnel', 'gamma', 'gammainc', 'gammaincc', 'gammainccinv', 'gammaincinv', 'gammaln', 'gammasgn', 'gdtr', 'gdtrc', 'gdtria', 'gdtrib', 'gdtrix', 'hankel1', 'hankel1e', 'hankel2', 'hankel2e', 'huber', 'hyp0f1', 'hyp1f1', 'hyp2f1', 'hyperu', 'i0', 'i0e', 'i1', 'i1e', 'inv_boxcox', 'inv_boxcox1p', 'it2i0k0', 'it2j0y0', 'it2struve0', 'itairy', 'iti0k0', 'itj0y0', 'itmodstruve0', 'itstruve0', 'iv', 'ive', 'j0', 'j1', 'jv', 'jve', 'k0', 'k0e', 'k1', 'k1e', 'kei', 'keip', 'kelvin', 'ker', 'kerp', 'kl_div', 'kn', 'kolmogi', 'kolmogorov', 'kv', 'kve', 'log1p', 'log_expit', 'log_ndtr', 'loggamma', 'logit', 'lpmv', 'mathieu_a', 'mathieu_b', 'mathieu_cem', 'mathieu_modcem1', 'mathieu_modcem2', 'mathieu_modsem1', 'mathieu_modsem2', 'mathieu_sem', 'modfresnelm', 'modfresnelp', 'modstruve', 'nbdtr', 'nbdtrc', 'nbdtri', 'nbdtrik', 'nbdtrin', 'ncfdtr', 'ncfdtri', 'ncfdtridfd', 'ncfdtridfn', 'ncfdtrinc', 'nctdtr', 'nctdtridf', 'nctdtrinc', 'nctdtrit', 'ndtr', 'ndtri', 'ndtri_exp', 'nrdtrimn', 'nrdtrisd', 'obl_ang1', 'obl_ang1_cv', 'obl_cv', 'obl_rad1', 'obl_rad1_cv', 'obl_rad2', 'obl_rad2_cv', 'owens_t', 'pbdv', 'pbvv', 'pbwa', 'pdtr', 'pdtrc', 'pdtri', 'pdtrik', 'poch', 'powm1', 'pro_ang1', 'pro_ang1_cv', 'pro_cv', 'pro_rad1', 'pro_rad1_cv', 'pro_rad2', 'pro_rad2_cv', 'pseudo_huber', 'psi', 'radian', 'rel_entr', 'rgamma', 'round', 'shichi', 'sici', 'sindg', 'smirnov', 'smirnovi', 'spence', 'sph_harm', 'stdtr', 'stdtridf', 'stdtrit', 'struve', 'tandg', 'tklmbda', 'voigt_profile', 'wofz', 'wright_bessel', 'wrightomega', 'xlog1py', 'xlogy', 'y0', 'y1', 'yn', 'yv', 'yve', 'zetac', 'geterr', 'seterr', 'errstate', 'jn']
+cdef void loop_D_DDDD__As_DDDD_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double complex, double complex, double complex, double complex) noexcept nogil>func)(<double complex>(<double complex*>ip0)[0], <double complex>(<double complex*>ip1)[0], <double complex>(<double complex*>ip2)[0], <double complex>(<double complex*>ip3)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_DDDD__As_FFFF_F(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double complex, double complex, double complex, double complex) noexcept nogil>func)(<double complex>(<float complex*>ip0)[0], <double complex>(<float complex*>ip1)[0], <double complex>(<float complex*>ip2)[0], <double complex>(<float complex*>ip3)[0])
+        (<float complex *>op0)[0] = <float complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_DDD__As_DDD_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double complex, double complex, double complex) noexcept nogil>func)(<double complex>(<double complex*>ip0)[0], <double complex>(<double complex*>ip1)[0], <double complex>(<double complex*>ip2)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_DDD__As_FFF_F(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double complex, double complex, double complex) noexcept nogil>func)(<double complex>(<float complex*>ip0)[0], <double complex>(<float complex*>ip1)[0], <double complex>(<float complex*>ip2)[0])
+        (<float complex *>op0)[0] = <float complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_DD__As_DD_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double complex, double complex) noexcept nogil>func)(<double complex>(<double complex*>ip0)[0], <double complex>(<double complex*>ip1)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_DD__As_FF_F(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double complex, double complex) noexcept nogil>func)(<double complex>(<float complex*>ip0)[0], <double complex>(<float complex*>ip1)[0])
+        (<float complex *>op0)[0] = <float complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_D__As_D_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double complex) noexcept nogil>func)(<double complex>(<double complex*>ip0)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        op0 += steps[1]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_D__As_F_F(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double complex) noexcept nogil>func)(<double complex>(<float complex*>ip0)[0])
+        (<float complex *>op0)[0] = <float complex>ov0
+        ip0 += steps[0]
+        op0 += steps[1]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_Dld__As_Dld_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double complex, long, double) noexcept nogil>func)(<double complex>(<double complex*>ip0)[0], <long>(<long*>ip1)[0], <double>(<double*>ip2)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_dD__As_dD_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double, double complex) noexcept nogil>func)(<double>(<double*>ip0)[0], <double complex>(<double complex*>ip1)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_dD__As_fF_F(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double, double complex) noexcept nogil>func)(<double>(<float*>ip0)[0], <double complex>(<float complex*>ip1)[0])
+        (<float complex *>op0)[0] = <float complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_ddD__As_ddD_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double, double, double complex) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <double complex>(<double complex*>ip2)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_ddD__As_ffF_F(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double, double, double complex) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], <double complex>(<float complex*>ip2)[0])
+        (<float complex *>op0)[0] = <float complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_dddD__As_dddD_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double, double, double, double complex) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0], <double complex>(<double complex*>ip3)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_dddD__As_fffF_F(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double, double, double, double complex) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], <double>(<float*>ip2)[0], <double complex>(<float complex*>ip3)[0])
+        (<float complex *>op0)[0] = <float complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_dddd__As_dddd_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double, double, double, double) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0], <double>(<double*>ip3)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_dddd__As_ffff_F(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(double, double, double, double) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], <double>(<float*>ip2)[0], <double>(<float*>ip3)[0])
+        (<float complex *>op0)[0] = <float complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_iidd__As_lldd_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double complex ov0
+    for i in range(n):
+        if <int>(<long*>ip0)[0] == (<long*>ip0)[0] and <int>(<long*>ip1)[0] == (<long*>ip1)[0]:
+            ov0 = (<double complex(*)(int, int, double, double) noexcept nogil>func)(<int>(<long*>ip0)[0], <int>(<long*>ip1)[0], <double>(<double*>ip2)[0], <double>(<double*>ip3)[0])
+        else:
+            sf_error.error(func_name, sf_error.DOMAIN, "invalid input argument")
+            ov0 = <double complex>NAN
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_D_lD__As_lD_D(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef double complex ov0
+    for i in range(n):
+        ov0 = (<double complex(*)(long, double complex) noexcept nogil>func)(<long>(<long*>ip0)[0], <double complex>(<double complex*>ip1)[0])
+        (<double complex *>op0)[0] = <double complex>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_d__As_d_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double) noexcept nogil>func)(<double>(<double*>ip0)[0])
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        op0 += steps[1]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_d__As_f_f(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double) noexcept nogil>func)(<double>(<float*>ip0)[0])
+        (<float *>op0)[0] = <float>ov0
+        ip0 += steps[0]
+        op0 += steps[1]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_dd__As_dd_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double, double) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0])
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_dd__As_ff_f(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double, double) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0])
+        (<float *>op0)[0] = <float>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_ddd__As_ddd_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double, double, double) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0])
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_ddd__As_fff_f(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double, double, double) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], <double>(<float*>ip2)[0])
+        (<float *>op0)[0] = <float>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_dddd__As_dddd_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double, double, double, double) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0], <double>(<double*>ip3)[0])
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_dddd__As_ffff_f(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double, double, double, double) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], <double>(<float*>ip2)[0], <double>(<float*>ip3)[0])
+        (<float *>op0)[0] = <float>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_dddd_d_As_dddd_dd(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef char *op1 = args[5]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        ov0 = (<double(*)(double, double, double, double, double *) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0], <double>(<double*>ip3)[0], &ov1)
+        (<double *>op0)[0] = <double>ov0
+        (<double *>op1)[0] = <double>ov1
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+        op1 += steps[5]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_dddd_d_As_ffff_ff(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef char *op1 = args[5]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        ov0 = (<double(*)(double, double, double, double, double *) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], <double>(<float*>ip2)[0], <double>(<float*>ip3)[0], &ov1)
+        (<float *>op0)[0] = <float>ov0
+        (<float *>op1)[0] = <float>ov1
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+        op1 += steps[5]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_ddddddd__As_ddddddd_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *ip4 = args[4]
+    cdef char *ip5 = args[5]
+    cdef char *ip6 = args[6]
+    cdef char *op0 = args[7]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double, double, double, double, double, double, double) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0], <double>(<double*>ip3)[0], <double>(<double*>ip4)[0], <double>(<double*>ip5)[0], <double>(<double*>ip6)[0])
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        ip4 += steps[4]
+        ip5 += steps[5]
+        ip6 += steps[6]
+        op0 += steps[7]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_ddddddd__As_fffffff_f(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *ip4 = args[4]
+    cdef char *ip5 = args[5]
+    cdef char *ip6 = args[6]
+    cdef char *op0 = args[7]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(double, double, double, double, double, double, double) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], <double>(<float*>ip2)[0], <double>(<float*>ip3)[0], <double>(<float*>ip4)[0], <double>(<float*>ip5)[0], <double>(<float*>ip6)[0])
+        (<float *>op0)[0] = <float>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        ip4 += steps[4]
+        ip5 += steps[5]
+        ip6 += steps[6]
+        op0 += steps[7]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_ddi_d_As_ddl_dd(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef char *op1 = args[4]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        if <int>(<long*>ip2)[0] == (<long*>ip2)[0]:
+            ov0 = (<double(*)(double, double, int, double *) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <int>(<long*>ip2)[0], &ov1)
+        else:
+            sf_error.error(func_name, sf_error.DOMAIN, "invalid input argument")
+            ov0 = <double>NAN
+            ov1 = <double>NAN
+        (<double *>op0)[0] = <double>ov0
+        (<double *>op1)[0] = <double>ov1
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+        op1 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_ddiiddd__As_ddllddd_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *ip4 = args[4]
+    cdef char *ip5 = args[5]
+    cdef char *ip6 = args[6]
+    cdef char *op0 = args[7]
+    cdef double ov0
+    for i in range(n):
+        if <int>(<long*>ip2)[0] == (<long*>ip2)[0] and <int>(<long*>ip3)[0] == (<long*>ip3)[0]:
+            ov0 = (<double(*)(double, double, int, int, double, double, double) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <int>(<long*>ip2)[0], <int>(<long*>ip3)[0], <double>(<double*>ip4)[0], <double>(<double*>ip5)[0], <double>(<double*>ip6)[0])
+        else:
+            sf_error.error(func_name, sf_error.DOMAIN, "invalid input argument")
+            ov0 = <double>NAN
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        ip4 += steps[4]
+        ip5 += steps[5]
+        ip6 += steps[6]
+        op0 += steps[7]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_did__As_dld_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double ov0
+    for i in range(n):
+        if <int>(<long*>ip1)[0] == (<long*>ip1)[0]:
+            ov0 = (<double(*)(double, int, double) noexcept nogil>func)(<double>(<double*>ip0)[0], <int>(<long*>ip1)[0], <double>(<double*>ip2)[0])
+        else:
+            sf_error.error(func_name, sf_error.DOMAIN, "invalid input argument")
+            ov0 = <double>NAN
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_id__As_ld_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef double ov0
+    for i in range(n):
+        if <int>(<long*>ip0)[0] == (<long*>ip0)[0]:
+            ov0 = (<double(*)(int, double) noexcept nogil>func)(<int>(<long*>ip0)[0], <double>(<double*>ip1)[0])
+        else:
+            sf_error.error(func_name, sf_error.DOMAIN, "invalid input argument")
+            ov0 = <double>NAN
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_iid__As_lld_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double ov0
+    for i in range(n):
+        if <int>(<long*>ip0)[0] == (<long*>ip0)[0] and <int>(<long*>ip1)[0] == (<long*>ip1)[0]:
+            ov0 = (<double(*)(int, int, double) noexcept nogil>func)(<int>(<long*>ip0)[0], <int>(<long*>ip1)[0], <double>(<double*>ip2)[0])
+        else:
+            sf_error.error(func_name, sf_error.DOMAIN, "invalid input argument")
+            ov0 = <double>NAN
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_ld__As_ld_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(long, double) noexcept nogil>func)(<long>(<long*>ip0)[0], <double>(<double*>ip1)[0])
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_ldd__As_ldd_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(long, double, double) noexcept nogil>func)(<long>(<long*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0])
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_d_lddd__As_lddd_d(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *op0 = args[4]
+    cdef double ov0
+    for i in range(n):
+        ov0 = (<double(*)(long, double, double, double) noexcept nogil>func)(<long>(<long*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0], <double>(<double*>ip3)[0])
+        (<double *>op0)[0] = <double>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        op0 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_f_f__As_f_f(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef float ov0
+    for i in range(n):
+        ov0 = (<float(*)(float) noexcept nogil>func)(<float>(<float*>ip0)[0])
+        (<float *>op0)[0] = <float>ov0
+        ip0 += steps[0]
+        op0 += steps[1]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_f_ff__As_ff_f(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef float ov0
+    for i in range(n):
+        ov0 = (<float(*)(float, float) noexcept nogil>func)(<float>(<float*>ip0)[0], <float>(<float*>ip1)[0])
+        (<float *>op0)[0] = <float>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_f_fff__As_fff_f(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef float ov0
+    for i in range(n):
+        ov0 = (<float(*)(float, float, float) noexcept nogil>func)(<float>(<float*>ip0)[0], <float>(<float*>ip1)[0], <float>(<float*>ip2)[0])
+        (<float *>op0)[0] = <float>ov0
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_g_g__As_g_g(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef long double ov0
+    for i in range(n):
+        ov0 = (<long double(*)(long double) noexcept nogil>func)(<long double>(<long double*>ip0)[0])
+        (<long double *>op0)[0] = <long double>ov0
+        ip0 += steps[0]
+        op0 += steps[1]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_D_DDDD_As_D_DDDD(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef char *op2 = args[3]
+    cdef char *op3 = args[4]
+    cdef double complex ov0
+    cdef double complex ov1
+    cdef double complex ov2
+    cdef double complex ov3
+    for i in range(n):
+        (<int(*)(double complex, double complex *, double complex *, double complex *, double complex *) noexcept nogil>func)(<double complex>(<double complex*>ip0)[0], &ov0, &ov1, &ov2, &ov3)
+        (<double complex *>op0)[0] = <double complex>ov0
+        (<double complex *>op1)[0] = <double complex>ov1
+        (<double complex *>op2)[0] = <double complex>ov2
+        (<double complex *>op3)[0] = <double complex>ov3
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+        op2 += steps[3]
+        op3 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_D_DDDD_As_F_FFFF(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef char *op2 = args[3]
+    cdef char *op3 = args[4]
+    cdef double complex ov0
+    cdef double complex ov1
+    cdef double complex ov2
+    cdef double complex ov3
+    for i in range(n):
+        (<int(*)(double complex, double complex *, double complex *, double complex *, double complex *) noexcept nogil>func)(<double complex>(<float complex*>ip0)[0], &ov0, &ov1, &ov2, &ov3)
+        (<float complex *>op0)[0] = <float complex>ov0
+        (<float complex *>op1)[0] = <float complex>ov1
+        (<float complex *>op2)[0] = <float complex>ov2
+        (<float complex *>op3)[0] = <float complex>ov3
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+        op2 += steps[3]
+        op3 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_D_DD_As_D_DD(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef double complex ov0
+    cdef double complex ov1
+    for i in range(n):
+        (<int(*)(double complex, double complex *, double complex *) noexcept nogil>func)(<double complex>(<double complex*>ip0)[0], &ov0, &ov1)
+        (<double complex *>op0)[0] = <double complex>ov0
+        (<double complex *>op1)[0] = <double complex>ov1
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_D_DD_As_F_FF(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef double complex ov0
+    cdef double complex ov1
+    for i in range(n):
+        (<int(*)(double complex, double complex *, double complex *) noexcept nogil>func)(<double complex>(<float complex*>ip0)[0], &ov0, &ov1)
+        (<float complex *>op0)[0] = <float complex>ov0
+        (<float complex *>op1)[0] = <float complex>ov1
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_d_DDDD_As_d_DDDD(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef char *op2 = args[3]
+    cdef char *op3 = args[4]
+    cdef double complex ov0
+    cdef double complex ov1
+    cdef double complex ov2
+    cdef double complex ov3
+    for i in range(n):
+        (<int(*)(double, double complex *, double complex *, double complex *, double complex *) noexcept nogil>func)(<double>(<double*>ip0)[0], &ov0, &ov1, &ov2, &ov3)
+        (<double complex *>op0)[0] = <double complex>ov0
+        (<double complex *>op1)[0] = <double complex>ov1
+        (<double complex *>op2)[0] = <double complex>ov2
+        (<double complex *>op3)[0] = <double complex>ov3
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+        op2 += steps[3]
+        op3 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_d_DDDD_As_f_FFFF(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef char *op2 = args[3]
+    cdef char *op3 = args[4]
+    cdef double complex ov0
+    cdef double complex ov1
+    cdef double complex ov2
+    cdef double complex ov3
+    for i in range(n):
+        (<int(*)(double, double complex *, double complex *, double complex *, double complex *) noexcept nogil>func)(<double>(<float*>ip0)[0], &ov0, &ov1, &ov2, &ov3)
+        (<float complex *>op0)[0] = <float complex>ov0
+        (<float complex *>op1)[0] = <float complex>ov1
+        (<float complex *>op2)[0] = <float complex>ov2
+        (<float complex *>op3)[0] = <float complex>ov3
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+        op2 += steps[3]
+        op3 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_d_DD_As_d_DD(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef double complex ov0
+    cdef double complex ov1
+    for i in range(n):
+        (<int(*)(double, double complex *, double complex *) noexcept nogil>func)(<double>(<double*>ip0)[0], &ov0, &ov1)
+        (<double complex *>op0)[0] = <double complex>ov0
+        (<double complex *>op1)[0] = <double complex>ov1
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_d_DD_As_f_FF(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef double complex ov0
+    cdef double complex ov1
+    for i in range(n):
+        (<int(*)(double, double complex *, double complex *) noexcept nogil>func)(<double>(<float*>ip0)[0], &ov0, &ov1)
+        (<float complex *>op0)[0] = <float complex>ov0
+        (<float complex *>op1)[0] = <float complex>ov1
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_d_dd_As_d_dd(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        (<int(*)(double, double *, double *) noexcept nogil>func)(<double>(<double*>ip0)[0], &ov0, &ov1)
+        (<double *>op0)[0] = <double>ov0
+        (<double *>op1)[0] = <double>ov1
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_d_dd_As_f_ff(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        (<int(*)(double, double *, double *) noexcept nogil>func)(<double>(<float*>ip0)[0], &ov0, &ov1)
+        (<float *>op0)[0] = <float>ov0
+        (<float *>op1)[0] = <float>ov1
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_d_dddd_As_d_dddd(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef char *op2 = args[3]
+    cdef char *op3 = args[4]
+    cdef double ov0
+    cdef double ov1
+    cdef double ov2
+    cdef double ov3
+    for i in range(n):
+        (<int(*)(double, double *, double *, double *, double *) noexcept nogil>func)(<double>(<double*>ip0)[0], &ov0, &ov1, &ov2, &ov3)
+        (<double *>op0)[0] = <double>ov0
+        (<double *>op1)[0] = <double>ov1
+        (<double *>op2)[0] = <double>ov2
+        (<double *>op3)[0] = <double>ov3
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+        op2 += steps[3]
+        op3 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_d_dddd_As_f_ffff(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef char *op1 = args[2]
+    cdef char *op2 = args[3]
+    cdef char *op3 = args[4]
+    cdef double ov0
+    cdef double ov1
+    cdef double ov2
+    cdef double ov3
+    for i in range(n):
+        (<int(*)(double, double *, double *, double *, double *) noexcept nogil>func)(<double>(<float*>ip0)[0], &ov0, &ov1, &ov2, &ov3)
+        (<float *>op0)[0] = <float>ov0
+        (<float *>op1)[0] = <float>ov1
+        (<float *>op2)[0] = <float>ov2
+        (<float *>op3)[0] = <float>ov3
+        ip0 += steps[0]
+        op0 += steps[1]
+        op1 += steps[2]
+        op2 += steps[3]
+        op3 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_dd_dd_As_dd_dd(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef char *op1 = args[3]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        (<int(*)(double, double, double *, double *) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], &ov0, &ov1)
+        (<double *>op0)[0] = <double>ov0
+        (<double *>op1)[0] = <double>ov1
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+        op1 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_dd_dd_As_ff_ff(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef char *op1 = args[3]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        (<int(*)(double, double, double *, double *) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], &ov0, &ov1)
+        (<float *>op0)[0] = <float>ov0
+        (<float *>op1)[0] = <float>ov1
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+        op1 += steps[3]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_dd_dddd_As_dd_dddd(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef char *op1 = args[3]
+    cdef char *op2 = args[4]
+    cdef char *op3 = args[5]
+    cdef double ov0
+    cdef double ov1
+    cdef double ov2
+    cdef double ov3
+    for i in range(n):
+        (<int(*)(double, double, double *, double *, double *, double *) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], &ov0, &ov1, &ov2, &ov3)
+        (<double *>op0)[0] = <double>ov0
+        (<double *>op1)[0] = <double>ov1
+        (<double *>op2)[0] = <double>ov2
+        (<double *>op3)[0] = <double>ov3
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+        op1 += steps[3]
+        op2 += steps[4]
+        op3 += steps[5]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_dd_dddd_As_ff_ffff(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *op0 = args[2]
+    cdef char *op1 = args[3]
+    cdef char *op2 = args[4]
+    cdef char *op3 = args[5]
+    cdef double ov0
+    cdef double ov1
+    cdef double ov2
+    cdef double ov3
+    for i in range(n):
+        (<int(*)(double, double, double *, double *, double *, double *) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], &ov0, &ov1, &ov2, &ov3)
+        (<float *>op0)[0] = <float>ov0
+        (<float *>op1)[0] = <float>ov1
+        (<float *>op2)[0] = <float>ov2
+        (<float *>op3)[0] = <float>ov3
+        ip0 += steps[0]
+        ip1 += steps[1]
+        op0 += steps[2]
+        op1 += steps[3]
+        op2 += steps[4]
+        op3 += steps[5]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_ddd_dd_As_ddd_dd(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef char *op1 = args[4]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        (<int(*)(double, double, double, double *, double *) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0], &ov0, &ov1)
+        (<double *>op0)[0] = <double>ov0
+        (<double *>op1)[0] = <double>ov1
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+        op1 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_ddd_dd_As_fff_ff(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *op0 = args[3]
+    cdef char *op1 = args[4]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        (<int(*)(double, double, double, double *, double *) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], <double>(<float*>ip2)[0], &ov0, &ov1)
+        (<float *>op0)[0] = <float>ov0
+        (<float *>op1)[0] = <float>ov1
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        op0 += steps[3]
+        op1 += steps[4]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_ddddd_dd_As_ddddd_dd(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *ip4 = args[4]
+    cdef char *op0 = args[5]
+    cdef char *op1 = args[6]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        (<int(*)(double, double, double, double, double, double *, double *) noexcept nogil>func)(<double>(<double*>ip0)[0], <double>(<double*>ip1)[0], <double>(<double*>ip2)[0], <double>(<double*>ip3)[0], <double>(<double*>ip4)[0], &ov0, &ov1)
+        (<double *>op0)[0] = <double>ov0
+        (<double *>op1)[0] = <double>ov1
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        ip4 += steps[4]
+        op0 += steps[5]
+        op1 += steps[6]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_ddddd_dd_As_fffff_ff(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *ip1 = args[1]
+    cdef char *ip2 = args[2]
+    cdef char *ip3 = args[3]
+    cdef char *ip4 = args[4]
+    cdef char *op0 = args[5]
+    cdef char *op1 = args[6]
+    cdef double ov0
+    cdef double ov1
+    for i in range(n):
+        (<int(*)(double, double, double, double, double, double *, double *) noexcept nogil>func)(<double>(<float*>ip0)[0], <double>(<float*>ip1)[0], <double>(<float*>ip2)[0], <double>(<float*>ip3)[0], <double>(<float*>ip4)[0], &ov0, &ov1)
+        (<float *>op0)[0] = <float>ov0
+        (<float *>op1)[0] = <float>ov1
+        ip0 += steps[0]
+        ip1 += steps[1]
+        ip2 += steps[2]
+        ip3 += steps[3]
+        ip4 += steps[4]
+        op0 += steps[5]
+        op1 += steps[6]
+    sf_error.check_fpe(func_name)
+
+cdef void loop_i_i__As_l_l(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) noexcept nogil:
+    cdef np.npy_intp i, n = dims[0]
+    cdef void *func = (<void**>data)[0]
+    cdef char *func_name = <char*>(<void**>data)[1]
+    cdef char *ip0 = args[0]
+    cdef char *op0 = args[1]
+    cdef int ov0
+    for i in range(n):
+        if <int>(<long*>ip0)[0] == (<long*>ip0)[0]:
+            ov0 = (<int(*)(int) noexcept nogil>func)(<int>(<long*>ip0)[0])
+        else:
+            sf_error.error(func_name, sf_error.DOMAIN, "invalid input argument")
+            ov0 = <int>0xbad0bad0
+        (<long *>op0)[0] = <long>ov0
+        ip0 += steps[0]
+        op0 += steps[1]
+    sf_error.check_fpe(func_name)
+
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cosine_cdf "cosine_cdf"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cosine_invcdf "cosine_invcdf"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cospi "cospi"(double) noexcept nogil
+from ._ellip_harm cimport ellip_harmonic as _func_ellip_harmonic
+ctypedef double _proto_ellip_harmonic_t(double, double, int, int, double, double, double) noexcept nogil
+cdef _proto_ellip_harmonic_t *_proto_ellip_harmonic_t_var = &_func_ellip_harmonic
+from ._legacy cimport ellip_harmonic_unsafe as _func_ellip_harmonic_unsafe
+ctypedef double _proto_ellip_harmonic_unsafe_t(double, double, double, double, double, double, double) noexcept nogil
+cdef _proto_ellip_harmonic_unsafe_t *_proto_ellip_harmonic_unsafe_t_var = &_func_ellip_harmonic_unsafe
+from ._factorial cimport _factorial as _func__factorial
+ctypedef double _proto__factorial_t(double) noexcept nogil
+cdef _proto__factorial_t *_proto__factorial_t_var = &_func__factorial
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_igam_fac "igam_fac"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_kolmogc "kolmogc"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_kolmogci "kolmogci"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_kolmogp "kolmogp"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_lanczos_sum_expg_scaled "lanczos_sum_expg_scaled"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_lgam1p "lgam1p"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_log1pmx "log1pmx"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_riemann_zeta "riemann_zeta"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_scaled_exp1 "scaled_exp1"(double) noexcept nogil
+from .sf_error cimport _sf_error_test_function as _func__sf_error_test_function
+ctypedef int _proto__sf_error_test_function_t(int) noexcept nogil
+cdef _proto__sf_error_test_function_t *_proto__sf_error_test_function_t_var = &_func__sf_error_test_function
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_sinpi "sinpi"(double) noexcept nogil
+from ._legacy cimport smirnovc_unsafe as _func_smirnovc_unsafe
+ctypedef double _proto_smirnovc_unsafe_t(double, double) noexcept nogil
+cdef _proto_smirnovc_unsafe_t *_proto_smirnovc_unsafe_t_var = &_func_smirnovc_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_smirnovc "smirnovc"(int, double) noexcept nogil
+from ._legacy cimport smirnovci_unsafe as _func_smirnovci_unsafe
+ctypedef double _proto_smirnovci_unsafe_t(double, double) noexcept nogil
+cdef _proto_smirnovci_unsafe_t *_proto_smirnovci_unsafe_t_var = &_func_smirnovci_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_smirnovci "smirnovci"(int, double) noexcept nogil
+from ._legacy cimport smirnovp_unsafe as _func_smirnovp_unsafe
+ctypedef double _proto_smirnovp_unsafe_t(double, double) noexcept nogil
+cdef _proto_smirnovp_unsafe_t *_proto_smirnovp_unsafe_t_var = &_func_smirnovp_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_smirnovp "smirnovp"(int, double) noexcept nogil
+from ._spherical_bessel cimport spherical_in_complex as _func_spherical_in_complex
+ctypedef double complex _proto_spherical_in_complex_t(long, double complex) noexcept nogil
+cdef _proto_spherical_in_complex_t *_proto_spherical_in_complex_t_var = &_func_spherical_in_complex
+from ._spherical_bessel cimport spherical_in_real as _func_spherical_in_real
+ctypedef double _proto_spherical_in_real_t(long, double) noexcept nogil
+cdef _proto_spherical_in_real_t *_proto_spherical_in_real_t_var = &_func_spherical_in_real
+from ._spherical_bessel cimport spherical_in_d_complex as _func_spherical_in_d_complex
+ctypedef double complex _proto_spherical_in_d_complex_t(long, double complex) noexcept nogil
+cdef _proto_spherical_in_d_complex_t *_proto_spherical_in_d_complex_t_var = &_func_spherical_in_d_complex
+from ._spherical_bessel cimport spherical_in_d_real as _func_spherical_in_d_real
+ctypedef double _proto_spherical_in_d_real_t(long, double) noexcept nogil
+cdef _proto_spherical_in_d_real_t *_proto_spherical_in_d_real_t_var = &_func_spherical_in_d_real
+from ._spherical_bessel cimport spherical_jn_complex as _func_spherical_jn_complex
+ctypedef double complex _proto_spherical_jn_complex_t(long, double complex) noexcept nogil
+cdef _proto_spherical_jn_complex_t *_proto_spherical_jn_complex_t_var = &_func_spherical_jn_complex
+from ._spherical_bessel cimport spherical_jn_real as _func_spherical_jn_real
+ctypedef double _proto_spherical_jn_real_t(long, double) noexcept nogil
+cdef _proto_spherical_jn_real_t *_proto_spherical_jn_real_t_var = &_func_spherical_jn_real
+from ._spherical_bessel cimport spherical_jn_d_complex as _func_spherical_jn_d_complex
+ctypedef double complex _proto_spherical_jn_d_complex_t(long, double complex) noexcept nogil
+cdef _proto_spherical_jn_d_complex_t *_proto_spherical_jn_d_complex_t_var = &_func_spherical_jn_d_complex
+from ._spherical_bessel cimport spherical_jn_d_real as _func_spherical_jn_d_real
+ctypedef double _proto_spherical_jn_d_real_t(long, double) noexcept nogil
+cdef _proto_spherical_jn_d_real_t *_proto_spherical_jn_d_real_t_var = &_func_spherical_jn_d_real
+from ._spherical_bessel cimport spherical_kn_complex as _func_spherical_kn_complex
+ctypedef double complex _proto_spherical_kn_complex_t(long, double complex) noexcept nogil
+cdef _proto_spherical_kn_complex_t *_proto_spherical_kn_complex_t_var = &_func_spherical_kn_complex
+from ._spherical_bessel cimport spherical_kn_real as _func_spherical_kn_real
+ctypedef double _proto_spherical_kn_real_t(long, double) noexcept nogil
+cdef _proto_spherical_kn_real_t *_proto_spherical_kn_real_t_var = &_func_spherical_kn_real
+from ._spherical_bessel cimport spherical_kn_d_complex as _func_spherical_kn_d_complex
+ctypedef double complex _proto_spherical_kn_d_complex_t(long, double complex) noexcept nogil
+cdef _proto_spherical_kn_d_complex_t *_proto_spherical_kn_d_complex_t_var = &_func_spherical_kn_d_complex
+from ._spherical_bessel cimport spherical_kn_d_real as _func_spherical_kn_d_real
+ctypedef double _proto_spherical_kn_d_real_t(long, double) noexcept nogil
+cdef _proto_spherical_kn_d_real_t *_proto_spherical_kn_d_real_t_var = &_func_spherical_kn_d_real
+from ._spherical_bessel cimport spherical_yn_complex as _func_spherical_yn_complex
+ctypedef double complex _proto_spherical_yn_complex_t(long, double complex) noexcept nogil
+cdef _proto_spherical_yn_complex_t *_proto_spherical_yn_complex_t_var = &_func_spherical_yn_complex
+from ._spherical_bessel cimport spherical_yn_real as _func_spherical_yn_real
+ctypedef double _proto_spherical_yn_real_t(long, double) noexcept nogil
+cdef _proto_spherical_yn_real_t *_proto_spherical_yn_real_t_var = &_func_spherical_yn_real
+from ._spherical_bessel cimport spherical_yn_d_complex as _func_spherical_yn_d_complex
+ctypedef double complex _proto_spherical_yn_d_complex_t(long, double complex) noexcept nogil
+cdef _proto_spherical_yn_d_complex_t *_proto_spherical_yn_d_complex_t_var = &_func_spherical_yn_d_complex
+from ._spherical_bessel cimport spherical_yn_d_real as _func_spherical_yn_d_real
+ctypedef double _proto_spherical_yn_d_real_t(long, double) noexcept nogil
+cdef _proto_spherical_yn_d_real_t *_proto_spherical_yn_d_real_t_var = &_func_spherical_yn_d_real
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_struve_asymp_large_z "struve_asymp_large_z"(double, double, int, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_struve_bessel_series "struve_bessel_series"(double, double, int, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_struve_power_series "struve_power_series"(double, double, int, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_zeta "zeta"(double, double) noexcept nogil
+from ._agm cimport agm as _func_agm
+ctypedef double _proto_agm_t(double, double) noexcept nogil
+cdef _proto_agm_t *_proto_agm_t_var = &_func_agm
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_airy_wrap "airy_wrap"(double, double *, double *, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_cairy_wrap "cairy_wrap"(double complex, double complex *, double complex *, double complex *, double complex *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_cairy_wrap_e "cairy_wrap_e"(double complex, double complex *, double complex *, double complex *, double complex *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_cairy_wrap_e_real "cairy_wrap_e_real"(double, double *, double *, double *, double *) noexcept nogil
+from ._legacy cimport bdtr_unsafe as _func_bdtr_unsafe
+ctypedef double _proto_bdtr_unsafe_t(double, double, double) noexcept nogil
+cdef _proto_bdtr_unsafe_t *_proto_bdtr_unsafe_t_var = &_func_bdtr_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_bdtr "bdtr"(double, int, double) noexcept nogil
+from ._legacy cimport bdtrc_unsafe as _func_bdtrc_unsafe
+ctypedef double _proto_bdtrc_unsafe_t(double, double, double) noexcept nogil
+cdef _proto_bdtrc_unsafe_t *_proto_bdtrc_unsafe_t_var = &_func_bdtrc_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_bdtrc "bdtrc"(double, int, double) noexcept nogil
+from ._legacy cimport bdtri_unsafe as _func_bdtri_unsafe
+ctypedef double _proto_bdtri_unsafe_t(double, double, double) noexcept nogil
+cdef _proto_bdtri_unsafe_t *_proto_bdtri_unsafe_t_var = &_func_bdtri_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_bdtri "bdtri"(double, int, double) noexcept nogil
+from ._cdflib_wrappers cimport bdtrik as _func_bdtrik
+ctypedef double _proto_bdtrik_t(double, double, double) noexcept nogil
+cdef _proto_bdtrik_t *_proto_bdtrik_t_var = &_func_bdtrik
+from ._cdflib_wrappers cimport bdtrin as _func_bdtrin
+ctypedef double _proto_bdtrin_t(double, double, double) noexcept nogil
+cdef _proto_bdtrin_t *_proto_bdtrin_t_var = &_func_bdtrin
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_bei_wrap "bei_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_beip_wrap "beip_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_ber_wrap "ber_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_berp_wrap "berp_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_besselpoly "besselpoly"(double, double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_beta "beta"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_lbeta "lbeta"(double, double) noexcept nogil
+from ._boxcox cimport boxcox as _func_boxcox
+ctypedef double _proto_boxcox_t(double, double) noexcept nogil
+cdef _proto_boxcox_t *_proto_boxcox_t_var = &_func_boxcox
+from ._boxcox cimport boxcox1p as _func_boxcox1p
+ctypedef double _proto_boxcox1p_t(double, double) noexcept nogil
+cdef _proto_boxcox1p_t *_proto_boxcox1p_t_var = &_func_boxcox1p
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_btdtr "btdtr"(double, double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_incbi "incbi"(double, double, double) noexcept nogil
+from ._cdflib_wrappers cimport btdtria as _func_btdtria
+ctypedef double _proto_btdtria_t(double, double, double) noexcept nogil
+cdef _proto_btdtria_t *_proto_btdtria_t_var = &_func_btdtria
+from ._cdflib_wrappers cimport btdtrib as _func_btdtrib
+ctypedef double _proto_btdtrib_t(double, double, double) noexcept nogil
+cdef _proto_btdtrib_t *_proto_btdtrib_t_var = &_func_btdtrib
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cbrt "cbrt"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_chdtr "chdtr"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_chdtrc "chdtrc"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_chdtri "chdtri"(double, double) noexcept nogil
+from ._cdflib_wrappers cimport chdtriv as _func_chdtriv
+ctypedef double _proto_chdtriv_t(double, double) noexcept nogil
+cdef _proto_chdtriv_t *_proto_chdtriv_t_var = &_func_chdtriv
+from ._cdflib_wrappers cimport chndtr as _func_chndtr
+ctypedef double _proto_chndtr_t(double, double, double) noexcept nogil
+cdef _proto_chndtr_t *_proto_chndtr_t_var = &_func_chndtr
+from ._cdflib_wrappers cimport chndtridf as _func_chndtridf
+ctypedef double _proto_chndtridf_t(double, double, double) noexcept nogil
+cdef _proto_chndtridf_t *_proto_chndtridf_t_var = &_func_chndtridf
+from ._cdflib_wrappers cimport chndtrinc as _func_chndtrinc
+ctypedef double _proto_chndtrinc_t(double, double, double) noexcept nogil
+cdef _proto_chndtrinc_t *_proto_chndtrinc_t_var = &_func_chndtrinc
+from ._cdflib_wrappers cimport chndtrix as _func_chndtrix
+ctypedef double _proto_chndtrix_t(double, double, double) noexcept nogil
+cdef _proto_chndtrix_t *_proto_chndtrix_t_var = &_func_chndtrix
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cosdg "cosdg"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cosm1 "cosm1"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cotdg "cotdg"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_ellpe "ellpe"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_ellie "ellie"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_ellpj "ellpj"(double, double, double *, double *, double *, double *) noexcept nogil
+from ._ellipk cimport ellipk as _func_ellipk
+ctypedef double _proto_ellipk_t(double) noexcept nogil
+cdef _proto_ellipk_t *_proto_ellipk_t_var = &_func_ellipk
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_ellik "ellik"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_ellpk "ellpk"(double) noexcept nogil
+from ._convex_analysis cimport entr as _func_entr
+ctypedef double _proto_entr_t(double) noexcept nogil
+cdef _proto_entr_t *_proto_entr_t_var = &_func_entr
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_erf "erf"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_erfc "erfc"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_erfcinv "erfcinv"(double) noexcept nogil
+from .orthogonal_eval cimport eval_chebyc as _func_eval_chebyc
+ctypedef double complex _proto_eval_chebyc_double_complex__t(double, double complex) noexcept nogil
+cdef _proto_eval_chebyc_double_complex__t *_proto_eval_chebyc_double_complex__t_var = &_func_eval_chebyc[double_complex]
+from .orthogonal_eval cimport eval_chebyc as _func_eval_chebyc
+ctypedef double _proto_eval_chebyc_double__t(double, double) noexcept nogil
+cdef _proto_eval_chebyc_double__t *_proto_eval_chebyc_double__t_var = &_func_eval_chebyc[double]
+from .orthogonal_eval cimport eval_chebyc_l as _func_eval_chebyc_l
+ctypedef double _proto_eval_chebyc_l_t(long, double) noexcept nogil
+cdef _proto_eval_chebyc_l_t *_proto_eval_chebyc_l_t_var = &_func_eval_chebyc_l
+from .orthogonal_eval cimport eval_chebys as _func_eval_chebys
+ctypedef double complex _proto_eval_chebys_double_complex__t(double, double complex) noexcept nogil
+cdef _proto_eval_chebys_double_complex__t *_proto_eval_chebys_double_complex__t_var = &_func_eval_chebys[double_complex]
+from .orthogonal_eval cimport eval_chebys as _func_eval_chebys
+ctypedef double _proto_eval_chebys_double__t(double, double) noexcept nogil
+cdef _proto_eval_chebys_double__t *_proto_eval_chebys_double__t_var = &_func_eval_chebys[double]
+from .orthogonal_eval cimport eval_chebys_l as _func_eval_chebys_l
+ctypedef double _proto_eval_chebys_l_t(long, double) noexcept nogil
+cdef _proto_eval_chebys_l_t *_proto_eval_chebys_l_t_var = &_func_eval_chebys_l
+from .orthogonal_eval cimport eval_chebyt as _func_eval_chebyt
+ctypedef double complex _proto_eval_chebyt_double_complex__t(double, double complex) noexcept nogil
+cdef _proto_eval_chebyt_double_complex__t *_proto_eval_chebyt_double_complex__t_var = &_func_eval_chebyt[double_complex]
+from .orthogonal_eval cimport eval_chebyt as _func_eval_chebyt
+ctypedef double _proto_eval_chebyt_double__t(double, double) noexcept nogil
+cdef _proto_eval_chebyt_double__t *_proto_eval_chebyt_double__t_var = &_func_eval_chebyt[double]
+from .orthogonal_eval cimport eval_chebyt_l as _func_eval_chebyt_l
+ctypedef double _proto_eval_chebyt_l_t(long, double) noexcept nogil
+cdef _proto_eval_chebyt_l_t *_proto_eval_chebyt_l_t_var = &_func_eval_chebyt_l
+from .orthogonal_eval cimport eval_chebyu as _func_eval_chebyu
+ctypedef double complex _proto_eval_chebyu_double_complex__t(double, double complex) noexcept nogil
+cdef _proto_eval_chebyu_double_complex__t *_proto_eval_chebyu_double_complex__t_var = &_func_eval_chebyu[double_complex]
+from .orthogonal_eval cimport eval_chebyu as _func_eval_chebyu
+ctypedef double _proto_eval_chebyu_double__t(double, double) noexcept nogil
+cdef _proto_eval_chebyu_double__t *_proto_eval_chebyu_double__t_var = &_func_eval_chebyu[double]
+from .orthogonal_eval cimport eval_chebyu_l as _func_eval_chebyu_l
+ctypedef double _proto_eval_chebyu_l_t(long, double) noexcept nogil
+cdef _proto_eval_chebyu_l_t *_proto_eval_chebyu_l_t_var = &_func_eval_chebyu_l
+from .orthogonal_eval cimport eval_gegenbauer as _func_eval_gegenbauer
+ctypedef double complex _proto_eval_gegenbauer_double_complex__t(double, double, double complex) noexcept nogil
+cdef _proto_eval_gegenbauer_double_complex__t *_proto_eval_gegenbauer_double_complex__t_var = &_func_eval_gegenbauer[double_complex]
+from .orthogonal_eval cimport eval_gegenbauer as _func_eval_gegenbauer
+ctypedef double _proto_eval_gegenbauer_double__t(double, double, double) noexcept nogil
+cdef _proto_eval_gegenbauer_double__t *_proto_eval_gegenbauer_double__t_var = &_func_eval_gegenbauer[double]
+from .orthogonal_eval cimport eval_gegenbauer_l as _func_eval_gegenbauer_l
+ctypedef double _proto_eval_gegenbauer_l_t(long, double, double) noexcept nogil
+cdef _proto_eval_gegenbauer_l_t *_proto_eval_gegenbauer_l_t_var = &_func_eval_gegenbauer_l
+from .orthogonal_eval cimport eval_genlaguerre as _func_eval_genlaguerre
+ctypedef double complex _proto_eval_genlaguerre_double_complex__t(double, double, double complex) noexcept nogil
+cdef _proto_eval_genlaguerre_double_complex__t *_proto_eval_genlaguerre_double_complex__t_var = &_func_eval_genlaguerre[double_complex]
+from .orthogonal_eval cimport eval_genlaguerre as _func_eval_genlaguerre
+ctypedef double _proto_eval_genlaguerre_double__t(double, double, double) noexcept nogil
+cdef _proto_eval_genlaguerre_double__t *_proto_eval_genlaguerre_double__t_var = &_func_eval_genlaguerre[double]
+from .orthogonal_eval cimport eval_genlaguerre_l as _func_eval_genlaguerre_l
+ctypedef double _proto_eval_genlaguerre_l_t(long, double, double) noexcept nogil
+cdef _proto_eval_genlaguerre_l_t *_proto_eval_genlaguerre_l_t_var = &_func_eval_genlaguerre_l
+from .orthogonal_eval cimport eval_hermite as _func_eval_hermite
+ctypedef double _proto_eval_hermite_t(long, double) noexcept nogil
+cdef _proto_eval_hermite_t *_proto_eval_hermite_t_var = &_func_eval_hermite
+from .orthogonal_eval cimport eval_hermitenorm as _func_eval_hermitenorm
+ctypedef double _proto_eval_hermitenorm_t(long, double) noexcept nogil
+cdef _proto_eval_hermitenorm_t *_proto_eval_hermitenorm_t_var = &_func_eval_hermitenorm
+from .orthogonal_eval cimport eval_jacobi as _func_eval_jacobi
+ctypedef double complex _proto_eval_jacobi_double_complex__t(double, double, double, double complex) noexcept nogil
+cdef _proto_eval_jacobi_double_complex__t *_proto_eval_jacobi_double_complex__t_var = &_func_eval_jacobi[double_complex]
+from .orthogonal_eval cimport eval_jacobi as _func_eval_jacobi
+ctypedef double _proto_eval_jacobi_double__t(double, double, double, double) noexcept nogil
+cdef _proto_eval_jacobi_double__t *_proto_eval_jacobi_double__t_var = &_func_eval_jacobi[double]
+from .orthogonal_eval cimport eval_jacobi_l as _func_eval_jacobi_l
+ctypedef double _proto_eval_jacobi_l_t(long, double, double, double) noexcept nogil
+cdef _proto_eval_jacobi_l_t *_proto_eval_jacobi_l_t_var = &_func_eval_jacobi_l
+from .orthogonal_eval cimport eval_laguerre as _func_eval_laguerre
+ctypedef double complex _proto_eval_laguerre_double_complex__t(double, double complex) noexcept nogil
+cdef _proto_eval_laguerre_double_complex__t *_proto_eval_laguerre_double_complex__t_var = &_func_eval_laguerre[double_complex]
+from .orthogonal_eval cimport eval_laguerre as _func_eval_laguerre
+ctypedef double _proto_eval_laguerre_double__t(double, double) noexcept nogil
+cdef _proto_eval_laguerre_double__t *_proto_eval_laguerre_double__t_var = &_func_eval_laguerre[double]
+from .orthogonal_eval cimport eval_laguerre_l as _func_eval_laguerre_l
+ctypedef double _proto_eval_laguerre_l_t(long, double) noexcept nogil
+cdef _proto_eval_laguerre_l_t *_proto_eval_laguerre_l_t_var = &_func_eval_laguerre_l
+from .orthogonal_eval cimport eval_legendre as _func_eval_legendre
+ctypedef double complex _proto_eval_legendre_double_complex__t(double, double complex) noexcept nogil
+cdef _proto_eval_legendre_double_complex__t *_proto_eval_legendre_double_complex__t_var = &_func_eval_legendre[double_complex]
+from .orthogonal_eval cimport eval_legendre as _func_eval_legendre
+ctypedef double _proto_eval_legendre_double__t(double, double) noexcept nogil
+cdef _proto_eval_legendre_double__t *_proto_eval_legendre_double__t_var = &_func_eval_legendre[double]
+from .orthogonal_eval cimport eval_legendre_l as _func_eval_legendre_l
+ctypedef double _proto_eval_legendre_l_t(long, double) noexcept nogil
+cdef _proto_eval_legendre_l_t *_proto_eval_legendre_l_t_var = &_func_eval_legendre_l
+from .orthogonal_eval cimport eval_sh_chebyt as _func_eval_sh_chebyt
+ctypedef double complex _proto_eval_sh_chebyt_double_complex__t(double, double complex) noexcept nogil
+cdef _proto_eval_sh_chebyt_double_complex__t *_proto_eval_sh_chebyt_double_complex__t_var = &_func_eval_sh_chebyt[double_complex]
+from .orthogonal_eval cimport eval_sh_chebyt as _func_eval_sh_chebyt
+ctypedef double _proto_eval_sh_chebyt_double__t(double, double) noexcept nogil
+cdef _proto_eval_sh_chebyt_double__t *_proto_eval_sh_chebyt_double__t_var = &_func_eval_sh_chebyt[double]
+from .orthogonal_eval cimport eval_sh_chebyt_l as _func_eval_sh_chebyt_l
+ctypedef double _proto_eval_sh_chebyt_l_t(long, double) noexcept nogil
+cdef _proto_eval_sh_chebyt_l_t *_proto_eval_sh_chebyt_l_t_var = &_func_eval_sh_chebyt_l
+from .orthogonal_eval cimport eval_sh_chebyu as _func_eval_sh_chebyu
+ctypedef double complex _proto_eval_sh_chebyu_double_complex__t(double, double complex) noexcept nogil
+cdef _proto_eval_sh_chebyu_double_complex__t *_proto_eval_sh_chebyu_double_complex__t_var = &_func_eval_sh_chebyu[double_complex]
+from .orthogonal_eval cimport eval_sh_chebyu as _func_eval_sh_chebyu
+ctypedef double _proto_eval_sh_chebyu_double__t(double, double) noexcept nogil
+cdef _proto_eval_sh_chebyu_double__t *_proto_eval_sh_chebyu_double__t_var = &_func_eval_sh_chebyu[double]
+from .orthogonal_eval cimport eval_sh_chebyu_l as _func_eval_sh_chebyu_l
+ctypedef double _proto_eval_sh_chebyu_l_t(long, double) noexcept nogil
+cdef _proto_eval_sh_chebyu_l_t *_proto_eval_sh_chebyu_l_t_var = &_func_eval_sh_chebyu_l
+from .orthogonal_eval cimport eval_sh_jacobi as _func_eval_sh_jacobi
+ctypedef double complex _proto_eval_sh_jacobi_double_complex__t(double, double, double, double complex) noexcept nogil
+cdef _proto_eval_sh_jacobi_double_complex__t *_proto_eval_sh_jacobi_double_complex__t_var = &_func_eval_sh_jacobi[double_complex]
+from .orthogonal_eval cimport eval_sh_jacobi as _func_eval_sh_jacobi
+ctypedef double _proto_eval_sh_jacobi_double__t(double, double, double, double) noexcept nogil
+cdef _proto_eval_sh_jacobi_double__t *_proto_eval_sh_jacobi_double__t_var = &_func_eval_sh_jacobi[double]
+from .orthogonal_eval cimport eval_sh_jacobi_l as _func_eval_sh_jacobi_l
+ctypedef double _proto_eval_sh_jacobi_l_t(long, double, double, double) noexcept nogil
+cdef _proto_eval_sh_jacobi_l_t *_proto_eval_sh_jacobi_l_t_var = &_func_eval_sh_jacobi_l
+from .orthogonal_eval cimport eval_sh_legendre as _func_eval_sh_legendre
+ctypedef double complex _proto_eval_sh_legendre_double_complex__t(double, double complex) noexcept nogil
+cdef _proto_eval_sh_legendre_double_complex__t *_proto_eval_sh_legendre_double_complex__t_var = &_func_eval_sh_legendre[double_complex]
+from .orthogonal_eval cimport eval_sh_legendre as _func_eval_sh_legendre
+ctypedef double _proto_eval_sh_legendre_double__t(double, double) noexcept nogil
+cdef _proto_eval_sh_legendre_double__t *_proto_eval_sh_legendre_double__t_var = &_func_eval_sh_legendre[double]
+from .orthogonal_eval cimport eval_sh_legendre_l as _func_eval_sh_legendre_l
+ctypedef double _proto_eval_sh_legendre_l_t(long, double) noexcept nogil
+cdef _proto_eval_sh_legendre_l_t *_proto_eval_sh_legendre_l_t_var = &_func_eval_sh_legendre_l
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cexp1_wrap "cexp1_wrap"(double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_exp1_wrap "exp1_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_exp10 "exp10"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_exp2 "exp2"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cexpi_wrap "cexpi_wrap"(double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_expi_wrap "expi_wrap"(double) noexcept nogil
+from ._cunity cimport cexpm1 as _func_cexpm1
+ctypedef double complex _proto_cexpm1_t(double complex) noexcept nogil
+cdef _proto_cexpm1_t *_proto_cexpm1_t_var = &_func_cexpm1
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_expm1 "expm1"(double) noexcept nogil
+from ._legacy cimport expn_unsafe as _func_expn_unsafe
+ctypedef double _proto_expn_unsafe_t(double, double) noexcept nogil
+cdef _proto_expn_unsafe_t *_proto_expn_unsafe_t_var = &_func_expn_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_expn "expn"(int, double) noexcept nogil
+from ._exprel cimport exprel as _func_exprel
+ctypedef double _proto_exprel_t(double) noexcept nogil
+cdef _proto_exprel_t *_proto_exprel_t_var = &_func_exprel
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_fdtr "fdtr"(double, double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_fdtrc "fdtrc"(double, double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_fdtri "fdtri"(double, double, double) noexcept nogil
+from ._cdflib_wrappers cimport fdtridfd as _func_fdtridfd
+ctypedef double _proto_fdtridfd_t(double, double, double) noexcept nogil
+cdef _proto_fdtridfd_t *_proto_fdtridfd_t_var = &_func_fdtridfd
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_fresnl "fresnl"(double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_cfresnl_wrap "cfresnl_wrap"(double complex, double complex *, double complex *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_Gamma "Gamma"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_igam "igam"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_igamc "igamc"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_igamci "igamci"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_igami "igami"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_lgam "lgam"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_gammasgn "gammasgn"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_gdtr "gdtr"(double, double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_gdtrc "gdtrc"(double, double, double) noexcept nogil
+from ._cdflib_wrappers cimport gdtria as _func_gdtria
+ctypedef double _proto_gdtria_t(double, double, double) noexcept nogil
+cdef _proto_gdtria_t *_proto_gdtria_t_var = &_func_gdtria
+from ._cdflib_wrappers cimport gdtrib as _func_gdtrib
+ctypedef double _proto_gdtrib_t(double, double, double) noexcept nogil
+cdef _proto_gdtrib_t *_proto_gdtrib_t_var = &_func_gdtrib
+from ._cdflib_wrappers cimport gdtrix as _func_gdtrix
+ctypedef double _proto_gdtrix_t(double, double, double) noexcept nogil
+cdef _proto_gdtrix_t *_proto_gdtrix_t_var = &_func_gdtrix
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesh_wrap1 "cbesh_wrap1"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesh_wrap1_e "cbesh_wrap1_e"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesh_wrap2 "cbesh_wrap2"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesh_wrap2_e "cbesh_wrap2_e"(double, double complex) noexcept nogil
+from ._convex_analysis cimport huber as _func_huber
+ctypedef double _proto_huber_t(double, double) noexcept nogil
+cdef _proto_huber_t *_proto_huber_t_var = &_func_huber
+from ._hyp0f1 cimport _hyp0f1_cmplx as _func__hyp0f1_cmplx
+ctypedef double complex _proto__hyp0f1_cmplx_t(double, double complex) noexcept nogil
+cdef _proto__hyp0f1_cmplx_t *_proto__hyp0f1_cmplx_t_var = &_func__hyp0f1_cmplx
+from ._hyp0f1 cimport _hyp0f1_real as _func__hyp0f1_real
+ctypedef double _proto__hyp0f1_real_t(double, double) noexcept nogil
+cdef _proto__hyp0f1_real_t *_proto__hyp0f1_real_t_var = &_func__hyp0f1_real
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_chyp1f1_wrap "chyp1f1_wrap"(double, double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_hyp2f1 "hyp2f1"(double, double, double, double) noexcept nogil
+from ._hyp2f1 cimport hyp2f1_complex as _func_hyp2f1_complex
+ctypedef double complex _proto_hyp2f1_complex_t(double, double, double, double complex) noexcept nogil
+cdef _proto_hyp2f1_complex_t *_proto_hyp2f1_complex_t_var = &_func_hyp2f1_complex
+from ._hypergeometric cimport hyperu as _func_hyperu
+ctypedef double _proto_hyperu_t(double, double, double) noexcept nogil
+cdef _proto_hyperu_t *_proto_hyperu_t_var = &_func_hyperu
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_i0 "i0"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_i0e "i0e"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_i1 "i1"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_i1e "i1e"(double) noexcept nogil
+from ._boxcox cimport inv_boxcox as _func_inv_boxcox
+ctypedef double _proto_inv_boxcox_t(double, double) noexcept nogil
+cdef _proto_inv_boxcox_t *_proto_inv_boxcox_t_var = &_func_inv_boxcox
+from ._boxcox cimport inv_boxcox1p as _func_inv_boxcox1p
+ctypedef double _proto_inv_boxcox1p_t(double, double) noexcept nogil
+cdef _proto_inv_boxcox1p_t *_proto_inv_boxcox1p_t_var = &_func_inv_boxcox1p
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_it2i0k0_wrap "it2i0k0_wrap"(double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_it2j0y0_wrap "it2j0y0_wrap"(double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_it2struve0_wrap "it2struve0_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_itairy_wrap "itairy_wrap"(double, double *, double *, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_it1i0k0_wrap "it1i0k0_wrap"(double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_it1j0y0_wrap "it1j0y0_wrap"(double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_itmodstruve0_wrap "itmodstruve0_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_itstruve0_wrap "itstruve0_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesi_wrap "cbesi_wrap"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_iv "iv"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesi_wrap_e "cbesi_wrap_e"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cbesi_wrap_e_real "cbesi_wrap_e_real"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_j0 "j0"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_j1 "j1"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesj_wrap "cbesj_wrap"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cbesj_wrap_real "cbesj_wrap_real"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesj_wrap_e "cbesj_wrap_e"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cbesj_wrap_e_real "cbesj_wrap_e_real"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_k0 "k0"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_k0e "k0e"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_k1 "k1"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_k1e "k1e"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_kei_wrap "kei_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_keip_wrap "keip_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_kelvin_wrap "kelvin_wrap"(double, double complex *, double complex *, double complex *, double complex *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_ker_wrap "ker_wrap"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_kerp_wrap "kerp_wrap"(double) noexcept nogil
+from ._convex_analysis cimport kl_div as _func_kl_div
+ctypedef double _proto_kl_div_t(double, double) noexcept nogil
+cdef _proto_kl_div_t *_proto_kl_div_t_var = &_func_kl_div
+from ._legacy cimport kn_unsafe as _func_kn_unsafe
+ctypedef double _proto_kn_unsafe_t(double, double) noexcept nogil
+cdef _proto_kn_unsafe_t *_proto_kn_unsafe_t_var = &_func_kn_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cbesk_wrap_real_int "cbesk_wrap_real_int"(int, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_kolmogi "kolmogi"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_kolmogorov "kolmogorov"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesk_wrap "cbesk_wrap"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cbesk_wrap_real "cbesk_wrap_real"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesk_wrap_e "cbesk_wrap_e"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cbesk_wrap_e_real "cbesk_wrap_e_real"(double, double) noexcept nogil
+from ._cunity cimport clog1p as _func_clog1p
+ctypedef double complex _proto_clog1p_t(double complex) noexcept nogil
+cdef _proto_clog1p_t *_proto_clog1p_t_var = &_func_clog1p
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_log1p "log1p"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_pmv_wrap "pmv_wrap"(double, double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cem_cva_wrap "cem_cva_wrap"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_sem_cva_wrap "sem_cva_wrap"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_cem_wrap "cem_wrap"(double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_mcm1_wrap "mcm1_wrap"(double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_mcm2_wrap "mcm2_wrap"(double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_msm1_wrap "msm1_wrap"(double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_msm2_wrap "msm2_wrap"(double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_sem_wrap "sem_wrap"(double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_modified_fresnel_minus_wrap "modified_fresnel_minus_wrap"(double, double complex *, double complex *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_modified_fresnel_plus_wrap "modified_fresnel_plus_wrap"(double, double complex *, double complex *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_struve_l "struve_l"(double, double) noexcept nogil
+from ._legacy cimport nbdtr_unsafe as _func_nbdtr_unsafe
+ctypedef double _proto_nbdtr_unsafe_t(double, double, double) noexcept nogil
+cdef _proto_nbdtr_unsafe_t *_proto_nbdtr_unsafe_t_var = &_func_nbdtr_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_nbdtr "nbdtr"(int, int, double) noexcept nogil
+from ._legacy cimport nbdtrc_unsafe as _func_nbdtrc_unsafe
+ctypedef double _proto_nbdtrc_unsafe_t(double, double, double) noexcept nogil
+cdef _proto_nbdtrc_unsafe_t *_proto_nbdtrc_unsafe_t_var = &_func_nbdtrc_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_nbdtrc "nbdtrc"(int, int, double) noexcept nogil
+from ._legacy cimport nbdtri_unsafe as _func_nbdtri_unsafe
+ctypedef double _proto_nbdtri_unsafe_t(double, double, double) noexcept nogil
+cdef _proto_nbdtri_unsafe_t *_proto_nbdtri_unsafe_t_var = &_func_nbdtri_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_nbdtri "nbdtri"(int, int, double) noexcept nogil
+from ._cdflib_wrappers cimport nbdtrik as _func_nbdtrik
+ctypedef double _proto_nbdtrik_t(double, double, double) noexcept nogil
+cdef _proto_nbdtrik_t *_proto_nbdtrik_t_var = &_func_nbdtrik
+from ._cdflib_wrappers cimport nbdtrin as _func_nbdtrin
+ctypedef double _proto_nbdtrin_t(double, double, double) noexcept nogil
+cdef _proto_nbdtrin_t *_proto_nbdtrin_t_var = &_func_nbdtrin
+from ._cdflib_wrappers cimport ncfdtr as _func_ncfdtr
+ctypedef double _proto_ncfdtr_t(double, double, double, double) noexcept nogil
+cdef _proto_ncfdtr_t *_proto_ncfdtr_t_var = &_func_ncfdtr
+from ._cdflib_wrappers cimport ncfdtri as _func_ncfdtri
+ctypedef double _proto_ncfdtri_t(double, double, double, double) noexcept nogil
+cdef _proto_ncfdtri_t *_proto_ncfdtri_t_var = &_func_ncfdtri
+from ._cdflib_wrappers cimport ncfdtridfd as _func_ncfdtridfd
+ctypedef double _proto_ncfdtridfd_t(double, double, double, double) noexcept nogil
+cdef _proto_ncfdtridfd_t *_proto_ncfdtridfd_t_var = &_func_ncfdtridfd
+from ._cdflib_wrappers cimport ncfdtridfn as _func_ncfdtridfn
+ctypedef double _proto_ncfdtridfn_t(double, double, double, double) noexcept nogil
+cdef _proto_ncfdtridfn_t *_proto_ncfdtridfn_t_var = &_func_ncfdtridfn
+from ._cdflib_wrappers cimport ncfdtrinc as _func_ncfdtrinc
+ctypedef double _proto_ncfdtrinc_t(double, double, double, double) noexcept nogil
+cdef _proto_ncfdtrinc_t *_proto_ncfdtrinc_t_var = &_func_ncfdtrinc
+from ._cdflib_wrappers cimport nctdtr as _func_nctdtr
+ctypedef double _proto_nctdtr_t(double, double, double) noexcept nogil
+cdef _proto_nctdtr_t *_proto_nctdtr_t_var = &_func_nctdtr
+from ._cdflib_wrappers cimport nctdtridf as _func_nctdtridf
+ctypedef double _proto_nctdtridf_t(double, double, double) noexcept nogil
+cdef _proto_nctdtridf_t *_proto_nctdtridf_t_var = &_func_nctdtridf
+from ._cdflib_wrappers cimport nctdtrinc as _func_nctdtrinc
+ctypedef double _proto_nctdtrinc_t(double, double, double) noexcept nogil
+cdef _proto_nctdtrinc_t *_proto_nctdtrinc_t_var = &_func_nctdtrinc
+from ._cdflib_wrappers cimport nctdtrit as _func_nctdtrit
+ctypedef double _proto_nctdtrit_t(double, double, double) noexcept nogil
+cdef _proto_nctdtrit_t *_proto_nctdtrit_t_var = &_func_nctdtrit
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_ndtr "ndtr"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_ndtri "ndtri"(double) noexcept nogil
+from ._ndtri_exp cimport ndtri_exp as _func_ndtri_exp
+ctypedef double _proto_ndtri_exp_t(double) noexcept nogil
+cdef _proto_ndtri_exp_t *_proto_ndtri_exp_t_var = &_func_ndtri_exp
+from ._cdflib_wrappers cimport nrdtrimn as _func_nrdtrimn
+ctypedef double _proto_nrdtrimn_t(double, double, double) noexcept nogil
+cdef _proto_nrdtrimn_t *_proto_nrdtrimn_t_var = &_func_nrdtrimn
+from ._cdflib_wrappers cimport nrdtrisd as _func_nrdtrisd
+ctypedef double _proto_nrdtrisd_t(double, double, double) noexcept nogil
+cdef _proto_nrdtrisd_t *_proto_nrdtrisd_t_var = &_func_nrdtrisd
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_oblate_aswfa_nocv_wrap "oblate_aswfa_nocv_wrap"(double, double, double, double, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_oblate_aswfa_wrap "oblate_aswfa_wrap"(double, double, double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_oblate_segv_wrap "oblate_segv_wrap"(double, double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_oblate_radial1_nocv_wrap "oblate_radial1_nocv_wrap"(double, double, double, double, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_oblate_radial1_wrap "oblate_radial1_wrap"(double, double, double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_oblate_radial2_nocv_wrap "oblate_radial2_nocv_wrap"(double, double, double, double, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_oblate_radial2_wrap "oblate_radial2_wrap"(double, double, double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_owens_t "owens_t"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_pbdv_wrap "pbdv_wrap"(double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_pbvv_wrap "pbvv_wrap"(double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_pbwa_wrap "pbwa_wrap"(double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_pdtr "pdtr"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_pdtrc "pdtrc"(double, double) noexcept nogil
+from ._legacy cimport pdtri_unsafe as _func_pdtri_unsafe
+ctypedef double _proto_pdtri_unsafe_t(double, double) noexcept nogil
+cdef _proto_pdtri_unsafe_t *_proto_pdtri_unsafe_t_var = &_func_pdtri_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_pdtri "pdtri"(int, double) noexcept nogil
+from ._cdflib_wrappers cimport pdtrik as _func_pdtrik
+ctypedef double _proto_pdtrik_t(double, double) noexcept nogil
+cdef _proto_pdtrik_t *_proto_pdtrik_t_var = &_func_pdtrik
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_poch "poch"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_prolate_aswfa_nocv_wrap "prolate_aswfa_nocv_wrap"(double, double, double, double, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_prolate_aswfa_wrap "prolate_aswfa_wrap"(double, double, double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_prolate_segv_wrap "prolate_segv_wrap"(double, double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_prolate_radial1_nocv_wrap "prolate_radial1_nocv_wrap"(double, double, double, double, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_prolate_radial1_wrap "prolate_radial1_wrap"(double, double, double, double, double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_prolate_radial2_nocv_wrap "prolate_radial2_nocv_wrap"(double, double, double, double, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_prolate_radial2_wrap "prolate_radial2_wrap"(double, double, double, double, double, double *, double *) noexcept nogil
+from ._convex_analysis cimport pseudo_huber as _func_pseudo_huber
+ctypedef double _proto_pseudo_huber_t(double, double) noexcept nogil
+cdef _proto_pseudo_huber_t *_proto_pseudo_huber_t_var = &_func_pseudo_huber
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_radian "radian"(double, double, double) noexcept nogil
+from ._convex_analysis cimport rel_entr as _func_rel_entr
+ctypedef double _proto_rel_entr_t(double, double) noexcept nogil
+cdef _proto_rel_entr_t *_proto_rel_entr_t_var = &_func_rel_entr
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_rgamma "rgamma"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_round "round"(double) noexcept nogil
+from ._sici cimport cshichi as _func_cshichi
+ctypedef int _proto_cshichi_t(double complex, double complex *, double complex *) noexcept nogil
+cdef _proto_cshichi_t *_proto_cshichi_t_var = &_func_cshichi
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_shichi "shichi"(double, double *, double *) noexcept nogil
+from ._sici cimport csici as _func_csici
+ctypedef int _proto_csici_t(double complex, double complex *, double complex *) noexcept nogil
+cdef _proto_csici_t *_proto_csici_t_var = &_func_csici
+cdef extern from r"_ufuncs_defs.h":
+    cdef int _func_sici "sici"(double, double *, double *) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_sindg "sindg"(double) noexcept nogil
+from ._legacy cimport smirnov_unsafe as _func_smirnov_unsafe
+ctypedef double _proto_smirnov_unsafe_t(double, double) noexcept nogil
+cdef _proto_smirnov_unsafe_t *_proto_smirnov_unsafe_t_var = &_func_smirnov_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_smirnov "smirnov"(int, double) noexcept nogil
+from ._legacy cimport smirnovi_unsafe as _func_smirnovi_unsafe
+ctypedef double _proto_smirnovi_unsafe_t(double, double) noexcept nogil
+cdef _proto_smirnovi_unsafe_t *_proto_smirnovi_unsafe_t_var = &_func_smirnovi_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_smirnovi "smirnovi"(int, double) noexcept nogil
+from ._spence cimport cspence as _func_cspence
+ctypedef double complex _proto_cspence_t(double complex) noexcept nogil
+cdef _proto_cspence_t *_proto_cspence_t_var = &_func_cspence
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_spence "spence"(double) noexcept nogil
+from ._legacy cimport sph_harmonic_unsafe as _func_sph_harmonic_unsafe
+ctypedef double complex _proto_sph_harmonic_unsafe_t(double, double, double, double) noexcept nogil
+cdef _proto_sph_harmonic_unsafe_t *_proto_sph_harmonic_unsafe_t_var = &_func_sph_harmonic_unsafe
+from .sph_harm cimport sph_harmonic as _func_sph_harmonic
+ctypedef double complex _proto_sph_harmonic_t(int, int, double, double) noexcept nogil
+cdef _proto_sph_harmonic_t *_proto_sph_harmonic_t_var = &_func_sph_harmonic
+from ._cdflib_wrappers cimport stdtr as _func_stdtr
+ctypedef double _proto_stdtr_t(double, double) noexcept nogil
+cdef _proto_stdtr_t *_proto_stdtr_t_var = &_func_stdtr
+from ._cdflib_wrappers cimport stdtridf as _func_stdtridf
+ctypedef double _proto_stdtridf_t(double, double) noexcept nogil
+cdef _proto_stdtridf_t *_proto_stdtridf_t_var = &_func_stdtridf
+from ._cdflib_wrappers cimport stdtrit as _func_stdtrit
+ctypedef double _proto_stdtrit_t(double, double) noexcept nogil
+cdef _proto_stdtrit_t *_proto_stdtrit_t_var = &_func_stdtrit
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_struve_h "struve_h"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_tandg "tandg"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_tukeylambdacdf "tukeylambdacdf"(double, double) noexcept nogil
+from ._wright_bessel cimport wright_bessel_scalar as _func_wright_bessel_scalar
+ctypedef double _proto_wright_bessel_scalar_t(double, double, double) noexcept nogil
+cdef _proto_wright_bessel_scalar_t *_proto_wright_bessel_scalar_t_var = &_func_wright_bessel_scalar
+from ._xlogy cimport xlog1py as _func_xlog1py
+ctypedef double _proto_xlog1py_double__t(double, double) noexcept nogil
+cdef _proto_xlog1py_double__t *_proto_xlog1py_double__t_var = &_func_xlog1py[double]
+from ._xlogy cimport xlog1py as _func_xlog1py
+ctypedef double complex _proto_xlog1py_double_complex__t(double complex, double complex) noexcept nogil
+cdef _proto_xlog1py_double_complex__t *_proto_xlog1py_double_complex__t_var = &_func_xlog1py[double_complex]
+from ._xlogy cimport xlogy as _func_xlogy
+ctypedef double _proto_xlogy_double__t(double, double) noexcept nogil
+cdef _proto_xlogy_double__t *_proto_xlogy_double__t_var = &_func_xlogy[double]
+from ._xlogy cimport xlogy as _func_xlogy
+ctypedef double complex _proto_xlogy_double_complex__t(double complex, double complex) noexcept nogil
+cdef _proto_xlogy_double_complex__t *_proto_xlogy_double_complex__t_var = &_func_xlogy[double_complex]
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_y0 "y0"(double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_y1 "y1"(double) noexcept nogil
+from ._legacy cimport yn_unsafe as _func_yn_unsafe
+ctypedef double _proto_yn_unsafe_t(double, double) noexcept nogil
+cdef _proto_yn_unsafe_t *_proto_yn_unsafe_t_var = &_func_yn_unsafe
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_yn "yn"(int, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesy_wrap "cbesy_wrap"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cbesy_wrap_real "cbesy_wrap_real"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double complex _func_cbesy_wrap_e "cbesy_wrap_e"(double, double complex) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_cbesy_wrap_e_real "cbesy_wrap_e_real"(double, double) noexcept nogil
+cdef extern from r"_ufuncs_defs.h":
+    cdef double _func_zetac "zetac"(double) noexcept nogil
+cdef np.PyUFuncGenericFunction ufunc__cosine_cdf_loops[2]
+cdef void *ufunc__cosine_cdf_ptr[4]
+cdef void *ufunc__cosine_cdf_data[2]
+cdef char ufunc__cosine_cdf_types[4]
+cdef char *ufunc__cosine_cdf_doc = (
+    "_cosine_cdf(x)\n"
+    "\n"
+    "Cumulative distribution function (CDF) of the cosine distribution::\n"
+    "\n"
+    "             {             0,              x < -pi\n"
+    "    cdf(x) = { (pi + x + sin(x))/(2*pi),   -pi <= x <= pi\n"
+    "             {             1,              x > pi\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    `x` must contain real numbers.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The cosine distribution CDF evaluated at `x`.")
+ufunc__cosine_cdf_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__cosine_cdf_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__cosine_cdf_types[0] = <char>NPY_FLOAT
+ufunc__cosine_cdf_types[1] = <char>NPY_FLOAT
+ufunc__cosine_cdf_types[2] = <char>NPY_DOUBLE
+ufunc__cosine_cdf_types[3] = <char>NPY_DOUBLE
+ufunc__cosine_cdf_ptr[2*0] = <void*>_func_cosine_cdf
+ufunc__cosine_cdf_ptr[2*0+1] = <void*>(<char*>"_cosine_cdf")
+ufunc__cosine_cdf_ptr[2*1] = <void*>_func_cosine_cdf
+ufunc__cosine_cdf_ptr[2*1+1] = <void*>(<char*>"_cosine_cdf")
+ufunc__cosine_cdf_data[0] = &ufunc__cosine_cdf_ptr[2*0]
+ufunc__cosine_cdf_data[1] = &ufunc__cosine_cdf_ptr[2*1]
+_cosine_cdf = np.PyUFunc_FromFuncAndData(ufunc__cosine_cdf_loops, ufunc__cosine_cdf_data, ufunc__cosine_cdf_types, 2, 1, 1, 0, "_cosine_cdf", ufunc__cosine_cdf_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__cosine_invcdf_loops[2]
+cdef void *ufunc__cosine_invcdf_ptr[4]
+cdef void *ufunc__cosine_invcdf_data[2]
+cdef char ufunc__cosine_invcdf_types[4]
+cdef char *ufunc__cosine_invcdf_doc = (
+    "_cosine_invcdf(p)\n"
+    "\n"
+    "Inverse of the cumulative distribution function (CDF) of the cosine\n"
+    "distribution.\n"
+    "\n"
+    "The CDF of the cosine distribution is::\n"
+    "\n"
+    "    cdf(x) = (pi + x + sin(x))/(2*pi)\n"
+    "\n"
+    "This function computes the inverse of cdf(x).\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    `p` must contain real numbers in the interval ``0 <= p <= 1``.\n"
+    "    `nan` is returned for values of `p` outside the interval [0, 1].\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The inverse of the cosine distribution CDF evaluated at `p`.")
+ufunc__cosine_invcdf_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__cosine_invcdf_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__cosine_invcdf_types[0] = <char>NPY_FLOAT
+ufunc__cosine_invcdf_types[1] = <char>NPY_FLOAT
+ufunc__cosine_invcdf_types[2] = <char>NPY_DOUBLE
+ufunc__cosine_invcdf_types[3] = <char>NPY_DOUBLE
+ufunc__cosine_invcdf_ptr[2*0] = <void*>_func_cosine_invcdf
+ufunc__cosine_invcdf_ptr[2*0+1] = <void*>(<char*>"_cosine_invcdf")
+ufunc__cosine_invcdf_ptr[2*1] = <void*>_func_cosine_invcdf
+ufunc__cosine_invcdf_ptr[2*1+1] = <void*>(<char*>"_cosine_invcdf")
+ufunc__cosine_invcdf_data[0] = &ufunc__cosine_invcdf_ptr[2*0]
+ufunc__cosine_invcdf_data[1] = &ufunc__cosine_invcdf_ptr[2*1]
+_cosine_invcdf = np.PyUFunc_FromFuncAndData(ufunc__cosine_invcdf_loops, ufunc__cosine_invcdf_data, ufunc__cosine_invcdf_types, 2, 1, 1, 0, "_cosine_invcdf", ufunc__cosine_invcdf_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__cospi_loops[4]
+cdef void *ufunc__cospi_ptr[8]
+cdef void *ufunc__cospi_data[4]
+cdef char ufunc__cospi_types[8]
+cdef char *ufunc__cospi_doc = (
+    "Internal function, do not use.")
+ufunc__cospi_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__cospi_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__cospi_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc__cospi_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc__cospi_types[0] = <char>NPY_FLOAT
+ufunc__cospi_types[1] = <char>NPY_FLOAT
+ufunc__cospi_types[2] = <char>NPY_DOUBLE
+ufunc__cospi_types[3] = <char>NPY_DOUBLE
+ufunc__cospi_types[4] = <char>NPY_CFLOAT
+ufunc__cospi_types[5] = <char>NPY_CFLOAT
+ufunc__cospi_types[6] = <char>NPY_CDOUBLE
+ufunc__cospi_types[7] = <char>NPY_CDOUBLE
+ufunc__cospi_ptr[2*0] = <void*>_func_cospi
+ufunc__cospi_ptr[2*0+1] = <void*>(<char*>"_cospi")
+ufunc__cospi_ptr[2*1] = <void*>_func_cospi
+ufunc__cospi_ptr[2*1+1] = <void*>(<char*>"_cospi")
+ufunc__cospi_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_ccospi
+ufunc__cospi_ptr[2*2+1] = <void*>(<char*>"_cospi")
+ufunc__cospi_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_ccospi
+ufunc__cospi_ptr[2*3+1] = <void*>(<char*>"_cospi")
+ufunc__cospi_data[0] = &ufunc__cospi_ptr[2*0]
+ufunc__cospi_data[1] = &ufunc__cospi_ptr[2*1]
+ufunc__cospi_data[2] = &ufunc__cospi_ptr[2*2]
+ufunc__cospi_data[3] = &ufunc__cospi_ptr[2*3]
+_cospi = np.PyUFunc_FromFuncAndData(ufunc__cospi_loops, ufunc__cospi_data, ufunc__cospi_types, 4, 1, 1, 0, "_cospi", ufunc__cospi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__ellip_harm_loops[3]
+cdef void *ufunc__ellip_harm_ptr[6]
+cdef void *ufunc__ellip_harm_data[3]
+cdef char ufunc__ellip_harm_types[24]
+cdef char *ufunc__ellip_harm_doc = (
+    "Internal function, use `ellip_harm` instead.")
+ufunc__ellip_harm_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddddddd__As_fffffff_f
+ufunc__ellip_harm_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddiiddd__As_ddllddd_d
+ufunc__ellip_harm_loops[2] = <np.PyUFuncGenericFunction>loop_d_ddddddd__As_ddddddd_d
+ufunc__ellip_harm_types[0] = <char>NPY_FLOAT
+ufunc__ellip_harm_types[1] = <char>NPY_FLOAT
+ufunc__ellip_harm_types[2] = <char>NPY_FLOAT
+ufunc__ellip_harm_types[3] = <char>NPY_FLOAT
+ufunc__ellip_harm_types[4] = <char>NPY_FLOAT
+ufunc__ellip_harm_types[5] = <char>NPY_FLOAT
+ufunc__ellip_harm_types[6] = <char>NPY_FLOAT
+ufunc__ellip_harm_types[7] = <char>NPY_FLOAT
+ufunc__ellip_harm_types[8] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[9] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[10] = <char>NPY_LONG
+ufunc__ellip_harm_types[11] = <char>NPY_LONG
+ufunc__ellip_harm_types[12] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[13] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[14] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[15] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[16] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[17] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[18] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[19] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[20] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[21] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[22] = <char>NPY_DOUBLE
+ufunc__ellip_harm_types[23] = <char>NPY_DOUBLE
+ufunc__ellip_harm_ptr[2*0] = <void*>_func_ellip_harmonic_unsafe
+ufunc__ellip_harm_ptr[2*0+1] = <void*>(<char*>"_ellip_harm")
+ufunc__ellip_harm_ptr[2*1] = <void*>_func_ellip_harmonic
+ufunc__ellip_harm_ptr[2*1+1] = <void*>(<char*>"_ellip_harm")
+ufunc__ellip_harm_ptr[2*2] = <void*>_func_ellip_harmonic_unsafe
+ufunc__ellip_harm_ptr[2*2+1] = <void*>(<char*>"_ellip_harm")
+ufunc__ellip_harm_data[0] = &ufunc__ellip_harm_ptr[2*0]
+ufunc__ellip_harm_data[1] = &ufunc__ellip_harm_ptr[2*1]
+ufunc__ellip_harm_data[2] = &ufunc__ellip_harm_ptr[2*2]
+_ellip_harm = np.PyUFunc_FromFuncAndData(ufunc__ellip_harm_loops, ufunc__ellip_harm_data, ufunc__ellip_harm_types, 3, 7, 1, 0, "_ellip_harm", ufunc__ellip_harm_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__factorial_loops[2]
+cdef void *ufunc__factorial_ptr[4]
+cdef void *ufunc__factorial_data[2]
+cdef char ufunc__factorial_types[4]
+cdef char *ufunc__factorial_doc = (
+    "Internal function, do not use.")
+ufunc__factorial_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__factorial_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__factorial_types[0] = <char>NPY_FLOAT
+ufunc__factorial_types[1] = <char>NPY_FLOAT
+ufunc__factorial_types[2] = <char>NPY_DOUBLE
+ufunc__factorial_types[3] = <char>NPY_DOUBLE
+ufunc__factorial_ptr[2*0] = <void*>_func__factorial
+ufunc__factorial_ptr[2*0+1] = <void*>(<char*>"_factorial")
+ufunc__factorial_ptr[2*1] = <void*>_func__factorial
+ufunc__factorial_ptr[2*1+1] = <void*>(<char*>"_factorial")
+ufunc__factorial_data[0] = &ufunc__factorial_ptr[2*0]
+ufunc__factorial_data[1] = &ufunc__factorial_ptr[2*1]
+_factorial = np.PyUFunc_FromFuncAndData(ufunc__factorial_loops, ufunc__factorial_data, ufunc__factorial_types, 2, 1, 1, 0, "_factorial", ufunc__factorial_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__igam_fac_loops[2]
+cdef void *ufunc__igam_fac_ptr[4]
+cdef void *ufunc__igam_fac_data[2]
+cdef char ufunc__igam_fac_types[6]
+cdef char *ufunc__igam_fac_doc = (
+    "Internal function, do not use.")
+ufunc__igam_fac_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc__igam_fac_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc__igam_fac_types[0] = <char>NPY_FLOAT
+ufunc__igam_fac_types[1] = <char>NPY_FLOAT
+ufunc__igam_fac_types[2] = <char>NPY_FLOAT
+ufunc__igam_fac_types[3] = <char>NPY_DOUBLE
+ufunc__igam_fac_types[4] = <char>NPY_DOUBLE
+ufunc__igam_fac_types[5] = <char>NPY_DOUBLE
+ufunc__igam_fac_ptr[2*0] = <void*>_func_igam_fac
+ufunc__igam_fac_ptr[2*0+1] = <void*>(<char*>"_igam_fac")
+ufunc__igam_fac_ptr[2*1] = <void*>_func_igam_fac
+ufunc__igam_fac_ptr[2*1+1] = <void*>(<char*>"_igam_fac")
+ufunc__igam_fac_data[0] = &ufunc__igam_fac_ptr[2*0]
+ufunc__igam_fac_data[1] = &ufunc__igam_fac_ptr[2*1]
+_igam_fac = np.PyUFunc_FromFuncAndData(ufunc__igam_fac_loops, ufunc__igam_fac_data, ufunc__igam_fac_types, 2, 2, 1, 0, "_igam_fac", ufunc__igam_fac_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__kolmogc_loops[2]
+cdef void *ufunc__kolmogc_ptr[4]
+cdef void *ufunc__kolmogc_data[2]
+cdef char ufunc__kolmogc_types[4]
+cdef char *ufunc__kolmogc_doc = (
+    "Internal function, do not use.")
+ufunc__kolmogc_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__kolmogc_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__kolmogc_types[0] = <char>NPY_FLOAT
+ufunc__kolmogc_types[1] = <char>NPY_FLOAT
+ufunc__kolmogc_types[2] = <char>NPY_DOUBLE
+ufunc__kolmogc_types[3] = <char>NPY_DOUBLE
+ufunc__kolmogc_ptr[2*0] = <void*>_func_kolmogc
+ufunc__kolmogc_ptr[2*0+1] = <void*>(<char*>"_kolmogc")
+ufunc__kolmogc_ptr[2*1] = <void*>_func_kolmogc
+ufunc__kolmogc_ptr[2*1+1] = <void*>(<char*>"_kolmogc")
+ufunc__kolmogc_data[0] = &ufunc__kolmogc_ptr[2*0]
+ufunc__kolmogc_data[1] = &ufunc__kolmogc_ptr[2*1]
+_kolmogc = np.PyUFunc_FromFuncAndData(ufunc__kolmogc_loops, ufunc__kolmogc_data, ufunc__kolmogc_types, 2, 1, 1, 0, "_kolmogc", ufunc__kolmogc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__kolmogci_loops[2]
+cdef void *ufunc__kolmogci_ptr[4]
+cdef void *ufunc__kolmogci_data[2]
+cdef char ufunc__kolmogci_types[4]
+cdef char *ufunc__kolmogci_doc = (
+    "Internal function, do not use.")
+ufunc__kolmogci_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__kolmogci_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__kolmogci_types[0] = <char>NPY_FLOAT
+ufunc__kolmogci_types[1] = <char>NPY_FLOAT
+ufunc__kolmogci_types[2] = <char>NPY_DOUBLE
+ufunc__kolmogci_types[3] = <char>NPY_DOUBLE
+ufunc__kolmogci_ptr[2*0] = <void*>_func_kolmogci
+ufunc__kolmogci_ptr[2*0+1] = <void*>(<char*>"_kolmogci")
+ufunc__kolmogci_ptr[2*1] = <void*>_func_kolmogci
+ufunc__kolmogci_ptr[2*1+1] = <void*>(<char*>"_kolmogci")
+ufunc__kolmogci_data[0] = &ufunc__kolmogci_ptr[2*0]
+ufunc__kolmogci_data[1] = &ufunc__kolmogci_ptr[2*1]
+_kolmogci = np.PyUFunc_FromFuncAndData(ufunc__kolmogci_loops, ufunc__kolmogci_data, ufunc__kolmogci_types, 2, 1, 1, 0, "_kolmogci", ufunc__kolmogci_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__kolmogp_loops[2]
+cdef void *ufunc__kolmogp_ptr[4]
+cdef void *ufunc__kolmogp_data[2]
+cdef char ufunc__kolmogp_types[4]
+cdef char *ufunc__kolmogp_doc = (
+    "Internal function, do not use.")
+ufunc__kolmogp_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__kolmogp_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__kolmogp_types[0] = <char>NPY_FLOAT
+ufunc__kolmogp_types[1] = <char>NPY_FLOAT
+ufunc__kolmogp_types[2] = <char>NPY_DOUBLE
+ufunc__kolmogp_types[3] = <char>NPY_DOUBLE
+ufunc__kolmogp_ptr[2*0] = <void*>_func_kolmogp
+ufunc__kolmogp_ptr[2*0+1] = <void*>(<char*>"_kolmogp")
+ufunc__kolmogp_ptr[2*1] = <void*>_func_kolmogp
+ufunc__kolmogp_ptr[2*1+1] = <void*>(<char*>"_kolmogp")
+ufunc__kolmogp_data[0] = &ufunc__kolmogp_ptr[2*0]
+ufunc__kolmogp_data[1] = &ufunc__kolmogp_ptr[2*1]
+_kolmogp = np.PyUFunc_FromFuncAndData(ufunc__kolmogp_loops, ufunc__kolmogp_data, ufunc__kolmogp_types, 2, 1, 1, 0, "_kolmogp", ufunc__kolmogp_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__lambertw_loops[1]
+cdef void *ufunc__lambertw_ptr[2]
+cdef void *ufunc__lambertw_data[1]
+cdef char ufunc__lambertw_types[4]
+cdef char *ufunc__lambertw_doc = (
+    "Internal function, use `lambertw` instead.")
+ufunc__lambertw_loops[0] = <np.PyUFuncGenericFunction>loop_D_Dld__As_Dld_D
+ufunc__lambertw_types[0] = <char>NPY_CDOUBLE
+ufunc__lambertw_types[1] = <char>NPY_LONG
+ufunc__lambertw_types[2] = <char>NPY_DOUBLE
+ufunc__lambertw_types[3] = <char>NPY_CDOUBLE
+ufunc__lambertw_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_lambertw_scalar
+ufunc__lambertw_ptr[2*0+1] = <void*>(<char*>"_lambertw")
+ufunc__lambertw_data[0] = &ufunc__lambertw_ptr[2*0]
+_lambertw = np.PyUFunc_FromFuncAndData(ufunc__lambertw_loops, ufunc__lambertw_data, ufunc__lambertw_types, 1, 3, 1, 0, "_lambertw", ufunc__lambertw_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__lanczos_sum_expg_scaled_loops[2]
+cdef void *ufunc__lanczos_sum_expg_scaled_ptr[4]
+cdef void *ufunc__lanczos_sum_expg_scaled_data[2]
+cdef char ufunc__lanczos_sum_expg_scaled_types[4]
+cdef char *ufunc__lanczos_sum_expg_scaled_doc = (
+    "Internal function, do not use.")
+ufunc__lanczos_sum_expg_scaled_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__lanczos_sum_expg_scaled_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__lanczos_sum_expg_scaled_types[0] = <char>NPY_FLOAT
+ufunc__lanczos_sum_expg_scaled_types[1] = <char>NPY_FLOAT
+ufunc__lanczos_sum_expg_scaled_types[2] = <char>NPY_DOUBLE
+ufunc__lanczos_sum_expg_scaled_types[3] = <char>NPY_DOUBLE
+ufunc__lanczos_sum_expg_scaled_ptr[2*0] = <void*>_func_lanczos_sum_expg_scaled
+ufunc__lanczos_sum_expg_scaled_ptr[2*0+1] = <void*>(<char*>"_lanczos_sum_expg_scaled")
+ufunc__lanczos_sum_expg_scaled_ptr[2*1] = <void*>_func_lanczos_sum_expg_scaled
+ufunc__lanczos_sum_expg_scaled_ptr[2*1+1] = <void*>(<char*>"_lanczos_sum_expg_scaled")
+ufunc__lanczos_sum_expg_scaled_data[0] = &ufunc__lanczos_sum_expg_scaled_ptr[2*0]
+ufunc__lanczos_sum_expg_scaled_data[1] = &ufunc__lanczos_sum_expg_scaled_ptr[2*1]
+_lanczos_sum_expg_scaled = np.PyUFunc_FromFuncAndData(ufunc__lanczos_sum_expg_scaled_loops, ufunc__lanczos_sum_expg_scaled_data, ufunc__lanczos_sum_expg_scaled_types, 2, 1, 1, 0, "_lanczos_sum_expg_scaled", ufunc__lanczos_sum_expg_scaled_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__lgam1p_loops[2]
+cdef void *ufunc__lgam1p_ptr[4]
+cdef void *ufunc__lgam1p_data[2]
+cdef char ufunc__lgam1p_types[4]
+cdef char *ufunc__lgam1p_doc = (
+    "Internal function, do not use.")
+ufunc__lgam1p_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__lgam1p_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__lgam1p_types[0] = <char>NPY_FLOAT
+ufunc__lgam1p_types[1] = <char>NPY_FLOAT
+ufunc__lgam1p_types[2] = <char>NPY_DOUBLE
+ufunc__lgam1p_types[3] = <char>NPY_DOUBLE
+ufunc__lgam1p_ptr[2*0] = <void*>_func_lgam1p
+ufunc__lgam1p_ptr[2*0+1] = <void*>(<char*>"_lgam1p")
+ufunc__lgam1p_ptr[2*1] = <void*>_func_lgam1p
+ufunc__lgam1p_ptr[2*1+1] = <void*>(<char*>"_lgam1p")
+ufunc__lgam1p_data[0] = &ufunc__lgam1p_ptr[2*0]
+ufunc__lgam1p_data[1] = &ufunc__lgam1p_ptr[2*1]
+_lgam1p = np.PyUFunc_FromFuncAndData(ufunc__lgam1p_loops, ufunc__lgam1p_data, ufunc__lgam1p_types, 2, 1, 1, 0, "_lgam1p", ufunc__lgam1p_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__log1pmx_loops[2]
+cdef void *ufunc__log1pmx_ptr[4]
+cdef void *ufunc__log1pmx_data[2]
+cdef char ufunc__log1pmx_types[4]
+cdef char *ufunc__log1pmx_doc = (
+    "Internal function, do not use.")
+ufunc__log1pmx_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__log1pmx_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__log1pmx_types[0] = <char>NPY_FLOAT
+ufunc__log1pmx_types[1] = <char>NPY_FLOAT
+ufunc__log1pmx_types[2] = <char>NPY_DOUBLE
+ufunc__log1pmx_types[3] = <char>NPY_DOUBLE
+ufunc__log1pmx_ptr[2*0] = <void*>_func_log1pmx
+ufunc__log1pmx_ptr[2*0+1] = <void*>(<char*>"_log1pmx")
+ufunc__log1pmx_ptr[2*1] = <void*>_func_log1pmx
+ufunc__log1pmx_ptr[2*1+1] = <void*>(<char*>"_log1pmx")
+ufunc__log1pmx_data[0] = &ufunc__log1pmx_ptr[2*0]
+ufunc__log1pmx_data[1] = &ufunc__log1pmx_ptr[2*1]
+_log1pmx = np.PyUFunc_FromFuncAndData(ufunc__log1pmx_loops, ufunc__log1pmx_data, ufunc__log1pmx_types, 2, 1, 1, 0, "_log1pmx", ufunc__log1pmx_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__riemann_zeta_loops[2]
+cdef void *ufunc__riemann_zeta_ptr[4]
+cdef void *ufunc__riemann_zeta_data[2]
+cdef char ufunc__riemann_zeta_types[4]
+cdef char *ufunc__riemann_zeta_doc = (
+    "Internal function, use `zeta` instead.")
+ufunc__riemann_zeta_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__riemann_zeta_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__riemann_zeta_types[0] = <char>NPY_FLOAT
+ufunc__riemann_zeta_types[1] = <char>NPY_FLOAT
+ufunc__riemann_zeta_types[2] = <char>NPY_DOUBLE
+ufunc__riemann_zeta_types[3] = <char>NPY_DOUBLE
+ufunc__riemann_zeta_ptr[2*0] = <void*>_func_riemann_zeta
+ufunc__riemann_zeta_ptr[2*0+1] = <void*>(<char*>"_riemann_zeta")
+ufunc__riemann_zeta_ptr[2*1] = <void*>_func_riemann_zeta
+ufunc__riemann_zeta_ptr[2*1+1] = <void*>(<char*>"_riemann_zeta")
+ufunc__riemann_zeta_data[0] = &ufunc__riemann_zeta_ptr[2*0]
+ufunc__riemann_zeta_data[1] = &ufunc__riemann_zeta_ptr[2*1]
+_riemann_zeta = np.PyUFunc_FromFuncAndData(ufunc__riemann_zeta_loops, ufunc__riemann_zeta_data, ufunc__riemann_zeta_types, 2, 1, 1, 0, "_riemann_zeta", ufunc__riemann_zeta_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__scaled_exp1_loops[2]
+cdef void *ufunc__scaled_exp1_ptr[4]
+cdef void *ufunc__scaled_exp1_data[2]
+cdef char ufunc__scaled_exp1_types[4]
+cdef char *ufunc__scaled_exp1_doc = (
+    "_scaled_exp1(x, out=None):\n"
+    "\n"
+    "Compute the scaled exponential integral.\n"
+    "\n"
+    "This is a private function, subject to change or removal with no\n"
+    "deprecation.\n"
+    "\n"
+    "This function computes F(x), where F is the factor remaining in E_1(x)\n"
+    "when exp(-x)/x is factored out.  That is,::\n"
+    "\n"
+    "    E_1(x) = exp(-x)/x * F(x)\n"
+    "\n"
+    "or\n"
+    "\n"
+    "    F(x) = x * exp(x) * E_1(x)\n"
+    "\n"
+    "The function is defined for real x >= 0.  For x < 0, nan is returned.\n"
+    "\n"
+    "F has the properties:\n"
+    "\n"
+    "* F(0) = 0\n"
+    "* F(x) is increasing on [0, inf).\n"
+    "* The limit as x goes to infinity of F(x) is 1.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x: array_like\n"
+    "    The input values. Must be real.  The implementation is limited to\n"
+    "    double precision floating point, so other types will be cast to\n"
+    "    to double precision.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the scaled exponential integral.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "exp1 : exponential integral E_1\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import _scaled_exp1\n"
+    ">>> _scaled_exp1([0, 0.1, 1, 10, 100])")
+ufunc__scaled_exp1_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__scaled_exp1_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__scaled_exp1_types[0] = <char>NPY_FLOAT
+ufunc__scaled_exp1_types[1] = <char>NPY_FLOAT
+ufunc__scaled_exp1_types[2] = <char>NPY_DOUBLE
+ufunc__scaled_exp1_types[3] = <char>NPY_DOUBLE
+ufunc__scaled_exp1_ptr[2*0] = <void*>_func_scaled_exp1
+ufunc__scaled_exp1_ptr[2*0+1] = <void*>(<char*>"_scaled_exp1")
+ufunc__scaled_exp1_ptr[2*1] = <void*>_func_scaled_exp1
+ufunc__scaled_exp1_ptr[2*1+1] = <void*>(<char*>"_scaled_exp1")
+ufunc__scaled_exp1_data[0] = &ufunc__scaled_exp1_ptr[2*0]
+ufunc__scaled_exp1_data[1] = &ufunc__scaled_exp1_ptr[2*1]
+_scaled_exp1 = np.PyUFunc_FromFuncAndData(ufunc__scaled_exp1_loops, ufunc__scaled_exp1_data, ufunc__scaled_exp1_types, 2, 1, 1, 0, "_scaled_exp1", ufunc__scaled_exp1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__sf_error_test_function_loops[1]
+cdef void *ufunc__sf_error_test_function_ptr[2]
+cdef void *ufunc__sf_error_test_function_data[1]
+cdef char ufunc__sf_error_test_function_types[2]
+cdef char *ufunc__sf_error_test_function_doc = (
+    "Private function; do not use.")
+ufunc__sf_error_test_function_loops[0] = <np.PyUFuncGenericFunction>loop_i_i__As_l_l
+ufunc__sf_error_test_function_types[0] = <char>NPY_LONG
+ufunc__sf_error_test_function_types[1] = <char>NPY_LONG
+ufunc__sf_error_test_function_ptr[2*0] = <void*>_func__sf_error_test_function
+ufunc__sf_error_test_function_ptr[2*0+1] = <void*>(<char*>"_sf_error_test_function")
+ufunc__sf_error_test_function_data[0] = &ufunc__sf_error_test_function_ptr[2*0]
+_sf_error_test_function = np.PyUFunc_FromFuncAndData(ufunc__sf_error_test_function_loops, ufunc__sf_error_test_function_data, ufunc__sf_error_test_function_types, 1, 1, 1, 0, "_sf_error_test_function", ufunc__sf_error_test_function_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__sinpi_loops[4]
+cdef void *ufunc__sinpi_ptr[8]
+cdef void *ufunc__sinpi_data[4]
+cdef char ufunc__sinpi_types[8]
+cdef char *ufunc__sinpi_doc = (
+    "Internal function, do not use.")
+ufunc__sinpi_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc__sinpi_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc__sinpi_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc__sinpi_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc__sinpi_types[0] = <char>NPY_FLOAT
+ufunc__sinpi_types[1] = <char>NPY_FLOAT
+ufunc__sinpi_types[2] = <char>NPY_DOUBLE
+ufunc__sinpi_types[3] = <char>NPY_DOUBLE
+ufunc__sinpi_types[4] = <char>NPY_CFLOAT
+ufunc__sinpi_types[5] = <char>NPY_CFLOAT
+ufunc__sinpi_types[6] = <char>NPY_CDOUBLE
+ufunc__sinpi_types[7] = <char>NPY_CDOUBLE
+ufunc__sinpi_ptr[2*0] = <void*>_func_sinpi
+ufunc__sinpi_ptr[2*0+1] = <void*>(<char*>"_sinpi")
+ufunc__sinpi_ptr[2*1] = <void*>_func_sinpi
+ufunc__sinpi_ptr[2*1+1] = <void*>(<char*>"_sinpi")
+ufunc__sinpi_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_csinpi
+ufunc__sinpi_ptr[2*2+1] = <void*>(<char*>"_sinpi")
+ufunc__sinpi_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_csinpi
+ufunc__sinpi_ptr[2*3+1] = <void*>(<char*>"_sinpi")
+ufunc__sinpi_data[0] = &ufunc__sinpi_ptr[2*0]
+ufunc__sinpi_data[1] = &ufunc__sinpi_ptr[2*1]
+ufunc__sinpi_data[2] = &ufunc__sinpi_ptr[2*2]
+ufunc__sinpi_data[3] = &ufunc__sinpi_ptr[2*3]
+_sinpi = np.PyUFunc_FromFuncAndData(ufunc__sinpi_loops, ufunc__sinpi_data, ufunc__sinpi_types, 4, 1, 1, 0, "_sinpi", ufunc__sinpi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__smirnovc_loops[3]
+cdef void *ufunc__smirnovc_ptr[6]
+cdef void *ufunc__smirnovc_data[3]
+cdef char ufunc__smirnovc_types[9]
+cdef char *ufunc__smirnovc_doc = (
+    "_smirnovc(n, d)\n"
+    " Internal function, do not use.")
+ufunc__smirnovc_loops[0] = <np.PyUFuncGenericFunction>loop_d_id__As_ld_d
+ufunc__smirnovc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc__smirnovc_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc__smirnovc_types[0] = <char>NPY_LONG
+ufunc__smirnovc_types[1] = <char>NPY_DOUBLE
+ufunc__smirnovc_types[2] = <char>NPY_DOUBLE
+ufunc__smirnovc_types[3] = <char>NPY_FLOAT
+ufunc__smirnovc_types[4] = <char>NPY_FLOAT
+ufunc__smirnovc_types[5] = <char>NPY_FLOAT
+ufunc__smirnovc_types[6] = <char>NPY_DOUBLE
+ufunc__smirnovc_types[7] = <char>NPY_DOUBLE
+ufunc__smirnovc_types[8] = <char>NPY_DOUBLE
+ufunc__smirnovc_ptr[2*0] = <void*>_func_smirnovc
+ufunc__smirnovc_ptr[2*0+1] = <void*>(<char*>"_smirnovc")
+ufunc__smirnovc_ptr[2*1] = <void*>_func_smirnovc_unsafe
+ufunc__smirnovc_ptr[2*1+1] = <void*>(<char*>"_smirnovc")
+ufunc__smirnovc_ptr[2*2] = <void*>_func_smirnovc_unsafe
+ufunc__smirnovc_ptr[2*2+1] = <void*>(<char*>"_smirnovc")
+ufunc__smirnovc_data[0] = &ufunc__smirnovc_ptr[2*0]
+ufunc__smirnovc_data[1] = &ufunc__smirnovc_ptr[2*1]
+ufunc__smirnovc_data[2] = &ufunc__smirnovc_ptr[2*2]
+_smirnovc = np.PyUFunc_FromFuncAndData(ufunc__smirnovc_loops, ufunc__smirnovc_data, ufunc__smirnovc_types, 3, 2, 1, 0, "_smirnovc", ufunc__smirnovc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__smirnovci_loops[3]
+cdef void *ufunc__smirnovci_ptr[6]
+cdef void *ufunc__smirnovci_data[3]
+cdef char ufunc__smirnovci_types[9]
+cdef char *ufunc__smirnovci_doc = (
+    "Internal function, do not use.")
+ufunc__smirnovci_loops[0] = <np.PyUFuncGenericFunction>loop_d_id__As_ld_d
+ufunc__smirnovci_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc__smirnovci_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc__smirnovci_types[0] = <char>NPY_LONG
+ufunc__smirnovci_types[1] = <char>NPY_DOUBLE
+ufunc__smirnovci_types[2] = <char>NPY_DOUBLE
+ufunc__smirnovci_types[3] = <char>NPY_FLOAT
+ufunc__smirnovci_types[4] = <char>NPY_FLOAT
+ufunc__smirnovci_types[5] = <char>NPY_FLOAT
+ufunc__smirnovci_types[6] = <char>NPY_DOUBLE
+ufunc__smirnovci_types[7] = <char>NPY_DOUBLE
+ufunc__smirnovci_types[8] = <char>NPY_DOUBLE
+ufunc__smirnovci_ptr[2*0] = <void*>_func_smirnovci
+ufunc__smirnovci_ptr[2*0+1] = <void*>(<char*>"_smirnovci")
+ufunc__smirnovci_ptr[2*1] = <void*>_func_smirnovci_unsafe
+ufunc__smirnovci_ptr[2*1+1] = <void*>(<char*>"_smirnovci")
+ufunc__smirnovci_ptr[2*2] = <void*>_func_smirnovci_unsafe
+ufunc__smirnovci_ptr[2*2+1] = <void*>(<char*>"_smirnovci")
+ufunc__smirnovci_data[0] = &ufunc__smirnovci_ptr[2*0]
+ufunc__smirnovci_data[1] = &ufunc__smirnovci_ptr[2*1]
+ufunc__smirnovci_data[2] = &ufunc__smirnovci_ptr[2*2]
+_smirnovci = np.PyUFunc_FromFuncAndData(ufunc__smirnovci_loops, ufunc__smirnovci_data, ufunc__smirnovci_types, 3, 2, 1, 0, "_smirnovci", ufunc__smirnovci_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__smirnovp_loops[3]
+cdef void *ufunc__smirnovp_ptr[6]
+cdef void *ufunc__smirnovp_data[3]
+cdef char ufunc__smirnovp_types[9]
+cdef char *ufunc__smirnovp_doc = (
+    "_smirnovp(n, p)\n"
+    " Internal function, do not use.")
+ufunc__smirnovp_loops[0] = <np.PyUFuncGenericFunction>loop_d_id__As_ld_d
+ufunc__smirnovp_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc__smirnovp_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc__smirnovp_types[0] = <char>NPY_LONG
+ufunc__smirnovp_types[1] = <char>NPY_DOUBLE
+ufunc__smirnovp_types[2] = <char>NPY_DOUBLE
+ufunc__smirnovp_types[3] = <char>NPY_FLOAT
+ufunc__smirnovp_types[4] = <char>NPY_FLOAT
+ufunc__smirnovp_types[5] = <char>NPY_FLOAT
+ufunc__smirnovp_types[6] = <char>NPY_DOUBLE
+ufunc__smirnovp_types[7] = <char>NPY_DOUBLE
+ufunc__smirnovp_types[8] = <char>NPY_DOUBLE
+ufunc__smirnovp_ptr[2*0] = <void*>_func_smirnovp
+ufunc__smirnovp_ptr[2*0+1] = <void*>(<char*>"_smirnovp")
+ufunc__smirnovp_ptr[2*1] = <void*>_func_smirnovp_unsafe
+ufunc__smirnovp_ptr[2*1+1] = <void*>(<char*>"_smirnovp")
+ufunc__smirnovp_ptr[2*2] = <void*>_func_smirnovp_unsafe
+ufunc__smirnovp_ptr[2*2+1] = <void*>(<char*>"_smirnovp")
+ufunc__smirnovp_data[0] = &ufunc__smirnovp_ptr[2*0]
+ufunc__smirnovp_data[1] = &ufunc__smirnovp_ptr[2*1]
+ufunc__smirnovp_data[2] = &ufunc__smirnovp_ptr[2*2]
+_smirnovp = np.PyUFunc_FromFuncAndData(ufunc__smirnovp_loops, ufunc__smirnovp_data, ufunc__smirnovp_types, 3, 2, 1, 0, "_smirnovp", ufunc__smirnovp_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__spherical_in_loops[2]
+cdef void *ufunc__spherical_in_ptr[4]
+cdef void *ufunc__spherical_in_data[2]
+cdef char ufunc__spherical_in_types[6]
+cdef char *ufunc__spherical_in_doc = (
+    "Internal function, use `spherical_in` instead.")
+ufunc__spherical_in_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc__spherical_in_loops[1] = <np.PyUFuncGenericFunction>loop_D_lD__As_lD_D
+ufunc__spherical_in_types[0] = <char>NPY_LONG
+ufunc__spherical_in_types[1] = <char>NPY_DOUBLE
+ufunc__spherical_in_types[2] = <char>NPY_DOUBLE
+ufunc__spherical_in_types[3] = <char>NPY_LONG
+ufunc__spherical_in_types[4] = <char>NPY_CDOUBLE
+ufunc__spherical_in_types[5] = <char>NPY_CDOUBLE
+ufunc__spherical_in_ptr[2*0] = <void*>_func_spherical_in_real
+ufunc__spherical_in_ptr[2*0+1] = <void*>(<char*>"_spherical_in")
+ufunc__spherical_in_ptr[2*1] = <void*>_func_spherical_in_complex
+ufunc__spherical_in_ptr[2*1+1] = <void*>(<char*>"_spherical_in")
+ufunc__spherical_in_data[0] = &ufunc__spherical_in_ptr[2*0]
+ufunc__spherical_in_data[1] = &ufunc__spherical_in_ptr[2*1]
+_spherical_in = np.PyUFunc_FromFuncAndData(ufunc__spherical_in_loops, ufunc__spherical_in_data, ufunc__spherical_in_types, 2, 2, 1, 0, "_spherical_in", ufunc__spherical_in_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__spherical_in_d_loops[2]
+cdef void *ufunc__spherical_in_d_ptr[4]
+cdef void *ufunc__spherical_in_d_data[2]
+cdef char ufunc__spherical_in_d_types[6]
+cdef char *ufunc__spherical_in_d_doc = (
+    "Internal function, use `spherical_in` instead.")
+ufunc__spherical_in_d_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc__spherical_in_d_loops[1] = <np.PyUFuncGenericFunction>loop_D_lD__As_lD_D
+ufunc__spherical_in_d_types[0] = <char>NPY_LONG
+ufunc__spherical_in_d_types[1] = <char>NPY_DOUBLE
+ufunc__spherical_in_d_types[2] = <char>NPY_DOUBLE
+ufunc__spherical_in_d_types[3] = <char>NPY_LONG
+ufunc__spherical_in_d_types[4] = <char>NPY_CDOUBLE
+ufunc__spherical_in_d_types[5] = <char>NPY_CDOUBLE
+ufunc__spherical_in_d_ptr[2*0] = <void*>_func_spherical_in_d_real
+ufunc__spherical_in_d_ptr[2*0+1] = <void*>(<char*>"_spherical_in_d")
+ufunc__spherical_in_d_ptr[2*1] = <void*>_func_spherical_in_d_complex
+ufunc__spherical_in_d_ptr[2*1+1] = <void*>(<char*>"_spherical_in_d")
+ufunc__spherical_in_d_data[0] = &ufunc__spherical_in_d_ptr[2*0]
+ufunc__spherical_in_d_data[1] = &ufunc__spherical_in_d_ptr[2*1]
+_spherical_in_d = np.PyUFunc_FromFuncAndData(ufunc__spherical_in_d_loops, ufunc__spherical_in_d_data, ufunc__spherical_in_d_types, 2, 2, 1, 0, "_spherical_in_d", ufunc__spherical_in_d_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__spherical_jn_loops[2]
+cdef void *ufunc__spherical_jn_ptr[4]
+cdef void *ufunc__spherical_jn_data[2]
+cdef char ufunc__spherical_jn_types[6]
+cdef char *ufunc__spherical_jn_doc = (
+    "Internal function, use `spherical_jn` instead.")
+ufunc__spherical_jn_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc__spherical_jn_loops[1] = <np.PyUFuncGenericFunction>loop_D_lD__As_lD_D
+ufunc__spherical_jn_types[0] = <char>NPY_LONG
+ufunc__spherical_jn_types[1] = <char>NPY_DOUBLE
+ufunc__spherical_jn_types[2] = <char>NPY_DOUBLE
+ufunc__spherical_jn_types[3] = <char>NPY_LONG
+ufunc__spherical_jn_types[4] = <char>NPY_CDOUBLE
+ufunc__spherical_jn_types[5] = <char>NPY_CDOUBLE
+ufunc__spherical_jn_ptr[2*0] = <void*>_func_spherical_jn_real
+ufunc__spherical_jn_ptr[2*0+1] = <void*>(<char*>"_spherical_jn")
+ufunc__spherical_jn_ptr[2*1] = <void*>_func_spherical_jn_complex
+ufunc__spherical_jn_ptr[2*1+1] = <void*>(<char*>"_spherical_jn")
+ufunc__spherical_jn_data[0] = &ufunc__spherical_jn_ptr[2*0]
+ufunc__spherical_jn_data[1] = &ufunc__spherical_jn_ptr[2*1]
+_spherical_jn = np.PyUFunc_FromFuncAndData(ufunc__spherical_jn_loops, ufunc__spherical_jn_data, ufunc__spherical_jn_types, 2, 2, 1, 0, "_spherical_jn", ufunc__spherical_jn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__spherical_jn_d_loops[2]
+cdef void *ufunc__spherical_jn_d_ptr[4]
+cdef void *ufunc__spherical_jn_d_data[2]
+cdef char ufunc__spherical_jn_d_types[6]
+cdef char *ufunc__spherical_jn_d_doc = (
+    "Internal function, use `spherical_jn` instead.")
+ufunc__spherical_jn_d_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc__spherical_jn_d_loops[1] = <np.PyUFuncGenericFunction>loop_D_lD__As_lD_D
+ufunc__spherical_jn_d_types[0] = <char>NPY_LONG
+ufunc__spherical_jn_d_types[1] = <char>NPY_DOUBLE
+ufunc__spherical_jn_d_types[2] = <char>NPY_DOUBLE
+ufunc__spherical_jn_d_types[3] = <char>NPY_LONG
+ufunc__spherical_jn_d_types[4] = <char>NPY_CDOUBLE
+ufunc__spherical_jn_d_types[5] = <char>NPY_CDOUBLE
+ufunc__spherical_jn_d_ptr[2*0] = <void*>_func_spherical_jn_d_real
+ufunc__spherical_jn_d_ptr[2*0+1] = <void*>(<char*>"_spherical_jn_d")
+ufunc__spherical_jn_d_ptr[2*1] = <void*>_func_spherical_jn_d_complex
+ufunc__spherical_jn_d_ptr[2*1+1] = <void*>(<char*>"_spherical_jn_d")
+ufunc__spherical_jn_d_data[0] = &ufunc__spherical_jn_d_ptr[2*0]
+ufunc__spherical_jn_d_data[1] = &ufunc__spherical_jn_d_ptr[2*1]
+_spherical_jn_d = np.PyUFunc_FromFuncAndData(ufunc__spherical_jn_d_loops, ufunc__spherical_jn_d_data, ufunc__spherical_jn_d_types, 2, 2, 1, 0, "_spherical_jn_d", ufunc__spherical_jn_d_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__spherical_kn_loops[2]
+cdef void *ufunc__spherical_kn_ptr[4]
+cdef void *ufunc__spherical_kn_data[2]
+cdef char ufunc__spherical_kn_types[6]
+cdef char *ufunc__spherical_kn_doc = (
+    "Internal function, use `spherical_kn` instead.")
+ufunc__spherical_kn_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc__spherical_kn_loops[1] = <np.PyUFuncGenericFunction>loop_D_lD__As_lD_D
+ufunc__spherical_kn_types[0] = <char>NPY_LONG
+ufunc__spherical_kn_types[1] = <char>NPY_DOUBLE
+ufunc__spherical_kn_types[2] = <char>NPY_DOUBLE
+ufunc__spherical_kn_types[3] = <char>NPY_LONG
+ufunc__spherical_kn_types[4] = <char>NPY_CDOUBLE
+ufunc__spherical_kn_types[5] = <char>NPY_CDOUBLE
+ufunc__spherical_kn_ptr[2*0] = <void*>_func_spherical_kn_real
+ufunc__spherical_kn_ptr[2*0+1] = <void*>(<char*>"_spherical_kn")
+ufunc__spherical_kn_ptr[2*1] = <void*>_func_spherical_kn_complex
+ufunc__spherical_kn_ptr[2*1+1] = <void*>(<char*>"_spherical_kn")
+ufunc__spherical_kn_data[0] = &ufunc__spherical_kn_ptr[2*0]
+ufunc__spherical_kn_data[1] = &ufunc__spherical_kn_ptr[2*1]
+_spherical_kn = np.PyUFunc_FromFuncAndData(ufunc__spherical_kn_loops, ufunc__spherical_kn_data, ufunc__spherical_kn_types, 2, 2, 1, 0, "_spherical_kn", ufunc__spherical_kn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__spherical_kn_d_loops[2]
+cdef void *ufunc__spherical_kn_d_ptr[4]
+cdef void *ufunc__spherical_kn_d_data[2]
+cdef char ufunc__spherical_kn_d_types[6]
+cdef char *ufunc__spherical_kn_d_doc = (
+    "Internal function, use `spherical_kn` instead.")
+ufunc__spherical_kn_d_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc__spherical_kn_d_loops[1] = <np.PyUFuncGenericFunction>loop_D_lD__As_lD_D
+ufunc__spherical_kn_d_types[0] = <char>NPY_LONG
+ufunc__spherical_kn_d_types[1] = <char>NPY_DOUBLE
+ufunc__spherical_kn_d_types[2] = <char>NPY_DOUBLE
+ufunc__spherical_kn_d_types[3] = <char>NPY_LONG
+ufunc__spherical_kn_d_types[4] = <char>NPY_CDOUBLE
+ufunc__spherical_kn_d_types[5] = <char>NPY_CDOUBLE
+ufunc__spherical_kn_d_ptr[2*0] = <void*>_func_spherical_kn_d_real
+ufunc__spherical_kn_d_ptr[2*0+1] = <void*>(<char*>"_spherical_kn_d")
+ufunc__spherical_kn_d_ptr[2*1] = <void*>_func_spherical_kn_d_complex
+ufunc__spherical_kn_d_ptr[2*1+1] = <void*>(<char*>"_spherical_kn_d")
+ufunc__spherical_kn_d_data[0] = &ufunc__spherical_kn_d_ptr[2*0]
+ufunc__spherical_kn_d_data[1] = &ufunc__spherical_kn_d_ptr[2*1]
+_spherical_kn_d = np.PyUFunc_FromFuncAndData(ufunc__spherical_kn_d_loops, ufunc__spherical_kn_d_data, ufunc__spherical_kn_d_types, 2, 2, 1, 0, "_spherical_kn_d", ufunc__spherical_kn_d_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__spherical_yn_loops[2]
+cdef void *ufunc__spherical_yn_ptr[4]
+cdef void *ufunc__spherical_yn_data[2]
+cdef char ufunc__spherical_yn_types[6]
+cdef char *ufunc__spherical_yn_doc = (
+    "Internal function, use `spherical_yn` instead.")
+ufunc__spherical_yn_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc__spherical_yn_loops[1] = <np.PyUFuncGenericFunction>loop_D_lD__As_lD_D
+ufunc__spherical_yn_types[0] = <char>NPY_LONG
+ufunc__spherical_yn_types[1] = <char>NPY_DOUBLE
+ufunc__spherical_yn_types[2] = <char>NPY_DOUBLE
+ufunc__spherical_yn_types[3] = <char>NPY_LONG
+ufunc__spherical_yn_types[4] = <char>NPY_CDOUBLE
+ufunc__spherical_yn_types[5] = <char>NPY_CDOUBLE
+ufunc__spherical_yn_ptr[2*0] = <void*>_func_spherical_yn_real
+ufunc__spherical_yn_ptr[2*0+1] = <void*>(<char*>"_spherical_yn")
+ufunc__spherical_yn_ptr[2*1] = <void*>_func_spherical_yn_complex
+ufunc__spherical_yn_ptr[2*1+1] = <void*>(<char*>"_spherical_yn")
+ufunc__spherical_yn_data[0] = &ufunc__spherical_yn_ptr[2*0]
+ufunc__spherical_yn_data[1] = &ufunc__spherical_yn_ptr[2*1]
+_spherical_yn = np.PyUFunc_FromFuncAndData(ufunc__spherical_yn_loops, ufunc__spherical_yn_data, ufunc__spherical_yn_types, 2, 2, 1, 0, "_spherical_yn", ufunc__spherical_yn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__spherical_yn_d_loops[2]
+cdef void *ufunc__spherical_yn_d_ptr[4]
+cdef void *ufunc__spherical_yn_d_data[2]
+cdef char ufunc__spherical_yn_d_types[6]
+cdef char *ufunc__spherical_yn_d_doc = (
+    "Internal function, use `spherical_yn` instead.")
+ufunc__spherical_yn_d_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc__spherical_yn_d_loops[1] = <np.PyUFuncGenericFunction>loop_D_lD__As_lD_D
+ufunc__spherical_yn_d_types[0] = <char>NPY_LONG
+ufunc__spherical_yn_d_types[1] = <char>NPY_DOUBLE
+ufunc__spherical_yn_d_types[2] = <char>NPY_DOUBLE
+ufunc__spherical_yn_d_types[3] = <char>NPY_LONG
+ufunc__spherical_yn_d_types[4] = <char>NPY_CDOUBLE
+ufunc__spherical_yn_d_types[5] = <char>NPY_CDOUBLE
+ufunc__spherical_yn_d_ptr[2*0] = <void*>_func_spherical_yn_d_real
+ufunc__spherical_yn_d_ptr[2*0+1] = <void*>(<char*>"_spherical_yn_d")
+ufunc__spherical_yn_d_ptr[2*1] = <void*>_func_spherical_yn_d_complex
+ufunc__spherical_yn_d_ptr[2*1+1] = <void*>(<char*>"_spherical_yn_d")
+ufunc__spherical_yn_d_data[0] = &ufunc__spherical_yn_d_ptr[2*0]
+ufunc__spherical_yn_d_data[1] = &ufunc__spherical_yn_d_ptr[2*1]
+_spherical_yn_d = np.PyUFunc_FromFuncAndData(ufunc__spherical_yn_d_loops, ufunc__spherical_yn_d_data, ufunc__spherical_yn_d_types, 2, 2, 1, 0, "_spherical_yn_d", ufunc__spherical_yn_d_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__stirling2_inexact_loops[2]
+cdef void *ufunc__stirling2_inexact_ptr[4]
+cdef void *ufunc__stirling2_inexact_data[2]
+cdef char ufunc__stirling2_inexact_types[6]
+cdef char *ufunc__stirling2_inexact_doc = (
+    "Internal function, do not use.")
+ufunc__stirling2_inexact_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc__stirling2_inexact_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc__stirling2_inexact_types[0] = <char>NPY_FLOAT
+ufunc__stirling2_inexact_types[1] = <char>NPY_FLOAT
+ufunc__stirling2_inexact_types[2] = <char>NPY_FLOAT
+ufunc__stirling2_inexact_types[3] = <char>NPY_DOUBLE
+ufunc__stirling2_inexact_types[4] = <char>NPY_DOUBLE
+ufunc__stirling2_inexact_types[5] = <char>NPY_DOUBLE
+ufunc__stirling2_inexact_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export__stirling2_inexact
+ufunc__stirling2_inexact_ptr[2*0+1] = <void*>(<char*>"_stirling2_inexact")
+ufunc__stirling2_inexact_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export__stirling2_inexact
+ufunc__stirling2_inexact_ptr[2*1+1] = <void*>(<char*>"_stirling2_inexact")
+ufunc__stirling2_inexact_data[0] = &ufunc__stirling2_inexact_ptr[2*0]
+ufunc__stirling2_inexact_data[1] = &ufunc__stirling2_inexact_ptr[2*1]
+_stirling2_inexact = np.PyUFunc_FromFuncAndData(ufunc__stirling2_inexact_loops, ufunc__stirling2_inexact_data, ufunc__stirling2_inexact_types, 2, 2, 1, 0, "_stirling2_inexact", ufunc__stirling2_inexact_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__struve_asymp_large_z_loops[1]
+cdef void *ufunc__struve_asymp_large_z_ptr[2]
+cdef void *ufunc__struve_asymp_large_z_data[1]
+cdef char ufunc__struve_asymp_large_z_types[5]
+cdef char *ufunc__struve_asymp_large_z_doc = (
+    "_struve_asymp_large_z(v, z, is_h)\n"
+    "\n"
+    "Internal function for testing `struve` & `modstruve`\n"
+    "\n"
+    "Evaluates using asymptotic expansion\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "v, err")
+ufunc__struve_asymp_large_z_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddi_d_As_ddl_dd
+ufunc__struve_asymp_large_z_types[0] = <char>NPY_DOUBLE
+ufunc__struve_asymp_large_z_types[1] = <char>NPY_DOUBLE
+ufunc__struve_asymp_large_z_types[2] = <char>NPY_LONG
+ufunc__struve_asymp_large_z_types[3] = <char>NPY_DOUBLE
+ufunc__struve_asymp_large_z_types[4] = <char>NPY_DOUBLE
+ufunc__struve_asymp_large_z_ptr[2*0] = <void*>_func_struve_asymp_large_z
+ufunc__struve_asymp_large_z_ptr[2*0+1] = <void*>(<char*>"_struve_asymp_large_z")
+ufunc__struve_asymp_large_z_data[0] = &ufunc__struve_asymp_large_z_ptr[2*0]
+_struve_asymp_large_z = np.PyUFunc_FromFuncAndData(ufunc__struve_asymp_large_z_loops, ufunc__struve_asymp_large_z_data, ufunc__struve_asymp_large_z_types, 1, 3, 2, 0, "_struve_asymp_large_z", ufunc__struve_asymp_large_z_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__struve_bessel_series_loops[1]
+cdef void *ufunc__struve_bessel_series_ptr[2]
+cdef void *ufunc__struve_bessel_series_data[1]
+cdef char ufunc__struve_bessel_series_types[5]
+cdef char *ufunc__struve_bessel_series_doc = (
+    "_struve_bessel_series(v, z, is_h)\n"
+    "\n"
+    "Internal function for testing `struve` & `modstruve`\n"
+    "\n"
+    "Evaluates using Bessel function series\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "v, err")
+ufunc__struve_bessel_series_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddi_d_As_ddl_dd
+ufunc__struve_bessel_series_types[0] = <char>NPY_DOUBLE
+ufunc__struve_bessel_series_types[1] = <char>NPY_DOUBLE
+ufunc__struve_bessel_series_types[2] = <char>NPY_LONG
+ufunc__struve_bessel_series_types[3] = <char>NPY_DOUBLE
+ufunc__struve_bessel_series_types[4] = <char>NPY_DOUBLE
+ufunc__struve_bessel_series_ptr[2*0] = <void*>_func_struve_bessel_series
+ufunc__struve_bessel_series_ptr[2*0+1] = <void*>(<char*>"_struve_bessel_series")
+ufunc__struve_bessel_series_data[0] = &ufunc__struve_bessel_series_ptr[2*0]
+_struve_bessel_series = np.PyUFunc_FromFuncAndData(ufunc__struve_bessel_series_loops, ufunc__struve_bessel_series_data, ufunc__struve_bessel_series_types, 1, 3, 2, 0, "_struve_bessel_series", ufunc__struve_bessel_series_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__struve_power_series_loops[1]
+cdef void *ufunc__struve_power_series_ptr[2]
+cdef void *ufunc__struve_power_series_data[1]
+cdef char ufunc__struve_power_series_types[5]
+cdef char *ufunc__struve_power_series_doc = (
+    "_struve_power_series(v, z, is_h)\n"
+    "\n"
+    "Internal function for testing `struve` & `modstruve`\n"
+    "\n"
+    "Evaluates using power series\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "v, err")
+ufunc__struve_power_series_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddi_d_As_ddl_dd
+ufunc__struve_power_series_types[0] = <char>NPY_DOUBLE
+ufunc__struve_power_series_types[1] = <char>NPY_DOUBLE
+ufunc__struve_power_series_types[2] = <char>NPY_LONG
+ufunc__struve_power_series_types[3] = <char>NPY_DOUBLE
+ufunc__struve_power_series_types[4] = <char>NPY_DOUBLE
+ufunc__struve_power_series_ptr[2*0] = <void*>_func_struve_power_series
+ufunc__struve_power_series_ptr[2*0+1] = <void*>(<char*>"_struve_power_series")
+ufunc__struve_power_series_data[0] = &ufunc__struve_power_series_ptr[2*0]
+_struve_power_series = np.PyUFunc_FromFuncAndData(ufunc__struve_power_series_loops, ufunc__struve_power_series_data, ufunc__struve_power_series_types, 1, 3, 2, 0, "_struve_power_series", ufunc__struve_power_series_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc__zeta_loops[2]
+cdef void *ufunc__zeta_ptr[4]
+cdef void *ufunc__zeta_data[2]
+cdef char ufunc__zeta_types[6]
+cdef char *ufunc__zeta_doc = (
+    "_zeta(x, q)\n"
+    "\n"
+    "Internal function, Hurwitz zeta.")
+ufunc__zeta_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc__zeta_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc__zeta_types[0] = <char>NPY_FLOAT
+ufunc__zeta_types[1] = <char>NPY_FLOAT
+ufunc__zeta_types[2] = <char>NPY_FLOAT
+ufunc__zeta_types[3] = <char>NPY_DOUBLE
+ufunc__zeta_types[4] = <char>NPY_DOUBLE
+ufunc__zeta_types[5] = <char>NPY_DOUBLE
+ufunc__zeta_ptr[2*0] = <void*>_func_zeta
+ufunc__zeta_ptr[2*0+1] = <void*>(<char*>"_zeta")
+ufunc__zeta_ptr[2*1] = <void*>_func_zeta
+ufunc__zeta_ptr[2*1+1] = <void*>(<char*>"_zeta")
+ufunc__zeta_data[0] = &ufunc__zeta_ptr[2*0]
+ufunc__zeta_data[1] = &ufunc__zeta_ptr[2*1]
+_zeta = np.PyUFunc_FromFuncAndData(ufunc__zeta_loops, ufunc__zeta_data, ufunc__zeta_types, 2, 2, 1, 0, "_zeta", ufunc__zeta_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_agm_loops[2]
+cdef void *ufunc_agm_ptr[4]
+cdef void *ufunc_agm_data[2]
+cdef char ufunc_agm_types[6]
+cdef char *ufunc_agm_doc = (
+    "agm(a, b, out=None)\n"
+    "\n"
+    "Compute the arithmetic-geometric mean of `a` and `b`.\n"
+    "\n"
+    "Start with a_0 = a and b_0 = b and iteratively compute::\n"
+    "\n"
+    "    a_{n+1} = (a_n + b_n)/2\n"
+    "    b_{n+1} = sqrt(a_n*b_n)\n"
+    "\n"
+    "a_n and b_n converge to the same limit as n increases; their common\n"
+    "limit is agm(a, b).\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b : array_like\n"
+    "    Real values only. If the values are both negative, the result\n"
+    "    is negative. If one value is negative and the other is positive,\n"
+    "    `nan` is returned.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The arithmetic-geometric mean of `a` and `b`.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import agm\n"
+    ">>> a, b = 24.0, 6.0\n"
+    ">>> agm(a, b)\n"
+    "13.458171481725614\n"
+    "\n"
+    "Compare that result to the iteration:\n"
+    "\n"
+    ">>> while a != b:\n"
+    "...     a, b = (a + b)/2, np.sqrt(a*b)\n"
+    "...     print(\"a = %19.16f  b=%19.16f\" % (a, b))\n"
+    "...\n"
+    "a = 15.0000000000000000  b=12.0000000000000000\n"
+    "a = 13.5000000000000000  b=13.4164078649987388\n"
+    "a = 13.4582039324993694  b=13.4581390309909850\n"
+    "a = 13.4581714817451772  b=13.4581714817060547\n"
+    "a = 13.4581714817256159  b=13.4581714817256159\n"
+    "\n"
+    "When array-like arguments are given, broadcasting applies:\n"
+    "\n"
+    ">>> a = np.array([[1.5], [3], [6]])  # a has shape (3, 1).\n"
+    ">>> b = np.array([6, 12, 24, 48])    # b has shape (4,).\n"
+    ">>> agm(a, b)\n"
+    "array([[  3.36454287,   5.42363427,   9.05798751,  15.53650756],\n"
+    "       [  4.37037309,   6.72908574,  10.84726853,  18.11597502],\n"
+    "       [  6.        ,   8.74074619,  13.45817148,  21.69453707]])")
+ufunc_agm_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_agm_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_agm_types[0] = <char>NPY_FLOAT
+ufunc_agm_types[1] = <char>NPY_FLOAT
+ufunc_agm_types[2] = <char>NPY_FLOAT
+ufunc_agm_types[3] = <char>NPY_DOUBLE
+ufunc_agm_types[4] = <char>NPY_DOUBLE
+ufunc_agm_types[5] = <char>NPY_DOUBLE
+ufunc_agm_ptr[2*0] = <void*>_func_agm
+ufunc_agm_ptr[2*0+1] = <void*>(<char*>"agm")
+ufunc_agm_ptr[2*1] = <void*>_func_agm
+ufunc_agm_ptr[2*1+1] = <void*>(<char*>"agm")
+ufunc_agm_data[0] = &ufunc_agm_ptr[2*0]
+ufunc_agm_data[1] = &ufunc_agm_ptr[2*1]
+agm = np.PyUFunc_FromFuncAndData(ufunc_agm_loops, ufunc_agm_data, ufunc_agm_types, 2, 2, 1, 0, "agm", ufunc_agm_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_airy_loops[4]
+cdef void *ufunc_airy_ptr[8]
+cdef void *ufunc_airy_data[4]
+cdef char ufunc_airy_types[20]
+cdef char *ufunc_airy_doc = (
+    "airy(z, out=None)\n"
+    "\n"
+    "Airy functions and their derivatives.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Real or complex argument.\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "Ai, Aip, Bi, Bip : 4-tuple of scalar or ndarray\n"
+    "    Airy functions Ai and Bi, and their derivatives Aip and Bip.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "airye : exponentially scaled Airy functions.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The Airy functions Ai and Bi are two independent solutions of\n"
+    "\n"
+    ".. math:: y''(x) = x y(x).\n"
+    "\n"
+    "For real `z` in [-10, 10], the computation is carried out by calling\n"
+    "the Cephes [1]_ `airy` routine, which uses power series summation\n"
+    "for small `z` and rational minimax approximations for large `z`.\n"
+    "\n"
+    "Outside this range, the AMOS [2]_ `zairy` and `zbiry` routines are\n"
+    "employed.  They are computed using power series for :math:`|z| < 1` and\n"
+    "the following relations to modified Bessel functions for larger `z`\n"
+    "(where :math:`t \\equiv 2 z^{3/2}/3`):\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    Ai(z) = \\frac{1}{\\pi \\sqrt{3}} K_{1/3}(t)\n"
+    "\n"
+    "    Ai'(z) = -\\frac{z}{\\pi \\sqrt{3}} K_{2/3}(t)\n"
+    "\n"
+    "    Bi(z) = \\sqrt{\\frac{z}{3}} \\left(I_{-1/3}(t) + I_{1/3}(t) \\right)\n"
+    "\n"
+    "    Bi'(z) = \\frac{z}{\\sqrt{3}} \\left(I_{-2/3}(t) + I_{2/3}(t)\\right)\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    ".. [2] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the Airy functions on the interval [-15, 5].\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    ">>> x = np.linspace(-15, 5, 201)\n"
+    ">>> ai, aip, bi, bip = special.airy(x)\n"
+    "\n"
+    "Plot Ai(x) and Bi(x).\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> plt.plot(x, ai, 'r', label='Ai(x)')\n"
+    ">>> plt.plot(x, bi, 'b--', label='Bi(x)')\n"
+    ">>> plt.ylim(-0.5, 1.0)\n"
+    ">>> plt.grid()\n"
+    ">>> plt.legend(loc='upper left')\n"
+    ">>> plt.show()")
+ufunc_airy_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dddd_As_f_ffff
+ufunc_airy_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dddd_As_d_dddd
+ufunc_airy_loops[2] = <np.PyUFuncGenericFunction>loop_i_D_DDDD_As_F_FFFF
+ufunc_airy_loops[3] = <np.PyUFuncGenericFunction>loop_i_D_DDDD_As_D_DDDD
+ufunc_airy_types[0] = <char>NPY_FLOAT
+ufunc_airy_types[1] = <char>NPY_FLOAT
+ufunc_airy_types[2] = <char>NPY_FLOAT
+ufunc_airy_types[3] = <char>NPY_FLOAT
+ufunc_airy_types[4] = <char>NPY_FLOAT
+ufunc_airy_types[5] = <char>NPY_DOUBLE
+ufunc_airy_types[6] = <char>NPY_DOUBLE
+ufunc_airy_types[7] = <char>NPY_DOUBLE
+ufunc_airy_types[8] = <char>NPY_DOUBLE
+ufunc_airy_types[9] = <char>NPY_DOUBLE
+ufunc_airy_types[10] = <char>NPY_CFLOAT
+ufunc_airy_types[11] = <char>NPY_CFLOAT
+ufunc_airy_types[12] = <char>NPY_CFLOAT
+ufunc_airy_types[13] = <char>NPY_CFLOAT
+ufunc_airy_types[14] = <char>NPY_CFLOAT
+ufunc_airy_types[15] = <char>NPY_CDOUBLE
+ufunc_airy_types[16] = <char>NPY_CDOUBLE
+ufunc_airy_types[17] = <char>NPY_CDOUBLE
+ufunc_airy_types[18] = <char>NPY_CDOUBLE
+ufunc_airy_types[19] = <char>NPY_CDOUBLE
+ufunc_airy_ptr[2*0] = <void*>_func_airy_wrap
+ufunc_airy_ptr[2*0+1] = <void*>(<char*>"airy")
+ufunc_airy_ptr[2*1] = <void*>_func_airy_wrap
+ufunc_airy_ptr[2*1+1] = <void*>(<char*>"airy")
+ufunc_airy_ptr[2*2] = <void*>_func_cairy_wrap
+ufunc_airy_ptr[2*2+1] = <void*>(<char*>"airy")
+ufunc_airy_ptr[2*3] = <void*>_func_cairy_wrap
+ufunc_airy_ptr[2*3+1] = <void*>(<char*>"airy")
+ufunc_airy_data[0] = &ufunc_airy_ptr[2*0]
+ufunc_airy_data[1] = &ufunc_airy_ptr[2*1]
+ufunc_airy_data[2] = &ufunc_airy_ptr[2*2]
+ufunc_airy_data[3] = &ufunc_airy_ptr[2*3]
+airy = np.PyUFunc_FromFuncAndData(ufunc_airy_loops, ufunc_airy_data, ufunc_airy_types, 4, 1, 4, 0, "airy", ufunc_airy_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_airye_loops[4]
+cdef void *ufunc_airye_ptr[8]
+cdef void *ufunc_airye_data[4]
+cdef char ufunc_airye_types[20]
+cdef char *ufunc_airye_doc = (
+    "airye(z, out=None)\n"
+    "\n"
+    "Exponentially scaled Airy functions and their derivatives.\n"
+    "\n"
+    "Scaling::\n"
+    "\n"
+    "    eAi  = Ai  * exp(2.0/3.0*z*sqrt(z))\n"
+    "    eAip = Aip * exp(2.0/3.0*z*sqrt(z))\n"
+    "    eBi  = Bi  * exp(-abs(2.0/3.0*(z*sqrt(z)).real))\n"
+    "    eBip = Bip * exp(-abs(2.0/3.0*(z*sqrt(z)).real))\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Real or complex argument.\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "eAi, eAip, eBi, eBip : 4-tuple of scalar or ndarray\n"
+    "    Exponentially scaled Airy functions eAi and eBi, and their derivatives\n"
+    "    eAip and eBip\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "airy\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the AMOS [1]_ routines `zairy` and `zbiry`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "We can compute exponentially scaled Airy functions and their derivatives:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import airye\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> z = np.linspace(0, 50, 500)\n"
+    ">>> eAi, eAip, eBi, eBip = airye(z)\n"
+    ">>> f, ax = plt.subplots(2, 1, sharex=True)\n"
+    ">>> for ind, data in enumerate([[eAi, eAip, [\"eAi\", \"eAip\"]],\n"
+    "...                             [eBi, eBip, [\"eBi\", \"eBip\"]]]):\n"
+    "...     ax[ind].plot(z, data[0], \"-r\", z, data[1], \"-b\")\n"
+    "...     ax[ind].legend(data[2])\n"
+    "...     ax[ind].grid(True)\n"
+    ">>> plt.show()\n"
+    "\n"
+    "We can compute these using usual non-scaled Airy functions by:\n"
+    "\n"
+    ">>> from scipy.special import airy\n"
+    ">>> Ai, Aip, Bi, Bip = airy(z)\n"
+    ">>> np.allclose(eAi, Ai * np.exp(2.0 / 3.0 * z * np.sqrt(z)))\n"
+    "True\n"
+    ">>> np.allclose(eAip, Aip * np.exp(2.0 / 3.0 * z * np.sqrt(z)))\n"
+    "True\n"
+    ">>> np.allclose(eBi, Bi * np.exp(-abs(np.real(2.0 / 3.0 * z * np.sqrt(z)))))\n"
+    "True\n"
+    ">>> np.allclose(eBip, Bip * np.exp(-abs(np.real(2.0 / 3.0 * z * np.sqrt(z)))))\n"
+    "True\n"
+    "\n"
+    "Comparing non-scaled and exponentially scaled ones, the usual non-scaled\n"
+    "function quickly underflows for large values, whereas the exponentially\n"
+    "scaled function does not.\n"
+    "\n"
+    ">>> airy(200)\n"
+    "(0.0, 0.0, nan, nan)\n"
+    ">>> airye(200)\n"
+    "(0.07501041684381093, -1.0609012305109042, 0.15003188417418148, 2.1215836725571093)")
+ufunc_airye_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dddd_As_f_ffff
+ufunc_airye_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dddd_As_d_dddd
+ufunc_airye_loops[2] = <np.PyUFuncGenericFunction>loop_i_D_DDDD_As_F_FFFF
+ufunc_airye_loops[3] = <np.PyUFuncGenericFunction>loop_i_D_DDDD_As_D_DDDD
+ufunc_airye_types[0] = <char>NPY_FLOAT
+ufunc_airye_types[1] = <char>NPY_FLOAT
+ufunc_airye_types[2] = <char>NPY_FLOAT
+ufunc_airye_types[3] = <char>NPY_FLOAT
+ufunc_airye_types[4] = <char>NPY_FLOAT
+ufunc_airye_types[5] = <char>NPY_DOUBLE
+ufunc_airye_types[6] = <char>NPY_DOUBLE
+ufunc_airye_types[7] = <char>NPY_DOUBLE
+ufunc_airye_types[8] = <char>NPY_DOUBLE
+ufunc_airye_types[9] = <char>NPY_DOUBLE
+ufunc_airye_types[10] = <char>NPY_CFLOAT
+ufunc_airye_types[11] = <char>NPY_CFLOAT
+ufunc_airye_types[12] = <char>NPY_CFLOAT
+ufunc_airye_types[13] = <char>NPY_CFLOAT
+ufunc_airye_types[14] = <char>NPY_CFLOAT
+ufunc_airye_types[15] = <char>NPY_CDOUBLE
+ufunc_airye_types[16] = <char>NPY_CDOUBLE
+ufunc_airye_types[17] = <char>NPY_CDOUBLE
+ufunc_airye_types[18] = <char>NPY_CDOUBLE
+ufunc_airye_types[19] = <char>NPY_CDOUBLE
+ufunc_airye_ptr[2*0] = <void*>_func_cairy_wrap_e_real
+ufunc_airye_ptr[2*0+1] = <void*>(<char*>"airye")
+ufunc_airye_ptr[2*1] = <void*>_func_cairy_wrap_e_real
+ufunc_airye_ptr[2*1+1] = <void*>(<char*>"airye")
+ufunc_airye_ptr[2*2] = <void*>_func_cairy_wrap_e
+ufunc_airye_ptr[2*2+1] = <void*>(<char*>"airye")
+ufunc_airye_ptr[2*3] = <void*>_func_cairy_wrap_e
+ufunc_airye_ptr[2*3+1] = <void*>(<char*>"airye")
+ufunc_airye_data[0] = &ufunc_airye_ptr[2*0]
+ufunc_airye_data[1] = &ufunc_airye_ptr[2*1]
+ufunc_airye_data[2] = &ufunc_airye_ptr[2*2]
+ufunc_airye_data[3] = &ufunc_airye_ptr[2*3]
+airye = np.PyUFunc_FromFuncAndData(ufunc_airye_loops, ufunc_airye_data, ufunc_airye_types, 4, 1, 4, 0, "airye", ufunc_airye_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_bdtr_loops[3]
+cdef void *ufunc_bdtr_ptr[6]
+cdef void *ufunc_bdtr_data[3]
+cdef char ufunc_bdtr_types[12]
+cdef char *ufunc_bdtr_doc = (
+    "bdtr(k, n, p, out=None)\n"
+    "\n"
+    "Binomial distribution cumulative distribution function.\n"
+    "\n"
+    "Sum of the terms 0 through `floor(k)` of the Binomial probability density.\n"
+    "\n"
+    ".. math::\n"
+    "    \\mathrm{bdtr}(k, n, p) =\n"
+    "    \\sum_{j=0}^{\\lfloor k \\rfloor} {{n}\\choose{j}} p^j (1-p)^{n-j}\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    Number of successes (double), rounded down to the nearest integer.\n"
+    "n : array_like\n"
+    "    Number of events (int).\n"
+    "p : array_like\n"
+    "    Probability of success in a single event (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Probability of `floor(k)` or fewer successes in `n` independent events with\n"
+    "    success probabilities of `p`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The terms are not summed directly; instead the regularized incomplete beta\n"
+    "function is employed, according to the formula,\n"
+    "\n"
+    ".. math::\n"
+    "    \\mathrm{bdtr}(k, n, p) =\n"
+    "    I_{1 - p}(n - \\lfloor k \\rfloor, \\lfloor k \\rfloor + 1).\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `bdtr`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/")
+ufunc_bdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_bdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_did__As_dld_d
+ufunc_bdtr_loops[2] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_bdtr_types[0] = <char>NPY_FLOAT
+ufunc_bdtr_types[1] = <char>NPY_FLOAT
+ufunc_bdtr_types[2] = <char>NPY_FLOAT
+ufunc_bdtr_types[3] = <char>NPY_FLOAT
+ufunc_bdtr_types[4] = <char>NPY_DOUBLE
+ufunc_bdtr_types[5] = <char>NPY_LONG
+ufunc_bdtr_types[6] = <char>NPY_DOUBLE
+ufunc_bdtr_types[7] = <char>NPY_DOUBLE
+ufunc_bdtr_types[8] = <char>NPY_DOUBLE
+ufunc_bdtr_types[9] = <char>NPY_DOUBLE
+ufunc_bdtr_types[10] = <char>NPY_DOUBLE
+ufunc_bdtr_types[11] = <char>NPY_DOUBLE
+ufunc_bdtr_ptr[2*0] = <void*>_func_bdtr_unsafe
+ufunc_bdtr_ptr[2*0+1] = <void*>(<char*>"bdtr")
+ufunc_bdtr_ptr[2*1] = <void*>_func_bdtr
+ufunc_bdtr_ptr[2*1+1] = <void*>(<char*>"bdtr")
+ufunc_bdtr_ptr[2*2] = <void*>_func_bdtr_unsafe
+ufunc_bdtr_ptr[2*2+1] = <void*>(<char*>"bdtr")
+ufunc_bdtr_data[0] = &ufunc_bdtr_ptr[2*0]
+ufunc_bdtr_data[1] = &ufunc_bdtr_ptr[2*1]
+ufunc_bdtr_data[2] = &ufunc_bdtr_ptr[2*2]
+bdtr = np.PyUFunc_FromFuncAndData(ufunc_bdtr_loops, ufunc_bdtr_data, ufunc_bdtr_types, 3, 3, 1, 0, "bdtr", ufunc_bdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_bdtrc_loops[3]
+cdef void *ufunc_bdtrc_ptr[6]
+cdef void *ufunc_bdtrc_data[3]
+cdef char ufunc_bdtrc_types[12]
+cdef char *ufunc_bdtrc_doc = (
+    "bdtrc(k, n, p, out=None)\n"
+    "\n"
+    "Binomial distribution survival function.\n"
+    "\n"
+    "Sum of the terms `floor(k) + 1` through `n` of the binomial probability\n"
+    "density,\n"
+    "\n"
+    ".. math::\n"
+    "    \\mathrm{bdtrc}(k, n, p) =\n"
+    "    \\sum_{j=\\lfloor k \\rfloor +1}^n {{n}\\choose{j}} p^j (1-p)^{n-j}\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    Number of successes (double), rounded down to nearest integer.\n"
+    "n : array_like\n"
+    "    Number of events (int)\n"
+    "p : array_like\n"
+    "    Probability of success in a single event.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Probability of `floor(k) + 1` or more successes in `n` independent\n"
+    "    events with success probabilities of `p`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "bdtr\n"
+    "betainc\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The terms are not summed directly; instead the regularized incomplete beta\n"
+    "function is employed, according to the formula,\n"
+    "\n"
+    ".. math::\n"
+    "    \\mathrm{bdtrc}(k, n, p) = I_{p}(\\lfloor k \\rfloor + 1, n - \\lfloor k \\rfloor).\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `bdtrc`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/")
+ufunc_bdtrc_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_bdtrc_loops[1] = <np.PyUFuncGenericFunction>loop_d_did__As_dld_d
+ufunc_bdtrc_loops[2] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_bdtrc_types[0] = <char>NPY_FLOAT
+ufunc_bdtrc_types[1] = <char>NPY_FLOAT
+ufunc_bdtrc_types[2] = <char>NPY_FLOAT
+ufunc_bdtrc_types[3] = <char>NPY_FLOAT
+ufunc_bdtrc_types[4] = <char>NPY_DOUBLE
+ufunc_bdtrc_types[5] = <char>NPY_LONG
+ufunc_bdtrc_types[6] = <char>NPY_DOUBLE
+ufunc_bdtrc_types[7] = <char>NPY_DOUBLE
+ufunc_bdtrc_types[8] = <char>NPY_DOUBLE
+ufunc_bdtrc_types[9] = <char>NPY_DOUBLE
+ufunc_bdtrc_types[10] = <char>NPY_DOUBLE
+ufunc_bdtrc_types[11] = <char>NPY_DOUBLE
+ufunc_bdtrc_ptr[2*0] = <void*>_func_bdtrc_unsafe
+ufunc_bdtrc_ptr[2*0+1] = <void*>(<char*>"bdtrc")
+ufunc_bdtrc_ptr[2*1] = <void*>_func_bdtrc
+ufunc_bdtrc_ptr[2*1+1] = <void*>(<char*>"bdtrc")
+ufunc_bdtrc_ptr[2*2] = <void*>_func_bdtrc_unsafe
+ufunc_bdtrc_ptr[2*2+1] = <void*>(<char*>"bdtrc")
+ufunc_bdtrc_data[0] = &ufunc_bdtrc_ptr[2*0]
+ufunc_bdtrc_data[1] = &ufunc_bdtrc_ptr[2*1]
+ufunc_bdtrc_data[2] = &ufunc_bdtrc_ptr[2*2]
+bdtrc = np.PyUFunc_FromFuncAndData(ufunc_bdtrc_loops, ufunc_bdtrc_data, ufunc_bdtrc_types, 3, 3, 1, 0, "bdtrc", ufunc_bdtrc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_bdtri_loops[3]
+cdef void *ufunc_bdtri_ptr[6]
+cdef void *ufunc_bdtri_data[3]
+cdef char ufunc_bdtri_types[12]
+cdef char *ufunc_bdtri_doc = (
+    "bdtri(k, n, y, out=None)\n"
+    "\n"
+    "Inverse function to `bdtr` with respect to `p`.\n"
+    "\n"
+    "Finds the event probability `p` such that the sum of the terms 0 through\n"
+    "`k` of the binomial probability density is equal to the given cumulative\n"
+    "probability `y`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    Number of successes (float), rounded down to the nearest integer.\n"
+    "n : array_like\n"
+    "    Number of events (float)\n"
+    "y : array_like\n"
+    "    Cumulative probability (probability of `k` or fewer successes in `n`\n"
+    "    events).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "p : scalar or ndarray\n"
+    "    The event probability such that `bdtr(\\lfloor k \\rfloor, n, p) = y`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "bdtr\n"
+    "betaincinv\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The computation is carried out using the inverse beta integral function\n"
+    "and the relation,::\n"
+    "\n"
+    "    1 - p = betaincinv(n - k, k + 1, y).\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `bdtri`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/")
+ufunc_bdtri_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_bdtri_loops[1] = <np.PyUFuncGenericFunction>loop_d_did__As_dld_d
+ufunc_bdtri_loops[2] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_bdtri_types[0] = <char>NPY_FLOAT
+ufunc_bdtri_types[1] = <char>NPY_FLOAT
+ufunc_bdtri_types[2] = <char>NPY_FLOAT
+ufunc_bdtri_types[3] = <char>NPY_FLOAT
+ufunc_bdtri_types[4] = <char>NPY_DOUBLE
+ufunc_bdtri_types[5] = <char>NPY_LONG
+ufunc_bdtri_types[6] = <char>NPY_DOUBLE
+ufunc_bdtri_types[7] = <char>NPY_DOUBLE
+ufunc_bdtri_types[8] = <char>NPY_DOUBLE
+ufunc_bdtri_types[9] = <char>NPY_DOUBLE
+ufunc_bdtri_types[10] = <char>NPY_DOUBLE
+ufunc_bdtri_types[11] = <char>NPY_DOUBLE
+ufunc_bdtri_ptr[2*0] = <void*>_func_bdtri_unsafe
+ufunc_bdtri_ptr[2*0+1] = <void*>(<char*>"bdtri")
+ufunc_bdtri_ptr[2*1] = <void*>_func_bdtri
+ufunc_bdtri_ptr[2*1+1] = <void*>(<char*>"bdtri")
+ufunc_bdtri_ptr[2*2] = <void*>_func_bdtri_unsafe
+ufunc_bdtri_ptr[2*2+1] = <void*>(<char*>"bdtri")
+ufunc_bdtri_data[0] = &ufunc_bdtri_ptr[2*0]
+ufunc_bdtri_data[1] = &ufunc_bdtri_ptr[2*1]
+ufunc_bdtri_data[2] = &ufunc_bdtri_ptr[2*2]
+bdtri = np.PyUFunc_FromFuncAndData(ufunc_bdtri_loops, ufunc_bdtri_data, ufunc_bdtri_types, 3, 3, 1, 0, "bdtri", ufunc_bdtri_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_bdtrik_loops[2]
+cdef void *ufunc_bdtrik_ptr[4]
+cdef void *ufunc_bdtrik_data[2]
+cdef char ufunc_bdtrik_types[8]
+cdef char *ufunc_bdtrik_doc = (
+    "bdtrik(y, n, p, out=None)\n"
+    "\n"
+    "Inverse function to `bdtr` with respect to `k`.\n"
+    "\n"
+    "Finds the number of successes `k` such that the sum of the terms 0 through\n"
+    "`k` of the Binomial probability density for `n` events with probability\n"
+    "`p` is equal to the given cumulative probability `y`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "y : array_like\n"
+    "    Cumulative probability (probability of `k` or fewer successes in `n`\n"
+    "    events).\n"
+    "n : array_like\n"
+    "    Number of events (float).\n"
+    "p : array_like\n"
+    "    Success probability (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "k : scalar or ndarray\n"
+    "    The number of successes `k` such that `bdtr(k, n, p) = y`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "bdtr\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Formula 26.5.24 of [1]_ is used to reduce the binomial distribution to the\n"
+    "cumulative incomplete beta distribution.\n"
+    "\n"
+    "Computation of `k` involves a search for a value that produces the desired\n"
+    "value of `y`. The search relies on the monotonicity of `y` with `k`.\n"
+    "\n"
+    "Wrapper for the CDFLIB [2]_ Fortran routine `cdfbin`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    ".. [2] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.")
+ufunc_bdtrik_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_bdtrik_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_bdtrik_types[0] = <char>NPY_FLOAT
+ufunc_bdtrik_types[1] = <char>NPY_FLOAT
+ufunc_bdtrik_types[2] = <char>NPY_FLOAT
+ufunc_bdtrik_types[3] = <char>NPY_FLOAT
+ufunc_bdtrik_types[4] = <char>NPY_DOUBLE
+ufunc_bdtrik_types[5] = <char>NPY_DOUBLE
+ufunc_bdtrik_types[6] = <char>NPY_DOUBLE
+ufunc_bdtrik_types[7] = <char>NPY_DOUBLE
+ufunc_bdtrik_ptr[2*0] = <void*>_func_bdtrik
+ufunc_bdtrik_ptr[2*0+1] = <void*>(<char*>"bdtrik")
+ufunc_bdtrik_ptr[2*1] = <void*>_func_bdtrik
+ufunc_bdtrik_ptr[2*1+1] = <void*>(<char*>"bdtrik")
+ufunc_bdtrik_data[0] = &ufunc_bdtrik_ptr[2*0]
+ufunc_bdtrik_data[1] = &ufunc_bdtrik_ptr[2*1]
+bdtrik = np.PyUFunc_FromFuncAndData(ufunc_bdtrik_loops, ufunc_bdtrik_data, ufunc_bdtrik_types, 2, 3, 1, 0, "bdtrik", ufunc_bdtrik_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_bdtrin_loops[2]
+cdef void *ufunc_bdtrin_ptr[4]
+cdef void *ufunc_bdtrin_data[2]
+cdef char ufunc_bdtrin_types[8]
+cdef char *ufunc_bdtrin_doc = (
+    "bdtrin(k, y, p, out=None)\n"
+    "\n"
+    "Inverse function to `bdtr` with respect to `n`.\n"
+    "\n"
+    "Finds the number of events `n` such that the sum of the terms 0 through\n"
+    "`k` of the Binomial probability density for events with probability `p` is\n"
+    "equal to the given cumulative probability `y`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    Number of successes (float).\n"
+    "y : array_like\n"
+    "    Cumulative probability (probability of `k` or fewer successes in `n`\n"
+    "    events).\n"
+    "p : array_like\n"
+    "    Success probability (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "n : scalar or ndarray\n"
+    "    The number of events `n` such that `bdtr(k, n, p) = y`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "bdtr\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Formula 26.5.24 of [1]_ is used to reduce the binomial distribution to the\n"
+    "cumulative incomplete beta distribution.\n"
+    "\n"
+    "Computation of `n` involves a search for a value that produces the desired\n"
+    "value of `y`. The search relies on the monotonicity of `y` with `n`.\n"
+    "\n"
+    "Wrapper for the CDFLIB [2]_ Fortran routine `cdfbin`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    ".. [2] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.")
+ufunc_bdtrin_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_bdtrin_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_bdtrin_types[0] = <char>NPY_FLOAT
+ufunc_bdtrin_types[1] = <char>NPY_FLOAT
+ufunc_bdtrin_types[2] = <char>NPY_FLOAT
+ufunc_bdtrin_types[3] = <char>NPY_FLOAT
+ufunc_bdtrin_types[4] = <char>NPY_DOUBLE
+ufunc_bdtrin_types[5] = <char>NPY_DOUBLE
+ufunc_bdtrin_types[6] = <char>NPY_DOUBLE
+ufunc_bdtrin_types[7] = <char>NPY_DOUBLE
+ufunc_bdtrin_ptr[2*0] = <void*>_func_bdtrin
+ufunc_bdtrin_ptr[2*0+1] = <void*>(<char*>"bdtrin")
+ufunc_bdtrin_ptr[2*1] = <void*>_func_bdtrin
+ufunc_bdtrin_ptr[2*1+1] = <void*>(<char*>"bdtrin")
+ufunc_bdtrin_data[0] = &ufunc_bdtrin_ptr[2*0]
+ufunc_bdtrin_data[1] = &ufunc_bdtrin_ptr[2*1]
+bdtrin = np.PyUFunc_FromFuncAndData(ufunc_bdtrin_loops, ufunc_bdtrin_data, ufunc_bdtrin_types, 2, 3, 1, 0, "bdtrin", ufunc_bdtrin_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_bei_loops[2]
+cdef void *ufunc_bei_ptr[4]
+cdef void *ufunc_bei_data[2]
+cdef char ufunc_bei_types[4]
+cdef char *ufunc_bei_doc = (
+    "bei(x, out=None)\n"
+    "\n"
+    "Kelvin function bei.\n"
+    "\n"
+    "Defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\mathrm{bei}(x) = \\Im[J_0(x e^{3 \\pi i / 4})]\n"
+    "\n"
+    "where :math:`J_0` is the Bessel function of the first kind of\n"
+    "order zero (see `jv`). See [dlmf]_ for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the Kelvin function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ber : the corresponding real part\n"
+    "beip : the derivative of bei\n"
+    "jv : Bessel function of the first kind\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST, Digital Library of Mathematical Functions,\n"
+    "    https://dlmf.nist.gov/10.61\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "It can be expressed using Bessel functions.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    ">>> x = np.array([1.0, 2.0, 3.0, 4.0])\n"
+    ">>> sc.jv(0, x * np.exp(3 * np.pi * 1j / 4)).imag\n"
+    "array([0.24956604, 0.97229163, 1.93758679, 2.29269032])\n"
+    ">>> sc.bei(x)\n"
+    "array([0.24956604, 0.97229163, 1.93758679, 2.29269032])")
+ufunc_bei_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_bei_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_bei_types[0] = <char>NPY_FLOAT
+ufunc_bei_types[1] = <char>NPY_FLOAT
+ufunc_bei_types[2] = <char>NPY_DOUBLE
+ufunc_bei_types[3] = <char>NPY_DOUBLE
+ufunc_bei_ptr[2*0] = <void*>_func_bei_wrap
+ufunc_bei_ptr[2*0+1] = <void*>(<char*>"bei")
+ufunc_bei_ptr[2*1] = <void*>_func_bei_wrap
+ufunc_bei_ptr[2*1+1] = <void*>(<char*>"bei")
+ufunc_bei_data[0] = &ufunc_bei_ptr[2*0]
+ufunc_bei_data[1] = &ufunc_bei_ptr[2*1]
+bei = np.PyUFunc_FromFuncAndData(ufunc_bei_loops, ufunc_bei_data, ufunc_bei_types, 2, 1, 1, 0, "bei", ufunc_bei_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_beip_loops[2]
+cdef void *ufunc_beip_ptr[4]
+cdef void *ufunc_beip_data[2]
+cdef char ufunc_beip_types[4]
+cdef char *ufunc_beip_doc = (
+    "beip(x, out=None)\n"
+    "\n"
+    "Derivative of the Kelvin function bei.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The values of the derivative of bei.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "bei\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST, Digital Library of Mathematical Functions,\n"
+    "    https://dlmf.nist.gov/10#PT5")
+ufunc_beip_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_beip_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_beip_types[0] = <char>NPY_FLOAT
+ufunc_beip_types[1] = <char>NPY_FLOAT
+ufunc_beip_types[2] = <char>NPY_DOUBLE
+ufunc_beip_types[3] = <char>NPY_DOUBLE
+ufunc_beip_ptr[2*0] = <void*>_func_beip_wrap
+ufunc_beip_ptr[2*0+1] = <void*>(<char*>"beip")
+ufunc_beip_ptr[2*1] = <void*>_func_beip_wrap
+ufunc_beip_ptr[2*1+1] = <void*>(<char*>"beip")
+ufunc_beip_data[0] = &ufunc_beip_ptr[2*0]
+ufunc_beip_data[1] = &ufunc_beip_ptr[2*1]
+beip = np.PyUFunc_FromFuncAndData(ufunc_beip_loops, ufunc_beip_data, ufunc_beip_types, 2, 1, 1, 0, "beip", ufunc_beip_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ber_loops[2]
+cdef void *ufunc_ber_ptr[4]
+cdef void *ufunc_ber_data[2]
+cdef char ufunc_ber_types[4]
+cdef char *ufunc_ber_doc = (
+    "ber(x, out=None)\n"
+    "\n"
+    "Kelvin function ber.\n"
+    "\n"
+    "Defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\mathrm{ber}(x) = \\Re[J_0(x e^{3 \\pi i / 4})]\n"
+    "\n"
+    "where :math:`J_0` is the Bessel function of the first kind of\n"
+    "order zero (see `jv`). See [dlmf]_ for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the Kelvin function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "bei : the corresponding real part\n"
+    "berp : the derivative of bei\n"
+    "jv : Bessel function of the first kind\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST, Digital Library of Mathematical Functions,\n"
+    "    https://dlmf.nist.gov/10.61\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "It can be expressed using Bessel functions.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    ">>> x = np.array([1.0, 2.0, 3.0, 4.0])\n"
+    ">>> sc.jv(0, x * np.exp(3 * np.pi * 1j / 4)).real\n"
+    "array([ 0.98438178,  0.75173418, -0.22138025, -2.56341656])\n"
+    ">>> sc.ber(x)\n"
+    "array([ 0.98438178,  0.75173418, -0.22138025, -2.56341656])")
+ufunc_ber_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_ber_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_ber_types[0] = <char>NPY_FLOAT
+ufunc_ber_types[1] = <char>NPY_FLOAT
+ufunc_ber_types[2] = <char>NPY_DOUBLE
+ufunc_ber_types[3] = <char>NPY_DOUBLE
+ufunc_ber_ptr[2*0] = <void*>_func_ber_wrap
+ufunc_ber_ptr[2*0+1] = <void*>(<char*>"ber")
+ufunc_ber_ptr[2*1] = <void*>_func_ber_wrap
+ufunc_ber_ptr[2*1+1] = <void*>(<char*>"ber")
+ufunc_ber_data[0] = &ufunc_ber_ptr[2*0]
+ufunc_ber_data[1] = &ufunc_ber_ptr[2*1]
+ber = np.PyUFunc_FromFuncAndData(ufunc_ber_loops, ufunc_ber_data, ufunc_ber_types, 2, 1, 1, 0, "ber", ufunc_ber_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_berp_loops[2]
+cdef void *ufunc_berp_ptr[4]
+cdef void *ufunc_berp_data[2]
+cdef char ufunc_berp_types[4]
+cdef char *ufunc_berp_doc = (
+    "berp(x, out=None)\n"
+    "\n"
+    "Derivative of the Kelvin function ber.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The values of the derivative of ber.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ber\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST, Digital Library of Mathematical Functions,\n"
+    "    https://dlmf.nist.gov/10#PT5")
+ufunc_berp_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_berp_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_berp_types[0] = <char>NPY_FLOAT
+ufunc_berp_types[1] = <char>NPY_FLOAT
+ufunc_berp_types[2] = <char>NPY_DOUBLE
+ufunc_berp_types[3] = <char>NPY_DOUBLE
+ufunc_berp_ptr[2*0] = <void*>_func_berp_wrap
+ufunc_berp_ptr[2*0+1] = <void*>(<char*>"berp")
+ufunc_berp_ptr[2*1] = <void*>_func_berp_wrap
+ufunc_berp_ptr[2*1+1] = <void*>(<char*>"berp")
+ufunc_berp_data[0] = &ufunc_berp_ptr[2*0]
+ufunc_berp_data[1] = &ufunc_berp_ptr[2*1]
+berp = np.PyUFunc_FromFuncAndData(ufunc_berp_loops, ufunc_berp_data, ufunc_berp_types, 2, 1, 1, 0, "berp", ufunc_berp_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_besselpoly_loops[2]
+cdef void *ufunc_besselpoly_ptr[4]
+cdef void *ufunc_besselpoly_data[2]
+cdef char ufunc_besselpoly_types[8]
+cdef char *ufunc_besselpoly_doc = (
+    "besselpoly(a, lmb, nu, out=None)\n"
+    "\n"
+    "Weighted integral of the Bessel function of the first kind.\n"
+    "\n"
+    "Computes\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   \\int_0^1 x^\\lambda J_\\nu(2 a x) \\, dx\n"
+    "\n"
+    "where :math:`J_\\nu` is a Bessel function and :math:`\\lambda=lmb`,\n"
+    ":math:`\\nu=nu`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    Scale factor inside the Bessel function.\n"
+    "lmb : array_like\n"
+    "    Power of `x`\n"
+    "nu : array_like\n"
+    "    Order of the Bessel function.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the integral.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the function for one parameter set.\n"
+    "\n"
+    ">>> from scipy.special import besselpoly\n"
+    ">>> besselpoly(1, 1, 1)\n"
+    "0.24449718372863877\n"
+    "\n"
+    "Evaluate the function for different scale factors.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> factors = np.array([0., 3., 6.])\n"
+    ">>> besselpoly(factors, 1, 1)\n"
+    "array([ 0.        , -0.00549029,  0.00140174])\n"
+    "\n"
+    "Plot the function for varying powers, orders and scales.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> powers = np.linspace(0, 10, 100)\n"
+    ">>> orders = [1, 2, 3]\n"
+    ">>> scales = [1, 2]\n"
+    ">>> all_combinations = [(order, scale) for order in orders\n"
+    "...                     for scale in scales]\n"
+    ">>> for order, scale in all_combinations:\n"
+    "...     ax.plot(powers, besselpoly(scale, powers, order),\n"
+    "...             label=rf\"$\\nu={order}, a={scale}$\")\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(r\"$\\lambda$\")\n"
+    ">>> ax.set_ylabel(r\"$\\int_0^1 x^{\\lambda} J_{\\nu}(2ax)\\,dx$\")\n"
+    ">>> plt.show()")
+ufunc_besselpoly_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_besselpoly_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_besselpoly_types[0] = <char>NPY_FLOAT
+ufunc_besselpoly_types[1] = <char>NPY_FLOAT
+ufunc_besselpoly_types[2] = <char>NPY_FLOAT
+ufunc_besselpoly_types[3] = <char>NPY_FLOAT
+ufunc_besselpoly_types[4] = <char>NPY_DOUBLE
+ufunc_besselpoly_types[5] = <char>NPY_DOUBLE
+ufunc_besselpoly_types[6] = <char>NPY_DOUBLE
+ufunc_besselpoly_types[7] = <char>NPY_DOUBLE
+ufunc_besselpoly_ptr[2*0] = <void*>_func_besselpoly
+ufunc_besselpoly_ptr[2*0+1] = <void*>(<char*>"besselpoly")
+ufunc_besselpoly_ptr[2*1] = <void*>_func_besselpoly
+ufunc_besselpoly_ptr[2*1+1] = <void*>(<char*>"besselpoly")
+ufunc_besselpoly_data[0] = &ufunc_besselpoly_ptr[2*0]
+ufunc_besselpoly_data[1] = &ufunc_besselpoly_ptr[2*1]
+besselpoly = np.PyUFunc_FromFuncAndData(ufunc_besselpoly_loops, ufunc_besselpoly_data, ufunc_besselpoly_types, 2, 3, 1, 0, "besselpoly", ufunc_besselpoly_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_beta_loops[2]
+cdef void *ufunc_beta_ptr[4]
+cdef void *ufunc_beta_data[2]
+cdef char ufunc_beta_types[6]
+cdef char *ufunc_beta_doc = (
+    "beta(a, b, out=None)\n"
+    "\n"
+    "Beta function.\n"
+    "\n"
+    "This function is defined in [1]_ as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    B(a, b) = \\int_0^1 t^{a-1}(1-t)^{b-1}dt\n"
+    "            = \\frac{\\Gamma(a)\\Gamma(b)}{\\Gamma(a+b)},\n"
+    "\n"
+    "where :math:`\\Gamma` is the gamma function.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b : array_like\n"
+    "    Real-valued arguments\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function result\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the beta function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gamma : the gamma function\n"
+    "betainc :  the regularized incomplete beta function\n"
+    "betaln : the natural logarithm of the absolute\n"
+    "         value of the beta function\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] NIST Digital Library of Mathematical Functions,\n"
+    "       Eq. 5.12.1. https://dlmf.nist.gov/5.12\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "The beta function relates to the gamma function by the\n"
+    "definition given above:\n"
+    "\n"
+    ">>> sc.beta(2, 3)\n"
+    "0.08333333333333333\n"
+    ">>> sc.gamma(2)*sc.gamma(3)/sc.gamma(2 + 3)\n"
+    "0.08333333333333333\n"
+    "\n"
+    "As this relationship demonstrates, the beta function\n"
+    "is symmetric:\n"
+    "\n"
+    ">>> sc.beta(1.7, 2.4)\n"
+    "0.16567527689031739\n"
+    ">>> sc.beta(2.4, 1.7)\n"
+    "0.16567527689031739\n"
+    "\n"
+    "This function satisfies :math:`B(1, b) = 1/b`:\n"
+    "\n"
+    ">>> sc.beta(1, 4)\n"
+    "0.25")
+ufunc_beta_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_beta_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_beta_types[0] = <char>NPY_FLOAT
+ufunc_beta_types[1] = <char>NPY_FLOAT
+ufunc_beta_types[2] = <char>NPY_FLOAT
+ufunc_beta_types[3] = <char>NPY_DOUBLE
+ufunc_beta_types[4] = <char>NPY_DOUBLE
+ufunc_beta_types[5] = <char>NPY_DOUBLE
+ufunc_beta_ptr[2*0] = <void*>_func_beta
+ufunc_beta_ptr[2*0+1] = <void*>(<char*>"beta")
+ufunc_beta_ptr[2*1] = <void*>_func_beta
+ufunc_beta_ptr[2*1+1] = <void*>(<char*>"beta")
+ufunc_beta_data[0] = &ufunc_beta_ptr[2*0]
+ufunc_beta_data[1] = &ufunc_beta_ptr[2*1]
+beta = np.PyUFunc_FromFuncAndData(ufunc_beta_loops, ufunc_beta_data, ufunc_beta_types, 2, 2, 1, 0, "beta", ufunc_beta_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_betainc_loops[2]
+cdef void *ufunc_betainc_ptr[4]
+cdef void *ufunc_betainc_data[2]
+cdef char ufunc_betainc_types[8]
+cdef char *ufunc_betainc_doc = (
+    "betainc(a, b, x, out=None)\n"
+    "\n"
+    "Regularized incomplete beta function.\n"
+    "\n"
+    "Computes the regularized incomplete beta function, defined as [1]_:\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    I_x(a, b) = \\frac{\\Gamma(a+b)}{\\Gamma(a)\\Gamma(b)} \\int_0^x\n"
+    "    t^{a-1}(1-t)^{b-1}dt,\n"
+    "\n"
+    "for :math:`0 \\leq x \\leq 1`.\n"
+    "\n"
+    "This function is the cumulative distribution function for the beta\n"
+    "distribution; its range is [0, 1].\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b : array_like\n"
+    "       Positive, real-valued parameters\n"
+    "x : array_like\n"
+    "    Real-valued such that :math:`0 \\leq x \\leq 1`,\n"
+    "    the upper limit of integration\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the regularized incomplete beta function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "beta : beta function\n"
+    "betaincinv : inverse of the regularized incomplete beta function\n"
+    "betaincc : complement of the regularized incomplete beta function\n"
+    "scipy.stats.beta : beta distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The term *regularized* in the name of this function refers to the\n"
+    "scaling of the function by the gamma function terms shown in the\n"
+    "formula.  When not qualified as *regularized*, the name *incomplete\n"
+    "beta function* often refers to just the integral expression,\n"
+    "without the gamma terms.  One can use the function `beta` from\n"
+    "`scipy.special` to get this \"nonregularized\" incomplete beta\n"
+    "function by multiplying the result of ``betainc(a, b, x)`` by\n"
+    "``beta(a, b)``.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] NIST Digital Library of Mathematical Functions\n"
+    "       https://dlmf.nist.gov/8.17\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "\n"
+    "Let :math:`B(a, b)` be the `beta` function.\n"
+    "\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "The coefficient in terms of `gamma` is equal to\n"
+    ":math:`1/B(a, b)`. Also, when :math:`x=1`\n"
+    "the integral is equal to :math:`B(a, b)`.\n"
+    "Therefore, :math:`I_{x=1}(a, b) = 1` for any :math:`a, b`.\n"
+    "\n"
+    ">>> sc.betainc(0.2, 3.5, 1.0)\n"
+    "1.0\n"
+    "\n"
+    "It satisfies\n"
+    ":math:`I_x(a, b) = x^a F(a, 1-b, a+1, x)/ (aB(a, b))`,\n"
+    "where :math:`F` is the hypergeometric function `hyp2f1`:\n"
+    "\n"
+    ">>> a, b, x = 1.4, 3.1, 0.5\n"
+    ">>> x**a * sc.hyp2f1(a, 1 - b, a + 1, x)/(a * sc.beta(a, b))\n"
+    "0.8148904036225295\n"
+    ">>> sc.betainc(a, b, x)\n"
+    "0.8148904036225296\n"
+    "\n"
+    "This functions satisfies the relationship\n"
+    ":math:`I_x(a, b) = 1 - I_{1-x}(b, a)`:\n"
+    "\n"
+    ">>> sc.betainc(2.2, 3.1, 0.4)\n"
+    "0.49339638807619446\n"
+    ">>> 1 - sc.betainc(3.1, 2.2, 1 - 0.4)\n"
+    "0.49339638807619446")
+ufunc_betainc_loops[0] = <np.PyUFuncGenericFunction>loop_f_fff__As_fff_f
+ufunc_betainc_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_betainc_types[0] = <char>NPY_FLOAT
+ufunc_betainc_types[1] = <char>NPY_FLOAT
+ufunc_betainc_types[2] = <char>NPY_FLOAT
+ufunc_betainc_types[3] = <char>NPY_FLOAT
+ufunc_betainc_types[4] = <char>NPY_DOUBLE
+ufunc_betainc_types[5] = <char>NPY_DOUBLE
+ufunc_betainc_types[6] = <char>NPY_DOUBLE
+ufunc_betainc_types[7] = <char>NPY_DOUBLE
+ufunc_betainc_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_ibeta_float
+ufunc_betainc_ptr[2*0+1] = <void*>(<char*>"betainc")
+ufunc_betainc_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_ibeta_double
+ufunc_betainc_ptr[2*1+1] = <void*>(<char*>"betainc")
+ufunc_betainc_data[0] = &ufunc_betainc_ptr[2*0]
+ufunc_betainc_data[1] = &ufunc_betainc_ptr[2*1]
+betainc = np.PyUFunc_FromFuncAndData(ufunc_betainc_loops, ufunc_betainc_data, ufunc_betainc_types, 2, 3, 1, 0, "betainc", ufunc_betainc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_betaincc_loops[2]
+cdef void *ufunc_betaincc_ptr[4]
+cdef void *ufunc_betaincc_data[2]
+cdef char ufunc_betaincc_types[8]
+cdef char *ufunc_betaincc_doc = (
+    "betaincc(a, b, x, out=None)\n"
+    "\n"
+    "Complement of the regularized incomplete beta function.\n"
+    "\n"
+    "Computes the complement of the regularized incomplete beta function,\n"
+    "defined as [1]_:\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\bar{I}_x(a, b) = 1 - I_x(a, b)\n"
+    "                    = 1 - \\frac{\\Gamma(a+b)}{\\Gamma(a)\\Gamma(b)} \\int_0^x\n"
+    "                              t^{a-1}(1-t)^{b-1}dt,\n"
+    "\n"
+    "for :math:`0 \\leq x \\leq 1`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b : array_like\n"
+    "       Positive, real-valued parameters\n"
+    "x : array_like\n"
+    "    Real-valued such that :math:`0 \\leq x \\leq 1`,\n"
+    "    the upper limit of integration\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the regularized incomplete beta function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "betainc : regularized incomplete beta function\n"
+    "betaincinv : inverse of the regularized incomplete beta function\n"
+    "betainccinv :\n"
+    "    inverse of the complement of the regularized incomplete beta function\n"
+    "beta : beta function\n"
+    "scipy.stats.beta : beta distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    ".. versionadded:: 1.11.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] NIST Digital Library of Mathematical Functions\n"
+    "       https://dlmf.nist.gov/8.17\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import betaincc, betainc\n"
+    "\n"
+    "The naive calculation ``1 - betainc(a, b, x)`` loses precision when\n"
+    "the values of ``betainc(a, b, x)`` are close to 1:\n"
+    "\n"
+    ">>> 1 - betainc(0.5, 8, [0.9, 0.99, 0.999])\n"
+    "array([2.0574632e-09, 0.0000000e+00, 0.0000000e+00])\n"
+    "\n"
+    "By using ``betaincc``, we get the correct values:\n"
+    "\n"
+    ">>> betaincc(0.5, 8, [0.9, 0.99, 0.999])\n"
+    "array([2.05746321e-09, 1.97259354e-17, 1.96467954e-25])")
+ufunc_betaincc_loops[0] = <np.PyUFuncGenericFunction>loop_f_fff__As_fff_f
+ufunc_betaincc_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_betaincc_types[0] = <char>NPY_FLOAT
+ufunc_betaincc_types[1] = <char>NPY_FLOAT
+ufunc_betaincc_types[2] = <char>NPY_FLOAT
+ufunc_betaincc_types[3] = <char>NPY_FLOAT
+ufunc_betaincc_types[4] = <char>NPY_DOUBLE
+ufunc_betaincc_types[5] = <char>NPY_DOUBLE
+ufunc_betaincc_types[6] = <char>NPY_DOUBLE
+ufunc_betaincc_types[7] = <char>NPY_DOUBLE
+ufunc_betaincc_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_ibetac_float
+ufunc_betaincc_ptr[2*0+1] = <void*>(<char*>"betaincc")
+ufunc_betaincc_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_ibetac_double
+ufunc_betaincc_ptr[2*1+1] = <void*>(<char*>"betaincc")
+ufunc_betaincc_data[0] = &ufunc_betaincc_ptr[2*0]
+ufunc_betaincc_data[1] = &ufunc_betaincc_ptr[2*1]
+betaincc = np.PyUFunc_FromFuncAndData(ufunc_betaincc_loops, ufunc_betaincc_data, ufunc_betaincc_types, 2, 3, 1, 0, "betaincc", ufunc_betaincc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_betainccinv_loops[2]
+cdef void *ufunc_betainccinv_ptr[4]
+cdef void *ufunc_betainccinv_data[2]
+cdef char ufunc_betainccinv_types[8]
+cdef char *ufunc_betainccinv_doc = (
+    "betainccinv(a, b, y, out=None)\n"
+    "\n"
+    "Inverse of the complemented regularized incomplete beta function.\n"
+    "\n"
+    "Computes :math:`x` such that:\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    y = 1 - I_x(a, b) = 1 - \\frac{\\Gamma(a+b)}{\\Gamma(a)\\Gamma(b)}\n"
+    "    \\int_0^x t^{a-1}(1-t)^{b-1}dt,\n"
+    "\n"
+    "where :math:`I_x` is the normalized incomplete beta function `betainc`\n"
+    "and :math:`\\Gamma` is the `gamma` function [1]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b : array_like\n"
+    "    Positive, real-valued parameters\n"
+    "y : array_like\n"
+    "    Real-valued input\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the inverse of the regularized incomplete beta function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "betainc : regularized incomplete beta function\n"
+    "betaincc : complement of the regularized incomplete beta function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    ".. versionadded:: 1.11.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] NIST Digital Library of Mathematical Functions\n"
+    "       https://dlmf.nist.gov/8.17\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import betainccinv, betaincc\n"
+    "\n"
+    "This function is the inverse of `betaincc` for fixed\n"
+    "values of :math:`a` and :math:`b`.\n"
+    "\n"
+    ">>> a, b = 1.2, 3.1\n"
+    ">>> y = betaincc(a, b, 0.2)\n"
+    ">>> betainccinv(a, b, y)\n"
+    "0.2\n"
+    "\n"
+    ">>> a, b = 7, 2.5\n"
+    ">>> x = betainccinv(a, b, 0.875)\n"
+    ">>> betaincc(a, b, x)\n"
+    "0.875")
+ufunc_betainccinv_loops[0] = <np.PyUFuncGenericFunction>loop_f_fff__As_fff_f
+ufunc_betainccinv_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_betainccinv_types[0] = <char>NPY_FLOAT
+ufunc_betainccinv_types[1] = <char>NPY_FLOAT
+ufunc_betainccinv_types[2] = <char>NPY_FLOAT
+ufunc_betainccinv_types[3] = <char>NPY_FLOAT
+ufunc_betainccinv_types[4] = <char>NPY_DOUBLE
+ufunc_betainccinv_types[5] = <char>NPY_DOUBLE
+ufunc_betainccinv_types[6] = <char>NPY_DOUBLE
+ufunc_betainccinv_types[7] = <char>NPY_DOUBLE
+ufunc_betainccinv_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_ibetac_inv_float
+ufunc_betainccinv_ptr[2*0+1] = <void*>(<char*>"betainccinv")
+ufunc_betainccinv_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_ibetac_inv_double
+ufunc_betainccinv_ptr[2*1+1] = <void*>(<char*>"betainccinv")
+ufunc_betainccinv_data[0] = &ufunc_betainccinv_ptr[2*0]
+ufunc_betainccinv_data[1] = &ufunc_betainccinv_ptr[2*1]
+betainccinv = np.PyUFunc_FromFuncAndData(ufunc_betainccinv_loops, ufunc_betainccinv_data, ufunc_betainccinv_types, 2, 3, 1, 0, "betainccinv", ufunc_betainccinv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_betaincinv_loops[2]
+cdef void *ufunc_betaincinv_ptr[4]
+cdef void *ufunc_betaincinv_data[2]
+cdef char ufunc_betaincinv_types[8]
+cdef char *ufunc_betaincinv_doc = (
+    "betaincinv(a, b, y, out=None)\n"
+    "\n"
+    "Inverse of the regularized incomplete beta function.\n"
+    "\n"
+    "Computes :math:`x` such that:\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    y = I_x(a, b) = \\frac{\\Gamma(a+b)}{\\Gamma(a)\\Gamma(b)}\n"
+    "    \\int_0^x t^{a-1}(1-t)^{b-1}dt,\n"
+    "\n"
+    "where :math:`I_x` is the normalized incomplete beta function `betainc`\n"
+    "and :math:`\\Gamma` is the `gamma` function [1]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b : array_like\n"
+    "    Positive, real-valued parameters\n"
+    "y : array_like\n"
+    "    Real-valued input\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the inverse of the regularized incomplete beta function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "betainc : regularized incomplete beta function\n"
+    "gamma : gamma function\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] NIST Digital Library of Mathematical Functions\n"
+    "       https://dlmf.nist.gov/8.17\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "This function is the inverse of `betainc` for fixed\n"
+    "values of :math:`a` and :math:`b`.\n"
+    "\n"
+    ">>> a, b = 1.2, 3.1\n"
+    ">>> y = sc.betainc(a, b, 0.2)\n"
+    ">>> sc.betaincinv(a, b, y)\n"
+    "0.2\n"
+    ">>>\n"
+    ">>> a, b = 7.5, 0.4\n"
+    ">>> x = sc.betaincinv(a, b, 0.5)\n"
+    ">>> sc.betainc(a, b, x)\n"
+    "0.5")
+ufunc_betaincinv_loops[0] = <np.PyUFuncGenericFunction>loop_f_fff__As_fff_f
+ufunc_betaincinv_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_betaincinv_types[0] = <char>NPY_FLOAT
+ufunc_betaincinv_types[1] = <char>NPY_FLOAT
+ufunc_betaincinv_types[2] = <char>NPY_FLOAT
+ufunc_betaincinv_types[3] = <char>NPY_FLOAT
+ufunc_betaincinv_types[4] = <char>NPY_DOUBLE
+ufunc_betaincinv_types[5] = <char>NPY_DOUBLE
+ufunc_betaincinv_types[6] = <char>NPY_DOUBLE
+ufunc_betaincinv_types[7] = <char>NPY_DOUBLE
+ufunc_betaincinv_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_ibeta_inv_float
+ufunc_betaincinv_ptr[2*0+1] = <void*>(<char*>"betaincinv")
+ufunc_betaincinv_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_ibeta_inv_double
+ufunc_betaincinv_ptr[2*1+1] = <void*>(<char*>"betaincinv")
+ufunc_betaincinv_data[0] = &ufunc_betaincinv_ptr[2*0]
+ufunc_betaincinv_data[1] = &ufunc_betaincinv_ptr[2*1]
+betaincinv = np.PyUFunc_FromFuncAndData(ufunc_betaincinv_loops, ufunc_betaincinv_data, ufunc_betaincinv_types, 2, 3, 1, 0, "betaincinv", ufunc_betaincinv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_betaln_loops[2]
+cdef void *ufunc_betaln_ptr[4]
+cdef void *ufunc_betaln_data[2]
+cdef char ufunc_betaln_types[6]
+cdef char *ufunc_betaln_doc = (
+    "betaln(a, b, out=None)\n"
+    "\n"
+    "Natural logarithm of absolute value of beta function.\n"
+    "\n"
+    "Computes ``ln(abs(beta(a, b)))``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b : array_like\n"
+    "    Positive, real-valued parameters\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the betaln function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gamma : the gamma function\n"
+    "betainc :  the regularized incomplete beta function\n"
+    "beta : the beta function\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import betaln, beta\n"
+    "\n"
+    "Verify that, for moderate values of ``a`` and ``b``, ``betaln(a, b)``\n"
+    "is the same as ``log(beta(a, b))``:\n"
+    "\n"
+    ">>> betaln(3, 4)\n"
+    "-4.0943445622221\n"
+    "\n"
+    ">>> np.log(beta(3, 4))\n"
+    "-4.0943445622221\n"
+    "\n"
+    "In the following ``beta(a, b)`` underflows to 0, so we can't compute\n"
+    "the logarithm of the actual value.\n"
+    "\n"
+    ">>> a = 400\n"
+    ">>> b = 900\n"
+    ">>> beta(a, b)\n"
+    "0.0\n"
+    "\n"
+    "We can compute the logarithm of ``beta(a, b)`` by using `betaln`:\n"
+    "\n"
+    ">>> betaln(a, b)\n"
+    "-804.3069951764146")
+ufunc_betaln_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_betaln_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_betaln_types[0] = <char>NPY_FLOAT
+ufunc_betaln_types[1] = <char>NPY_FLOAT
+ufunc_betaln_types[2] = <char>NPY_FLOAT
+ufunc_betaln_types[3] = <char>NPY_DOUBLE
+ufunc_betaln_types[4] = <char>NPY_DOUBLE
+ufunc_betaln_types[5] = <char>NPY_DOUBLE
+ufunc_betaln_ptr[2*0] = <void*>_func_lbeta
+ufunc_betaln_ptr[2*0+1] = <void*>(<char*>"betaln")
+ufunc_betaln_ptr[2*1] = <void*>_func_lbeta
+ufunc_betaln_ptr[2*1+1] = <void*>(<char*>"betaln")
+ufunc_betaln_data[0] = &ufunc_betaln_ptr[2*0]
+ufunc_betaln_data[1] = &ufunc_betaln_ptr[2*1]
+betaln = np.PyUFunc_FromFuncAndData(ufunc_betaln_loops, ufunc_betaln_data, ufunc_betaln_types, 2, 2, 1, 0, "betaln", ufunc_betaln_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_binom_loops[2]
+cdef void *ufunc_binom_ptr[4]
+cdef void *ufunc_binom_data[2]
+cdef char ufunc_binom_types[6]
+cdef char *ufunc_binom_doc = (
+    "binom(x, y, out=None)\n"
+    "\n"
+    "Binomial coefficient considered as a function of two real variables.\n"
+    "\n"
+    "For real arguments, the binomial coefficient is defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\binom{x}{y} = \\frac{\\Gamma(x + 1)}{\\Gamma(y + 1)\\Gamma(x - y + 1)} =\n"
+    "        \\frac{1}{(x + 1)\\mathrm{B}(x - y + 1, y + 1)}\n"
+    "\n"
+    "Where :math:`\\Gamma` is the Gamma function (`gamma`) and :math:`\\mathrm{B}`\n"
+    "is the Beta function (`beta`) [1]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x, y: array_like\n"
+    "   Real arguments to :math:`\\binom{x}{y}`.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of binomial coefficient.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "comb : The number of combinations of N things taken k at a time.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The Gamma function has poles at non-positive integers and tends to either\n"
+    "positive or negative infinity depending on the direction on the real line\n"
+    "from which a pole is approached. When considered as a function of two real\n"
+    "variables, :math:`\\binom{x}{y}` is thus undefined when `x` is a negative\n"
+    "integer.  `binom` returns ``nan`` when ``x`` is a negative integer. This\n"
+    "is the case even when ``x`` is a negative integer and ``y`` an integer,\n"
+    "contrary to the usual convention for defining :math:`\\binom{n}{k}` when it\n"
+    "is considered as a function of two integer variables.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] https://en.wikipedia.org/wiki/Binomial_coefficient\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "The following examples illustrate the ways in which `binom` differs from\n"
+    "the function `comb`.\n"
+    "\n"
+    ">>> from scipy.special import binom, comb\n"
+    "\n"
+    "When ``exact=False`` and ``x`` and ``y`` are both positive, `comb` calls\n"
+    "`binom` internally.\n"
+    "\n"
+    ">>> x, y = 3, 2\n"
+    ">>> (binom(x, y), comb(x, y), comb(x, y, exact=True))\n"
+    "(3.0, 3.0, 3)\n"
+    "\n"
+    "For larger values, `comb` with ``exact=True`` no longer agrees\n"
+    "with `binom`.\n"
+    "\n"
+    ">>> x, y = 43, 23\n"
+    ">>> (binom(x, y), comb(x, y), comb(x, y, exact=True))\n"
+    "(960566918219.9999, 960566918219.9999, 960566918220)\n"
+    "\n"
+    "`binom` returns ``nan`` when ``x`` is a negative integer, but is otherwise\n"
+    "defined for negative arguments. `comb` returns 0 whenever one of ``x`` or\n"
+    "``y`` is negative or ``x`` is less than ``y``.\n"
+    "\n"
+    ">>> x, y = -3, 2\n"
+    ">>> (binom(x, y), comb(x, y), comb(x, y, exact=True))\n"
+    "(nan, 0.0, 0)\n"
+    "\n"
+    ">>> x, y = -3.1, 2.2\n"
+    ">>> (binom(x, y), comb(x, y), comb(x, y, exact=True))\n"
+    "(18.714147876804432, 0.0, 0)\n"
+    "\n"
+    ">>> x, y = 2.2, 3.1\n"
+    ">>> (binom(x, y), comb(x, y), comb(x, y, exact=True))\n"
+    "(0.037399983365134115, 0.0, 0)")
+ufunc_binom_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_binom_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_binom_types[0] = <char>NPY_FLOAT
+ufunc_binom_types[1] = <char>NPY_FLOAT
+ufunc_binom_types[2] = <char>NPY_FLOAT
+ufunc_binom_types[3] = <char>NPY_DOUBLE
+ufunc_binom_types[4] = <char>NPY_DOUBLE
+ufunc_binom_types[5] = <char>NPY_DOUBLE
+ufunc_binom_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_binom
+ufunc_binom_ptr[2*0+1] = <void*>(<char*>"binom")
+ufunc_binom_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_binom
+ufunc_binom_ptr[2*1+1] = <void*>(<char*>"binom")
+ufunc_binom_data[0] = &ufunc_binom_ptr[2*0]
+ufunc_binom_data[1] = &ufunc_binom_ptr[2*1]
+binom = np.PyUFunc_FromFuncAndData(ufunc_binom_loops, ufunc_binom_data, ufunc_binom_types, 2, 2, 1, 0, "binom", ufunc_binom_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_boxcox_loops[2]
+cdef void *ufunc_boxcox_ptr[4]
+cdef void *ufunc_boxcox_data[2]
+cdef char ufunc_boxcox_types[6]
+cdef char *ufunc_boxcox_doc = (
+    "boxcox(x, lmbda, out=None)\n"
+    "\n"
+    "Compute the Box-Cox transformation.\n"
+    "\n"
+    "The Box-Cox transformation is::\n"
+    "\n"
+    "    y = (x**lmbda - 1) / lmbda  if lmbda != 0\n"
+    "        log(x)                  if lmbda == 0\n"
+    "\n"
+    "Returns `nan` if ``x < 0``.\n"
+    "Returns `-inf` if ``x == 0`` and ``lmbda < 0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Data to be transformed.\n"
+    "lmbda : array_like\n"
+    "    Power parameter of the Box-Cox transform.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Transformed data.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "\n"
+    ".. versionadded:: 0.14.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import boxcox\n"
+    ">>> boxcox([1, 4, 10], 2.5)\n"
+    "array([   0.        ,   12.4       ,  126.09110641])\n"
+    ">>> boxcox(2, [0, 1, 2])\n"
+    "array([ 0.69314718,  1.        ,  1.5       ])")
+ufunc_boxcox_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_boxcox_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_boxcox_types[0] = <char>NPY_FLOAT
+ufunc_boxcox_types[1] = <char>NPY_FLOAT
+ufunc_boxcox_types[2] = <char>NPY_FLOAT
+ufunc_boxcox_types[3] = <char>NPY_DOUBLE
+ufunc_boxcox_types[4] = <char>NPY_DOUBLE
+ufunc_boxcox_types[5] = <char>NPY_DOUBLE
+ufunc_boxcox_ptr[2*0] = <void*>_func_boxcox
+ufunc_boxcox_ptr[2*0+1] = <void*>(<char*>"boxcox")
+ufunc_boxcox_ptr[2*1] = <void*>_func_boxcox
+ufunc_boxcox_ptr[2*1+1] = <void*>(<char*>"boxcox")
+ufunc_boxcox_data[0] = &ufunc_boxcox_ptr[2*0]
+ufunc_boxcox_data[1] = &ufunc_boxcox_ptr[2*1]
+boxcox = np.PyUFunc_FromFuncAndData(ufunc_boxcox_loops, ufunc_boxcox_data, ufunc_boxcox_types, 2, 2, 1, 0, "boxcox", ufunc_boxcox_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_boxcox1p_loops[2]
+cdef void *ufunc_boxcox1p_ptr[4]
+cdef void *ufunc_boxcox1p_data[2]
+cdef char ufunc_boxcox1p_types[6]
+cdef char *ufunc_boxcox1p_doc = (
+    "boxcox1p(x, lmbda, out=None)\n"
+    "\n"
+    "Compute the Box-Cox transformation of 1 + `x`.\n"
+    "\n"
+    "The Box-Cox transformation computed by `boxcox1p` is::\n"
+    "\n"
+    "    y = ((1+x)**lmbda - 1) / lmbda  if lmbda != 0\n"
+    "        log(1+x)                    if lmbda == 0\n"
+    "\n"
+    "Returns `nan` if ``x < -1``.\n"
+    "Returns `-inf` if ``x == -1`` and ``lmbda < 0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Data to be transformed.\n"
+    "lmbda : array_like\n"
+    "    Power parameter of the Box-Cox transform.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Transformed data.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "\n"
+    ".. versionadded:: 0.14.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import boxcox1p\n"
+    ">>> boxcox1p(1e-4, [0, 0.5, 1])\n"
+    "array([  9.99950003e-05,   9.99975001e-05,   1.00000000e-04])\n"
+    ">>> boxcox1p([0.01, 0.1], 0.25)\n"
+    "array([ 0.00996272,  0.09645476])")
+ufunc_boxcox1p_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_boxcox1p_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_boxcox1p_types[0] = <char>NPY_FLOAT
+ufunc_boxcox1p_types[1] = <char>NPY_FLOAT
+ufunc_boxcox1p_types[2] = <char>NPY_FLOAT
+ufunc_boxcox1p_types[3] = <char>NPY_DOUBLE
+ufunc_boxcox1p_types[4] = <char>NPY_DOUBLE
+ufunc_boxcox1p_types[5] = <char>NPY_DOUBLE
+ufunc_boxcox1p_ptr[2*0] = <void*>_func_boxcox1p
+ufunc_boxcox1p_ptr[2*0+1] = <void*>(<char*>"boxcox1p")
+ufunc_boxcox1p_ptr[2*1] = <void*>_func_boxcox1p
+ufunc_boxcox1p_ptr[2*1+1] = <void*>(<char*>"boxcox1p")
+ufunc_boxcox1p_data[0] = &ufunc_boxcox1p_ptr[2*0]
+ufunc_boxcox1p_data[1] = &ufunc_boxcox1p_ptr[2*1]
+boxcox1p = np.PyUFunc_FromFuncAndData(ufunc_boxcox1p_loops, ufunc_boxcox1p_data, ufunc_boxcox1p_types, 2, 2, 1, 0, "boxcox1p", ufunc_boxcox1p_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_btdtr_loops[2]
+cdef void *ufunc_btdtr_ptr[4]
+cdef void *ufunc_btdtr_data[2]
+cdef char ufunc_btdtr_types[8]
+cdef char *ufunc_btdtr_doc = (
+    "btdtr(a, b, x, out=None)\n"
+    "\n"
+    "Cumulative distribution function of the beta distribution.\n"
+    "\n"
+    "Returns the integral from zero to `x` of the beta probability density\n"
+    "function,\n"
+    "\n"
+    ".. math::\n"
+    "    I = \\int_0^x \\frac{\\Gamma(a + b)}{\\Gamma(a)\\Gamma(b)} t^{a-1} (1-t)^{b-1}\\,dt\n"
+    "\n"
+    "where :math:`\\Gamma` is the gamma function.\n"
+    "\n"
+    ".. deprecated:: 1.12.0\n"
+    "    This function is deprecated and will be removed from SciPy 1.14.0.\n"
+    "    Use `scipy.special.betainc` instead.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    Shape parameter (a > 0).\n"
+    "b : array_like\n"
+    "    Shape parameter (b > 0).\n"
+    "x : array_like\n"
+    "    Upper limit of integration, in [0, 1].\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "I : scalar or ndarray\n"
+    "    Cumulative distribution function of the beta distribution with\n"
+    "    parameters `a` and `b` at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "betainc\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "This function is identical to the incomplete beta integral function\n"
+    "`betainc`.\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `btdtr`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/")
+ufunc_btdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_btdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_btdtr_types[0] = <char>NPY_FLOAT
+ufunc_btdtr_types[1] = <char>NPY_FLOAT
+ufunc_btdtr_types[2] = <char>NPY_FLOAT
+ufunc_btdtr_types[3] = <char>NPY_FLOAT
+ufunc_btdtr_types[4] = <char>NPY_DOUBLE
+ufunc_btdtr_types[5] = <char>NPY_DOUBLE
+ufunc_btdtr_types[6] = <char>NPY_DOUBLE
+ufunc_btdtr_types[7] = <char>NPY_DOUBLE
+ufunc_btdtr_ptr[2*0] = <void*>_func_btdtr
+ufunc_btdtr_ptr[2*0+1] = <void*>(<char*>"btdtr")
+ufunc_btdtr_ptr[2*1] = <void*>_func_btdtr
+ufunc_btdtr_ptr[2*1+1] = <void*>(<char*>"btdtr")
+ufunc_btdtr_data[0] = &ufunc_btdtr_ptr[2*0]
+ufunc_btdtr_data[1] = &ufunc_btdtr_ptr[2*1]
+btdtr = np.PyUFunc_FromFuncAndData(ufunc_btdtr_loops, ufunc_btdtr_data, ufunc_btdtr_types, 2, 3, 1, 0, "btdtr", ufunc_btdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_btdtri_loops[2]
+cdef void *ufunc_btdtri_ptr[4]
+cdef void *ufunc_btdtri_data[2]
+cdef char ufunc_btdtri_types[8]
+cdef char *ufunc_btdtri_doc = (
+    "btdtri(a, b, p, out=None)\n"
+    "\n"
+    "The `p`-th quantile of the beta distribution.\n"
+    "\n"
+    "This function is the inverse of the beta cumulative distribution function,\n"
+    "`btdtr`, returning the value of `x` for which `btdtr(a, b, x) = p`, or\n"
+    "\n"
+    ".. math::\n"
+    "    p = \\int_0^x \\frac{\\Gamma(a + b)}{\\Gamma(a)\\Gamma(b)} t^{a-1} (1-t)^{b-1}\\,dt\n"
+    "\n"
+    ".. deprecated:: 1.12.0\n"
+    "    This function is deprecated and will be removed from SciPy 1.14.0.\n"
+    "    Use `scipy.special.betaincinv` instead.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    Shape parameter (`a` > 0).\n"
+    "b : array_like\n"
+    "    Shape parameter (`b` > 0).\n"
+    "p : array_like\n"
+    "    Cumulative probability, in [0, 1].\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "x : scalar or ndarray\n"
+    "    The quantile corresponding to `p`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "betaincinv\n"
+    "btdtr\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The value of `x` is found by interval halving or Newton iterations.\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `incbi`, which solves the equivalent\n"
+    "problem of finding the inverse of the incomplete beta integral.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/")
+ufunc_btdtri_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_btdtri_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_btdtri_types[0] = <char>NPY_FLOAT
+ufunc_btdtri_types[1] = <char>NPY_FLOAT
+ufunc_btdtri_types[2] = <char>NPY_FLOAT
+ufunc_btdtri_types[3] = <char>NPY_FLOAT
+ufunc_btdtri_types[4] = <char>NPY_DOUBLE
+ufunc_btdtri_types[5] = <char>NPY_DOUBLE
+ufunc_btdtri_types[6] = <char>NPY_DOUBLE
+ufunc_btdtri_types[7] = <char>NPY_DOUBLE
+ufunc_btdtri_ptr[2*0] = <void*>_func_incbi
+ufunc_btdtri_ptr[2*0+1] = <void*>(<char*>"btdtri")
+ufunc_btdtri_ptr[2*1] = <void*>_func_incbi
+ufunc_btdtri_ptr[2*1+1] = <void*>(<char*>"btdtri")
+ufunc_btdtri_data[0] = &ufunc_btdtri_ptr[2*0]
+ufunc_btdtri_data[1] = &ufunc_btdtri_ptr[2*1]
+btdtri = np.PyUFunc_FromFuncAndData(ufunc_btdtri_loops, ufunc_btdtri_data, ufunc_btdtri_types, 2, 3, 1, 0, "btdtri", ufunc_btdtri_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_btdtria_loops[2]
+cdef void *ufunc_btdtria_ptr[4]
+cdef void *ufunc_btdtria_data[2]
+cdef char ufunc_btdtria_types[8]
+cdef char *ufunc_btdtria_doc = (
+    "btdtria(p, b, x, out=None)\n"
+    "\n"
+    "Inverse of `btdtr` with respect to `a`.\n"
+    "\n"
+    "This is the inverse of the beta cumulative distribution function, `btdtr`,\n"
+    "considered as a function of `a`, returning the value of `a` for which\n"
+    "`btdtr(a, b, x) = p`, or\n"
+    "\n"
+    ".. math::\n"
+    "    p = \\int_0^x \\frac{\\Gamma(a + b)}{\\Gamma(a)\\Gamma(b)} t^{a-1} (1-t)^{b-1}\\,dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    Cumulative probability, in [0, 1].\n"
+    "b : array_like\n"
+    "    Shape parameter (`b` > 0).\n"
+    "x : array_like\n"
+    "    The quantile, in [0, 1].\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "a : scalar or ndarray\n"
+    "    The value of the shape parameter `a` such that `btdtr(a, b, x) = p`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "btdtr : Cumulative distribution function of the beta distribution.\n"
+    "btdtri : Inverse with respect to `x`.\n"
+    "btdtrib : Inverse with respect to `b`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the CDFLIB [1]_ Fortran routine `cdfbet`.\n"
+    "\n"
+    "The cumulative distribution function `p` is computed using a routine by\n"
+    "DiDinato and Morris [2]_. Computation of `a` involves a search for a value\n"
+    "that produces the desired value of `p`. The search relies on the\n"
+    "monotonicity of `p` with `a`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.\n"
+    ".. [2] DiDinato, A. R. and Morris, A. H.,\n"
+    "       Algorithm 708: Significant Digit Computation of the Incomplete Beta\n"
+    "       Function Ratios. ACM Trans. Math. Softw. 18 (1993), 360-373.")
+ufunc_btdtria_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_btdtria_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_btdtria_types[0] = <char>NPY_FLOAT
+ufunc_btdtria_types[1] = <char>NPY_FLOAT
+ufunc_btdtria_types[2] = <char>NPY_FLOAT
+ufunc_btdtria_types[3] = <char>NPY_FLOAT
+ufunc_btdtria_types[4] = <char>NPY_DOUBLE
+ufunc_btdtria_types[5] = <char>NPY_DOUBLE
+ufunc_btdtria_types[6] = <char>NPY_DOUBLE
+ufunc_btdtria_types[7] = <char>NPY_DOUBLE
+ufunc_btdtria_ptr[2*0] = <void*>_func_btdtria
+ufunc_btdtria_ptr[2*0+1] = <void*>(<char*>"btdtria")
+ufunc_btdtria_ptr[2*1] = <void*>_func_btdtria
+ufunc_btdtria_ptr[2*1+1] = <void*>(<char*>"btdtria")
+ufunc_btdtria_data[0] = &ufunc_btdtria_ptr[2*0]
+ufunc_btdtria_data[1] = &ufunc_btdtria_ptr[2*1]
+btdtria = np.PyUFunc_FromFuncAndData(ufunc_btdtria_loops, ufunc_btdtria_data, ufunc_btdtria_types, 2, 3, 1, 0, "btdtria", ufunc_btdtria_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_btdtrib_loops[2]
+cdef void *ufunc_btdtrib_ptr[4]
+cdef void *ufunc_btdtrib_data[2]
+cdef char ufunc_btdtrib_types[8]
+cdef char *ufunc_btdtrib_doc = (
+    "btdtria(a, p, x, out=None)\n"
+    "\n"
+    "Inverse of `btdtr` with respect to `b`.\n"
+    "\n"
+    "This is the inverse of the beta cumulative distribution function, `btdtr`,\n"
+    "considered as a function of `b`, returning the value of `b` for which\n"
+    "`btdtr(a, b, x) = p`, or\n"
+    "\n"
+    ".. math::\n"
+    "    p = \\int_0^x \\frac{\\Gamma(a + b)}{\\Gamma(a)\\Gamma(b)} t^{a-1} (1-t)^{b-1}\\,dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    Shape parameter (`a` > 0).\n"
+    "p : array_like\n"
+    "    Cumulative probability, in [0, 1].\n"
+    "x : array_like\n"
+    "    The quantile, in [0, 1].\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "b : scalar or ndarray\n"
+    "    The value of the shape parameter `b` such that `btdtr(a, b, x) = p`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "btdtr : Cumulative distribution function of the beta distribution.\n"
+    "btdtri : Inverse with respect to `x`.\n"
+    "btdtria : Inverse with respect to `a`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the CDFLIB [1]_ Fortran routine `cdfbet`.\n"
+    "\n"
+    "The cumulative distribution function `p` is computed using a routine by\n"
+    "DiDinato and Morris [2]_. Computation of `b` involves a search for a value\n"
+    "that produces the desired value of `p`. The search relies on the\n"
+    "monotonicity of `p` with `b`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.\n"
+    ".. [2] DiDinato, A. R. and Morris, A. H.,\n"
+    "       Algorithm 708: Significant Digit Computation of the Incomplete Beta\n"
+    "       Function Ratios. ACM Trans. Math. Softw. 18 (1993), 360-373.")
+ufunc_btdtrib_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_btdtrib_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_btdtrib_types[0] = <char>NPY_FLOAT
+ufunc_btdtrib_types[1] = <char>NPY_FLOAT
+ufunc_btdtrib_types[2] = <char>NPY_FLOAT
+ufunc_btdtrib_types[3] = <char>NPY_FLOAT
+ufunc_btdtrib_types[4] = <char>NPY_DOUBLE
+ufunc_btdtrib_types[5] = <char>NPY_DOUBLE
+ufunc_btdtrib_types[6] = <char>NPY_DOUBLE
+ufunc_btdtrib_types[7] = <char>NPY_DOUBLE
+ufunc_btdtrib_ptr[2*0] = <void*>_func_btdtrib
+ufunc_btdtrib_ptr[2*0+1] = <void*>(<char*>"btdtrib")
+ufunc_btdtrib_ptr[2*1] = <void*>_func_btdtrib
+ufunc_btdtrib_ptr[2*1+1] = <void*>(<char*>"btdtrib")
+ufunc_btdtrib_data[0] = &ufunc_btdtrib_ptr[2*0]
+ufunc_btdtrib_data[1] = &ufunc_btdtrib_ptr[2*1]
+btdtrib = np.PyUFunc_FromFuncAndData(ufunc_btdtrib_loops, ufunc_btdtrib_data, ufunc_btdtrib_types, 2, 3, 1, 0, "btdtrib", ufunc_btdtrib_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_cbrt_loops[2]
+cdef void *ufunc_cbrt_ptr[4]
+cdef void *ufunc_cbrt_data[2]
+cdef char ufunc_cbrt_types[4]
+cdef char *ufunc_cbrt_doc = (
+    "cbrt(x, out=None)\n"
+    "\n"
+    "Element-wise cube root of `x`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    `x` must contain real numbers.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The cube root of each value in `x`.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import cbrt\n"
+    "\n"
+    ">>> cbrt(8)\n"
+    "2.0\n"
+    ">>> cbrt([-8, -3, 0.125, 1.331])\n"
+    "array([-2.        , -1.44224957,  0.5       ,  1.1       ])")
+ufunc_cbrt_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_cbrt_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_cbrt_types[0] = <char>NPY_FLOAT
+ufunc_cbrt_types[1] = <char>NPY_FLOAT
+ufunc_cbrt_types[2] = <char>NPY_DOUBLE
+ufunc_cbrt_types[3] = <char>NPY_DOUBLE
+ufunc_cbrt_ptr[2*0] = <void*>_func_cbrt
+ufunc_cbrt_ptr[2*0+1] = <void*>(<char*>"cbrt")
+ufunc_cbrt_ptr[2*1] = <void*>_func_cbrt
+ufunc_cbrt_ptr[2*1+1] = <void*>(<char*>"cbrt")
+ufunc_cbrt_data[0] = &ufunc_cbrt_ptr[2*0]
+ufunc_cbrt_data[1] = &ufunc_cbrt_ptr[2*1]
+cbrt = np.PyUFunc_FromFuncAndData(ufunc_cbrt_loops, ufunc_cbrt_data, ufunc_cbrt_types, 2, 1, 1, 0, "cbrt", ufunc_cbrt_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_chdtr_loops[2]
+cdef void *ufunc_chdtr_ptr[4]
+cdef void *ufunc_chdtr_data[2]
+cdef char ufunc_chdtr_types[6]
+cdef char *ufunc_chdtr_doc = (
+    "chdtr(v, x, out=None)\n"
+    "\n"
+    "Chi square cumulative distribution function.\n"
+    "\n"
+    "Returns the area under the left tail (from 0 to `x`) of the Chi\n"
+    "square probability density function with `v` degrees of freedom:\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\frac{1}{2^{v/2} \\Gamma(v/2)} \\int_0^x t^{v/2 - 1} e^{-t/2} dt\n"
+    "\n"
+    "Here :math:`\\Gamma` is the Gamma function; see `gamma`. This\n"
+    "integral can be expressed in terms of the regularized lower\n"
+    "incomplete gamma function `gammainc` as\n"
+    "``gammainc(v / 2, x / 2)``. [1]_\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Degrees of freedom.\n"
+    "x : array_like\n"
+    "    Upper bound of the integral.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the cumulative distribution function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "chdtrc, chdtri, chdtriv, gammainc\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Chi-Square distribution,\n"
+    "    https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It can be expressed in terms of the regularized lower incomplete\n"
+    "gamma function.\n"
+    "\n"
+    ">>> v = 1\n"
+    ">>> x = np.arange(4)\n"
+    ">>> sc.chdtr(v, x)\n"
+    "array([0.        , 0.68268949, 0.84270079, 0.91673548])\n"
+    ">>> sc.gammainc(v / 2, x / 2)\n"
+    "array([0.        , 0.68268949, 0.84270079, 0.91673548])")
+ufunc_chdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_chdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_chdtr_types[0] = <char>NPY_FLOAT
+ufunc_chdtr_types[1] = <char>NPY_FLOAT
+ufunc_chdtr_types[2] = <char>NPY_FLOAT
+ufunc_chdtr_types[3] = <char>NPY_DOUBLE
+ufunc_chdtr_types[4] = <char>NPY_DOUBLE
+ufunc_chdtr_types[5] = <char>NPY_DOUBLE
+ufunc_chdtr_ptr[2*0] = <void*>_func_chdtr
+ufunc_chdtr_ptr[2*0+1] = <void*>(<char*>"chdtr")
+ufunc_chdtr_ptr[2*1] = <void*>_func_chdtr
+ufunc_chdtr_ptr[2*1+1] = <void*>(<char*>"chdtr")
+ufunc_chdtr_data[0] = &ufunc_chdtr_ptr[2*0]
+ufunc_chdtr_data[1] = &ufunc_chdtr_ptr[2*1]
+chdtr = np.PyUFunc_FromFuncAndData(ufunc_chdtr_loops, ufunc_chdtr_data, ufunc_chdtr_types, 2, 2, 1, 0, "chdtr", ufunc_chdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_chdtrc_loops[2]
+cdef void *ufunc_chdtrc_ptr[4]
+cdef void *ufunc_chdtrc_data[2]
+cdef char ufunc_chdtrc_types[6]
+cdef char *ufunc_chdtrc_doc = (
+    "chdtrc(v, x, out=None)\n"
+    "\n"
+    "Chi square survival function.\n"
+    "\n"
+    "Returns the area under the right hand tail (from `x` to infinity)\n"
+    "of the Chi square probability density function with `v` degrees of\n"
+    "freedom:\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\frac{1}{2^{v/2} \\Gamma(v/2)} \\int_x^\\infty t^{v/2 - 1} e^{-t/2} dt\n"
+    "\n"
+    "Here :math:`\\Gamma` is the Gamma function; see `gamma`. This\n"
+    "integral can be expressed in terms of the regularized upper\n"
+    "incomplete gamma function `gammaincc` as\n"
+    "``gammaincc(v / 2, x / 2)``. [1]_\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Degrees of freedom.\n"
+    "x : array_like\n"
+    "    Lower bound of the integral.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the survival function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "chdtr, chdtri, chdtriv, gammaincc\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Chi-Square distribution,\n"
+    "    https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It can be expressed in terms of the regularized upper incomplete\n"
+    "gamma function.\n"
+    "\n"
+    ">>> v = 1\n"
+    ">>> x = np.arange(4)\n"
+    ">>> sc.chdtrc(v, x)\n"
+    "array([1.        , 0.31731051, 0.15729921, 0.08326452])\n"
+    ">>> sc.gammaincc(v / 2, x / 2)\n"
+    "array([1.        , 0.31731051, 0.15729921, 0.08326452])")
+ufunc_chdtrc_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_chdtrc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_chdtrc_types[0] = <char>NPY_FLOAT
+ufunc_chdtrc_types[1] = <char>NPY_FLOAT
+ufunc_chdtrc_types[2] = <char>NPY_FLOAT
+ufunc_chdtrc_types[3] = <char>NPY_DOUBLE
+ufunc_chdtrc_types[4] = <char>NPY_DOUBLE
+ufunc_chdtrc_types[5] = <char>NPY_DOUBLE
+ufunc_chdtrc_ptr[2*0] = <void*>_func_chdtrc
+ufunc_chdtrc_ptr[2*0+1] = <void*>(<char*>"chdtrc")
+ufunc_chdtrc_ptr[2*1] = <void*>_func_chdtrc
+ufunc_chdtrc_ptr[2*1+1] = <void*>(<char*>"chdtrc")
+ufunc_chdtrc_data[0] = &ufunc_chdtrc_ptr[2*0]
+ufunc_chdtrc_data[1] = &ufunc_chdtrc_ptr[2*1]
+chdtrc = np.PyUFunc_FromFuncAndData(ufunc_chdtrc_loops, ufunc_chdtrc_data, ufunc_chdtrc_types, 2, 2, 1, 0, "chdtrc", ufunc_chdtrc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_chdtri_loops[2]
+cdef void *ufunc_chdtri_ptr[4]
+cdef void *ufunc_chdtri_data[2]
+cdef char ufunc_chdtri_types[6]
+cdef char *ufunc_chdtri_doc = (
+    "chdtri(v, p, out=None)\n"
+    "\n"
+    "Inverse to `chdtrc` with respect to `x`.\n"
+    "\n"
+    "Returns `x` such that ``chdtrc(v, x) == p``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Degrees of freedom.\n"
+    "p : array_like\n"
+    "    Probability.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "x : scalar or ndarray\n"
+    "    Value so that the probability a Chi square random variable\n"
+    "    with `v` degrees of freedom is greater than `x` equals `p`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "chdtrc, chdtr, chdtriv\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Chi-Square distribution,\n"
+    "    https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It inverts `chdtrc`.\n"
+    "\n"
+    ">>> v, p = 1, 0.3\n"
+    ">>> sc.chdtrc(v, sc.chdtri(v, p))\n"
+    "0.3\n"
+    ">>> x = 1\n"
+    ">>> sc.chdtri(v, sc.chdtrc(v, x))\n"
+    "1.0")
+ufunc_chdtri_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_chdtri_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_chdtri_types[0] = <char>NPY_FLOAT
+ufunc_chdtri_types[1] = <char>NPY_FLOAT
+ufunc_chdtri_types[2] = <char>NPY_FLOAT
+ufunc_chdtri_types[3] = <char>NPY_DOUBLE
+ufunc_chdtri_types[4] = <char>NPY_DOUBLE
+ufunc_chdtri_types[5] = <char>NPY_DOUBLE
+ufunc_chdtri_ptr[2*0] = <void*>_func_chdtri
+ufunc_chdtri_ptr[2*0+1] = <void*>(<char*>"chdtri")
+ufunc_chdtri_ptr[2*1] = <void*>_func_chdtri
+ufunc_chdtri_ptr[2*1+1] = <void*>(<char*>"chdtri")
+ufunc_chdtri_data[0] = &ufunc_chdtri_ptr[2*0]
+ufunc_chdtri_data[1] = &ufunc_chdtri_ptr[2*1]
+chdtri = np.PyUFunc_FromFuncAndData(ufunc_chdtri_loops, ufunc_chdtri_data, ufunc_chdtri_types, 2, 2, 1, 0, "chdtri", ufunc_chdtri_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_chdtriv_loops[2]
+cdef void *ufunc_chdtriv_ptr[4]
+cdef void *ufunc_chdtriv_data[2]
+cdef char ufunc_chdtriv_types[6]
+cdef char *ufunc_chdtriv_doc = (
+    "chdtriv(p, x, out=None)\n"
+    "\n"
+    "Inverse to `chdtr` with respect to `v`.\n"
+    "\n"
+    "Returns `v` such that ``chdtr(v, x) == p``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    Probability that the Chi square random variable is less than\n"
+    "    or equal to `x`.\n"
+    "x : array_like\n"
+    "    Nonnegative input.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Degrees of freedom.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "chdtr, chdtrc, chdtri\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Chi-Square distribution,\n"
+    "    https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It inverts `chdtr`.\n"
+    "\n"
+    ">>> p, x = 0.5, 1\n"
+    ">>> sc.chdtr(sc.chdtriv(p, x), x)\n"
+    "0.5000000000202172\n"
+    ">>> v = 1\n"
+    ">>> sc.chdtriv(sc.chdtr(v, x), v)\n"
+    "1.0000000000000013")
+ufunc_chdtriv_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_chdtriv_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_chdtriv_types[0] = <char>NPY_FLOAT
+ufunc_chdtriv_types[1] = <char>NPY_FLOAT
+ufunc_chdtriv_types[2] = <char>NPY_FLOAT
+ufunc_chdtriv_types[3] = <char>NPY_DOUBLE
+ufunc_chdtriv_types[4] = <char>NPY_DOUBLE
+ufunc_chdtriv_types[5] = <char>NPY_DOUBLE
+ufunc_chdtriv_ptr[2*0] = <void*>_func_chdtriv
+ufunc_chdtriv_ptr[2*0+1] = <void*>(<char*>"chdtriv")
+ufunc_chdtriv_ptr[2*1] = <void*>_func_chdtriv
+ufunc_chdtriv_ptr[2*1+1] = <void*>(<char*>"chdtriv")
+ufunc_chdtriv_data[0] = &ufunc_chdtriv_ptr[2*0]
+ufunc_chdtriv_data[1] = &ufunc_chdtriv_ptr[2*1]
+chdtriv = np.PyUFunc_FromFuncAndData(ufunc_chdtriv_loops, ufunc_chdtriv_data, ufunc_chdtriv_types, 2, 2, 1, 0, "chdtriv", ufunc_chdtriv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_chndtr_loops[2]
+cdef void *ufunc_chndtr_ptr[4]
+cdef void *ufunc_chndtr_data[2]
+cdef char ufunc_chndtr_types[8]
+cdef char *ufunc_chndtr_doc = (
+    "chndtr(x, df, nc, out=None)\n"
+    "\n"
+    "Non-central chi square cumulative distribution function\n"
+    "\n"
+    "The cumulative distribution function is given by:\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    P(\\chi^{\\prime 2} \\vert \\nu, \\lambda) =\\sum_{j=0}^{\\infty}\n"
+    "    e^{-\\lambda /2}\n"
+    "    \\frac{(\\lambda /2)^j}{j!} P(\\chi^{\\prime 2} \\vert \\nu + 2j),\n"
+    "\n"
+    "where :math:`\\nu > 0` is the degrees of freedom (``df``) and\n"
+    ":math:`\\lambda \\geq 0` is the non-centrality parameter (``nc``).\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Upper bound of the integral; must satisfy ``x >= 0``\n"
+    "df : array_like\n"
+    "    Degrees of freedom; must satisfy ``df > 0``\n"
+    "nc : array_like\n"
+    "    Non-centrality parameter; must satisfy ``nc >= 0``\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "x : scalar or ndarray\n"
+    "    Value of the non-central chi square cumulative distribution function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "chndtrix, chndtridf, chndtrinc")
+ufunc_chndtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_chndtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_chndtr_types[0] = <char>NPY_FLOAT
+ufunc_chndtr_types[1] = <char>NPY_FLOAT
+ufunc_chndtr_types[2] = <char>NPY_FLOAT
+ufunc_chndtr_types[3] = <char>NPY_FLOAT
+ufunc_chndtr_types[4] = <char>NPY_DOUBLE
+ufunc_chndtr_types[5] = <char>NPY_DOUBLE
+ufunc_chndtr_types[6] = <char>NPY_DOUBLE
+ufunc_chndtr_types[7] = <char>NPY_DOUBLE
+ufunc_chndtr_ptr[2*0] = <void*>_func_chndtr
+ufunc_chndtr_ptr[2*0+1] = <void*>(<char*>"chndtr")
+ufunc_chndtr_ptr[2*1] = <void*>_func_chndtr
+ufunc_chndtr_ptr[2*1+1] = <void*>(<char*>"chndtr")
+ufunc_chndtr_data[0] = &ufunc_chndtr_ptr[2*0]
+ufunc_chndtr_data[1] = &ufunc_chndtr_ptr[2*1]
+chndtr = np.PyUFunc_FromFuncAndData(ufunc_chndtr_loops, ufunc_chndtr_data, ufunc_chndtr_types, 2, 3, 1, 0, "chndtr", ufunc_chndtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_chndtridf_loops[2]
+cdef void *ufunc_chndtridf_ptr[4]
+cdef void *ufunc_chndtridf_data[2]
+cdef char ufunc_chndtridf_types[8]
+cdef char *ufunc_chndtridf_doc = (
+    "chndtridf(x, p, nc, out=None)\n"
+    "\n"
+    "Inverse to `chndtr` vs `df`\n"
+    "\n"
+    "Calculated using a search to find a value for `df` that produces the\n"
+    "desired value of `p`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Upper bound of the integral; must satisfy ``x >= 0``\n"
+    "p : array_like\n"
+    "    Probability; must satisfy ``0 <= p < 1``\n"
+    "nc : array_like\n"
+    "    Non-centrality parameter; must satisfy ``nc >= 0``\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "df : scalar or ndarray\n"
+    "    Degrees of freedom\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "chndtr, chndtrix, chndtrinc")
+ufunc_chndtridf_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_chndtridf_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_chndtridf_types[0] = <char>NPY_FLOAT
+ufunc_chndtridf_types[1] = <char>NPY_FLOAT
+ufunc_chndtridf_types[2] = <char>NPY_FLOAT
+ufunc_chndtridf_types[3] = <char>NPY_FLOAT
+ufunc_chndtridf_types[4] = <char>NPY_DOUBLE
+ufunc_chndtridf_types[5] = <char>NPY_DOUBLE
+ufunc_chndtridf_types[6] = <char>NPY_DOUBLE
+ufunc_chndtridf_types[7] = <char>NPY_DOUBLE
+ufunc_chndtridf_ptr[2*0] = <void*>_func_chndtridf
+ufunc_chndtridf_ptr[2*0+1] = <void*>(<char*>"chndtridf")
+ufunc_chndtridf_ptr[2*1] = <void*>_func_chndtridf
+ufunc_chndtridf_ptr[2*1+1] = <void*>(<char*>"chndtridf")
+ufunc_chndtridf_data[0] = &ufunc_chndtridf_ptr[2*0]
+ufunc_chndtridf_data[1] = &ufunc_chndtridf_ptr[2*1]
+chndtridf = np.PyUFunc_FromFuncAndData(ufunc_chndtridf_loops, ufunc_chndtridf_data, ufunc_chndtridf_types, 2, 3, 1, 0, "chndtridf", ufunc_chndtridf_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_chndtrinc_loops[2]
+cdef void *ufunc_chndtrinc_ptr[4]
+cdef void *ufunc_chndtrinc_data[2]
+cdef char ufunc_chndtrinc_types[8]
+cdef char *ufunc_chndtrinc_doc = (
+    "chndtrinc(x, df, p, out=None)\n"
+    "\n"
+    "Inverse to `chndtr` vs `nc`\n"
+    "\n"
+    "Calculated using a search to find a value for `df` that produces the\n"
+    "desired value of `p`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Upper bound of the integral; must satisfy ``x >= 0``\n"
+    "df : array_like\n"
+    "    Degrees of freedom; must satisfy ``df > 0``\n"
+    "p : array_like\n"
+    "    Probability; must satisfy ``0 <= p < 1``\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "nc : scalar or ndarray\n"
+    "    Non-centrality\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "chndtr, chndtrix, chndtrinc")
+ufunc_chndtrinc_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_chndtrinc_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_chndtrinc_types[0] = <char>NPY_FLOAT
+ufunc_chndtrinc_types[1] = <char>NPY_FLOAT
+ufunc_chndtrinc_types[2] = <char>NPY_FLOAT
+ufunc_chndtrinc_types[3] = <char>NPY_FLOAT
+ufunc_chndtrinc_types[4] = <char>NPY_DOUBLE
+ufunc_chndtrinc_types[5] = <char>NPY_DOUBLE
+ufunc_chndtrinc_types[6] = <char>NPY_DOUBLE
+ufunc_chndtrinc_types[7] = <char>NPY_DOUBLE
+ufunc_chndtrinc_ptr[2*0] = <void*>_func_chndtrinc
+ufunc_chndtrinc_ptr[2*0+1] = <void*>(<char*>"chndtrinc")
+ufunc_chndtrinc_ptr[2*1] = <void*>_func_chndtrinc
+ufunc_chndtrinc_ptr[2*1+1] = <void*>(<char*>"chndtrinc")
+ufunc_chndtrinc_data[0] = &ufunc_chndtrinc_ptr[2*0]
+ufunc_chndtrinc_data[1] = &ufunc_chndtrinc_ptr[2*1]
+chndtrinc = np.PyUFunc_FromFuncAndData(ufunc_chndtrinc_loops, ufunc_chndtrinc_data, ufunc_chndtrinc_types, 2, 3, 1, 0, "chndtrinc", ufunc_chndtrinc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_chndtrix_loops[2]
+cdef void *ufunc_chndtrix_ptr[4]
+cdef void *ufunc_chndtrix_data[2]
+cdef char ufunc_chndtrix_types[8]
+cdef char *ufunc_chndtrix_doc = (
+    "chndtrix(p, df, nc, out=None)\n"
+    "\n"
+    "Inverse to `chndtr` vs `x`\n"
+    "\n"
+    "Calculated using a search to find a value for `x` that produces the\n"
+    "desired value of `p`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    Probability; must satisfy ``0 <= p < 1``\n"
+    "df : array_like\n"
+    "    Degrees of freedom; must satisfy ``df > 0``\n"
+    "nc : array_like\n"
+    "    Non-centrality parameter; must satisfy ``nc >= 0``\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "x : scalar or ndarray\n"
+    "    Value so that the probability a non-central Chi square random variable\n"
+    "    with `df` degrees of freedom and non-centrality, `nc`, is greater than\n"
+    "    `x` equals `p`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "chndtr, chndtridf, chndtrinc")
+ufunc_chndtrix_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_chndtrix_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_chndtrix_types[0] = <char>NPY_FLOAT
+ufunc_chndtrix_types[1] = <char>NPY_FLOAT
+ufunc_chndtrix_types[2] = <char>NPY_FLOAT
+ufunc_chndtrix_types[3] = <char>NPY_FLOAT
+ufunc_chndtrix_types[4] = <char>NPY_DOUBLE
+ufunc_chndtrix_types[5] = <char>NPY_DOUBLE
+ufunc_chndtrix_types[6] = <char>NPY_DOUBLE
+ufunc_chndtrix_types[7] = <char>NPY_DOUBLE
+ufunc_chndtrix_ptr[2*0] = <void*>_func_chndtrix
+ufunc_chndtrix_ptr[2*0+1] = <void*>(<char*>"chndtrix")
+ufunc_chndtrix_ptr[2*1] = <void*>_func_chndtrix
+ufunc_chndtrix_ptr[2*1+1] = <void*>(<char*>"chndtrix")
+ufunc_chndtrix_data[0] = &ufunc_chndtrix_ptr[2*0]
+ufunc_chndtrix_data[1] = &ufunc_chndtrix_ptr[2*1]
+chndtrix = np.PyUFunc_FromFuncAndData(ufunc_chndtrix_loops, ufunc_chndtrix_data, ufunc_chndtrix_types, 2, 3, 1, 0, "chndtrix", ufunc_chndtrix_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_cosdg_loops[2]
+cdef void *ufunc_cosdg_ptr[4]
+cdef void *ufunc_cosdg_data[2]
+cdef char ufunc_cosdg_types[4]
+cdef char *ufunc_cosdg_doc = (
+    "cosdg(x, out=None)\n"
+    "\n"
+    "Cosine of the angle `x` given in degrees.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Angle, given in degrees.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Cosine of the input.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "sindg, tandg, cotdg\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is more accurate than using cosine directly.\n"
+    "\n"
+    ">>> x = 90 + 180 * np.arange(3)\n"
+    ">>> sc.cosdg(x)\n"
+    "array([-0.,  0., -0.])\n"
+    ">>> np.cos(x * np.pi / 180)\n"
+    "array([ 6.1232340e-17, -1.8369702e-16,  3.0616170e-16])")
+ufunc_cosdg_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_cosdg_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_cosdg_types[0] = <char>NPY_FLOAT
+ufunc_cosdg_types[1] = <char>NPY_FLOAT
+ufunc_cosdg_types[2] = <char>NPY_DOUBLE
+ufunc_cosdg_types[3] = <char>NPY_DOUBLE
+ufunc_cosdg_ptr[2*0] = <void*>_func_cosdg
+ufunc_cosdg_ptr[2*0+1] = <void*>(<char*>"cosdg")
+ufunc_cosdg_ptr[2*1] = <void*>_func_cosdg
+ufunc_cosdg_ptr[2*1+1] = <void*>(<char*>"cosdg")
+ufunc_cosdg_data[0] = &ufunc_cosdg_ptr[2*0]
+ufunc_cosdg_data[1] = &ufunc_cosdg_ptr[2*1]
+cosdg = np.PyUFunc_FromFuncAndData(ufunc_cosdg_loops, ufunc_cosdg_data, ufunc_cosdg_types, 2, 1, 1, 0, "cosdg", ufunc_cosdg_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_cosm1_loops[2]
+cdef void *ufunc_cosm1_ptr[4]
+cdef void *ufunc_cosm1_data[2]
+cdef char ufunc_cosm1_types[4]
+cdef char *ufunc_cosm1_doc = (
+    "cosm1(x, out=None)\n"
+    "\n"
+    "cos(x) - 1 for use when `x` is near zero.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real valued argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of ``cos(x) - 1``.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "expm1, log1p\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is more accurate than computing ``cos(x) - 1`` directly for\n"
+    "``x`` around 0.\n"
+    "\n"
+    ">>> x = 1e-30\n"
+    ">>> np.cos(x) - 1\n"
+    "0.0\n"
+    ">>> sc.cosm1(x)\n"
+    "-5.0000000000000005e-61")
+ufunc_cosm1_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_cosm1_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_cosm1_types[0] = <char>NPY_FLOAT
+ufunc_cosm1_types[1] = <char>NPY_FLOAT
+ufunc_cosm1_types[2] = <char>NPY_DOUBLE
+ufunc_cosm1_types[3] = <char>NPY_DOUBLE
+ufunc_cosm1_ptr[2*0] = <void*>_func_cosm1
+ufunc_cosm1_ptr[2*0+1] = <void*>(<char*>"cosm1")
+ufunc_cosm1_ptr[2*1] = <void*>_func_cosm1
+ufunc_cosm1_ptr[2*1+1] = <void*>(<char*>"cosm1")
+ufunc_cosm1_data[0] = &ufunc_cosm1_ptr[2*0]
+ufunc_cosm1_data[1] = &ufunc_cosm1_ptr[2*1]
+cosm1 = np.PyUFunc_FromFuncAndData(ufunc_cosm1_loops, ufunc_cosm1_data, ufunc_cosm1_types, 2, 1, 1, 0, "cosm1", ufunc_cosm1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_cotdg_loops[2]
+cdef void *ufunc_cotdg_ptr[4]
+cdef void *ufunc_cotdg_data[2]
+cdef char ufunc_cotdg_types[4]
+cdef char *ufunc_cotdg_doc = (
+    "cotdg(x, out=None)\n"
+    "\n"
+    "Cotangent of the angle `x` given in degrees.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Angle, given in degrees.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Cotangent at the input.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "sindg, cosdg, tandg\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is more accurate than using cotangent directly.\n"
+    "\n"
+    ">>> x = 90 + 180 * np.arange(3)\n"
+    ">>> sc.cotdg(x)\n"
+    "array([0., 0., 0.])\n"
+    ">>> 1 / np.tan(x * np.pi / 180)\n"
+    "array([6.1232340e-17, 1.8369702e-16, 3.0616170e-16])")
+ufunc_cotdg_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_cotdg_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_cotdg_types[0] = <char>NPY_FLOAT
+ufunc_cotdg_types[1] = <char>NPY_FLOAT
+ufunc_cotdg_types[2] = <char>NPY_DOUBLE
+ufunc_cotdg_types[3] = <char>NPY_DOUBLE
+ufunc_cotdg_ptr[2*0] = <void*>_func_cotdg
+ufunc_cotdg_ptr[2*0+1] = <void*>(<char*>"cotdg")
+ufunc_cotdg_ptr[2*1] = <void*>_func_cotdg
+ufunc_cotdg_ptr[2*1+1] = <void*>(<char*>"cotdg")
+ufunc_cotdg_data[0] = &ufunc_cotdg_ptr[2*0]
+ufunc_cotdg_data[1] = &ufunc_cotdg_ptr[2*1]
+cotdg = np.PyUFunc_FromFuncAndData(ufunc_cotdg_loops, ufunc_cotdg_data, ufunc_cotdg_types, 2, 1, 1, 0, "cotdg", ufunc_cotdg_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_dawsn_loops[4]
+cdef void *ufunc_dawsn_ptr[8]
+cdef void *ufunc_dawsn_data[4]
+cdef char ufunc_dawsn_types[8]
+cdef char *ufunc_dawsn_doc = (
+    "dawsn(x, out=None)\n"
+    "\n"
+    "Dawson's integral.\n"
+    "\n"
+    "Computes::\n"
+    "\n"
+    "    exp(-x**2) * integral(exp(t**2), t=0..x).\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Function parameter.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Value of the integral.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "wofz, erf, erfc, erfcx, erfi\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Steven G. Johnson, Faddeeva W function implementation.\n"
+    "   http://ab-initio.mit.edu/Faddeeva\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-15, 15, num=1000)\n"
+    ">>> plt.plot(x, special.dawsn(x))\n"
+    ">>> plt.xlabel('$x$')\n"
+    ">>> plt.ylabel('$dawsn(x)$')\n"
+    ">>> plt.show()")
+ufunc_dawsn_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_dawsn_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_dawsn_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_dawsn_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_dawsn_types[0] = <char>NPY_FLOAT
+ufunc_dawsn_types[1] = <char>NPY_FLOAT
+ufunc_dawsn_types[2] = <char>NPY_DOUBLE
+ufunc_dawsn_types[3] = <char>NPY_DOUBLE
+ufunc_dawsn_types[4] = <char>NPY_CFLOAT
+ufunc_dawsn_types[5] = <char>NPY_CFLOAT
+ufunc_dawsn_types[6] = <char>NPY_CDOUBLE
+ufunc_dawsn_types[7] = <char>NPY_CDOUBLE
+ufunc_dawsn_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_dawsn
+ufunc_dawsn_ptr[2*0+1] = <void*>(<char*>"dawsn")
+ufunc_dawsn_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_dawsn
+ufunc_dawsn_ptr[2*1+1] = <void*>(<char*>"dawsn")
+ufunc_dawsn_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_dawsn_complex
+ufunc_dawsn_ptr[2*2+1] = <void*>(<char*>"dawsn")
+ufunc_dawsn_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_dawsn_complex
+ufunc_dawsn_ptr[2*3+1] = <void*>(<char*>"dawsn")
+ufunc_dawsn_data[0] = &ufunc_dawsn_ptr[2*0]
+ufunc_dawsn_data[1] = &ufunc_dawsn_ptr[2*1]
+ufunc_dawsn_data[2] = &ufunc_dawsn_ptr[2*2]
+ufunc_dawsn_data[3] = &ufunc_dawsn_ptr[2*3]
+dawsn = np.PyUFunc_FromFuncAndData(ufunc_dawsn_loops, ufunc_dawsn_data, ufunc_dawsn_types, 4, 1, 1, 0, "dawsn", ufunc_dawsn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ellipe_loops[2]
+cdef void *ufunc_ellipe_ptr[4]
+cdef void *ufunc_ellipe_data[2]
+cdef char ufunc_ellipe_types[4]
+cdef char *ufunc_ellipe_doc = (
+    "ellipe(m, out=None)\n"
+    "\n"
+    "Complete elliptic integral of the second kind\n"
+    "\n"
+    "This function is defined as\n"
+    "\n"
+    ".. math:: E(m) = \\int_0^{\\pi/2} [1 - m \\sin(t)^2]^{1/2} dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Defines the parameter of the elliptic integral.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "E : scalar or ndarray\n"
+    "    Value of the elliptic integral.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ellipkm1 : Complete elliptic integral of the first kind, near `m` = 1\n"
+    "ellipk : Complete elliptic integral of the first kind\n"
+    "ellipkinc : Incomplete elliptic integral of the first kind\n"
+    "ellipeinc : Incomplete elliptic integral of the second kind\n"
+    "elliprd : Symmetric elliptic integral of the second kind.\n"
+    "elliprg : Completely-symmetric elliptic integral of the second kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the Cephes [1]_ routine `ellpe`.\n"
+    "\n"
+    "For `m > 0` the computation uses the approximation,\n"
+    "\n"
+    ".. math:: E(m) \\approx P(1-m) - (1-m) \\log(1-m) Q(1-m),\n"
+    "\n"
+    "where :math:`P` and :math:`Q` are tenth-order polynomials.  For\n"
+    "`m < 0`, the relation\n"
+    "\n"
+    ".. math:: E(m) = E(m/(m - 1)) \\sqrt(1-m)\n"
+    "\n"
+    "is used.\n"
+    "\n"
+    "The parameterization in terms of :math:`m` follows that of section\n"
+    "17.2 in [2]_. Other parameterizations in terms of the\n"
+    "complementary parameter :math:`1 - m`, modular angle\n"
+    ":math:`\\sin^2(\\alpha) = m`, or modulus :math:`k^2 = m` are also\n"
+    "used, so be careful that you choose the correct parameter.\n"
+    "\n"
+    "The Legendre E integral is related to Carlson's symmetric R_D or R_G\n"
+    "functions in multiple ways [3]_. For example,\n"
+    "\n"
+    ".. math:: E(m) = 2 R_G(0, 1-k^2, 1) .\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    ".. [2] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    ".. [3] NIST Digital Library of Mathematical\n"
+    "       Functions. http://dlmf.nist.gov/, Release 1.0.28 of\n"
+    "       2020-09-15. See Sec. 19.25(i) https://dlmf.nist.gov/19.25#i\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "This function is used in finding the circumference of an\n"
+    "ellipse with semi-major axis `a` and semi-minor axis `b`.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    "\n"
+    ">>> a = 3.5\n"
+    ">>> b = 2.1\n"
+    ">>> e_sq = 1.0 - b**2/a**2  # eccentricity squared\n"
+    "\n"
+    "Then the circumference is found using the following:\n"
+    "\n"
+    ">>> C = 4*a*special.ellipe(e_sq)  # circumference formula\n"
+    ">>> C\n"
+    "17.868899204378693\n"
+    "\n"
+    "When `a` and `b` are the same (meaning eccentricity is 0),\n"
+    "this reduces to the circumference of a circle.\n"
+    "\n"
+    ">>> 4*a*special.ellipe(0.0)  # formula for ellipse with a = b\n"
+    "21.991148575128552\n"
+    ">>> 2*np.pi*a  # formula for circle of radius a\n"
+    "21.991148575128552")
+ufunc_ellipe_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_ellipe_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_ellipe_types[0] = <char>NPY_FLOAT
+ufunc_ellipe_types[1] = <char>NPY_FLOAT
+ufunc_ellipe_types[2] = <char>NPY_DOUBLE
+ufunc_ellipe_types[3] = <char>NPY_DOUBLE
+ufunc_ellipe_ptr[2*0] = <void*>_func_ellpe
+ufunc_ellipe_ptr[2*0+1] = <void*>(<char*>"ellipe")
+ufunc_ellipe_ptr[2*1] = <void*>_func_ellpe
+ufunc_ellipe_ptr[2*1+1] = <void*>(<char*>"ellipe")
+ufunc_ellipe_data[0] = &ufunc_ellipe_ptr[2*0]
+ufunc_ellipe_data[1] = &ufunc_ellipe_ptr[2*1]
+ellipe = np.PyUFunc_FromFuncAndData(ufunc_ellipe_loops, ufunc_ellipe_data, ufunc_ellipe_types, 2, 1, 1, 0, "ellipe", ufunc_ellipe_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ellipeinc_loops[2]
+cdef void *ufunc_ellipeinc_ptr[4]
+cdef void *ufunc_ellipeinc_data[2]
+cdef char ufunc_ellipeinc_types[6]
+cdef char *ufunc_ellipeinc_doc = (
+    "ellipeinc(phi, m, out=None)\n"
+    "\n"
+    "Incomplete elliptic integral of the second kind\n"
+    "\n"
+    "This function is defined as\n"
+    "\n"
+    ".. math:: E(\\phi, m) = \\int_0^{\\phi} [1 - m \\sin(t)^2]^{1/2} dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "phi : array_like\n"
+    "    amplitude of the elliptic integral.\n"
+    "m : array_like\n"
+    "    parameter of the elliptic integral.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "E : scalar or ndarray\n"
+    "    Value of the elliptic integral.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ellipkm1 : Complete elliptic integral of the first kind, near `m` = 1\n"
+    "ellipk : Complete elliptic integral of the first kind\n"
+    "ellipkinc : Incomplete elliptic integral of the first kind\n"
+    "ellipe : Complete elliptic integral of the second kind\n"
+    "elliprd : Symmetric elliptic integral of the second kind.\n"
+    "elliprf : Completely-symmetric elliptic integral of the first kind.\n"
+    "elliprg : Completely-symmetric elliptic integral of the second kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the Cephes [1]_ routine `ellie`.\n"
+    "\n"
+    "Computation uses arithmetic-geometric means algorithm.\n"
+    "\n"
+    "The parameterization in terms of :math:`m` follows that of section\n"
+    "17.2 in [2]_. Other parameterizations in terms of the\n"
+    "complementary parameter :math:`1 - m`, modular angle\n"
+    ":math:`\\sin^2(\\alpha) = m`, or modulus :math:`k^2 = m` are also\n"
+    "used, so be careful that you choose the correct parameter.\n"
+    "\n"
+    "The Legendre E incomplete integral can be related to combinations\n"
+    "of Carlson's symmetric integrals R_D, R_F, and R_G in multiple\n"
+    "ways [3]_. For example, with :math:`c = \\csc^2\\phi`,\n"
+    "\n"
+    ".. math::\n"
+    "  E(\\phi, m) = R_F(c-1, c-k^2, c)\n"
+    "    - \\frac{1}{3} k^2 R_D(c-1, c-k^2, c) .\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    ".. [2] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    ".. [3] NIST Digital Library of Mathematical\n"
+    "       Functions. http://dlmf.nist.gov/, Release 1.0.28 of\n"
+    "       2020-09-15. See Sec. 19.25(i) https://dlmf.nist.gov/19.25#i")
+ufunc_ellipeinc_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_ellipeinc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_ellipeinc_types[0] = <char>NPY_FLOAT
+ufunc_ellipeinc_types[1] = <char>NPY_FLOAT
+ufunc_ellipeinc_types[2] = <char>NPY_FLOAT
+ufunc_ellipeinc_types[3] = <char>NPY_DOUBLE
+ufunc_ellipeinc_types[4] = <char>NPY_DOUBLE
+ufunc_ellipeinc_types[5] = <char>NPY_DOUBLE
+ufunc_ellipeinc_ptr[2*0] = <void*>_func_ellie
+ufunc_ellipeinc_ptr[2*0+1] = <void*>(<char*>"ellipeinc")
+ufunc_ellipeinc_ptr[2*1] = <void*>_func_ellie
+ufunc_ellipeinc_ptr[2*1+1] = <void*>(<char*>"ellipeinc")
+ufunc_ellipeinc_data[0] = &ufunc_ellipeinc_ptr[2*0]
+ufunc_ellipeinc_data[1] = &ufunc_ellipeinc_ptr[2*1]
+ellipeinc = np.PyUFunc_FromFuncAndData(ufunc_ellipeinc_loops, ufunc_ellipeinc_data, ufunc_ellipeinc_types, 2, 2, 1, 0, "ellipeinc", ufunc_ellipeinc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ellipj_loops[2]
+cdef void *ufunc_ellipj_ptr[4]
+cdef void *ufunc_ellipj_data[2]
+cdef char ufunc_ellipj_types[12]
+cdef char *ufunc_ellipj_doc = (
+    "ellipj(u, m, out=None)\n"
+    "\n"
+    "Jacobian elliptic functions\n"
+    "\n"
+    "Calculates the Jacobian elliptic functions of parameter `m` between\n"
+    "0 and 1, and real argument `u`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Parameter.\n"
+    "u : array_like\n"
+    "    Argument.\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "sn, cn, dn, ph : 4-tuple of scalar or ndarray\n"
+    "    The returned functions::\n"
+    "\n"
+    "        sn(u|m), cn(u|m), dn(u|m)\n"
+    "\n"
+    "    The value `ph` is such that if `u = ellipkinc(ph, m)`,\n"
+    "    then `sn(u|m) = sin(ph)` and `cn(u|m) = cos(ph)`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ellipk : Complete elliptic integral of the first kind\n"
+    "ellipkinc : Incomplete elliptic integral of the first kind\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the Cephes [1]_ routine `ellpj`.\n"
+    "\n"
+    "These functions are periodic, with quarter-period on the real axis\n"
+    "equal to the complete elliptic integral `ellipk(m)`.\n"
+    "\n"
+    "Relation to incomplete elliptic integral: If `u = ellipkinc(phi,m)`, then\n"
+    "`sn(u|m) = sin(phi)`, and `cn(u|m) = cos(phi)`. The `phi` is called\n"
+    "the amplitude of `u`.\n"
+    "\n"
+    "Computation is by means of the arithmetic-geometric mean algorithm,\n"
+    "except when `m` is within 1e-9 of 0 or 1. In the latter case with `m`\n"
+    "close to 1, the approximation applies only for `phi < pi/2`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/")
+ufunc_ellipj_loops[0] = <np.PyUFuncGenericFunction>loop_i_dd_dddd_As_ff_ffff
+ufunc_ellipj_loops[1] = <np.PyUFuncGenericFunction>loop_i_dd_dddd_As_dd_dddd
+ufunc_ellipj_types[0] = <char>NPY_FLOAT
+ufunc_ellipj_types[1] = <char>NPY_FLOAT
+ufunc_ellipj_types[2] = <char>NPY_FLOAT
+ufunc_ellipj_types[3] = <char>NPY_FLOAT
+ufunc_ellipj_types[4] = <char>NPY_FLOAT
+ufunc_ellipj_types[5] = <char>NPY_FLOAT
+ufunc_ellipj_types[6] = <char>NPY_DOUBLE
+ufunc_ellipj_types[7] = <char>NPY_DOUBLE
+ufunc_ellipj_types[8] = <char>NPY_DOUBLE
+ufunc_ellipj_types[9] = <char>NPY_DOUBLE
+ufunc_ellipj_types[10] = <char>NPY_DOUBLE
+ufunc_ellipj_types[11] = <char>NPY_DOUBLE
+ufunc_ellipj_ptr[2*0] = <void*>_func_ellpj
+ufunc_ellipj_ptr[2*0+1] = <void*>(<char*>"ellipj")
+ufunc_ellipj_ptr[2*1] = <void*>_func_ellpj
+ufunc_ellipj_ptr[2*1+1] = <void*>(<char*>"ellipj")
+ufunc_ellipj_data[0] = &ufunc_ellipj_ptr[2*0]
+ufunc_ellipj_data[1] = &ufunc_ellipj_ptr[2*1]
+ellipj = np.PyUFunc_FromFuncAndData(ufunc_ellipj_loops, ufunc_ellipj_data, ufunc_ellipj_types, 2, 2, 4, 0, "ellipj", ufunc_ellipj_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ellipk_loops[2]
+cdef void *ufunc_ellipk_ptr[4]
+cdef void *ufunc_ellipk_data[2]
+cdef char ufunc_ellipk_types[4]
+cdef char *ufunc_ellipk_doc = (
+    "ellipk(m, out=None)\n"
+    "\n"
+    "Complete elliptic integral of the first kind.\n"
+    "\n"
+    "This function is defined as\n"
+    "\n"
+    ".. math:: K(m) = \\int_0^{\\pi/2} [1 - m \\sin(t)^2]^{-1/2} dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    The parameter of the elliptic integral.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "K : scalar or ndarray\n"
+    "    Value of the elliptic integral.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ellipkm1 : Complete elliptic integral of the first kind around m = 1\n"
+    "ellipkinc : Incomplete elliptic integral of the first kind\n"
+    "ellipe : Complete elliptic integral of the second kind\n"
+    "ellipeinc : Incomplete elliptic integral of the second kind\n"
+    "elliprf : Completely-symmetric elliptic integral of the first kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For more precision around point m = 1, use `ellipkm1`, which this\n"
+    "function calls.\n"
+    "\n"
+    "The parameterization in terms of :math:`m` follows that of section\n"
+    "17.2 in [1]_. Other parameterizations in terms of the\n"
+    "complementary parameter :math:`1 - m`, modular angle\n"
+    ":math:`\\sin^2(\\alpha) = m`, or modulus :math:`k^2 = m` are also\n"
+    "used, so be careful that you choose the correct parameter.\n"
+    "\n"
+    "The Legendre K integral is related to Carlson's symmetric R_F\n"
+    "function by [2]_:\n"
+    "\n"
+    ".. math:: K(m) = R_F(0, 1-k^2, 1) .\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    ".. [2] NIST Digital Library of Mathematical\n"
+    "       Functions. http://dlmf.nist.gov/, Release 1.0.28 of\n"
+    "       2020-09-15. See Sec. 19.25(i) https://dlmf.nist.gov/19.25#i")
+ufunc_ellipk_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_ellipk_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_ellipk_types[0] = <char>NPY_FLOAT
+ufunc_ellipk_types[1] = <char>NPY_FLOAT
+ufunc_ellipk_types[2] = <char>NPY_DOUBLE
+ufunc_ellipk_types[3] = <char>NPY_DOUBLE
+ufunc_ellipk_ptr[2*0] = <void*>_func_ellipk
+ufunc_ellipk_ptr[2*0+1] = <void*>(<char*>"ellipk")
+ufunc_ellipk_ptr[2*1] = <void*>_func_ellipk
+ufunc_ellipk_ptr[2*1+1] = <void*>(<char*>"ellipk")
+ufunc_ellipk_data[0] = &ufunc_ellipk_ptr[2*0]
+ufunc_ellipk_data[1] = &ufunc_ellipk_ptr[2*1]
+ellipk = np.PyUFunc_FromFuncAndData(ufunc_ellipk_loops, ufunc_ellipk_data, ufunc_ellipk_types, 2, 1, 1, 0, "ellipk", ufunc_ellipk_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ellipkinc_loops[2]
+cdef void *ufunc_ellipkinc_ptr[4]
+cdef void *ufunc_ellipkinc_data[2]
+cdef char ufunc_ellipkinc_types[6]
+cdef char *ufunc_ellipkinc_doc = (
+    "ellipkinc(phi, m, out=None)\n"
+    "\n"
+    "Incomplete elliptic integral of the first kind\n"
+    "\n"
+    "This function is defined as\n"
+    "\n"
+    ".. math:: K(\\phi, m) = \\int_0^{\\phi} [1 - m \\sin(t)^2]^{-1/2} dt\n"
+    "\n"
+    "This function is also called :math:`F(\\phi, m)`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "phi : array_like\n"
+    "    amplitude of the elliptic integral\n"
+    "m : array_like\n"
+    "    parameter of the elliptic integral\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "K : scalar or ndarray\n"
+    "    Value of the elliptic integral\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ellipkm1 : Complete elliptic integral of the first kind, near `m` = 1\n"
+    "ellipk : Complete elliptic integral of the first kind\n"
+    "ellipe : Complete elliptic integral of the second kind\n"
+    "ellipeinc : Incomplete elliptic integral of the second kind\n"
+    "elliprf : Completely-symmetric elliptic integral of the first kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the Cephes [1]_ routine `ellik`.  The computation is\n"
+    "carried out using the arithmetic-geometric mean algorithm.\n"
+    "\n"
+    "The parameterization in terms of :math:`m` follows that of section\n"
+    "17.2 in [2]_. Other parameterizations in terms of the\n"
+    "complementary parameter :math:`1 - m`, modular angle\n"
+    ":math:`\\sin^2(\\alpha) = m`, or modulus :math:`k^2 = m` are also\n"
+    "used, so be careful that you choose the correct parameter.\n"
+    "\n"
+    "The Legendre K incomplete integral (or F integral) is related to\n"
+    "Carlson's symmetric R_F function [3]_.\n"
+    "Setting :math:`c = \\csc^2\\phi`,\n"
+    "\n"
+    ".. math:: F(\\phi, m) = R_F(c-1, c-k^2, c) .\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    ".. [2] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    ".. [3] NIST Digital Library of Mathematical\n"
+    "       Functions. http://dlmf.nist.gov/, Release 1.0.28 of\n"
+    "       2020-09-15. See Sec. 19.25(i) https://dlmf.nist.gov/19.25#i")
+ufunc_ellipkinc_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_ellipkinc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_ellipkinc_types[0] = <char>NPY_FLOAT
+ufunc_ellipkinc_types[1] = <char>NPY_FLOAT
+ufunc_ellipkinc_types[2] = <char>NPY_FLOAT
+ufunc_ellipkinc_types[3] = <char>NPY_DOUBLE
+ufunc_ellipkinc_types[4] = <char>NPY_DOUBLE
+ufunc_ellipkinc_types[5] = <char>NPY_DOUBLE
+ufunc_ellipkinc_ptr[2*0] = <void*>_func_ellik
+ufunc_ellipkinc_ptr[2*0+1] = <void*>(<char*>"ellipkinc")
+ufunc_ellipkinc_ptr[2*1] = <void*>_func_ellik
+ufunc_ellipkinc_ptr[2*1+1] = <void*>(<char*>"ellipkinc")
+ufunc_ellipkinc_data[0] = &ufunc_ellipkinc_ptr[2*0]
+ufunc_ellipkinc_data[1] = &ufunc_ellipkinc_ptr[2*1]
+ellipkinc = np.PyUFunc_FromFuncAndData(ufunc_ellipkinc_loops, ufunc_ellipkinc_data, ufunc_ellipkinc_types, 2, 2, 1, 0, "ellipkinc", ufunc_ellipkinc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ellipkm1_loops[2]
+cdef void *ufunc_ellipkm1_ptr[4]
+cdef void *ufunc_ellipkm1_data[2]
+cdef char ufunc_ellipkm1_types[4]
+cdef char *ufunc_ellipkm1_doc = (
+    "ellipkm1(p, out=None)\n"
+    "\n"
+    "Complete elliptic integral of the first kind around `m` = 1\n"
+    "\n"
+    "This function is defined as\n"
+    "\n"
+    ".. math:: K(p) = \\int_0^{\\pi/2} [1 - m \\sin(t)^2]^{-1/2} dt\n"
+    "\n"
+    "where `m = 1 - p`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    Defines the parameter of the elliptic integral as `m = 1 - p`.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "K : scalar or ndarray\n"
+    "    Value of the elliptic integral.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ellipk : Complete elliptic integral of the first kind\n"
+    "ellipkinc : Incomplete elliptic integral of the first kind\n"
+    "ellipe : Complete elliptic integral of the second kind\n"
+    "ellipeinc : Incomplete elliptic integral of the second kind\n"
+    "elliprf : Completely-symmetric elliptic integral of the first kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the Cephes [1]_ routine `ellpk`.\n"
+    "\n"
+    "For `p <= 1`, computation uses the approximation,\n"
+    "\n"
+    ".. math:: K(p) \\approx P(p) - \\log(p) Q(p),\n"
+    "\n"
+    "where :math:`P` and :math:`Q` are tenth-order polynomials.  The\n"
+    "argument `p` is used internally rather than `m` so that the logarithmic\n"
+    "singularity at `m = 1` will be shifted to the origin; this preserves\n"
+    "maximum accuracy.  For `p > 1`, the identity\n"
+    "\n"
+    ".. math:: K(p) = K(1/p)/\\sqrt(p)\n"
+    "\n"
+    "is used.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/")
+ufunc_ellipkm1_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_ellipkm1_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_ellipkm1_types[0] = <char>NPY_FLOAT
+ufunc_ellipkm1_types[1] = <char>NPY_FLOAT
+ufunc_ellipkm1_types[2] = <char>NPY_DOUBLE
+ufunc_ellipkm1_types[3] = <char>NPY_DOUBLE
+ufunc_ellipkm1_ptr[2*0] = <void*>_func_ellpk
+ufunc_ellipkm1_ptr[2*0+1] = <void*>(<char*>"ellipkm1")
+ufunc_ellipkm1_ptr[2*1] = <void*>_func_ellpk
+ufunc_ellipkm1_ptr[2*1+1] = <void*>(<char*>"ellipkm1")
+ufunc_ellipkm1_data[0] = &ufunc_ellipkm1_ptr[2*0]
+ufunc_ellipkm1_data[1] = &ufunc_ellipkm1_ptr[2*1]
+ellipkm1 = np.PyUFunc_FromFuncAndData(ufunc_ellipkm1_loops, ufunc_ellipkm1_data, ufunc_ellipkm1_types, 2, 1, 1, 0, "ellipkm1", ufunc_ellipkm1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_elliprc_loops[4]
+cdef void *ufunc_elliprc_ptr[8]
+cdef void *ufunc_elliprc_data[4]
+cdef char ufunc_elliprc_types[12]
+cdef char *ufunc_elliprc_doc = (
+    "elliprc(x, y, out=None)\n"
+    "\n"
+    "Degenerate symmetric elliptic integral.\n"
+    "\n"
+    "The function RC is defined as [1]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    R_{\\mathrm{C}}(x, y) =\n"
+    "       \\frac{1}{2} \\int_0^{+\\infty} (t + x)^{-1/2} (t + y)^{-1} dt\n"
+    "       = R_{\\mathrm{F}}(x, y, y)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x, y : array_like\n"
+    "    Real or complex input parameters. `x` can be any number in the\n"
+    "    complex plane cut along the negative real axis. `y` must be non-zero.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "R : scalar or ndarray\n"
+    "    Value of the integral. If `y` is real and negative, the Cauchy\n"
+    "    principal value is returned. If both of `x` and `y` are real, the\n"
+    "    return value is real. Otherwise, the return value is complex.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "elliprf : Completely-symmetric elliptic integral of the first kind.\n"
+    "elliprd : Symmetric elliptic integral of the second kind.\n"
+    "elliprg : Completely-symmetric elliptic integral of the second kind.\n"
+    "elliprj : Symmetric elliptic integral of the third kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "RC is a degenerate case of the symmetric integral RF: ``elliprc(x, y) ==\n"
+    "elliprf(x, y, y)``. It is an elementary function rather than an elliptic\n"
+    "integral.\n"
+    "\n"
+    "The code implements Carlson's algorithm based on the duplication theorems\n"
+    "and series expansion up to the 7th order. [2]_\n"
+    "\n"
+    ".. versionadded:: 1.8.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] B. C. Carlson, ed., Chapter 19 in \"Digital Library of Mathematical\n"
+    "       Functions,\" NIST, US Dept. of Commerce.\n"
+    "       https://dlmf.nist.gov/19.16.E6\n"
+    ".. [2] B. C. Carlson, \"Numerical computation of real or complex elliptic\n"
+    "       integrals,\" Numer. Algorithm, vol. 10, no. 1, pp. 13-26, 1995.\n"
+    "       https://arxiv.org/abs/math/9409227\n"
+    "       https://doi.org/10.1007/BF02198293\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Basic homogeneity property:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import elliprc\n"
+    "\n"
+    ">>> x = 1.2 + 3.4j\n"
+    ">>> y = 5.\n"
+    ">>> scale = 0.3 + 0.4j\n"
+    ">>> elliprc(scale*x, scale*y)\n"
+    "(0.5484493976710874-0.4169557678995833j)\n"
+    "\n"
+    ">>> elliprc(x, y)/np.sqrt(scale)\n"
+    "(0.5484493976710874-0.41695576789958333j)\n"
+    "\n"
+    "When the two arguments coincide, the integral is particularly\n"
+    "simple:\n"
+    "\n"
+    ">>> x = 1.2 + 3.4j\n"
+    ">>> elliprc(x, x)\n"
+    "(0.4299173120614631-0.3041729818745595j)\n"
+    "\n"
+    ">>> 1/np.sqrt(x)\n"
+    "(0.4299173120614631-0.30417298187455954j)\n"
+    "\n"
+    "Another simple case: the first argument vanishes:\n"
+    "\n"
+    ">>> y = 1.2 + 3.4j\n"
+    ">>> elliprc(0, y)\n"
+    "(0.6753125346116815-0.47779380263880866j)\n"
+    "\n"
+    ">>> np.pi/2/np.sqrt(y)\n"
+    "(0.6753125346116815-0.4777938026388088j)\n"
+    "\n"
+    "When `x` and `y` are both positive, we can express\n"
+    ":math:`R_C(x,y)` in terms of more elementary functions.  For the\n"
+    "case :math:`0 \\le x < y`,\n"
+    "\n"
+    ">>> x = 3.2\n"
+    ">>> y = 6.\n"
+    ">>> elliprc(x, y)\n"
+    "0.44942991498453444\n"
+    "\n"
+    ">>> np.arctan(np.sqrt((y-x)/x))/np.sqrt(y-x)\n"
+    "0.44942991498453433\n"
+    "\n"
+    "And for the case :math:`0 \\le y < x`,\n"
+    "\n"
+    ">>> x = 6.\n"
+    ">>> y = 3.2\n"
+    ">>> elliprc(x,y)\n"
+    "0.4989837501576147\n"
+    "\n"
+    ">>> np.log((np.sqrt(x)+np.sqrt(x-y))/np.sqrt(y))/np.sqrt(x-y)\n"
+    "0.49898375015761476")
+ufunc_elliprc_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_elliprc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_elliprc_loops[2] = <np.PyUFuncGenericFunction>loop_D_DD__As_FF_F
+ufunc_elliprc_loops[3] = <np.PyUFuncGenericFunction>loop_D_DD__As_DD_D
+ufunc_elliprc_types[0] = <char>NPY_FLOAT
+ufunc_elliprc_types[1] = <char>NPY_FLOAT
+ufunc_elliprc_types[2] = <char>NPY_FLOAT
+ufunc_elliprc_types[3] = <char>NPY_DOUBLE
+ufunc_elliprc_types[4] = <char>NPY_DOUBLE
+ufunc_elliprc_types[5] = <char>NPY_DOUBLE
+ufunc_elliprc_types[6] = <char>NPY_CFLOAT
+ufunc_elliprc_types[7] = <char>NPY_CFLOAT
+ufunc_elliprc_types[8] = <char>NPY_CFLOAT
+ufunc_elliprc_types[9] = <char>NPY_CDOUBLE
+ufunc_elliprc_types[10] = <char>NPY_CDOUBLE
+ufunc_elliprc_types[11] = <char>NPY_CDOUBLE
+ufunc_elliprc_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_fellint_RC
+ufunc_elliprc_ptr[2*0+1] = <void*>(<char*>"elliprc")
+ufunc_elliprc_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_fellint_RC
+ufunc_elliprc_ptr[2*1+1] = <void*>(<char*>"elliprc")
+ufunc_elliprc_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_cellint_RC
+ufunc_elliprc_ptr[2*2+1] = <void*>(<char*>"elliprc")
+ufunc_elliprc_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_cellint_RC
+ufunc_elliprc_ptr[2*3+1] = <void*>(<char*>"elliprc")
+ufunc_elliprc_data[0] = &ufunc_elliprc_ptr[2*0]
+ufunc_elliprc_data[1] = &ufunc_elliprc_ptr[2*1]
+ufunc_elliprc_data[2] = &ufunc_elliprc_ptr[2*2]
+ufunc_elliprc_data[3] = &ufunc_elliprc_ptr[2*3]
+elliprc = np.PyUFunc_FromFuncAndData(ufunc_elliprc_loops, ufunc_elliprc_data, ufunc_elliprc_types, 4, 2, 1, 0, "elliprc", ufunc_elliprc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_elliprd_loops[4]
+cdef void *ufunc_elliprd_ptr[8]
+cdef void *ufunc_elliprd_data[4]
+cdef char ufunc_elliprd_types[16]
+cdef char *ufunc_elliprd_doc = (
+    "elliprd(x, y, z, out=None)\n"
+    "\n"
+    "Symmetric elliptic integral of the second kind.\n"
+    "\n"
+    "The function RD is defined as [1]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    R_{\\mathrm{D}}(x, y, z) =\n"
+    "       \\frac{3}{2} \\int_0^{+\\infty} [(t + x) (t + y)]^{-1/2} (t + z)^{-3/2}\n"
+    "       dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x, y, z : array_like\n"
+    "    Real or complex input parameters. `x` or `y` can be any number in the\n"
+    "    complex plane cut along the negative real axis, but at most one of them\n"
+    "    can be zero, while `z` must be non-zero.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "R : scalar or ndarray\n"
+    "    Value of the integral. If all of `x`, `y`, and `z` are real, the\n"
+    "    return value is real. Otherwise, the return value is complex.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "elliprc : Degenerate symmetric elliptic integral.\n"
+    "elliprf : Completely-symmetric elliptic integral of the first kind.\n"
+    "elliprg : Completely-symmetric elliptic integral of the second kind.\n"
+    "elliprj : Symmetric elliptic integral of the third kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "RD is a degenerate case of the elliptic integral RJ: ``elliprd(x, y, z) ==\n"
+    "elliprj(x, y, z, z)``.\n"
+    "\n"
+    "The code implements Carlson's algorithm based on the duplication theorems\n"
+    "and series expansion up to the 7th order. [2]_\n"
+    "\n"
+    ".. versionadded:: 1.8.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] B. C. Carlson, ed., Chapter 19 in \"Digital Library of Mathematical\n"
+    "       Functions,\" NIST, US Dept. of Commerce.\n"
+    "       https://dlmf.nist.gov/19.16.E5\n"
+    ".. [2] B. C. Carlson, \"Numerical computation of real or complex elliptic\n"
+    "       integrals,\" Numer. Algorithm, vol. 10, no. 1, pp. 13-26, 1995.\n"
+    "       https://arxiv.org/abs/math/9409227\n"
+    "       https://doi.org/10.1007/BF02198293\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Basic homogeneity property:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import elliprd\n"
+    "\n"
+    ">>> x = 1.2 + 3.4j\n"
+    ">>> y = 5.\n"
+    ">>> z = 6.\n"
+    ">>> scale = 0.3 + 0.4j\n"
+    ">>> elliprd(scale*x, scale*y, scale*z)\n"
+    "(-0.03703043835680379-0.24500934665683802j)\n"
+    "\n"
+    ">>> elliprd(x, y, z)*np.power(scale, -1.5)\n"
+    "(-0.0370304383568038-0.24500934665683805j)\n"
+    "\n"
+    "All three arguments coincide:\n"
+    "\n"
+    ">>> x = 1.2 + 3.4j\n"
+    ">>> elliprd(x, x, x)\n"
+    "(-0.03986825876151896-0.14051741840449586j)\n"
+    "\n"
+    ">>> np.power(x, -1.5)\n"
+    "(-0.03986825876151894-0.14051741840449583j)\n"
+    "\n"
+    "The so-called \"second lemniscate constant\":\n"
+    "\n"
+    ">>> elliprd(0, 2, 1)/3\n"
+    "0.5990701173677961\n"
+    "\n"
+    ">>> from scipy.special import gamma\n"
+    ">>> gamma(0.75)**2/np.sqrt(2*np.pi)\n"
+    "0.5990701173677959")
+ufunc_elliprd_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_elliprd_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_elliprd_loops[2] = <np.PyUFuncGenericFunction>loop_D_DDD__As_FFF_F
+ufunc_elliprd_loops[3] = <np.PyUFuncGenericFunction>loop_D_DDD__As_DDD_D
+ufunc_elliprd_types[0] = <char>NPY_FLOAT
+ufunc_elliprd_types[1] = <char>NPY_FLOAT
+ufunc_elliprd_types[2] = <char>NPY_FLOAT
+ufunc_elliprd_types[3] = <char>NPY_FLOAT
+ufunc_elliprd_types[4] = <char>NPY_DOUBLE
+ufunc_elliprd_types[5] = <char>NPY_DOUBLE
+ufunc_elliprd_types[6] = <char>NPY_DOUBLE
+ufunc_elliprd_types[7] = <char>NPY_DOUBLE
+ufunc_elliprd_types[8] = <char>NPY_CFLOAT
+ufunc_elliprd_types[9] = <char>NPY_CFLOAT
+ufunc_elliprd_types[10] = <char>NPY_CFLOAT
+ufunc_elliprd_types[11] = <char>NPY_CFLOAT
+ufunc_elliprd_types[12] = <char>NPY_CDOUBLE
+ufunc_elliprd_types[13] = <char>NPY_CDOUBLE
+ufunc_elliprd_types[14] = <char>NPY_CDOUBLE
+ufunc_elliprd_types[15] = <char>NPY_CDOUBLE
+ufunc_elliprd_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_fellint_RD
+ufunc_elliprd_ptr[2*0+1] = <void*>(<char*>"elliprd")
+ufunc_elliprd_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_fellint_RD
+ufunc_elliprd_ptr[2*1+1] = <void*>(<char*>"elliprd")
+ufunc_elliprd_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_cellint_RD
+ufunc_elliprd_ptr[2*2+1] = <void*>(<char*>"elliprd")
+ufunc_elliprd_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_cellint_RD
+ufunc_elliprd_ptr[2*3+1] = <void*>(<char*>"elliprd")
+ufunc_elliprd_data[0] = &ufunc_elliprd_ptr[2*0]
+ufunc_elliprd_data[1] = &ufunc_elliprd_ptr[2*1]
+ufunc_elliprd_data[2] = &ufunc_elliprd_ptr[2*2]
+ufunc_elliprd_data[3] = &ufunc_elliprd_ptr[2*3]
+elliprd = np.PyUFunc_FromFuncAndData(ufunc_elliprd_loops, ufunc_elliprd_data, ufunc_elliprd_types, 4, 3, 1, 0, "elliprd", ufunc_elliprd_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_elliprf_loops[4]
+cdef void *ufunc_elliprf_ptr[8]
+cdef void *ufunc_elliprf_data[4]
+cdef char ufunc_elliprf_types[16]
+cdef char *ufunc_elliprf_doc = (
+    "elliprf(x, y, z, out=None)\n"
+    "\n"
+    "Completely-symmetric elliptic integral of the first kind.\n"
+    "\n"
+    "The function RF is defined as [1]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    R_{\\mathrm{F}}(x, y, z) =\n"
+    "       \\frac{1}{2} \\int_0^{+\\infty} [(t + x) (t + y) (t + z)]^{-1/2} dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x, y, z : array_like\n"
+    "    Real or complex input parameters. `x`, `y`, or `z` can be any number in\n"
+    "    the complex plane cut along the negative real axis, but at most one of\n"
+    "    them can be zero.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "R : scalar or ndarray\n"
+    "    Value of the integral. If all of `x`, `y`, and `z` are real, the return\n"
+    "    value is real. Otherwise, the return value is complex.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "elliprc : Degenerate symmetric integral.\n"
+    "elliprd : Symmetric elliptic integral of the second kind.\n"
+    "elliprg : Completely-symmetric elliptic integral of the second kind.\n"
+    "elliprj : Symmetric elliptic integral of the third kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The code implements Carlson's algorithm based on the duplication theorems\n"
+    "and series expansion up to the 7th order (cf.:\n"
+    "https://dlmf.nist.gov/19.36.i) and the AGM algorithm for the complete\n"
+    "integral. [2]_\n"
+    "\n"
+    ".. versionadded:: 1.8.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] B. C. Carlson, ed., Chapter 19 in \"Digital Library of Mathematical\n"
+    "       Functions,\" NIST, US Dept. of Commerce.\n"
+    "       https://dlmf.nist.gov/19.16.E1\n"
+    ".. [2] B. C. Carlson, \"Numerical computation of real or complex elliptic\n"
+    "       integrals,\" Numer. Algorithm, vol. 10, no. 1, pp. 13-26, 1995.\n"
+    "       https://arxiv.org/abs/math/9409227\n"
+    "       https://doi.org/10.1007/BF02198293\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Basic homogeneity property:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import elliprf\n"
+    "\n"
+    ">>> x = 1.2 + 3.4j\n"
+    ">>> y = 5.\n"
+    ">>> z = 6.\n"
+    ">>> scale = 0.3 + 0.4j\n"
+    ">>> elliprf(scale*x, scale*y, scale*z)\n"
+    "(0.5328051227278146-0.4008623567957094j)\n"
+    "\n"
+    ">>> elliprf(x, y, z)/np.sqrt(scale)\n"
+    "(0.5328051227278147-0.4008623567957095j)\n"
+    "\n"
+    "All three arguments coincide:\n"
+    "\n"
+    ">>> x = 1.2 + 3.4j\n"
+    ">>> elliprf(x, x, x)\n"
+    "(0.42991731206146316-0.30417298187455954j)\n"
+    "\n"
+    ">>> 1/np.sqrt(x)\n"
+    "(0.4299173120614631-0.30417298187455954j)\n"
+    "\n"
+    "The so-called \"first lemniscate constant\":\n"
+    "\n"
+    ">>> elliprf(0, 1, 2)\n"
+    "1.3110287771460598\n"
+    "\n"
+    ">>> from scipy.special import gamma\n"
+    ">>> gamma(0.25)**2/(4*np.sqrt(2*np.pi))\n"
+    "1.3110287771460598")
+ufunc_elliprf_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_elliprf_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_elliprf_loops[2] = <np.PyUFuncGenericFunction>loop_D_DDD__As_FFF_F
+ufunc_elliprf_loops[3] = <np.PyUFuncGenericFunction>loop_D_DDD__As_DDD_D
+ufunc_elliprf_types[0] = <char>NPY_FLOAT
+ufunc_elliprf_types[1] = <char>NPY_FLOAT
+ufunc_elliprf_types[2] = <char>NPY_FLOAT
+ufunc_elliprf_types[3] = <char>NPY_FLOAT
+ufunc_elliprf_types[4] = <char>NPY_DOUBLE
+ufunc_elliprf_types[5] = <char>NPY_DOUBLE
+ufunc_elliprf_types[6] = <char>NPY_DOUBLE
+ufunc_elliprf_types[7] = <char>NPY_DOUBLE
+ufunc_elliprf_types[8] = <char>NPY_CFLOAT
+ufunc_elliprf_types[9] = <char>NPY_CFLOAT
+ufunc_elliprf_types[10] = <char>NPY_CFLOAT
+ufunc_elliprf_types[11] = <char>NPY_CFLOAT
+ufunc_elliprf_types[12] = <char>NPY_CDOUBLE
+ufunc_elliprf_types[13] = <char>NPY_CDOUBLE
+ufunc_elliprf_types[14] = <char>NPY_CDOUBLE
+ufunc_elliprf_types[15] = <char>NPY_CDOUBLE
+ufunc_elliprf_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_fellint_RF
+ufunc_elliprf_ptr[2*0+1] = <void*>(<char*>"elliprf")
+ufunc_elliprf_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_fellint_RF
+ufunc_elliprf_ptr[2*1+1] = <void*>(<char*>"elliprf")
+ufunc_elliprf_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_cellint_RF
+ufunc_elliprf_ptr[2*2+1] = <void*>(<char*>"elliprf")
+ufunc_elliprf_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_cellint_RF
+ufunc_elliprf_ptr[2*3+1] = <void*>(<char*>"elliprf")
+ufunc_elliprf_data[0] = &ufunc_elliprf_ptr[2*0]
+ufunc_elliprf_data[1] = &ufunc_elliprf_ptr[2*1]
+ufunc_elliprf_data[2] = &ufunc_elliprf_ptr[2*2]
+ufunc_elliprf_data[3] = &ufunc_elliprf_ptr[2*3]
+elliprf = np.PyUFunc_FromFuncAndData(ufunc_elliprf_loops, ufunc_elliprf_data, ufunc_elliprf_types, 4, 3, 1, 0, "elliprf", ufunc_elliprf_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_elliprg_loops[4]
+cdef void *ufunc_elliprg_ptr[8]
+cdef void *ufunc_elliprg_data[4]
+cdef char ufunc_elliprg_types[16]
+cdef char *ufunc_elliprg_doc = (
+    "elliprg(x, y, z, out=None)\n"
+    "\n"
+    "Completely-symmetric elliptic integral of the second kind.\n"
+    "\n"
+    "The function RG is defined as [1]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    R_{\\mathrm{G}}(x, y, z) =\n"
+    "       \\frac{1}{4} \\int_0^{+\\infty} [(t + x) (t + y) (t + z)]^{-1/2}\n"
+    "       \\left(\\frac{x}{t + x} + \\frac{y}{t + y} + \\frac{z}{t + z}\\right) t\n"
+    "       dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x, y, z : array_like\n"
+    "    Real or complex input parameters. `x`, `y`, or `z` can be any number in\n"
+    "    the complex plane cut along the negative real axis.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "R : scalar or ndarray\n"
+    "    Value of the integral. If all of `x`, `y`, and `z` are real, the return\n"
+    "    value is real. Otherwise, the return value is complex.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "elliprc : Degenerate symmetric integral.\n"
+    "elliprd : Symmetric elliptic integral of the second kind.\n"
+    "elliprf : Completely-symmetric elliptic integral of the first kind.\n"
+    "elliprj : Symmetric elliptic integral of the third kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The implementation uses the relation [1]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    2 R_{\\mathrm{G}}(x, y, z) =\n"
+    "       z R_{\\mathrm{F}}(x, y, z) -\n"
+    "       \\frac{1}{3} (x - z) (y - z) R_{\\mathrm{D}}(x, y, z) +\n"
+    "       \\sqrt{\\frac{x y}{z}}\n"
+    "\n"
+    "and the symmetry of `x`, `y`, `z` when at least one non-zero parameter can\n"
+    "be chosen as the pivot. When one of the arguments is close to zero, the AGM\n"
+    "method is applied instead. Other special cases are computed following Ref.\n"
+    "[2]_\n"
+    "\n"
+    ".. versionadded:: 1.8.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] B. C. Carlson, \"Numerical computation of real or complex elliptic\n"
+    "       integrals,\" Numer. Algorithm, vol. 10, no. 1, pp. 13-26, 1995.\n"
+    "       https://arxiv.org/abs/math/9409227\n"
+    "       https://doi.org/10.1007/BF02198293\n"
+    ".. [2] B. C. Carlson, ed., Chapter 19 in \"Digital Library of Mathematical\n"
+    "       Functions,\" NIST, US Dept. of Commerce.\n"
+    "       https://dlmf.nist.gov/19.16.E1\n"
+    "       https://dlmf.nist.gov/19.20.ii\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Basic homogeneity property:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import elliprg\n"
+    "\n"
+    ">>> x = 1.2 + 3.4j\n"
+    ">>> y = 5.\n"
+    ">>> z = 6.\n"
+    ">>> scale = 0.3 + 0.4j\n"
+    ">>> elliprg(scale*x, scale*y, scale*z)\n"
+    "(1.195936862005246+0.8470988320464167j)\n"
+    "\n"
+    ">>> elliprg(x, y, z)*np.sqrt(scale)\n"
+    "(1.195936862005246+0.8470988320464165j)\n"
+    "\n"
+    "Simplifications:\n"
+    "\n"
+    ">>> elliprg(0, y, y)\n"
+    "1.756203682760182\n"
+    "\n"
+    ">>> 0.25*np.pi*np.sqrt(y)\n"
+    "1.7562036827601817\n"
+    "\n"
+    ">>> elliprg(0, 0, z)\n"
+    "1.224744871391589\n"
+    "\n"
+    ">>> 0.5*np.sqrt(z)\n"
+    "1.224744871391589\n"
+    "\n"
+    "The surface area of a triaxial ellipsoid with semiaxes ``a``, ``b``, and\n"
+    "``c`` is given by\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    S = 4 \\pi a b c R_{\\mathrm{G}}(1 / a^2, 1 / b^2, 1 / c^2).\n"
+    "\n"
+    ">>> def ellipsoid_area(a, b, c):\n"
+    "...     r = 4.0 * np.pi * a * b * c\n"
+    "...     return r * elliprg(1.0 / (a * a), 1.0 / (b * b), 1.0 / (c * c))\n"
+    ">>> print(ellipsoid_area(1, 3, 5))\n"
+    "108.62688289491807")
+ufunc_elliprg_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_elliprg_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_elliprg_loops[2] = <np.PyUFuncGenericFunction>loop_D_DDD__As_FFF_F
+ufunc_elliprg_loops[3] = <np.PyUFuncGenericFunction>loop_D_DDD__As_DDD_D
+ufunc_elliprg_types[0] = <char>NPY_FLOAT
+ufunc_elliprg_types[1] = <char>NPY_FLOAT
+ufunc_elliprg_types[2] = <char>NPY_FLOAT
+ufunc_elliprg_types[3] = <char>NPY_FLOAT
+ufunc_elliprg_types[4] = <char>NPY_DOUBLE
+ufunc_elliprg_types[5] = <char>NPY_DOUBLE
+ufunc_elliprg_types[6] = <char>NPY_DOUBLE
+ufunc_elliprg_types[7] = <char>NPY_DOUBLE
+ufunc_elliprg_types[8] = <char>NPY_CFLOAT
+ufunc_elliprg_types[9] = <char>NPY_CFLOAT
+ufunc_elliprg_types[10] = <char>NPY_CFLOAT
+ufunc_elliprg_types[11] = <char>NPY_CFLOAT
+ufunc_elliprg_types[12] = <char>NPY_CDOUBLE
+ufunc_elliprg_types[13] = <char>NPY_CDOUBLE
+ufunc_elliprg_types[14] = <char>NPY_CDOUBLE
+ufunc_elliprg_types[15] = <char>NPY_CDOUBLE
+ufunc_elliprg_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_fellint_RG
+ufunc_elliprg_ptr[2*0+1] = <void*>(<char*>"elliprg")
+ufunc_elliprg_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_fellint_RG
+ufunc_elliprg_ptr[2*1+1] = <void*>(<char*>"elliprg")
+ufunc_elliprg_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_cellint_RG
+ufunc_elliprg_ptr[2*2+1] = <void*>(<char*>"elliprg")
+ufunc_elliprg_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_cellint_RG
+ufunc_elliprg_ptr[2*3+1] = <void*>(<char*>"elliprg")
+ufunc_elliprg_data[0] = &ufunc_elliprg_ptr[2*0]
+ufunc_elliprg_data[1] = &ufunc_elliprg_ptr[2*1]
+ufunc_elliprg_data[2] = &ufunc_elliprg_ptr[2*2]
+ufunc_elliprg_data[3] = &ufunc_elliprg_ptr[2*3]
+elliprg = np.PyUFunc_FromFuncAndData(ufunc_elliprg_loops, ufunc_elliprg_data, ufunc_elliprg_types, 4, 3, 1, 0, "elliprg", ufunc_elliprg_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_elliprj_loops[4]
+cdef void *ufunc_elliprj_ptr[8]
+cdef void *ufunc_elliprj_data[4]
+cdef char ufunc_elliprj_types[20]
+cdef char *ufunc_elliprj_doc = (
+    "elliprj(x, y, z, p, out=None)\n"
+    "\n"
+    "Symmetric elliptic integral of the third kind.\n"
+    "\n"
+    "The function RJ is defined as [1]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    R_{\\mathrm{J}}(x, y, z, p) =\n"
+    "       \\frac{3}{2} \\int_0^{+\\infty} [(t + x) (t + y) (t + z)]^{-1/2}\n"
+    "       (t + p)^{-1} dt\n"
+    "\n"
+    ".. warning::\n"
+    "    This function should be considered experimental when the inputs are\n"
+    "    unbalanced.  Check correctness with another independent implementation.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x, y, z, p : array_like\n"
+    "    Real or complex input parameters. `x`, `y`, or `z` are numbers in\n"
+    "    the complex plane cut along the negative real axis (subject to further\n"
+    "    constraints, see Notes), and at most one of them can be zero. `p` must\n"
+    "    be non-zero.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "R : scalar or ndarray\n"
+    "    Value of the integral. If all of `x`, `y`, `z`, and `p` are real, the\n"
+    "    return value is real. Otherwise, the return value is complex.\n"
+    "\n"
+    "    If `p` is real and negative, while `x`, `y`, and `z` are real,\n"
+    "    non-negative, and at most one of them is zero, the Cauchy principal\n"
+    "    value is returned. [1]_ [2]_\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "elliprc : Degenerate symmetric integral.\n"
+    "elliprd : Symmetric elliptic integral of the second kind.\n"
+    "elliprf : Completely-symmetric elliptic integral of the first kind.\n"
+    "elliprg : Completely-symmetric elliptic integral of the second kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The code implements Carlson's algorithm based on the duplication theorems\n"
+    "and series expansion up to the 7th order. [3]_ The algorithm is slightly\n"
+    "different from its earlier incarnation as it appears in [1]_, in that the\n"
+    "call to `elliprc` (or ``atan``/``atanh``, see [4]_) is no longer needed in\n"
+    "the inner loop. Asymptotic approximations are used where arguments differ\n"
+    "widely in the order of magnitude. [5]_\n"
+    "\n"
+    "The input values are subject to certain sufficient but not necessary\n"
+    "constraints when input arguments are complex. Notably, ``x``, ``y``, and\n"
+    "``z`` must have non-negative real parts, unless two of them are\n"
+    "non-negative and complex-conjugates to each other while the other is a real\n"
+    "non-negative number. [1]_ If the inputs do not satisfy the sufficient\n"
+    "condition described in Ref. [1]_ they are rejected outright with the output\n"
+    "set to NaN.\n"
+    "\n"
+    "In the case where one of ``x``, ``y``, and ``z`` is equal to ``p``, the\n"
+    "function ``elliprd`` should be preferred because of its less restrictive\n"
+    "domain.\n"
+    "\n"
+    ".. versionadded:: 1.8.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] B. C. Carlson, \"Numerical computation of real or complex elliptic\n"
+    "       integrals,\" Numer. Algorithm, vol. 10, no. 1, pp. 13-26, 1995.\n"
+    "       https://arxiv.org/abs/math/9409227\n"
+    "       https://doi.org/10.1007/BF02198293\n"
+    ".. [2] B. C. Carlson, ed., Chapter 19 in \"Digital Library of Mathematical\n"
+    "       Functions,\" NIST, US Dept. of Commerce.\n"
+    "       https://dlmf.nist.gov/19.20.iii\n"
+    ".. [3] B. C. Carlson, J. FitzSimmons, \"Reduction Theorems for Elliptic\n"
+    "       Integrands with the Square Root of Two Quadratic Factors,\" J.\n"
+    "       Comput. Appl. Math., vol. 118, nos. 1-2, pp. 71-85, 2000.\n"
+    "       https://doi.org/10.1016/S0377-0427(00)00282-X\n"
+    ".. [4] F. Johansson, \"Numerical Evaluation of Elliptic Functions, Elliptic\n"
+    "       Integrals and Modular Forms,\" in J. Blumlein, C. Schneider, P.\n"
+    "       Paule, eds., \"Elliptic Integrals, Elliptic Functions and Modular\n"
+    "       Forms in Quantum Field Theory,\" pp. 269-293, 2019 (Cham,\n"
+    "       Switzerland: Springer Nature Switzerland)\n"
+    "       https://arxiv.org/abs/1806.06725\n"
+    "       https://doi.org/10.1007/978-3-030-04480-0\n"
+    ".. [5] B. C. Carlson, J. L. Gustafson, \"Asymptotic Approximations for\n"
+    "       Symmetric Elliptic Integrals,\" SIAM J. Math. Anls., vol. 25, no. 2,\n"
+    "       pp. 288-303, 1994.\n"
+    "       https://arxiv.org/abs/math/9310223\n"
+    "       https://doi.org/10.1137/S0036141092228477\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Basic homogeneity property:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import elliprj\n"
+    "\n"
+    ">>> x = 1.2 + 3.4j\n"
+    ">>> y = 5.\n"
+    ">>> z = 6.\n"
+    ">>> p = 7.\n"
+    ">>> scale = 0.3 - 0.4j\n"
+    ">>> elliprj(scale*x, scale*y, scale*z, scale*p)\n"
+    "(0.10834905565679157+0.19694950747103812j)\n"
+    "\n"
+    ">>> elliprj(x, y, z, p)*np.power(scale, -1.5)\n"
+    "(0.10834905565679556+0.19694950747103854j)\n"
+    "\n"
+    "Reduction to simpler elliptic integral:\n"
+    "\n"
+    ">>> elliprj(x, y, z, z)\n"
+    "(0.08288462362195129-0.028376809745123258j)\n"
+    "\n"
+    ">>> from scipy.special import elliprd\n"
+    ">>> elliprd(x, y, z)\n"
+    "(0.08288462362195136-0.028376809745123296j)\n"
+    "\n"
+    "All arguments coincide:\n"
+    "\n"
+    ">>> elliprj(x, x, x, x)\n"
+    "(-0.03986825876151896-0.14051741840449586j)\n"
+    "\n"
+    ">>> np.power(x, -1.5)\n"
+    "(-0.03986825876151894-0.14051741840449583j)")
+ufunc_elliprj_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd__As_ffff_f
+ufunc_elliprj_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd__As_dddd_d
+ufunc_elliprj_loops[2] = <np.PyUFuncGenericFunction>loop_D_DDDD__As_FFFF_F
+ufunc_elliprj_loops[3] = <np.PyUFuncGenericFunction>loop_D_DDDD__As_DDDD_D
+ufunc_elliprj_types[0] = <char>NPY_FLOAT
+ufunc_elliprj_types[1] = <char>NPY_FLOAT
+ufunc_elliprj_types[2] = <char>NPY_FLOAT
+ufunc_elliprj_types[3] = <char>NPY_FLOAT
+ufunc_elliprj_types[4] = <char>NPY_FLOAT
+ufunc_elliprj_types[5] = <char>NPY_DOUBLE
+ufunc_elliprj_types[6] = <char>NPY_DOUBLE
+ufunc_elliprj_types[7] = <char>NPY_DOUBLE
+ufunc_elliprj_types[8] = <char>NPY_DOUBLE
+ufunc_elliprj_types[9] = <char>NPY_DOUBLE
+ufunc_elliprj_types[10] = <char>NPY_CFLOAT
+ufunc_elliprj_types[11] = <char>NPY_CFLOAT
+ufunc_elliprj_types[12] = <char>NPY_CFLOAT
+ufunc_elliprj_types[13] = <char>NPY_CFLOAT
+ufunc_elliprj_types[14] = <char>NPY_CFLOAT
+ufunc_elliprj_types[15] = <char>NPY_CDOUBLE
+ufunc_elliprj_types[16] = <char>NPY_CDOUBLE
+ufunc_elliprj_types[17] = <char>NPY_CDOUBLE
+ufunc_elliprj_types[18] = <char>NPY_CDOUBLE
+ufunc_elliprj_types[19] = <char>NPY_CDOUBLE
+ufunc_elliprj_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_fellint_RJ
+ufunc_elliprj_ptr[2*0+1] = <void*>(<char*>"elliprj")
+ufunc_elliprj_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_fellint_RJ
+ufunc_elliprj_ptr[2*1+1] = <void*>(<char*>"elliprj")
+ufunc_elliprj_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_cellint_RJ
+ufunc_elliprj_ptr[2*2+1] = <void*>(<char*>"elliprj")
+ufunc_elliprj_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_cellint_RJ
+ufunc_elliprj_ptr[2*3+1] = <void*>(<char*>"elliprj")
+ufunc_elliprj_data[0] = &ufunc_elliprj_ptr[2*0]
+ufunc_elliprj_data[1] = &ufunc_elliprj_ptr[2*1]
+ufunc_elliprj_data[2] = &ufunc_elliprj_ptr[2*2]
+ufunc_elliprj_data[3] = &ufunc_elliprj_ptr[2*3]
+elliprj = np.PyUFunc_FromFuncAndData(ufunc_elliprj_loops, ufunc_elliprj_data, ufunc_elliprj_types, 4, 4, 1, 0, "elliprj", ufunc_elliprj_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_entr_loops[2]
+cdef void *ufunc_entr_ptr[4]
+cdef void *ufunc_entr_data[2]
+cdef char ufunc_entr_types[4]
+cdef char *ufunc_entr_doc = (
+    "entr(x, out=None)\n"
+    "\n"
+    "Elementwise function for computing entropy.\n"
+    "\n"
+    ".. math:: \\text{entr}(x) = \\begin{cases} - x \\log(x) & x > 0  \\\\ 0 & x = 0\n"
+    "          \\\\ -\\infty & \\text{otherwise} \\end{cases}\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : ndarray\n"
+    "    Input array.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "res : scalar or ndarray\n"
+    "    The value of the elementwise entropy function at the given points `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kl_div, rel_entr, scipy.stats.entropy\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    ".. versionadded:: 0.15.0\n"
+    "\n"
+    "This function is concave.\n"
+    "\n"
+    "The origin of this function is in convex programming; see [1]_.\n"
+    "Given a probability distribution :math:`p_1, \\ldots, p_n`,\n"
+    "the definition of entropy in the context of *information theory* is\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\sum_{i = 1}^n \\mathrm{entr}(p_i).\n"
+    "\n"
+    "To compute the latter quantity, use `scipy.stats.entropy`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Boyd, Stephen and Lieven Vandenberghe. *Convex optimization*.\n"
+    "       Cambridge University Press, 2004.\n"
+    "       :doi:`https://doi.org/10.1017/CBO9780511804441`")
+ufunc_entr_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_entr_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_entr_types[0] = <char>NPY_FLOAT
+ufunc_entr_types[1] = <char>NPY_FLOAT
+ufunc_entr_types[2] = <char>NPY_DOUBLE
+ufunc_entr_types[3] = <char>NPY_DOUBLE
+ufunc_entr_ptr[2*0] = <void*>_func_entr
+ufunc_entr_ptr[2*0+1] = <void*>(<char*>"entr")
+ufunc_entr_ptr[2*1] = <void*>_func_entr
+ufunc_entr_ptr[2*1+1] = <void*>(<char*>"entr")
+ufunc_entr_data[0] = &ufunc_entr_ptr[2*0]
+ufunc_entr_data[1] = &ufunc_entr_ptr[2*1]
+entr = np.PyUFunc_FromFuncAndData(ufunc_entr_loops, ufunc_entr_data, ufunc_entr_types, 2, 1, 1, 0, "entr", ufunc_entr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_erf_loops[4]
+cdef void *ufunc_erf_ptr[8]
+cdef void *ufunc_erf_data[4]
+cdef char ufunc_erf_types[8]
+cdef char *ufunc_erf_doc = (
+    "erf(z, out=None)\n"
+    "\n"
+    "Returns the error function of complex argument.\n"
+    "\n"
+    "It is defined as ``2/sqrt(pi)*integral(exp(-t**2), t=0..z)``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : ndarray\n"
+    "    Input array.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "res : scalar or ndarray\n"
+    "    The values of the error function at the given points `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "erfc, erfinv, erfcinv, wofz, erfcx, erfi\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The cumulative of the unit normal distribution is given by\n"
+    "``Phi(z) = 1/2[1 + erf(z/sqrt(2))]``.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] https://en.wikipedia.org/wiki/Error_function\n"
+    ".. [2] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover,\n"
+    "    1972. http://www.math.sfu.ca/~cbm/aands/page_297.htm\n"
+    ".. [3] Steven G. Johnson, Faddeeva W function implementation.\n"
+    "   http://ab-initio.mit.edu/Faddeeva\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-3, 3)\n"
+    ">>> plt.plot(x, special.erf(x))\n"
+    ">>> plt.xlabel('$x$')\n"
+    ">>> plt.ylabel('$erf(x)$')\n"
+    ">>> plt.show()")
+ufunc_erf_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_erf_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_erf_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_erf_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_erf_types[0] = <char>NPY_FLOAT
+ufunc_erf_types[1] = <char>NPY_FLOAT
+ufunc_erf_types[2] = <char>NPY_DOUBLE
+ufunc_erf_types[3] = <char>NPY_DOUBLE
+ufunc_erf_types[4] = <char>NPY_CFLOAT
+ufunc_erf_types[5] = <char>NPY_CFLOAT
+ufunc_erf_types[6] = <char>NPY_CDOUBLE
+ufunc_erf_types[7] = <char>NPY_CDOUBLE
+ufunc_erf_ptr[2*0] = <void*>_func_erf
+ufunc_erf_ptr[2*0+1] = <void*>(<char*>"erf")
+ufunc_erf_ptr[2*1] = <void*>_func_erf
+ufunc_erf_ptr[2*1+1] = <void*>(<char*>"erf")
+ufunc_erf_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erf
+ufunc_erf_ptr[2*2+1] = <void*>(<char*>"erf")
+ufunc_erf_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erf
+ufunc_erf_ptr[2*3+1] = <void*>(<char*>"erf")
+ufunc_erf_data[0] = &ufunc_erf_ptr[2*0]
+ufunc_erf_data[1] = &ufunc_erf_ptr[2*1]
+ufunc_erf_data[2] = &ufunc_erf_ptr[2*2]
+ufunc_erf_data[3] = &ufunc_erf_ptr[2*3]
+erf = np.PyUFunc_FromFuncAndData(ufunc_erf_loops, ufunc_erf_data, ufunc_erf_types, 4, 1, 1, 0, "erf", ufunc_erf_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_erfc_loops[4]
+cdef void *ufunc_erfc_ptr[8]
+cdef void *ufunc_erfc_data[4]
+cdef char ufunc_erfc_types[8]
+cdef char *ufunc_erfc_doc = (
+    "erfc(x, out=None)\n"
+    "\n"
+    "Complementary error function, ``1 - erf(x)``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real or complex valued argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the complementary error function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "erf, erfi, erfcx, dawsn, wofz\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Steven G. Johnson, Faddeeva W function implementation.\n"
+    "   http://ab-initio.mit.edu/Faddeeva\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-3, 3)\n"
+    ">>> plt.plot(x, special.erfc(x))\n"
+    ">>> plt.xlabel('$x$')\n"
+    ">>> plt.ylabel('$erfc(x)$')\n"
+    ">>> plt.show()")
+ufunc_erfc_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_erfc_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_erfc_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_erfc_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_erfc_types[0] = <char>NPY_FLOAT
+ufunc_erfc_types[1] = <char>NPY_FLOAT
+ufunc_erfc_types[2] = <char>NPY_DOUBLE
+ufunc_erfc_types[3] = <char>NPY_DOUBLE
+ufunc_erfc_types[4] = <char>NPY_CFLOAT
+ufunc_erfc_types[5] = <char>NPY_CFLOAT
+ufunc_erfc_types[6] = <char>NPY_CDOUBLE
+ufunc_erfc_types[7] = <char>NPY_CDOUBLE
+ufunc_erfc_ptr[2*0] = <void*>_func_erfc
+ufunc_erfc_ptr[2*0+1] = <void*>(<char*>"erfc")
+ufunc_erfc_ptr[2*1] = <void*>_func_erfc
+ufunc_erfc_ptr[2*1+1] = <void*>(<char*>"erfc")
+ufunc_erfc_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfc_complex
+ufunc_erfc_ptr[2*2+1] = <void*>(<char*>"erfc")
+ufunc_erfc_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfc_complex
+ufunc_erfc_ptr[2*3+1] = <void*>(<char*>"erfc")
+ufunc_erfc_data[0] = &ufunc_erfc_ptr[2*0]
+ufunc_erfc_data[1] = &ufunc_erfc_ptr[2*1]
+ufunc_erfc_data[2] = &ufunc_erfc_ptr[2*2]
+ufunc_erfc_data[3] = &ufunc_erfc_ptr[2*3]
+erfc = np.PyUFunc_FromFuncAndData(ufunc_erfc_loops, ufunc_erfc_data, ufunc_erfc_types, 4, 1, 1, 0, "erfc", ufunc_erfc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_erfcinv_loops[2]
+cdef void *ufunc_erfcinv_ptr[4]
+cdef void *ufunc_erfcinv_data[2]
+cdef char ufunc_erfcinv_types[4]
+cdef char *ufunc_erfcinv_doc = (
+    "erfcinv(y, out=None)\n"
+    "\n"
+    "Inverse of the complementary error function.\n"
+    "\n"
+    "Computes the inverse of the complementary error function.\n"
+    "\n"
+    "In the complex domain, there is no unique complex number w satisfying\n"
+    "erfc(w)=z. This indicates a true inverse function would be multivalued.\n"
+    "When the domain restricts to the real, 0 < x < 2, there is a unique real\n"
+    "number satisfying erfc(erfcinv(x)) = erfcinv(erfc(x)).\n"
+    "\n"
+    "It is related to inverse of the error function by erfcinv(1-x) = erfinv(x)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "y : ndarray\n"
+    "    Argument at which to evaluate. Domain: [0, 2]\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "erfcinv : scalar or ndarray\n"
+    "    The inverse of erfc of y, element-wise\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "erf : Error function of a complex argument\n"
+    "erfc : Complementary error function, ``1 - erf(x)``\n"
+    "erfinv : Inverse of the error function\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> from scipy.special import erfcinv\n"
+    "\n"
+    ">>> erfcinv(0.5)\n"
+    "0.4769362762044699\n"
+    "\n"
+    ">>> y = np.linspace(0.0, 2.0, num=11)\n"
+    ">>> erfcinv(y)\n"
+    "array([        inf,  0.9061938 ,  0.59511608,  0.37080716,  0.17914345,\n"
+    "       -0.        , -0.17914345, -0.37080716, -0.59511608, -0.9061938 ,\n"
+    "              -inf])\n"
+    "\n"
+    "Plot the function:\n"
+    "\n"
+    ">>> y = np.linspace(0, 2, 200)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> ax.plot(y, erfcinv(y))\n"
+    ">>> ax.grid(True)\n"
+    ">>> ax.set_xlabel('y')\n"
+    ">>> ax.set_title('erfcinv(y)')\n"
+    ">>> plt.show()")
+ufunc_erfcinv_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_erfcinv_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_erfcinv_types[0] = <char>NPY_FLOAT
+ufunc_erfcinv_types[1] = <char>NPY_FLOAT
+ufunc_erfcinv_types[2] = <char>NPY_DOUBLE
+ufunc_erfcinv_types[3] = <char>NPY_DOUBLE
+ufunc_erfcinv_ptr[2*0] = <void*>_func_erfcinv
+ufunc_erfcinv_ptr[2*0+1] = <void*>(<char*>"erfcinv")
+ufunc_erfcinv_ptr[2*1] = <void*>_func_erfcinv
+ufunc_erfcinv_ptr[2*1+1] = <void*>(<char*>"erfcinv")
+ufunc_erfcinv_data[0] = &ufunc_erfcinv_ptr[2*0]
+ufunc_erfcinv_data[1] = &ufunc_erfcinv_ptr[2*1]
+erfcinv = np.PyUFunc_FromFuncAndData(ufunc_erfcinv_loops, ufunc_erfcinv_data, ufunc_erfcinv_types, 2, 1, 1, 0, "erfcinv", ufunc_erfcinv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_erfcx_loops[4]
+cdef void *ufunc_erfcx_ptr[8]
+cdef void *ufunc_erfcx_data[4]
+cdef char ufunc_erfcx_types[8]
+cdef char *ufunc_erfcx_doc = (
+    "erfcx(x, out=None)\n"
+    "\n"
+    "Scaled complementary error function, ``exp(x**2) * erfc(x)``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real or complex valued argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the scaled complementary error function\n"
+    "\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "erf, erfc, erfi, dawsn, wofz\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "\n"
+    ".. versionadded:: 0.12.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Steven G. Johnson, Faddeeva W function implementation.\n"
+    "   http://ab-initio.mit.edu/Faddeeva\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-3, 3)\n"
+    ">>> plt.plot(x, special.erfcx(x))\n"
+    ">>> plt.xlabel('$x$')\n"
+    ">>> plt.ylabel('$erfcx(x)$')\n"
+    ">>> plt.show()")
+ufunc_erfcx_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_erfcx_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_erfcx_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_erfcx_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_erfcx_types[0] = <char>NPY_FLOAT
+ufunc_erfcx_types[1] = <char>NPY_FLOAT
+ufunc_erfcx_types[2] = <char>NPY_DOUBLE
+ufunc_erfcx_types[3] = <char>NPY_DOUBLE
+ufunc_erfcx_types[4] = <char>NPY_CFLOAT
+ufunc_erfcx_types[5] = <char>NPY_CFLOAT
+ufunc_erfcx_types[6] = <char>NPY_CDOUBLE
+ufunc_erfcx_types[7] = <char>NPY_CDOUBLE
+ufunc_erfcx_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfcx
+ufunc_erfcx_ptr[2*0+1] = <void*>(<char*>"erfcx")
+ufunc_erfcx_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfcx
+ufunc_erfcx_ptr[2*1+1] = <void*>(<char*>"erfcx")
+ufunc_erfcx_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfcx_complex
+ufunc_erfcx_ptr[2*2+1] = <void*>(<char*>"erfcx")
+ufunc_erfcx_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfcx_complex
+ufunc_erfcx_ptr[2*3+1] = <void*>(<char*>"erfcx")
+ufunc_erfcx_data[0] = &ufunc_erfcx_ptr[2*0]
+ufunc_erfcx_data[1] = &ufunc_erfcx_ptr[2*1]
+ufunc_erfcx_data[2] = &ufunc_erfcx_ptr[2*2]
+ufunc_erfcx_data[3] = &ufunc_erfcx_ptr[2*3]
+erfcx = np.PyUFunc_FromFuncAndData(ufunc_erfcx_loops, ufunc_erfcx_data, ufunc_erfcx_types, 4, 1, 1, 0, "erfcx", ufunc_erfcx_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_erfi_loops[4]
+cdef void *ufunc_erfi_ptr[8]
+cdef void *ufunc_erfi_data[4]
+cdef char ufunc_erfi_types[8]
+cdef char *ufunc_erfi_doc = (
+    "erfi(z, out=None)\n"
+    "\n"
+    "Imaginary error function, ``-i erf(i z)``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Real or complex valued argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the imaginary error function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "erf, erfc, erfcx, dawsn, wofz\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "\n"
+    ".. versionadded:: 0.12.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Steven G. Johnson, Faddeeva W function implementation.\n"
+    "   http://ab-initio.mit.edu/Faddeeva\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-3, 3)\n"
+    ">>> plt.plot(x, special.erfi(x))\n"
+    ">>> plt.xlabel('$x$')\n"
+    ">>> plt.ylabel('$erfi(x)$')\n"
+    ">>> plt.show()")
+ufunc_erfi_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_erfi_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_erfi_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_erfi_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_erfi_types[0] = <char>NPY_FLOAT
+ufunc_erfi_types[1] = <char>NPY_FLOAT
+ufunc_erfi_types[2] = <char>NPY_DOUBLE
+ufunc_erfi_types[3] = <char>NPY_DOUBLE
+ufunc_erfi_types[4] = <char>NPY_CFLOAT
+ufunc_erfi_types[5] = <char>NPY_CFLOAT
+ufunc_erfi_types[6] = <char>NPY_CDOUBLE
+ufunc_erfi_types[7] = <char>NPY_CDOUBLE
+ufunc_erfi_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfi
+ufunc_erfi_ptr[2*0+1] = <void*>(<char*>"erfi")
+ufunc_erfi_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfi
+ufunc_erfi_ptr[2*1+1] = <void*>(<char*>"erfi")
+ufunc_erfi_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfi_complex
+ufunc_erfi_ptr[2*2+1] = <void*>(<char*>"erfi")
+ufunc_erfi_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_erfi_complex
+ufunc_erfi_ptr[2*3+1] = <void*>(<char*>"erfi")
+ufunc_erfi_data[0] = &ufunc_erfi_ptr[2*0]
+ufunc_erfi_data[1] = &ufunc_erfi_ptr[2*1]
+ufunc_erfi_data[2] = &ufunc_erfi_ptr[2*2]
+ufunc_erfi_data[3] = &ufunc_erfi_ptr[2*3]
+erfi = np.PyUFunc_FromFuncAndData(ufunc_erfi_loops, ufunc_erfi_data, ufunc_erfi_types, 4, 1, 1, 0, "erfi", ufunc_erfi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_erfinv_loops[2]
+cdef void *ufunc_erfinv_ptr[4]
+cdef void *ufunc_erfinv_data[2]
+cdef char ufunc_erfinv_types[4]
+cdef char *ufunc_erfinv_doc = (
+    "erfinv(y, out=None)\n"
+    "\n"
+    "Inverse of the error function.\n"
+    "\n"
+    "Computes the inverse of the error function.\n"
+    "\n"
+    "In the complex domain, there is no unique complex number w satisfying\n"
+    "erf(w)=z. This indicates a true inverse function would be multivalued.\n"
+    "When the domain restricts to the real, -1 < x < 1, there is a unique real\n"
+    "number satisfying erf(erfinv(x)) = x.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "y : ndarray\n"
+    "    Argument at which to evaluate. Domain: [-1, 1]\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "erfinv : scalar or ndarray\n"
+    "    The inverse of erf of y, element-wise\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "erf : Error function of a complex argument\n"
+    "erfc : Complementary error function, ``1 - erf(x)``\n"
+    "erfcinv : Inverse of the complementary error function\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> from scipy.special import erfinv, erf\n"
+    "\n"
+    ">>> erfinv(0.5)\n"
+    "0.4769362762044699\n"
+    "\n"
+    ">>> y = np.linspace(-1.0, 1.0, num=9)\n"
+    ">>> x = erfinv(y)\n"
+    ">>> x\n"
+    "array([       -inf, -0.81341985, -0.47693628, -0.22531206,  0.        ,\n"
+    "        0.22531206,  0.47693628,  0.81341985,         inf])\n"
+    "\n"
+    "Verify that ``erf(erfinv(y))`` is ``y``.\n"
+    "\n"
+    ">>> erf(x)\n"
+    "array([-1.  , -0.75, -0.5 , -0.25,  0.  ,  0.25,  0.5 ,  0.75,  1.  ])\n"
+    "\n"
+    "Plot the function:\n"
+    "\n"
+    ">>> y = np.linspace(-1, 1, 200)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> ax.plot(y, erfinv(y))\n"
+    ">>> ax.grid(True)\n"
+    ">>> ax.set_xlabel('y')\n"
+    ">>> ax.set_title('erfinv(y)')\n"
+    ">>> plt.show()")
+ufunc_erfinv_loops[0] = <np.PyUFuncGenericFunction>loop_f_f__As_f_f
+ufunc_erfinv_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_erfinv_types[0] = <char>NPY_FLOAT
+ufunc_erfinv_types[1] = <char>NPY_FLOAT
+ufunc_erfinv_types[2] = <char>NPY_DOUBLE
+ufunc_erfinv_types[3] = <char>NPY_DOUBLE
+ufunc_erfinv_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_erfinv_float
+ufunc_erfinv_ptr[2*0+1] = <void*>(<char*>"erfinv")
+ufunc_erfinv_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_erfinv_double
+ufunc_erfinv_ptr[2*1+1] = <void*>(<char*>"erfinv")
+ufunc_erfinv_data[0] = &ufunc_erfinv_ptr[2*0]
+ufunc_erfinv_data[1] = &ufunc_erfinv_ptr[2*1]
+erfinv = np.PyUFunc_FromFuncAndData(ufunc_erfinv_loops, ufunc_erfinv_data, ufunc_erfinv_types, 2, 1, 1, 0, "erfinv", ufunc_erfinv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_chebyc_loops[5]
+cdef void *ufunc_eval_chebyc_ptr[10]
+cdef void *ufunc_eval_chebyc_data[5]
+cdef char ufunc_eval_chebyc_types[15]
+cdef char *ufunc_eval_chebyc_doc = (
+    "eval_chebyc(n, x, out=None)\n"
+    "\n"
+    "Evaluate Chebyshev polynomial of the first kind on [-2, 2] at a\n"
+    "point.\n"
+    "\n"
+    "These polynomials are defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    C_n(x) = 2 T_n(x/2)\n"
+    "\n"
+    "where :math:`T_n` is a Chebyshev polynomial of the first kind. See\n"
+    "22.5.11 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to `eval_chebyt`.\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the Chebyshev polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "C : scalar or ndarray\n"
+    "    Values of the Chebyshev polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_chebyc : roots and quadrature weights of Chebyshev\n"
+    "               polynomials of the first kind on [-2, 2]\n"
+    "chebyc : Chebyshev polynomial object\n"
+    "numpy.polynomial.chebyshev.Chebyshev : Chebyshev series\n"
+    "eval_chebyt : evaluate Chebycshev polynomials of the first kind\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "They are a scaled version of the Chebyshev polynomials of the\n"
+    "first kind.\n"
+    "\n"
+    ">>> x = np.linspace(-2, 2, 6)\n"
+    ">>> sc.eval_chebyc(3, x)\n"
+    "array([-2.   ,  1.872,  1.136, -1.136, -1.872,  2.   ])\n"
+    ">>> 2 * sc.eval_chebyt(3, x / 2)\n"
+    "array([-2.   ,  1.872,  1.136, -1.136, -1.872,  2.   ])")
+ufunc_eval_chebyc_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_chebyc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_eval_chebyc_loops[2] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_eval_chebyc_loops[3] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_eval_chebyc_loops[4] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_eval_chebyc_types[0] = <char>NPY_LONG
+ufunc_eval_chebyc_types[1] = <char>NPY_DOUBLE
+ufunc_eval_chebyc_types[2] = <char>NPY_DOUBLE
+ufunc_eval_chebyc_types[3] = <char>NPY_FLOAT
+ufunc_eval_chebyc_types[4] = <char>NPY_FLOAT
+ufunc_eval_chebyc_types[5] = <char>NPY_FLOAT
+ufunc_eval_chebyc_types[6] = <char>NPY_FLOAT
+ufunc_eval_chebyc_types[7] = <char>NPY_CFLOAT
+ufunc_eval_chebyc_types[8] = <char>NPY_CFLOAT
+ufunc_eval_chebyc_types[9] = <char>NPY_DOUBLE
+ufunc_eval_chebyc_types[10] = <char>NPY_DOUBLE
+ufunc_eval_chebyc_types[11] = <char>NPY_DOUBLE
+ufunc_eval_chebyc_types[12] = <char>NPY_DOUBLE
+ufunc_eval_chebyc_types[13] = <char>NPY_CDOUBLE
+ufunc_eval_chebyc_types[14] = <char>NPY_CDOUBLE
+ufunc_eval_chebyc_ptr[2*0] = <void*>_func_eval_chebyc_l
+ufunc_eval_chebyc_ptr[2*0+1] = <void*>(<char*>"eval_chebyc")
+ufunc_eval_chebyc_ptr[2*1] = <void*>_func_eval_chebyc[double]
+ufunc_eval_chebyc_ptr[2*1+1] = <void*>(<char*>"eval_chebyc")
+ufunc_eval_chebyc_ptr[2*2] = <void*>_func_eval_chebyc[double_complex]
+ufunc_eval_chebyc_ptr[2*2+1] = <void*>(<char*>"eval_chebyc")
+ufunc_eval_chebyc_ptr[2*3] = <void*>_func_eval_chebyc[double]
+ufunc_eval_chebyc_ptr[2*3+1] = <void*>(<char*>"eval_chebyc")
+ufunc_eval_chebyc_ptr[2*4] = <void*>_func_eval_chebyc[double_complex]
+ufunc_eval_chebyc_ptr[2*4+1] = <void*>(<char*>"eval_chebyc")
+ufunc_eval_chebyc_data[0] = &ufunc_eval_chebyc_ptr[2*0]
+ufunc_eval_chebyc_data[1] = &ufunc_eval_chebyc_ptr[2*1]
+ufunc_eval_chebyc_data[2] = &ufunc_eval_chebyc_ptr[2*2]
+ufunc_eval_chebyc_data[3] = &ufunc_eval_chebyc_ptr[2*3]
+ufunc_eval_chebyc_data[4] = &ufunc_eval_chebyc_ptr[2*4]
+eval_chebyc = np.PyUFunc_FromFuncAndData(ufunc_eval_chebyc_loops, ufunc_eval_chebyc_data, ufunc_eval_chebyc_types, 5, 2, 1, 0, "eval_chebyc", ufunc_eval_chebyc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_chebys_loops[5]
+cdef void *ufunc_eval_chebys_ptr[10]
+cdef void *ufunc_eval_chebys_data[5]
+cdef char ufunc_eval_chebys_types[15]
+cdef char *ufunc_eval_chebys_doc = (
+    "eval_chebys(n, x, out=None)\n"
+    "\n"
+    "Evaluate Chebyshev polynomial of the second kind on [-2, 2] at a\n"
+    "point.\n"
+    "\n"
+    "These polynomials are defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    S_n(x) = U_n(x/2)\n"
+    "\n"
+    "where :math:`U_n` is a Chebyshev polynomial of the second\n"
+    "kind. See 22.5.13 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to `eval_chebyu`.\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the Chebyshev polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "S : scalar or ndarray\n"
+    "    Values of the Chebyshev polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_chebys : roots and quadrature weights of Chebyshev\n"
+    "               polynomials of the second kind on [-2, 2]\n"
+    "chebys : Chebyshev polynomial object\n"
+    "eval_chebyu : evaluate Chebyshev polynomials of the second kind\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "They are a scaled version of the Chebyshev polynomials of the\n"
+    "second kind.\n"
+    "\n"
+    ">>> x = np.linspace(-2, 2, 6)\n"
+    ">>> sc.eval_chebys(3, x)\n"
+    "array([-4.   ,  0.672,  0.736, -0.736, -0.672,  4.   ])\n"
+    ">>> sc.eval_chebyu(3, x / 2)\n"
+    "array([-4.   ,  0.672,  0.736, -0.736, -0.672,  4.   ])")
+ufunc_eval_chebys_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_chebys_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_eval_chebys_loops[2] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_eval_chebys_loops[3] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_eval_chebys_loops[4] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_eval_chebys_types[0] = <char>NPY_LONG
+ufunc_eval_chebys_types[1] = <char>NPY_DOUBLE
+ufunc_eval_chebys_types[2] = <char>NPY_DOUBLE
+ufunc_eval_chebys_types[3] = <char>NPY_FLOAT
+ufunc_eval_chebys_types[4] = <char>NPY_FLOAT
+ufunc_eval_chebys_types[5] = <char>NPY_FLOAT
+ufunc_eval_chebys_types[6] = <char>NPY_FLOAT
+ufunc_eval_chebys_types[7] = <char>NPY_CFLOAT
+ufunc_eval_chebys_types[8] = <char>NPY_CFLOAT
+ufunc_eval_chebys_types[9] = <char>NPY_DOUBLE
+ufunc_eval_chebys_types[10] = <char>NPY_DOUBLE
+ufunc_eval_chebys_types[11] = <char>NPY_DOUBLE
+ufunc_eval_chebys_types[12] = <char>NPY_DOUBLE
+ufunc_eval_chebys_types[13] = <char>NPY_CDOUBLE
+ufunc_eval_chebys_types[14] = <char>NPY_CDOUBLE
+ufunc_eval_chebys_ptr[2*0] = <void*>_func_eval_chebys_l
+ufunc_eval_chebys_ptr[2*0+1] = <void*>(<char*>"eval_chebys")
+ufunc_eval_chebys_ptr[2*1] = <void*>_func_eval_chebys[double]
+ufunc_eval_chebys_ptr[2*1+1] = <void*>(<char*>"eval_chebys")
+ufunc_eval_chebys_ptr[2*2] = <void*>_func_eval_chebys[double_complex]
+ufunc_eval_chebys_ptr[2*2+1] = <void*>(<char*>"eval_chebys")
+ufunc_eval_chebys_ptr[2*3] = <void*>_func_eval_chebys[double]
+ufunc_eval_chebys_ptr[2*3+1] = <void*>(<char*>"eval_chebys")
+ufunc_eval_chebys_ptr[2*4] = <void*>_func_eval_chebys[double_complex]
+ufunc_eval_chebys_ptr[2*4+1] = <void*>(<char*>"eval_chebys")
+ufunc_eval_chebys_data[0] = &ufunc_eval_chebys_ptr[2*0]
+ufunc_eval_chebys_data[1] = &ufunc_eval_chebys_ptr[2*1]
+ufunc_eval_chebys_data[2] = &ufunc_eval_chebys_ptr[2*2]
+ufunc_eval_chebys_data[3] = &ufunc_eval_chebys_ptr[2*3]
+ufunc_eval_chebys_data[4] = &ufunc_eval_chebys_ptr[2*4]
+eval_chebys = np.PyUFunc_FromFuncAndData(ufunc_eval_chebys_loops, ufunc_eval_chebys_data, ufunc_eval_chebys_types, 5, 2, 1, 0, "eval_chebys", ufunc_eval_chebys_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_chebyt_loops[5]
+cdef void *ufunc_eval_chebyt_ptr[10]
+cdef void *ufunc_eval_chebyt_data[5]
+cdef char ufunc_eval_chebyt_types[15]
+cdef char *ufunc_eval_chebyt_doc = (
+    "eval_chebyt(n, x, out=None)\n"
+    "\n"
+    "Evaluate Chebyshev polynomial of the first kind at a point.\n"
+    "\n"
+    "The Chebyshev polynomials of the first kind can be defined via the\n"
+    "Gauss hypergeometric function :math:`{}_2F_1` as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    T_n(x) = {}_2F_1(n, -n; 1/2; (1 - x)/2).\n"
+    "\n"
+    "When :math:`n` is an integer the result is a polynomial of degree\n"
+    ":math:`n`. See 22.5.47 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to the Gauss hypergeometric\n"
+    "    function.\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the Chebyshev polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "T : scalar or ndarray\n"
+    "    Values of the Chebyshev polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_chebyt : roots and quadrature weights of Chebyshev\n"
+    "               polynomials of the first kind\n"
+    "chebyu : Chebychev polynomial object\n"
+    "eval_chebyu : evaluate Chebyshev polynomials of the second kind\n"
+    "hyp2f1 : Gauss hypergeometric function\n"
+    "numpy.polynomial.chebyshev.Chebyshev : Chebyshev series\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "This routine is numerically stable for `x` in ``[-1, 1]`` at least\n"
+    "up to order ``10000``.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_chebyt_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_chebyt_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_eval_chebyt_loops[2] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_eval_chebyt_loops[3] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_eval_chebyt_loops[4] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_eval_chebyt_types[0] = <char>NPY_LONG
+ufunc_eval_chebyt_types[1] = <char>NPY_DOUBLE
+ufunc_eval_chebyt_types[2] = <char>NPY_DOUBLE
+ufunc_eval_chebyt_types[3] = <char>NPY_FLOAT
+ufunc_eval_chebyt_types[4] = <char>NPY_FLOAT
+ufunc_eval_chebyt_types[5] = <char>NPY_FLOAT
+ufunc_eval_chebyt_types[6] = <char>NPY_FLOAT
+ufunc_eval_chebyt_types[7] = <char>NPY_CFLOAT
+ufunc_eval_chebyt_types[8] = <char>NPY_CFLOAT
+ufunc_eval_chebyt_types[9] = <char>NPY_DOUBLE
+ufunc_eval_chebyt_types[10] = <char>NPY_DOUBLE
+ufunc_eval_chebyt_types[11] = <char>NPY_DOUBLE
+ufunc_eval_chebyt_types[12] = <char>NPY_DOUBLE
+ufunc_eval_chebyt_types[13] = <char>NPY_CDOUBLE
+ufunc_eval_chebyt_types[14] = <char>NPY_CDOUBLE
+ufunc_eval_chebyt_ptr[2*0] = <void*>_func_eval_chebyt_l
+ufunc_eval_chebyt_ptr[2*0+1] = <void*>(<char*>"eval_chebyt")
+ufunc_eval_chebyt_ptr[2*1] = <void*>_func_eval_chebyt[double]
+ufunc_eval_chebyt_ptr[2*1+1] = <void*>(<char*>"eval_chebyt")
+ufunc_eval_chebyt_ptr[2*2] = <void*>_func_eval_chebyt[double_complex]
+ufunc_eval_chebyt_ptr[2*2+1] = <void*>(<char*>"eval_chebyt")
+ufunc_eval_chebyt_ptr[2*3] = <void*>_func_eval_chebyt[double]
+ufunc_eval_chebyt_ptr[2*3+1] = <void*>(<char*>"eval_chebyt")
+ufunc_eval_chebyt_ptr[2*4] = <void*>_func_eval_chebyt[double_complex]
+ufunc_eval_chebyt_ptr[2*4+1] = <void*>(<char*>"eval_chebyt")
+ufunc_eval_chebyt_data[0] = &ufunc_eval_chebyt_ptr[2*0]
+ufunc_eval_chebyt_data[1] = &ufunc_eval_chebyt_ptr[2*1]
+ufunc_eval_chebyt_data[2] = &ufunc_eval_chebyt_ptr[2*2]
+ufunc_eval_chebyt_data[3] = &ufunc_eval_chebyt_ptr[2*3]
+ufunc_eval_chebyt_data[4] = &ufunc_eval_chebyt_ptr[2*4]
+eval_chebyt = np.PyUFunc_FromFuncAndData(ufunc_eval_chebyt_loops, ufunc_eval_chebyt_data, ufunc_eval_chebyt_types, 5, 2, 1, 0, "eval_chebyt", ufunc_eval_chebyt_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_chebyu_loops[5]
+cdef void *ufunc_eval_chebyu_ptr[10]
+cdef void *ufunc_eval_chebyu_data[5]
+cdef char ufunc_eval_chebyu_types[15]
+cdef char *ufunc_eval_chebyu_doc = (
+    "eval_chebyu(n, x, out=None)\n"
+    "\n"
+    "Evaluate Chebyshev polynomial of the second kind at a point.\n"
+    "\n"
+    "The Chebyshev polynomials of the second kind can be defined via\n"
+    "the Gauss hypergeometric function :math:`{}_2F_1` as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    U_n(x) = (n + 1) {}_2F_1(-n, n + 2; 3/2; (1 - x)/2).\n"
+    "\n"
+    "When :math:`n` is an integer the result is a polynomial of degree\n"
+    ":math:`n`. See 22.5.48 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to the Gauss hypergeometric\n"
+    "    function.\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the Chebyshev polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "U : scalar or ndarray\n"
+    "    Values of the Chebyshev polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_chebyu : roots and quadrature weights of Chebyshev\n"
+    "               polynomials of the second kind\n"
+    "chebyu : Chebyshev polynomial object\n"
+    "eval_chebyt : evaluate Chebyshev polynomials of the first kind\n"
+    "hyp2f1 : Gauss hypergeometric function\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_chebyu_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_chebyu_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_eval_chebyu_loops[2] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_eval_chebyu_loops[3] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_eval_chebyu_loops[4] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_eval_chebyu_types[0] = <char>NPY_LONG
+ufunc_eval_chebyu_types[1] = <char>NPY_DOUBLE
+ufunc_eval_chebyu_types[2] = <char>NPY_DOUBLE
+ufunc_eval_chebyu_types[3] = <char>NPY_FLOAT
+ufunc_eval_chebyu_types[4] = <char>NPY_FLOAT
+ufunc_eval_chebyu_types[5] = <char>NPY_FLOAT
+ufunc_eval_chebyu_types[6] = <char>NPY_FLOAT
+ufunc_eval_chebyu_types[7] = <char>NPY_CFLOAT
+ufunc_eval_chebyu_types[8] = <char>NPY_CFLOAT
+ufunc_eval_chebyu_types[9] = <char>NPY_DOUBLE
+ufunc_eval_chebyu_types[10] = <char>NPY_DOUBLE
+ufunc_eval_chebyu_types[11] = <char>NPY_DOUBLE
+ufunc_eval_chebyu_types[12] = <char>NPY_DOUBLE
+ufunc_eval_chebyu_types[13] = <char>NPY_CDOUBLE
+ufunc_eval_chebyu_types[14] = <char>NPY_CDOUBLE
+ufunc_eval_chebyu_ptr[2*0] = <void*>_func_eval_chebyu_l
+ufunc_eval_chebyu_ptr[2*0+1] = <void*>(<char*>"eval_chebyu")
+ufunc_eval_chebyu_ptr[2*1] = <void*>_func_eval_chebyu[double]
+ufunc_eval_chebyu_ptr[2*1+1] = <void*>(<char*>"eval_chebyu")
+ufunc_eval_chebyu_ptr[2*2] = <void*>_func_eval_chebyu[double_complex]
+ufunc_eval_chebyu_ptr[2*2+1] = <void*>(<char*>"eval_chebyu")
+ufunc_eval_chebyu_ptr[2*3] = <void*>_func_eval_chebyu[double]
+ufunc_eval_chebyu_ptr[2*3+1] = <void*>(<char*>"eval_chebyu")
+ufunc_eval_chebyu_ptr[2*4] = <void*>_func_eval_chebyu[double_complex]
+ufunc_eval_chebyu_ptr[2*4+1] = <void*>(<char*>"eval_chebyu")
+ufunc_eval_chebyu_data[0] = &ufunc_eval_chebyu_ptr[2*0]
+ufunc_eval_chebyu_data[1] = &ufunc_eval_chebyu_ptr[2*1]
+ufunc_eval_chebyu_data[2] = &ufunc_eval_chebyu_ptr[2*2]
+ufunc_eval_chebyu_data[3] = &ufunc_eval_chebyu_ptr[2*3]
+ufunc_eval_chebyu_data[4] = &ufunc_eval_chebyu_ptr[2*4]
+eval_chebyu = np.PyUFunc_FromFuncAndData(ufunc_eval_chebyu_loops, ufunc_eval_chebyu_data, ufunc_eval_chebyu_types, 5, 2, 1, 0, "eval_chebyu", ufunc_eval_chebyu_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_gegenbauer_loops[5]
+cdef void *ufunc_eval_gegenbauer_ptr[10]
+cdef void *ufunc_eval_gegenbauer_data[5]
+cdef char ufunc_eval_gegenbauer_types[20]
+cdef char *ufunc_eval_gegenbauer_doc = (
+    "eval_gegenbauer(n, alpha, x, out=None)\n"
+    "\n"
+    "Evaluate Gegenbauer polynomial at a point.\n"
+    "\n"
+    "The Gegenbauer polynomials can be defined via the Gauss\n"
+    "hypergeometric function :math:`{}_2F_1` as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    C_n^{(\\alpha)} = \\frac{(2\\alpha)_n}{\\Gamma(n + 1)}\n"
+    "      {}_2F_1(-n, 2\\alpha + n; \\alpha + 1/2; (1 - z)/2).\n"
+    "\n"
+    "When :math:`n` is an integer the result is a polynomial of degree\n"
+    ":math:`n`. See 22.5.46 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to the Gauss hypergeometric\n"
+    "    function.\n"
+    "alpha : array_like\n"
+    "    Parameter\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the Gegenbauer polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "C : scalar or ndarray\n"
+    "    Values of the Gegenbauer polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_gegenbauer : roots and quadrature weights of Gegenbauer\n"
+    "                   polynomials\n"
+    "gegenbauer : Gegenbauer polynomial object\n"
+    "hyp2f1 : Gauss hypergeometric function\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_gegenbauer_loops[0] = <np.PyUFuncGenericFunction>loop_d_ldd__As_ldd_d
+ufunc_eval_gegenbauer_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_eval_gegenbauer_loops[2] = <np.PyUFuncGenericFunction>loop_D_ddD__As_ffF_F
+ufunc_eval_gegenbauer_loops[3] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_eval_gegenbauer_loops[4] = <np.PyUFuncGenericFunction>loop_D_ddD__As_ddD_D
+ufunc_eval_gegenbauer_types[0] = <char>NPY_LONG
+ufunc_eval_gegenbauer_types[1] = <char>NPY_DOUBLE
+ufunc_eval_gegenbauer_types[2] = <char>NPY_DOUBLE
+ufunc_eval_gegenbauer_types[3] = <char>NPY_DOUBLE
+ufunc_eval_gegenbauer_types[4] = <char>NPY_FLOAT
+ufunc_eval_gegenbauer_types[5] = <char>NPY_FLOAT
+ufunc_eval_gegenbauer_types[6] = <char>NPY_FLOAT
+ufunc_eval_gegenbauer_types[7] = <char>NPY_FLOAT
+ufunc_eval_gegenbauer_types[8] = <char>NPY_FLOAT
+ufunc_eval_gegenbauer_types[9] = <char>NPY_FLOAT
+ufunc_eval_gegenbauer_types[10] = <char>NPY_CFLOAT
+ufunc_eval_gegenbauer_types[11] = <char>NPY_CFLOAT
+ufunc_eval_gegenbauer_types[12] = <char>NPY_DOUBLE
+ufunc_eval_gegenbauer_types[13] = <char>NPY_DOUBLE
+ufunc_eval_gegenbauer_types[14] = <char>NPY_DOUBLE
+ufunc_eval_gegenbauer_types[15] = <char>NPY_DOUBLE
+ufunc_eval_gegenbauer_types[16] = <char>NPY_DOUBLE
+ufunc_eval_gegenbauer_types[17] = <char>NPY_DOUBLE
+ufunc_eval_gegenbauer_types[18] = <char>NPY_CDOUBLE
+ufunc_eval_gegenbauer_types[19] = <char>NPY_CDOUBLE
+ufunc_eval_gegenbauer_ptr[2*0] = <void*>_func_eval_gegenbauer_l
+ufunc_eval_gegenbauer_ptr[2*0+1] = <void*>(<char*>"eval_gegenbauer")
+ufunc_eval_gegenbauer_ptr[2*1] = <void*>_func_eval_gegenbauer[double]
+ufunc_eval_gegenbauer_ptr[2*1+1] = <void*>(<char*>"eval_gegenbauer")
+ufunc_eval_gegenbauer_ptr[2*2] = <void*>_func_eval_gegenbauer[double_complex]
+ufunc_eval_gegenbauer_ptr[2*2+1] = <void*>(<char*>"eval_gegenbauer")
+ufunc_eval_gegenbauer_ptr[2*3] = <void*>_func_eval_gegenbauer[double]
+ufunc_eval_gegenbauer_ptr[2*3+1] = <void*>(<char*>"eval_gegenbauer")
+ufunc_eval_gegenbauer_ptr[2*4] = <void*>_func_eval_gegenbauer[double_complex]
+ufunc_eval_gegenbauer_ptr[2*4+1] = <void*>(<char*>"eval_gegenbauer")
+ufunc_eval_gegenbauer_data[0] = &ufunc_eval_gegenbauer_ptr[2*0]
+ufunc_eval_gegenbauer_data[1] = &ufunc_eval_gegenbauer_ptr[2*1]
+ufunc_eval_gegenbauer_data[2] = &ufunc_eval_gegenbauer_ptr[2*2]
+ufunc_eval_gegenbauer_data[3] = &ufunc_eval_gegenbauer_ptr[2*3]
+ufunc_eval_gegenbauer_data[4] = &ufunc_eval_gegenbauer_ptr[2*4]
+eval_gegenbauer = np.PyUFunc_FromFuncAndData(ufunc_eval_gegenbauer_loops, ufunc_eval_gegenbauer_data, ufunc_eval_gegenbauer_types, 5, 3, 1, 0, "eval_gegenbauer", ufunc_eval_gegenbauer_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_genlaguerre_loops[5]
+cdef void *ufunc_eval_genlaguerre_ptr[10]
+cdef void *ufunc_eval_genlaguerre_data[5]
+cdef char ufunc_eval_genlaguerre_types[20]
+cdef char *ufunc_eval_genlaguerre_doc = (
+    "eval_genlaguerre(n, alpha, x, out=None)\n"
+    "\n"
+    "Evaluate generalized Laguerre polynomial at a point.\n"
+    "\n"
+    "The generalized Laguerre polynomials can be defined via the\n"
+    "confluent hypergeometric function :math:`{}_1F_1` as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    L_n^{(\\alpha)}(x) = \\binom{n + \\alpha}{n}\n"
+    "      {}_1F_1(-n, \\alpha + 1, x).\n"
+    "\n"
+    "When :math:`n` is an integer the result is a polynomial of degree\n"
+    ":math:`n`. See 22.5.54 in [AS]_ for details. The Laguerre\n"
+    "polynomials are the special case where :math:`\\alpha = 0`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to the confluent hypergeometric\n"
+    "    function.\n"
+    "alpha : array_like\n"
+    "    Parameter; must have ``alpha > -1``\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the generalized Laguerre\n"
+    "    polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "L : scalar or ndarray\n"
+    "    Values of the generalized Laguerre polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_genlaguerre : roots and quadrature weights of generalized\n"
+    "                    Laguerre polynomials\n"
+    "genlaguerre : generalized Laguerre polynomial object\n"
+    "hyp1f1 : confluent hypergeometric function\n"
+    "eval_laguerre : evaluate Laguerre polynomials\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_genlaguerre_loops[0] = <np.PyUFuncGenericFunction>loop_d_ldd__As_ldd_d
+ufunc_eval_genlaguerre_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_eval_genlaguerre_loops[2] = <np.PyUFuncGenericFunction>loop_D_ddD__As_ffF_F
+ufunc_eval_genlaguerre_loops[3] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_eval_genlaguerre_loops[4] = <np.PyUFuncGenericFunction>loop_D_ddD__As_ddD_D
+ufunc_eval_genlaguerre_types[0] = <char>NPY_LONG
+ufunc_eval_genlaguerre_types[1] = <char>NPY_DOUBLE
+ufunc_eval_genlaguerre_types[2] = <char>NPY_DOUBLE
+ufunc_eval_genlaguerre_types[3] = <char>NPY_DOUBLE
+ufunc_eval_genlaguerre_types[4] = <char>NPY_FLOAT
+ufunc_eval_genlaguerre_types[5] = <char>NPY_FLOAT
+ufunc_eval_genlaguerre_types[6] = <char>NPY_FLOAT
+ufunc_eval_genlaguerre_types[7] = <char>NPY_FLOAT
+ufunc_eval_genlaguerre_types[8] = <char>NPY_FLOAT
+ufunc_eval_genlaguerre_types[9] = <char>NPY_FLOAT
+ufunc_eval_genlaguerre_types[10] = <char>NPY_CFLOAT
+ufunc_eval_genlaguerre_types[11] = <char>NPY_CFLOAT
+ufunc_eval_genlaguerre_types[12] = <char>NPY_DOUBLE
+ufunc_eval_genlaguerre_types[13] = <char>NPY_DOUBLE
+ufunc_eval_genlaguerre_types[14] = <char>NPY_DOUBLE
+ufunc_eval_genlaguerre_types[15] = <char>NPY_DOUBLE
+ufunc_eval_genlaguerre_types[16] = <char>NPY_DOUBLE
+ufunc_eval_genlaguerre_types[17] = <char>NPY_DOUBLE
+ufunc_eval_genlaguerre_types[18] = <char>NPY_CDOUBLE
+ufunc_eval_genlaguerre_types[19] = <char>NPY_CDOUBLE
+ufunc_eval_genlaguerre_ptr[2*0] = <void*>_func_eval_genlaguerre_l
+ufunc_eval_genlaguerre_ptr[2*0+1] = <void*>(<char*>"eval_genlaguerre")
+ufunc_eval_genlaguerre_ptr[2*1] = <void*>_func_eval_genlaguerre[double]
+ufunc_eval_genlaguerre_ptr[2*1+1] = <void*>(<char*>"eval_genlaguerre")
+ufunc_eval_genlaguerre_ptr[2*2] = <void*>_func_eval_genlaguerre[double_complex]
+ufunc_eval_genlaguerre_ptr[2*2+1] = <void*>(<char*>"eval_genlaguerre")
+ufunc_eval_genlaguerre_ptr[2*3] = <void*>_func_eval_genlaguerre[double]
+ufunc_eval_genlaguerre_ptr[2*3+1] = <void*>(<char*>"eval_genlaguerre")
+ufunc_eval_genlaguerre_ptr[2*4] = <void*>_func_eval_genlaguerre[double_complex]
+ufunc_eval_genlaguerre_ptr[2*4+1] = <void*>(<char*>"eval_genlaguerre")
+ufunc_eval_genlaguerre_data[0] = &ufunc_eval_genlaguerre_ptr[2*0]
+ufunc_eval_genlaguerre_data[1] = &ufunc_eval_genlaguerre_ptr[2*1]
+ufunc_eval_genlaguerre_data[2] = &ufunc_eval_genlaguerre_ptr[2*2]
+ufunc_eval_genlaguerre_data[3] = &ufunc_eval_genlaguerre_ptr[2*3]
+ufunc_eval_genlaguerre_data[4] = &ufunc_eval_genlaguerre_ptr[2*4]
+eval_genlaguerre = np.PyUFunc_FromFuncAndData(ufunc_eval_genlaguerre_loops, ufunc_eval_genlaguerre_data, ufunc_eval_genlaguerre_types, 5, 3, 1, 0, "eval_genlaguerre", ufunc_eval_genlaguerre_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_hermite_loops[1]
+cdef void *ufunc_eval_hermite_ptr[2]
+cdef void *ufunc_eval_hermite_data[1]
+cdef char ufunc_eval_hermite_types[3]
+cdef char *ufunc_eval_hermite_doc = (
+    "eval_hermite(n, x, out=None)\n"
+    "\n"
+    "Evaluate physicist's Hermite polynomial at a point.\n"
+    "\n"
+    "Defined by\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    H_n(x) = (-1)^n e^{x^2} \\frac{d^n}{dx^n} e^{-x^2};\n"
+    "\n"
+    ":math:`H_n` is a polynomial of degree :math:`n`. See 22.11.7 in\n"
+    "[AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the Hermite polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "H : scalar or ndarray\n"
+    "    Values of the Hermite polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_hermite : roots and quadrature weights of physicist's\n"
+    "                Hermite polynomials\n"
+    "hermite : physicist's Hermite polynomial object\n"
+    "numpy.polynomial.hermite.Hermite : Physicist's Hermite series\n"
+    "eval_hermitenorm : evaluate Probabilist's Hermite polynomials\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_hermite_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_hermite_types[0] = <char>NPY_LONG
+ufunc_eval_hermite_types[1] = <char>NPY_DOUBLE
+ufunc_eval_hermite_types[2] = <char>NPY_DOUBLE
+ufunc_eval_hermite_ptr[2*0] = <void*>_func_eval_hermite
+ufunc_eval_hermite_ptr[2*0+1] = <void*>(<char*>"eval_hermite")
+ufunc_eval_hermite_data[0] = &ufunc_eval_hermite_ptr[2*0]
+eval_hermite = np.PyUFunc_FromFuncAndData(ufunc_eval_hermite_loops, ufunc_eval_hermite_data, ufunc_eval_hermite_types, 1, 2, 1, 0, "eval_hermite", ufunc_eval_hermite_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_hermitenorm_loops[1]
+cdef void *ufunc_eval_hermitenorm_ptr[2]
+cdef void *ufunc_eval_hermitenorm_data[1]
+cdef char ufunc_eval_hermitenorm_types[3]
+cdef char *ufunc_eval_hermitenorm_doc = (
+    "eval_hermitenorm(n, x, out=None)\n"
+    "\n"
+    "Evaluate probabilist's (normalized) Hermite polynomial at a\n"
+    "point.\n"
+    "\n"
+    "Defined by\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    He_n(x) = (-1)^n e^{x^2/2} \\frac{d^n}{dx^n} e^{-x^2/2};\n"
+    "\n"
+    ":math:`He_n` is a polynomial of degree :math:`n`. See 22.11.8 in\n"
+    "[AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the Hermite polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "He : scalar or ndarray\n"
+    "    Values of the Hermite polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_hermitenorm : roots and quadrature weights of probabilist's\n"
+    "                    Hermite polynomials\n"
+    "hermitenorm : probabilist's Hermite polynomial object\n"
+    "numpy.polynomial.hermite_e.HermiteE : Probabilist's Hermite series\n"
+    "eval_hermite : evaluate physicist's Hermite polynomials\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_hermitenorm_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_hermitenorm_types[0] = <char>NPY_LONG
+ufunc_eval_hermitenorm_types[1] = <char>NPY_DOUBLE
+ufunc_eval_hermitenorm_types[2] = <char>NPY_DOUBLE
+ufunc_eval_hermitenorm_ptr[2*0] = <void*>_func_eval_hermitenorm
+ufunc_eval_hermitenorm_ptr[2*0+1] = <void*>(<char*>"eval_hermitenorm")
+ufunc_eval_hermitenorm_data[0] = &ufunc_eval_hermitenorm_ptr[2*0]
+eval_hermitenorm = np.PyUFunc_FromFuncAndData(ufunc_eval_hermitenorm_loops, ufunc_eval_hermitenorm_data, ufunc_eval_hermitenorm_types, 1, 2, 1, 0, "eval_hermitenorm", ufunc_eval_hermitenorm_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_jacobi_loops[5]
+cdef void *ufunc_eval_jacobi_ptr[10]
+cdef void *ufunc_eval_jacobi_data[5]
+cdef char ufunc_eval_jacobi_types[25]
+cdef char *ufunc_eval_jacobi_doc = (
+    "eval_jacobi(n, alpha, beta, x, out=None)\n"
+    "\n"
+    "Evaluate Jacobi polynomial at a point.\n"
+    "\n"
+    "The Jacobi polynomials can be defined via the Gauss hypergeometric\n"
+    "function :math:`{}_2F_1` as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    P_n^{(\\alpha, \\beta)}(x) = \\frac{(\\alpha + 1)_n}{\\Gamma(n + 1)}\n"
+    "      {}_2F_1(-n, 1 + \\alpha + \\beta + n; \\alpha + 1; (1 - z)/2)\n"
+    "\n"
+    "where :math:`(\\cdot)_n` is the Pochhammer symbol; see `poch`. When\n"
+    ":math:`n` is an integer the result is a polynomial of degree\n"
+    ":math:`n`. See 22.5.42 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer the result is\n"
+    "    determined via the relation to the Gauss hypergeometric\n"
+    "    function.\n"
+    "alpha : array_like\n"
+    "    Parameter\n"
+    "beta : array_like\n"
+    "    Parameter\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "P : scalar or ndarray\n"
+    "    Values of the Jacobi polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_jacobi : roots and quadrature weights of Jacobi polynomials\n"
+    "jacobi : Jacobi polynomial object\n"
+    "hyp2f1 : Gauss hypergeometric function\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_jacobi_loops[0] = <np.PyUFuncGenericFunction>loop_d_lddd__As_lddd_d
+ufunc_eval_jacobi_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd__As_ffff_f
+ufunc_eval_jacobi_loops[2] = <np.PyUFuncGenericFunction>loop_D_dddD__As_fffF_F
+ufunc_eval_jacobi_loops[3] = <np.PyUFuncGenericFunction>loop_d_dddd__As_dddd_d
+ufunc_eval_jacobi_loops[4] = <np.PyUFuncGenericFunction>loop_D_dddD__As_dddD_D
+ufunc_eval_jacobi_types[0] = <char>NPY_LONG
+ufunc_eval_jacobi_types[1] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[2] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[3] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[4] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[5] = <char>NPY_FLOAT
+ufunc_eval_jacobi_types[6] = <char>NPY_FLOAT
+ufunc_eval_jacobi_types[7] = <char>NPY_FLOAT
+ufunc_eval_jacobi_types[8] = <char>NPY_FLOAT
+ufunc_eval_jacobi_types[9] = <char>NPY_FLOAT
+ufunc_eval_jacobi_types[10] = <char>NPY_FLOAT
+ufunc_eval_jacobi_types[11] = <char>NPY_FLOAT
+ufunc_eval_jacobi_types[12] = <char>NPY_FLOAT
+ufunc_eval_jacobi_types[13] = <char>NPY_CFLOAT
+ufunc_eval_jacobi_types[14] = <char>NPY_CFLOAT
+ufunc_eval_jacobi_types[15] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[16] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[17] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[18] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[19] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[20] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[21] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[22] = <char>NPY_DOUBLE
+ufunc_eval_jacobi_types[23] = <char>NPY_CDOUBLE
+ufunc_eval_jacobi_types[24] = <char>NPY_CDOUBLE
+ufunc_eval_jacobi_ptr[2*0] = <void*>_func_eval_jacobi_l
+ufunc_eval_jacobi_ptr[2*0+1] = <void*>(<char*>"eval_jacobi")
+ufunc_eval_jacobi_ptr[2*1] = <void*>_func_eval_jacobi[double]
+ufunc_eval_jacobi_ptr[2*1+1] = <void*>(<char*>"eval_jacobi")
+ufunc_eval_jacobi_ptr[2*2] = <void*>_func_eval_jacobi[double_complex]
+ufunc_eval_jacobi_ptr[2*2+1] = <void*>(<char*>"eval_jacobi")
+ufunc_eval_jacobi_ptr[2*3] = <void*>_func_eval_jacobi[double]
+ufunc_eval_jacobi_ptr[2*3+1] = <void*>(<char*>"eval_jacobi")
+ufunc_eval_jacobi_ptr[2*4] = <void*>_func_eval_jacobi[double_complex]
+ufunc_eval_jacobi_ptr[2*4+1] = <void*>(<char*>"eval_jacobi")
+ufunc_eval_jacobi_data[0] = &ufunc_eval_jacobi_ptr[2*0]
+ufunc_eval_jacobi_data[1] = &ufunc_eval_jacobi_ptr[2*1]
+ufunc_eval_jacobi_data[2] = &ufunc_eval_jacobi_ptr[2*2]
+ufunc_eval_jacobi_data[3] = &ufunc_eval_jacobi_ptr[2*3]
+ufunc_eval_jacobi_data[4] = &ufunc_eval_jacobi_ptr[2*4]
+eval_jacobi = np.PyUFunc_FromFuncAndData(ufunc_eval_jacobi_loops, ufunc_eval_jacobi_data, ufunc_eval_jacobi_types, 5, 4, 1, 0, "eval_jacobi", ufunc_eval_jacobi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_laguerre_loops[5]
+cdef void *ufunc_eval_laguerre_ptr[10]
+cdef void *ufunc_eval_laguerre_data[5]
+cdef char ufunc_eval_laguerre_types[15]
+cdef char *ufunc_eval_laguerre_doc = (
+    "eval_laguerre(n, x, out=None)\n"
+    "\n"
+    "Evaluate Laguerre polynomial at a point.\n"
+    "\n"
+    "The Laguerre polynomials can be defined via the confluent\n"
+    "hypergeometric function :math:`{}_1F_1` as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    L_n(x) = {}_1F_1(-n, 1, x).\n"
+    "\n"
+    "See 22.5.16 and 22.5.54 in [AS]_ for details. When :math:`n` is an\n"
+    "integer the result is a polynomial of degree :math:`n`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer the result is\n"
+    "    determined via the relation to the confluent hypergeometric\n"
+    "    function.\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the Laguerre polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "L : scalar or ndarray\n"
+    "    Values of the Laguerre polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_laguerre : roots and quadrature weights of Laguerre\n"
+    "                 polynomials\n"
+    "laguerre : Laguerre polynomial object\n"
+    "numpy.polynomial.laguerre.Laguerre : Laguerre series\n"
+    "eval_genlaguerre : evaluate generalized Laguerre polynomials\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_laguerre_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_laguerre_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_eval_laguerre_loops[2] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_eval_laguerre_loops[3] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_eval_laguerre_loops[4] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_eval_laguerre_types[0] = <char>NPY_LONG
+ufunc_eval_laguerre_types[1] = <char>NPY_DOUBLE
+ufunc_eval_laguerre_types[2] = <char>NPY_DOUBLE
+ufunc_eval_laguerre_types[3] = <char>NPY_FLOAT
+ufunc_eval_laguerre_types[4] = <char>NPY_FLOAT
+ufunc_eval_laguerre_types[5] = <char>NPY_FLOAT
+ufunc_eval_laguerre_types[6] = <char>NPY_FLOAT
+ufunc_eval_laguerre_types[7] = <char>NPY_CFLOAT
+ufunc_eval_laguerre_types[8] = <char>NPY_CFLOAT
+ufunc_eval_laguerre_types[9] = <char>NPY_DOUBLE
+ufunc_eval_laguerre_types[10] = <char>NPY_DOUBLE
+ufunc_eval_laguerre_types[11] = <char>NPY_DOUBLE
+ufunc_eval_laguerre_types[12] = <char>NPY_DOUBLE
+ufunc_eval_laguerre_types[13] = <char>NPY_CDOUBLE
+ufunc_eval_laguerre_types[14] = <char>NPY_CDOUBLE
+ufunc_eval_laguerre_ptr[2*0] = <void*>_func_eval_laguerre_l
+ufunc_eval_laguerre_ptr[2*0+1] = <void*>(<char*>"eval_laguerre")
+ufunc_eval_laguerre_ptr[2*1] = <void*>_func_eval_laguerre[double]
+ufunc_eval_laguerre_ptr[2*1+1] = <void*>(<char*>"eval_laguerre")
+ufunc_eval_laguerre_ptr[2*2] = <void*>_func_eval_laguerre[double_complex]
+ufunc_eval_laguerre_ptr[2*2+1] = <void*>(<char*>"eval_laguerre")
+ufunc_eval_laguerre_ptr[2*3] = <void*>_func_eval_laguerre[double]
+ufunc_eval_laguerre_ptr[2*3+1] = <void*>(<char*>"eval_laguerre")
+ufunc_eval_laguerre_ptr[2*4] = <void*>_func_eval_laguerre[double_complex]
+ufunc_eval_laguerre_ptr[2*4+1] = <void*>(<char*>"eval_laguerre")
+ufunc_eval_laguerre_data[0] = &ufunc_eval_laguerre_ptr[2*0]
+ufunc_eval_laguerre_data[1] = &ufunc_eval_laguerre_ptr[2*1]
+ufunc_eval_laguerre_data[2] = &ufunc_eval_laguerre_ptr[2*2]
+ufunc_eval_laguerre_data[3] = &ufunc_eval_laguerre_ptr[2*3]
+ufunc_eval_laguerre_data[4] = &ufunc_eval_laguerre_ptr[2*4]
+eval_laguerre = np.PyUFunc_FromFuncAndData(ufunc_eval_laguerre_loops, ufunc_eval_laguerre_data, ufunc_eval_laguerre_types, 5, 2, 1, 0, "eval_laguerre", ufunc_eval_laguerre_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_legendre_loops[5]
+cdef void *ufunc_eval_legendre_ptr[10]
+cdef void *ufunc_eval_legendre_data[5]
+cdef char ufunc_eval_legendre_types[15]
+cdef char *ufunc_eval_legendre_doc = (
+    "eval_legendre(n, x, out=None)\n"
+    "\n"
+    "Evaluate Legendre polynomial at a point.\n"
+    "\n"
+    "The Legendre polynomials can be defined via the Gauss\n"
+    "hypergeometric function :math:`{}_2F_1` as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    P_n(x) = {}_2F_1(-n, n + 1; 1; (1 - x)/2).\n"
+    "\n"
+    "When :math:`n` is an integer the result is a polynomial of degree\n"
+    ":math:`n`. See 22.5.49 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to the Gauss hypergeometric\n"
+    "    function.\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the Legendre polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "P : scalar or ndarray\n"
+    "    Values of the Legendre polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_legendre : roots and quadrature weights of Legendre\n"
+    "                 polynomials\n"
+    "legendre : Legendre polynomial object\n"
+    "hyp2f1 : Gauss hypergeometric function\n"
+    "numpy.polynomial.legendre.Legendre : Legendre series\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import eval_legendre\n"
+    "\n"
+    "Evaluate the zero-order Legendre polynomial at x = 0\n"
+    "\n"
+    ">>> eval_legendre(0, 0)\n"
+    "1.0\n"
+    "\n"
+    "Evaluate the first-order Legendre polynomial between -1 and 1\n"
+    "\n"
+    ">>> X = np.linspace(-1, 1, 5)  # Domain of Legendre polynomials\n"
+    ">>> eval_legendre(1, X)\n"
+    "array([-1. , -0.5,  0. ,  0.5,  1. ])\n"
+    "\n"
+    "Evaluate Legendre polynomials of order 0 through 4 at x = 0\n"
+    "\n"
+    ">>> N = range(0, 5)\n"
+    ">>> eval_legendre(N, 0)\n"
+    "array([ 1.   ,  0.   , -0.5  ,  0.   ,  0.375])\n"
+    "\n"
+    "Plot Legendre polynomials of order 0 through 4\n"
+    "\n"
+    ">>> X = np.linspace(-1, 1)\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> for n in range(0, 5):\n"
+    "...     y = eval_legendre(n, X)\n"
+    "...     plt.plot(X, y, label=r'$P_{}(x)$'.format(n))\n"
+    "\n"
+    ">>> plt.title(\"Legendre Polynomials\")\n"
+    ">>> plt.xlabel(\"x\")\n"
+    ">>> plt.ylabel(r'$P_n(x)$')\n"
+    ">>> plt.legend(loc='lower right')\n"
+    ">>> plt.show()")
+ufunc_eval_legendre_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_legendre_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_eval_legendre_loops[2] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_eval_legendre_loops[3] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_eval_legendre_loops[4] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_eval_legendre_types[0] = <char>NPY_LONG
+ufunc_eval_legendre_types[1] = <char>NPY_DOUBLE
+ufunc_eval_legendre_types[2] = <char>NPY_DOUBLE
+ufunc_eval_legendre_types[3] = <char>NPY_FLOAT
+ufunc_eval_legendre_types[4] = <char>NPY_FLOAT
+ufunc_eval_legendre_types[5] = <char>NPY_FLOAT
+ufunc_eval_legendre_types[6] = <char>NPY_FLOAT
+ufunc_eval_legendre_types[7] = <char>NPY_CFLOAT
+ufunc_eval_legendre_types[8] = <char>NPY_CFLOAT
+ufunc_eval_legendre_types[9] = <char>NPY_DOUBLE
+ufunc_eval_legendre_types[10] = <char>NPY_DOUBLE
+ufunc_eval_legendre_types[11] = <char>NPY_DOUBLE
+ufunc_eval_legendre_types[12] = <char>NPY_DOUBLE
+ufunc_eval_legendre_types[13] = <char>NPY_CDOUBLE
+ufunc_eval_legendre_types[14] = <char>NPY_CDOUBLE
+ufunc_eval_legendre_ptr[2*0] = <void*>_func_eval_legendre_l
+ufunc_eval_legendre_ptr[2*0+1] = <void*>(<char*>"eval_legendre")
+ufunc_eval_legendre_ptr[2*1] = <void*>_func_eval_legendre[double]
+ufunc_eval_legendre_ptr[2*1+1] = <void*>(<char*>"eval_legendre")
+ufunc_eval_legendre_ptr[2*2] = <void*>_func_eval_legendre[double_complex]
+ufunc_eval_legendre_ptr[2*2+1] = <void*>(<char*>"eval_legendre")
+ufunc_eval_legendre_ptr[2*3] = <void*>_func_eval_legendre[double]
+ufunc_eval_legendre_ptr[2*3+1] = <void*>(<char*>"eval_legendre")
+ufunc_eval_legendre_ptr[2*4] = <void*>_func_eval_legendre[double_complex]
+ufunc_eval_legendre_ptr[2*4+1] = <void*>(<char*>"eval_legendre")
+ufunc_eval_legendre_data[0] = &ufunc_eval_legendre_ptr[2*0]
+ufunc_eval_legendre_data[1] = &ufunc_eval_legendre_ptr[2*1]
+ufunc_eval_legendre_data[2] = &ufunc_eval_legendre_ptr[2*2]
+ufunc_eval_legendre_data[3] = &ufunc_eval_legendre_ptr[2*3]
+ufunc_eval_legendre_data[4] = &ufunc_eval_legendre_ptr[2*4]
+eval_legendre = np.PyUFunc_FromFuncAndData(ufunc_eval_legendre_loops, ufunc_eval_legendre_data, ufunc_eval_legendre_types, 5, 2, 1, 0, "eval_legendre", ufunc_eval_legendre_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_sh_chebyt_loops[5]
+cdef void *ufunc_eval_sh_chebyt_ptr[10]
+cdef void *ufunc_eval_sh_chebyt_data[5]
+cdef char ufunc_eval_sh_chebyt_types[15]
+cdef char *ufunc_eval_sh_chebyt_doc = (
+    "eval_sh_chebyt(n, x, out=None)\n"
+    "\n"
+    "Evaluate shifted Chebyshev polynomial of the first kind at a\n"
+    "point.\n"
+    "\n"
+    "These polynomials are defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    T_n^*(x) = T_n(2x - 1)\n"
+    "\n"
+    "where :math:`T_n` is a Chebyshev polynomial of the first kind. See\n"
+    "22.5.14 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to `eval_chebyt`.\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the shifted Chebyshev polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "T : scalar or ndarray\n"
+    "    Values of the shifted Chebyshev polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_sh_chebyt : roots and quadrature weights of shifted\n"
+    "                  Chebyshev polynomials of the first kind\n"
+    "sh_chebyt : shifted Chebyshev polynomial object\n"
+    "eval_chebyt : evaluate Chebyshev polynomials of the first kind\n"
+    "numpy.polynomial.chebyshev.Chebyshev : Chebyshev series\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_sh_chebyt_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_sh_chebyt_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_eval_sh_chebyt_loops[2] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_eval_sh_chebyt_loops[3] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_eval_sh_chebyt_loops[4] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_eval_sh_chebyt_types[0] = <char>NPY_LONG
+ufunc_eval_sh_chebyt_types[1] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyt_types[2] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyt_types[3] = <char>NPY_FLOAT
+ufunc_eval_sh_chebyt_types[4] = <char>NPY_FLOAT
+ufunc_eval_sh_chebyt_types[5] = <char>NPY_FLOAT
+ufunc_eval_sh_chebyt_types[6] = <char>NPY_FLOAT
+ufunc_eval_sh_chebyt_types[7] = <char>NPY_CFLOAT
+ufunc_eval_sh_chebyt_types[8] = <char>NPY_CFLOAT
+ufunc_eval_sh_chebyt_types[9] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyt_types[10] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyt_types[11] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyt_types[12] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyt_types[13] = <char>NPY_CDOUBLE
+ufunc_eval_sh_chebyt_types[14] = <char>NPY_CDOUBLE
+ufunc_eval_sh_chebyt_ptr[2*0] = <void*>_func_eval_sh_chebyt_l
+ufunc_eval_sh_chebyt_ptr[2*0+1] = <void*>(<char*>"eval_sh_chebyt")
+ufunc_eval_sh_chebyt_ptr[2*1] = <void*>_func_eval_sh_chebyt[double]
+ufunc_eval_sh_chebyt_ptr[2*1+1] = <void*>(<char*>"eval_sh_chebyt")
+ufunc_eval_sh_chebyt_ptr[2*2] = <void*>_func_eval_sh_chebyt[double_complex]
+ufunc_eval_sh_chebyt_ptr[2*2+1] = <void*>(<char*>"eval_sh_chebyt")
+ufunc_eval_sh_chebyt_ptr[2*3] = <void*>_func_eval_sh_chebyt[double]
+ufunc_eval_sh_chebyt_ptr[2*3+1] = <void*>(<char*>"eval_sh_chebyt")
+ufunc_eval_sh_chebyt_ptr[2*4] = <void*>_func_eval_sh_chebyt[double_complex]
+ufunc_eval_sh_chebyt_ptr[2*4+1] = <void*>(<char*>"eval_sh_chebyt")
+ufunc_eval_sh_chebyt_data[0] = &ufunc_eval_sh_chebyt_ptr[2*0]
+ufunc_eval_sh_chebyt_data[1] = &ufunc_eval_sh_chebyt_ptr[2*1]
+ufunc_eval_sh_chebyt_data[2] = &ufunc_eval_sh_chebyt_ptr[2*2]
+ufunc_eval_sh_chebyt_data[3] = &ufunc_eval_sh_chebyt_ptr[2*3]
+ufunc_eval_sh_chebyt_data[4] = &ufunc_eval_sh_chebyt_ptr[2*4]
+eval_sh_chebyt = np.PyUFunc_FromFuncAndData(ufunc_eval_sh_chebyt_loops, ufunc_eval_sh_chebyt_data, ufunc_eval_sh_chebyt_types, 5, 2, 1, 0, "eval_sh_chebyt", ufunc_eval_sh_chebyt_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_sh_chebyu_loops[5]
+cdef void *ufunc_eval_sh_chebyu_ptr[10]
+cdef void *ufunc_eval_sh_chebyu_data[5]
+cdef char ufunc_eval_sh_chebyu_types[15]
+cdef char *ufunc_eval_sh_chebyu_doc = (
+    "eval_sh_chebyu(n, x, out=None)\n"
+    "\n"
+    "Evaluate shifted Chebyshev polynomial of the second kind at a\n"
+    "point.\n"
+    "\n"
+    "These polynomials are defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    U_n^*(x) = U_n(2x - 1)\n"
+    "\n"
+    "where :math:`U_n` is a Chebyshev polynomial of the first kind. See\n"
+    "22.5.15 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to `eval_chebyu`.\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the shifted Chebyshev polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "U : scalar or ndarray\n"
+    "    Values of the shifted Chebyshev polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_sh_chebyu : roots and quadrature weights of shifted\n"
+    "                  Chebychev polynomials of the second kind\n"
+    "sh_chebyu : shifted Chebyshev polynomial object\n"
+    "eval_chebyu : evaluate Chebyshev polynomials of the second kind\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_sh_chebyu_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_sh_chebyu_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_eval_sh_chebyu_loops[2] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_eval_sh_chebyu_loops[3] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_eval_sh_chebyu_loops[4] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_eval_sh_chebyu_types[0] = <char>NPY_LONG
+ufunc_eval_sh_chebyu_types[1] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyu_types[2] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyu_types[3] = <char>NPY_FLOAT
+ufunc_eval_sh_chebyu_types[4] = <char>NPY_FLOAT
+ufunc_eval_sh_chebyu_types[5] = <char>NPY_FLOAT
+ufunc_eval_sh_chebyu_types[6] = <char>NPY_FLOAT
+ufunc_eval_sh_chebyu_types[7] = <char>NPY_CFLOAT
+ufunc_eval_sh_chebyu_types[8] = <char>NPY_CFLOAT
+ufunc_eval_sh_chebyu_types[9] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyu_types[10] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyu_types[11] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyu_types[12] = <char>NPY_DOUBLE
+ufunc_eval_sh_chebyu_types[13] = <char>NPY_CDOUBLE
+ufunc_eval_sh_chebyu_types[14] = <char>NPY_CDOUBLE
+ufunc_eval_sh_chebyu_ptr[2*0] = <void*>_func_eval_sh_chebyu_l
+ufunc_eval_sh_chebyu_ptr[2*0+1] = <void*>(<char*>"eval_sh_chebyu")
+ufunc_eval_sh_chebyu_ptr[2*1] = <void*>_func_eval_sh_chebyu[double]
+ufunc_eval_sh_chebyu_ptr[2*1+1] = <void*>(<char*>"eval_sh_chebyu")
+ufunc_eval_sh_chebyu_ptr[2*2] = <void*>_func_eval_sh_chebyu[double_complex]
+ufunc_eval_sh_chebyu_ptr[2*2+1] = <void*>(<char*>"eval_sh_chebyu")
+ufunc_eval_sh_chebyu_ptr[2*3] = <void*>_func_eval_sh_chebyu[double]
+ufunc_eval_sh_chebyu_ptr[2*3+1] = <void*>(<char*>"eval_sh_chebyu")
+ufunc_eval_sh_chebyu_ptr[2*4] = <void*>_func_eval_sh_chebyu[double_complex]
+ufunc_eval_sh_chebyu_ptr[2*4+1] = <void*>(<char*>"eval_sh_chebyu")
+ufunc_eval_sh_chebyu_data[0] = &ufunc_eval_sh_chebyu_ptr[2*0]
+ufunc_eval_sh_chebyu_data[1] = &ufunc_eval_sh_chebyu_ptr[2*1]
+ufunc_eval_sh_chebyu_data[2] = &ufunc_eval_sh_chebyu_ptr[2*2]
+ufunc_eval_sh_chebyu_data[3] = &ufunc_eval_sh_chebyu_ptr[2*3]
+ufunc_eval_sh_chebyu_data[4] = &ufunc_eval_sh_chebyu_ptr[2*4]
+eval_sh_chebyu = np.PyUFunc_FromFuncAndData(ufunc_eval_sh_chebyu_loops, ufunc_eval_sh_chebyu_data, ufunc_eval_sh_chebyu_types, 5, 2, 1, 0, "eval_sh_chebyu", ufunc_eval_sh_chebyu_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_sh_jacobi_loops[5]
+cdef void *ufunc_eval_sh_jacobi_ptr[10]
+cdef void *ufunc_eval_sh_jacobi_data[5]
+cdef char ufunc_eval_sh_jacobi_types[25]
+cdef char *ufunc_eval_sh_jacobi_doc = (
+    "eval_sh_jacobi(n, p, q, x, out=None)\n"
+    "\n"
+    "Evaluate shifted Jacobi polynomial at a point.\n"
+    "\n"
+    "Defined by\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    G_n^{(p, q)}(x)\n"
+    "      = \\binom{2n + p - 1}{n}^{-1} P_n^{(p - q, q - 1)}(2x - 1),\n"
+    "\n"
+    "where :math:`P_n^{(\\cdot, \\cdot)}` is the n-th Jacobi\n"
+    "polynomial. See 22.5.2 in [AS]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : int\n"
+    "    Degree of the polynomial. If not an integer, the result is\n"
+    "    determined via the relation to `binom` and `eval_jacobi`.\n"
+    "p : float\n"
+    "    Parameter\n"
+    "q : float\n"
+    "    Parameter\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "G : scalar or ndarray\n"
+    "    Values of the shifted Jacobi polynomial.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_sh_jacobi : roots and quadrature weights of shifted Jacobi\n"
+    "                  polynomials\n"
+    "sh_jacobi : shifted Jacobi polynomial object\n"
+    "eval_jacobi : evaluate Jacobi polynomials\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_sh_jacobi_loops[0] = <np.PyUFuncGenericFunction>loop_d_lddd__As_lddd_d
+ufunc_eval_sh_jacobi_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd__As_ffff_f
+ufunc_eval_sh_jacobi_loops[2] = <np.PyUFuncGenericFunction>loop_D_dddD__As_fffF_F
+ufunc_eval_sh_jacobi_loops[3] = <np.PyUFuncGenericFunction>loop_d_dddd__As_dddd_d
+ufunc_eval_sh_jacobi_loops[4] = <np.PyUFuncGenericFunction>loop_D_dddD__As_dddD_D
+ufunc_eval_sh_jacobi_types[0] = <char>NPY_LONG
+ufunc_eval_sh_jacobi_types[1] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[2] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[3] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[4] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[5] = <char>NPY_FLOAT
+ufunc_eval_sh_jacobi_types[6] = <char>NPY_FLOAT
+ufunc_eval_sh_jacobi_types[7] = <char>NPY_FLOAT
+ufunc_eval_sh_jacobi_types[8] = <char>NPY_FLOAT
+ufunc_eval_sh_jacobi_types[9] = <char>NPY_FLOAT
+ufunc_eval_sh_jacobi_types[10] = <char>NPY_FLOAT
+ufunc_eval_sh_jacobi_types[11] = <char>NPY_FLOAT
+ufunc_eval_sh_jacobi_types[12] = <char>NPY_FLOAT
+ufunc_eval_sh_jacobi_types[13] = <char>NPY_CFLOAT
+ufunc_eval_sh_jacobi_types[14] = <char>NPY_CFLOAT
+ufunc_eval_sh_jacobi_types[15] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[16] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[17] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[18] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[19] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[20] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[21] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[22] = <char>NPY_DOUBLE
+ufunc_eval_sh_jacobi_types[23] = <char>NPY_CDOUBLE
+ufunc_eval_sh_jacobi_types[24] = <char>NPY_CDOUBLE
+ufunc_eval_sh_jacobi_ptr[2*0] = <void*>_func_eval_sh_jacobi_l
+ufunc_eval_sh_jacobi_ptr[2*0+1] = <void*>(<char*>"eval_sh_jacobi")
+ufunc_eval_sh_jacobi_ptr[2*1] = <void*>_func_eval_sh_jacobi[double]
+ufunc_eval_sh_jacobi_ptr[2*1+1] = <void*>(<char*>"eval_sh_jacobi")
+ufunc_eval_sh_jacobi_ptr[2*2] = <void*>_func_eval_sh_jacobi[double_complex]
+ufunc_eval_sh_jacobi_ptr[2*2+1] = <void*>(<char*>"eval_sh_jacobi")
+ufunc_eval_sh_jacobi_ptr[2*3] = <void*>_func_eval_sh_jacobi[double]
+ufunc_eval_sh_jacobi_ptr[2*3+1] = <void*>(<char*>"eval_sh_jacobi")
+ufunc_eval_sh_jacobi_ptr[2*4] = <void*>_func_eval_sh_jacobi[double_complex]
+ufunc_eval_sh_jacobi_ptr[2*4+1] = <void*>(<char*>"eval_sh_jacobi")
+ufunc_eval_sh_jacobi_data[0] = &ufunc_eval_sh_jacobi_ptr[2*0]
+ufunc_eval_sh_jacobi_data[1] = &ufunc_eval_sh_jacobi_ptr[2*1]
+ufunc_eval_sh_jacobi_data[2] = &ufunc_eval_sh_jacobi_ptr[2*2]
+ufunc_eval_sh_jacobi_data[3] = &ufunc_eval_sh_jacobi_ptr[2*3]
+ufunc_eval_sh_jacobi_data[4] = &ufunc_eval_sh_jacobi_ptr[2*4]
+eval_sh_jacobi = np.PyUFunc_FromFuncAndData(ufunc_eval_sh_jacobi_loops, ufunc_eval_sh_jacobi_data, ufunc_eval_sh_jacobi_types, 5, 4, 1, 0, "eval_sh_jacobi", ufunc_eval_sh_jacobi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_eval_sh_legendre_loops[5]
+cdef void *ufunc_eval_sh_legendre_ptr[10]
+cdef void *ufunc_eval_sh_legendre_data[5]
+cdef char ufunc_eval_sh_legendre_types[15]
+cdef char *ufunc_eval_sh_legendre_doc = (
+    "eval_sh_legendre(n, x, out=None)\n"
+    "\n"
+    "Evaluate shifted Legendre polynomial at a point.\n"
+    "\n"
+    "These polynomials are defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    P_n^*(x) = P_n(2x - 1)\n"
+    "\n"
+    "where :math:`P_n` is a Legendre polynomial. See 2.2.11 in [AS]_\n"
+    "for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Degree of the polynomial. If not an integer, the value is\n"
+    "    determined via the relation to `eval_legendre`.\n"
+    "x : array_like\n"
+    "    Points at which to evaluate the shifted Legendre polynomial\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "P : scalar or ndarray\n"
+    "    Values of the shifted Legendre polynomial\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "roots_sh_legendre : roots and quadrature weights of shifted\n"
+    "                    Legendre polynomials\n"
+    "sh_legendre : shifted Legendre polynomial object\n"
+    "eval_legendre : evaluate Legendre polynomials\n"
+    "numpy.polynomial.legendre.Legendre : Legendre series\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "    Handbook of Mathematical Functions with Formulas,\n"
+    "    Graphs, and Mathematical Tables. New York: Dover, 1972.")
+ufunc_eval_sh_legendre_loops[0] = <np.PyUFuncGenericFunction>loop_d_ld__As_ld_d
+ufunc_eval_sh_legendre_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_eval_sh_legendre_loops[2] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_eval_sh_legendre_loops[3] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_eval_sh_legendre_loops[4] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_eval_sh_legendre_types[0] = <char>NPY_LONG
+ufunc_eval_sh_legendre_types[1] = <char>NPY_DOUBLE
+ufunc_eval_sh_legendre_types[2] = <char>NPY_DOUBLE
+ufunc_eval_sh_legendre_types[3] = <char>NPY_FLOAT
+ufunc_eval_sh_legendre_types[4] = <char>NPY_FLOAT
+ufunc_eval_sh_legendre_types[5] = <char>NPY_FLOAT
+ufunc_eval_sh_legendre_types[6] = <char>NPY_FLOAT
+ufunc_eval_sh_legendre_types[7] = <char>NPY_CFLOAT
+ufunc_eval_sh_legendre_types[8] = <char>NPY_CFLOAT
+ufunc_eval_sh_legendre_types[9] = <char>NPY_DOUBLE
+ufunc_eval_sh_legendre_types[10] = <char>NPY_DOUBLE
+ufunc_eval_sh_legendre_types[11] = <char>NPY_DOUBLE
+ufunc_eval_sh_legendre_types[12] = <char>NPY_DOUBLE
+ufunc_eval_sh_legendre_types[13] = <char>NPY_CDOUBLE
+ufunc_eval_sh_legendre_types[14] = <char>NPY_CDOUBLE
+ufunc_eval_sh_legendre_ptr[2*0] = <void*>_func_eval_sh_legendre_l
+ufunc_eval_sh_legendre_ptr[2*0+1] = <void*>(<char*>"eval_sh_legendre")
+ufunc_eval_sh_legendre_ptr[2*1] = <void*>_func_eval_sh_legendre[double]
+ufunc_eval_sh_legendre_ptr[2*1+1] = <void*>(<char*>"eval_sh_legendre")
+ufunc_eval_sh_legendre_ptr[2*2] = <void*>_func_eval_sh_legendre[double_complex]
+ufunc_eval_sh_legendre_ptr[2*2+1] = <void*>(<char*>"eval_sh_legendre")
+ufunc_eval_sh_legendre_ptr[2*3] = <void*>_func_eval_sh_legendre[double]
+ufunc_eval_sh_legendre_ptr[2*3+1] = <void*>(<char*>"eval_sh_legendre")
+ufunc_eval_sh_legendre_ptr[2*4] = <void*>_func_eval_sh_legendre[double_complex]
+ufunc_eval_sh_legendre_ptr[2*4+1] = <void*>(<char*>"eval_sh_legendre")
+ufunc_eval_sh_legendre_data[0] = &ufunc_eval_sh_legendre_ptr[2*0]
+ufunc_eval_sh_legendre_data[1] = &ufunc_eval_sh_legendre_ptr[2*1]
+ufunc_eval_sh_legendre_data[2] = &ufunc_eval_sh_legendre_ptr[2*2]
+ufunc_eval_sh_legendre_data[3] = &ufunc_eval_sh_legendre_ptr[2*3]
+ufunc_eval_sh_legendre_data[4] = &ufunc_eval_sh_legendre_ptr[2*4]
+eval_sh_legendre = np.PyUFunc_FromFuncAndData(ufunc_eval_sh_legendre_loops, ufunc_eval_sh_legendre_data, ufunc_eval_sh_legendre_types, 5, 2, 1, 0, "eval_sh_legendre", ufunc_eval_sh_legendre_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_exp1_loops[4]
+cdef void *ufunc_exp1_ptr[8]
+cdef void *ufunc_exp1_data[4]
+cdef char ufunc_exp1_types[8]
+cdef char *ufunc_exp1_doc = (
+    "exp1(z, out=None)\n"
+    "\n"
+    "Exponential integral E1.\n"
+    "\n"
+    "For complex :math:`z \\ne 0` the exponential integral can be defined as\n"
+    "[1]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   E_1(z) = \\int_z^\\infty \\frac{e^{-t}}{t} dt,\n"
+    "\n"
+    "where the path of the integral does not cross the negative real\n"
+    "axis or pass through the origin.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z: array_like\n"
+    "    Real or complex argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the exponential integral E1\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "expi : exponential integral :math:`Ei`\n"
+    "expn : generalization of :math:`E_1`\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For :math:`x > 0` it is related to the exponential integral\n"
+    ":math:`Ei` (see `expi`) via the relation\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   E_1(x) = -Ei(-x).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Digital Library of Mathematical Functions, 6.2.1\n"
+    "       https://dlmf.nist.gov/6.2#E1\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It has a pole at 0.\n"
+    "\n"
+    ">>> sc.exp1(0)\n"
+    "inf\n"
+    "\n"
+    "It has a branch cut on the negative real axis.\n"
+    "\n"
+    ">>> sc.exp1(-1)\n"
+    "nan\n"
+    ">>> sc.exp1(complex(-1, 0))\n"
+    "(-1.8951178163559368-3.141592653589793j)\n"
+    ">>> sc.exp1(complex(-1, -0.0))\n"
+    "(-1.8951178163559368+3.141592653589793j)\n"
+    "\n"
+    "It approaches 0 along the positive real axis.\n"
+    "\n"
+    ">>> sc.exp1([1, 10, 100, 1000])\n"
+    "array([2.19383934e-01, 4.15696893e-06, 3.68359776e-46, 0.00000000e+00])\n"
+    "\n"
+    "It is related to `expi`.\n"
+    "\n"
+    ">>> x = np.array([1, 2, 3, 4])\n"
+    ">>> sc.exp1(x)\n"
+    "array([0.21938393, 0.04890051, 0.01304838, 0.00377935])\n"
+    ">>> -sc.expi(-x)\n"
+    "array([0.21938393, 0.04890051, 0.01304838, 0.00377935])")
+ufunc_exp1_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_exp1_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_exp1_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_exp1_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_exp1_types[0] = <char>NPY_FLOAT
+ufunc_exp1_types[1] = <char>NPY_FLOAT
+ufunc_exp1_types[2] = <char>NPY_DOUBLE
+ufunc_exp1_types[3] = <char>NPY_DOUBLE
+ufunc_exp1_types[4] = <char>NPY_CFLOAT
+ufunc_exp1_types[5] = <char>NPY_CFLOAT
+ufunc_exp1_types[6] = <char>NPY_CDOUBLE
+ufunc_exp1_types[7] = <char>NPY_CDOUBLE
+ufunc_exp1_ptr[2*0] = <void*>_func_exp1_wrap
+ufunc_exp1_ptr[2*0+1] = <void*>(<char*>"exp1")
+ufunc_exp1_ptr[2*1] = <void*>_func_exp1_wrap
+ufunc_exp1_ptr[2*1+1] = <void*>(<char*>"exp1")
+ufunc_exp1_ptr[2*2] = <void*>_func_cexp1_wrap
+ufunc_exp1_ptr[2*2+1] = <void*>(<char*>"exp1")
+ufunc_exp1_ptr[2*3] = <void*>_func_cexp1_wrap
+ufunc_exp1_ptr[2*3+1] = <void*>(<char*>"exp1")
+ufunc_exp1_data[0] = &ufunc_exp1_ptr[2*0]
+ufunc_exp1_data[1] = &ufunc_exp1_ptr[2*1]
+ufunc_exp1_data[2] = &ufunc_exp1_ptr[2*2]
+ufunc_exp1_data[3] = &ufunc_exp1_ptr[2*3]
+exp1 = np.PyUFunc_FromFuncAndData(ufunc_exp1_loops, ufunc_exp1_data, ufunc_exp1_types, 4, 1, 1, 0, "exp1", ufunc_exp1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_exp10_loops[2]
+cdef void *ufunc_exp10_ptr[4]
+cdef void *ufunc_exp10_data[2]
+cdef char ufunc_exp10_types[4]
+cdef char *ufunc_exp10_doc = (
+    "exp10(x, out=None)\n"
+    "\n"
+    "Compute ``10**x`` element-wise.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    `x` must contain real numbers.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    ``10**x``, computed element-wise.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import exp10\n"
+    "\n"
+    ">>> exp10(3)\n"
+    "1000.0\n"
+    ">>> x = np.array([[-1, -0.5, 0], [0.5, 1, 1.5]])\n"
+    ">>> exp10(x)\n"
+    "array([[  0.1       ,   0.31622777,   1.        ],\n"
+    "       [  3.16227766,  10.        ,  31.6227766 ]])")
+ufunc_exp10_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_exp10_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_exp10_types[0] = <char>NPY_FLOAT
+ufunc_exp10_types[1] = <char>NPY_FLOAT
+ufunc_exp10_types[2] = <char>NPY_DOUBLE
+ufunc_exp10_types[3] = <char>NPY_DOUBLE
+ufunc_exp10_ptr[2*0] = <void*>_func_exp10
+ufunc_exp10_ptr[2*0+1] = <void*>(<char*>"exp10")
+ufunc_exp10_ptr[2*1] = <void*>_func_exp10
+ufunc_exp10_ptr[2*1+1] = <void*>(<char*>"exp10")
+ufunc_exp10_data[0] = &ufunc_exp10_ptr[2*0]
+ufunc_exp10_data[1] = &ufunc_exp10_ptr[2*1]
+exp10 = np.PyUFunc_FromFuncAndData(ufunc_exp10_loops, ufunc_exp10_data, ufunc_exp10_types, 2, 1, 1, 0, "exp10", ufunc_exp10_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_exp2_loops[2]
+cdef void *ufunc_exp2_ptr[4]
+cdef void *ufunc_exp2_data[2]
+cdef char ufunc_exp2_types[4]
+cdef char *ufunc_exp2_doc = (
+    "exp2(x, out=None)\n"
+    "\n"
+    "Compute ``2**x`` element-wise.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    `x` must contain real numbers.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    ``2**x``, computed element-wise.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import exp2\n"
+    "\n"
+    ">>> exp2(3)\n"
+    "8.0\n"
+    ">>> x = np.array([[-1, -0.5, 0], [0.5, 1, 1.5]])\n"
+    ">>> exp2(x)\n"
+    "array([[ 0.5       ,  0.70710678,  1.        ],\n"
+    "       [ 1.41421356,  2.        ,  2.82842712]])")
+ufunc_exp2_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_exp2_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_exp2_types[0] = <char>NPY_FLOAT
+ufunc_exp2_types[1] = <char>NPY_FLOAT
+ufunc_exp2_types[2] = <char>NPY_DOUBLE
+ufunc_exp2_types[3] = <char>NPY_DOUBLE
+ufunc_exp2_ptr[2*0] = <void*>_func_exp2
+ufunc_exp2_ptr[2*0+1] = <void*>(<char*>"exp2")
+ufunc_exp2_ptr[2*1] = <void*>_func_exp2
+ufunc_exp2_ptr[2*1+1] = <void*>(<char*>"exp2")
+ufunc_exp2_data[0] = &ufunc_exp2_ptr[2*0]
+ufunc_exp2_data[1] = &ufunc_exp2_ptr[2*1]
+exp2 = np.PyUFunc_FromFuncAndData(ufunc_exp2_loops, ufunc_exp2_data, ufunc_exp2_types, 2, 1, 1, 0, "exp2", ufunc_exp2_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_expi_loops[4]
+cdef void *ufunc_expi_ptr[8]
+cdef void *ufunc_expi_data[4]
+cdef char ufunc_expi_types[8]
+cdef char *ufunc_expi_doc = (
+    "expi(x, out=None)\n"
+    "\n"
+    "Exponential integral Ei.\n"
+    "\n"
+    "For real :math:`x`, the exponential integral is defined as [1]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    Ei(x) = \\int_{-\\infty}^x \\frac{e^t}{t} dt.\n"
+    "\n"
+    "For :math:`x > 0` the integral is understood as a Cauchy principal\n"
+    "value.\n"
+    "\n"
+    "It is extended to the complex plane by analytic continuation of\n"
+    "the function on the interval :math:`(0, \\infty)`. The complex\n"
+    "variant has a branch cut on the negative real axis.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real or complex valued argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the exponential integral\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "exp1 : Exponential integral :math:`E_1`\n"
+    "expn : Generalized exponential integral :math:`E_n`\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The exponential integrals :math:`E_1` and :math:`Ei` satisfy the\n"
+    "relation\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    E_1(x) = -Ei(-x)\n"
+    "\n"
+    "for :math:`x > 0`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Digital Library of Mathematical Functions, 6.2.5\n"
+    "       https://dlmf.nist.gov/6.2#E5\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is related to `exp1`.\n"
+    "\n"
+    ">>> x = np.array([1, 2, 3, 4])\n"
+    ">>> -sc.expi(-x)\n"
+    "array([0.21938393, 0.04890051, 0.01304838, 0.00377935])\n"
+    ">>> sc.exp1(x)\n"
+    "array([0.21938393, 0.04890051, 0.01304838, 0.00377935])\n"
+    "\n"
+    "The complex variant has a branch cut on the negative real axis.\n"
+    "\n"
+    ">>> sc.expi(-1 + 1e-12j)\n"
+    "(-0.21938393439552062+3.1415926535894254j)\n"
+    ">>> sc.expi(-1 - 1e-12j)\n"
+    "(-0.21938393439552062-3.1415926535894254j)\n"
+    "\n"
+    "As the complex variant approaches the branch cut, the real parts\n"
+    "approach the value of the real variant.\n"
+    "\n"
+    ">>> sc.expi(-1)\n"
+    "-0.21938393439552062\n"
+    "\n"
+    "The SciPy implementation returns the real variant for complex\n"
+    "values on the branch cut.\n"
+    "\n"
+    ">>> sc.expi(complex(-1, 0.0))\n"
+    "(-0.21938393439552062-0j)\n"
+    ">>> sc.expi(complex(-1, -0.0))\n"
+    "(-0.21938393439552062-0j)")
+ufunc_expi_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_expi_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_expi_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_expi_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_expi_types[0] = <char>NPY_FLOAT
+ufunc_expi_types[1] = <char>NPY_FLOAT
+ufunc_expi_types[2] = <char>NPY_DOUBLE
+ufunc_expi_types[3] = <char>NPY_DOUBLE
+ufunc_expi_types[4] = <char>NPY_CFLOAT
+ufunc_expi_types[5] = <char>NPY_CFLOAT
+ufunc_expi_types[6] = <char>NPY_CDOUBLE
+ufunc_expi_types[7] = <char>NPY_CDOUBLE
+ufunc_expi_ptr[2*0] = <void*>_func_expi_wrap
+ufunc_expi_ptr[2*0+1] = <void*>(<char*>"expi")
+ufunc_expi_ptr[2*1] = <void*>_func_expi_wrap
+ufunc_expi_ptr[2*1+1] = <void*>(<char*>"expi")
+ufunc_expi_ptr[2*2] = <void*>_func_cexpi_wrap
+ufunc_expi_ptr[2*2+1] = <void*>(<char*>"expi")
+ufunc_expi_ptr[2*3] = <void*>_func_cexpi_wrap
+ufunc_expi_ptr[2*3+1] = <void*>(<char*>"expi")
+ufunc_expi_data[0] = &ufunc_expi_ptr[2*0]
+ufunc_expi_data[1] = &ufunc_expi_ptr[2*1]
+ufunc_expi_data[2] = &ufunc_expi_ptr[2*2]
+ufunc_expi_data[3] = &ufunc_expi_ptr[2*3]
+expi = np.PyUFunc_FromFuncAndData(ufunc_expi_loops, ufunc_expi_data, ufunc_expi_types, 4, 1, 1, 0, "expi", ufunc_expi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_expit_loops[3]
+cdef void *ufunc_expit_ptr[6]
+cdef void *ufunc_expit_data[3]
+cdef char ufunc_expit_types[6]
+cdef char *ufunc_expit_doc = (
+    "expit(x, out=None)\n"
+    "\n"
+    "Expit (a.k.a. logistic sigmoid) ufunc for ndarrays.\n"
+    "\n"
+    "The expit function, also known as the logistic sigmoid function, is\n"
+    "defined as ``expit(x) = 1/(1+exp(-x))``.  It is the inverse of the\n"
+    "logit function.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : ndarray\n"
+    "    The ndarray to apply expit to element-wise.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    An ndarray of the same shape as x. Its entries\n"
+    "    are `expit` of the corresponding entry of x.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "logit\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "As a ufunc expit takes a number of optional\n"
+    "keyword arguments. For more information\n"
+    "see `ufuncs <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_\n"
+    "\n"
+    ".. versionadded:: 0.10.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import expit, logit\n"
+    "\n"
+    ">>> expit([-np.inf, -1.5, 0, 1.5, np.inf])\n"
+    "array([ 0.        ,  0.18242552,  0.5       ,  0.81757448,  1.        ])\n"
+    "\n"
+    "`logit` is the inverse of `expit`:\n"
+    "\n"
+    ">>> logit(expit([-2.5, 0, 3.1, 5.0]))\n"
+    "array([-2.5,  0. ,  3.1,  5. ])\n"
+    "\n"
+    "Plot expit(x) for x in [-6, 6]:\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-6, 6, 121)\n"
+    ">>> y = expit(x)\n"
+    ">>> plt.plot(x, y)\n"
+    ">>> plt.grid()\n"
+    ">>> plt.xlim(-6, 6)\n"
+    ">>> plt.xlabel('x')\n"
+    ">>> plt.title('expit(x)')\n"
+    ">>> plt.show()")
+ufunc_expit_loops[0] = <np.PyUFuncGenericFunction>loop_f_f__As_f_f
+ufunc_expit_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_expit_loops[2] = <np.PyUFuncGenericFunction>loop_g_g__As_g_g
+ufunc_expit_types[0] = <char>NPY_FLOAT
+ufunc_expit_types[1] = <char>NPY_FLOAT
+ufunc_expit_types[2] = <char>NPY_DOUBLE
+ufunc_expit_types[3] = <char>NPY_DOUBLE
+ufunc_expit_types[4] = <char>NPY_LONGDOUBLE
+ufunc_expit_types[5] = <char>NPY_LONGDOUBLE
+ufunc_expit_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_expitf
+ufunc_expit_ptr[2*0+1] = <void*>(<char*>"expit")
+ufunc_expit_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_expit
+ufunc_expit_ptr[2*1+1] = <void*>(<char*>"expit")
+ufunc_expit_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_expitl
+ufunc_expit_ptr[2*2+1] = <void*>(<char*>"expit")
+ufunc_expit_data[0] = &ufunc_expit_ptr[2*0]
+ufunc_expit_data[1] = &ufunc_expit_ptr[2*1]
+ufunc_expit_data[2] = &ufunc_expit_ptr[2*2]
+expit = np.PyUFunc_FromFuncAndData(ufunc_expit_loops, ufunc_expit_data, ufunc_expit_types, 3, 1, 1, 0, "expit", ufunc_expit_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_expm1_loops[4]
+cdef void *ufunc_expm1_ptr[8]
+cdef void *ufunc_expm1_data[4]
+cdef char ufunc_expm1_types[8]
+cdef char *ufunc_expm1_doc = (
+    "expm1(x, out=None)\n"
+    "\n"
+    "Compute ``exp(x) - 1``.\n"
+    "\n"
+    "When `x` is near zero, ``exp(x)`` is near 1, so the numerical calculation\n"
+    "of ``exp(x) - 1`` can suffer from catastrophic loss of precision.\n"
+    "``expm1(x)`` is implemented to avoid the loss of precision that occurs when\n"
+    "`x` is near zero.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    `x` must contain real numbers.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    ``exp(x) - 1`` computed element-wise.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import expm1\n"
+    "\n"
+    ">>> expm1(1.0)\n"
+    "1.7182818284590451\n"
+    ">>> expm1([-0.2, -0.1, 0, 0.1, 0.2])\n"
+    "array([-0.18126925, -0.09516258,  0.        ,  0.10517092,  0.22140276])\n"
+    "\n"
+    "The exact value of ``exp(7.5e-13) - 1`` is::\n"
+    "\n"
+    "    7.5000000000028125000000007031250000001318...*10**-13.\n"
+    "\n"
+    "Here is what ``expm1(7.5e-13)`` gives:\n"
+    "\n"
+    ">>> expm1(7.5e-13)\n"
+    "7.5000000000028135e-13\n"
+    "\n"
+    "Compare that to ``exp(7.5e-13) - 1``, where the subtraction results in\n"
+    "a \"catastrophic\" loss of precision:\n"
+    "\n"
+    ">>> np.exp(7.5e-13) - 1\n"
+    "7.5006667543675576e-13")
+ufunc_expm1_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_expm1_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_expm1_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_expm1_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_expm1_types[0] = <char>NPY_FLOAT
+ufunc_expm1_types[1] = <char>NPY_FLOAT
+ufunc_expm1_types[2] = <char>NPY_DOUBLE
+ufunc_expm1_types[3] = <char>NPY_DOUBLE
+ufunc_expm1_types[4] = <char>NPY_CFLOAT
+ufunc_expm1_types[5] = <char>NPY_CFLOAT
+ufunc_expm1_types[6] = <char>NPY_CDOUBLE
+ufunc_expm1_types[7] = <char>NPY_CDOUBLE
+ufunc_expm1_ptr[2*0] = <void*>_func_expm1
+ufunc_expm1_ptr[2*0+1] = <void*>(<char*>"expm1")
+ufunc_expm1_ptr[2*1] = <void*>_func_expm1
+ufunc_expm1_ptr[2*1+1] = <void*>(<char*>"expm1")
+ufunc_expm1_ptr[2*2] = <void*>_func_cexpm1
+ufunc_expm1_ptr[2*2+1] = <void*>(<char*>"expm1")
+ufunc_expm1_ptr[2*3] = <void*>_func_cexpm1
+ufunc_expm1_ptr[2*3+1] = <void*>(<char*>"expm1")
+ufunc_expm1_data[0] = &ufunc_expm1_ptr[2*0]
+ufunc_expm1_data[1] = &ufunc_expm1_ptr[2*1]
+ufunc_expm1_data[2] = &ufunc_expm1_ptr[2*2]
+ufunc_expm1_data[3] = &ufunc_expm1_ptr[2*3]
+expm1 = np.PyUFunc_FromFuncAndData(ufunc_expm1_loops, ufunc_expm1_data, ufunc_expm1_types, 4, 1, 1, 0, "expm1", ufunc_expm1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_expn_loops[3]
+cdef void *ufunc_expn_ptr[6]
+cdef void *ufunc_expn_data[3]
+cdef char ufunc_expn_types[9]
+cdef char *ufunc_expn_doc = (
+    "expn(n, x, out=None)\n"
+    "\n"
+    "Generalized exponential integral En.\n"
+    "\n"
+    "For integer :math:`n \\geq 0` and real :math:`x \\geq 0` the\n"
+    "generalized exponential integral is defined as [dlmf]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    E_n(x) = x^{n - 1} \\int_x^\\infty \\frac{e^{-t}}{t^n} dt.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Non-negative integers\n"
+    "x : array_like\n"
+    "    Real argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the generalized exponential integral\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "exp1 : special case of :math:`E_n` for :math:`n = 1`\n"
+    "expi : related to :math:`E_n` when :math:`n = 1`\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] Digital Library of Mathematical Functions, 8.19.2\n"
+    "          https://dlmf.nist.gov/8.19#E2\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "Its domain is nonnegative n and x.\n"
+    "\n"
+    ">>> sc.expn(-1, 1.0), sc.expn(1, -1.0)\n"
+    "(nan, nan)\n"
+    "\n"
+    "It has a pole at ``x = 0`` for ``n = 1, 2``; for larger ``n`` it\n"
+    "is equal to ``1 / (n - 1)``.\n"
+    "\n"
+    ">>> sc.expn([0, 1, 2, 3, 4], 0)\n"
+    "array([       inf,        inf, 1.        , 0.5       , 0.33333333])\n"
+    "\n"
+    "For n equal to 0 it reduces to ``exp(-x) / x``.\n"
+    "\n"
+    ">>> x = np.array([1, 2, 3, 4])\n"
+    ">>> sc.expn(0, x)\n"
+    "array([0.36787944, 0.06766764, 0.01659569, 0.00457891])\n"
+    ">>> np.exp(-x) / x\n"
+    "array([0.36787944, 0.06766764, 0.01659569, 0.00457891])\n"
+    "\n"
+    "For n equal to 1 it reduces to `exp1`.\n"
+    "\n"
+    ">>> sc.expn(1, x)\n"
+    "array([0.21938393, 0.04890051, 0.01304838, 0.00377935])\n"
+    ">>> sc.exp1(x)\n"
+    "array([0.21938393, 0.04890051, 0.01304838, 0.00377935])")
+ufunc_expn_loops[0] = <np.PyUFuncGenericFunction>loop_d_id__As_ld_d
+ufunc_expn_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_expn_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_expn_types[0] = <char>NPY_LONG
+ufunc_expn_types[1] = <char>NPY_DOUBLE
+ufunc_expn_types[2] = <char>NPY_DOUBLE
+ufunc_expn_types[3] = <char>NPY_FLOAT
+ufunc_expn_types[4] = <char>NPY_FLOAT
+ufunc_expn_types[5] = <char>NPY_FLOAT
+ufunc_expn_types[6] = <char>NPY_DOUBLE
+ufunc_expn_types[7] = <char>NPY_DOUBLE
+ufunc_expn_types[8] = <char>NPY_DOUBLE
+ufunc_expn_ptr[2*0] = <void*>_func_expn
+ufunc_expn_ptr[2*0+1] = <void*>(<char*>"expn")
+ufunc_expn_ptr[2*1] = <void*>_func_expn_unsafe
+ufunc_expn_ptr[2*1+1] = <void*>(<char*>"expn")
+ufunc_expn_ptr[2*2] = <void*>_func_expn_unsafe
+ufunc_expn_ptr[2*2+1] = <void*>(<char*>"expn")
+ufunc_expn_data[0] = &ufunc_expn_ptr[2*0]
+ufunc_expn_data[1] = &ufunc_expn_ptr[2*1]
+ufunc_expn_data[2] = &ufunc_expn_ptr[2*2]
+expn = np.PyUFunc_FromFuncAndData(ufunc_expn_loops, ufunc_expn_data, ufunc_expn_types, 3, 2, 1, 0, "expn", ufunc_expn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_exprel_loops[2]
+cdef void *ufunc_exprel_ptr[4]
+cdef void *ufunc_exprel_data[2]
+cdef char ufunc_exprel_types[4]
+cdef char *ufunc_exprel_doc = (
+    "exprel(x, out=None)\n"
+    "\n"
+    "Relative error exponential, ``(exp(x) - 1)/x``.\n"
+    "\n"
+    "When `x` is near zero, ``exp(x)`` is near 1, so the numerical calculation\n"
+    "of ``exp(x) - 1`` can suffer from catastrophic loss of precision.\n"
+    "``exprel(x)`` is implemented to avoid the loss of precision that occurs when\n"
+    "`x` is near zero.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : ndarray\n"
+    "    Input array.  `x` must contain real numbers.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    ``(exp(x) - 1)/x``, computed element-wise.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "expm1\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    ".. versionadded:: 0.17.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import exprel\n"
+    "\n"
+    ">>> exprel(0.01)\n"
+    "1.0050167084168056\n"
+    ">>> exprel([-0.25, -0.1, 0, 0.1, 0.25])\n"
+    "array([ 0.88479687,  0.95162582,  1.        ,  1.05170918,  1.13610167])\n"
+    "\n"
+    "Compare ``exprel(5e-9)`` to the naive calculation.  The exact value\n"
+    "is ``1.00000000250000000416...``.\n"
+    "\n"
+    ">>> exprel(5e-9)\n"
+    "1.0000000025\n"
+    "\n"
+    ">>> (np.exp(5e-9) - 1)/5e-9\n"
+    "0.99999999392252903")
+ufunc_exprel_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_exprel_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_exprel_types[0] = <char>NPY_FLOAT
+ufunc_exprel_types[1] = <char>NPY_FLOAT
+ufunc_exprel_types[2] = <char>NPY_DOUBLE
+ufunc_exprel_types[3] = <char>NPY_DOUBLE
+ufunc_exprel_ptr[2*0] = <void*>_func_exprel
+ufunc_exprel_ptr[2*0+1] = <void*>(<char*>"exprel")
+ufunc_exprel_ptr[2*1] = <void*>_func_exprel
+ufunc_exprel_ptr[2*1+1] = <void*>(<char*>"exprel")
+ufunc_exprel_data[0] = &ufunc_exprel_ptr[2*0]
+ufunc_exprel_data[1] = &ufunc_exprel_ptr[2*1]
+exprel = np.PyUFunc_FromFuncAndData(ufunc_exprel_loops, ufunc_exprel_data, ufunc_exprel_types, 2, 1, 1, 0, "exprel", ufunc_exprel_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_fdtr_loops[2]
+cdef void *ufunc_fdtr_ptr[4]
+cdef void *ufunc_fdtr_data[2]
+cdef char ufunc_fdtr_types[8]
+cdef char *ufunc_fdtr_doc = (
+    "fdtr(dfn, dfd, x, out=None)\n"
+    "\n"
+    "F cumulative distribution function.\n"
+    "\n"
+    "Returns the value of the cumulative distribution function of the\n"
+    "F-distribution, also known as Snedecor's F-distribution or the\n"
+    "Fisher-Snedecor distribution.\n"
+    "\n"
+    "The F-distribution with parameters :math:`d_n` and :math:`d_d` is the\n"
+    "distribution of the random variable,\n"
+    "\n"
+    ".. math::\n"
+    "    X = \\frac{U_n/d_n}{U_d/d_d},\n"
+    "\n"
+    "where :math:`U_n` and :math:`U_d` are random variables distributed\n"
+    ":math:`\\chi^2`, with :math:`d_n` and :math:`d_d` degrees of freedom,\n"
+    "respectively.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "dfn : array_like\n"
+    "    First parameter (positive float).\n"
+    "dfd : array_like\n"
+    "    Second parameter (positive float).\n"
+    "x : array_like\n"
+    "    Argument (nonnegative float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    The CDF of the F-distribution with parameters `dfn` and `dfd` at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "fdtrc : F distribution survival function\n"
+    "fdtri : F distribution inverse cumulative distribution\n"
+    "scipy.stats.f : F distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The regularized incomplete beta function is used, according to the\n"
+    "formula,\n"
+    "\n"
+    ".. math::\n"
+    "    F(d_n, d_d; x) = I_{xd_n/(d_d + xd_n)}(d_n/2, d_d/2).\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `fdtr`. The F distribution is also\n"
+    "available as `scipy.stats.f`. Calling `fdtr` directly can improve\n"
+    "performance compared to the ``cdf`` method of `scipy.stats.f` (see last\n"
+    "example below).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function for ``dfn=1`` and ``dfd=2`` at ``x=1``.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import fdtr\n"
+    ">>> fdtr(1, 2, 1)\n"
+    "0.5773502691896258\n"
+    "\n"
+    "Calculate the function at several points by providing a NumPy array for\n"
+    "`x`.\n"
+    "\n"
+    ">>> x = np.array([0.5, 2., 3.])\n"
+    ">>> fdtr(1, 2, x)\n"
+    "array([0.4472136 , 0.70710678, 0.77459667])\n"
+    "\n"
+    "Plot the function for several parameter sets.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> dfn_parameters = [1, 5, 10, 50]\n"
+    ">>> dfd_parameters = [1, 1, 2, 3]\n"
+    ">>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']\n"
+    ">>> parameters_list = list(zip(dfn_parameters, dfd_parameters,\n"
+    "...                            linestyles))\n"
+    ">>> x = np.linspace(0, 30, 1000)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> for parameter_set in parameters_list:\n"
+    "...     dfn, dfd, style = parameter_set\n"
+    "...     fdtr_vals = fdtr(dfn, dfd, x)\n"
+    "...     ax.plot(x, fdtr_vals, label=rf\"$d_n={dfn},\\, d_d={dfd}$\",\n"
+    "...             ls=style)\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(\"$x$\")\n"
+    ">>> ax.set_title(\"F distribution cumulative distribution function\")\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The F distribution is also available as `scipy.stats.f`. Using `fdtr`\n"
+    "directly can be much faster than calling the ``cdf`` method of\n"
+    "`scipy.stats.f`, especially for small arrays or individual values.\n"
+    "To get the same results one must use the following parametrization:\n"
+    "``stats.f(dfn, dfd).cdf(x)=fdtr(dfn, dfd, x)``.\n"
+    "\n"
+    ">>> from scipy.stats import f\n"
+    ">>> dfn, dfd = 1, 2\n"
+    ">>> x = 1\n"
+    ">>> fdtr_res = fdtr(dfn, dfd, x)  # this will often be faster than below\n"
+    ">>> f_dist_res = f(dfn, dfd).cdf(x)\n"
+    ">>> fdtr_res == f_dist_res  # test that results are equal\n"
+    "True")
+ufunc_fdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_fdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_fdtr_types[0] = <char>NPY_FLOAT
+ufunc_fdtr_types[1] = <char>NPY_FLOAT
+ufunc_fdtr_types[2] = <char>NPY_FLOAT
+ufunc_fdtr_types[3] = <char>NPY_FLOAT
+ufunc_fdtr_types[4] = <char>NPY_DOUBLE
+ufunc_fdtr_types[5] = <char>NPY_DOUBLE
+ufunc_fdtr_types[6] = <char>NPY_DOUBLE
+ufunc_fdtr_types[7] = <char>NPY_DOUBLE
+ufunc_fdtr_ptr[2*0] = <void*>_func_fdtr
+ufunc_fdtr_ptr[2*0+1] = <void*>(<char*>"fdtr")
+ufunc_fdtr_ptr[2*1] = <void*>_func_fdtr
+ufunc_fdtr_ptr[2*1+1] = <void*>(<char*>"fdtr")
+ufunc_fdtr_data[0] = &ufunc_fdtr_ptr[2*0]
+ufunc_fdtr_data[1] = &ufunc_fdtr_ptr[2*1]
+fdtr = np.PyUFunc_FromFuncAndData(ufunc_fdtr_loops, ufunc_fdtr_data, ufunc_fdtr_types, 2, 3, 1, 0, "fdtr", ufunc_fdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_fdtrc_loops[2]
+cdef void *ufunc_fdtrc_ptr[4]
+cdef void *ufunc_fdtrc_data[2]
+cdef char ufunc_fdtrc_types[8]
+cdef char *ufunc_fdtrc_doc = (
+    "fdtrc(dfn, dfd, x, out=None)\n"
+    "\n"
+    "F survival function.\n"
+    "\n"
+    "Returns the complemented F-distribution function (the integral of the\n"
+    "density from `x` to infinity).\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "dfn : array_like\n"
+    "    First parameter (positive float).\n"
+    "dfd : array_like\n"
+    "    Second parameter (positive float).\n"
+    "x : array_like\n"
+    "    Argument (nonnegative float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    The complemented F-distribution function with parameters `dfn` and\n"
+    "    `dfd` at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "fdtr : F distribution cumulative distribution function\n"
+    "fdtri : F distribution inverse cumulative distribution function\n"
+    "scipy.stats.f : F distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The regularized incomplete beta function is used, according to the\n"
+    "formula,\n"
+    "\n"
+    ".. math::\n"
+    "    F(d_n, d_d; x) = I_{d_d/(d_d + xd_n)}(d_d/2, d_n/2).\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `fdtrc`. The F distribution is also\n"
+    "available as `scipy.stats.f`. Calling `fdtrc` directly can improve\n"
+    "performance compared to the ``sf`` method of `scipy.stats.f` (see last\n"
+    "example below).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function for ``dfn=1`` and ``dfd=2`` at ``x=1``.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import fdtrc\n"
+    ">>> fdtrc(1, 2, 1)\n"
+    "0.42264973081037427\n"
+    "\n"
+    "Calculate the function at several points by providing a NumPy array for\n"
+    "`x`.\n"
+    "\n"
+    ">>> x = np.array([0.5, 2., 3.])\n"
+    ">>> fdtrc(1, 2, x)\n"
+    "array([0.5527864 , 0.29289322, 0.22540333])\n"
+    "\n"
+    "Plot the function for several parameter sets.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> dfn_parameters = [1, 5, 10, 50]\n"
+    ">>> dfd_parameters = [1, 1, 2, 3]\n"
+    ">>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']\n"
+    ">>> parameters_list = list(zip(dfn_parameters, dfd_parameters,\n"
+    "...                            linestyles))\n"
+    ">>> x = np.linspace(0, 30, 1000)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> for parameter_set in parameters_list:\n"
+    "...     dfn, dfd, style = parameter_set\n"
+    "...     fdtrc_vals = fdtrc(dfn, dfd, x)\n"
+    "...     ax.plot(x, fdtrc_vals, label=rf\"$d_n={dfn},\\, d_d={dfd}$\",\n"
+    "...             ls=style)\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(\"$x$\")\n"
+    ">>> ax.set_title(\"F distribution survival function\")\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The F distribution is also available as `scipy.stats.f`. Using `fdtrc`\n"
+    "directly can be much faster than calling the ``sf`` method of\n"
+    "`scipy.stats.f`, especially for small arrays or individual values.\n"
+    "To get the same results one must use the following parametrization:\n"
+    "``stats.f(dfn, dfd).sf(x)=fdtrc(dfn, dfd, x)``.\n"
+    "\n"
+    ">>> from scipy.stats import f\n"
+    ">>> dfn, dfd = 1, 2\n"
+    ">>> x = 1\n"
+    ">>> fdtrc_res = fdtrc(dfn, dfd, x)  # this will often be faster than below\n"
+    ">>> f_dist_res = f(dfn, dfd).sf(x)\n"
+    ">>> f_dist_res == fdtrc_res  # test that results are equal\n"
+    "True")
+ufunc_fdtrc_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_fdtrc_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_fdtrc_types[0] = <char>NPY_FLOAT
+ufunc_fdtrc_types[1] = <char>NPY_FLOAT
+ufunc_fdtrc_types[2] = <char>NPY_FLOAT
+ufunc_fdtrc_types[3] = <char>NPY_FLOAT
+ufunc_fdtrc_types[4] = <char>NPY_DOUBLE
+ufunc_fdtrc_types[5] = <char>NPY_DOUBLE
+ufunc_fdtrc_types[6] = <char>NPY_DOUBLE
+ufunc_fdtrc_types[7] = <char>NPY_DOUBLE
+ufunc_fdtrc_ptr[2*0] = <void*>_func_fdtrc
+ufunc_fdtrc_ptr[2*0+1] = <void*>(<char*>"fdtrc")
+ufunc_fdtrc_ptr[2*1] = <void*>_func_fdtrc
+ufunc_fdtrc_ptr[2*1+1] = <void*>(<char*>"fdtrc")
+ufunc_fdtrc_data[0] = &ufunc_fdtrc_ptr[2*0]
+ufunc_fdtrc_data[1] = &ufunc_fdtrc_ptr[2*1]
+fdtrc = np.PyUFunc_FromFuncAndData(ufunc_fdtrc_loops, ufunc_fdtrc_data, ufunc_fdtrc_types, 2, 3, 1, 0, "fdtrc", ufunc_fdtrc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_fdtri_loops[2]
+cdef void *ufunc_fdtri_ptr[4]
+cdef void *ufunc_fdtri_data[2]
+cdef char ufunc_fdtri_types[8]
+cdef char *ufunc_fdtri_doc = (
+    "fdtri(dfn, dfd, p, out=None)\n"
+    "\n"
+    "The `p`-th quantile of the F-distribution.\n"
+    "\n"
+    "This function is the inverse of the F-distribution CDF, `fdtr`, returning\n"
+    "the `x` such that `fdtr(dfn, dfd, x) = p`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "dfn : array_like\n"
+    "    First parameter (positive float).\n"
+    "dfd : array_like\n"
+    "    Second parameter (positive float).\n"
+    "p : array_like\n"
+    "    Cumulative probability, in [0, 1].\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "x : scalar or ndarray\n"
+    "    The quantile corresponding to `p`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "fdtr : F distribution cumulative distribution function\n"
+    "fdtrc : F distribution survival function\n"
+    "scipy.stats.f : F distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The computation is carried out using the relation to the inverse\n"
+    "regularized beta function, :math:`I^{-1}_x(a, b)`.  Let\n"
+    ":math:`z = I^{-1}_p(d_d/2, d_n/2).`  Then,\n"
+    "\n"
+    ".. math::\n"
+    "    x = \\frac{d_d (1 - z)}{d_n z}.\n"
+    "\n"
+    "If `p` is such that :math:`x < 0.5`, the following relation is used\n"
+    "instead for improved stability: let\n"
+    ":math:`z' = I^{-1}_{1 - p}(d_n/2, d_d/2).` Then,\n"
+    "\n"
+    ".. math::\n"
+    "    x = \\frac{d_d z'}{d_n (1 - z')}.\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `fdtri`.\n"
+    "\n"
+    "The F distribution is also available as `scipy.stats.f`. Calling\n"
+    "`fdtri` directly can improve performance compared to the ``ppf``\n"
+    "method of `scipy.stats.f` (see last example below).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "`fdtri` represents the inverse of the F distribution CDF which is\n"
+    "available as `fdtr`. Here, we calculate the CDF for ``df1=1``, ``df2=2``\n"
+    "at ``x=3``. `fdtri` then returns ``3`` given the same values for `df1`,\n"
+    "`df2` and the computed CDF value.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import fdtri, fdtr\n"
+    ">>> df1, df2 = 1, 2\n"
+    ">>> x = 3\n"
+    ">>> cdf_value =  fdtr(df1, df2, x)\n"
+    ">>> fdtri(df1, df2, cdf_value)\n"
+    "3.000000000000006\n"
+    "\n"
+    "Calculate the function at several points by providing a NumPy array for\n"
+    "`x`.\n"
+    "\n"
+    ">>> x = np.array([0.1, 0.4, 0.7])\n"
+    ">>> fdtri(1, 2, x)\n"
+    "array([0.02020202, 0.38095238, 1.92156863])\n"
+    "\n"
+    "Plot the function for several parameter sets.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> dfn_parameters = [50, 10, 1, 50]\n"
+    ">>> dfd_parameters = [0.5, 1, 1, 5]\n"
+    ">>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']\n"
+    ">>> parameters_list = list(zip(dfn_parameters, dfd_parameters,\n"
+    "...                            linestyles))\n"
+    ">>> x = np.linspace(0, 1, 1000)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> for parameter_set in parameters_list:\n"
+    "...     dfn, dfd, style = parameter_set\n"
+    "...     fdtri_vals = fdtri(dfn, dfd, x)\n"
+    "...     ax.plot(x, fdtri_vals, label=rf\"$d_n={dfn},\\, d_d={dfd}$\",\n"
+    "...             ls=style)\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(\"$x$\")\n"
+    ">>> title = \"F distribution inverse cumulative distribution function\"\n"
+    ">>> ax.set_title(title)\n"
+    ">>> ax.set_ylim(0, 30)\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The F distribution is also available as `scipy.stats.f`. Using `fdtri`\n"
+    "directly can be much faster than calling the ``ppf`` method of\n"
+    "`scipy.stats.f`, especially for small arrays or individual values.\n"
+    "To get the same results one must use the following parametrization:\n"
+    "``stats.f(dfn, dfd).ppf(x)=fdtri(dfn, dfd, x)``.\n"
+    "\n"
+    ">>> from scipy.stats import f\n"
+    ">>> dfn, dfd = 1, 2\n"
+    ">>> x = 0.7\n"
+    ">>> fdtri_res = fdtri(dfn, dfd, x)  # this will often be faster than below\n"
+    ">>> f_dist_res = f(dfn, dfd).ppf(x)\n"
+    ">>> f_dist_res == fdtri_res  # test that results are equal\n"
+    "True")
+ufunc_fdtri_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_fdtri_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_fdtri_types[0] = <char>NPY_FLOAT
+ufunc_fdtri_types[1] = <char>NPY_FLOAT
+ufunc_fdtri_types[2] = <char>NPY_FLOAT
+ufunc_fdtri_types[3] = <char>NPY_FLOAT
+ufunc_fdtri_types[4] = <char>NPY_DOUBLE
+ufunc_fdtri_types[5] = <char>NPY_DOUBLE
+ufunc_fdtri_types[6] = <char>NPY_DOUBLE
+ufunc_fdtri_types[7] = <char>NPY_DOUBLE
+ufunc_fdtri_ptr[2*0] = <void*>_func_fdtri
+ufunc_fdtri_ptr[2*0+1] = <void*>(<char*>"fdtri")
+ufunc_fdtri_ptr[2*1] = <void*>_func_fdtri
+ufunc_fdtri_ptr[2*1+1] = <void*>(<char*>"fdtri")
+ufunc_fdtri_data[0] = &ufunc_fdtri_ptr[2*0]
+ufunc_fdtri_data[1] = &ufunc_fdtri_ptr[2*1]
+fdtri = np.PyUFunc_FromFuncAndData(ufunc_fdtri_loops, ufunc_fdtri_data, ufunc_fdtri_types, 2, 3, 1, 0, "fdtri", ufunc_fdtri_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_fdtridfd_loops[2]
+cdef void *ufunc_fdtridfd_ptr[4]
+cdef void *ufunc_fdtridfd_data[2]
+cdef char ufunc_fdtridfd_types[8]
+cdef char *ufunc_fdtridfd_doc = (
+    "fdtridfd(dfn, p, x, out=None)\n"
+    "\n"
+    "Inverse to `fdtr` vs dfd\n"
+    "\n"
+    "Finds the F density argument dfd such that ``fdtr(dfn, dfd, x) == p``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "dfn : array_like\n"
+    "    First parameter (positive float).\n"
+    "p : array_like\n"
+    "    Cumulative probability, in [0, 1].\n"
+    "x : array_like\n"
+    "    Argument (nonnegative float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "dfd : scalar or ndarray\n"
+    "    `dfd` such that ``fdtr(dfn, dfd, x) == p``.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "fdtr : F distribution cumulative distribution function\n"
+    "fdtrc : F distribution survival function\n"
+    "fdtri : F distribution quantile function\n"
+    "scipy.stats.f : F distribution\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the F distribution cumulative distribution function for one\n"
+    "parameter set.\n"
+    "\n"
+    ">>> from scipy.special import fdtridfd, fdtr\n"
+    ">>> dfn, dfd, x = 10, 5, 2\n"
+    ">>> cdf_value = fdtr(dfn, dfd, x)\n"
+    ">>> cdf_value\n"
+    "0.7700248806501017\n"
+    "\n"
+    "Verify that `fdtridfd` recovers the original value for `dfd`:\n"
+    "\n"
+    ">>> fdtridfd(dfn, cdf_value, x)\n"
+    "5.0")
+ufunc_fdtridfd_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_fdtridfd_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_fdtridfd_types[0] = <char>NPY_FLOAT
+ufunc_fdtridfd_types[1] = <char>NPY_FLOAT
+ufunc_fdtridfd_types[2] = <char>NPY_FLOAT
+ufunc_fdtridfd_types[3] = <char>NPY_FLOAT
+ufunc_fdtridfd_types[4] = <char>NPY_DOUBLE
+ufunc_fdtridfd_types[5] = <char>NPY_DOUBLE
+ufunc_fdtridfd_types[6] = <char>NPY_DOUBLE
+ufunc_fdtridfd_types[7] = <char>NPY_DOUBLE
+ufunc_fdtridfd_ptr[2*0] = <void*>_func_fdtridfd
+ufunc_fdtridfd_ptr[2*0+1] = <void*>(<char*>"fdtridfd")
+ufunc_fdtridfd_ptr[2*1] = <void*>_func_fdtridfd
+ufunc_fdtridfd_ptr[2*1+1] = <void*>(<char*>"fdtridfd")
+ufunc_fdtridfd_data[0] = &ufunc_fdtridfd_ptr[2*0]
+ufunc_fdtridfd_data[1] = &ufunc_fdtridfd_ptr[2*1]
+fdtridfd = np.PyUFunc_FromFuncAndData(ufunc_fdtridfd_loops, ufunc_fdtridfd_data, ufunc_fdtridfd_types, 2, 3, 1, 0, "fdtridfd", ufunc_fdtridfd_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_fresnel_loops[4]
+cdef void *ufunc_fresnel_ptr[8]
+cdef void *ufunc_fresnel_data[4]
+cdef char ufunc_fresnel_types[12]
+cdef char *ufunc_fresnel_doc = (
+    "fresnel(z, out=None)\n"
+    "\n"
+    "Fresnel integrals.\n"
+    "\n"
+    "The Fresnel integrals are defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   S(z) &= \\int_0^z \\sin(\\pi t^2 /2) dt \\\\\n"
+    "   C(z) &= \\int_0^z \\cos(\\pi t^2 /2) dt.\n"
+    "\n"
+    "See [dlmf]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Real or complex valued argument\n"
+    "out : 2-tuple of ndarrays, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "S, C : 2-tuple of scalar or ndarray\n"
+    "    Values of the Fresnel integrals\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "fresnel_zeros : zeros of the Fresnel integrals\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical Functions\n"
+    "          https://dlmf.nist.gov/7.2#iii\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "As z goes to infinity along the real axis, S and C converge to 0.5.\n"
+    "\n"
+    ">>> S, C = sc.fresnel([0.1, 1, 10, 100, np.inf])\n"
+    ">>> S\n"
+    "array([0.00052359, 0.43825915, 0.46816998, 0.4968169 , 0.5       ])\n"
+    ">>> C\n"
+    "array([0.09999753, 0.7798934 , 0.49989869, 0.4999999 , 0.5       ])\n"
+    "\n"
+    "They are related to the error function `erf`.\n"
+    "\n"
+    ">>> z = np.array([1, 2, 3, 4])\n"
+    ">>> zeta = 0.5 * np.sqrt(np.pi) * (1 - 1j) * z\n"
+    ">>> S, C = sc.fresnel(z)\n"
+    ">>> C + 1j*S\n"
+    "array([0.7798934 +0.43825915j, 0.48825341+0.34341568j,\n"
+    "       0.60572079+0.496313j  , 0.49842603+0.42051575j])\n"
+    ">>> 0.5 * (1 + 1j) * sc.erf(zeta)\n"
+    "array([0.7798934 +0.43825915j, 0.48825341+0.34341568j,\n"
+    "       0.60572079+0.496313j  , 0.49842603+0.42051575j])")
+ufunc_fresnel_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_f_ff
+ufunc_fresnel_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_d_dd
+ufunc_fresnel_loops[2] = <np.PyUFuncGenericFunction>loop_i_D_DD_As_F_FF
+ufunc_fresnel_loops[3] = <np.PyUFuncGenericFunction>loop_i_D_DD_As_D_DD
+ufunc_fresnel_types[0] = <char>NPY_FLOAT
+ufunc_fresnel_types[1] = <char>NPY_FLOAT
+ufunc_fresnel_types[2] = <char>NPY_FLOAT
+ufunc_fresnel_types[3] = <char>NPY_DOUBLE
+ufunc_fresnel_types[4] = <char>NPY_DOUBLE
+ufunc_fresnel_types[5] = <char>NPY_DOUBLE
+ufunc_fresnel_types[6] = <char>NPY_CFLOAT
+ufunc_fresnel_types[7] = <char>NPY_CFLOAT
+ufunc_fresnel_types[8] = <char>NPY_CFLOAT
+ufunc_fresnel_types[9] = <char>NPY_CDOUBLE
+ufunc_fresnel_types[10] = <char>NPY_CDOUBLE
+ufunc_fresnel_types[11] = <char>NPY_CDOUBLE
+ufunc_fresnel_ptr[2*0] = <void*>_func_fresnl
+ufunc_fresnel_ptr[2*0+1] = <void*>(<char*>"fresnel")
+ufunc_fresnel_ptr[2*1] = <void*>_func_fresnl
+ufunc_fresnel_ptr[2*1+1] = <void*>(<char*>"fresnel")
+ufunc_fresnel_ptr[2*2] = <void*>_func_cfresnl_wrap
+ufunc_fresnel_ptr[2*2+1] = <void*>(<char*>"fresnel")
+ufunc_fresnel_ptr[2*3] = <void*>_func_cfresnl_wrap
+ufunc_fresnel_ptr[2*3+1] = <void*>(<char*>"fresnel")
+ufunc_fresnel_data[0] = &ufunc_fresnel_ptr[2*0]
+ufunc_fresnel_data[1] = &ufunc_fresnel_ptr[2*1]
+ufunc_fresnel_data[2] = &ufunc_fresnel_ptr[2*2]
+ufunc_fresnel_data[3] = &ufunc_fresnel_ptr[2*3]
+fresnel = np.PyUFunc_FromFuncAndData(ufunc_fresnel_loops, ufunc_fresnel_data, ufunc_fresnel_types, 4, 1, 2, 0, "fresnel", ufunc_fresnel_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gamma_loops[4]
+cdef void *ufunc_gamma_ptr[8]
+cdef void *ufunc_gamma_data[4]
+cdef char ufunc_gamma_types[8]
+cdef char *ufunc_gamma_doc = (
+    "gamma(z, out=None)\n"
+    "\n"
+    "gamma function.\n"
+    "\n"
+    "The gamma function is defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   \\Gamma(z) = \\int_0^\\infty t^{z-1} e^{-t} dt\n"
+    "\n"
+    "for :math:`\\Re(z) > 0` and is extended to the rest of the complex\n"
+    "plane by analytic continuation. See [dlmf]_ for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Real or complex valued argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the gamma function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The gamma function is often referred to as the generalized\n"
+    "factorial since :math:`\\Gamma(n + 1) = n!` for natural numbers\n"
+    ":math:`n`. More generally it satisfies the recurrence relation\n"
+    ":math:`\\Gamma(z + 1) = z \\cdot \\Gamma(z)` for complex :math:`z`,\n"
+    "which, combined with the fact that :math:`\\Gamma(1) = 1`, implies\n"
+    "the above identity for :math:`z = n`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical Functions\n"
+    "          https://dlmf.nist.gov/5.2#E1\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import gamma, factorial\n"
+    "\n"
+    ">>> gamma([0, 0.5, 1, 5])\n"
+    "array([         inf,   1.77245385,   1.        ,  24.        ])\n"
+    "\n"
+    ">>> z = 2.5 + 1j\n"
+    ">>> gamma(z)\n"
+    "(0.77476210455108352+0.70763120437959293j)\n"
+    ">>> gamma(z+1), z*gamma(z)  # Recurrence property\n"
+    "((1.2292740569981171+2.5438401155000685j),\n"
+    " (1.2292740569981158+2.5438401155000658j))\n"
+    "\n"
+    ">>> gamma(0.5)**2  # gamma(0.5) = sqrt(pi)\n"
+    "3.1415926535897927\n"
+    "\n"
+    "Plot gamma(x) for real x\n"
+    "\n"
+    ">>> x = np.linspace(-3.5, 5.5, 2251)\n"
+    ">>> y = gamma(x)\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> plt.plot(x, y, 'b', alpha=0.6, label='gamma(x)')\n"
+    ">>> k = np.arange(1, 7)\n"
+    ">>> plt.plot(k, factorial(k-1), 'k*', alpha=0.6,\n"
+    "...          label='(x-1)!, x = 1, 2, ...')\n"
+    ">>> plt.xlim(-3.5, 5.5)\n"
+    ">>> plt.ylim(-10, 25)\n"
+    ">>> plt.grid()\n"
+    ">>> plt.xlabel('x')\n"
+    ">>> plt.legend(loc='lower right')\n"
+    ">>> plt.show()")
+ufunc_gamma_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_gamma_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_gamma_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_gamma_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_gamma_types[0] = <char>NPY_FLOAT
+ufunc_gamma_types[1] = <char>NPY_FLOAT
+ufunc_gamma_types[2] = <char>NPY_DOUBLE
+ufunc_gamma_types[3] = <char>NPY_DOUBLE
+ufunc_gamma_types[4] = <char>NPY_CFLOAT
+ufunc_gamma_types[5] = <char>NPY_CFLOAT
+ufunc_gamma_types[6] = <char>NPY_CDOUBLE
+ufunc_gamma_types[7] = <char>NPY_CDOUBLE
+ufunc_gamma_ptr[2*0] = <void*>_func_Gamma
+ufunc_gamma_ptr[2*0+1] = <void*>(<char*>"gamma")
+ufunc_gamma_ptr[2*1] = <void*>_func_Gamma
+ufunc_gamma_ptr[2*1+1] = <void*>(<char*>"gamma")
+ufunc_gamma_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_cgamma
+ufunc_gamma_ptr[2*2+1] = <void*>(<char*>"gamma")
+ufunc_gamma_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_cgamma
+ufunc_gamma_ptr[2*3+1] = <void*>(<char*>"gamma")
+ufunc_gamma_data[0] = &ufunc_gamma_ptr[2*0]
+ufunc_gamma_data[1] = &ufunc_gamma_ptr[2*1]
+ufunc_gamma_data[2] = &ufunc_gamma_ptr[2*2]
+ufunc_gamma_data[3] = &ufunc_gamma_ptr[2*3]
+gamma = np.PyUFunc_FromFuncAndData(ufunc_gamma_loops, ufunc_gamma_data, ufunc_gamma_types, 4, 1, 1, 0, "gamma", ufunc_gamma_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gammainc_loops[2]
+cdef void *ufunc_gammainc_ptr[4]
+cdef void *ufunc_gammainc_data[2]
+cdef char ufunc_gammainc_types[6]
+cdef char *ufunc_gammainc_doc = (
+    "gammainc(a, x, out=None)\n"
+    "\n"
+    "Regularized lower incomplete gamma function.\n"
+    "\n"
+    "It is defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    P(a, x) = \\frac{1}{\\Gamma(a)} \\int_0^x t^{a - 1}e^{-t} dt\n"
+    "\n"
+    "for :math:`a > 0` and :math:`x \\geq 0`. See [dlmf]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    Positive parameter\n"
+    "x : array_like\n"
+    "    Nonnegative argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the lower incomplete gamma function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gammaincc : regularized upper incomplete gamma function\n"
+    "gammaincinv : inverse of the regularized lower incomplete gamma function\n"
+    "gammainccinv : inverse of the regularized upper incomplete gamma function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The function satisfies the relation ``gammainc(a, x) +\n"
+    "gammaincc(a, x) = 1`` where `gammaincc` is the regularized upper\n"
+    "incomplete gamma function.\n"
+    "\n"
+    "The implementation largely follows that of [boost]_.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical functions\n"
+    "          https://dlmf.nist.gov/8.2#E4\n"
+    ".. [boost] Maddock et. al., \"Incomplete Gamma Functions\",\n"
+    "   https://www.boost.org/doc/libs/1_61_0/libs/math/doc/html/math_toolkit/sf_gamma/igamma.html\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is the CDF of the gamma distribution, so it starts at 0 and\n"
+    "monotonically increases to 1.\n"
+    "\n"
+    ">>> sc.gammainc(0.5, [0, 1, 10, 100])\n"
+    "array([0.        , 0.84270079, 0.99999226, 1.        ])\n"
+    "\n"
+    "It is equal to one minus the upper incomplete gamma function.\n"
+    "\n"
+    ">>> a, x = 0.5, 0.4\n"
+    ">>> sc.gammainc(a, x)\n"
+    "0.6289066304773024\n"
+    ">>> 1 - sc.gammaincc(a, x)\n"
+    "0.6289066304773024")
+ufunc_gammainc_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_gammainc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_gammainc_types[0] = <char>NPY_FLOAT
+ufunc_gammainc_types[1] = <char>NPY_FLOAT
+ufunc_gammainc_types[2] = <char>NPY_FLOAT
+ufunc_gammainc_types[3] = <char>NPY_DOUBLE
+ufunc_gammainc_types[4] = <char>NPY_DOUBLE
+ufunc_gammainc_types[5] = <char>NPY_DOUBLE
+ufunc_gammainc_ptr[2*0] = <void*>_func_igam
+ufunc_gammainc_ptr[2*0+1] = <void*>(<char*>"gammainc")
+ufunc_gammainc_ptr[2*1] = <void*>_func_igam
+ufunc_gammainc_ptr[2*1+1] = <void*>(<char*>"gammainc")
+ufunc_gammainc_data[0] = &ufunc_gammainc_ptr[2*0]
+ufunc_gammainc_data[1] = &ufunc_gammainc_ptr[2*1]
+gammainc = np.PyUFunc_FromFuncAndData(ufunc_gammainc_loops, ufunc_gammainc_data, ufunc_gammainc_types, 2, 2, 1, 0, "gammainc", ufunc_gammainc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gammaincc_loops[2]
+cdef void *ufunc_gammaincc_ptr[4]
+cdef void *ufunc_gammaincc_data[2]
+cdef char ufunc_gammaincc_types[6]
+cdef char *ufunc_gammaincc_doc = (
+    "gammaincc(a, x, out=None)\n"
+    "\n"
+    "Regularized upper incomplete gamma function.\n"
+    "\n"
+    "It is defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    Q(a, x) = \\frac{1}{\\Gamma(a)} \\int_x^\\infty t^{a - 1}e^{-t} dt\n"
+    "\n"
+    "for :math:`a > 0` and :math:`x \\geq 0`. See [dlmf]_ for details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    Positive parameter\n"
+    "x : array_like\n"
+    "    Nonnegative argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the upper incomplete gamma function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gammainc : regularized lower incomplete gamma function\n"
+    "gammaincinv : inverse of the regularized lower incomplete gamma function\n"
+    "gammainccinv : inverse of the regularized upper incomplete gamma function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The function satisfies the relation ``gammainc(a, x) +\n"
+    "gammaincc(a, x) = 1`` where `gammainc` is the regularized lower\n"
+    "incomplete gamma function.\n"
+    "\n"
+    "The implementation largely follows that of [boost]_.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical functions\n"
+    "          https://dlmf.nist.gov/8.2#E4\n"
+    ".. [boost] Maddock et. al., \"Incomplete Gamma Functions\",\n"
+    "   https://www.boost.org/doc/libs/1_61_0/libs/math/doc/html/math_toolkit/sf_gamma/igamma.html\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is the survival function of the gamma distribution, so it\n"
+    "starts at 1 and monotonically decreases to 0.\n"
+    "\n"
+    ">>> sc.gammaincc(0.5, [0, 1, 10, 100, 1000])\n"
+    "array([1.00000000e+00, 1.57299207e-01, 7.74421643e-06, 2.08848758e-45,\n"
+    "       0.00000000e+00])\n"
+    "\n"
+    "It is equal to one minus the lower incomplete gamma function.\n"
+    "\n"
+    ">>> a, x = 0.5, 0.4\n"
+    ">>> sc.gammaincc(a, x)\n"
+    "0.37109336952269756\n"
+    ">>> 1 - sc.gammainc(a, x)\n"
+    "0.37109336952269756")
+ufunc_gammaincc_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_gammaincc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_gammaincc_types[0] = <char>NPY_FLOAT
+ufunc_gammaincc_types[1] = <char>NPY_FLOAT
+ufunc_gammaincc_types[2] = <char>NPY_FLOAT
+ufunc_gammaincc_types[3] = <char>NPY_DOUBLE
+ufunc_gammaincc_types[4] = <char>NPY_DOUBLE
+ufunc_gammaincc_types[5] = <char>NPY_DOUBLE
+ufunc_gammaincc_ptr[2*0] = <void*>_func_igamc
+ufunc_gammaincc_ptr[2*0+1] = <void*>(<char*>"gammaincc")
+ufunc_gammaincc_ptr[2*1] = <void*>_func_igamc
+ufunc_gammaincc_ptr[2*1+1] = <void*>(<char*>"gammaincc")
+ufunc_gammaincc_data[0] = &ufunc_gammaincc_ptr[2*0]
+ufunc_gammaincc_data[1] = &ufunc_gammaincc_ptr[2*1]
+gammaincc = np.PyUFunc_FromFuncAndData(ufunc_gammaincc_loops, ufunc_gammaincc_data, ufunc_gammaincc_types, 2, 2, 1, 0, "gammaincc", ufunc_gammaincc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gammainccinv_loops[2]
+cdef void *ufunc_gammainccinv_ptr[4]
+cdef void *ufunc_gammainccinv_data[2]
+cdef char ufunc_gammainccinv_types[6]
+cdef char *ufunc_gammainccinv_doc = (
+    "gammainccinv(a, y, out=None)\n"
+    "\n"
+    "Inverse of the regularized upper incomplete gamma function.\n"
+    "\n"
+    "Given an input :math:`y` between 0 and 1, returns :math:`x` such\n"
+    "that :math:`y = Q(a, x)`. Here :math:`Q` is the regularized upper\n"
+    "incomplete gamma function; see `gammaincc`. This is well-defined\n"
+    "because the upper incomplete gamma function is monotonic as can\n"
+    "be seen from its definition in [dlmf]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    Positive parameter\n"
+    "y : array_like\n"
+    "    Argument between 0 and 1, inclusive\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the inverse of the upper incomplete gamma function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gammaincc : regularized upper incomplete gamma function\n"
+    "gammainc : regularized lower incomplete gamma function\n"
+    "gammaincinv : inverse of the regularized lower incomplete gamma function\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical Functions\n"
+    "          https://dlmf.nist.gov/8.2#E4\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It starts at infinity and monotonically decreases to 0.\n"
+    "\n"
+    ">>> sc.gammainccinv(0.5, [0, 0.1, 0.5, 1])\n"
+    "array([       inf, 1.35277173, 0.22746821, 0.        ])\n"
+    "\n"
+    "It inverts the upper incomplete gamma function.\n"
+    "\n"
+    ">>> a, x = 0.5, [0, 0.1, 0.5, 1]\n"
+    ">>> sc.gammaincc(a, sc.gammainccinv(a, x))\n"
+    "array([0. , 0.1, 0.5, 1. ])\n"
+    "\n"
+    ">>> a, x = 0.5, [0, 10, 50]\n"
+    ">>> sc.gammainccinv(a, sc.gammaincc(a, x))\n"
+    "array([ 0., 10., 50.])")
+ufunc_gammainccinv_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_gammainccinv_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_gammainccinv_types[0] = <char>NPY_FLOAT
+ufunc_gammainccinv_types[1] = <char>NPY_FLOAT
+ufunc_gammainccinv_types[2] = <char>NPY_FLOAT
+ufunc_gammainccinv_types[3] = <char>NPY_DOUBLE
+ufunc_gammainccinv_types[4] = <char>NPY_DOUBLE
+ufunc_gammainccinv_types[5] = <char>NPY_DOUBLE
+ufunc_gammainccinv_ptr[2*0] = <void*>_func_igamci
+ufunc_gammainccinv_ptr[2*0+1] = <void*>(<char*>"gammainccinv")
+ufunc_gammainccinv_ptr[2*1] = <void*>_func_igamci
+ufunc_gammainccinv_ptr[2*1+1] = <void*>(<char*>"gammainccinv")
+ufunc_gammainccinv_data[0] = &ufunc_gammainccinv_ptr[2*0]
+ufunc_gammainccinv_data[1] = &ufunc_gammainccinv_ptr[2*1]
+gammainccinv = np.PyUFunc_FromFuncAndData(ufunc_gammainccinv_loops, ufunc_gammainccinv_data, ufunc_gammainccinv_types, 2, 2, 1, 0, "gammainccinv", ufunc_gammainccinv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gammaincinv_loops[2]
+cdef void *ufunc_gammaincinv_ptr[4]
+cdef void *ufunc_gammaincinv_data[2]
+cdef char ufunc_gammaincinv_types[6]
+cdef char *ufunc_gammaincinv_doc = (
+    "gammaincinv(a, y, out=None)\n"
+    "\n"
+    "Inverse to the regularized lower incomplete gamma function.\n"
+    "\n"
+    "Given an input :math:`y` between 0 and 1, returns :math:`x` such\n"
+    "that :math:`y = P(a, x)`. Here :math:`P` is the regularized lower\n"
+    "incomplete gamma function; see `gammainc`. This is well-defined\n"
+    "because the lower incomplete gamma function is monotonic as can be\n"
+    "seen from its definition in [dlmf]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    Positive parameter\n"
+    "y : array_like\n"
+    "    Parameter between 0 and 1, inclusive\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the inverse of the lower incomplete gamma function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gammainc : regularized lower incomplete gamma function\n"
+    "gammaincc : regularized upper incomplete gamma function\n"
+    "gammainccinv : inverse of the regularized upper incomplete gamma function\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical Functions\n"
+    "          https://dlmf.nist.gov/8.2#E4\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It starts at 0 and monotonically increases to infinity.\n"
+    "\n"
+    ">>> sc.gammaincinv(0.5, [0, 0.1 ,0.5, 1])\n"
+    "array([0.        , 0.00789539, 0.22746821,        inf])\n"
+    "\n"
+    "It inverts the lower incomplete gamma function.\n"
+    "\n"
+    ">>> a, x = 0.5, [0, 0.1, 0.5, 1]\n"
+    ">>> sc.gammainc(a, sc.gammaincinv(a, x))\n"
+    "array([0. , 0.1, 0.5, 1. ])\n"
+    "\n"
+    ">>> a, x = 0.5, [0, 10, 25]\n"
+    ">>> sc.gammaincinv(a, sc.gammainc(a, x))\n"
+    "array([ 0.        , 10.        , 25.00001465])")
+ufunc_gammaincinv_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_gammaincinv_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_gammaincinv_types[0] = <char>NPY_FLOAT
+ufunc_gammaincinv_types[1] = <char>NPY_FLOAT
+ufunc_gammaincinv_types[2] = <char>NPY_FLOAT
+ufunc_gammaincinv_types[3] = <char>NPY_DOUBLE
+ufunc_gammaincinv_types[4] = <char>NPY_DOUBLE
+ufunc_gammaincinv_types[5] = <char>NPY_DOUBLE
+ufunc_gammaincinv_ptr[2*0] = <void*>_func_igami
+ufunc_gammaincinv_ptr[2*0+1] = <void*>(<char*>"gammaincinv")
+ufunc_gammaincinv_ptr[2*1] = <void*>_func_igami
+ufunc_gammaincinv_ptr[2*1+1] = <void*>(<char*>"gammaincinv")
+ufunc_gammaincinv_data[0] = &ufunc_gammaincinv_ptr[2*0]
+ufunc_gammaincinv_data[1] = &ufunc_gammaincinv_ptr[2*1]
+gammaincinv = np.PyUFunc_FromFuncAndData(ufunc_gammaincinv_loops, ufunc_gammaincinv_data, ufunc_gammaincinv_types, 2, 2, 1, 0, "gammaincinv", ufunc_gammaincinv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gammaln_loops[2]
+cdef void *ufunc_gammaln_ptr[4]
+cdef void *ufunc_gammaln_data[2]
+cdef char ufunc_gammaln_types[4]
+cdef char *ufunc_gammaln_doc = (
+    "gammaln(x, out=None)\n"
+    "\n"
+    "Logarithm of the absolute value of the gamma function.\n"
+    "\n"
+    "Defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   \\ln(\\lvert\\Gamma(x)\\rvert)\n"
+    "\n"
+    "where :math:`\\Gamma` is the gamma function. For more details on\n"
+    "the gamma function, see [dlmf]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the log of the absolute value of gamma\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gammasgn : sign of the gamma function\n"
+    "loggamma : principal branch of the logarithm of the gamma function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "It is the same function as the Python standard library function\n"
+    ":func:`math.lgamma`.\n"
+    "\n"
+    "When used in conjunction with `gammasgn`, this function is useful\n"
+    "for working in logspace on the real axis without having to deal\n"
+    "with complex numbers via the relation ``exp(gammaln(x)) =\n"
+    "gammasgn(x) * gamma(x)``.\n"
+    "\n"
+    "For complex-valued log-gamma, use `loggamma` instead of `gammaln`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical Functions\n"
+    "          https://dlmf.nist.gov/5\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It has two positive zeros.\n"
+    "\n"
+    ">>> sc.gammaln([1, 2])\n"
+    "array([0., 0.])\n"
+    "\n"
+    "It has poles at nonpositive integers.\n"
+    "\n"
+    ">>> sc.gammaln([0, -1, -2, -3, -4])\n"
+    "array([inf, inf, inf, inf, inf])\n"
+    "\n"
+    "It asymptotically approaches ``x * log(x)`` (Stirling's formula).\n"
+    "\n"
+    ">>> x = np.array([1e10, 1e20, 1e40, 1e80])\n"
+    ">>> sc.gammaln(x)\n"
+    "array([2.20258509e+11, 4.50517019e+21, 9.11034037e+41, 1.83206807e+82])\n"
+    ">>> x * np.log(x)\n"
+    "array([2.30258509e+11, 4.60517019e+21, 9.21034037e+41, 1.84206807e+82])")
+ufunc_gammaln_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_gammaln_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_gammaln_types[0] = <char>NPY_FLOAT
+ufunc_gammaln_types[1] = <char>NPY_FLOAT
+ufunc_gammaln_types[2] = <char>NPY_DOUBLE
+ufunc_gammaln_types[3] = <char>NPY_DOUBLE
+ufunc_gammaln_ptr[2*0] = <void*>_func_lgam
+ufunc_gammaln_ptr[2*0+1] = <void*>(<char*>"gammaln")
+ufunc_gammaln_ptr[2*1] = <void*>_func_lgam
+ufunc_gammaln_ptr[2*1+1] = <void*>(<char*>"gammaln")
+ufunc_gammaln_data[0] = &ufunc_gammaln_ptr[2*0]
+ufunc_gammaln_data[1] = &ufunc_gammaln_ptr[2*1]
+gammaln = np.PyUFunc_FromFuncAndData(ufunc_gammaln_loops, ufunc_gammaln_data, ufunc_gammaln_types, 2, 1, 1, 0, "gammaln", ufunc_gammaln_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gammasgn_loops[2]
+cdef void *ufunc_gammasgn_ptr[4]
+cdef void *ufunc_gammasgn_data[2]
+cdef char ufunc_gammasgn_types[4]
+cdef char *ufunc_gammasgn_doc = (
+    "gammasgn(x, out=None)\n"
+    "\n"
+    "Sign of the gamma function.\n"
+    "\n"
+    "It is defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   \\text{gammasgn}(x) =\n"
+    "   \\begin{cases}\n"
+    "     +1 & \\Gamma(x) > 0 \\\\\n"
+    "     -1 & \\Gamma(x) < 0\n"
+    "   \\end{cases}\n"
+    "\n"
+    "where :math:`\\Gamma` is the gamma function; see `gamma`. This\n"
+    "definition is complete since the gamma function is never zero;\n"
+    "see the discussion after [dlmf]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Sign of the gamma function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gamma : the gamma function\n"
+    "gammaln : log of the absolute value of the gamma function\n"
+    "loggamma : analytic continuation of the log of the gamma function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The gamma function can be computed as ``gammasgn(x) *\n"
+    "np.exp(gammaln(x))``.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical Functions\n"
+    "          https://dlmf.nist.gov/5.2#E1\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is 1 for `x > 0`.\n"
+    "\n"
+    ">>> sc.gammasgn([1, 2, 3, 4])\n"
+    "array([1., 1., 1., 1.])\n"
+    "\n"
+    "It alternates between -1 and 1 for negative integers.\n"
+    "\n"
+    ">>> sc.gammasgn([-0.5, -1.5, -2.5, -3.5])\n"
+    "array([-1.,  1., -1.,  1.])\n"
+    "\n"
+    "It can be used to compute the gamma function.\n"
+    "\n"
+    ">>> x = [1.5, 0.5, -0.5, -1.5]\n"
+    ">>> sc.gammasgn(x) * np.exp(sc.gammaln(x))\n"
+    "array([ 0.88622693,  1.77245385, -3.5449077 ,  2.3632718 ])\n"
+    ">>> sc.gamma(x)\n"
+    "array([ 0.88622693,  1.77245385, -3.5449077 ,  2.3632718 ])")
+ufunc_gammasgn_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_gammasgn_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_gammasgn_types[0] = <char>NPY_FLOAT
+ufunc_gammasgn_types[1] = <char>NPY_FLOAT
+ufunc_gammasgn_types[2] = <char>NPY_DOUBLE
+ufunc_gammasgn_types[3] = <char>NPY_DOUBLE
+ufunc_gammasgn_ptr[2*0] = <void*>_func_gammasgn
+ufunc_gammasgn_ptr[2*0+1] = <void*>(<char*>"gammasgn")
+ufunc_gammasgn_ptr[2*1] = <void*>_func_gammasgn
+ufunc_gammasgn_ptr[2*1+1] = <void*>(<char*>"gammasgn")
+ufunc_gammasgn_data[0] = &ufunc_gammasgn_ptr[2*0]
+ufunc_gammasgn_data[1] = &ufunc_gammasgn_ptr[2*1]
+gammasgn = np.PyUFunc_FromFuncAndData(ufunc_gammasgn_loops, ufunc_gammasgn_data, ufunc_gammasgn_types, 2, 1, 1, 0, "gammasgn", ufunc_gammasgn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gdtr_loops[2]
+cdef void *ufunc_gdtr_ptr[4]
+cdef void *ufunc_gdtr_data[2]
+cdef char ufunc_gdtr_types[8]
+cdef char *ufunc_gdtr_doc = (
+    "gdtr(a, b, x, out=None)\n"
+    "\n"
+    "Gamma distribution cumulative distribution function.\n"
+    "\n"
+    "Returns the integral from zero to `x` of the gamma probability density\n"
+    "function,\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    F = \\int_0^x \\frac{a^b}{\\Gamma(b)} t^{b-1} e^{-at}\\,dt,\n"
+    "\n"
+    "where :math:`\\Gamma` is the gamma function.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    The rate parameter of the gamma distribution, sometimes denoted\n"
+    "    :math:`\\beta` (float).  It is also the reciprocal of the scale\n"
+    "    parameter :math:`\\theta`.\n"
+    "b : array_like\n"
+    "    The shape parameter of the gamma distribution, sometimes denoted\n"
+    "    :math:`\\alpha` (float).\n"
+    "x : array_like\n"
+    "    The quantile (upper limit of integration; float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "F : scalar or ndarray\n"
+    "    The CDF of the gamma distribution with parameters `a` and `b`\n"
+    "    evaluated at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gdtrc : 1 - CDF of the gamma distribution.\n"
+    "scipy.stats.gamma: Gamma distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The evaluation is carried out using the relation to the incomplete gamma\n"
+    "integral (regularized gamma function).\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `gdtr`. Calling `gdtr` directly can\n"
+    "improve performance compared to the ``cdf`` method of `scipy.stats.gamma`\n"
+    "(see last example below).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the function for ``a=1``, ``b=2`` at ``x=5``.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import gdtr\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> gdtr(1., 2., 5.)\n"
+    "0.9595723180054873\n"
+    "\n"
+    "Compute the function for ``a=1`` and ``b=2`` at several points by\n"
+    "providing a NumPy array for `x`.\n"
+    "\n"
+    ">>> xvalues = np.array([1., 2., 3., 4])\n"
+    ">>> gdtr(1., 1., xvalues)\n"
+    "array([0.63212056, 0.86466472, 0.95021293, 0.98168436])\n"
+    "\n"
+    "`gdtr` can evaluate different parameter sets by providing arrays with\n"
+    "broadcasting compatible shapes for `a`, `b` and `x`. Here we compute the\n"
+    "function for three different `a` at four positions `x` and ``b=3``,\n"
+    "resulting in a 3x4 array.\n"
+    "\n"
+    ">>> a = np.array([[0.5], [1.5], [2.5]])\n"
+    ">>> x = np.array([1., 2., 3., 4])\n"
+    ">>> a.shape, x.shape\n"
+    "((3, 1), (4,))\n"
+    "\n"
+    ">>> gdtr(a, 3., x)\n"
+    "array([[0.01438768, 0.0803014 , 0.19115317, 0.32332358],\n"
+    "       [0.19115317, 0.57680992, 0.82642193, 0.9380312 ],\n"
+    "       [0.45618688, 0.87534798, 0.97974328, 0.9972306 ]])\n"
+    "\n"
+    "Plot the function for four different parameter sets.\n"
+    "\n"
+    ">>> a_parameters = [0.3, 1, 2, 6]\n"
+    ">>> b_parameters = [2, 10, 15, 20]\n"
+    ">>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']\n"
+    ">>> parameters_list = list(zip(a_parameters, b_parameters, linestyles))\n"
+    ">>> x = np.linspace(0, 30, 1000)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> for parameter_set in parameters_list:\n"
+    "...     a, b, style = parameter_set\n"
+    "...     gdtr_vals = gdtr(a, b, x)\n"
+    "...     ax.plot(x, gdtr_vals, label=fr\"$a= {a},\\, b={b}$\", ls=style)\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(\"$x$\")\n"
+    ">>> ax.set_title(\"Gamma distribution cumulative distribution function\")\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The gamma distribution is also available as `scipy.stats.gamma`. Using\n"
+    "`gdtr` directly can be much faster than calling the ``cdf`` method of\n"
+    "`scipy.stats.gamma`, especially for small arrays or individual values.\n"
+    "To get the same results one must use the following parametrization:\n"
+    "``stats.gamma(b, scale=1/a).cdf(x)=gdtr(a, b, x)``.\n"
+    "\n"
+    ">>> from scipy.stats import gamma\n"
+    ">>> a = 2.\n"
+    ">>> b = 3\n"
+    ">>> x = 1.\n"
+    ">>> gdtr_result = gdtr(a, b, x)  # this will often be faster than below\n"
+    ">>> gamma_dist_result = gamma(b, scale=1/a).cdf(x)\n"
+    ">>> gdtr_result == gamma_dist_result  # test that results are equal\n"
+    "True")
+ufunc_gdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_gdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_gdtr_types[0] = <char>NPY_FLOAT
+ufunc_gdtr_types[1] = <char>NPY_FLOAT
+ufunc_gdtr_types[2] = <char>NPY_FLOAT
+ufunc_gdtr_types[3] = <char>NPY_FLOAT
+ufunc_gdtr_types[4] = <char>NPY_DOUBLE
+ufunc_gdtr_types[5] = <char>NPY_DOUBLE
+ufunc_gdtr_types[6] = <char>NPY_DOUBLE
+ufunc_gdtr_types[7] = <char>NPY_DOUBLE
+ufunc_gdtr_ptr[2*0] = <void*>_func_gdtr
+ufunc_gdtr_ptr[2*0+1] = <void*>(<char*>"gdtr")
+ufunc_gdtr_ptr[2*1] = <void*>_func_gdtr
+ufunc_gdtr_ptr[2*1+1] = <void*>(<char*>"gdtr")
+ufunc_gdtr_data[0] = &ufunc_gdtr_ptr[2*0]
+ufunc_gdtr_data[1] = &ufunc_gdtr_ptr[2*1]
+gdtr = np.PyUFunc_FromFuncAndData(ufunc_gdtr_loops, ufunc_gdtr_data, ufunc_gdtr_types, 2, 3, 1, 0, "gdtr", ufunc_gdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gdtrc_loops[2]
+cdef void *ufunc_gdtrc_ptr[4]
+cdef void *ufunc_gdtrc_data[2]
+cdef char ufunc_gdtrc_types[8]
+cdef char *ufunc_gdtrc_doc = (
+    "gdtrc(a, b, x, out=None)\n"
+    "\n"
+    "Gamma distribution survival function.\n"
+    "\n"
+    "Integral from `x` to infinity of the gamma probability density function,\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    F = \\int_x^\\infty \\frac{a^b}{\\Gamma(b)} t^{b-1} e^{-at}\\,dt,\n"
+    "\n"
+    "where :math:`\\Gamma` is the gamma function.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    The rate parameter of the gamma distribution, sometimes denoted\n"
+    "    :math:`\\beta` (float). It is also the reciprocal of the scale\n"
+    "    parameter :math:`\\theta`.\n"
+    "b : array_like\n"
+    "    The shape parameter of the gamma distribution, sometimes denoted\n"
+    "    :math:`\\alpha` (float).\n"
+    "x : array_like\n"
+    "    The quantile (lower limit of integration; float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "F : scalar or ndarray\n"
+    "    The survival function of the gamma distribution with parameters `a`\n"
+    "    and `b` evaluated at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gdtr: Gamma distribution cumulative distribution function\n"
+    "scipy.stats.gamma: Gamma distribution\n"
+    "gdtrix\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The evaluation is carried out using the relation to the incomplete gamma\n"
+    "integral (regularized gamma function).\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `gdtrc`. Calling `gdtrc` directly can\n"
+    "improve performance compared to the ``sf`` method of `scipy.stats.gamma`\n"
+    "(see last example below).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the function for ``a=1`` and ``b=2`` at ``x=5``.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import gdtrc\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> gdtrc(1., 2., 5.)\n"
+    "0.04042768199451279\n"
+    "\n"
+    "Compute the function for ``a=1``, ``b=2`` at several points by providing\n"
+    "a NumPy array for `x`.\n"
+    "\n"
+    ">>> xvalues = np.array([1., 2., 3., 4])\n"
+    ">>> gdtrc(1., 1., xvalues)\n"
+    "array([0.36787944, 0.13533528, 0.04978707, 0.01831564])\n"
+    "\n"
+    "`gdtrc` can evaluate different parameter sets by providing arrays with\n"
+    "broadcasting compatible shapes for `a`, `b` and `x`. Here we compute the\n"
+    "function for three different `a` at four positions `x` and ``b=3``,\n"
+    "resulting in a 3x4 array.\n"
+    "\n"
+    ">>> a = np.array([[0.5], [1.5], [2.5]])\n"
+    ">>> x = np.array([1., 2., 3., 4])\n"
+    ">>> a.shape, x.shape\n"
+    "((3, 1), (4,))\n"
+    "\n"
+    ">>> gdtrc(a, 3., x)\n"
+    "array([[0.98561232, 0.9196986 , 0.80884683, 0.67667642],\n"
+    "       [0.80884683, 0.42319008, 0.17357807, 0.0619688 ],\n"
+    "       [0.54381312, 0.12465202, 0.02025672, 0.0027694 ]])\n"
+    "\n"
+    "Plot the function for four different parameter sets.\n"
+    "\n"
+    ">>> a_parameters = [0.3, 1, 2, 6]\n"
+    ">>> b_parameters = [2, 10, 15, 20]\n"
+    ">>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']\n"
+    ">>> parameters_list = list(zip(a_parameters, b_parameters, linestyles))\n"
+    ">>> x = np.linspace(0, 30, 1000)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> for parameter_set in parameters_list:\n"
+    "...     a, b, style = parameter_set\n"
+    "...     gdtrc_vals = gdtrc(a, b, x)\n"
+    "...     ax.plot(x, gdtrc_vals, label=fr\"$a= {a},\\, b={b}$\", ls=style)\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(\"$x$\")\n"
+    ">>> ax.set_title(\"Gamma distribution survival function\")\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The gamma distribution is also available as `scipy.stats.gamma`.\n"
+    "Using `gdtrc` directly can be much faster than calling the ``sf`` method\n"
+    "of `scipy.stats.gamma`, especially for small arrays or individual\n"
+    "values. To get the same results one must use the following parametrization:\n"
+    "``stats.gamma(b, scale=1/a).sf(x)=gdtrc(a, b, x)``.\n"
+    "\n"
+    ">>> from scipy.stats import gamma\n"
+    ">>> a = 2\n"
+    ">>> b = 3\n"
+    ">>> x = 1.\n"
+    ">>> gdtrc_result = gdtrc(a, b, x)  # this will often be faster than below\n"
+    ">>> gamma_dist_result = gamma(b, scale=1/a).sf(x)\n"
+    ">>> gdtrc_result == gamma_dist_result  # test that results are equal\n"
+    "True")
+ufunc_gdtrc_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_gdtrc_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_gdtrc_types[0] = <char>NPY_FLOAT
+ufunc_gdtrc_types[1] = <char>NPY_FLOAT
+ufunc_gdtrc_types[2] = <char>NPY_FLOAT
+ufunc_gdtrc_types[3] = <char>NPY_FLOAT
+ufunc_gdtrc_types[4] = <char>NPY_DOUBLE
+ufunc_gdtrc_types[5] = <char>NPY_DOUBLE
+ufunc_gdtrc_types[6] = <char>NPY_DOUBLE
+ufunc_gdtrc_types[7] = <char>NPY_DOUBLE
+ufunc_gdtrc_ptr[2*0] = <void*>_func_gdtrc
+ufunc_gdtrc_ptr[2*0+1] = <void*>(<char*>"gdtrc")
+ufunc_gdtrc_ptr[2*1] = <void*>_func_gdtrc
+ufunc_gdtrc_ptr[2*1+1] = <void*>(<char*>"gdtrc")
+ufunc_gdtrc_data[0] = &ufunc_gdtrc_ptr[2*0]
+ufunc_gdtrc_data[1] = &ufunc_gdtrc_ptr[2*1]
+gdtrc = np.PyUFunc_FromFuncAndData(ufunc_gdtrc_loops, ufunc_gdtrc_data, ufunc_gdtrc_types, 2, 3, 1, 0, "gdtrc", ufunc_gdtrc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gdtria_loops[2]
+cdef void *ufunc_gdtria_ptr[4]
+cdef void *ufunc_gdtria_data[2]
+cdef char ufunc_gdtria_types[8]
+cdef char *ufunc_gdtria_doc = (
+    "gdtria(p, b, x, out=None)\n"
+    "\n"
+    "Inverse of `gdtr` vs a.\n"
+    "\n"
+    "Returns the inverse with respect to the parameter `a` of ``p =\n"
+    "gdtr(a, b, x)``, the cumulative distribution function of the gamma\n"
+    "distribution.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    Probability values.\n"
+    "b : array_like\n"
+    "    `b` parameter values of `gdtr(a, b, x)`. `b` is the \"shape\" parameter\n"
+    "    of the gamma distribution.\n"
+    "x : array_like\n"
+    "    Nonnegative real values, from the domain of the gamma distribution.\n"
+    "out : ndarray, optional\n"
+    "    If a fourth argument is given, it must be a numpy.ndarray whose size\n"
+    "    matches the broadcast result of `a`, `b` and `x`.  `out` is then the\n"
+    "    array returned by the function.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "a : scalar or ndarray\n"
+    "    Values of the `a` parameter such that `p = gdtr(a, b, x)`.  `1/a`\n"
+    "    is the \"scale\" parameter of the gamma distribution.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gdtr : CDF of the gamma distribution.\n"
+    "gdtrib : Inverse with respect to `b` of `gdtr(a, b, x)`.\n"
+    "gdtrix : Inverse with respect to `x` of `gdtr(a, b, x)`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the CDFLIB [1]_ Fortran routine `cdfgam`.\n"
+    "\n"
+    "The cumulative distribution function `p` is computed using a routine by\n"
+    "DiDinato and Morris [2]_. Computation of `a` involves a search for a value\n"
+    "that produces the desired value of `p`. The search relies on the\n"
+    "monotonicity of `p` with `a`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.\n"
+    ".. [2] DiDinato, A. R. and Morris, A. H.,\n"
+    "       Computation of the incomplete gamma function ratios and their\n"
+    "       inverse.  ACM Trans. Math. Softw. 12 (1986), 377-393.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "First evaluate `gdtr`.\n"
+    "\n"
+    ">>> from scipy.special import gdtr, gdtria\n"
+    ">>> p = gdtr(1.2, 3.4, 5.6)\n"
+    ">>> print(p)\n"
+    "0.94378087442\n"
+    "\n"
+    "Verify the inverse.\n"
+    "\n"
+    ">>> gdtria(p, 3.4, 5.6)\n"
+    "1.2")
+ufunc_gdtria_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_gdtria_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_gdtria_types[0] = <char>NPY_FLOAT
+ufunc_gdtria_types[1] = <char>NPY_FLOAT
+ufunc_gdtria_types[2] = <char>NPY_FLOAT
+ufunc_gdtria_types[3] = <char>NPY_FLOAT
+ufunc_gdtria_types[4] = <char>NPY_DOUBLE
+ufunc_gdtria_types[5] = <char>NPY_DOUBLE
+ufunc_gdtria_types[6] = <char>NPY_DOUBLE
+ufunc_gdtria_types[7] = <char>NPY_DOUBLE
+ufunc_gdtria_ptr[2*0] = <void*>_func_gdtria
+ufunc_gdtria_ptr[2*0+1] = <void*>(<char*>"gdtria")
+ufunc_gdtria_ptr[2*1] = <void*>_func_gdtria
+ufunc_gdtria_ptr[2*1+1] = <void*>(<char*>"gdtria")
+ufunc_gdtria_data[0] = &ufunc_gdtria_ptr[2*0]
+ufunc_gdtria_data[1] = &ufunc_gdtria_ptr[2*1]
+gdtria = np.PyUFunc_FromFuncAndData(ufunc_gdtria_loops, ufunc_gdtria_data, ufunc_gdtria_types, 2, 3, 1, 0, "gdtria", ufunc_gdtria_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gdtrib_loops[2]
+cdef void *ufunc_gdtrib_ptr[4]
+cdef void *ufunc_gdtrib_data[2]
+cdef char ufunc_gdtrib_types[8]
+cdef char *ufunc_gdtrib_doc = (
+    "gdtrib(a, p, x, out=None)\n"
+    "\n"
+    "Inverse of `gdtr` vs b.\n"
+    "\n"
+    "Returns the inverse with respect to the parameter `b` of ``p =\n"
+    "gdtr(a, b, x)``, the cumulative distribution function of the gamma\n"
+    "distribution.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    `a` parameter values of `gdtr(a, b, x)`. `1/a` is the \"scale\"\n"
+    "    parameter of the gamma distribution.\n"
+    "p : array_like\n"
+    "    Probability values.\n"
+    "x : array_like\n"
+    "    Nonnegative real values, from the domain of the gamma distribution.\n"
+    "out : ndarray, optional\n"
+    "    If a fourth argument is given, it must be a numpy.ndarray whose size\n"
+    "    matches the broadcast result of `a`, `b` and `x`.  `out` is then the\n"
+    "    array returned by the function.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "b : scalar or ndarray\n"
+    "    Values of the `b` parameter such that `p = gdtr(a, b, x)`.  `b` is\n"
+    "    the \"shape\" parameter of the gamma distribution.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gdtr : CDF of the gamma distribution.\n"
+    "gdtria : Inverse with respect to `a` of `gdtr(a, b, x)`.\n"
+    "gdtrix : Inverse with respect to `x` of `gdtr(a, b, x)`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the CDFLIB [1]_ Fortran routine `cdfgam`.\n"
+    "\n"
+    "The cumulative distribution function `p` is computed using a routine by\n"
+    "DiDinato and Morris [2]_. Computation of `b` involves a search for a value\n"
+    "that produces the desired value of `p`. The search relies on the\n"
+    "monotonicity of `p` with `b`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.\n"
+    ".. [2] DiDinato, A. R. and Morris, A. H.,\n"
+    "       Computation of the incomplete gamma function ratios and their\n"
+    "       inverse.  ACM Trans. Math. Softw. 12 (1986), 377-393.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "First evaluate `gdtr`.\n"
+    "\n"
+    ">>> from scipy.special import gdtr, gdtrib\n"
+    ">>> p = gdtr(1.2, 3.4, 5.6)\n"
+    ">>> print(p)\n"
+    "0.94378087442\n"
+    "\n"
+    "Verify the inverse.\n"
+    "\n"
+    ">>> gdtrib(1.2, p, 5.6)\n"
+    "3.3999999999723882")
+ufunc_gdtrib_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_gdtrib_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_gdtrib_types[0] = <char>NPY_FLOAT
+ufunc_gdtrib_types[1] = <char>NPY_FLOAT
+ufunc_gdtrib_types[2] = <char>NPY_FLOAT
+ufunc_gdtrib_types[3] = <char>NPY_FLOAT
+ufunc_gdtrib_types[4] = <char>NPY_DOUBLE
+ufunc_gdtrib_types[5] = <char>NPY_DOUBLE
+ufunc_gdtrib_types[6] = <char>NPY_DOUBLE
+ufunc_gdtrib_types[7] = <char>NPY_DOUBLE
+ufunc_gdtrib_ptr[2*0] = <void*>_func_gdtrib
+ufunc_gdtrib_ptr[2*0+1] = <void*>(<char*>"gdtrib")
+ufunc_gdtrib_ptr[2*1] = <void*>_func_gdtrib
+ufunc_gdtrib_ptr[2*1+1] = <void*>(<char*>"gdtrib")
+ufunc_gdtrib_data[0] = &ufunc_gdtrib_ptr[2*0]
+ufunc_gdtrib_data[1] = &ufunc_gdtrib_ptr[2*1]
+gdtrib = np.PyUFunc_FromFuncAndData(ufunc_gdtrib_loops, ufunc_gdtrib_data, ufunc_gdtrib_types, 2, 3, 1, 0, "gdtrib", ufunc_gdtrib_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_gdtrix_loops[2]
+cdef void *ufunc_gdtrix_ptr[4]
+cdef void *ufunc_gdtrix_data[2]
+cdef char ufunc_gdtrix_types[8]
+cdef char *ufunc_gdtrix_doc = (
+    "gdtrix(a, b, p, out=None)\n"
+    "\n"
+    "Inverse of `gdtr` vs x.\n"
+    "\n"
+    "Returns the inverse with respect to the parameter `x` of ``p =\n"
+    "gdtr(a, b, x)``, the cumulative distribution function of the gamma\n"
+    "distribution. This is also known as the pth quantile of the\n"
+    "distribution.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    `a` parameter values of `gdtr(a, b, x)`. `1/a` is the \"scale\"\n"
+    "    parameter of the gamma distribution.\n"
+    "b : array_like\n"
+    "    `b` parameter values of `gdtr(a, b, x)`. `b` is the \"shape\" parameter\n"
+    "    of the gamma distribution.\n"
+    "p : array_like\n"
+    "    Probability values.\n"
+    "out : ndarray, optional\n"
+    "    If a fourth argument is given, it must be a numpy.ndarray whose size\n"
+    "    matches the broadcast result of `a`, `b` and `x`. `out` is then the\n"
+    "    array returned by the function.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "x : scalar or ndarray\n"
+    "    Values of the `x` parameter such that `p = gdtr(a, b, x)`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gdtr : CDF of the gamma distribution.\n"
+    "gdtria : Inverse with respect to `a` of `gdtr(a, b, x)`.\n"
+    "gdtrib : Inverse with respect to `b` of `gdtr(a, b, x)`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the CDFLIB [1]_ Fortran routine `cdfgam`.\n"
+    "\n"
+    "The cumulative distribution function `p` is computed using a routine by\n"
+    "DiDinato and Morris [2]_. Computation of `x` involves a search for a value\n"
+    "that produces the desired value of `p`. The search relies on the\n"
+    "monotonicity of `p` with `x`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.\n"
+    ".. [2] DiDinato, A. R. and Morris, A. H.,\n"
+    "       Computation of the incomplete gamma function ratios and their\n"
+    "       inverse.  ACM Trans. Math. Softw. 12 (1986), 377-393.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "First evaluate `gdtr`.\n"
+    "\n"
+    ">>> from scipy.special import gdtr, gdtrix\n"
+    ">>> p = gdtr(1.2, 3.4, 5.6)\n"
+    ">>> print(p)\n"
+    "0.94378087442\n"
+    "\n"
+    "Verify the inverse.\n"
+    "\n"
+    ">>> gdtrix(1.2, 3.4, p)\n"
+    "5.5999999999999996")
+ufunc_gdtrix_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_gdtrix_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_gdtrix_types[0] = <char>NPY_FLOAT
+ufunc_gdtrix_types[1] = <char>NPY_FLOAT
+ufunc_gdtrix_types[2] = <char>NPY_FLOAT
+ufunc_gdtrix_types[3] = <char>NPY_FLOAT
+ufunc_gdtrix_types[4] = <char>NPY_DOUBLE
+ufunc_gdtrix_types[5] = <char>NPY_DOUBLE
+ufunc_gdtrix_types[6] = <char>NPY_DOUBLE
+ufunc_gdtrix_types[7] = <char>NPY_DOUBLE
+ufunc_gdtrix_ptr[2*0] = <void*>_func_gdtrix
+ufunc_gdtrix_ptr[2*0+1] = <void*>(<char*>"gdtrix")
+ufunc_gdtrix_ptr[2*1] = <void*>_func_gdtrix
+ufunc_gdtrix_ptr[2*1+1] = <void*>(<char*>"gdtrix")
+ufunc_gdtrix_data[0] = &ufunc_gdtrix_ptr[2*0]
+ufunc_gdtrix_data[1] = &ufunc_gdtrix_ptr[2*1]
+gdtrix = np.PyUFunc_FromFuncAndData(ufunc_gdtrix_loops, ufunc_gdtrix_data, ufunc_gdtrix_types, 2, 3, 1, 0, "gdtrix", ufunc_gdtrix_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_hankel1_loops[2]
+cdef void *ufunc_hankel1_ptr[4]
+cdef void *ufunc_hankel1_data[2]
+cdef char ufunc_hankel1_types[6]
+cdef char *ufunc_hankel1_doc = (
+    "hankel1(v, z, out=None)\n"
+    "\n"
+    "Hankel function of the first kind\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order (float).\n"
+    "z : array_like\n"
+    "    Argument (float or complex).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the Hankel function of the first kind.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "hankel1e : ndarray\n"
+    "    This function with leading exponential behavior stripped off.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "A wrapper for the AMOS [1]_ routine `zbesh`, which carries out the\n"
+    "computation using the relation,\n"
+    "\n"
+    ".. math:: H^{(1)}_v(z) =\n"
+    "          \\frac{2}{\\imath\\pi} \\exp(-\\imath \\pi v/2) K_v(z \\exp(-\\imath\\pi/2))\n"
+    "\n"
+    "where :math:`K_v` is the modified Bessel function of the second kind.\n"
+    "For negative orders, the relation\n"
+    "\n"
+    ".. math:: H^{(1)}_{-v}(z) = H^{(1)}_v(z) \\exp(\\imath\\pi v)\n"
+    "\n"
+    "is used.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/")
+ufunc_hankel1_loops[0] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_hankel1_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_hankel1_types[0] = <char>NPY_FLOAT
+ufunc_hankel1_types[1] = <char>NPY_CFLOAT
+ufunc_hankel1_types[2] = <char>NPY_CFLOAT
+ufunc_hankel1_types[3] = <char>NPY_DOUBLE
+ufunc_hankel1_types[4] = <char>NPY_CDOUBLE
+ufunc_hankel1_types[5] = <char>NPY_CDOUBLE
+ufunc_hankel1_ptr[2*0] = <void*>_func_cbesh_wrap1
+ufunc_hankel1_ptr[2*0+1] = <void*>(<char*>"hankel1")
+ufunc_hankel1_ptr[2*1] = <void*>_func_cbesh_wrap1
+ufunc_hankel1_ptr[2*1+1] = <void*>(<char*>"hankel1")
+ufunc_hankel1_data[0] = &ufunc_hankel1_ptr[2*0]
+ufunc_hankel1_data[1] = &ufunc_hankel1_ptr[2*1]
+hankel1 = np.PyUFunc_FromFuncAndData(ufunc_hankel1_loops, ufunc_hankel1_data, ufunc_hankel1_types, 2, 2, 1, 0, "hankel1", ufunc_hankel1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_hankel1e_loops[2]
+cdef void *ufunc_hankel1e_ptr[4]
+cdef void *ufunc_hankel1e_data[2]
+cdef char ufunc_hankel1e_types[6]
+cdef char *ufunc_hankel1e_doc = (
+    "hankel1e(v, z, out=None)\n"
+    "\n"
+    "Exponentially scaled Hankel function of the first kind\n"
+    "\n"
+    "Defined as::\n"
+    "\n"
+    "    hankel1e(v, z) = hankel1(v, z) * exp(-1j * z)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order (float).\n"
+    "z : array_like\n"
+    "    Argument (float or complex).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the exponentially scaled Hankel function.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "A wrapper for the AMOS [1]_ routine `zbesh`, which carries out the\n"
+    "computation using the relation,\n"
+    "\n"
+    ".. math:: H^{(1)}_v(z) =\n"
+    "          \\frac{2}{\\imath\\pi} \\exp(-\\imath \\pi v/2) K_v(z \\exp(-\\imath\\pi/2))\n"
+    "\n"
+    "where :math:`K_v` is the modified Bessel function of the second kind.\n"
+    "For negative orders, the relation\n"
+    "\n"
+    ".. math:: H^{(1)}_{-v}(z) = H^{(1)}_v(z) \\exp(\\imath\\pi v)\n"
+    "\n"
+    "is used.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/")
+ufunc_hankel1e_loops[0] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_hankel1e_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_hankel1e_types[0] = <char>NPY_FLOAT
+ufunc_hankel1e_types[1] = <char>NPY_CFLOAT
+ufunc_hankel1e_types[2] = <char>NPY_CFLOAT
+ufunc_hankel1e_types[3] = <char>NPY_DOUBLE
+ufunc_hankel1e_types[4] = <char>NPY_CDOUBLE
+ufunc_hankel1e_types[5] = <char>NPY_CDOUBLE
+ufunc_hankel1e_ptr[2*0] = <void*>_func_cbesh_wrap1_e
+ufunc_hankel1e_ptr[2*0+1] = <void*>(<char*>"hankel1e")
+ufunc_hankel1e_ptr[2*1] = <void*>_func_cbesh_wrap1_e
+ufunc_hankel1e_ptr[2*1+1] = <void*>(<char*>"hankel1e")
+ufunc_hankel1e_data[0] = &ufunc_hankel1e_ptr[2*0]
+ufunc_hankel1e_data[1] = &ufunc_hankel1e_ptr[2*1]
+hankel1e = np.PyUFunc_FromFuncAndData(ufunc_hankel1e_loops, ufunc_hankel1e_data, ufunc_hankel1e_types, 2, 2, 1, 0, "hankel1e", ufunc_hankel1e_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_hankel2_loops[2]
+cdef void *ufunc_hankel2_ptr[4]
+cdef void *ufunc_hankel2_data[2]
+cdef char ufunc_hankel2_types[6]
+cdef char *ufunc_hankel2_doc = (
+    "hankel2(v, z, out=None)\n"
+    "\n"
+    "Hankel function of the second kind\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order (float).\n"
+    "z : array_like\n"
+    "    Argument (float or complex).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the Hankel function of the second kind.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "hankel2e : this function with leading exponential behavior stripped off.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "A wrapper for the AMOS [1]_ routine `zbesh`, which carries out the\n"
+    "computation using the relation,\n"
+    "\n"
+    ".. math:: H^{(2)}_v(z) =\n"
+    "          -\\frac{2}{\\imath\\pi} \\exp(\\imath \\pi v/2) K_v(z \\exp(\\imath\\pi/2))\n"
+    "\n"
+    "where :math:`K_v` is the modified Bessel function of the second kind.\n"
+    "For negative orders, the relation\n"
+    "\n"
+    ".. math:: H^{(2)}_{-v}(z) = H^{(2)}_v(z) \\exp(-\\imath\\pi v)\n"
+    "\n"
+    "is used.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/")
+ufunc_hankel2_loops[0] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_hankel2_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_hankel2_types[0] = <char>NPY_FLOAT
+ufunc_hankel2_types[1] = <char>NPY_CFLOAT
+ufunc_hankel2_types[2] = <char>NPY_CFLOAT
+ufunc_hankel2_types[3] = <char>NPY_DOUBLE
+ufunc_hankel2_types[4] = <char>NPY_CDOUBLE
+ufunc_hankel2_types[5] = <char>NPY_CDOUBLE
+ufunc_hankel2_ptr[2*0] = <void*>_func_cbesh_wrap2
+ufunc_hankel2_ptr[2*0+1] = <void*>(<char*>"hankel2")
+ufunc_hankel2_ptr[2*1] = <void*>_func_cbesh_wrap2
+ufunc_hankel2_ptr[2*1+1] = <void*>(<char*>"hankel2")
+ufunc_hankel2_data[0] = &ufunc_hankel2_ptr[2*0]
+ufunc_hankel2_data[1] = &ufunc_hankel2_ptr[2*1]
+hankel2 = np.PyUFunc_FromFuncAndData(ufunc_hankel2_loops, ufunc_hankel2_data, ufunc_hankel2_types, 2, 2, 1, 0, "hankel2", ufunc_hankel2_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_hankel2e_loops[2]
+cdef void *ufunc_hankel2e_ptr[4]
+cdef void *ufunc_hankel2e_data[2]
+cdef char ufunc_hankel2e_types[6]
+cdef char *ufunc_hankel2e_doc = (
+    "hankel2e(v, z, out=None)\n"
+    "\n"
+    "Exponentially scaled Hankel function of the second kind\n"
+    "\n"
+    "Defined as::\n"
+    "\n"
+    "    hankel2e(v, z) = hankel2(v, z) * exp(1j * z)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order (float).\n"
+    "z : array_like\n"
+    "    Argument (float or complex).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the exponentially scaled Hankel function of the second kind.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "A wrapper for the AMOS [1]_ routine `zbesh`, which carries out the\n"
+    "computation using the relation,\n"
+    "\n"
+    ".. math:: H^{(2)}_v(z) = -\\frac{2}{\\imath\\pi}\n"
+    "          \\exp(\\frac{\\imath \\pi v}{2}) K_v(z exp(\\frac{\\imath\\pi}{2}))\n"
+    "\n"
+    "where :math:`K_v` is the modified Bessel function of the second kind.\n"
+    "For negative orders, the relation\n"
+    "\n"
+    ".. math:: H^{(2)}_{-v}(z) = H^{(2)}_v(z) \\exp(-\\imath\\pi v)\n"
+    "\n"
+    "is used.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/")
+ufunc_hankel2e_loops[0] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_hankel2e_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_hankel2e_types[0] = <char>NPY_FLOAT
+ufunc_hankel2e_types[1] = <char>NPY_CFLOAT
+ufunc_hankel2e_types[2] = <char>NPY_CFLOAT
+ufunc_hankel2e_types[3] = <char>NPY_DOUBLE
+ufunc_hankel2e_types[4] = <char>NPY_CDOUBLE
+ufunc_hankel2e_types[5] = <char>NPY_CDOUBLE
+ufunc_hankel2e_ptr[2*0] = <void*>_func_cbesh_wrap2_e
+ufunc_hankel2e_ptr[2*0+1] = <void*>(<char*>"hankel2e")
+ufunc_hankel2e_ptr[2*1] = <void*>_func_cbesh_wrap2_e
+ufunc_hankel2e_ptr[2*1+1] = <void*>(<char*>"hankel2e")
+ufunc_hankel2e_data[0] = &ufunc_hankel2e_ptr[2*0]
+ufunc_hankel2e_data[1] = &ufunc_hankel2e_ptr[2*1]
+hankel2e = np.PyUFunc_FromFuncAndData(ufunc_hankel2e_loops, ufunc_hankel2e_data, ufunc_hankel2e_types, 2, 2, 1, 0, "hankel2e", ufunc_hankel2e_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_huber_loops[2]
+cdef void *ufunc_huber_ptr[4]
+cdef void *ufunc_huber_data[2]
+cdef char ufunc_huber_types[6]
+cdef char *ufunc_huber_doc = (
+    "huber(delta, r, out=None)\n"
+    "\n"
+    "Huber loss function.\n"
+    "\n"
+    ".. math:: \\text{huber}(\\delta, r) = \\begin{cases} \\infty & \\delta < 0  \\\\\n"
+    "          \\frac{1}{2}r^2 & 0 \\le \\delta, | r | \\le \\delta \\\\\n"
+    "          \\delta ( |r| - \\frac{1}{2}\\delta ) & \\text{otherwise} \\end{cases}\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "delta : ndarray\n"
+    "    Input array, indicating the quadratic vs. linear loss changepoint.\n"
+    "r : ndarray\n"
+    "    Input array, possibly representing residuals.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The computed Huber loss function values.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "pseudo_huber : smooth approximation of this function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "`huber` is useful as a loss function in robust statistics or machine\n"
+    "learning to reduce the influence of outliers as compared to the common\n"
+    "squared error loss, residuals with a magnitude higher than `delta` are\n"
+    "not squared [1]_.\n"
+    "\n"
+    "Typically, `r` represents residuals, the difference\n"
+    "between a model prediction and data. Then, for :math:`|r|\\leq\\delta`,\n"
+    "`huber` resembles the squared error and for :math:`|r|>\\delta` the\n"
+    "absolute error. This way, the Huber loss often achieves\n"
+    "a fast convergence in model fitting for small residuals like the squared\n"
+    "error loss function and still reduces the influence of outliers\n"
+    "(:math:`|r|>\\delta`) like the absolute error loss. As :math:`\\delta` is\n"
+    "the cutoff between squared and absolute error regimes, it has\n"
+    "to be tuned carefully for each problem. `huber` is also\n"
+    "convex, making it suitable for gradient based optimization.\n"
+    "\n"
+    ".. versionadded:: 0.15.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Peter Huber. \"Robust Estimation of a Location Parameter\",\n"
+    "       1964. Annals of Statistics. 53 (1): 73 - 101.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Import all necessary modules.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import huber\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    "\n"
+    "Compute the function for ``delta=1`` at ``r=2``\n"
+    "\n"
+    ">>> huber(1., 2.)\n"
+    "1.5\n"
+    "\n"
+    "Compute the function for different `delta` by providing a NumPy array or\n"
+    "list for `delta`.\n"
+    "\n"
+    ">>> huber([1., 3., 5.], 4.)\n"
+    "array([3.5, 7.5, 8. ])\n"
+    "\n"
+    "Compute the function at different points by providing a NumPy array or\n"
+    "list for `r`.\n"
+    "\n"
+    ">>> huber(2., np.array([1., 1.5, 3.]))\n"
+    "array([0.5  , 1.125, 4.   ])\n"
+    "\n"
+    "The function can be calculated for different `delta` and `r` by\n"
+    "providing arrays for both with compatible shapes for broadcasting.\n"
+    "\n"
+    ">>> r = np.array([1., 2.5, 8., 10.])\n"
+    ">>> deltas = np.array([[1.], [5.], [9.]])\n"
+    ">>> print(r.shape, deltas.shape)\n"
+    "(4,) (3, 1)\n"
+    "\n"
+    ">>> huber(deltas, r)\n"
+    "array([[ 0.5  ,  2.   ,  7.5  ,  9.5  ],\n"
+    "       [ 0.5  ,  3.125, 27.5  , 37.5  ],\n"
+    "       [ 0.5  ,  3.125, 32.   , 49.5  ]])\n"
+    "\n"
+    "Plot the function for different `delta`.\n"
+    "\n"
+    ">>> x = np.linspace(-4, 4, 500)\n"
+    ">>> deltas = [1, 2, 3]\n"
+    ">>> linestyles = [\"dashed\", \"dotted\", \"dashdot\"]\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> combined_plot_parameters = list(zip(deltas, linestyles))\n"
+    ">>> for delta, style in combined_plot_parameters:\n"
+    "...     ax.plot(x, huber(delta, x), label=fr\"$\\delta={delta}$\", ls=style)\n"
+    ">>> ax.legend(loc=\"upper center\")\n"
+    ">>> ax.set_xlabel(\"$x$\")\n"
+    ">>> ax.set_title(r\"Huber loss function $h_{\\delta}(x)$\")\n"
+    ">>> ax.set_xlim(-4, 4)\n"
+    ">>> ax.set_ylim(0, 8)\n"
+    ">>> plt.show()")
+ufunc_huber_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_huber_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_huber_types[0] = <char>NPY_FLOAT
+ufunc_huber_types[1] = <char>NPY_FLOAT
+ufunc_huber_types[2] = <char>NPY_FLOAT
+ufunc_huber_types[3] = <char>NPY_DOUBLE
+ufunc_huber_types[4] = <char>NPY_DOUBLE
+ufunc_huber_types[5] = <char>NPY_DOUBLE
+ufunc_huber_ptr[2*0] = <void*>_func_huber
+ufunc_huber_ptr[2*0+1] = <void*>(<char*>"huber")
+ufunc_huber_ptr[2*1] = <void*>_func_huber
+ufunc_huber_ptr[2*1+1] = <void*>(<char*>"huber")
+ufunc_huber_data[0] = &ufunc_huber_ptr[2*0]
+ufunc_huber_data[1] = &ufunc_huber_ptr[2*1]
+huber = np.PyUFunc_FromFuncAndData(ufunc_huber_loops, ufunc_huber_data, ufunc_huber_types, 2, 2, 1, 0, "huber", ufunc_huber_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_hyp0f1_loops[4]
+cdef void *ufunc_hyp0f1_ptr[8]
+cdef void *ufunc_hyp0f1_data[4]
+cdef char ufunc_hyp0f1_types[12]
+cdef char *ufunc_hyp0f1_doc = (
+    "hyp0f1(v, z, out=None)\n"
+    "\n"
+    "Confluent hypergeometric limit function 0F1.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Real-valued parameter\n"
+    "z : array_like\n"
+    "    Real- or complex-valued argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The confluent hypergeometric limit function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "This function is defined as:\n"
+    "\n"
+    ".. math:: _0F_1(v, z) = \\sum_{k=0}^{\\infty}\\frac{z^k}{(v)_k k!}.\n"
+    "\n"
+    "It's also the limit as :math:`q \\to \\infty` of :math:`_1F_1(q; v; z/q)`,\n"
+    "and satisfies the differential equation :math:`f''(z) + vf'(z) =\n"
+    "f(z)`. See [1]_ for more information.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Wolfram MathWorld, \"Confluent Hypergeometric Limit Function\",\n"
+    "       http://mathworld.wolfram.com/ConfluentHypergeometricLimitFunction.html\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is one when `z` is zero.\n"
+    "\n"
+    ">>> sc.hyp0f1(1, 0)\n"
+    "1.0\n"
+    "\n"
+    "It is the limit of the confluent hypergeometric function as `q`\n"
+    "goes to infinity.\n"
+    "\n"
+    ">>> q = np.array([1, 10, 100, 1000])\n"
+    ">>> v = 1\n"
+    ">>> z = 1\n"
+    ">>> sc.hyp1f1(q, v, z / q)\n"
+    "array([2.71828183, 2.31481985, 2.28303778, 2.27992985])\n"
+    ">>> sc.hyp0f1(v, z)\n"
+    "2.2795853023360673\n"
+    "\n"
+    "It is related to Bessel functions.\n"
+    "\n"
+    ">>> n = 1\n"
+    ">>> x = np.linspace(0, 1, 5)\n"
+    ">>> sc.jv(n, x)\n"
+    "array([0.        , 0.12402598, 0.24226846, 0.3492436 , 0.44005059])\n"
+    ">>> (0.5 * x)**n / sc.factorial(n) * sc.hyp0f1(n + 1, -0.25 * x**2)\n"
+    "array([0.        , 0.12402598, 0.24226846, 0.3492436 , 0.44005059])")
+ufunc_hyp0f1_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_hyp0f1_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_hyp0f1_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_hyp0f1_loops[3] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_hyp0f1_types[0] = <char>NPY_FLOAT
+ufunc_hyp0f1_types[1] = <char>NPY_FLOAT
+ufunc_hyp0f1_types[2] = <char>NPY_FLOAT
+ufunc_hyp0f1_types[3] = <char>NPY_FLOAT
+ufunc_hyp0f1_types[4] = <char>NPY_CFLOAT
+ufunc_hyp0f1_types[5] = <char>NPY_CFLOAT
+ufunc_hyp0f1_types[6] = <char>NPY_DOUBLE
+ufunc_hyp0f1_types[7] = <char>NPY_DOUBLE
+ufunc_hyp0f1_types[8] = <char>NPY_DOUBLE
+ufunc_hyp0f1_types[9] = <char>NPY_DOUBLE
+ufunc_hyp0f1_types[10] = <char>NPY_CDOUBLE
+ufunc_hyp0f1_types[11] = <char>NPY_CDOUBLE
+ufunc_hyp0f1_ptr[2*0] = <void*>_func__hyp0f1_real
+ufunc_hyp0f1_ptr[2*0+1] = <void*>(<char*>"hyp0f1")
+ufunc_hyp0f1_ptr[2*1] = <void*>_func__hyp0f1_cmplx
+ufunc_hyp0f1_ptr[2*1+1] = <void*>(<char*>"hyp0f1")
+ufunc_hyp0f1_ptr[2*2] = <void*>_func__hyp0f1_real
+ufunc_hyp0f1_ptr[2*2+1] = <void*>(<char*>"hyp0f1")
+ufunc_hyp0f1_ptr[2*3] = <void*>_func__hyp0f1_cmplx
+ufunc_hyp0f1_ptr[2*3+1] = <void*>(<char*>"hyp0f1")
+ufunc_hyp0f1_data[0] = &ufunc_hyp0f1_ptr[2*0]
+ufunc_hyp0f1_data[1] = &ufunc_hyp0f1_ptr[2*1]
+ufunc_hyp0f1_data[2] = &ufunc_hyp0f1_ptr[2*2]
+ufunc_hyp0f1_data[3] = &ufunc_hyp0f1_ptr[2*3]
+hyp0f1 = np.PyUFunc_FromFuncAndData(ufunc_hyp0f1_loops, ufunc_hyp0f1_data, ufunc_hyp0f1_types, 4, 2, 1, 0, "hyp0f1", ufunc_hyp0f1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_hyp1f1_loops[4]
+cdef void *ufunc_hyp1f1_ptr[8]
+cdef void *ufunc_hyp1f1_data[4]
+cdef char ufunc_hyp1f1_types[16]
+cdef char *ufunc_hyp1f1_doc = (
+    "hyp1f1(a, b, x, out=None)\n"
+    "\n"
+    "Confluent hypergeometric function 1F1.\n"
+    "\n"
+    "The confluent hypergeometric function is defined by the series\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   {}_1F_1(a; b; x) = \\sum_{k = 0}^\\infty \\frac{(a)_k}{(b)_k k!} x^k.\n"
+    "\n"
+    "See [dlmf]_ for more details. Here :math:`(\\cdot)_k` is the\n"
+    "Pochhammer symbol; see `poch`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b : array_like\n"
+    "    Real parameters\n"
+    "x : array_like\n"
+    "    Real or complex argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the confluent hypergeometric function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "hyperu : another confluent hypergeometric function\n"
+    "hyp0f1 : confluent hypergeometric limit function\n"
+    "hyp2f1 : Gaussian hypergeometric function\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical Functions\n"
+    "          https://dlmf.nist.gov/13.2#E2\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is one when `x` is zero:\n"
+    "\n"
+    ">>> sc.hyp1f1(0.5, 0.5, 0)\n"
+    "1.0\n"
+    "\n"
+    "It is singular when `b` is a nonpositive integer.\n"
+    "\n"
+    ">>> sc.hyp1f1(0.5, -1, 0)\n"
+    "inf\n"
+    "\n"
+    "It is a polynomial when `a` is a nonpositive integer.\n"
+    "\n"
+    ">>> a, b, x = -1, 0.5, np.array([1.0, 2.0, 3.0, 4.0])\n"
+    ">>> sc.hyp1f1(a, b, x)\n"
+    "array([-1., -3., -5., -7.])\n"
+    ">>> 1 + (a / b) * x\n"
+    "array([-1., -3., -5., -7.])\n"
+    "\n"
+    "It reduces to the exponential function when `a = b`.\n"
+    "\n"
+    ">>> sc.hyp1f1(2, 2, [1, 2, 3, 4])\n"
+    "array([ 2.71828183,  7.3890561 , 20.08553692, 54.59815003])\n"
+    ">>> np.exp([1, 2, 3, 4])\n"
+    "array([ 2.71828183,  7.3890561 , 20.08553692, 54.59815003])")
+ufunc_hyp1f1_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_hyp1f1_loops[1] = <np.PyUFuncGenericFunction>loop_D_ddD__As_ffF_F
+ufunc_hyp1f1_loops[2] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_hyp1f1_loops[3] = <np.PyUFuncGenericFunction>loop_D_ddD__As_ddD_D
+ufunc_hyp1f1_types[0] = <char>NPY_FLOAT
+ufunc_hyp1f1_types[1] = <char>NPY_FLOAT
+ufunc_hyp1f1_types[2] = <char>NPY_FLOAT
+ufunc_hyp1f1_types[3] = <char>NPY_FLOAT
+ufunc_hyp1f1_types[4] = <char>NPY_FLOAT
+ufunc_hyp1f1_types[5] = <char>NPY_FLOAT
+ufunc_hyp1f1_types[6] = <char>NPY_CFLOAT
+ufunc_hyp1f1_types[7] = <char>NPY_CFLOAT
+ufunc_hyp1f1_types[8] = <char>NPY_DOUBLE
+ufunc_hyp1f1_types[9] = <char>NPY_DOUBLE
+ufunc_hyp1f1_types[10] = <char>NPY_DOUBLE
+ufunc_hyp1f1_types[11] = <char>NPY_DOUBLE
+ufunc_hyp1f1_types[12] = <char>NPY_DOUBLE
+ufunc_hyp1f1_types[13] = <char>NPY_DOUBLE
+ufunc_hyp1f1_types[14] = <char>NPY_CDOUBLE
+ufunc_hyp1f1_types[15] = <char>NPY_CDOUBLE
+ufunc_hyp1f1_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_hyp1f1_double
+ufunc_hyp1f1_ptr[2*0+1] = <void*>(<char*>"hyp1f1")
+ufunc_hyp1f1_ptr[2*1] = <void*>_func_chyp1f1_wrap
+ufunc_hyp1f1_ptr[2*1+1] = <void*>(<char*>"hyp1f1")
+ufunc_hyp1f1_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_hyp1f1_double
+ufunc_hyp1f1_ptr[2*2+1] = <void*>(<char*>"hyp1f1")
+ufunc_hyp1f1_ptr[2*3] = <void*>_func_chyp1f1_wrap
+ufunc_hyp1f1_ptr[2*3+1] = <void*>(<char*>"hyp1f1")
+ufunc_hyp1f1_data[0] = &ufunc_hyp1f1_ptr[2*0]
+ufunc_hyp1f1_data[1] = &ufunc_hyp1f1_ptr[2*1]
+ufunc_hyp1f1_data[2] = &ufunc_hyp1f1_ptr[2*2]
+ufunc_hyp1f1_data[3] = &ufunc_hyp1f1_ptr[2*3]
+hyp1f1 = np.PyUFunc_FromFuncAndData(ufunc_hyp1f1_loops, ufunc_hyp1f1_data, ufunc_hyp1f1_types, 4, 3, 1, 0, "hyp1f1", ufunc_hyp1f1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_hyp2f1_loops[4]
+cdef void *ufunc_hyp2f1_ptr[8]
+cdef void *ufunc_hyp2f1_data[4]
+cdef char ufunc_hyp2f1_types[20]
+cdef char *ufunc_hyp2f1_doc = (
+    "hyp2f1(a, b, c, z, out=None)\n"
+    "\n"
+    "Gauss hypergeometric function 2F1(a, b; c; z)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b, c : array_like\n"
+    "    Arguments, should be real-valued.\n"
+    "z : array_like\n"
+    "    Argument, real or complex.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "hyp2f1 : scalar or ndarray\n"
+    "    The values of the gaussian hypergeometric function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "hyp0f1 : confluent hypergeometric limit function.\n"
+    "hyp1f1 : Kummer's (confluent hypergeometric) function.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "This function is defined for :math:`|z| < 1` as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   \\mathrm{hyp2f1}(a, b, c, z) = \\sum_{n=0}^\\infty\n"
+    "   \\frac{(a)_n (b)_n}{(c)_n}\\frac{z^n}{n!},\n"
+    "\n"
+    "and defined on the rest of the complex z-plane by analytic\n"
+    "continuation [1]_.\n"
+    "Here :math:`(\\cdot)_n` is the Pochhammer symbol; see `poch`. When\n"
+    ":math:`n` is an integer the result is a polynomial of degree :math:`n`.\n"
+    "\n"
+    "The implementation for complex values of ``z`` is described in [2]_,\n"
+    "except for ``z`` in the region defined by\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "     0.9 <= \\left|z\\right| < 1.1,\n"
+    "     \\left|1 - z\\right| >= 0.9,\n"
+    "     \\mathrm{real}(z) >= 0\n"
+    "\n"
+    "in which the implementation follows [4]_.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] NIST Digital Library of Mathematical Functions\n"
+    "       https://dlmf.nist.gov/15.2\n"
+    ".. [2] S. Zhang and J.M. Jin, \"Computation of Special Functions\", Wiley 1996\n"
+    ".. [3] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    ".. [4] J.L. Lopez and N.M. Temme, \"New series expansions of the Gauss\n"
+    "       hypergeometric function\", Adv Comput Math 39, 349-365 (2013).\n"
+    "       https://doi.org/10.1007/s10444-012-9283-y\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It has poles when `c` is a negative integer.\n"
+    "\n"
+    ">>> sc.hyp2f1(1, 1, -2, 1)\n"
+    "inf\n"
+    "\n"
+    "It is a polynomial when `a` or `b` is a negative integer.\n"
+    "\n"
+    ">>> a, b, c = -1, 1, 1.5\n"
+    ">>> z = np.linspace(0, 1, 5)\n"
+    ">>> sc.hyp2f1(a, b, c, z)\n"
+    "array([1.        , 0.83333333, 0.66666667, 0.5       , 0.33333333])\n"
+    ">>> 1 + a * b * z / c\n"
+    "array([1.        , 0.83333333, 0.66666667, 0.5       , 0.33333333])\n"
+    "\n"
+    "It is symmetric in `a` and `b`.\n"
+    "\n"
+    ">>> a = np.linspace(0, 1, 5)\n"
+    ">>> b = np.linspace(0, 1, 5)\n"
+    ">>> sc.hyp2f1(a, b, 1, 0.5)\n"
+    "array([1.        , 1.03997334, 1.1803406 , 1.47074441, 2.        ])\n"
+    ">>> sc.hyp2f1(b, a, 1, 0.5)\n"
+    "array([1.        , 1.03997334, 1.1803406 , 1.47074441, 2.        ])\n"
+    "\n"
+    "It contains many other functions as special cases.\n"
+    "\n"
+    ">>> z = 0.5\n"
+    ">>> sc.hyp2f1(1, 1, 2, z)\n"
+    "1.3862943611198901\n"
+    ">>> -np.log(1 - z) / z\n"
+    "1.3862943611198906\n"
+    "\n"
+    ">>> sc.hyp2f1(0.5, 1, 1.5, z**2)\n"
+    "1.098612288668109\n"
+    ">>> np.log((1 + z) / (1 - z)) / (2 * z)\n"
+    "1.0986122886681098\n"
+    "\n"
+    ">>> sc.hyp2f1(0.5, 1, 1.5, -z**2)\n"
+    "0.9272952180016117\n"
+    ">>> np.arctan(z) / z\n"
+    "0.9272952180016122")
+ufunc_hyp2f1_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd__As_ffff_f
+ufunc_hyp2f1_loops[1] = <np.PyUFuncGenericFunction>loop_D_dddD__As_fffF_F
+ufunc_hyp2f1_loops[2] = <np.PyUFuncGenericFunction>loop_d_dddd__As_dddd_d
+ufunc_hyp2f1_loops[3] = <np.PyUFuncGenericFunction>loop_D_dddD__As_dddD_D
+ufunc_hyp2f1_types[0] = <char>NPY_FLOAT
+ufunc_hyp2f1_types[1] = <char>NPY_FLOAT
+ufunc_hyp2f1_types[2] = <char>NPY_FLOAT
+ufunc_hyp2f1_types[3] = <char>NPY_FLOAT
+ufunc_hyp2f1_types[4] = <char>NPY_FLOAT
+ufunc_hyp2f1_types[5] = <char>NPY_FLOAT
+ufunc_hyp2f1_types[6] = <char>NPY_FLOAT
+ufunc_hyp2f1_types[7] = <char>NPY_FLOAT
+ufunc_hyp2f1_types[8] = <char>NPY_CFLOAT
+ufunc_hyp2f1_types[9] = <char>NPY_CFLOAT
+ufunc_hyp2f1_types[10] = <char>NPY_DOUBLE
+ufunc_hyp2f1_types[11] = <char>NPY_DOUBLE
+ufunc_hyp2f1_types[12] = <char>NPY_DOUBLE
+ufunc_hyp2f1_types[13] = <char>NPY_DOUBLE
+ufunc_hyp2f1_types[14] = <char>NPY_DOUBLE
+ufunc_hyp2f1_types[15] = <char>NPY_DOUBLE
+ufunc_hyp2f1_types[16] = <char>NPY_DOUBLE
+ufunc_hyp2f1_types[17] = <char>NPY_DOUBLE
+ufunc_hyp2f1_types[18] = <char>NPY_CDOUBLE
+ufunc_hyp2f1_types[19] = <char>NPY_CDOUBLE
+ufunc_hyp2f1_ptr[2*0] = <void*>_func_hyp2f1
+ufunc_hyp2f1_ptr[2*0+1] = <void*>(<char*>"hyp2f1")
+ufunc_hyp2f1_ptr[2*1] = <void*>_func_hyp2f1_complex
+ufunc_hyp2f1_ptr[2*1+1] = <void*>(<char*>"hyp2f1")
+ufunc_hyp2f1_ptr[2*2] = <void*>_func_hyp2f1
+ufunc_hyp2f1_ptr[2*2+1] = <void*>(<char*>"hyp2f1")
+ufunc_hyp2f1_ptr[2*3] = <void*>_func_hyp2f1_complex
+ufunc_hyp2f1_ptr[2*3+1] = <void*>(<char*>"hyp2f1")
+ufunc_hyp2f1_data[0] = &ufunc_hyp2f1_ptr[2*0]
+ufunc_hyp2f1_data[1] = &ufunc_hyp2f1_ptr[2*1]
+ufunc_hyp2f1_data[2] = &ufunc_hyp2f1_ptr[2*2]
+ufunc_hyp2f1_data[3] = &ufunc_hyp2f1_ptr[2*3]
+hyp2f1 = np.PyUFunc_FromFuncAndData(ufunc_hyp2f1_loops, ufunc_hyp2f1_data, ufunc_hyp2f1_types, 4, 4, 1, 0, "hyp2f1", ufunc_hyp2f1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_hyperu_loops[2]
+cdef void *ufunc_hyperu_ptr[4]
+cdef void *ufunc_hyperu_data[2]
+cdef char ufunc_hyperu_types[8]
+cdef char *ufunc_hyperu_doc = (
+    "hyperu(a, b, x, out=None)\n"
+    "\n"
+    "Confluent hypergeometric function U\n"
+    "\n"
+    "It is defined as the solution to the equation\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   x \\frac{d^2w}{dx^2} + (b - x) \\frac{dw}{dx} - aw = 0\n"
+    "\n"
+    "which satisfies the property\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   U(a, b, x) \\sim x^{-a}\n"
+    "\n"
+    "as :math:`x \\to \\infty`. See [dlmf]_ for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a, b : array_like\n"
+    "    Real-valued parameters\n"
+    "x : array_like\n"
+    "    Real-valued argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of `U`\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematics Functions\n"
+    "          https://dlmf.nist.gov/13.2#E6\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It has a branch cut along the negative `x` axis.\n"
+    "\n"
+    ">>> x = np.linspace(-0.1, -10, 5)\n"
+    ">>> sc.hyperu(1, 1, x)\n"
+    "array([nan, nan, nan, nan, nan])\n"
+    "\n"
+    "It approaches zero as `x` goes to infinity.\n"
+    "\n"
+    ">>> x = np.array([1, 10, 100])\n"
+    ">>> sc.hyperu(1, 1, x)\n"
+    "array([0.59634736, 0.09156333, 0.00990194])\n"
+    "\n"
+    "It satisfies Kummer's transformation.\n"
+    "\n"
+    ">>> a, b, x = 2, 1, 1\n"
+    ">>> sc.hyperu(a, b, x)\n"
+    "0.1926947246463881\n"
+    ">>> x**(1 - b) * sc.hyperu(a - b + 1, 2 - b, x)\n"
+    "0.1926947246463881")
+ufunc_hyperu_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_hyperu_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_hyperu_types[0] = <char>NPY_FLOAT
+ufunc_hyperu_types[1] = <char>NPY_FLOAT
+ufunc_hyperu_types[2] = <char>NPY_FLOAT
+ufunc_hyperu_types[3] = <char>NPY_FLOAT
+ufunc_hyperu_types[4] = <char>NPY_DOUBLE
+ufunc_hyperu_types[5] = <char>NPY_DOUBLE
+ufunc_hyperu_types[6] = <char>NPY_DOUBLE
+ufunc_hyperu_types[7] = <char>NPY_DOUBLE
+ufunc_hyperu_ptr[2*0] = <void*>_func_hyperu
+ufunc_hyperu_ptr[2*0+1] = <void*>(<char*>"hyperu")
+ufunc_hyperu_ptr[2*1] = <void*>_func_hyperu
+ufunc_hyperu_ptr[2*1+1] = <void*>(<char*>"hyperu")
+ufunc_hyperu_data[0] = &ufunc_hyperu_ptr[2*0]
+ufunc_hyperu_data[1] = &ufunc_hyperu_ptr[2*1]
+hyperu = np.PyUFunc_FromFuncAndData(ufunc_hyperu_loops, ufunc_hyperu_data, ufunc_hyperu_types, 2, 3, 1, 0, "hyperu", ufunc_hyperu_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_i0_loops[2]
+cdef void *ufunc_i0_ptr[4]
+cdef void *ufunc_i0_data[2]
+cdef char ufunc_i0_types[4]
+cdef char *ufunc_i0_doc = (
+    "i0(x, out=None)\n"
+    "\n"
+    "Modified Bessel function of order 0.\n"
+    "\n"
+    "Defined as,\n"
+    "\n"
+    ".. math::\n"
+    "    I_0(x) = \\sum_{k=0}^\\infty \\frac{(x^2/4)^k}{(k!)^2} = J_0(\\imath x),\n"
+    "\n"
+    "where :math:`J_0` is the Bessel function of the first kind of order 0.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "I : scalar or ndarray\n"
+    "    Value of the modified Bessel function of order 0 at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "iv: Modified Bessel function of any order\n"
+    "i0e: Exponentially scaled modified Bessel function of order 0\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The range is partitioned into the two intervals [0, 8] and (8, infinity).\n"
+    "Chebyshev polynomial expansions are employed in each interval.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `i0`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function at one point:\n"
+    "\n"
+    ">>> from scipy.special import i0\n"
+    ">>> i0(1.)\n"
+    "1.2660658777520082\n"
+    "\n"
+    "Calculate at several points:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> i0(np.array([-2., 0., 3.5]))\n"
+    "array([2.2795853 , 1.        , 7.37820343])\n"
+    "\n"
+    "Plot the function from -10 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-10., 10., 1000)\n"
+    ">>> y = i0(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_i0_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_i0_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_i0_types[0] = <char>NPY_FLOAT
+ufunc_i0_types[1] = <char>NPY_FLOAT
+ufunc_i0_types[2] = <char>NPY_DOUBLE
+ufunc_i0_types[3] = <char>NPY_DOUBLE
+ufunc_i0_ptr[2*0] = <void*>_func_i0
+ufunc_i0_ptr[2*0+1] = <void*>(<char*>"i0")
+ufunc_i0_ptr[2*1] = <void*>_func_i0
+ufunc_i0_ptr[2*1+1] = <void*>(<char*>"i0")
+ufunc_i0_data[0] = &ufunc_i0_ptr[2*0]
+ufunc_i0_data[1] = &ufunc_i0_ptr[2*1]
+i0 = np.PyUFunc_FromFuncAndData(ufunc_i0_loops, ufunc_i0_data, ufunc_i0_types, 2, 1, 1, 0, "i0", ufunc_i0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_i0e_loops[2]
+cdef void *ufunc_i0e_ptr[4]
+cdef void *ufunc_i0e_data[2]
+cdef char ufunc_i0e_types[4]
+cdef char *ufunc_i0e_doc = (
+    "i0e(x, out=None)\n"
+    "\n"
+    "Exponentially scaled modified Bessel function of order 0.\n"
+    "\n"
+    "Defined as::\n"
+    "\n"
+    "    i0e(x) = exp(-abs(x)) * i0(x).\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "I : scalar or ndarray\n"
+    "    Value of the exponentially scaled modified Bessel function of order 0\n"
+    "    at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "iv: Modified Bessel function of the first kind\n"
+    "i0: Modified Bessel function of order 0\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The range is partitioned into the two intervals [0, 8] and (8, infinity).\n"
+    "Chebyshev polynomial expansions are employed in each interval. The\n"
+    "polynomial expansions used are the same as those in `i0`, but\n"
+    "they are not multiplied by the dominant exponential factor.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `i0e`. `i0e`\n"
+    "is useful for large arguments `x`: for these, `i0` quickly overflows.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "In the following example `i0` returns infinity whereas `i0e` still returns\n"
+    "a finite number.\n"
+    "\n"
+    ">>> from scipy.special import i0, i0e\n"
+    ">>> i0(1000.), i0e(1000.)\n"
+    "(inf, 0.012617240455891257)\n"
+    "\n"
+    "Calculate the function at several points by providing a NumPy array or\n"
+    "list for `x`:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> i0e(np.array([-2., 0., 3.]))\n"
+    "array([0.30850832, 1.        , 0.24300035])\n"
+    "\n"
+    "Plot the function from -10 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-10., 10., 1000)\n"
+    ">>> y = i0e(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_i0e_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_i0e_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_i0e_types[0] = <char>NPY_FLOAT
+ufunc_i0e_types[1] = <char>NPY_FLOAT
+ufunc_i0e_types[2] = <char>NPY_DOUBLE
+ufunc_i0e_types[3] = <char>NPY_DOUBLE
+ufunc_i0e_ptr[2*0] = <void*>_func_i0e
+ufunc_i0e_ptr[2*0+1] = <void*>(<char*>"i0e")
+ufunc_i0e_ptr[2*1] = <void*>_func_i0e
+ufunc_i0e_ptr[2*1+1] = <void*>(<char*>"i0e")
+ufunc_i0e_data[0] = &ufunc_i0e_ptr[2*0]
+ufunc_i0e_data[1] = &ufunc_i0e_ptr[2*1]
+i0e = np.PyUFunc_FromFuncAndData(ufunc_i0e_loops, ufunc_i0e_data, ufunc_i0e_types, 2, 1, 1, 0, "i0e", ufunc_i0e_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_i1_loops[2]
+cdef void *ufunc_i1_ptr[4]
+cdef void *ufunc_i1_data[2]
+cdef char ufunc_i1_types[4]
+cdef char *ufunc_i1_doc = (
+    "i1(x, out=None)\n"
+    "\n"
+    "Modified Bessel function of order 1.\n"
+    "\n"
+    "Defined as,\n"
+    "\n"
+    ".. math::\n"
+    "    I_1(x) = \\frac{1}{2}x \\sum_{k=0}^\\infty \\frac{(x^2/4)^k}{k! (k + 1)!}\n"
+    "           = -\\imath J_1(\\imath x),\n"
+    "\n"
+    "where :math:`J_1` is the Bessel function of the first kind of order 1.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "I : scalar or ndarray\n"
+    "    Value of the modified Bessel function of order 1 at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "iv: Modified Bessel function of the first kind\n"
+    "i1e: Exponentially scaled modified Bessel function of order 1\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The range is partitioned into the two intervals [0, 8] and (8, infinity).\n"
+    "Chebyshev polynomial expansions are employed in each interval.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `i1`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function at one point:\n"
+    "\n"
+    ">>> from scipy.special import i1\n"
+    ">>> i1(1.)\n"
+    "0.5651591039924851\n"
+    "\n"
+    "Calculate the function at several points:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> i1(np.array([-2., 0., 6.]))\n"
+    "array([-1.59063685,  0.        , 61.34193678])\n"
+    "\n"
+    "Plot the function between -10 and 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-10., 10., 1000)\n"
+    ">>> y = i1(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_i1_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_i1_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_i1_types[0] = <char>NPY_FLOAT
+ufunc_i1_types[1] = <char>NPY_FLOAT
+ufunc_i1_types[2] = <char>NPY_DOUBLE
+ufunc_i1_types[3] = <char>NPY_DOUBLE
+ufunc_i1_ptr[2*0] = <void*>_func_i1
+ufunc_i1_ptr[2*0+1] = <void*>(<char*>"i1")
+ufunc_i1_ptr[2*1] = <void*>_func_i1
+ufunc_i1_ptr[2*1+1] = <void*>(<char*>"i1")
+ufunc_i1_data[0] = &ufunc_i1_ptr[2*0]
+ufunc_i1_data[1] = &ufunc_i1_ptr[2*1]
+i1 = np.PyUFunc_FromFuncAndData(ufunc_i1_loops, ufunc_i1_data, ufunc_i1_types, 2, 1, 1, 0, "i1", ufunc_i1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_i1e_loops[2]
+cdef void *ufunc_i1e_ptr[4]
+cdef void *ufunc_i1e_data[2]
+cdef char ufunc_i1e_types[4]
+cdef char *ufunc_i1e_doc = (
+    "i1e(x, out=None)\n"
+    "\n"
+    "Exponentially scaled modified Bessel function of order 1.\n"
+    "\n"
+    "Defined as::\n"
+    "\n"
+    "    i1e(x) = exp(-abs(x)) * i1(x)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "I : scalar or ndarray\n"
+    "    Value of the exponentially scaled modified Bessel function of order 1\n"
+    "    at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "iv: Modified Bessel function of the first kind\n"
+    "i1: Modified Bessel function of order 1\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The range is partitioned into the two intervals [0, 8] and (8, infinity).\n"
+    "Chebyshev polynomial expansions are employed in each interval. The\n"
+    "polynomial expansions used are the same as those in `i1`, but\n"
+    "they are not multiplied by the dominant exponential factor.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `i1e`. `i1e`\n"
+    "is useful for large arguments `x`: for these, `i1` quickly overflows.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "In the following example `i1` returns infinity whereas `i1e` still returns\n"
+    "a finite number.\n"
+    "\n"
+    ">>> from scipy.special import i1, i1e\n"
+    ">>> i1(1000.), i1e(1000.)\n"
+    "(inf, 0.01261093025692863)\n"
+    "\n"
+    "Calculate the function at several points by providing a NumPy array or\n"
+    "list for `x`:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> i1e(np.array([-2., 0., 6.]))\n"
+    "array([-0.21526929,  0.        ,  0.15205146])\n"
+    "\n"
+    "Plot the function between -10 and 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-10., 10., 1000)\n"
+    ">>> y = i1e(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_i1e_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_i1e_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_i1e_types[0] = <char>NPY_FLOAT
+ufunc_i1e_types[1] = <char>NPY_FLOAT
+ufunc_i1e_types[2] = <char>NPY_DOUBLE
+ufunc_i1e_types[3] = <char>NPY_DOUBLE
+ufunc_i1e_ptr[2*0] = <void*>_func_i1e
+ufunc_i1e_ptr[2*0+1] = <void*>(<char*>"i1e")
+ufunc_i1e_ptr[2*1] = <void*>_func_i1e
+ufunc_i1e_ptr[2*1+1] = <void*>(<char*>"i1e")
+ufunc_i1e_data[0] = &ufunc_i1e_ptr[2*0]
+ufunc_i1e_data[1] = &ufunc_i1e_ptr[2*1]
+i1e = np.PyUFunc_FromFuncAndData(ufunc_i1e_loops, ufunc_i1e_data, ufunc_i1e_types, 2, 1, 1, 0, "i1e", ufunc_i1e_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_inv_boxcox_loops[2]
+cdef void *ufunc_inv_boxcox_ptr[4]
+cdef void *ufunc_inv_boxcox_data[2]
+cdef char ufunc_inv_boxcox_types[6]
+cdef char *ufunc_inv_boxcox_doc = (
+    "inv_boxcox(y, lmbda, out=None)\n"
+    "\n"
+    "Compute the inverse of the Box-Cox transformation.\n"
+    "\n"
+    "Find ``x`` such that::\n"
+    "\n"
+    "    y = (x**lmbda - 1) / lmbda  if lmbda != 0\n"
+    "        log(x)                  if lmbda == 0\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "y : array_like\n"
+    "    Data to be transformed.\n"
+    "lmbda : array_like\n"
+    "    Power parameter of the Box-Cox transform.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "x : scalar or ndarray\n"
+    "    Transformed data.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "\n"
+    ".. versionadded:: 0.16.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import boxcox, inv_boxcox\n"
+    ">>> y = boxcox([1, 4, 10], 2.5)\n"
+    ">>> inv_boxcox(y, 2.5)\n"
+    "array([1., 4., 10.])")
+ufunc_inv_boxcox_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_inv_boxcox_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_inv_boxcox_types[0] = <char>NPY_FLOAT
+ufunc_inv_boxcox_types[1] = <char>NPY_FLOAT
+ufunc_inv_boxcox_types[2] = <char>NPY_FLOAT
+ufunc_inv_boxcox_types[3] = <char>NPY_DOUBLE
+ufunc_inv_boxcox_types[4] = <char>NPY_DOUBLE
+ufunc_inv_boxcox_types[5] = <char>NPY_DOUBLE
+ufunc_inv_boxcox_ptr[2*0] = <void*>_func_inv_boxcox
+ufunc_inv_boxcox_ptr[2*0+1] = <void*>(<char*>"inv_boxcox")
+ufunc_inv_boxcox_ptr[2*1] = <void*>_func_inv_boxcox
+ufunc_inv_boxcox_ptr[2*1+1] = <void*>(<char*>"inv_boxcox")
+ufunc_inv_boxcox_data[0] = &ufunc_inv_boxcox_ptr[2*0]
+ufunc_inv_boxcox_data[1] = &ufunc_inv_boxcox_ptr[2*1]
+inv_boxcox = np.PyUFunc_FromFuncAndData(ufunc_inv_boxcox_loops, ufunc_inv_boxcox_data, ufunc_inv_boxcox_types, 2, 2, 1, 0, "inv_boxcox", ufunc_inv_boxcox_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_inv_boxcox1p_loops[2]
+cdef void *ufunc_inv_boxcox1p_ptr[4]
+cdef void *ufunc_inv_boxcox1p_data[2]
+cdef char ufunc_inv_boxcox1p_types[6]
+cdef char *ufunc_inv_boxcox1p_doc = (
+    "inv_boxcox1p(y, lmbda, out=None)\n"
+    "\n"
+    "Compute the inverse of the Box-Cox transformation.\n"
+    "\n"
+    "Find ``x`` such that::\n"
+    "\n"
+    "    y = ((1+x)**lmbda - 1) / lmbda  if lmbda != 0\n"
+    "        log(1+x)                    if lmbda == 0\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "y : array_like\n"
+    "    Data to be transformed.\n"
+    "lmbda : array_like\n"
+    "    Power parameter of the Box-Cox transform.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "x : scalar or ndarray\n"
+    "    Transformed data.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "\n"
+    ".. versionadded:: 0.16.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import boxcox1p, inv_boxcox1p\n"
+    ">>> y = boxcox1p([1, 4, 10], 2.5)\n"
+    ">>> inv_boxcox1p(y, 2.5)\n"
+    "array([1., 4., 10.])")
+ufunc_inv_boxcox1p_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_inv_boxcox1p_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_inv_boxcox1p_types[0] = <char>NPY_FLOAT
+ufunc_inv_boxcox1p_types[1] = <char>NPY_FLOAT
+ufunc_inv_boxcox1p_types[2] = <char>NPY_FLOAT
+ufunc_inv_boxcox1p_types[3] = <char>NPY_DOUBLE
+ufunc_inv_boxcox1p_types[4] = <char>NPY_DOUBLE
+ufunc_inv_boxcox1p_types[5] = <char>NPY_DOUBLE
+ufunc_inv_boxcox1p_ptr[2*0] = <void*>_func_inv_boxcox1p
+ufunc_inv_boxcox1p_ptr[2*0+1] = <void*>(<char*>"inv_boxcox1p")
+ufunc_inv_boxcox1p_ptr[2*1] = <void*>_func_inv_boxcox1p
+ufunc_inv_boxcox1p_ptr[2*1+1] = <void*>(<char*>"inv_boxcox1p")
+ufunc_inv_boxcox1p_data[0] = &ufunc_inv_boxcox1p_ptr[2*0]
+ufunc_inv_boxcox1p_data[1] = &ufunc_inv_boxcox1p_ptr[2*1]
+inv_boxcox1p = np.PyUFunc_FromFuncAndData(ufunc_inv_boxcox1p_loops, ufunc_inv_boxcox1p_data, ufunc_inv_boxcox1p_types, 2, 2, 1, 0, "inv_boxcox1p", ufunc_inv_boxcox1p_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_it2i0k0_loops[2]
+cdef void *ufunc_it2i0k0_ptr[4]
+cdef void *ufunc_it2i0k0_data[2]
+cdef char ufunc_it2i0k0_types[6]
+cdef char *ufunc_it2i0k0_doc = (
+    "it2i0k0(x, out=None)\n"
+    "\n"
+    "Integrals related to modified Bessel functions of order 0.\n"
+    "\n"
+    "Computes the integrals\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\int_0^x \\frac{I_0(t) - 1}{t} dt \\\\\n"
+    "    \\int_x^\\infty \\frac{K_0(t)}{t} dt.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Values at which to evaluate the integrals.\n"
+    "out : tuple of ndarrays, optional\n"
+    "    Optional output arrays for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "ii0 : scalar or ndarray\n"
+    "    The integral for `i0`\n"
+    "ik0 : scalar or ndarray\n"
+    "    The integral for `k0`\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] S. Zhang and J.M. Jin, \"Computation of Special Functions\",\n"
+    "       Wiley 1996\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the functions at one point.\n"
+    "\n"
+    ">>> from scipy.special import it2i0k0\n"
+    ">>> int_i, int_k = it2i0k0(1.)\n"
+    ">>> int_i, int_k\n"
+    "(0.12897944249456852, 0.2085182909001295)\n"
+    "\n"
+    "Evaluate the functions at several points.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> points = np.array([0.5, 1.5, 3.])\n"
+    ">>> int_i, int_k = it2i0k0(points)\n"
+    ">>> int_i, int_k\n"
+    "(array([0.03149527, 0.30187149, 1.50012461]),\n"
+    " array([0.66575102, 0.0823715 , 0.00823631]))\n"
+    "\n"
+    "Plot the functions from 0 to 5.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 5., 1000)\n"
+    ">>> int_i, int_k = it2i0k0(x)\n"
+    ">>> ax.plot(x, int_i, label=r\"$\\int_0^x \\frac{I_0(t)-1}{t}\\,dt$\")\n"
+    ">>> ax.plot(x, int_k, label=r\"$\\int_x^{\\infty} \\frac{K_0(t)}{t}\\,dt$\")\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_ylim(0, 10)\n"
+    ">>> plt.show()")
+ufunc_it2i0k0_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_f_ff
+ufunc_it2i0k0_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_d_dd
+ufunc_it2i0k0_types[0] = <char>NPY_FLOAT
+ufunc_it2i0k0_types[1] = <char>NPY_FLOAT
+ufunc_it2i0k0_types[2] = <char>NPY_FLOAT
+ufunc_it2i0k0_types[3] = <char>NPY_DOUBLE
+ufunc_it2i0k0_types[4] = <char>NPY_DOUBLE
+ufunc_it2i0k0_types[5] = <char>NPY_DOUBLE
+ufunc_it2i0k0_ptr[2*0] = <void*>_func_it2i0k0_wrap
+ufunc_it2i0k0_ptr[2*0+1] = <void*>(<char*>"it2i0k0")
+ufunc_it2i0k0_ptr[2*1] = <void*>_func_it2i0k0_wrap
+ufunc_it2i0k0_ptr[2*1+1] = <void*>(<char*>"it2i0k0")
+ufunc_it2i0k0_data[0] = &ufunc_it2i0k0_ptr[2*0]
+ufunc_it2i0k0_data[1] = &ufunc_it2i0k0_ptr[2*1]
+it2i0k0 = np.PyUFunc_FromFuncAndData(ufunc_it2i0k0_loops, ufunc_it2i0k0_data, ufunc_it2i0k0_types, 2, 1, 2, 0, "it2i0k0", ufunc_it2i0k0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_it2j0y0_loops[2]
+cdef void *ufunc_it2j0y0_ptr[4]
+cdef void *ufunc_it2j0y0_data[2]
+cdef char ufunc_it2j0y0_types[6]
+cdef char *ufunc_it2j0y0_doc = (
+    "it2j0y0(x, out=None)\n"
+    "\n"
+    "Integrals related to Bessel functions of the first kind of order 0.\n"
+    "\n"
+    "Computes the integrals\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\int_0^x \\frac{1 - J_0(t)}{t} dt \\\\\n"
+    "    \\int_x^\\infty \\frac{Y_0(t)}{t} dt.\n"
+    "\n"
+    "For more on :math:`J_0` and :math:`Y_0` see `j0` and `y0`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Values at which to evaluate the integrals.\n"
+    "out : tuple of ndarrays, optional\n"
+    "    Optional output arrays for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "ij0 : scalar or ndarray\n"
+    "    The integral for `j0`\n"
+    "iy0 : scalar or ndarray\n"
+    "    The integral for `y0`\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] S. Zhang and J.M. Jin, \"Computation of Special Functions\",\n"
+    "       Wiley 1996\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the functions at one point.\n"
+    "\n"
+    ">>> from scipy.special import it2j0y0\n"
+    ">>> int_j, int_y = it2j0y0(1.)\n"
+    ">>> int_j, int_y\n"
+    "(0.12116524699506871, 0.39527290169929336)\n"
+    "\n"
+    "Evaluate the functions at several points.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> points = np.array([0.5, 1.5, 3.])\n"
+    ">>> int_j, int_y = it2j0y0(points)\n"
+    ">>> int_j, int_y\n"
+    "(array([0.03100699, 0.26227724, 0.85614669]),\n"
+    " array([ 0.26968854,  0.29769696, -0.02987272]))\n"
+    "\n"
+    "Plot the functions from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> int_j, int_y = it2j0y0(x)\n"
+    ">>> ax.plot(x, int_j, label=r\"$\\int_0^x \\frac{1-J_0(t)}{t}\\,dt$\")\n"
+    ">>> ax.plot(x, int_y, label=r\"$\\int_x^{\\infty} \\frac{Y_0(t)}{t}\\,dt$\")\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_ylim(-2.5, 2.5)\n"
+    ">>> plt.show()")
+ufunc_it2j0y0_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_f_ff
+ufunc_it2j0y0_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_d_dd
+ufunc_it2j0y0_types[0] = <char>NPY_FLOAT
+ufunc_it2j0y0_types[1] = <char>NPY_FLOAT
+ufunc_it2j0y0_types[2] = <char>NPY_FLOAT
+ufunc_it2j0y0_types[3] = <char>NPY_DOUBLE
+ufunc_it2j0y0_types[4] = <char>NPY_DOUBLE
+ufunc_it2j0y0_types[5] = <char>NPY_DOUBLE
+ufunc_it2j0y0_ptr[2*0] = <void*>_func_it2j0y0_wrap
+ufunc_it2j0y0_ptr[2*0+1] = <void*>(<char*>"it2j0y0")
+ufunc_it2j0y0_ptr[2*1] = <void*>_func_it2j0y0_wrap
+ufunc_it2j0y0_ptr[2*1+1] = <void*>(<char*>"it2j0y0")
+ufunc_it2j0y0_data[0] = &ufunc_it2j0y0_ptr[2*0]
+ufunc_it2j0y0_data[1] = &ufunc_it2j0y0_ptr[2*1]
+it2j0y0 = np.PyUFunc_FromFuncAndData(ufunc_it2j0y0_loops, ufunc_it2j0y0_data, ufunc_it2j0y0_types, 2, 1, 2, 0, "it2j0y0", ufunc_it2j0y0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_it2struve0_loops[2]
+cdef void *ufunc_it2struve0_ptr[4]
+cdef void *ufunc_it2struve0_data[2]
+cdef char ufunc_it2struve0_types[4]
+cdef char *ufunc_it2struve0_doc = (
+    "it2struve0(x, out=None)\n"
+    "\n"
+    "Integral related to the Struve function of order 0.\n"
+    "\n"
+    "Returns the integral,\n"
+    "\n"
+    ".. math::\n"
+    "    \\int_x^\\infty \\frac{H_0(t)}{t}\\,dt\n"
+    "\n"
+    "where :math:`H_0` is the Struve function of order 0.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Lower limit of integration.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "I : scalar or ndarray\n"
+    "    The value of the integral.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "struve\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for a Fortran routine created by Shanjie Zhang and Jianming\n"
+    "Jin [1]_.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n"
+    "       Functions\", John Wiley and Sons, 1996.\n"
+    "       https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the function at one point.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import it2struve0\n"
+    ">>> it2struve0(1.)\n"
+    "0.9571973506383524\n"
+    "\n"
+    "Evaluate the function at several points by supplying\n"
+    "an array for `x`.\n"
+    "\n"
+    ">>> points = np.array([1., 2., 3.5])\n"
+    ">>> it2struve0(points)\n"
+    "array([0.95719735, 0.46909296, 0.10366042])\n"
+    "\n"
+    "Plot the function from -10 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-10., 10., 1000)\n"
+    ">>> it2struve0_values = it2struve0(x)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> ax.plot(x, it2struve0_values)\n"
+    ">>> ax.set_xlabel(r'$x$')\n"
+    ">>> ax.set_ylabel(r'$\\int_x^{\\infty}\\frac{H_0(t)}{t}\\,dt$')\n"
+    ">>> plt.show()")
+ufunc_it2struve0_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_it2struve0_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_it2struve0_types[0] = <char>NPY_FLOAT
+ufunc_it2struve0_types[1] = <char>NPY_FLOAT
+ufunc_it2struve0_types[2] = <char>NPY_DOUBLE
+ufunc_it2struve0_types[3] = <char>NPY_DOUBLE
+ufunc_it2struve0_ptr[2*0] = <void*>_func_it2struve0_wrap
+ufunc_it2struve0_ptr[2*0+1] = <void*>(<char*>"it2struve0")
+ufunc_it2struve0_ptr[2*1] = <void*>_func_it2struve0_wrap
+ufunc_it2struve0_ptr[2*1+1] = <void*>(<char*>"it2struve0")
+ufunc_it2struve0_data[0] = &ufunc_it2struve0_ptr[2*0]
+ufunc_it2struve0_data[1] = &ufunc_it2struve0_ptr[2*1]
+it2struve0 = np.PyUFunc_FromFuncAndData(ufunc_it2struve0_loops, ufunc_it2struve0_data, ufunc_it2struve0_types, 2, 1, 1, 0, "it2struve0", ufunc_it2struve0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_itairy_loops[2]
+cdef void *ufunc_itairy_ptr[4]
+cdef void *ufunc_itairy_data[2]
+cdef char ufunc_itairy_types[10]
+cdef char *ufunc_itairy_doc = (
+    "itairy(x, out=None)\n"
+    "\n"
+    "Integrals of Airy functions\n"
+    "\n"
+    "Calculates the integrals of Airy functions from 0 to `x`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "\n"
+    "x : array_like\n"
+    "    Upper limit of integration (float).\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "Apt : scalar or ndarray\n"
+    "    Integral of Ai(t) from 0 to x.\n"
+    "Bpt : scalar or ndarray\n"
+    "    Integral of Bi(t) from 0 to x.\n"
+    "Ant : scalar or ndarray\n"
+    "    Integral of Ai(-t) from 0 to x.\n"
+    "Bnt : scalar or ndarray\n"
+    "    Integral of Bi(-t) from 0 to x.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "\n"
+    "Wrapper for a Fortran routine created by Shanjie Zhang and Jianming\n"
+    "Jin [1]_.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    "\n"
+    ".. [1] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n"
+    "       Functions\", John Wiley and Sons, 1996.\n"
+    "       https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the functions at ``x=1.``.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import itairy\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> apt, bpt, ant, bnt = itairy(1.)\n"
+    ">>> apt, bpt, ant, bnt\n"
+    "(0.23631734191710949,\n"
+    " 0.8727691167380077,\n"
+    " 0.46567398346706845,\n"
+    " 0.3730050096342943)\n"
+    "\n"
+    "Compute the functions at several points by providing a NumPy array for `x`.\n"
+    "\n"
+    ">>> x = np.array([1., 1.5, 2.5, 5])\n"
+    ">>> apt, bpt, ant, bnt = itairy(x)\n"
+    ">>> apt, bpt, ant, bnt\n"
+    "(array([0.23631734, 0.28678675, 0.324638  , 0.33328759]),\n"
+    " array([  0.87276912,   1.62470809,   5.20906691, 321.47831857]),\n"
+    " array([0.46567398, 0.72232876, 0.93187776, 0.7178822 ]),\n"
+    " array([ 0.37300501,  0.35038814, -0.02812939,  0.15873094]))\n"
+    "\n"
+    "Plot the functions from -10 to 10.\n"
+    "\n"
+    ">>> x = np.linspace(-10, 10, 500)\n"
+    ">>> apt, bpt, ant, bnt = itairy(x)\n"
+    ">>> fig, ax = plt.subplots(figsize=(6, 5))\n"
+    ">>> ax.plot(x, apt, label=r\"$\\int_0^x\\, Ai(t)\\, dt$\")\n"
+    ">>> ax.plot(x, bpt, ls=\"dashed\", label=r\"$\\int_0^x\\, Bi(t)\\, dt$\")\n"
+    ">>> ax.plot(x, ant, ls=\"dashdot\", label=r\"$\\int_0^x\\, Ai(-t)\\, dt$\")\n"
+    ">>> ax.plot(x, bnt, ls=\"dotted\", label=r\"$\\int_0^x\\, Bi(-t)\\, dt$\")\n"
+    ">>> ax.set_ylim(-2, 1.5)\n"
+    ">>> ax.legend(loc=\"lower right\")\n"
+    ">>> plt.show()")
+ufunc_itairy_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dddd_As_f_ffff
+ufunc_itairy_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dddd_As_d_dddd
+ufunc_itairy_types[0] = <char>NPY_FLOAT
+ufunc_itairy_types[1] = <char>NPY_FLOAT
+ufunc_itairy_types[2] = <char>NPY_FLOAT
+ufunc_itairy_types[3] = <char>NPY_FLOAT
+ufunc_itairy_types[4] = <char>NPY_FLOAT
+ufunc_itairy_types[5] = <char>NPY_DOUBLE
+ufunc_itairy_types[6] = <char>NPY_DOUBLE
+ufunc_itairy_types[7] = <char>NPY_DOUBLE
+ufunc_itairy_types[8] = <char>NPY_DOUBLE
+ufunc_itairy_types[9] = <char>NPY_DOUBLE
+ufunc_itairy_ptr[2*0] = <void*>_func_itairy_wrap
+ufunc_itairy_ptr[2*0+1] = <void*>(<char*>"itairy")
+ufunc_itairy_ptr[2*1] = <void*>_func_itairy_wrap
+ufunc_itairy_ptr[2*1+1] = <void*>(<char*>"itairy")
+ufunc_itairy_data[0] = &ufunc_itairy_ptr[2*0]
+ufunc_itairy_data[1] = &ufunc_itairy_ptr[2*1]
+itairy = np.PyUFunc_FromFuncAndData(ufunc_itairy_loops, ufunc_itairy_data, ufunc_itairy_types, 2, 1, 4, 0, "itairy", ufunc_itairy_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_iti0k0_loops[2]
+cdef void *ufunc_iti0k0_ptr[4]
+cdef void *ufunc_iti0k0_data[2]
+cdef char ufunc_iti0k0_types[6]
+cdef char *ufunc_iti0k0_doc = (
+    "iti0k0(x, out=None)\n"
+    "\n"
+    "Integrals of modified Bessel functions of order 0.\n"
+    "\n"
+    "Computes the integrals\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\int_0^x I_0(t) dt \\\\\n"
+    "    \\int_0^x K_0(t) dt.\n"
+    "\n"
+    "For more on :math:`I_0` and :math:`K_0` see `i0` and `k0`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Values at which to evaluate the integrals.\n"
+    "out : tuple of ndarrays, optional\n"
+    "    Optional output arrays for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "ii0 : scalar or ndarray\n"
+    "    The integral for `i0`\n"
+    "ik0 : scalar or ndarray\n"
+    "    The integral for `k0`\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] S. Zhang and J.M. Jin, \"Computation of Special Functions\",\n"
+    "       Wiley 1996\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the functions at one point.\n"
+    "\n"
+    ">>> from scipy.special import iti0k0\n"
+    ">>> int_i, int_k = iti0k0(1.)\n"
+    ">>> int_i, int_k\n"
+    "(1.0865210970235892, 1.2425098486237771)\n"
+    "\n"
+    "Evaluate the functions at several points.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> points = np.array([0., 1.5, 3.])\n"
+    ">>> int_i, int_k = iti0k0(points)\n"
+    ">>> int_i, int_k\n"
+    "(array([0.        , 1.80606937, 6.16096149]),\n"
+    " array([0.        , 1.39458246, 1.53994809]))\n"
+    "\n"
+    "Plot the functions from 0 to 5.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 5., 1000)\n"
+    ">>> int_i, int_k = iti0k0(x)\n"
+    ">>> ax.plot(x, int_i, label=r\"$\\int_0^x I_0(t)\\,dt$\")\n"
+    ">>> ax.plot(x, int_k, label=r\"$\\int_0^x K_0(t)\\,dt$\")\n"
+    ">>> ax.legend()\n"
+    ">>> plt.show()")
+ufunc_iti0k0_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_f_ff
+ufunc_iti0k0_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_d_dd
+ufunc_iti0k0_types[0] = <char>NPY_FLOAT
+ufunc_iti0k0_types[1] = <char>NPY_FLOAT
+ufunc_iti0k0_types[2] = <char>NPY_FLOAT
+ufunc_iti0k0_types[3] = <char>NPY_DOUBLE
+ufunc_iti0k0_types[4] = <char>NPY_DOUBLE
+ufunc_iti0k0_types[5] = <char>NPY_DOUBLE
+ufunc_iti0k0_ptr[2*0] = <void*>_func_it1i0k0_wrap
+ufunc_iti0k0_ptr[2*0+1] = <void*>(<char*>"iti0k0")
+ufunc_iti0k0_ptr[2*1] = <void*>_func_it1i0k0_wrap
+ufunc_iti0k0_ptr[2*1+1] = <void*>(<char*>"iti0k0")
+ufunc_iti0k0_data[0] = &ufunc_iti0k0_ptr[2*0]
+ufunc_iti0k0_data[1] = &ufunc_iti0k0_ptr[2*1]
+iti0k0 = np.PyUFunc_FromFuncAndData(ufunc_iti0k0_loops, ufunc_iti0k0_data, ufunc_iti0k0_types, 2, 1, 2, 0, "iti0k0", ufunc_iti0k0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_itj0y0_loops[2]
+cdef void *ufunc_itj0y0_ptr[4]
+cdef void *ufunc_itj0y0_data[2]
+cdef char ufunc_itj0y0_types[6]
+cdef char *ufunc_itj0y0_doc = (
+    "itj0y0(x, out=None)\n"
+    "\n"
+    "Integrals of Bessel functions of the first kind of order 0.\n"
+    "\n"
+    "Computes the integrals\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\int_0^x J_0(t) dt \\\\\n"
+    "    \\int_0^x Y_0(t) dt.\n"
+    "\n"
+    "For more on :math:`J_0` and :math:`Y_0` see `j0` and `y0`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Values at which to evaluate the integrals.\n"
+    "out : tuple of ndarrays, optional\n"
+    "    Optional output arrays for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "ij0 : scalar or ndarray\n"
+    "    The integral of `j0`\n"
+    "iy0 : scalar or ndarray\n"
+    "    The integral of `y0`\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] S. Zhang and J.M. Jin, \"Computation of Special Functions\",\n"
+    "       Wiley 1996\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the functions at one point.\n"
+    "\n"
+    ">>> from scipy.special import itj0y0\n"
+    ">>> int_j, int_y = itj0y0(1.)\n"
+    ">>> int_j, int_y\n"
+    "(0.9197304100897596, -0.637069376607422)\n"
+    "\n"
+    "Evaluate the functions at several points.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> points = np.array([0., 1.5, 3.])\n"
+    ">>> int_j, int_y = itj0y0(points)\n"
+    ">>> int_j, int_y\n"
+    "(array([0.        , 1.24144951, 1.38756725]),\n"
+    " array([ 0.        , -0.51175903,  0.19765826]))\n"
+    "\n"
+    "Plot the functions from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> int_j, int_y = itj0y0(x)\n"
+    ">>> ax.plot(x, int_j, label=r\"$\\int_0^x J_0(t)\\,dt$\")\n"
+    ">>> ax.plot(x, int_y, label=r\"$\\int_0^x Y_0(t)\\,dt$\")\n"
+    ">>> ax.legend()\n"
+    ">>> plt.show()")
+ufunc_itj0y0_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_f_ff
+ufunc_itj0y0_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_d_dd
+ufunc_itj0y0_types[0] = <char>NPY_FLOAT
+ufunc_itj0y0_types[1] = <char>NPY_FLOAT
+ufunc_itj0y0_types[2] = <char>NPY_FLOAT
+ufunc_itj0y0_types[3] = <char>NPY_DOUBLE
+ufunc_itj0y0_types[4] = <char>NPY_DOUBLE
+ufunc_itj0y0_types[5] = <char>NPY_DOUBLE
+ufunc_itj0y0_ptr[2*0] = <void*>_func_it1j0y0_wrap
+ufunc_itj0y0_ptr[2*0+1] = <void*>(<char*>"itj0y0")
+ufunc_itj0y0_ptr[2*1] = <void*>_func_it1j0y0_wrap
+ufunc_itj0y0_ptr[2*1+1] = <void*>(<char*>"itj0y0")
+ufunc_itj0y0_data[0] = &ufunc_itj0y0_ptr[2*0]
+ufunc_itj0y0_data[1] = &ufunc_itj0y0_ptr[2*1]
+itj0y0 = np.PyUFunc_FromFuncAndData(ufunc_itj0y0_loops, ufunc_itj0y0_data, ufunc_itj0y0_types, 2, 1, 2, 0, "itj0y0", ufunc_itj0y0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_itmodstruve0_loops[2]
+cdef void *ufunc_itmodstruve0_ptr[4]
+cdef void *ufunc_itmodstruve0_data[2]
+cdef char ufunc_itmodstruve0_types[4]
+cdef char *ufunc_itmodstruve0_doc = (
+    "itmodstruve0(x, out=None)\n"
+    "\n"
+    "Integral of the modified Struve function of order 0.\n"
+    "\n"
+    ".. math::\n"
+    "    I = \\int_0^x L_0(t)\\,dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Upper limit of integration (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "I : scalar or ndarray\n"
+    "    The integral of :math:`L_0` from 0 to `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "modstruve: Modified Struve function which is integrated by this function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for a Fortran routine created by Shanjie Zhang and Jianming\n"
+    "Jin [1]_.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n"
+    "       Functions\", John Wiley and Sons, 1996.\n"
+    "       https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the function at one point.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import itmodstruve0\n"
+    ">>> itmodstruve0(1.)\n"
+    "0.3364726286440384\n"
+    "\n"
+    "Evaluate the function at several points by supplying\n"
+    "an array for `x`.\n"
+    "\n"
+    ">>> points = np.array([1., 2., 3.5])\n"
+    ">>> itmodstruve0(points)\n"
+    "array([0.33647263, 1.588285  , 7.60382578])\n"
+    "\n"
+    "Plot the function from -10 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-10., 10., 1000)\n"
+    ">>> itmodstruve0_values = itmodstruve0(x)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> ax.plot(x, itmodstruve0_values)\n"
+    ">>> ax.set_xlabel(r'$x$')\n"
+    ">>> ax.set_ylabel(r'$\\int_0^xL_0(t)\\,dt$')\n"
+    ">>> plt.show()")
+ufunc_itmodstruve0_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_itmodstruve0_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_itmodstruve0_types[0] = <char>NPY_FLOAT
+ufunc_itmodstruve0_types[1] = <char>NPY_FLOAT
+ufunc_itmodstruve0_types[2] = <char>NPY_DOUBLE
+ufunc_itmodstruve0_types[3] = <char>NPY_DOUBLE
+ufunc_itmodstruve0_ptr[2*0] = <void*>_func_itmodstruve0_wrap
+ufunc_itmodstruve0_ptr[2*0+1] = <void*>(<char*>"itmodstruve0")
+ufunc_itmodstruve0_ptr[2*1] = <void*>_func_itmodstruve0_wrap
+ufunc_itmodstruve0_ptr[2*1+1] = <void*>(<char*>"itmodstruve0")
+ufunc_itmodstruve0_data[0] = &ufunc_itmodstruve0_ptr[2*0]
+ufunc_itmodstruve0_data[1] = &ufunc_itmodstruve0_ptr[2*1]
+itmodstruve0 = np.PyUFunc_FromFuncAndData(ufunc_itmodstruve0_loops, ufunc_itmodstruve0_data, ufunc_itmodstruve0_types, 2, 1, 1, 0, "itmodstruve0", ufunc_itmodstruve0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_itstruve0_loops[2]
+cdef void *ufunc_itstruve0_ptr[4]
+cdef void *ufunc_itstruve0_data[2]
+cdef char ufunc_itstruve0_types[4]
+cdef char *ufunc_itstruve0_doc = (
+    "itstruve0(x, out=None)\n"
+    "\n"
+    "Integral of the Struve function of order 0.\n"
+    "\n"
+    ".. math::\n"
+    "    I = \\int_0^x H_0(t)\\,dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Upper limit of integration (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "I : scalar or ndarray\n"
+    "    The integral of :math:`H_0` from 0 to `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "struve: Function which is integrated by this function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for a Fortran routine created by Shanjie Zhang and Jianming\n"
+    "Jin [1]_.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n"
+    "       Functions\", John Wiley and Sons, 1996.\n"
+    "       https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the function at one point.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import itstruve0\n"
+    ">>> itstruve0(1.)\n"
+    "0.30109042670805547\n"
+    "\n"
+    "Evaluate the function at several points by supplying\n"
+    "an array for `x`.\n"
+    "\n"
+    ">>> points = np.array([1., 2., 3.5])\n"
+    ">>> itstruve0(points)\n"
+    "array([0.30109043, 1.01870116, 1.96804581])\n"
+    "\n"
+    "Plot the function from -20 to 20.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-20., 20., 1000)\n"
+    ">>> istruve0_values = itstruve0(x)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> ax.plot(x, istruve0_values)\n"
+    ">>> ax.set_xlabel(r'$x$')\n"
+    ">>> ax.set_ylabel(r'$\\int_0^{x}H_0(t)\\,dt$')\n"
+    ">>> plt.show()")
+ufunc_itstruve0_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_itstruve0_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_itstruve0_types[0] = <char>NPY_FLOAT
+ufunc_itstruve0_types[1] = <char>NPY_FLOAT
+ufunc_itstruve0_types[2] = <char>NPY_DOUBLE
+ufunc_itstruve0_types[3] = <char>NPY_DOUBLE
+ufunc_itstruve0_ptr[2*0] = <void*>_func_itstruve0_wrap
+ufunc_itstruve0_ptr[2*0+1] = <void*>(<char*>"itstruve0")
+ufunc_itstruve0_ptr[2*1] = <void*>_func_itstruve0_wrap
+ufunc_itstruve0_ptr[2*1+1] = <void*>(<char*>"itstruve0")
+ufunc_itstruve0_data[0] = &ufunc_itstruve0_ptr[2*0]
+ufunc_itstruve0_data[1] = &ufunc_itstruve0_ptr[2*1]
+itstruve0 = np.PyUFunc_FromFuncAndData(ufunc_itstruve0_loops, ufunc_itstruve0_data, ufunc_itstruve0_types, 2, 1, 1, 0, "itstruve0", ufunc_itstruve0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_iv_loops[4]
+cdef void *ufunc_iv_ptr[8]
+cdef void *ufunc_iv_data[4]
+cdef char ufunc_iv_types[12]
+cdef char *ufunc_iv_doc = (
+    "iv(v, z, out=None)\n"
+    "\n"
+    "Modified Bessel function of the first kind of real order.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order. If `z` is of real type and negative, `v` must be integer\n"
+    "    valued.\n"
+    "z : array_like of float or complex\n"
+    "    Argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the modified Bessel function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ive : This function with leading exponential behavior stripped off.\n"
+    "i0 : Faster version of this function for order 0.\n"
+    "i1 : Faster version of this function for order 1.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For real `z` and :math:`v \\in [-50, 50]`, the evaluation is carried out\n"
+    "using Temme's method [1]_.  For larger orders, uniform asymptotic\n"
+    "expansions are applied.\n"
+    "\n"
+    "For complex `z` and positive `v`, the AMOS [2]_ `zbesi` routine is\n"
+    "called. It uses a power series for small `z`, the asymptotic expansion\n"
+    "for large `abs(z)`, the Miller algorithm normalized by the Wronskian\n"
+    "and a Neumann series for intermediate magnitudes, and the uniform\n"
+    "asymptotic expansions for :math:`I_v(z)` and :math:`J_v(z)` for large\n"
+    "orders. Backward recurrence is used to generate sequences or reduce\n"
+    "orders when necessary.\n"
+    "\n"
+    "The calculations above are done in the right half plane and continued\n"
+    "into the left half plane by the formula,\n"
+    "\n"
+    ".. math:: I_v(z \\exp(\\pm\\imath\\pi)) = \\exp(\\pm\\pi v) I_v(z)\n"
+    "\n"
+    "(valid when the real part of `z` is positive).  For negative `v`, the\n"
+    "formula\n"
+    "\n"
+    ".. math:: I_{-v}(z) = I_v(z) + \\frac{2}{\\pi} \\sin(\\pi v) K_v(z)\n"
+    "\n"
+    "is used, where :math:`K_v(z)` is the modified Bessel function of the\n"
+    "second kind, evaluated using the AMOS routine `zbesk`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Temme, Journal of Computational Physics, vol 21, 343 (1976)\n"
+    ".. [2] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the function of order 0 at one point.\n"
+    "\n"
+    ">>> from scipy.special import iv\n"
+    ">>> iv(0, 1.)\n"
+    "1.2660658777520084\n"
+    "\n"
+    "Evaluate the function at one point for different orders.\n"
+    "\n"
+    ">>> iv(0, 1.), iv(1, 1.), iv(1.5, 1.)\n"
+    "(1.2660658777520084, 0.565159103992485, 0.2935253263474798)\n"
+    "\n"
+    "The evaluation for different orders can be carried out in one call by\n"
+    "providing a list or NumPy array as argument for the `v` parameter:\n"
+    "\n"
+    ">>> iv([0, 1, 1.5], 1.)\n"
+    "array([1.26606588, 0.5651591 , 0.29352533])\n"
+    "\n"
+    "Evaluate the function at several points for order 0 by providing an\n"
+    "array for `z`.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> points = np.array([-2., 0., 3.])\n"
+    ">>> iv(0, points)\n"
+    "array([2.2795853 , 1.        , 4.88079259])\n"
+    "\n"
+    "If `z` is an array, the order parameter `v` must be broadcastable to\n"
+    "the correct shape if different orders shall be computed in one call.\n"
+    "To calculate the orders 0 and 1 for an 1D array:\n"
+    "\n"
+    ">>> orders = np.array([[0], [1]])\n"
+    ">>> orders.shape\n"
+    "(2, 1)\n"
+    "\n"
+    ">>> iv(orders, points)\n"
+    "array([[ 2.2795853 ,  1.        ,  4.88079259],\n"
+    "       [-1.59063685,  0.        ,  3.95337022]])\n"
+    "\n"
+    "Plot the functions of order 0 to 3 from -5 to 5.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-5., 5., 1000)\n"
+    ">>> for i in range(4):\n"
+    "...     ax.plot(x, iv(i, x), label=f'$I_{i!r}$')\n"
+    ">>> ax.legend()\n"
+    ">>> plt.show()")
+ufunc_iv_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_iv_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_iv_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_iv_loops[3] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_iv_types[0] = <char>NPY_FLOAT
+ufunc_iv_types[1] = <char>NPY_FLOAT
+ufunc_iv_types[2] = <char>NPY_FLOAT
+ufunc_iv_types[3] = <char>NPY_FLOAT
+ufunc_iv_types[4] = <char>NPY_CFLOAT
+ufunc_iv_types[5] = <char>NPY_CFLOAT
+ufunc_iv_types[6] = <char>NPY_DOUBLE
+ufunc_iv_types[7] = <char>NPY_DOUBLE
+ufunc_iv_types[8] = <char>NPY_DOUBLE
+ufunc_iv_types[9] = <char>NPY_DOUBLE
+ufunc_iv_types[10] = <char>NPY_CDOUBLE
+ufunc_iv_types[11] = <char>NPY_CDOUBLE
+ufunc_iv_ptr[2*0] = <void*>_func_iv
+ufunc_iv_ptr[2*0+1] = <void*>(<char*>"iv")
+ufunc_iv_ptr[2*1] = <void*>_func_cbesi_wrap
+ufunc_iv_ptr[2*1+1] = <void*>(<char*>"iv")
+ufunc_iv_ptr[2*2] = <void*>_func_iv
+ufunc_iv_ptr[2*2+1] = <void*>(<char*>"iv")
+ufunc_iv_ptr[2*3] = <void*>_func_cbesi_wrap
+ufunc_iv_ptr[2*3+1] = <void*>(<char*>"iv")
+ufunc_iv_data[0] = &ufunc_iv_ptr[2*0]
+ufunc_iv_data[1] = &ufunc_iv_ptr[2*1]
+ufunc_iv_data[2] = &ufunc_iv_ptr[2*2]
+ufunc_iv_data[3] = &ufunc_iv_ptr[2*3]
+iv = np.PyUFunc_FromFuncAndData(ufunc_iv_loops, ufunc_iv_data, ufunc_iv_types, 4, 2, 1, 0, "iv", ufunc_iv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ive_loops[4]
+cdef void *ufunc_ive_ptr[8]
+cdef void *ufunc_ive_data[4]
+cdef char ufunc_ive_types[12]
+cdef char *ufunc_ive_doc = (
+    "ive(v, z, out=None)\n"
+    "\n"
+    "Exponentially scaled modified Bessel function of the first kind.\n"
+    "\n"
+    "Defined as::\n"
+    "\n"
+    "    ive(v, z) = iv(v, z) * exp(-abs(z.real))\n"
+    "\n"
+    "For imaginary numbers without a real part, returns the unscaled\n"
+    "Bessel function of the first kind `iv`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like of float\n"
+    "    Order.\n"
+    "z : array_like of float or complex\n"
+    "    Argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the exponentially scaled modified Bessel function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "iv: Modified Bessel function of the first kind\n"
+    "i0e: Faster implementation of this function for order 0\n"
+    "i1e: Faster implementation of this function for order 1\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For positive `v`, the AMOS [1]_ `zbesi` routine is called. It uses a\n"
+    "power series for small `z`, the asymptotic expansion for large\n"
+    "`abs(z)`, the Miller algorithm normalized by the Wronskian and a\n"
+    "Neumann series for intermediate magnitudes, and the uniform asymptotic\n"
+    "expansions for :math:`I_v(z)` and :math:`J_v(z)` for large orders.\n"
+    "Backward recurrence is used to generate sequences or reduce orders when\n"
+    "necessary.\n"
+    "\n"
+    "The calculations above are done in the right half plane and continued\n"
+    "into the left half plane by the formula,\n"
+    "\n"
+    ".. math:: I_v(z \\exp(\\pm\\imath\\pi)) = \\exp(\\pm\\pi v) I_v(z)\n"
+    "\n"
+    "(valid when the real part of `z` is positive).  For negative `v`, the\n"
+    "formula\n"
+    "\n"
+    ".. math:: I_{-v}(z) = I_v(z) + \\frac{2}{\\pi} \\sin(\\pi v) K_v(z)\n"
+    "\n"
+    "is used, where :math:`K_v(z)` is the modified Bessel function of the\n"
+    "second kind, evaluated using the AMOS routine `zbesk`.\n"
+    "\n"
+    "`ive` is useful for large arguments `z`: for these, `iv` easily overflows,\n"
+    "while `ive` does not due to the exponential scaling.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "In the following example `iv` returns infinity whereas `ive` still returns\n"
+    "a finite number.\n"
+    "\n"
+    ">>> from scipy.special import iv, ive\n"
+    ">>> import numpy as np\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> iv(3, 1000.), ive(3, 1000.)\n"
+    "(inf, 0.01256056218254712)\n"
+    "\n"
+    "Evaluate the function at one point for different orders by\n"
+    "providing a list or NumPy array as argument for the `v` parameter:\n"
+    "\n"
+    ">>> ive([0, 1, 1.5], 1.)\n"
+    "array([0.46575961, 0.20791042, 0.10798193])\n"
+    "\n"
+    "Evaluate the function at several points for order 0 by providing an\n"
+    "array for `z`.\n"
+    "\n"
+    ">>> points = np.array([-2., 0., 3.])\n"
+    ">>> ive(0, points)\n"
+    "array([0.30850832, 1.        , 0.24300035])\n"
+    "\n"
+    "Evaluate the function at several points for different orders by\n"
+    "providing arrays for both `v` for `z`. Both arrays have to be\n"
+    "broadcastable to the correct shape. To calculate the orders 0, 1\n"
+    "and 2 for a 1D array of points:\n"
+    "\n"
+    ">>> ive([[0], [1], [2]], points)\n"
+    "array([[ 0.30850832,  1.        ,  0.24300035],\n"
+    "       [-0.21526929,  0.        ,  0.19682671],\n"
+    "       [ 0.09323903,  0.        ,  0.11178255]])\n"
+    "\n"
+    "Plot the functions of order 0 to 3 from -5 to 5.\n"
+    "\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-5., 5., 1000)\n"
+    ">>> for i in range(4):\n"
+    "...     ax.plot(x, ive(i, x), label=fr'$I_{i!r}(z)\\cdot e^{{-|z|}}$')\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(r\"$z$\")\n"
+    ">>> plt.show()")
+ufunc_ive_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_ive_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_ive_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_ive_loops[3] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_ive_types[0] = <char>NPY_FLOAT
+ufunc_ive_types[1] = <char>NPY_FLOAT
+ufunc_ive_types[2] = <char>NPY_FLOAT
+ufunc_ive_types[3] = <char>NPY_FLOAT
+ufunc_ive_types[4] = <char>NPY_CFLOAT
+ufunc_ive_types[5] = <char>NPY_CFLOAT
+ufunc_ive_types[6] = <char>NPY_DOUBLE
+ufunc_ive_types[7] = <char>NPY_DOUBLE
+ufunc_ive_types[8] = <char>NPY_DOUBLE
+ufunc_ive_types[9] = <char>NPY_DOUBLE
+ufunc_ive_types[10] = <char>NPY_CDOUBLE
+ufunc_ive_types[11] = <char>NPY_CDOUBLE
+ufunc_ive_ptr[2*0] = <void*>_func_cbesi_wrap_e_real
+ufunc_ive_ptr[2*0+1] = <void*>(<char*>"ive")
+ufunc_ive_ptr[2*1] = <void*>_func_cbesi_wrap_e
+ufunc_ive_ptr[2*1+1] = <void*>(<char*>"ive")
+ufunc_ive_ptr[2*2] = <void*>_func_cbesi_wrap_e_real
+ufunc_ive_ptr[2*2+1] = <void*>(<char*>"ive")
+ufunc_ive_ptr[2*3] = <void*>_func_cbesi_wrap_e
+ufunc_ive_ptr[2*3+1] = <void*>(<char*>"ive")
+ufunc_ive_data[0] = &ufunc_ive_ptr[2*0]
+ufunc_ive_data[1] = &ufunc_ive_ptr[2*1]
+ufunc_ive_data[2] = &ufunc_ive_ptr[2*2]
+ufunc_ive_data[3] = &ufunc_ive_ptr[2*3]
+ive = np.PyUFunc_FromFuncAndData(ufunc_ive_loops, ufunc_ive_data, ufunc_ive_types, 4, 2, 1, 0, "ive", ufunc_ive_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_j0_loops[2]
+cdef void *ufunc_j0_ptr[4]
+cdef void *ufunc_j0_data[2]
+cdef char ufunc_j0_types[4]
+cdef char *ufunc_j0_doc = (
+    "j0(x, out=None)\n"
+    "\n"
+    "Bessel function of the first kind of order 0.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "J : scalar or ndarray\n"
+    "    Value of the Bessel function of the first kind of order 0 at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "jv : Bessel function of real order and complex argument.\n"
+    "spherical_jn : spherical Bessel functions.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The domain is divided into the intervals [0, 5] and (5, infinity). In the\n"
+    "first interval the following rational approximation is used:\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    J_0(x) \\approx (w - r_1^2)(w - r_2^2) \\frac{P_3(w)}{Q_8(w)},\n"
+    "\n"
+    "where :math:`w = x^2` and :math:`r_1`, :math:`r_2` are the zeros of\n"
+    ":math:`J_0`, and :math:`P_3` and :math:`Q_8` are polynomials of degrees 3\n"
+    "and 8, respectively.\n"
+    "\n"
+    "In the second interval, the Hankel asymptotic expansion is employed with\n"
+    "two rational functions of degree 6/6 and 7/7.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `j0`.\n"
+    "It should not be confused with the spherical Bessel functions (see\n"
+    "`spherical_jn`).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function at one point:\n"
+    "\n"
+    ">>> from scipy.special import j0\n"
+    ">>> j0(1.)\n"
+    "0.7651976865579665\n"
+    "\n"
+    "Calculate the function at several points:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> j0(np.array([-2., 0., 4.]))\n"
+    "array([ 0.22389078,  1.        , -0.39714981])\n"
+    "\n"
+    "Plot the function from -20 to 20.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-20., 20., 1000)\n"
+    ">>> y = j0(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_j0_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_j0_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_j0_types[0] = <char>NPY_FLOAT
+ufunc_j0_types[1] = <char>NPY_FLOAT
+ufunc_j0_types[2] = <char>NPY_DOUBLE
+ufunc_j0_types[3] = <char>NPY_DOUBLE
+ufunc_j0_ptr[2*0] = <void*>_func_j0
+ufunc_j0_ptr[2*0+1] = <void*>(<char*>"j0")
+ufunc_j0_ptr[2*1] = <void*>_func_j0
+ufunc_j0_ptr[2*1+1] = <void*>(<char*>"j0")
+ufunc_j0_data[0] = &ufunc_j0_ptr[2*0]
+ufunc_j0_data[1] = &ufunc_j0_ptr[2*1]
+j0 = np.PyUFunc_FromFuncAndData(ufunc_j0_loops, ufunc_j0_data, ufunc_j0_types, 2, 1, 1, 0, "j0", ufunc_j0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_j1_loops[2]
+cdef void *ufunc_j1_ptr[4]
+cdef void *ufunc_j1_data[2]
+cdef char ufunc_j1_types[4]
+cdef char *ufunc_j1_doc = (
+    "j1(x, out=None)\n"
+    "\n"
+    "Bessel function of the first kind of order 1.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "J : scalar or ndarray\n"
+    "    Value of the Bessel function of the first kind of order 1 at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "jv: Bessel function of the first kind\n"
+    "spherical_jn: spherical Bessel functions.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The domain is divided into the intervals [0, 8] and (8, infinity). In the\n"
+    "first interval a 24 term Chebyshev expansion is used. In the second, the\n"
+    "asymptotic trigonometric representation is employed using two rational\n"
+    "functions of degree 5/5.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `j1`.\n"
+    "It should not be confused with the spherical Bessel functions (see\n"
+    "`spherical_jn`).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function at one point:\n"
+    "\n"
+    ">>> from scipy.special import j1\n"
+    ">>> j1(1.)\n"
+    "0.44005058574493355\n"
+    "\n"
+    "Calculate the function at several points:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> j1(np.array([-2., 0., 4.]))\n"
+    "array([-0.57672481,  0.        , -0.06604333])\n"
+    "\n"
+    "Plot the function from -20 to 20.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-20., 20., 1000)\n"
+    ">>> y = j1(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_j1_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_j1_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_j1_types[0] = <char>NPY_FLOAT
+ufunc_j1_types[1] = <char>NPY_FLOAT
+ufunc_j1_types[2] = <char>NPY_DOUBLE
+ufunc_j1_types[3] = <char>NPY_DOUBLE
+ufunc_j1_ptr[2*0] = <void*>_func_j1
+ufunc_j1_ptr[2*0+1] = <void*>(<char*>"j1")
+ufunc_j1_ptr[2*1] = <void*>_func_j1
+ufunc_j1_ptr[2*1+1] = <void*>(<char*>"j1")
+ufunc_j1_data[0] = &ufunc_j1_ptr[2*0]
+ufunc_j1_data[1] = &ufunc_j1_ptr[2*1]
+j1 = np.PyUFunc_FromFuncAndData(ufunc_j1_loops, ufunc_j1_data, ufunc_j1_types, 2, 1, 1, 0, "j1", ufunc_j1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_jv_loops[4]
+cdef void *ufunc_jv_ptr[8]
+cdef void *ufunc_jv_data[4]
+cdef char ufunc_jv_types[12]
+cdef char *ufunc_jv_doc = (
+    "jv(v, z, out=None)\n"
+    "\n"
+    "Bessel function of the first kind of real order and complex argument.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order (float).\n"
+    "z : array_like\n"
+    "    Argument (float or complex).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "J : scalar or ndarray\n"
+    "    Value of the Bessel function, :math:`J_v(z)`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "jve : :math:`J_v` with leading exponential behavior stripped off.\n"
+    "spherical_jn : spherical Bessel functions.\n"
+    "j0 : faster version of this function for order 0.\n"
+    "j1 : faster version of this function for order 1.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For positive `v` values, the computation is carried out using the AMOS\n"
+    "[1]_ `zbesj` routine, which exploits the connection to the modified\n"
+    "Bessel function :math:`I_v`,\n"
+    "\n"
+    ".. math::\n"
+    "    J_v(z) = \\exp(v\\pi\\imath/2) I_v(-\\imath z)\\qquad (\\Im z > 0)\n"
+    "\n"
+    "    J_v(z) = \\exp(-v\\pi\\imath/2) I_v(\\imath z)\\qquad (\\Im z < 0)\n"
+    "\n"
+    "For negative `v` values the formula,\n"
+    "\n"
+    ".. math:: J_{-v}(z) = J_v(z) \\cos(\\pi v) - Y_v(z) \\sin(\\pi v)\n"
+    "\n"
+    "is used, where :math:`Y_v(z)` is the Bessel function of the second\n"
+    "kind, computed using the AMOS routine `zbesy`.  Note that the second\n"
+    "term is exactly zero for integer `v`; to improve accuracy the second\n"
+    "term is explicitly omitted for `v` values such that `v = floor(v)`.\n"
+    "\n"
+    "Not to be confused with the spherical Bessel functions (see `spherical_jn`).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the function of order 0 at one point.\n"
+    "\n"
+    ">>> from scipy.special import jv\n"
+    ">>> jv(0, 1.)\n"
+    "0.7651976865579666\n"
+    "\n"
+    "Evaluate the function at one point for different orders.\n"
+    "\n"
+    ">>> jv(0, 1.), jv(1, 1.), jv(1.5, 1.)\n"
+    "(0.7651976865579666, 0.44005058574493355, 0.24029783912342725)\n"
+    "\n"
+    "The evaluation for different orders can be carried out in one call by\n"
+    "providing a list or NumPy array as argument for the `v` parameter:\n"
+    "\n"
+    ">>> jv([0, 1, 1.5], 1.)\n"
+    "array([0.76519769, 0.44005059, 0.24029784])\n"
+    "\n"
+    "Evaluate the function at several points for order 0 by providing an\n"
+    "array for `z`.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> points = np.array([-2., 0., 3.])\n"
+    ">>> jv(0, points)\n"
+    "array([ 0.22389078,  1.        , -0.26005195])\n"
+    "\n"
+    "If `z` is an array, the order parameter `v` must be broadcastable to\n"
+    "the correct shape if different orders shall be computed in one call.\n"
+    "To calculate the orders 0 and 1 for an 1D array:\n"
+    "\n"
+    ">>> orders = np.array([[0], [1]])\n"
+    ">>> orders.shape\n"
+    "(2, 1)\n"
+    "\n"
+    ">>> jv(orders, points)\n"
+    "array([[ 0.22389078,  1.        , -0.26005195],\n"
+    "       [-0.57672481,  0.        ,  0.33905896]])\n"
+    "\n"
+    "Plot the functions of order 0 to 3 from -10 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-10., 10., 1000)\n"
+    ">>> for i in range(4):\n"
+    "...     ax.plot(x, jv(i, x), label=f'$J_{i!r}$')\n"
+    ">>> ax.legend()\n"
+    ">>> plt.show()")
+ufunc_jv_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_jv_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_jv_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_jv_loops[3] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_jv_types[0] = <char>NPY_FLOAT
+ufunc_jv_types[1] = <char>NPY_FLOAT
+ufunc_jv_types[2] = <char>NPY_FLOAT
+ufunc_jv_types[3] = <char>NPY_FLOAT
+ufunc_jv_types[4] = <char>NPY_CFLOAT
+ufunc_jv_types[5] = <char>NPY_CFLOAT
+ufunc_jv_types[6] = <char>NPY_DOUBLE
+ufunc_jv_types[7] = <char>NPY_DOUBLE
+ufunc_jv_types[8] = <char>NPY_DOUBLE
+ufunc_jv_types[9] = <char>NPY_DOUBLE
+ufunc_jv_types[10] = <char>NPY_CDOUBLE
+ufunc_jv_types[11] = <char>NPY_CDOUBLE
+ufunc_jv_ptr[2*0] = <void*>_func_cbesj_wrap_real
+ufunc_jv_ptr[2*0+1] = <void*>(<char*>"jv")
+ufunc_jv_ptr[2*1] = <void*>_func_cbesj_wrap
+ufunc_jv_ptr[2*1+1] = <void*>(<char*>"jv")
+ufunc_jv_ptr[2*2] = <void*>_func_cbesj_wrap_real
+ufunc_jv_ptr[2*2+1] = <void*>(<char*>"jv")
+ufunc_jv_ptr[2*3] = <void*>_func_cbesj_wrap
+ufunc_jv_ptr[2*3+1] = <void*>(<char*>"jv")
+ufunc_jv_data[0] = &ufunc_jv_ptr[2*0]
+ufunc_jv_data[1] = &ufunc_jv_ptr[2*1]
+ufunc_jv_data[2] = &ufunc_jv_ptr[2*2]
+ufunc_jv_data[3] = &ufunc_jv_ptr[2*3]
+jv = np.PyUFunc_FromFuncAndData(ufunc_jv_loops, ufunc_jv_data, ufunc_jv_types, 4, 2, 1, 0, "jv", ufunc_jv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_jve_loops[4]
+cdef void *ufunc_jve_ptr[8]
+cdef void *ufunc_jve_data[4]
+cdef char ufunc_jve_types[12]
+cdef char *ufunc_jve_doc = (
+    "jve(v, z, out=None)\n"
+    "\n"
+    "Exponentially scaled Bessel function of the first kind of order `v`.\n"
+    "\n"
+    "Defined as::\n"
+    "\n"
+    "    jve(v, z) = jv(v, z) * exp(-abs(z.imag))\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order (float).\n"
+    "z : array_like\n"
+    "    Argument (float or complex).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "J : scalar or ndarray\n"
+    "    Value of the exponentially scaled Bessel function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "jv: Unscaled Bessel function of the first kind\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For positive `v` values, the computation is carried out using the AMOS\n"
+    "[1]_ `zbesj` routine, which exploits the connection to the modified\n"
+    "Bessel function :math:`I_v`,\n"
+    "\n"
+    ".. math::\n"
+    "    J_v(z) = \\exp(v\\pi\\imath/2) I_v(-\\imath z)\\qquad (\\Im z > 0)\n"
+    "\n"
+    "    J_v(z) = \\exp(-v\\pi\\imath/2) I_v(\\imath z)\\qquad (\\Im z < 0)\n"
+    "\n"
+    "For negative `v` values the formula,\n"
+    "\n"
+    ".. math:: J_{-v}(z) = J_v(z) \\cos(\\pi v) - Y_v(z) \\sin(\\pi v)\n"
+    "\n"
+    "is used, where :math:`Y_v(z)` is the Bessel function of the second\n"
+    "kind, computed using the AMOS routine `zbesy`.  Note that the second\n"
+    "term is exactly zero for integer `v`; to improve accuracy the second\n"
+    "term is explicitly omitted for `v` values such that `v = floor(v)`.\n"
+    "\n"
+    "Exponentially scaled Bessel functions are useful for large arguments `z`:\n"
+    "for these, the unscaled Bessel functions can easily under-or overflow.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compare the output of `jv` and `jve` for large complex arguments for `z`\n"
+    "by computing their values for order ``v=1`` at ``z=1000j``. We see that\n"
+    "`jv` overflows but `jve` returns a finite number:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import jv, jve\n"
+    ">>> v = 1\n"
+    ">>> z = 1000j\n"
+    ">>> jv(v, z), jve(v, z)\n"
+    "((inf+infj), (7.721967686709077e-19+0.012610930256928629j))\n"
+    "\n"
+    "For real arguments for `z`, `jve` returns the same as `jv`.\n"
+    "\n"
+    ">>> v, z = 1, 1000\n"
+    ">>> jv(v, z), jve(v, z)\n"
+    "(0.004728311907089523, 0.004728311907089523)\n"
+    "\n"
+    "The function can be evaluated for several orders at the same time by\n"
+    "providing a list or NumPy array for `v`:\n"
+    "\n"
+    ">>> jve([1, 3, 5], 1j)\n"
+    "array([1.27304208e-17+2.07910415e-01j, -4.99352086e-19-8.15530777e-03j,\n"
+    "       6.11480940e-21+9.98657141e-05j])\n"
+    "\n"
+    "In the same way, the function can be evaluated at several points in one\n"
+    "call by providing a list or NumPy array for `z`:\n"
+    "\n"
+    ">>> jve(1, np.array([1j, 2j, 3j]))\n"
+    "array([1.27308412e-17+0.20791042j, 1.31814423e-17+0.21526929j,\n"
+    "       1.20521602e-17+0.19682671j])\n"
+    "\n"
+    "It is also possible to evaluate several orders at several points\n"
+    "at the same time by providing arrays for `v` and `z` with\n"
+    "compatible shapes for broadcasting. Compute `jve` for two different orders\n"
+    "`v` and three points `z` resulting in a 2x3 array.\n"
+    "\n"
+    ">>> v = np.array([[1], [3]])\n"
+    ">>> z = np.array([1j, 2j, 3j])\n"
+    ">>> v.shape, z.shape\n"
+    "((2, 1), (3,))\n"
+    "\n"
+    ">>> jve(v, z)\n"
+    "array([[1.27304208e-17+0.20791042j,  1.31810070e-17+0.21526929j,\n"
+    "        1.20517622e-17+0.19682671j],\n"
+    "       [-4.99352086e-19-0.00815531j, -1.76289571e-18-0.02879122j,\n"
+    "        -2.92578784e-18-0.04778332j]])")
+ufunc_jve_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_jve_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_jve_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_jve_loops[3] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_jve_types[0] = <char>NPY_FLOAT
+ufunc_jve_types[1] = <char>NPY_FLOAT
+ufunc_jve_types[2] = <char>NPY_FLOAT
+ufunc_jve_types[3] = <char>NPY_FLOAT
+ufunc_jve_types[4] = <char>NPY_CFLOAT
+ufunc_jve_types[5] = <char>NPY_CFLOAT
+ufunc_jve_types[6] = <char>NPY_DOUBLE
+ufunc_jve_types[7] = <char>NPY_DOUBLE
+ufunc_jve_types[8] = <char>NPY_DOUBLE
+ufunc_jve_types[9] = <char>NPY_DOUBLE
+ufunc_jve_types[10] = <char>NPY_CDOUBLE
+ufunc_jve_types[11] = <char>NPY_CDOUBLE
+ufunc_jve_ptr[2*0] = <void*>_func_cbesj_wrap_e_real
+ufunc_jve_ptr[2*0+1] = <void*>(<char*>"jve")
+ufunc_jve_ptr[2*1] = <void*>_func_cbesj_wrap_e
+ufunc_jve_ptr[2*1+1] = <void*>(<char*>"jve")
+ufunc_jve_ptr[2*2] = <void*>_func_cbesj_wrap_e_real
+ufunc_jve_ptr[2*2+1] = <void*>(<char*>"jve")
+ufunc_jve_ptr[2*3] = <void*>_func_cbesj_wrap_e
+ufunc_jve_ptr[2*3+1] = <void*>(<char*>"jve")
+ufunc_jve_data[0] = &ufunc_jve_ptr[2*0]
+ufunc_jve_data[1] = &ufunc_jve_ptr[2*1]
+ufunc_jve_data[2] = &ufunc_jve_ptr[2*2]
+ufunc_jve_data[3] = &ufunc_jve_ptr[2*3]
+jve = np.PyUFunc_FromFuncAndData(ufunc_jve_loops, ufunc_jve_data, ufunc_jve_types, 4, 2, 1, 0, "jve", ufunc_jve_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_k0_loops[2]
+cdef void *ufunc_k0_ptr[4]
+cdef void *ufunc_k0_data[2]
+cdef char ufunc_k0_types[4]
+cdef char *ufunc_k0_doc = (
+    "k0(x, out=None)\n"
+    "\n"
+    "Modified Bessel function of the second kind of order 0, :math:`K_0`.\n"
+    "\n"
+    "This function is also sometimes referred to as the modified Bessel\n"
+    "function of the third kind of order 0.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "K : scalar or ndarray\n"
+    "    Value of the modified Bessel function :math:`K_0` at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kv: Modified Bessel function of the second kind of any order\n"
+    "k0e: Exponentially scaled modified Bessel function of the second kind\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The range is partitioned into the two intervals [0, 2] and (2, infinity).\n"
+    "Chebyshev polynomial expansions are employed in each interval.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `k0`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function at one point:\n"
+    "\n"
+    ">>> from scipy.special import k0\n"
+    ">>> k0(1.)\n"
+    "0.42102443824070823\n"
+    "\n"
+    "Calculate the function at several points:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> k0(np.array([0.5, 2., 3.]))\n"
+    "array([0.92441907, 0.11389387, 0.0347395 ])\n"
+    "\n"
+    "Plot the function from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> y = k0(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_k0_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_k0_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_k0_types[0] = <char>NPY_FLOAT
+ufunc_k0_types[1] = <char>NPY_FLOAT
+ufunc_k0_types[2] = <char>NPY_DOUBLE
+ufunc_k0_types[3] = <char>NPY_DOUBLE
+ufunc_k0_ptr[2*0] = <void*>_func_k0
+ufunc_k0_ptr[2*0+1] = <void*>(<char*>"k0")
+ufunc_k0_ptr[2*1] = <void*>_func_k0
+ufunc_k0_ptr[2*1+1] = <void*>(<char*>"k0")
+ufunc_k0_data[0] = &ufunc_k0_ptr[2*0]
+ufunc_k0_data[1] = &ufunc_k0_ptr[2*1]
+k0 = np.PyUFunc_FromFuncAndData(ufunc_k0_loops, ufunc_k0_data, ufunc_k0_types, 2, 1, 1, 0, "k0", ufunc_k0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_k0e_loops[2]
+cdef void *ufunc_k0e_ptr[4]
+cdef void *ufunc_k0e_data[2]
+cdef char ufunc_k0e_types[4]
+cdef char *ufunc_k0e_doc = (
+    "k0e(x, out=None)\n"
+    "\n"
+    "Exponentially scaled modified Bessel function K of order 0\n"
+    "\n"
+    "Defined as::\n"
+    "\n"
+    "    k0e(x) = exp(x) * k0(x).\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "K : scalar or ndarray\n"
+    "    Value of the exponentially scaled modified Bessel function K of order\n"
+    "    0 at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kv: Modified Bessel function of the second kind of any order\n"
+    "k0: Modified Bessel function of the second kind\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The range is partitioned into the two intervals [0, 2] and (2, infinity).\n"
+    "Chebyshev polynomial expansions are employed in each interval.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `k0e`. `k0e` is\n"
+    "useful for large arguments: for these, `k0` easily underflows.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "In the following example `k0` returns 0 whereas `k0e` still returns a\n"
+    "useful finite number:\n"
+    "\n"
+    ">>> from scipy.special import k0, k0e\n"
+    ">>> k0(1000.), k0e(1000)\n"
+    "(0., 0.03962832160075422)\n"
+    "\n"
+    "Calculate the function at several points by providing a NumPy array or\n"
+    "list for `x`:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> k0e(np.array([0.5, 2., 3.]))\n"
+    "array([1.52410939, 0.84156822, 0.6977616 ])\n"
+    "\n"
+    "Plot the function from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> y = k0e(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_k0e_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_k0e_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_k0e_types[0] = <char>NPY_FLOAT
+ufunc_k0e_types[1] = <char>NPY_FLOAT
+ufunc_k0e_types[2] = <char>NPY_DOUBLE
+ufunc_k0e_types[3] = <char>NPY_DOUBLE
+ufunc_k0e_ptr[2*0] = <void*>_func_k0e
+ufunc_k0e_ptr[2*0+1] = <void*>(<char*>"k0e")
+ufunc_k0e_ptr[2*1] = <void*>_func_k0e
+ufunc_k0e_ptr[2*1+1] = <void*>(<char*>"k0e")
+ufunc_k0e_data[0] = &ufunc_k0e_ptr[2*0]
+ufunc_k0e_data[1] = &ufunc_k0e_ptr[2*1]
+k0e = np.PyUFunc_FromFuncAndData(ufunc_k0e_loops, ufunc_k0e_data, ufunc_k0e_types, 2, 1, 1, 0, "k0e", ufunc_k0e_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_k1_loops[2]
+cdef void *ufunc_k1_ptr[4]
+cdef void *ufunc_k1_data[2]
+cdef char ufunc_k1_types[4]
+cdef char *ufunc_k1_doc = (
+    "k1(x, out=None)\n"
+    "\n"
+    "Modified Bessel function of the second kind of order 1, :math:`K_1(x)`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "K : scalar or ndarray\n"
+    "    Value of the modified Bessel function K of order 1 at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kv: Modified Bessel function of the second kind of any order\n"
+    "k1e: Exponentially scaled modified Bessel function K of order 1\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The range is partitioned into the two intervals [0, 2] and (2, infinity).\n"
+    "Chebyshev polynomial expansions are employed in each interval.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `k1`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function at one point:\n"
+    "\n"
+    ">>> from scipy.special import k1\n"
+    ">>> k1(1.)\n"
+    "0.6019072301972346\n"
+    "\n"
+    "Calculate the function at several points:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> k1(np.array([0.5, 2., 3.]))\n"
+    "array([1.65644112, 0.13986588, 0.04015643])\n"
+    "\n"
+    "Plot the function from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> y = k1(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_k1_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_k1_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_k1_types[0] = <char>NPY_FLOAT
+ufunc_k1_types[1] = <char>NPY_FLOAT
+ufunc_k1_types[2] = <char>NPY_DOUBLE
+ufunc_k1_types[3] = <char>NPY_DOUBLE
+ufunc_k1_ptr[2*0] = <void*>_func_k1
+ufunc_k1_ptr[2*0+1] = <void*>(<char*>"k1")
+ufunc_k1_ptr[2*1] = <void*>_func_k1
+ufunc_k1_ptr[2*1+1] = <void*>(<char*>"k1")
+ufunc_k1_data[0] = &ufunc_k1_ptr[2*0]
+ufunc_k1_data[1] = &ufunc_k1_ptr[2*1]
+k1 = np.PyUFunc_FromFuncAndData(ufunc_k1_loops, ufunc_k1_data, ufunc_k1_types, 2, 1, 1, 0, "k1", ufunc_k1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_k1e_loops[2]
+cdef void *ufunc_k1e_ptr[4]
+cdef void *ufunc_k1e_data[2]
+cdef char ufunc_k1e_types[4]
+cdef char *ufunc_k1e_doc = (
+    "k1e(x, out=None)\n"
+    "\n"
+    "Exponentially scaled modified Bessel function K of order 1\n"
+    "\n"
+    "Defined as::\n"
+    "\n"
+    "    k1e(x) = exp(x) * k1(x)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "K : scalar or ndarray\n"
+    "    Value of the exponentially scaled modified Bessel function K of order\n"
+    "    1 at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kv: Modified Bessel function of the second kind of any order\n"
+    "k1: Modified Bessel function of the second kind of order 1\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The range is partitioned into the two intervals [0, 2] and (2, infinity).\n"
+    "Chebyshev polynomial expansions are employed in each interval.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `k1e`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "In the following example `k1` returns 0 whereas `k1e` still returns a\n"
+    "useful floating point number.\n"
+    "\n"
+    ">>> from scipy.special import k1, k1e\n"
+    ">>> k1(1000.), k1e(1000.)\n"
+    "(0., 0.03964813081296021)\n"
+    "\n"
+    "Calculate the function at several points by providing a NumPy array or\n"
+    "list for `x`:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> k1e(np.array([0.5, 2., 3.]))\n"
+    "array([2.73100971, 1.03347685, 0.80656348])\n"
+    "\n"
+    "Plot the function from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> y = k1e(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_k1e_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_k1e_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_k1e_types[0] = <char>NPY_FLOAT
+ufunc_k1e_types[1] = <char>NPY_FLOAT
+ufunc_k1e_types[2] = <char>NPY_DOUBLE
+ufunc_k1e_types[3] = <char>NPY_DOUBLE
+ufunc_k1e_ptr[2*0] = <void*>_func_k1e
+ufunc_k1e_ptr[2*0+1] = <void*>(<char*>"k1e")
+ufunc_k1e_ptr[2*1] = <void*>_func_k1e
+ufunc_k1e_ptr[2*1+1] = <void*>(<char*>"k1e")
+ufunc_k1e_data[0] = &ufunc_k1e_ptr[2*0]
+ufunc_k1e_data[1] = &ufunc_k1e_ptr[2*1]
+k1e = np.PyUFunc_FromFuncAndData(ufunc_k1e_loops, ufunc_k1e_data, ufunc_k1e_types, 2, 1, 1, 0, "k1e", ufunc_k1e_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_kei_loops[2]
+cdef void *ufunc_kei_ptr[4]
+cdef void *ufunc_kei_data[2]
+cdef char ufunc_kei_types[4]
+cdef char *ufunc_kei_doc = (
+    "kei(x, out=None)\n"
+    "\n"
+    "Kelvin function kei.\n"
+    "\n"
+    "Defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\mathrm{kei}(x) = \\Im[K_0(x e^{\\pi i / 4})]\n"
+    "\n"
+    "where :math:`K_0` is the modified Bessel function of the second\n"
+    "kind (see `kv`). See [dlmf]_ for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the Kelvin function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ker : the corresponding real part\n"
+    "keip : the derivative of kei\n"
+    "kv : modified Bessel function of the second kind\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST, Digital Library of Mathematical Functions,\n"
+    "    https://dlmf.nist.gov/10.61\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "It can be expressed using the modified Bessel function of the\n"
+    "second kind.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    ">>> x = np.array([1.0, 2.0, 3.0, 4.0])\n"
+    ">>> sc.kv(0, x * np.exp(np.pi * 1j / 4)).imag\n"
+    "array([-0.49499464, -0.20240007, -0.05112188,  0.0021984 ])\n"
+    ">>> sc.kei(x)\n"
+    "array([-0.49499464, -0.20240007, -0.05112188,  0.0021984 ])")
+ufunc_kei_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_kei_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_kei_types[0] = <char>NPY_FLOAT
+ufunc_kei_types[1] = <char>NPY_FLOAT
+ufunc_kei_types[2] = <char>NPY_DOUBLE
+ufunc_kei_types[3] = <char>NPY_DOUBLE
+ufunc_kei_ptr[2*0] = <void*>_func_kei_wrap
+ufunc_kei_ptr[2*0+1] = <void*>(<char*>"kei")
+ufunc_kei_ptr[2*1] = <void*>_func_kei_wrap
+ufunc_kei_ptr[2*1+1] = <void*>(<char*>"kei")
+ufunc_kei_data[0] = &ufunc_kei_ptr[2*0]
+ufunc_kei_data[1] = &ufunc_kei_ptr[2*1]
+kei = np.PyUFunc_FromFuncAndData(ufunc_kei_loops, ufunc_kei_data, ufunc_kei_types, 2, 1, 1, 0, "kei", ufunc_kei_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_keip_loops[2]
+cdef void *ufunc_keip_ptr[4]
+cdef void *ufunc_keip_data[2]
+cdef char ufunc_keip_types[4]
+cdef char *ufunc_keip_doc = (
+    "keip(x, out=None)\n"
+    "\n"
+    "Derivative of the Kelvin function kei.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The values of the derivative of kei.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kei\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST, Digital Library of Mathematical Functions,\n"
+    "    https://dlmf.nist.gov/10#PT5")
+ufunc_keip_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_keip_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_keip_types[0] = <char>NPY_FLOAT
+ufunc_keip_types[1] = <char>NPY_FLOAT
+ufunc_keip_types[2] = <char>NPY_DOUBLE
+ufunc_keip_types[3] = <char>NPY_DOUBLE
+ufunc_keip_ptr[2*0] = <void*>_func_keip_wrap
+ufunc_keip_ptr[2*0+1] = <void*>(<char*>"keip")
+ufunc_keip_ptr[2*1] = <void*>_func_keip_wrap
+ufunc_keip_ptr[2*1+1] = <void*>(<char*>"keip")
+ufunc_keip_data[0] = &ufunc_keip_ptr[2*0]
+ufunc_keip_data[1] = &ufunc_keip_ptr[2*1]
+keip = np.PyUFunc_FromFuncAndData(ufunc_keip_loops, ufunc_keip_data, ufunc_keip_types, 2, 1, 1, 0, "keip", ufunc_keip_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_kelvin_loops[2]
+cdef void *ufunc_kelvin_ptr[4]
+cdef void *ufunc_kelvin_data[2]
+cdef char ufunc_kelvin_types[10]
+cdef char *ufunc_kelvin_doc = (
+    "kelvin(x, out=None)\n"
+    "\n"
+    "Kelvin functions as complex numbers\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "Be, Ke, Bep, Kep : 4-tuple of scalar or ndarray\n"
+    "    The tuple (Be, Ke, Bep, Kep) contains complex numbers\n"
+    "    representing the real and imaginary Kelvin functions and their\n"
+    "    derivatives evaluated at `x`.  For example, kelvin(x)[0].real =\n"
+    "    ber x and kelvin(x)[0].imag = bei x with similar relationships\n"
+    "    for ker and kei.")
+ufunc_kelvin_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_DDDD_As_f_FFFF
+ufunc_kelvin_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_DDDD_As_d_DDDD
+ufunc_kelvin_types[0] = <char>NPY_FLOAT
+ufunc_kelvin_types[1] = <char>NPY_CFLOAT
+ufunc_kelvin_types[2] = <char>NPY_CFLOAT
+ufunc_kelvin_types[3] = <char>NPY_CFLOAT
+ufunc_kelvin_types[4] = <char>NPY_CFLOAT
+ufunc_kelvin_types[5] = <char>NPY_DOUBLE
+ufunc_kelvin_types[6] = <char>NPY_CDOUBLE
+ufunc_kelvin_types[7] = <char>NPY_CDOUBLE
+ufunc_kelvin_types[8] = <char>NPY_CDOUBLE
+ufunc_kelvin_types[9] = <char>NPY_CDOUBLE
+ufunc_kelvin_ptr[2*0] = <void*>_func_kelvin_wrap
+ufunc_kelvin_ptr[2*0+1] = <void*>(<char*>"kelvin")
+ufunc_kelvin_ptr[2*1] = <void*>_func_kelvin_wrap
+ufunc_kelvin_ptr[2*1+1] = <void*>(<char*>"kelvin")
+ufunc_kelvin_data[0] = &ufunc_kelvin_ptr[2*0]
+ufunc_kelvin_data[1] = &ufunc_kelvin_ptr[2*1]
+kelvin = np.PyUFunc_FromFuncAndData(ufunc_kelvin_loops, ufunc_kelvin_data, ufunc_kelvin_types, 2, 1, 4, 0, "kelvin", ufunc_kelvin_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ker_loops[2]
+cdef void *ufunc_ker_ptr[4]
+cdef void *ufunc_ker_data[2]
+cdef char ufunc_ker_types[4]
+cdef char *ufunc_ker_doc = (
+    "ker(x, out=None)\n"
+    "\n"
+    "Kelvin function ker.\n"
+    "\n"
+    "Defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\mathrm{ker}(x) = \\Re[K_0(x e^{\\pi i / 4})]\n"
+    "\n"
+    "Where :math:`K_0` is the modified Bessel function of the second\n"
+    "kind (see `kv`). See [dlmf]_ for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the Kelvin function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kei : the corresponding imaginary part\n"
+    "kerp : the derivative of ker\n"
+    "kv : modified Bessel function of the second kind\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST, Digital Library of Mathematical Functions,\n"
+    "    https://dlmf.nist.gov/10.61\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "It can be expressed using the modified Bessel function of the\n"
+    "second kind.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    ">>> x = np.array([1.0, 2.0, 3.0, 4.0])\n"
+    ">>> sc.kv(0, x * np.exp(np.pi * 1j / 4)).real\n"
+    "array([ 0.28670621, -0.04166451, -0.06702923, -0.03617885])\n"
+    ">>> sc.ker(x)\n"
+    "array([ 0.28670621, -0.04166451, -0.06702923, -0.03617885])")
+ufunc_ker_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_ker_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_ker_types[0] = <char>NPY_FLOAT
+ufunc_ker_types[1] = <char>NPY_FLOAT
+ufunc_ker_types[2] = <char>NPY_DOUBLE
+ufunc_ker_types[3] = <char>NPY_DOUBLE
+ufunc_ker_ptr[2*0] = <void*>_func_ker_wrap
+ufunc_ker_ptr[2*0+1] = <void*>(<char*>"ker")
+ufunc_ker_ptr[2*1] = <void*>_func_ker_wrap
+ufunc_ker_ptr[2*1+1] = <void*>(<char*>"ker")
+ufunc_ker_data[0] = &ufunc_ker_ptr[2*0]
+ufunc_ker_data[1] = &ufunc_ker_ptr[2*1]
+ker = np.PyUFunc_FromFuncAndData(ufunc_ker_loops, ufunc_ker_data, ufunc_ker_types, 2, 1, 1, 0, "ker", ufunc_ker_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_kerp_loops[2]
+cdef void *ufunc_kerp_ptr[4]
+cdef void *ufunc_kerp_data[2]
+cdef char ufunc_kerp_types[4]
+cdef char *ufunc_kerp_doc = (
+    "kerp(x, out=None)\n"
+    "\n"
+    "Derivative of the Kelvin function ker.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the derivative of ker.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ker\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST, Digital Library of Mathematical Functions,\n"
+    "    https://dlmf.nist.gov/10#PT5")
+ufunc_kerp_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_kerp_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_kerp_types[0] = <char>NPY_FLOAT
+ufunc_kerp_types[1] = <char>NPY_FLOAT
+ufunc_kerp_types[2] = <char>NPY_DOUBLE
+ufunc_kerp_types[3] = <char>NPY_DOUBLE
+ufunc_kerp_ptr[2*0] = <void*>_func_kerp_wrap
+ufunc_kerp_ptr[2*0+1] = <void*>(<char*>"kerp")
+ufunc_kerp_ptr[2*1] = <void*>_func_kerp_wrap
+ufunc_kerp_ptr[2*1+1] = <void*>(<char*>"kerp")
+ufunc_kerp_data[0] = &ufunc_kerp_ptr[2*0]
+ufunc_kerp_data[1] = &ufunc_kerp_ptr[2*1]
+kerp = np.PyUFunc_FromFuncAndData(ufunc_kerp_loops, ufunc_kerp_data, ufunc_kerp_types, 2, 1, 1, 0, "kerp", ufunc_kerp_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_kl_div_loops[2]
+cdef void *ufunc_kl_div_ptr[4]
+cdef void *ufunc_kl_div_data[2]
+cdef char ufunc_kl_div_types[6]
+cdef char *ufunc_kl_div_doc = (
+    "kl_div(x, y, out=None)\n"
+    "\n"
+    "Elementwise function for computing Kullback-Leibler divergence.\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\mathrm{kl\\_div}(x, y) =\n"
+    "      \\begin{cases}\n"
+    "        x \\log(x / y) - x + y & x > 0, y > 0 \\\\\n"
+    "        y & x = 0, y \\ge 0 \\\\\n"
+    "        \\infty & \\text{otherwise}\n"
+    "      \\end{cases}\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x, y : array_like\n"
+    "    Real arguments\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the Kullback-Liebler divergence.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "entr, rel_entr, scipy.stats.entropy\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    ".. versionadded:: 0.15.0\n"
+    "\n"
+    "This function is non-negative and is jointly convex in `x` and `y`.\n"
+    "\n"
+    "The origin of this function is in convex programming; see [1]_ for\n"
+    "details. This is why the function contains the extra :math:`-x\n"
+    "+ y` terms over what might be expected from the Kullback-Leibler\n"
+    "divergence. For a version of the function without the extra terms,\n"
+    "see `rel_entr`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Boyd, Stephen and Lieven Vandenberghe. *Convex optimization*.\n"
+    "       Cambridge University Press, 2004.\n"
+    "       :doi:`https://doi.org/10.1017/CBO9780511804441`")
+ufunc_kl_div_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_kl_div_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_kl_div_types[0] = <char>NPY_FLOAT
+ufunc_kl_div_types[1] = <char>NPY_FLOAT
+ufunc_kl_div_types[2] = <char>NPY_FLOAT
+ufunc_kl_div_types[3] = <char>NPY_DOUBLE
+ufunc_kl_div_types[4] = <char>NPY_DOUBLE
+ufunc_kl_div_types[5] = <char>NPY_DOUBLE
+ufunc_kl_div_ptr[2*0] = <void*>_func_kl_div
+ufunc_kl_div_ptr[2*0+1] = <void*>(<char*>"kl_div")
+ufunc_kl_div_ptr[2*1] = <void*>_func_kl_div
+ufunc_kl_div_ptr[2*1+1] = <void*>(<char*>"kl_div")
+ufunc_kl_div_data[0] = &ufunc_kl_div_ptr[2*0]
+ufunc_kl_div_data[1] = &ufunc_kl_div_ptr[2*1]
+kl_div = np.PyUFunc_FromFuncAndData(ufunc_kl_div_loops, ufunc_kl_div_data, ufunc_kl_div_types, 2, 2, 1, 0, "kl_div", ufunc_kl_div_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_kn_loops[3]
+cdef void *ufunc_kn_ptr[6]
+cdef void *ufunc_kn_data[3]
+cdef char ufunc_kn_types[9]
+cdef char *ufunc_kn_doc = (
+    "kn(n, x, out=None)\n"
+    "\n"
+    "Modified Bessel function of the second kind of integer order `n`\n"
+    "\n"
+    "Returns the modified Bessel function of the second kind for integer order\n"
+    "`n` at real `z`.\n"
+    "\n"
+    "These are also sometimes called functions of the third kind, Basset\n"
+    "functions, or Macdonald functions.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like of int\n"
+    "    Order of Bessel functions (floats will truncate with a warning)\n"
+    "x : array_like of float\n"
+    "    Argument at which to evaluate the Bessel functions\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the Modified Bessel function of the second kind,\n"
+    "    :math:`K_n(x)`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kv : Same function, but accepts real order and complex argument\n"
+    "kvp : Derivative of this function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for AMOS [1]_ routine `zbesk`.  For a discussion of the\n"
+    "algorithm used, see [2]_ and the references therein.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    ".. [2] Donald E. Amos, \"Algorithm 644: A portable package for Bessel\n"
+    "       functions of a complex argument and nonnegative order\", ACM\n"
+    "       TOMS Vol. 12 Issue 3, Sept. 1986, p. 265\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Plot the function of several orders for real input:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import kn\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(0, 5, 1000)\n"
+    ">>> for N in range(6):\n"
+    "...     plt.plot(x, kn(N, x), label='$K_{}(x)$'.format(N))\n"
+    ">>> plt.ylim(0, 10)\n"
+    ">>> plt.legend()\n"
+    ">>> plt.title(r'Modified Bessel function of the second kind $K_n(x)$')\n"
+    ">>> plt.show()\n"
+    "\n"
+    "Calculate for a single value at multiple orders:\n"
+    "\n"
+    ">>> kn([4, 5, 6], 1)\n"
+    "array([   44.23241585,   360.9605896 ,  3653.83831186])")
+ufunc_kn_loops[0] = <np.PyUFuncGenericFunction>loop_d_id__As_ld_d
+ufunc_kn_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_kn_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_kn_types[0] = <char>NPY_LONG
+ufunc_kn_types[1] = <char>NPY_DOUBLE
+ufunc_kn_types[2] = <char>NPY_DOUBLE
+ufunc_kn_types[3] = <char>NPY_FLOAT
+ufunc_kn_types[4] = <char>NPY_FLOAT
+ufunc_kn_types[5] = <char>NPY_FLOAT
+ufunc_kn_types[6] = <char>NPY_DOUBLE
+ufunc_kn_types[7] = <char>NPY_DOUBLE
+ufunc_kn_types[8] = <char>NPY_DOUBLE
+ufunc_kn_ptr[2*0] = <void*>_func_cbesk_wrap_real_int
+ufunc_kn_ptr[2*0+1] = <void*>(<char*>"kn")
+ufunc_kn_ptr[2*1] = <void*>_func_kn_unsafe
+ufunc_kn_ptr[2*1+1] = <void*>(<char*>"kn")
+ufunc_kn_ptr[2*2] = <void*>_func_kn_unsafe
+ufunc_kn_ptr[2*2+1] = <void*>(<char*>"kn")
+ufunc_kn_data[0] = &ufunc_kn_ptr[2*0]
+ufunc_kn_data[1] = &ufunc_kn_ptr[2*1]
+ufunc_kn_data[2] = &ufunc_kn_ptr[2*2]
+kn = np.PyUFunc_FromFuncAndData(ufunc_kn_loops, ufunc_kn_data, ufunc_kn_types, 3, 2, 1, 0, "kn", ufunc_kn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_kolmogi_loops[2]
+cdef void *ufunc_kolmogi_ptr[4]
+cdef void *ufunc_kolmogi_data[2]
+cdef char ufunc_kolmogi_types[4]
+cdef char *ufunc_kolmogi_doc = (
+    "kolmogi(p, out=None)\n"
+    "\n"
+    "Inverse Survival Function of Kolmogorov distribution\n"
+    "\n"
+    "It is the inverse function to `kolmogorov`.\n"
+    "Returns y such that ``kolmogorov(y) == p``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : float array_like\n"
+    "    Probability\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The value(s) of kolmogi(p)\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kolmogorov : The Survival Function for the distribution\n"
+    "scipy.stats.kstwobign : Provides the functionality as a continuous distribution\n"
+    "smirnov, smirnovi : Functions for the one-sided distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "`kolmogorov` is used by `stats.kstest` in the application of the\n"
+    "Kolmogorov-Smirnov Goodness of Fit test. For historical reasons this\n"
+    "function is exposed in `scpy.special`, but the recommended way to achieve\n"
+    "the most accurate CDF/SF/PDF/PPF/ISF computations is to use the\n"
+    "`stats.kstwobign` distribution.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import kolmogi\n"
+    ">>> kolmogi([0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0])\n"
+    "array([        inf,  1.22384787,  1.01918472,  0.82757356,  0.67644769,\n"
+    "        0.57117327,  0.        ])")
+ufunc_kolmogi_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_kolmogi_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_kolmogi_types[0] = <char>NPY_FLOAT
+ufunc_kolmogi_types[1] = <char>NPY_FLOAT
+ufunc_kolmogi_types[2] = <char>NPY_DOUBLE
+ufunc_kolmogi_types[3] = <char>NPY_DOUBLE
+ufunc_kolmogi_ptr[2*0] = <void*>_func_kolmogi
+ufunc_kolmogi_ptr[2*0+1] = <void*>(<char*>"kolmogi")
+ufunc_kolmogi_ptr[2*1] = <void*>_func_kolmogi
+ufunc_kolmogi_ptr[2*1+1] = <void*>(<char*>"kolmogi")
+ufunc_kolmogi_data[0] = &ufunc_kolmogi_ptr[2*0]
+ufunc_kolmogi_data[1] = &ufunc_kolmogi_ptr[2*1]
+kolmogi = np.PyUFunc_FromFuncAndData(ufunc_kolmogi_loops, ufunc_kolmogi_data, ufunc_kolmogi_types, 2, 1, 1, 0, "kolmogi", ufunc_kolmogi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_kolmogorov_loops[2]
+cdef void *ufunc_kolmogorov_ptr[4]
+cdef void *ufunc_kolmogorov_data[2]
+cdef char ufunc_kolmogorov_types[4]
+cdef char *ufunc_kolmogorov_doc = (
+    "kolmogorov(y, out=None)\n"
+    "\n"
+    "Complementary cumulative distribution (Survival Function) function of\n"
+    "Kolmogorov distribution.\n"
+    "\n"
+    "Returns the complementary cumulative distribution function of\n"
+    "Kolmogorov's limiting distribution (``D_n*\\sqrt(n)`` as n goes to infinity)\n"
+    "of a two-sided test for equality between an empirical and a theoretical\n"
+    "distribution. It is equal to the (limit as n->infinity of the)\n"
+    "probability that ``sqrt(n) * max absolute deviation > y``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "y : float array_like\n"
+    "  Absolute deviation between the Empirical CDF (ECDF) and the target CDF,\n"
+    "  multiplied by sqrt(n).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The value(s) of kolmogorov(y)\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kolmogi : The Inverse Survival Function for the distribution\n"
+    "scipy.stats.kstwobign : Provides the functionality as a continuous distribution\n"
+    "smirnov, smirnovi : Functions for the one-sided distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "`kolmogorov` is used by `stats.kstest` in the application of the\n"
+    "Kolmogorov-Smirnov Goodness of Fit test. For historical reasons this\n"
+    "function is exposed in `scpy.special`, but the recommended way to achieve\n"
+    "the most accurate CDF/SF/PDF/PPF/ISF computations is to use the\n"
+    "`stats.kstwobign` distribution.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Show the probability of a gap at least as big as 0, 0.5 and 1.0.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import kolmogorov\n"
+    ">>> from scipy.stats import kstwobign\n"
+    ">>> kolmogorov([0, 0.5, 1.0])\n"
+    "array([ 1.        ,  0.96394524,  0.26999967])\n"
+    "\n"
+    "Compare a sample of size 1000 drawn from a Laplace(0, 1) distribution against\n"
+    "the target distribution, a Normal(0, 1) distribution.\n"
+    "\n"
+    ">>> from scipy.stats import norm, laplace\n"
+    ">>> rng = np.random.default_rng()\n"
+    ">>> n = 1000\n"
+    ">>> lap01 = laplace(0, 1)\n"
+    ">>> x = np.sort(lap01.rvs(n, random_state=rng))\n"
+    ">>> np.mean(x), np.std(x)\n"
+    "(-0.05841730131499543, 1.3968109101997568)\n"
+    "\n"
+    "Construct the Empirical CDF and the K-S statistic Dn.\n"
+    "\n"
+    ">>> target = norm(0,1)  # Normal mean 0, stddev 1\n"
+    ">>> cdfs = target.cdf(x)\n"
+    ">>> ecdfs = np.arange(n+1, dtype=float)/n\n"
+    ">>> gaps = np.column_stack([cdfs - ecdfs[:n], ecdfs[1:] - cdfs])\n"
+    ">>> Dn = np.max(gaps)\n"
+    ">>> Kn = np.sqrt(n) * Dn\n"
+    ">>> print('Dn=%f, sqrt(n)*Dn=%f' % (Dn, Kn))\n"
+    "Dn=0.043363, sqrt(n)*Dn=1.371265\n"
+    ">>> print(chr(10).join(['For a sample of size n drawn from a N(0, 1) distribution:',\n"
+    "...   ' the approximate Kolmogorov probability that sqrt(n)*Dn>=%f is %f' %\n"
+    "...    (Kn, kolmogorov(Kn)),\n"
+    "...   ' the approximate Kolmogorov probability that sqrt(n)*Dn<=%f is %f' %\n"
+    "...    (Kn, kstwobign.cdf(Kn))]))\n"
+    "For a sample of size n drawn from a N(0, 1) distribution:\n"
+    " the approximate Kolmogorov probability that sqrt(n)*Dn>=1.371265 is 0.046533\n"
+    " the approximate Kolmogorov probability that sqrt(n)*Dn<=1.371265 is 0.953467\n"
+    "\n"
+    "Plot the Empirical CDF against the target N(0, 1) CDF.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> plt.step(np.concatenate([[-3], x]), ecdfs, where='post', label='Empirical CDF')\n"
+    ">>> x3 = np.linspace(-3, 3, 100)\n"
+    ">>> plt.plot(x3, target.cdf(x3), label='CDF for N(0, 1)')\n"
+    ">>> plt.ylim([0, 1]); plt.grid(True); plt.legend();\n"
+    ">>> # Add vertical lines marking Dn+ and Dn-\n"
+    ">>> iminus, iplus = np.argmax(gaps, axis=0)\n"
+    ">>> plt.vlines([x[iminus]], ecdfs[iminus], cdfs[iminus],\n"
+    "...            color='r', linestyle='dashed', lw=4)\n"
+    ">>> plt.vlines([x[iplus]], cdfs[iplus], ecdfs[iplus+1],\n"
+    "...            color='r', linestyle='dashed', lw=4)\n"
+    ">>> plt.show()")
+ufunc_kolmogorov_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_kolmogorov_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_kolmogorov_types[0] = <char>NPY_FLOAT
+ufunc_kolmogorov_types[1] = <char>NPY_FLOAT
+ufunc_kolmogorov_types[2] = <char>NPY_DOUBLE
+ufunc_kolmogorov_types[3] = <char>NPY_DOUBLE
+ufunc_kolmogorov_ptr[2*0] = <void*>_func_kolmogorov
+ufunc_kolmogorov_ptr[2*0+1] = <void*>(<char*>"kolmogorov")
+ufunc_kolmogorov_ptr[2*1] = <void*>_func_kolmogorov
+ufunc_kolmogorov_ptr[2*1+1] = <void*>(<char*>"kolmogorov")
+ufunc_kolmogorov_data[0] = &ufunc_kolmogorov_ptr[2*0]
+ufunc_kolmogorov_data[1] = &ufunc_kolmogorov_ptr[2*1]
+kolmogorov = np.PyUFunc_FromFuncAndData(ufunc_kolmogorov_loops, ufunc_kolmogorov_data, ufunc_kolmogorov_types, 2, 1, 1, 0, "kolmogorov", ufunc_kolmogorov_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_kv_loops[4]
+cdef void *ufunc_kv_ptr[8]
+cdef void *ufunc_kv_data[4]
+cdef char ufunc_kv_types[12]
+cdef char *ufunc_kv_doc = (
+    "kv(v, z, out=None)\n"
+    "\n"
+    "Modified Bessel function of the second kind of real order `v`\n"
+    "\n"
+    "Returns the modified Bessel function of the second kind for real order\n"
+    "`v` at complex `z`.\n"
+    "\n"
+    "These are also sometimes called functions of the third kind, Basset\n"
+    "functions, or Macdonald functions.  They are defined as those solutions\n"
+    "of the modified Bessel equation for which,\n"
+    "\n"
+    ".. math::\n"
+    "    K_v(x) \\sim \\sqrt{\\pi/(2x)} \\exp(-x)\n"
+    "\n"
+    "as :math:`x \\to \\infty` [3]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like of float\n"
+    "    Order of Bessel functions\n"
+    "z : array_like of complex\n"
+    "    Argument at which to evaluate the Bessel functions\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The results. Note that input must be of complex type to get complex\n"
+    "    output, e.g. ``kv(3, -2+0j)`` instead of ``kv(3, -2)``.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kve : This function with leading exponential behavior stripped off.\n"
+    "kvp : Derivative of this function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for AMOS [1]_ routine `zbesk`.  For a discussion of the\n"
+    "algorithm used, see [2]_ and the references therein.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    ".. [2] Donald E. Amos, \"Algorithm 644: A portable package for Bessel\n"
+    "       functions of a complex argument and nonnegative order\", ACM\n"
+    "       TOMS Vol. 12 Issue 3, Sept. 1986, p. 265\n"
+    ".. [3] NIST Digital Library of Mathematical Functions,\n"
+    "       Eq. 10.25.E3. https://dlmf.nist.gov/10.25.E3\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Plot the function of several orders for real input:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import kv\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(0, 5, 1000)\n"
+    ">>> for N in np.linspace(0, 6, 5):\n"
+    "...     plt.plot(x, kv(N, x), label='$K_{{{}}}(x)$'.format(N))\n"
+    ">>> plt.ylim(0, 10)\n"
+    ">>> plt.legend()\n"
+    ">>> plt.title(r'Modified Bessel function of the second kind $K_\\nu(x)$')\n"
+    ">>> plt.show()\n"
+    "\n"
+    "Calculate for a single value at multiple orders:\n"
+    "\n"
+    ">>> kv([4, 4.5, 5], 1+2j)\n"
+    "array([ 0.1992+2.3892j,  2.3493+3.6j   ,  7.2827+3.8104j])")
+ufunc_kv_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_kv_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_kv_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_kv_loops[3] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_kv_types[0] = <char>NPY_FLOAT
+ufunc_kv_types[1] = <char>NPY_FLOAT
+ufunc_kv_types[2] = <char>NPY_FLOAT
+ufunc_kv_types[3] = <char>NPY_FLOAT
+ufunc_kv_types[4] = <char>NPY_CFLOAT
+ufunc_kv_types[5] = <char>NPY_CFLOAT
+ufunc_kv_types[6] = <char>NPY_DOUBLE
+ufunc_kv_types[7] = <char>NPY_DOUBLE
+ufunc_kv_types[8] = <char>NPY_DOUBLE
+ufunc_kv_types[9] = <char>NPY_DOUBLE
+ufunc_kv_types[10] = <char>NPY_CDOUBLE
+ufunc_kv_types[11] = <char>NPY_CDOUBLE
+ufunc_kv_ptr[2*0] = <void*>_func_cbesk_wrap_real
+ufunc_kv_ptr[2*0+1] = <void*>(<char*>"kv")
+ufunc_kv_ptr[2*1] = <void*>_func_cbesk_wrap
+ufunc_kv_ptr[2*1+1] = <void*>(<char*>"kv")
+ufunc_kv_ptr[2*2] = <void*>_func_cbesk_wrap_real
+ufunc_kv_ptr[2*2+1] = <void*>(<char*>"kv")
+ufunc_kv_ptr[2*3] = <void*>_func_cbesk_wrap
+ufunc_kv_ptr[2*3+1] = <void*>(<char*>"kv")
+ufunc_kv_data[0] = &ufunc_kv_ptr[2*0]
+ufunc_kv_data[1] = &ufunc_kv_ptr[2*1]
+ufunc_kv_data[2] = &ufunc_kv_ptr[2*2]
+ufunc_kv_data[3] = &ufunc_kv_ptr[2*3]
+kv = np.PyUFunc_FromFuncAndData(ufunc_kv_loops, ufunc_kv_data, ufunc_kv_types, 4, 2, 1, 0, "kv", ufunc_kv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_kve_loops[4]
+cdef void *ufunc_kve_ptr[8]
+cdef void *ufunc_kve_data[4]
+cdef char ufunc_kve_types[12]
+cdef char *ufunc_kve_doc = (
+    "kve(v, z, out=None)\n"
+    "\n"
+    "Exponentially scaled modified Bessel function of the second kind.\n"
+    "\n"
+    "Returns the exponentially scaled, modified Bessel function of the\n"
+    "second kind (sometimes called the third kind) for real order `v` at\n"
+    "complex `z`::\n"
+    "\n"
+    "    kve(v, z) = kv(v, z) * exp(z)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like of float\n"
+    "    Order of Bessel functions\n"
+    "z : array_like of complex\n"
+    "    Argument at which to evaluate the Bessel functions\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The exponentially scaled modified Bessel function of the second kind.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "kv : This function without exponential scaling.\n"
+    "k0e : Faster version of this function for order 0.\n"
+    "k1e : Faster version of this function for order 1.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for AMOS [1]_ routine `zbesk`.  For a discussion of the\n"
+    "algorithm used, see [2]_ and the references therein.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    ".. [2] Donald E. Amos, \"Algorithm 644: A portable package for Bessel\n"
+    "       functions of a complex argument and nonnegative order\", ACM\n"
+    "       TOMS Vol. 12 Issue 3, Sept. 1986, p. 265\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "In the following example `kv` returns 0 whereas `kve` still returns\n"
+    "a useful finite number.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import kv, kve\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> kv(3, 1000.), kve(3, 1000.)\n"
+    "(0.0, 0.03980696128440973)\n"
+    "\n"
+    "Evaluate the function at one point for different orders by\n"
+    "providing a list or NumPy array as argument for the `v` parameter:\n"
+    "\n"
+    ">>> kve([0, 1, 1.5], 1.)\n"
+    "array([1.14446308, 1.63615349, 2.50662827])\n"
+    "\n"
+    "Evaluate the function at several points for order 0 by providing an\n"
+    "array for `z`.\n"
+    "\n"
+    ">>> points = np.array([1., 3., 10.])\n"
+    ">>> kve(0, points)\n"
+    "array([1.14446308, 0.6977616 , 0.39163193])\n"
+    "\n"
+    "Evaluate the function at several points for different orders by\n"
+    "providing arrays for both `v` for `z`. Both arrays have to be\n"
+    "broadcastable to the correct shape. To calculate the orders 0, 1\n"
+    "and 2 for a 1D array of points:\n"
+    "\n"
+    ">>> kve([[0], [1], [2]], points)\n"
+    "array([[1.14446308, 0.6977616 , 0.39163193],\n"
+    "       [1.63615349, 0.80656348, 0.41076657],\n"
+    "       [4.41677005, 1.23547058, 0.47378525]])\n"
+    "\n"
+    "Plot the functions of order 0 to 3 from 0 to 5.\n"
+    "\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 5., 1000)\n"
+    ">>> for i in range(4):\n"
+    "...     ax.plot(x, kve(i, x), label=fr'$K_{i!r}(z)\\cdot e^z$')\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(r\"$z$\")\n"
+    ">>> ax.set_ylim(0, 4)\n"
+    ">>> ax.set_xlim(0, 5)\n"
+    ">>> plt.show()")
+ufunc_kve_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_kve_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_kve_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_kve_loops[3] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_kve_types[0] = <char>NPY_FLOAT
+ufunc_kve_types[1] = <char>NPY_FLOAT
+ufunc_kve_types[2] = <char>NPY_FLOAT
+ufunc_kve_types[3] = <char>NPY_FLOAT
+ufunc_kve_types[4] = <char>NPY_CFLOAT
+ufunc_kve_types[5] = <char>NPY_CFLOAT
+ufunc_kve_types[6] = <char>NPY_DOUBLE
+ufunc_kve_types[7] = <char>NPY_DOUBLE
+ufunc_kve_types[8] = <char>NPY_DOUBLE
+ufunc_kve_types[9] = <char>NPY_DOUBLE
+ufunc_kve_types[10] = <char>NPY_CDOUBLE
+ufunc_kve_types[11] = <char>NPY_CDOUBLE
+ufunc_kve_ptr[2*0] = <void*>_func_cbesk_wrap_e_real
+ufunc_kve_ptr[2*0+1] = <void*>(<char*>"kve")
+ufunc_kve_ptr[2*1] = <void*>_func_cbesk_wrap_e
+ufunc_kve_ptr[2*1+1] = <void*>(<char*>"kve")
+ufunc_kve_ptr[2*2] = <void*>_func_cbesk_wrap_e_real
+ufunc_kve_ptr[2*2+1] = <void*>(<char*>"kve")
+ufunc_kve_ptr[2*3] = <void*>_func_cbesk_wrap_e
+ufunc_kve_ptr[2*3+1] = <void*>(<char*>"kve")
+ufunc_kve_data[0] = &ufunc_kve_ptr[2*0]
+ufunc_kve_data[1] = &ufunc_kve_ptr[2*1]
+ufunc_kve_data[2] = &ufunc_kve_ptr[2*2]
+ufunc_kve_data[3] = &ufunc_kve_ptr[2*3]
+kve = np.PyUFunc_FromFuncAndData(ufunc_kve_loops, ufunc_kve_data, ufunc_kve_types, 4, 2, 1, 0, "kve", ufunc_kve_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_log1p_loops[4]
+cdef void *ufunc_log1p_ptr[8]
+cdef void *ufunc_log1p_data[4]
+cdef char ufunc_log1p_types[8]
+cdef char *ufunc_log1p_doc = (
+    "log1p(x, out=None)\n"
+    "\n"
+    "Calculates log(1 + x) for use when `x` is near zero.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real or complex valued input.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of ``log(1 + x)``.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "expm1, cosm1\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is more accurate than using ``log(1 + x)`` directly for ``x``\n"
+    "near 0. Note that in the below example ``1 + 1e-17 == 1`` to\n"
+    "double precision.\n"
+    "\n"
+    ">>> sc.log1p(1e-17)\n"
+    "1e-17\n"
+    ">>> np.log(1 + 1e-17)\n"
+    "0.0")
+ufunc_log1p_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_log1p_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_log1p_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_log1p_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_log1p_types[0] = <char>NPY_FLOAT
+ufunc_log1p_types[1] = <char>NPY_FLOAT
+ufunc_log1p_types[2] = <char>NPY_DOUBLE
+ufunc_log1p_types[3] = <char>NPY_DOUBLE
+ufunc_log1p_types[4] = <char>NPY_CFLOAT
+ufunc_log1p_types[5] = <char>NPY_CFLOAT
+ufunc_log1p_types[6] = <char>NPY_CDOUBLE
+ufunc_log1p_types[7] = <char>NPY_CDOUBLE
+ufunc_log1p_ptr[2*0] = <void*>_func_log1p
+ufunc_log1p_ptr[2*0+1] = <void*>(<char*>"log1p")
+ufunc_log1p_ptr[2*1] = <void*>_func_log1p
+ufunc_log1p_ptr[2*1+1] = <void*>(<char*>"log1p")
+ufunc_log1p_ptr[2*2] = <void*>_func_clog1p
+ufunc_log1p_ptr[2*2+1] = <void*>(<char*>"log1p")
+ufunc_log1p_ptr[2*3] = <void*>_func_clog1p
+ufunc_log1p_ptr[2*3+1] = <void*>(<char*>"log1p")
+ufunc_log1p_data[0] = &ufunc_log1p_ptr[2*0]
+ufunc_log1p_data[1] = &ufunc_log1p_ptr[2*1]
+ufunc_log1p_data[2] = &ufunc_log1p_ptr[2*2]
+ufunc_log1p_data[3] = &ufunc_log1p_ptr[2*3]
+log1p = np.PyUFunc_FromFuncAndData(ufunc_log1p_loops, ufunc_log1p_data, ufunc_log1p_types, 4, 1, 1, 0, "log1p", ufunc_log1p_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_log_expit_loops[3]
+cdef void *ufunc_log_expit_ptr[6]
+cdef void *ufunc_log_expit_data[3]
+cdef char ufunc_log_expit_types[6]
+cdef char *ufunc_log_expit_doc = (
+    "log_expit(x, out=None)\n"
+    "\n"
+    "Logarithm of the logistic sigmoid function.\n"
+    "\n"
+    "The SciPy implementation of the logistic sigmoid function is\n"
+    "`scipy.special.expit`, so this function is called ``log_expit``.\n"
+    "\n"
+    "The function is mathematically equivalent to ``log(expit(x))``, but\n"
+    "is formulated to avoid loss of precision for inputs with large\n"
+    "(positive or negative) magnitude.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    The values to apply ``log_expit`` to element-wise.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "out : scalar or ndarray\n"
+    "    The computed values, an ndarray of the same shape as ``x``.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "expit\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "As a ufunc, ``log_expit`` takes a number of optional keyword arguments.\n"
+    "For more information see\n"
+    "`ufuncs <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_\n"
+    "\n"
+    ".. versionadded:: 1.8.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import log_expit, expit\n"
+    "\n"
+    ">>> log_expit([-3.0, 0.25, 2.5, 5.0])\n"
+    "array([-3.04858735, -0.57593942, -0.07888973, -0.00671535])\n"
+    "\n"
+    "Large negative values:\n"
+    "\n"
+    ">>> log_expit([-100, -500, -1000])\n"
+    "array([ -100.,  -500., -1000.])\n"
+    "\n"
+    "Note that ``expit(-1000)`` returns 0, so the naive implementation\n"
+    "``log(expit(-1000))`` return ``-inf``.\n"
+    "\n"
+    "Large positive values:\n"
+    "\n"
+    ">>> log_expit([29, 120, 400])\n"
+    "array([-2.54366565e-013, -7.66764807e-053, -1.91516960e-174])\n"
+    "\n"
+    "Compare that to the naive implementation:\n"
+    "\n"
+    ">>> np.log(expit([29, 120, 400]))\n"
+    "array([-2.54463117e-13,  0.00000000e+00,  0.00000000e+00])\n"
+    "\n"
+    "The first value is accurate to only 3 digits, and the larger inputs\n"
+    "lose all precision and return 0.")
+ufunc_log_expit_loops[0] = <np.PyUFuncGenericFunction>loop_f_f__As_f_f
+ufunc_log_expit_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_log_expit_loops[2] = <np.PyUFuncGenericFunction>loop_g_g__As_g_g
+ufunc_log_expit_types[0] = <char>NPY_FLOAT
+ufunc_log_expit_types[1] = <char>NPY_FLOAT
+ufunc_log_expit_types[2] = <char>NPY_DOUBLE
+ufunc_log_expit_types[3] = <char>NPY_DOUBLE
+ufunc_log_expit_types[4] = <char>NPY_LONGDOUBLE
+ufunc_log_expit_types[5] = <char>NPY_LONGDOUBLE
+ufunc_log_expit_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_log_expitf
+ufunc_log_expit_ptr[2*0+1] = <void*>(<char*>"log_expit")
+ufunc_log_expit_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_log_expit
+ufunc_log_expit_ptr[2*1+1] = <void*>(<char*>"log_expit")
+ufunc_log_expit_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_log_expitl
+ufunc_log_expit_ptr[2*2+1] = <void*>(<char*>"log_expit")
+ufunc_log_expit_data[0] = &ufunc_log_expit_ptr[2*0]
+ufunc_log_expit_data[1] = &ufunc_log_expit_ptr[2*1]
+ufunc_log_expit_data[2] = &ufunc_log_expit_ptr[2*2]
+log_expit = np.PyUFunc_FromFuncAndData(ufunc_log_expit_loops, ufunc_log_expit_data, ufunc_log_expit_types, 3, 1, 1, 0, "log_expit", ufunc_log_expit_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_log_ndtr_loops[4]
+cdef void *ufunc_log_ndtr_ptr[8]
+cdef void *ufunc_log_ndtr_data[4]
+cdef char ufunc_log_ndtr_types[8]
+cdef char *ufunc_log_ndtr_doc = (
+    "log_ndtr(x, out=None)\n"
+    "\n"
+    "Logarithm of Gaussian cumulative distribution function.\n"
+    "\n"
+    "Returns the log of the area under the standard Gaussian probability\n"
+    "density function, integrated from minus infinity to `x`::\n"
+    "\n"
+    "    log(1/sqrt(2*pi) * integral(exp(-t**2 / 2), t=-inf..x))\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like, real or complex\n"
+    "    Argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The value of the log of the normal CDF evaluated at `x`\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "erf\n"
+    "erfc\n"
+    "scipy.stats.norm\n"
+    "ndtr\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import log_ndtr, ndtr\n"
+    "\n"
+    "The benefit of ``log_ndtr(x)`` over the naive implementation\n"
+    "``np.log(ndtr(x))`` is most evident with moderate to large positive\n"
+    "values of ``x``:\n"
+    "\n"
+    ">>> x = np.array([6, 7, 9, 12, 15, 25])\n"
+    ">>> log_ndtr(x)\n"
+    "array([-9.86587646e-010, -1.27981254e-012, -1.12858841e-019,\n"
+    "       -1.77648211e-033, -3.67096620e-051, -3.05669671e-138])\n"
+    "\n"
+    "The results of the naive calculation for the moderate ``x`` values\n"
+    "have only 5 or 6 correct significant digits. For values of ``x``\n"
+    "greater than approximately 8.3, the naive expression returns 0:\n"
+    "\n"
+    ">>> np.log(ndtr(x))\n"
+    "array([-9.86587701e-10, -1.27986510e-12,  0.00000000e+00,\n"
+    "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00])")
+ufunc_log_ndtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_log_ndtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_log_ndtr_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_log_ndtr_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_log_ndtr_types[0] = <char>NPY_FLOAT
+ufunc_log_ndtr_types[1] = <char>NPY_FLOAT
+ufunc_log_ndtr_types[2] = <char>NPY_DOUBLE
+ufunc_log_ndtr_types[3] = <char>NPY_DOUBLE
+ufunc_log_ndtr_types[4] = <char>NPY_CFLOAT
+ufunc_log_ndtr_types[5] = <char>NPY_CFLOAT
+ufunc_log_ndtr_types[6] = <char>NPY_CDOUBLE
+ufunc_log_ndtr_types[7] = <char>NPY_CDOUBLE
+ufunc_log_ndtr_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_log_ndtr
+ufunc_log_ndtr_ptr[2*0+1] = <void*>(<char*>"log_ndtr")
+ufunc_log_ndtr_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_log_ndtr
+ufunc_log_ndtr_ptr[2*1+1] = <void*>(<char*>"log_ndtr")
+ufunc_log_ndtr_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_log_ndtr_complex
+ufunc_log_ndtr_ptr[2*2+1] = <void*>(<char*>"log_ndtr")
+ufunc_log_ndtr_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_log_ndtr_complex
+ufunc_log_ndtr_ptr[2*3+1] = <void*>(<char*>"log_ndtr")
+ufunc_log_ndtr_data[0] = &ufunc_log_ndtr_ptr[2*0]
+ufunc_log_ndtr_data[1] = &ufunc_log_ndtr_ptr[2*1]
+ufunc_log_ndtr_data[2] = &ufunc_log_ndtr_ptr[2*2]
+ufunc_log_ndtr_data[3] = &ufunc_log_ndtr_ptr[2*3]
+log_ndtr = np.PyUFunc_FromFuncAndData(ufunc_log_ndtr_loops, ufunc_log_ndtr_data, ufunc_log_ndtr_types, 4, 1, 1, 0, "log_ndtr", ufunc_log_ndtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_loggamma_loops[4]
+cdef void *ufunc_loggamma_ptr[8]
+cdef void *ufunc_loggamma_data[4]
+cdef char ufunc_loggamma_types[8]
+cdef char *ufunc_loggamma_doc = (
+    "loggamma(z, out=None)\n"
+    "\n"
+    "Principal branch of the logarithm of the gamma function.\n"
+    "\n"
+    "Defined to be :math:`\\log(\\Gamma(x))` for :math:`x > 0` and\n"
+    "extended to the complex plane by analytic continuation. The\n"
+    "function has a single branch cut on the negative real axis.\n"
+    "\n"
+    ".. versionadded:: 0.18.0\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Values in the complex plane at which to compute ``loggamma``\n"
+    "out : ndarray, optional\n"
+    "    Output array for computed values of ``loggamma``\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "loggamma : scalar or ndarray\n"
+    "    Values of ``loggamma`` at z.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gammaln : logarithm of the absolute value of the gamma function\n"
+    "gammasgn : sign of the gamma function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "It is not generally true that :math:`\\log\\Gamma(z) =\n"
+    "\\log(\\Gamma(z))`, though the real parts of the functions do\n"
+    "agree. The benefit of not defining `loggamma` as\n"
+    ":math:`\\log(\\Gamma(z))` is that the latter function has a\n"
+    "complicated branch cut structure whereas `loggamma` is analytic\n"
+    "except for on the negative real axis.\n"
+    "\n"
+    "The identities\n"
+    "\n"
+    ".. math::\n"
+    "  \\exp(\\log\\Gamma(z)) &= \\Gamma(z) \\\\\n"
+    "  \\log\\Gamma(z + 1) &= \\log(z) + \\log\\Gamma(z)\n"
+    "\n"
+    "make `loggamma` useful for working in complex logspace.\n"
+    "\n"
+    "On the real line `loggamma` is related to `gammaln` via\n"
+    "``exp(loggamma(x + 0j)) = gammasgn(x)*exp(gammaln(x))``, up to\n"
+    "rounding error.\n"
+    "\n"
+    "The implementation here is based on [hare1997]_.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [hare1997] D.E.G. Hare,\n"
+    "  *Computing the Principal Branch of log-Gamma*,\n"
+    "  Journal of Algorithms, Volume 25, Issue 2, November 1997, pages 221-236.")
+ufunc_loggamma_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_loggamma_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_loggamma_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_loggamma_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_loggamma_types[0] = <char>NPY_FLOAT
+ufunc_loggamma_types[1] = <char>NPY_FLOAT
+ufunc_loggamma_types[2] = <char>NPY_DOUBLE
+ufunc_loggamma_types[3] = <char>NPY_DOUBLE
+ufunc_loggamma_types[4] = <char>NPY_CFLOAT
+ufunc_loggamma_types[5] = <char>NPY_CFLOAT
+ufunc_loggamma_types[6] = <char>NPY_CDOUBLE
+ufunc_loggamma_types[7] = <char>NPY_CDOUBLE
+ufunc_loggamma_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_loggamma_real
+ufunc_loggamma_ptr[2*0+1] = <void*>(<char*>"loggamma")
+ufunc_loggamma_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_loggamma_real
+ufunc_loggamma_ptr[2*1+1] = <void*>(<char*>"loggamma")
+ufunc_loggamma_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_loggamma
+ufunc_loggamma_ptr[2*2+1] = <void*>(<char*>"loggamma")
+ufunc_loggamma_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_loggamma
+ufunc_loggamma_ptr[2*3+1] = <void*>(<char*>"loggamma")
+ufunc_loggamma_data[0] = &ufunc_loggamma_ptr[2*0]
+ufunc_loggamma_data[1] = &ufunc_loggamma_ptr[2*1]
+ufunc_loggamma_data[2] = &ufunc_loggamma_ptr[2*2]
+ufunc_loggamma_data[3] = &ufunc_loggamma_ptr[2*3]
+loggamma = np.PyUFunc_FromFuncAndData(ufunc_loggamma_loops, ufunc_loggamma_data, ufunc_loggamma_types, 4, 1, 1, 0, "loggamma", ufunc_loggamma_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_logit_loops[3]
+cdef void *ufunc_logit_ptr[6]
+cdef void *ufunc_logit_data[3]
+cdef char ufunc_logit_types[6]
+cdef char *ufunc_logit_doc = (
+    "logit(x, out=None)\n"
+    "\n"
+    "Logit ufunc for ndarrays.\n"
+    "\n"
+    "The logit function is defined as logit(p) = log(p/(1-p)).\n"
+    "Note that logit(0) = -inf, logit(1) = inf, and logit(p)\n"
+    "for p<0 or p>1 yields nan.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : ndarray\n"
+    "    The ndarray to apply logit to element-wise.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    An ndarray of the same shape as x. Its entries\n"
+    "    are logit of the corresponding entry of x.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "expit\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "As a ufunc logit takes a number of optional\n"
+    "keyword arguments. For more information\n"
+    "see `ufuncs <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_\n"
+    "\n"
+    ".. versionadded:: 0.10.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import logit, expit\n"
+    "\n"
+    ">>> logit([0, 0.25, 0.5, 0.75, 1])\n"
+    "array([       -inf, -1.09861229,  0.        ,  1.09861229,         inf])\n"
+    "\n"
+    "`expit` is the inverse of `logit`:\n"
+    "\n"
+    ">>> expit(logit([0.1, 0.75, 0.999]))\n"
+    "array([ 0.1  ,  0.75 ,  0.999])\n"
+    "\n"
+    "Plot logit(x) for x in [0, 1]:\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(0, 1, 501)\n"
+    ">>> y = logit(x)\n"
+    ">>> plt.plot(x, y)\n"
+    ">>> plt.grid()\n"
+    ">>> plt.ylim(-6, 6)\n"
+    ">>> plt.xlabel('x')\n"
+    ">>> plt.title('logit(x)')\n"
+    ">>> plt.show()")
+ufunc_logit_loops[0] = <np.PyUFuncGenericFunction>loop_f_f__As_f_f
+ufunc_logit_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_logit_loops[2] = <np.PyUFuncGenericFunction>loop_g_g__As_g_g
+ufunc_logit_types[0] = <char>NPY_FLOAT
+ufunc_logit_types[1] = <char>NPY_FLOAT
+ufunc_logit_types[2] = <char>NPY_DOUBLE
+ufunc_logit_types[3] = <char>NPY_DOUBLE
+ufunc_logit_types[4] = <char>NPY_LONGDOUBLE
+ufunc_logit_types[5] = <char>NPY_LONGDOUBLE
+ufunc_logit_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_logitf
+ufunc_logit_ptr[2*0+1] = <void*>(<char*>"logit")
+ufunc_logit_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_logit
+ufunc_logit_ptr[2*1+1] = <void*>(<char*>"logit")
+ufunc_logit_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_logitl
+ufunc_logit_ptr[2*2+1] = <void*>(<char*>"logit")
+ufunc_logit_data[0] = &ufunc_logit_ptr[2*0]
+ufunc_logit_data[1] = &ufunc_logit_ptr[2*1]
+ufunc_logit_data[2] = &ufunc_logit_ptr[2*2]
+logit = np.PyUFunc_FromFuncAndData(ufunc_logit_loops, ufunc_logit_data, ufunc_logit_types, 3, 1, 1, 0, "logit", ufunc_logit_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_lpmv_loops[2]
+cdef void *ufunc_lpmv_ptr[4]
+cdef void *ufunc_lpmv_data[2]
+cdef char ufunc_lpmv_types[8]
+cdef char *ufunc_lpmv_doc = (
+    "lpmv(m, v, x, out=None)\n"
+    "\n"
+    "Associated Legendre function of integer order and real degree.\n"
+    "\n"
+    "Defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    P_v^m = (-1)^m (1 - x^2)^{m/2} \\frac{d^m}{dx^m} P_v(x)\n"
+    "\n"
+    "where\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    P_v = \\sum_{k = 0}^\\infty \\frac{(-v)_k (v + 1)_k}{(k!)^2}\n"
+    "            \\left(\\frac{1 - x}{2}\\right)^k\n"
+    "\n"
+    "is the Legendre function of the first kind. Here :math:`(\\cdot)_k`\n"
+    "is the Pochhammer symbol; see `poch`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order (int or float). If passed a float not equal to an\n"
+    "    integer the function returns NaN.\n"
+    "v : array_like\n"
+    "    Degree (float).\n"
+    "x : array_like\n"
+    "    Argument (float). Must have ``|x| <= 1``.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "pmv : scalar or ndarray\n"
+    "    Value of the associated Legendre function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "lpmn : Compute the associated Legendre function for all orders\n"
+    "       ``0, ..., m`` and degrees ``0, ..., n``.\n"
+    "clpmn : Compute the associated Legendre function at complex\n"
+    "        arguments.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Note that this implementation includes the Condon-Shortley phase.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Zhang, Jin, \"Computation of Special Functions\", John Wiley\n"
+    "       and Sons, Inc, 1996.")
+ufunc_lpmv_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_lpmv_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_lpmv_types[0] = <char>NPY_FLOAT
+ufunc_lpmv_types[1] = <char>NPY_FLOAT
+ufunc_lpmv_types[2] = <char>NPY_FLOAT
+ufunc_lpmv_types[3] = <char>NPY_FLOAT
+ufunc_lpmv_types[4] = <char>NPY_DOUBLE
+ufunc_lpmv_types[5] = <char>NPY_DOUBLE
+ufunc_lpmv_types[6] = <char>NPY_DOUBLE
+ufunc_lpmv_types[7] = <char>NPY_DOUBLE
+ufunc_lpmv_ptr[2*0] = <void*>_func_pmv_wrap
+ufunc_lpmv_ptr[2*0+1] = <void*>(<char*>"lpmv")
+ufunc_lpmv_ptr[2*1] = <void*>_func_pmv_wrap
+ufunc_lpmv_ptr[2*1+1] = <void*>(<char*>"lpmv")
+ufunc_lpmv_data[0] = &ufunc_lpmv_ptr[2*0]
+ufunc_lpmv_data[1] = &ufunc_lpmv_ptr[2*1]
+lpmv = np.PyUFunc_FromFuncAndData(ufunc_lpmv_loops, ufunc_lpmv_data, ufunc_lpmv_types, 2, 3, 1, 0, "lpmv", ufunc_lpmv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_mathieu_a_loops[2]
+cdef void *ufunc_mathieu_a_ptr[4]
+cdef void *ufunc_mathieu_a_data[2]
+cdef char ufunc_mathieu_a_types[6]
+cdef char *ufunc_mathieu_a_doc = (
+    "mathieu_a(m, q, out=None)\n"
+    "\n"
+    "Characteristic value of even Mathieu functions\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order of the function\n"
+    "q : array_like\n"
+    "    Parameter of the function\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Characteristic value for the even solution, ``ce_m(z, q)``, of\n"
+    "    Mathieu's equation.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "mathieu_b, mathieu_cem, mathieu_sem")
+ufunc_mathieu_a_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_mathieu_a_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_mathieu_a_types[0] = <char>NPY_FLOAT
+ufunc_mathieu_a_types[1] = <char>NPY_FLOAT
+ufunc_mathieu_a_types[2] = <char>NPY_FLOAT
+ufunc_mathieu_a_types[3] = <char>NPY_DOUBLE
+ufunc_mathieu_a_types[4] = <char>NPY_DOUBLE
+ufunc_mathieu_a_types[5] = <char>NPY_DOUBLE
+ufunc_mathieu_a_ptr[2*0] = <void*>_func_cem_cva_wrap
+ufunc_mathieu_a_ptr[2*0+1] = <void*>(<char*>"mathieu_a")
+ufunc_mathieu_a_ptr[2*1] = <void*>_func_cem_cva_wrap
+ufunc_mathieu_a_ptr[2*1+1] = <void*>(<char*>"mathieu_a")
+ufunc_mathieu_a_data[0] = &ufunc_mathieu_a_ptr[2*0]
+ufunc_mathieu_a_data[1] = &ufunc_mathieu_a_ptr[2*1]
+mathieu_a = np.PyUFunc_FromFuncAndData(ufunc_mathieu_a_loops, ufunc_mathieu_a_data, ufunc_mathieu_a_types, 2, 2, 1, 0, "mathieu_a", ufunc_mathieu_a_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_mathieu_b_loops[2]
+cdef void *ufunc_mathieu_b_ptr[4]
+cdef void *ufunc_mathieu_b_data[2]
+cdef char ufunc_mathieu_b_types[6]
+cdef char *ufunc_mathieu_b_doc = (
+    "mathieu_b(m, q, out=None)\n"
+    "\n"
+    "Characteristic value of odd Mathieu functions\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order of the function\n"
+    "q : array_like\n"
+    "    Parameter of the function\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Characteristic value for the odd solution, ``se_m(z, q)``, of Mathieu's\n"
+    "    equation.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "mathieu_a, mathieu_cem, mathieu_sem")
+ufunc_mathieu_b_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_mathieu_b_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_mathieu_b_types[0] = <char>NPY_FLOAT
+ufunc_mathieu_b_types[1] = <char>NPY_FLOAT
+ufunc_mathieu_b_types[2] = <char>NPY_FLOAT
+ufunc_mathieu_b_types[3] = <char>NPY_DOUBLE
+ufunc_mathieu_b_types[4] = <char>NPY_DOUBLE
+ufunc_mathieu_b_types[5] = <char>NPY_DOUBLE
+ufunc_mathieu_b_ptr[2*0] = <void*>_func_sem_cva_wrap
+ufunc_mathieu_b_ptr[2*0+1] = <void*>(<char*>"mathieu_b")
+ufunc_mathieu_b_ptr[2*1] = <void*>_func_sem_cva_wrap
+ufunc_mathieu_b_ptr[2*1+1] = <void*>(<char*>"mathieu_b")
+ufunc_mathieu_b_data[0] = &ufunc_mathieu_b_ptr[2*0]
+ufunc_mathieu_b_data[1] = &ufunc_mathieu_b_ptr[2*1]
+mathieu_b = np.PyUFunc_FromFuncAndData(ufunc_mathieu_b_loops, ufunc_mathieu_b_data, ufunc_mathieu_b_types, 2, 2, 1, 0, "mathieu_b", ufunc_mathieu_b_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_mathieu_cem_loops[2]
+cdef void *ufunc_mathieu_cem_ptr[4]
+cdef void *ufunc_mathieu_cem_data[2]
+cdef char ufunc_mathieu_cem_types[10]
+cdef char *ufunc_mathieu_cem_doc = (
+    "mathieu_cem(m, q, x, out=None)\n"
+    "\n"
+    "Even Mathieu function and its derivative\n"
+    "\n"
+    "Returns the even Mathieu function, ``ce_m(x, q)``, of order `m` and\n"
+    "parameter `q` evaluated at `x` (given in degrees).  Also returns the\n"
+    "derivative with respect to `x` of ce_m(x, q)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order of the function\n"
+    "q : array_like\n"
+    "    Parameter of the function\n"
+    "x : array_like\n"
+    "    Argument of the function, *given in degrees, not radians*\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Value of the function\n"
+    "yp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "mathieu_a, mathieu_b, mathieu_sem")
+ufunc_mathieu_cem_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_fff_ff
+ufunc_mathieu_cem_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_ddd_dd
+ufunc_mathieu_cem_types[0] = <char>NPY_FLOAT
+ufunc_mathieu_cem_types[1] = <char>NPY_FLOAT
+ufunc_mathieu_cem_types[2] = <char>NPY_FLOAT
+ufunc_mathieu_cem_types[3] = <char>NPY_FLOAT
+ufunc_mathieu_cem_types[4] = <char>NPY_FLOAT
+ufunc_mathieu_cem_types[5] = <char>NPY_DOUBLE
+ufunc_mathieu_cem_types[6] = <char>NPY_DOUBLE
+ufunc_mathieu_cem_types[7] = <char>NPY_DOUBLE
+ufunc_mathieu_cem_types[8] = <char>NPY_DOUBLE
+ufunc_mathieu_cem_types[9] = <char>NPY_DOUBLE
+ufunc_mathieu_cem_ptr[2*0] = <void*>_func_cem_wrap
+ufunc_mathieu_cem_ptr[2*0+1] = <void*>(<char*>"mathieu_cem")
+ufunc_mathieu_cem_ptr[2*1] = <void*>_func_cem_wrap
+ufunc_mathieu_cem_ptr[2*1+1] = <void*>(<char*>"mathieu_cem")
+ufunc_mathieu_cem_data[0] = &ufunc_mathieu_cem_ptr[2*0]
+ufunc_mathieu_cem_data[1] = &ufunc_mathieu_cem_ptr[2*1]
+mathieu_cem = np.PyUFunc_FromFuncAndData(ufunc_mathieu_cem_loops, ufunc_mathieu_cem_data, ufunc_mathieu_cem_types, 2, 3, 2, 0, "mathieu_cem", ufunc_mathieu_cem_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_mathieu_modcem1_loops[2]
+cdef void *ufunc_mathieu_modcem1_ptr[4]
+cdef void *ufunc_mathieu_modcem1_data[2]
+cdef char ufunc_mathieu_modcem1_types[10]
+cdef char *ufunc_mathieu_modcem1_doc = (
+    "mathieu_modcem1(m, q, x, out=None)\n"
+    "\n"
+    "Even modified Mathieu function of the first kind and its derivative\n"
+    "\n"
+    "Evaluates the even modified Mathieu function of the first kind,\n"
+    "``Mc1m(x, q)``, and its derivative at `x` for order `m` and parameter\n"
+    "`q`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order of the function\n"
+    "q : array_like\n"
+    "    Parameter of the function\n"
+    "x : array_like\n"
+    "    Argument of the function, *given in degrees, not radians*\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Value of the function\n"
+    "yp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "mathieu_modsem1")
+ufunc_mathieu_modcem1_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_fff_ff
+ufunc_mathieu_modcem1_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_ddd_dd
+ufunc_mathieu_modcem1_types[0] = <char>NPY_FLOAT
+ufunc_mathieu_modcem1_types[1] = <char>NPY_FLOAT
+ufunc_mathieu_modcem1_types[2] = <char>NPY_FLOAT
+ufunc_mathieu_modcem1_types[3] = <char>NPY_FLOAT
+ufunc_mathieu_modcem1_types[4] = <char>NPY_FLOAT
+ufunc_mathieu_modcem1_types[5] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem1_types[6] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem1_types[7] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem1_types[8] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem1_types[9] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem1_ptr[2*0] = <void*>_func_mcm1_wrap
+ufunc_mathieu_modcem1_ptr[2*0+1] = <void*>(<char*>"mathieu_modcem1")
+ufunc_mathieu_modcem1_ptr[2*1] = <void*>_func_mcm1_wrap
+ufunc_mathieu_modcem1_ptr[2*1+1] = <void*>(<char*>"mathieu_modcem1")
+ufunc_mathieu_modcem1_data[0] = &ufunc_mathieu_modcem1_ptr[2*0]
+ufunc_mathieu_modcem1_data[1] = &ufunc_mathieu_modcem1_ptr[2*1]
+mathieu_modcem1 = np.PyUFunc_FromFuncAndData(ufunc_mathieu_modcem1_loops, ufunc_mathieu_modcem1_data, ufunc_mathieu_modcem1_types, 2, 3, 2, 0, "mathieu_modcem1", ufunc_mathieu_modcem1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_mathieu_modcem2_loops[2]
+cdef void *ufunc_mathieu_modcem2_ptr[4]
+cdef void *ufunc_mathieu_modcem2_data[2]
+cdef char ufunc_mathieu_modcem2_types[10]
+cdef char *ufunc_mathieu_modcem2_doc = (
+    "mathieu_modcem2(m, q, x, out=None)\n"
+    "\n"
+    "Even modified Mathieu function of the second kind and its derivative\n"
+    "\n"
+    "Evaluates the even modified Mathieu function of the second kind,\n"
+    "Mc2m(x, q), and its derivative at `x` (given in degrees) for order `m`\n"
+    "and parameter `q`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order of the function\n"
+    "q : array_like\n"
+    "    Parameter of the function\n"
+    "x : array_like\n"
+    "    Argument of the function, *given in degrees, not radians*\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Value of the function\n"
+    "yp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "mathieu_modsem2")
+ufunc_mathieu_modcem2_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_fff_ff
+ufunc_mathieu_modcem2_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_ddd_dd
+ufunc_mathieu_modcem2_types[0] = <char>NPY_FLOAT
+ufunc_mathieu_modcem2_types[1] = <char>NPY_FLOAT
+ufunc_mathieu_modcem2_types[2] = <char>NPY_FLOAT
+ufunc_mathieu_modcem2_types[3] = <char>NPY_FLOAT
+ufunc_mathieu_modcem2_types[4] = <char>NPY_FLOAT
+ufunc_mathieu_modcem2_types[5] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem2_types[6] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem2_types[7] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem2_types[8] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem2_types[9] = <char>NPY_DOUBLE
+ufunc_mathieu_modcem2_ptr[2*0] = <void*>_func_mcm2_wrap
+ufunc_mathieu_modcem2_ptr[2*0+1] = <void*>(<char*>"mathieu_modcem2")
+ufunc_mathieu_modcem2_ptr[2*1] = <void*>_func_mcm2_wrap
+ufunc_mathieu_modcem2_ptr[2*1+1] = <void*>(<char*>"mathieu_modcem2")
+ufunc_mathieu_modcem2_data[0] = &ufunc_mathieu_modcem2_ptr[2*0]
+ufunc_mathieu_modcem2_data[1] = &ufunc_mathieu_modcem2_ptr[2*1]
+mathieu_modcem2 = np.PyUFunc_FromFuncAndData(ufunc_mathieu_modcem2_loops, ufunc_mathieu_modcem2_data, ufunc_mathieu_modcem2_types, 2, 3, 2, 0, "mathieu_modcem2", ufunc_mathieu_modcem2_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_mathieu_modsem1_loops[2]
+cdef void *ufunc_mathieu_modsem1_ptr[4]
+cdef void *ufunc_mathieu_modsem1_data[2]
+cdef char ufunc_mathieu_modsem1_types[10]
+cdef char *ufunc_mathieu_modsem1_doc = (
+    "mathieu_modsem1(m, q, x, out=None)\n"
+    "\n"
+    "Odd modified Mathieu function of the first kind and its derivative\n"
+    "\n"
+    "Evaluates the odd modified Mathieu function of the first kind,\n"
+    "Ms1m(x, q), and its derivative at `x` (given in degrees) for order `m`\n"
+    "and parameter `q`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order of the function\n"
+    "q : array_like\n"
+    "    Parameter of the function\n"
+    "x : array_like\n"
+    "    Argument of the function, *given in degrees, not radians*\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Value of the function\n"
+    "yp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "mathieu_modcem1")
+ufunc_mathieu_modsem1_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_fff_ff
+ufunc_mathieu_modsem1_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_ddd_dd
+ufunc_mathieu_modsem1_types[0] = <char>NPY_FLOAT
+ufunc_mathieu_modsem1_types[1] = <char>NPY_FLOAT
+ufunc_mathieu_modsem1_types[2] = <char>NPY_FLOAT
+ufunc_mathieu_modsem1_types[3] = <char>NPY_FLOAT
+ufunc_mathieu_modsem1_types[4] = <char>NPY_FLOAT
+ufunc_mathieu_modsem1_types[5] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem1_types[6] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem1_types[7] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem1_types[8] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem1_types[9] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem1_ptr[2*0] = <void*>_func_msm1_wrap
+ufunc_mathieu_modsem1_ptr[2*0+1] = <void*>(<char*>"mathieu_modsem1")
+ufunc_mathieu_modsem1_ptr[2*1] = <void*>_func_msm1_wrap
+ufunc_mathieu_modsem1_ptr[2*1+1] = <void*>(<char*>"mathieu_modsem1")
+ufunc_mathieu_modsem1_data[0] = &ufunc_mathieu_modsem1_ptr[2*0]
+ufunc_mathieu_modsem1_data[1] = &ufunc_mathieu_modsem1_ptr[2*1]
+mathieu_modsem1 = np.PyUFunc_FromFuncAndData(ufunc_mathieu_modsem1_loops, ufunc_mathieu_modsem1_data, ufunc_mathieu_modsem1_types, 2, 3, 2, 0, "mathieu_modsem1", ufunc_mathieu_modsem1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_mathieu_modsem2_loops[2]
+cdef void *ufunc_mathieu_modsem2_ptr[4]
+cdef void *ufunc_mathieu_modsem2_data[2]
+cdef char ufunc_mathieu_modsem2_types[10]
+cdef char *ufunc_mathieu_modsem2_doc = (
+    "mathieu_modsem2(m, q, x, out=None)\n"
+    "\n"
+    "Odd modified Mathieu function of the second kind and its derivative\n"
+    "\n"
+    "Evaluates the odd modified Mathieu function of the second kind,\n"
+    "Ms2m(x, q), and its derivative at `x` (given in degrees) for order `m`\n"
+    "and parameter q.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order of the function\n"
+    "q : array_like\n"
+    "    Parameter of the function\n"
+    "x : array_like\n"
+    "    Argument of the function, *given in degrees, not radians*\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Value of the function\n"
+    "yp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "mathieu_modcem2")
+ufunc_mathieu_modsem2_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_fff_ff
+ufunc_mathieu_modsem2_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_ddd_dd
+ufunc_mathieu_modsem2_types[0] = <char>NPY_FLOAT
+ufunc_mathieu_modsem2_types[1] = <char>NPY_FLOAT
+ufunc_mathieu_modsem2_types[2] = <char>NPY_FLOAT
+ufunc_mathieu_modsem2_types[3] = <char>NPY_FLOAT
+ufunc_mathieu_modsem2_types[4] = <char>NPY_FLOAT
+ufunc_mathieu_modsem2_types[5] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem2_types[6] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem2_types[7] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem2_types[8] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem2_types[9] = <char>NPY_DOUBLE
+ufunc_mathieu_modsem2_ptr[2*0] = <void*>_func_msm2_wrap
+ufunc_mathieu_modsem2_ptr[2*0+1] = <void*>(<char*>"mathieu_modsem2")
+ufunc_mathieu_modsem2_ptr[2*1] = <void*>_func_msm2_wrap
+ufunc_mathieu_modsem2_ptr[2*1+1] = <void*>(<char*>"mathieu_modsem2")
+ufunc_mathieu_modsem2_data[0] = &ufunc_mathieu_modsem2_ptr[2*0]
+ufunc_mathieu_modsem2_data[1] = &ufunc_mathieu_modsem2_ptr[2*1]
+mathieu_modsem2 = np.PyUFunc_FromFuncAndData(ufunc_mathieu_modsem2_loops, ufunc_mathieu_modsem2_data, ufunc_mathieu_modsem2_types, 2, 3, 2, 0, "mathieu_modsem2", ufunc_mathieu_modsem2_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_mathieu_sem_loops[2]
+cdef void *ufunc_mathieu_sem_ptr[4]
+cdef void *ufunc_mathieu_sem_data[2]
+cdef char ufunc_mathieu_sem_types[10]
+cdef char *ufunc_mathieu_sem_doc = (
+    "mathieu_sem(m, q, x, out=None)\n"
+    "\n"
+    "Odd Mathieu function and its derivative\n"
+    "\n"
+    "Returns the odd Mathieu function, se_m(x, q), of order `m` and\n"
+    "parameter `q` evaluated at `x` (given in degrees).  Also returns the\n"
+    "derivative with respect to `x` of se_m(x, q).\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order of the function\n"
+    "q : array_like\n"
+    "    Parameter of the function\n"
+    "x : array_like\n"
+    "    Argument of the function, *given in degrees, not radians*.\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y : scalar or ndarray\n"
+    "    Value of the function\n"
+    "yp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "mathieu_a, mathieu_b, mathieu_cem")
+ufunc_mathieu_sem_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_fff_ff
+ufunc_mathieu_sem_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddd_dd_As_ddd_dd
+ufunc_mathieu_sem_types[0] = <char>NPY_FLOAT
+ufunc_mathieu_sem_types[1] = <char>NPY_FLOAT
+ufunc_mathieu_sem_types[2] = <char>NPY_FLOAT
+ufunc_mathieu_sem_types[3] = <char>NPY_FLOAT
+ufunc_mathieu_sem_types[4] = <char>NPY_FLOAT
+ufunc_mathieu_sem_types[5] = <char>NPY_DOUBLE
+ufunc_mathieu_sem_types[6] = <char>NPY_DOUBLE
+ufunc_mathieu_sem_types[7] = <char>NPY_DOUBLE
+ufunc_mathieu_sem_types[8] = <char>NPY_DOUBLE
+ufunc_mathieu_sem_types[9] = <char>NPY_DOUBLE
+ufunc_mathieu_sem_ptr[2*0] = <void*>_func_sem_wrap
+ufunc_mathieu_sem_ptr[2*0+1] = <void*>(<char*>"mathieu_sem")
+ufunc_mathieu_sem_ptr[2*1] = <void*>_func_sem_wrap
+ufunc_mathieu_sem_ptr[2*1+1] = <void*>(<char*>"mathieu_sem")
+ufunc_mathieu_sem_data[0] = &ufunc_mathieu_sem_ptr[2*0]
+ufunc_mathieu_sem_data[1] = &ufunc_mathieu_sem_ptr[2*1]
+mathieu_sem = np.PyUFunc_FromFuncAndData(ufunc_mathieu_sem_loops, ufunc_mathieu_sem_data, ufunc_mathieu_sem_types, 2, 3, 2, 0, "mathieu_sem", ufunc_mathieu_sem_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_modfresnelm_loops[2]
+cdef void *ufunc_modfresnelm_ptr[4]
+cdef void *ufunc_modfresnelm_data[2]
+cdef char ufunc_modfresnelm_types[6]
+cdef char *ufunc_modfresnelm_doc = (
+    "modfresnelm(x, out=None)\n"
+    "\n"
+    "Modified Fresnel negative integrals\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Function argument\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "fm : scalar or ndarray\n"
+    "    Integral ``F_-(x)``: ``integral(exp(-1j*t*t), t=x..inf)``\n"
+    "km : scalar or ndarray\n"
+    "    Integral ``K_-(x)``: ``1/sqrt(pi)*exp(1j*(x*x+pi/4))*fp``\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "modfresnelp")
+ufunc_modfresnelm_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_DD_As_f_FF
+ufunc_modfresnelm_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_DD_As_d_DD
+ufunc_modfresnelm_types[0] = <char>NPY_FLOAT
+ufunc_modfresnelm_types[1] = <char>NPY_CFLOAT
+ufunc_modfresnelm_types[2] = <char>NPY_CFLOAT
+ufunc_modfresnelm_types[3] = <char>NPY_DOUBLE
+ufunc_modfresnelm_types[4] = <char>NPY_CDOUBLE
+ufunc_modfresnelm_types[5] = <char>NPY_CDOUBLE
+ufunc_modfresnelm_ptr[2*0] = <void*>_func_modified_fresnel_minus_wrap
+ufunc_modfresnelm_ptr[2*0+1] = <void*>(<char*>"modfresnelm")
+ufunc_modfresnelm_ptr[2*1] = <void*>_func_modified_fresnel_minus_wrap
+ufunc_modfresnelm_ptr[2*1+1] = <void*>(<char*>"modfresnelm")
+ufunc_modfresnelm_data[0] = &ufunc_modfresnelm_ptr[2*0]
+ufunc_modfresnelm_data[1] = &ufunc_modfresnelm_ptr[2*1]
+modfresnelm = np.PyUFunc_FromFuncAndData(ufunc_modfresnelm_loops, ufunc_modfresnelm_data, ufunc_modfresnelm_types, 2, 1, 2, 0, "modfresnelm", ufunc_modfresnelm_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_modfresnelp_loops[2]
+cdef void *ufunc_modfresnelp_ptr[4]
+cdef void *ufunc_modfresnelp_data[2]
+cdef char ufunc_modfresnelp_types[6]
+cdef char *ufunc_modfresnelp_doc = (
+    "modfresnelp(x, out=None)\n"
+    "\n"
+    "Modified Fresnel positive integrals\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Function argument\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "fp : scalar or ndarray\n"
+    "    Integral ``F_+(x)``: ``integral(exp(1j*t*t), t=x..inf)``\n"
+    "kp : scalar or ndarray\n"
+    "    Integral ``K_+(x)``: ``1/sqrt(pi)*exp(-1j*(x*x+pi/4))*fp``\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "modfresnelm")
+ufunc_modfresnelp_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_DD_As_f_FF
+ufunc_modfresnelp_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_DD_As_d_DD
+ufunc_modfresnelp_types[0] = <char>NPY_FLOAT
+ufunc_modfresnelp_types[1] = <char>NPY_CFLOAT
+ufunc_modfresnelp_types[2] = <char>NPY_CFLOAT
+ufunc_modfresnelp_types[3] = <char>NPY_DOUBLE
+ufunc_modfresnelp_types[4] = <char>NPY_CDOUBLE
+ufunc_modfresnelp_types[5] = <char>NPY_CDOUBLE
+ufunc_modfresnelp_ptr[2*0] = <void*>_func_modified_fresnel_plus_wrap
+ufunc_modfresnelp_ptr[2*0+1] = <void*>(<char*>"modfresnelp")
+ufunc_modfresnelp_ptr[2*1] = <void*>_func_modified_fresnel_plus_wrap
+ufunc_modfresnelp_ptr[2*1+1] = <void*>(<char*>"modfresnelp")
+ufunc_modfresnelp_data[0] = &ufunc_modfresnelp_ptr[2*0]
+ufunc_modfresnelp_data[1] = &ufunc_modfresnelp_ptr[2*1]
+modfresnelp = np.PyUFunc_FromFuncAndData(ufunc_modfresnelp_loops, ufunc_modfresnelp_data, ufunc_modfresnelp_types, 2, 1, 2, 0, "modfresnelp", ufunc_modfresnelp_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_modstruve_loops[2]
+cdef void *ufunc_modstruve_ptr[4]
+cdef void *ufunc_modstruve_data[2]
+cdef char ufunc_modstruve_types[6]
+cdef char *ufunc_modstruve_doc = (
+    "modstruve(v, x, out=None)\n"
+    "\n"
+    "Modified Struve function.\n"
+    "\n"
+    "Return the value of the modified Struve function of order `v` at `x`.  The\n"
+    "modified Struve function is defined as,\n"
+    "\n"
+    ".. math::\n"
+    "    L_v(x) = -\\imath \\exp(-\\pi\\imath v/2) H_v(\\imath x),\n"
+    "\n"
+    "where :math:`H_v` is the Struve function.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order of the modified Struve function (float).\n"
+    "x : array_like\n"
+    "    Argument of the Struve function (float; must be positive unless `v` is\n"
+    "    an integer).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "L : scalar or ndarray\n"
+    "    Value of the modified Struve function of order `v` at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "struve\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Three methods discussed in [1]_ are used to evaluate the function:\n"
+    "\n"
+    "- power series\n"
+    "- expansion in Bessel functions (if :math:`|x| < |v| + 20`)\n"
+    "- asymptotic large-x expansion (if :math:`x \\geq 0.7v + 12`)\n"
+    "\n"
+    "Rounding errors are estimated based on the largest terms in the sums, and\n"
+    "the result associated with the smallest error is returned.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] NIST Digital Library of Mathematical Functions\n"
+    "       https://dlmf.nist.gov/11\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the modified Struve function of order 1 at 2.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import modstruve\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> modstruve(1, 2.)\n"
+    "1.102759787367716\n"
+    "\n"
+    "Calculate the modified Struve function at 2 for orders 1, 2 and 3 by\n"
+    "providing a list for the order parameter `v`.\n"
+    "\n"
+    ">>> modstruve([1, 2, 3], 2.)\n"
+    "array([1.10275979, 0.41026079, 0.11247294])\n"
+    "\n"
+    "Calculate the modified Struve function of order 1 for several points\n"
+    "by providing an array for `x`.\n"
+    "\n"
+    ">>> points = np.array([2., 5., 8.])\n"
+    ">>> modstruve(1, points)\n"
+    "array([  1.10275979,  23.72821578, 399.24709139])\n"
+    "\n"
+    "Compute the modified Struve function for several orders at several\n"
+    "points by providing arrays for `v` and `z`. The arrays have to be\n"
+    "broadcastable to the correct shapes.\n"
+    "\n"
+    ">>> orders = np.array([[1], [2], [3]])\n"
+    ">>> points.shape, orders.shape\n"
+    "((3,), (3, 1))\n"
+    "\n"
+    ">>> modstruve(orders, points)\n"
+    "array([[1.10275979e+00, 2.37282158e+01, 3.99247091e+02],\n"
+    "       [4.10260789e-01, 1.65535979e+01, 3.25973609e+02],\n"
+    "       [1.12472937e-01, 9.42430454e+00, 2.33544042e+02]])\n"
+    "\n"
+    "Plot the modified Struve functions of order 0 to 3 from -5 to 5.\n"
+    "\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-5., 5., 1000)\n"
+    ">>> for i in range(4):\n"
+    "...     ax.plot(x, modstruve(i, x), label=f'$L_{i!r}$')\n"
+    ">>> ax.legend(ncol=2)\n"
+    ">>> ax.set_xlim(-5, 5)\n"
+    ">>> ax.set_title(r\"Modified Struve functions $L_{\\nu}$\")\n"
+    ">>> plt.show()")
+ufunc_modstruve_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_modstruve_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_modstruve_types[0] = <char>NPY_FLOAT
+ufunc_modstruve_types[1] = <char>NPY_FLOAT
+ufunc_modstruve_types[2] = <char>NPY_FLOAT
+ufunc_modstruve_types[3] = <char>NPY_DOUBLE
+ufunc_modstruve_types[4] = <char>NPY_DOUBLE
+ufunc_modstruve_types[5] = <char>NPY_DOUBLE
+ufunc_modstruve_ptr[2*0] = <void*>_func_struve_l
+ufunc_modstruve_ptr[2*0+1] = <void*>(<char*>"modstruve")
+ufunc_modstruve_ptr[2*1] = <void*>_func_struve_l
+ufunc_modstruve_ptr[2*1+1] = <void*>(<char*>"modstruve")
+ufunc_modstruve_data[0] = &ufunc_modstruve_ptr[2*0]
+ufunc_modstruve_data[1] = &ufunc_modstruve_ptr[2*1]
+modstruve = np.PyUFunc_FromFuncAndData(ufunc_modstruve_loops, ufunc_modstruve_data, ufunc_modstruve_types, 2, 2, 1, 0, "modstruve", ufunc_modstruve_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nbdtr_loops[3]
+cdef void *ufunc_nbdtr_ptr[6]
+cdef void *ufunc_nbdtr_data[3]
+cdef char ufunc_nbdtr_types[12]
+cdef char *ufunc_nbdtr_doc = (
+    "nbdtr(k, n, p, out=None)\n"
+    "\n"
+    "Negative binomial cumulative distribution function.\n"
+    "\n"
+    "Returns the sum of the terms 0 through `k` of the negative binomial\n"
+    "distribution probability mass function,\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    F = \\sum_{j=0}^k {{n + j - 1}\\choose{j}} p^n (1 - p)^j.\n"
+    "\n"
+    "In a sequence of Bernoulli trials with individual success probabilities\n"
+    "`p`, this is the probability that `k` or fewer failures precede the nth\n"
+    "success.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    The maximum number of allowed failures (nonnegative int).\n"
+    "n : array_like\n"
+    "    The target number of successes (positive int).\n"
+    "p : array_like\n"
+    "    Probability of success in a single event (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "F : scalar or ndarray\n"
+    "    The probability of `k` or fewer failures before `n` successes in a\n"
+    "    sequence of events with individual success probability `p`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "nbdtrc : Negative binomial survival function\n"
+    "nbdtrik : Negative binomial quantile function\n"
+    "scipy.stats.nbinom : Negative binomial distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "If floating point values are passed for `k` or `n`, they will be truncated\n"
+    "to integers.\n"
+    "\n"
+    "The terms are not summed directly; instead the regularized incomplete beta\n"
+    "function is employed, according to the formula,\n"
+    "\n"
+    ".. math::\n"
+    "    \\mathrm{nbdtr}(k, n, p) = I_{p}(n, k + 1).\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `nbdtr`.\n"
+    "\n"
+    "The negative binomial distribution is also available as\n"
+    "`scipy.stats.nbinom`. Using `nbdtr` directly can improve performance\n"
+    "compared to the ``cdf`` method of `scipy.stats.nbinom` (see last example).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the function for ``k=10`` and ``n=5`` at ``p=0.5``.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import nbdtr\n"
+    ">>> nbdtr(10, 5, 0.5)\n"
+    "0.940765380859375\n"
+    "\n"
+    "Compute the function for ``n=10`` and ``p=0.5`` at several points by\n"
+    "providing a NumPy array or list for `k`.\n"
+    "\n"
+    ">>> nbdtr([5, 10, 15], 10, 0.5)\n"
+    "array([0.15087891, 0.58809853, 0.88523853])\n"
+    "\n"
+    "Plot the function for four different parameter sets.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> k = np.arange(130)\n"
+    ">>> n_parameters = [20, 20, 20, 80]\n"
+    ">>> p_parameters = [0.2, 0.5, 0.8, 0.5]\n"
+    ">>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']\n"
+    ">>> parameters_list = list(zip(p_parameters, n_parameters,\n"
+    "...                            linestyles))\n"
+    ">>> fig, ax = plt.subplots(figsize=(8, 8))\n"
+    ">>> for parameter_set in parameters_list:\n"
+    "...     p, n, style = parameter_set\n"
+    "...     nbdtr_vals = nbdtr(k, n, p)\n"
+    "...     ax.plot(k, nbdtr_vals, label=rf\"$n={n},\\, p={p}$\",\n"
+    "...             ls=style)\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(\"$k$\")\n"
+    ">>> ax.set_title(\"Negative binomial cumulative distribution function\")\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The negative binomial distribution is also available as\n"
+    "`scipy.stats.nbinom`. Using `nbdtr` directly can be much faster than\n"
+    "calling the ``cdf`` method of `scipy.stats.nbinom`, especially for small\n"
+    "arrays or individual values. To get the same results one must use the\n"
+    "following parametrization: ``nbinom(n, p).cdf(k)=nbdtr(k, n, p)``.\n"
+    "\n"
+    ">>> from scipy.stats import nbinom\n"
+    ">>> k, n, p = 5, 3, 0.5\n"
+    ">>> nbdtr_res = nbdtr(k, n, p)  # this will often be faster than below\n"
+    ">>> stats_res = nbinom(n, p).cdf(k)\n"
+    ">>> stats_res, nbdtr_res  # test that results are equal\n"
+    "(0.85546875, 0.85546875)\n"
+    "\n"
+    "`nbdtr` can evaluate different parameter sets by providing arrays with\n"
+    "shapes compatible for broadcasting for `k`, `n` and `p`. Here we compute\n"
+    "the function for three different `k` at four locations `p`, resulting in\n"
+    "a 3x4 array.\n"
+    "\n"
+    ">>> k = np.array([[5], [10], [15]])\n"
+    ">>> p = np.array([0.3, 0.5, 0.7, 0.9])\n"
+    ">>> k.shape, p.shape\n"
+    "((3, 1), (4,))\n"
+    "\n"
+    ">>> nbdtr(k, 5, p)\n"
+    "array([[0.15026833, 0.62304687, 0.95265101, 0.9998531 ],\n"
+    "       [0.48450894, 0.94076538, 0.99932777, 0.99999999],\n"
+    "       [0.76249222, 0.99409103, 0.99999445, 1.        ]])")
+ufunc_nbdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_iid__As_lld_d
+ufunc_nbdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nbdtr_loops[2] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nbdtr_types[0] = <char>NPY_LONG
+ufunc_nbdtr_types[1] = <char>NPY_LONG
+ufunc_nbdtr_types[2] = <char>NPY_DOUBLE
+ufunc_nbdtr_types[3] = <char>NPY_DOUBLE
+ufunc_nbdtr_types[4] = <char>NPY_FLOAT
+ufunc_nbdtr_types[5] = <char>NPY_FLOAT
+ufunc_nbdtr_types[6] = <char>NPY_FLOAT
+ufunc_nbdtr_types[7] = <char>NPY_FLOAT
+ufunc_nbdtr_types[8] = <char>NPY_DOUBLE
+ufunc_nbdtr_types[9] = <char>NPY_DOUBLE
+ufunc_nbdtr_types[10] = <char>NPY_DOUBLE
+ufunc_nbdtr_types[11] = <char>NPY_DOUBLE
+ufunc_nbdtr_ptr[2*0] = <void*>_func_nbdtr
+ufunc_nbdtr_ptr[2*0+1] = <void*>(<char*>"nbdtr")
+ufunc_nbdtr_ptr[2*1] = <void*>_func_nbdtr_unsafe
+ufunc_nbdtr_ptr[2*1+1] = <void*>(<char*>"nbdtr")
+ufunc_nbdtr_ptr[2*2] = <void*>_func_nbdtr_unsafe
+ufunc_nbdtr_ptr[2*2+1] = <void*>(<char*>"nbdtr")
+ufunc_nbdtr_data[0] = &ufunc_nbdtr_ptr[2*0]
+ufunc_nbdtr_data[1] = &ufunc_nbdtr_ptr[2*1]
+ufunc_nbdtr_data[2] = &ufunc_nbdtr_ptr[2*2]
+nbdtr = np.PyUFunc_FromFuncAndData(ufunc_nbdtr_loops, ufunc_nbdtr_data, ufunc_nbdtr_types, 3, 3, 1, 0, "nbdtr", ufunc_nbdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nbdtrc_loops[3]
+cdef void *ufunc_nbdtrc_ptr[6]
+cdef void *ufunc_nbdtrc_data[3]
+cdef char ufunc_nbdtrc_types[12]
+cdef char *ufunc_nbdtrc_doc = (
+    "nbdtrc(k, n, p, out=None)\n"
+    "\n"
+    "Negative binomial survival function.\n"
+    "\n"
+    "Returns the sum of the terms `k + 1` to infinity of the negative binomial\n"
+    "distribution probability mass function,\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    F = \\sum_{j=k + 1}^\\infty {{n + j - 1}\\choose{j}} p^n (1 - p)^j.\n"
+    "\n"
+    "In a sequence of Bernoulli trials with individual success probabilities\n"
+    "`p`, this is the probability that more than `k` failures precede the nth\n"
+    "success.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    The maximum number of allowed failures (nonnegative int).\n"
+    "n : array_like\n"
+    "    The target number of successes (positive int).\n"
+    "p : array_like\n"
+    "    Probability of success in a single event (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "F : scalar or ndarray\n"
+    "    The probability of `k + 1` or more failures before `n` successes in a\n"
+    "    sequence of events with individual success probability `p`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "nbdtr : Negative binomial cumulative distribution function\n"
+    "nbdtrik : Negative binomial percentile function\n"
+    "scipy.stats.nbinom : Negative binomial distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "If floating point values are passed for `k` or `n`, they will be truncated\n"
+    "to integers.\n"
+    "\n"
+    "The terms are not summed directly; instead the regularized incomplete beta\n"
+    "function is employed, according to the formula,\n"
+    "\n"
+    ".. math::\n"
+    "    \\mathrm{nbdtrc}(k, n, p) = I_{1 - p}(k + 1, n).\n"
+    "\n"
+    "Wrapper for the Cephes [1]_ routine `nbdtrc`.\n"
+    "\n"
+    "The negative binomial distribution is also available as\n"
+    "`scipy.stats.nbinom`. Using `nbdtrc` directly can improve performance\n"
+    "compared to the ``sf`` method of `scipy.stats.nbinom` (see last example).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the function for ``k=10`` and ``n=5`` at ``p=0.5``.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import nbdtrc\n"
+    ">>> nbdtrc(10, 5, 0.5)\n"
+    "0.059234619140624986\n"
+    "\n"
+    "Compute the function for ``n=10`` and ``p=0.5`` at several points by\n"
+    "providing a NumPy array or list for `k`.\n"
+    "\n"
+    ">>> nbdtrc([5, 10, 15], 10, 0.5)\n"
+    "array([0.84912109, 0.41190147, 0.11476147])\n"
+    "\n"
+    "Plot the function for four different parameter sets.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> k = np.arange(130)\n"
+    ">>> n_parameters = [20, 20, 20, 80]\n"
+    ">>> p_parameters = [0.2, 0.5, 0.8, 0.5]\n"
+    ">>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']\n"
+    ">>> parameters_list = list(zip(p_parameters, n_parameters,\n"
+    "...                            linestyles))\n"
+    ">>> fig, ax = plt.subplots(figsize=(8, 8))\n"
+    ">>> for parameter_set in parameters_list:\n"
+    "...     p, n, style = parameter_set\n"
+    "...     nbdtrc_vals = nbdtrc(k, n, p)\n"
+    "...     ax.plot(k, nbdtrc_vals, label=rf\"$n={n},\\, p={p}$\",\n"
+    "...             ls=style)\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_xlabel(\"$k$\")\n"
+    ">>> ax.set_title(\"Negative binomial distribution survival function\")\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The negative binomial distribution is also available as\n"
+    "`scipy.stats.nbinom`. Using `nbdtrc` directly can be much faster than\n"
+    "calling the ``sf`` method of `scipy.stats.nbinom`, especially for small\n"
+    "arrays or individual values. To get the same results one must use the\n"
+    "following parametrization: ``nbinom(n, p).sf(k)=nbdtrc(k, n, p)``.\n"
+    "\n"
+    ">>> from scipy.stats import nbinom\n"
+    ">>> k, n, p = 3, 5, 0.5\n"
+    ">>> nbdtr_res = nbdtrc(k, n, p)  # this will often be faster than below\n"
+    ">>> stats_res = nbinom(n, p).sf(k)\n"
+    ">>> stats_res, nbdtr_res  # test that results are equal\n"
+    "(0.6367187499999999, 0.6367187499999999)\n"
+    "\n"
+    "`nbdtrc` can evaluate different parameter sets by providing arrays with\n"
+    "shapes compatible for broadcasting for `k`, `n` and `p`. Here we compute\n"
+    "the function for three different `k` at four locations `p`, resulting in\n"
+    "a 3x4 array.\n"
+    "\n"
+    ">>> k = np.array([[5], [10], [15]])\n"
+    ">>> p = np.array([0.3, 0.5, 0.7, 0.9])\n"
+    ">>> k.shape, p.shape\n"
+    "((3, 1), (4,))\n"
+    "\n"
+    ">>> nbdtrc(k, 5, p)\n"
+    "array([[8.49731667e-01, 3.76953125e-01, 4.73489874e-02, 1.46902600e-04],\n"
+    "       [5.15491059e-01, 5.92346191e-02, 6.72234070e-04, 9.29610100e-09],\n"
+    "       [2.37507779e-01, 5.90896606e-03, 5.55025308e-06, 3.26346760e-13]])")
+ufunc_nbdtrc_loops[0] = <np.PyUFuncGenericFunction>loop_d_iid__As_lld_d
+ufunc_nbdtrc_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nbdtrc_loops[2] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nbdtrc_types[0] = <char>NPY_LONG
+ufunc_nbdtrc_types[1] = <char>NPY_LONG
+ufunc_nbdtrc_types[2] = <char>NPY_DOUBLE
+ufunc_nbdtrc_types[3] = <char>NPY_DOUBLE
+ufunc_nbdtrc_types[4] = <char>NPY_FLOAT
+ufunc_nbdtrc_types[5] = <char>NPY_FLOAT
+ufunc_nbdtrc_types[6] = <char>NPY_FLOAT
+ufunc_nbdtrc_types[7] = <char>NPY_FLOAT
+ufunc_nbdtrc_types[8] = <char>NPY_DOUBLE
+ufunc_nbdtrc_types[9] = <char>NPY_DOUBLE
+ufunc_nbdtrc_types[10] = <char>NPY_DOUBLE
+ufunc_nbdtrc_types[11] = <char>NPY_DOUBLE
+ufunc_nbdtrc_ptr[2*0] = <void*>_func_nbdtrc
+ufunc_nbdtrc_ptr[2*0+1] = <void*>(<char*>"nbdtrc")
+ufunc_nbdtrc_ptr[2*1] = <void*>_func_nbdtrc_unsafe
+ufunc_nbdtrc_ptr[2*1+1] = <void*>(<char*>"nbdtrc")
+ufunc_nbdtrc_ptr[2*2] = <void*>_func_nbdtrc_unsafe
+ufunc_nbdtrc_ptr[2*2+1] = <void*>(<char*>"nbdtrc")
+ufunc_nbdtrc_data[0] = &ufunc_nbdtrc_ptr[2*0]
+ufunc_nbdtrc_data[1] = &ufunc_nbdtrc_ptr[2*1]
+ufunc_nbdtrc_data[2] = &ufunc_nbdtrc_ptr[2*2]
+nbdtrc = np.PyUFunc_FromFuncAndData(ufunc_nbdtrc_loops, ufunc_nbdtrc_data, ufunc_nbdtrc_types, 3, 3, 1, 0, "nbdtrc", ufunc_nbdtrc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nbdtri_loops[3]
+cdef void *ufunc_nbdtri_ptr[6]
+cdef void *ufunc_nbdtri_data[3]
+cdef char ufunc_nbdtri_types[12]
+cdef char *ufunc_nbdtri_doc = (
+    "nbdtri(k, n, y, out=None)\n"
+    "\n"
+    "Returns the inverse with respect to the parameter `p` of\n"
+    "`y = nbdtr(k, n, p)`, the negative binomial cumulative distribution\n"
+    "function.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    The maximum number of allowed failures (nonnegative int).\n"
+    "n : array_like\n"
+    "    The target number of successes (positive int).\n"
+    "y : array_like\n"
+    "    The probability of `k` or fewer failures before `n` successes (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "p : scalar or ndarray\n"
+    "    Probability of success in a single event (float) such that\n"
+    "    `nbdtr(k, n, p) = y`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "nbdtr : Cumulative distribution function of the negative binomial.\n"
+    "nbdtrc : Negative binomial survival function.\n"
+    "scipy.stats.nbinom : negative binomial distribution.\n"
+    "nbdtrik : Inverse with respect to `k` of `nbdtr(k, n, p)`.\n"
+    "nbdtrin : Inverse with respect to `n` of `nbdtr(k, n, p)`.\n"
+    "scipy.stats.nbinom : Negative binomial distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the Cephes [1]_ routine `nbdtri`.\n"
+    "\n"
+    "The negative binomial distribution is also available as\n"
+    "`scipy.stats.nbinom`. Using `nbdtri` directly can improve performance\n"
+    "compared to the ``ppf`` method of `scipy.stats.nbinom`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "`nbdtri` is the inverse of `nbdtr` with respect to `p`.\n"
+    "Up to floating point errors the following holds:\n"
+    "``nbdtri(k, n, nbdtr(k, n, p))=p``.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import nbdtri, nbdtr\n"
+    ">>> k, n, y = 5, 10, 0.2\n"
+    ">>> cdf_val = nbdtr(k, n, y)\n"
+    ">>> nbdtri(k, n, cdf_val)\n"
+    "0.20000000000000004\n"
+    "\n"
+    "Compute the function for ``k=10`` and ``n=5`` at several points by\n"
+    "providing a NumPy array or list for `y`.\n"
+    "\n"
+    ">>> y = np.array([0.1, 0.4, 0.8])\n"
+    ">>> nbdtri(3, 5, y)\n"
+    "array([0.34462319, 0.51653095, 0.69677416])\n"
+    "\n"
+    "Plot the function for three different parameter sets.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> n_parameters = [5, 20, 30, 30]\n"
+    ">>> k_parameters = [20, 20, 60, 80]\n"
+    ">>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']\n"
+    ">>> parameters_list = list(zip(n_parameters, k_parameters, linestyles))\n"
+    ">>> cdf_vals = np.linspace(0, 1, 1000)\n"
+    ">>> fig, ax = plt.subplots(figsize=(8, 8))\n"
+    ">>> for parameter_set in parameters_list:\n"
+    "...     n, k, style = parameter_set\n"
+    "...     nbdtri_vals = nbdtri(k, n, cdf_vals)\n"
+    "...     ax.plot(cdf_vals, nbdtri_vals, label=rf\"$k={k},\\ n={n}$\",\n"
+    "...             ls=style)\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_ylabel(\"$p$\")\n"
+    ">>> ax.set_xlabel(\"$CDF$\")\n"
+    ">>> title = \"nbdtri: inverse of negative binomial CDF with respect to $p$\"\n"
+    ">>> ax.set_title(title)\n"
+    ">>> plt.show()\n"
+    "\n"
+    "`nbdtri` can evaluate different parameter sets by providing arrays with\n"
+    "shapes compatible for broadcasting for `k`, `n` and `p`. Here we compute\n"
+    "the function for three different `k` at four locations `p`, resulting in\n"
+    "a 3x4 array.\n"
+    "\n"
+    ">>> k = np.array([[5], [10], [15]])\n"
+    ">>> y = np.array([0.3, 0.5, 0.7, 0.9])\n"
+    ">>> k.shape, y.shape\n"
+    "((3, 1), (4,))\n"
+    "\n"
+    ">>> nbdtri(k, 5, y)\n"
+    "array([[0.37258157, 0.45169416, 0.53249956, 0.64578407],\n"
+    "       [0.24588501, 0.30451981, 0.36778453, 0.46397088],\n"
+    "       [0.18362101, 0.22966758, 0.28054743, 0.36066188]])")
+ufunc_nbdtri_loops[0] = <np.PyUFuncGenericFunction>loop_d_iid__As_lld_d
+ufunc_nbdtri_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nbdtri_loops[2] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nbdtri_types[0] = <char>NPY_LONG
+ufunc_nbdtri_types[1] = <char>NPY_LONG
+ufunc_nbdtri_types[2] = <char>NPY_DOUBLE
+ufunc_nbdtri_types[3] = <char>NPY_DOUBLE
+ufunc_nbdtri_types[4] = <char>NPY_FLOAT
+ufunc_nbdtri_types[5] = <char>NPY_FLOAT
+ufunc_nbdtri_types[6] = <char>NPY_FLOAT
+ufunc_nbdtri_types[7] = <char>NPY_FLOAT
+ufunc_nbdtri_types[8] = <char>NPY_DOUBLE
+ufunc_nbdtri_types[9] = <char>NPY_DOUBLE
+ufunc_nbdtri_types[10] = <char>NPY_DOUBLE
+ufunc_nbdtri_types[11] = <char>NPY_DOUBLE
+ufunc_nbdtri_ptr[2*0] = <void*>_func_nbdtri
+ufunc_nbdtri_ptr[2*0+1] = <void*>(<char*>"nbdtri")
+ufunc_nbdtri_ptr[2*1] = <void*>_func_nbdtri_unsafe
+ufunc_nbdtri_ptr[2*1+1] = <void*>(<char*>"nbdtri")
+ufunc_nbdtri_ptr[2*2] = <void*>_func_nbdtri_unsafe
+ufunc_nbdtri_ptr[2*2+1] = <void*>(<char*>"nbdtri")
+ufunc_nbdtri_data[0] = &ufunc_nbdtri_ptr[2*0]
+ufunc_nbdtri_data[1] = &ufunc_nbdtri_ptr[2*1]
+ufunc_nbdtri_data[2] = &ufunc_nbdtri_ptr[2*2]
+nbdtri = np.PyUFunc_FromFuncAndData(ufunc_nbdtri_loops, ufunc_nbdtri_data, ufunc_nbdtri_types, 3, 3, 1, 0, "nbdtri", ufunc_nbdtri_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nbdtrik_loops[2]
+cdef void *ufunc_nbdtrik_ptr[4]
+cdef void *ufunc_nbdtrik_data[2]
+cdef char ufunc_nbdtrik_types[8]
+cdef char *ufunc_nbdtrik_doc = (
+    "nbdtrik(y, n, p, out=None)\n"
+    "\n"
+    "Negative binomial percentile function.\n"
+    "\n"
+    "Returns the inverse with respect to the parameter `k` of\n"
+    "`y = nbdtr(k, n, p)`, the negative binomial cumulative distribution\n"
+    "function.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "y : array_like\n"
+    "    The probability of `k` or fewer failures before `n` successes (float).\n"
+    "n : array_like\n"
+    "    The target number of successes (positive int).\n"
+    "p : array_like\n"
+    "    Probability of success in a single event (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "k : scalar or ndarray\n"
+    "    The maximum number of allowed failures such that `nbdtr(k, n, p) = y`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "nbdtr : Cumulative distribution function of the negative binomial.\n"
+    "nbdtrc : Survival function of the negative binomial.\n"
+    "nbdtri : Inverse with respect to `p` of `nbdtr(k, n, p)`.\n"
+    "nbdtrin : Inverse with respect to `n` of `nbdtr(k, n, p)`.\n"
+    "scipy.stats.nbinom : Negative binomial distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the CDFLIB [1]_ Fortran routine `cdfnbn`.\n"
+    "\n"
+    "Formula 26.5.26 of [2]_,\n"
+    "\n"
+    ".. math::\n"
+    "    \\sum_{j=k + 1}^\\infty {{n + j - 1}\n"
+    "    \\choose{j}} p^n (1 - p)^j = I_{1 - p}(k + 1, n),\n"
+    "\n"
+    "is used to reduce calculation of the cumulative distribution function to\n"
+    "that of a regularized incomplete beta :math:`I`.\n"
+    "\n"
+    "Computation of `k` involves a search for a value that produces the desired\n"
+    "value of `y`.  The search relies on the monotonicity of `y` with `k`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.\n"
+    ".. [2] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the negative binomial cumulative distribution function for an\n"
+    "exemplary parameter set.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import nbdtr, nbdtrik\n"
+    ">>> k, n, p = 5, 2, 0.5\n"
+    ">>> cdf_value = nbdtr(k, n, p)\n"
+    ">>> cdf_value\n"
+    "0.9375\n"
+    "\n"
+    "Verify that `nbdtrik` recovers the original value for `k`.\n"
+    "\n"
+    ">>> nbdtrik(cdf_value, n, p)\n"
+    "5.0\n"
+    "\n"
+    "Plot the function for different parameter sets.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> p_parameters = [0.2, 0.5, 0.7, 0.5]\n"
+    ">>> n_parameters = [30, 30, 30, 80]\n"
+    ">>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']\n"
+    ">>> parameters_list = list(zip(p_parameters, n_parameters, linestyles))\n"
+    ">>> cdf_vals = np.linspace(0, 1, 1000)\n"
+    ">>> fig, ax = plt.subplots(figsize=(8, 8))\n"
+    ">>> for parameter_set in parameters_list:\n"
+    "...     p, n, style = parameter_set\n"
+    "...     nbdtrik_vals = nbdtrik(cdf_vals, n, p)\n"
+    "...     ax.plot(cdf_vals, nbdtrik_vals, label=rf\"$n={n},\\ p={p}$\",\n"
+    "...             ls=style)\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_ylabel(\"$k$\")\n"
+    ">>> ax.set_xlabel(\"$CDF$\")\n"
+    ">>> ax.set_title(\"Negative binomial percentile function\")\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The negative binomial distribution is also available as\n"
+    "`scipy.stats.nbinom`. The percentile function  method ``ppf``\n"
+    "returns the result of `nbdtrik` rounded up to integers:\n"
+    "\n"
+    ">>> from scipy.stats import nbinom\n"
+    ">>> q, n, p = 0.6, 5, 0.5\n"
+    ">>> nbinom.ppf(q, n, p), nbdtrik(q, n, p)\n"
+    "(5.0, 4.800428460273882)")
+ufunc_nbdtrik_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nbdtrik_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nbdtrik_types[0] = <char>NPY_FLOAT
+ufunc_nbdtrik_types[1] = <char>NPY_FLOAT
+ufunc_nbdtrik_types[2] = <char>NPY_FLOAT
+ufunc_nbdtrik_types[3] = <char>NPY_FLOAT
+ufunc_nbdtrik_types[4] = <char>NPY_DOUBLE
+ufunc_nbdtrik_types[5] = <char>NPY_DOUBLE
+ufunc_nbdtrik_types[6] = <char>NPY_DOUBLE
+ufunc_nbdtrik_types[7] = <char>NPY_DOUBLE
+ufunc_nbdtrik_ptr[2*0] = <void*>_func_nbdtrik
+ufunc_nbdtrik_ptr[2*0+1] = <void*>(<char*>"nbdtrik")
+ufunc_nbdtrik_ptr[2*1] = <void*>_func_nbdtrik
+ufunc_nbdtrik_ptr[2*1+1] = <void*>(<char*>"nbdtrik")
+ufunc_nbdtrik_data[0] = &ufunc_nbdtrik_ptr[2*0]
+ufunc_nbdtrik_data[1] = &ufunc_nbdtrik_ptr[2*1]
+nbdtrik = np.PyUFunc_FromFuncAndData(ufunc_nbdtrik_loops, ufunc_nbdtrik_data, ufunc_nbdtrik_types, 2, 3, 1, 0, "nbdtrik", ufunc_nbdtrik_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nbdtrin_loops[2]
+cdef void *ufunc_nbdtrin_ptr[4]
+cdef void *ufunc_nbdtrin_data[2]
+cdef char ufunc_nbdtrin_types[8]
+cdef char *ufunc_nbdtrin_doc = (
+    "nbdtrin(k, y, p, out=None)\n"
+    "\n"
+    "Inverse of `nbdtr` vs `n`.\n"
+    "\n"
+    "Returns the inverse with respect to the parameter `n` of\n"
+    "`y = nbdtr(k, n, p)`, the negative binomial cumulative distribution\n"
+    "function.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    The maximum number of allowed failures (nonnegative int).\n"
+    "y : array_like\n"
+    "    The probability of `k` or fewer failures before `n` successes (float).\n"
+    "p : array_like\n"
+    "    Probability of success in a single event (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "n : scalar or ndarray\n"
+    "    The number of successes `n` such that `nbdtr(k, n, p) = y`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "nbdtr : Cumulative distribution function of the negative binomial.\n"
+    "nbdtri : Inverse with respect to `p` of `nbdtr(k, n, p)`.\n"
+    "nbdtrik : Inverse with respect to `k` of `nbdtr(k, n, p)`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the CDFLIB [1]_ Fortran routine `cdfnbn`.\n"
+    "\n"
+    "Formula 26.5.26 of [2]_,\n"
+    "\n"
+    ".. math::\n"
+    "    \\sum_{j=k + 1}^\\infty {{n + j - 1}\n"
+    "    \\choose{j}} p^n (1 - p)^j = I_{1 - p}(k + 1, n),\n"
+    "\n"
+    "is used to reduce calculation of the cumulative distribution function to\n"
+    "that of a regularized incomplete beta :math:`I`.\n"
+    "\n"
+    "Computation of `n` involves a search for a value that produces the desired\n"
+    "value of `y`.  The search relies on the monotonicity of `y` with `n`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.\n"
+    ".. [2] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the negative binomial cumulative distribution function for an\n"
+    "exemplary parameter set.\n"
+    "\n"
+    ">>> from scipy.special import nbdtr, nbdtrin\n"
+    ">>> k, n, p = 5, 2, 0.5\n"
+    ">>> cdf_value = nbdtr(k, n, p)\n"
+    ">>> cdf_value\n"
+    "0.9375\n"
+    "\n"
+    "Verify that `nbdtrin` recovers the original value for `n` up to floating\n"
+    "point accuracy.\n"
+    "\n"
+    ">>> nbdtrin(k, cdf_value, p)\n"
+    "1.999999999998137")
+ufunc_nbdtrin_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nbdtrin_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nbdtrin_types[0] = <char>NPY_FLOAT
+ufunc_nbdtrin_types[1] = <char>NPY_FLOAT
+ufunc_nbdtrin_types[2] = <char>NPY_FLOAT
+ufunc_nbdtrin_types[3] = <char>NPY_FLOAT
+ufunc_nbdtrin_types[4] = <char>NPY_DOUBLE
+ufunc_nbdtrin_types[5] = <char>NPY_DOUBLE
+ufunc_nbdtrin_types[6] = <char>NPY_DOUBLE
+ufunc_nbdtrin_types[7] = <char>NPY_DOUBLE
+ufunc_nbdtrin_ptr[2*0] = <void*>_func_nbdtrin
+ufunc_nbdtrin_ptr[2*0+1] = <void*>(<char*>"nbdtrin")
+ufunc_nbdtrin_ptr[2*1] = <void*>_func_nbdtrin
+ufunc_nbdtrin_ptr[2*1+1] = <void*>(<char*>"nbdtrin")
+ufunc_nbdtrin_data[0] = &ufunc_nbdtrin_ptr[2*0]
+ufunc_nbdtrin_data[1] = &ufunc_nbdtrin_ptr[2*1]
+nbdtrin = np.PyUFunc_FromFuncAndData(ufunc_nbdtrin_loops, ufunc_nbdtrin_data, ufunc_nbdtrin_types, 2, 3, 1, 0, "nbdtrin", ufunc_nbdtrin_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ncfdtr_loops[2]
+cdef void *ufunc_ncfdtr_ptr[4]
+cdef void *ufunc_ncfdtr_data[2]
+cdef char ufunc_ncfdtr_types[10]
+cdef char *ufunc_ncfdtr_doc = (
+    "ncfdtr(dfn, dfd, nc, f, out=None)\n"
+    "\n"
+    "Cumulative distribution function of the non-central F distribution.\n"
+    "\n"
+    "The non-central F describes the distribution of,\n"
+    "\n"
+    ".. math::\n"
+    "    Z = \\frac{X/d_n}{Y/d_d}\n"
+    "\n"
+    "where :math:`X` and :math:`Y` are independently distributed, with\n"
+    ":math:`X` distributed non-central :math:`\\chi^2` with noncentrality\n"
+    "parameter `nc` and :math:`d_n` degrees of freedom, and :math:`Y`\n"
+    "distributed :math:`\\chi^2` with :math:`d_d` degrees of freedom.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "dfn : array_like\n"
+    "    Degrees of freedom of the numerator sum of squares.  Range (0, inf).\n"
+    "dfd : array_like\n"
+    "    Degrees of freedom of the denominator sum of squares.  Range (0, inf).\n"
+    "nc : array_like\n"
+    "    Noncentrality parameter.  Should be in range (0, 1e4).\n"
+    "f : array_like\n"
+    "    Quantiles, i.e. the upper limit of integration.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "cdf : scalar or ndarray\n"
+    "    The calculated CDF.  If all inputs are scalar, the return will be a\n"
+    "    float.  Otherwise it will be an array.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ncfdtri : Quantile function; inverse of `ncfdtr` with respect to `f`.\n"
+    "ncfdtridfd : Inverse of `ncfdtr` with respect to `dfd`.\n"
+    "ncfdtridfn : Inverse of `ncfdtr` with respect to `dfn`.\n"
+    "ncfdtrinc : Inverse of `ncfdtr` with respect to `nc`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the CDFLIB [1]_ Fortran routine `cdffnc`.\n"
+    "\n"
+    "The cumulative distribution function is computed using Formula 26.6.20 of\n"
+    "[2]_:\n"
+    "\n"
+    ".. math::\n"
+    "    F(d_n, d_d, n_c, f) = \\sum_{j=0}^\\infty e^{-n_c/2}\n"
+    "    \\frac{(n_c/2)^j}{j!} I_{x}(\\frac{d_n}{2} + j, \\frac{d_d}{2}),\n"
+    "\n"
+    "where :math:`I` is the regularized incomplete beta function, and\n"
+    ":math:`x = f d_n/(f d_n + d_d)`.\n"
+    "\n"
+    "The computation time required for this routine is proportional to the\n"
+    "noncentrality parameter `nc`.  Very large values of this parameter can\n"
+    "consume immense computer resources.  This is why the search range is\n"
+    "bounded by 10,000.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Barry Brown, James Lovato, and Kathy Russell,\n"
+    "       CDFLIB: Library of Fortran Routines for Cumulative Distribution\n"
+    "       Functions, Inverses, and Other Parameters.\n"
+    ".. [2] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    ">>> from scipy import stats\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    "\n"
+    "Plot the CDF of the non-central F distribution, for nc=0.  Compare with the\n"
+    "F-distribution from scipy.stats:\n"
+    "\n"
+    ">>> x = np.linspace(-1, 8, num=500)\n"
+    ">>> dfn = 3\n"
+    ">>> dfd = 2\n"
+    ">>> ncf_stats = stats.f.cdf(x, dfn, dfd)\n"
+    ">>> ncf_special = special.ncfdtr(dfn, dfd, 0, x)\n"
+    "\n"
+    ">>> fig = plt.figure()\n"
+    ">>> ax = fig.add_subplot(111)\n"
+    ">>> ax.plot(x, ncf_stats, 'b-', lw=3)\n"
+    ">>> ax.plot(x, ncf_special, 'r-')\n"
+    ">>> plt.show()")
+ufunc_ncfdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd__As_ffff_f
+ufunc_ncfdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd__As_dddd_d
+ufunc_ncfdtr_types[0] = <char>NPY_FLOAT
+ufunc_ncfdtr_types[1] = <char>NPY_FLOAT
+ufunc_ncfdtr_types[2] = <char>NPY_FLOAT
+ufunc_ncfdtr_types[3] = <char>NPY_FLOAT
+ufunc_ncfdtr_types[4] = <char>NPY_FLOAT
+ufunc_ncfdtr_types[5] = <char>NPY_DOUBLE
+ufunc_ncfdtr_types[6] = <char>NPY_DOUBLE
+ufunc_ncfdtr_types[7] = <char>NPY_DOUBLE
+ufunc_ncfdtr_types[8] = <char>NPY_DOUBLE
+ufunc_ncfdtr_types[9] = <char>NPY_DOUBLE
+ufunc_ncfdtr_ptr[2*0] = <void*>_func_ncfdtr
+ufunc_ncfdtr_ptr[2*0+1] = <void*>(<char*>"ncfdtr")
+ufunc_ncfdtr_ptr[2*1] = <void*>_func_ncfdtr
+ufunc_ncfdtr_ptr[2*1+1] = <void*>(<char*>"ncfdtr")
+ufunc_ncfdtr_data[0] = &ufunc_ncfdtr_ptr[2*0]
+ufunc_ncfdtr_data[1] = &ufunc_ncfdtr_ptr[2*1]
+ncfdtr = np.PyUFunc_FromFuncAndData(ufunc_ncfdtr_loops, ufunc_ncfdtr_data, ufunc_ncfdtr_types, 2, 4, 1, 0, "ncfdtr", ufunc_ncfdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ncfdtri_loops[2]
+cdef void *ufunc_ncfdtri_ptr[4]
+cdef void *ufunc_ncfdtri_data[2]
+cdef char ufunc_ncfdtri_types[10]
+cdef char *ufunc_ncfdtri_doc = (
+    "ncfdtri(dfn, dfd, nc, p, out=None)\n"
+    "\n"
+    "Inverse with respect to `f` of the CDF of the non-central F distribution.\n"
+    "\n"
+    "See `ncfdtr` for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "dfn : array_like\n"
+    "    Degrees of freedom of the numerator sum of squares.  Range (0, inf).\n"
+    "dfd : array_like\n"
+    "    Degrees of freedom of the denominator sum of squares.  Range (0, inf).\n"
+    "nc : array_like\n"
+    "    Noncentrality parameter.  Should be in range (0, 1e4).\n"
+    "p : array_like\n"
+    "    Value of the cumulative distribution function.  Must be in the\n"
+    "    range [0, 1].\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "f : scalar or ndarray\n"
+    "    Quantiles, i.e., the upper limit of integration.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ncfdtr : CDF of the non-central F distribution.\n"
+    "ncfdtridfd : Inverse of `ncfdtr` with respect to `dfd`.\n"
+    "ncfdtridfn : Inverse of `ncfdtr` with respect to `dfn`.\n"
+    "ncfdtrinc : Inverse of `ncfdtr` with respect to `nc`.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import ncfdtr, ncfdtri\n"
+    "\n"
+    "Compute the CDF for several values of `f`:\n"
+    "\n"
+    ">>> f = [0.5, 1, 1.5]\n"
+    ">>> p = ncfdtr(2, 3, 1.5, f)\n"
+    ">>> p\n"
+    "array([ 0.20782291,  0.36107392,  0.47345752])\n"
+    "\n"
+    "Compute the inverse.  We recover the values of `f`, as expected:\n"
+    "\n"
+    ">>> ncfdtri(2, 3, 1.5, p)\n"
+    "array([ 0.5,  1. ,  1.5])")
+ufunc_ncfdtri_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd__As_ffff_f
+ufunc_ncfdtri_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd__As_dddd_d
+ufunc_ncfdtri_types[0] = <char>NPY_FLOAT
+ufunc_ncfdtri_types[1] = <char>NPY_FLOAT
+ufunc_ncfdtri_types[2] = <char>NPY_FLOAT
+ufunc_ncfdtri_types[3] = <char>NPY_FLOAT
+ufunc_ncfdtri_types[4] = <char>NPY_FLOAT
+ufunc_ncfdtri_types[5] = <char>NPY_DOUBLE
+ufunc_ncfdtri_types[6] = <char>NPY_DOUBLE
+ufunc_ncfdtri_types[7] = <char>NPY_DOUBLE
+ufunc_ncfdtri_types[8] = <char>NPY_DOUBLE
+ufunc_ncfdtri_types[9] = <char>NPY_DOUBLE
+ufunc_ncfdtri_ptr[2*0] = <void*>_func_ncfdtri
+ufunc_ncfdtri_ptr[2*0+1] = <void*>(<char*>"ncfdtri")
+ufunc_ncfdtri_ptr[2*1] = <void*>_func_ncfdtri
+ufunc_ncfdtri_ptr[2*1+1] = <void*>(<char*>"ncfdtri")
+ufunc_ncfdtri_data[0] = &ufunc_ncfdtri_ptr[2*0]
+ufunc_ncfdtri_data[1] = &ufunc_ncfdtri_ptr[2*1]
+ncfdtri = np.PyUFunc_FromFuncAndData(ufunc_ncfdtri_loops, ufunc_ncfdtri_data, ufunc_ncfdtri_types, 2, 4, 1, 0, "ncfdtri", ufunc_ncfdtri_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ncfdtridfd_loops[2]
+cdef void *ufunc_ncfdtridfd_ptr[4]
+cdef void *ufunc_ncfdtridfd_data[2]
+cdef char ufunc_ncfdtridfd_types[10]
+cdef char *ufunc_ncfdtridfd_doc = (
+    "ncfdtridfd(dfn, p, nc, f, out=None)\n"
+    "\n"
+    "Calculate degrees of freedom (denominator) for the noncentral F-distribution.\n"
+    "\n"
+    "This is the inverse with respect to `dfd` of `ncfdtr`.\n"
+    "See `ncfdtr` for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "dfn : array_like\n"
+    "    Degrees of freedom of the numerator sum of squares.  Range (0, inf).\n"
+    "p : array_like\n"
+    "    Value of the cumulative distribution function.  Must be in the\n"
+    "    range [0, 1].\n"
+    "nc : array_like\n"
+    "    Noncentrality parameter.  Should be in range (0, 1e4).\n"
+    "f : array_like\n"
+    "    Quantiles, i.e., the upper limit of integration.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "dfd : scalar or ndarray\n"
+    "    Degrees of freedom of the denominator sum of squares.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ncfdtr : CDF of the non-central F distribution.\n"
+    "ncfdtri : Quantile function; inverse of `ncfdtr` with respect to `f`.\n"
+    "ncfdtridfn : Inverse of `ncfdtr` with respect to `dfn`.\n"
+    "ncfdtrinc : Inverse of `ncfdtr` with respect to `nc`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The value of the cumulative noncentral F distribution is not necessarily\n"
+    "monotone in either degrees of freedom. There thus may be two values that\n"
+    "provide a given CDF value. This routine assumes monotonicity and will\n"
+    "find an arbitrary one of the two values.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import ncfdtr, ncfdtridfd\n"
+    "\n"
+    "Compute the CDF for several values of `dfd`:\n"
+    "\n"
+    ">>> dfd = [1, 2, 3]\n"
+    ">>> p = ncfdtr(2, dfd, 0.25, 15)\n"
+    ">>> p\n"
+    "array([ 0.8097138 ,  0.93020416,  0.96787852])\n"
+    "\n"
+    "Compute the inverse.  We recover the values of `dfd`, as expected:\n"
+    "\n"
+    ">>> ncfdtridfd(2, p, 0.25, 15)\n"
+    "array([ 1.,  2.,  3.])")
+ufunc_ncfdtridfd_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd__As_ffff_f
+ufunc_ncfdtridfd_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd__As_dddd_d
+ufunc_ncfdtridfd_types[0] = <char>NPY_FLOAT
+ufunc_ncfdtridfd_types[1] = <char>NPY_FLOAT
+ufunc_ncfdtridfd_types[2] = <char>NPY_FLOAT
+ufunc_ncfdtridfd_types[3] = <char>NPY_FLOAT
+ufunc_ncfdtridfd_types[4] = <char>NPY_FLOAT
+ufunc_ncfdtridfd_types[5] = <char>NPY_DOUBLE
+ufunc_ncfdtridfd_types[6] = <char>NPY_DOUBLE
+ufunc_ncfdtridfd_types[7] = <char>NPY_DOUBLE
+ufunc_ncfdtridfd_types[8] = <char>NPY_DOUBLE
+ufunc_ncfdtridfd_types[9] = <char>NPY_DOUBLE
+ufunc_ncfdtridfd_ptr[2*0] = <void*>_func_ncfdtridfd
+ufunc_ncfdtridfd_ptr[2*0+1] = <void*>(<char*>"ncfdtridfd")
+ufunc_ncfdtridfd_ptr[2*1] = <void*>_func_ncfdtridfd
+ufunc_ncfdtridfd_ptr[2*1+1] = <void*>(<char*>"ncfdtridfd")
+ufunc_ncfdtridfd_data[0] = &ufunc_ncfdtridfd_ptr[2*0]
+ufunc_ncfdtridfd_data[1] = &ufunc_ncfdtridfd_ptr[2*1]
+ncfdtridfd = np.PyUFunc_FromFuncAndData(ufunc_ncfdtridfd_loops, ufunc_ncfdtridfd_data, ufunc_ncfdtridfd_types, 2, 4, 1, 0, "ncfdtridfd", ufunc_ncfdtridfd_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ncfdtridfn_loops[2]
+cdef void *ufunc_ncfdtridfn_ptr[4]
+cdef void *ufunc_ncfdtridfn_data[2]
+cdef char ufunc_ncfdtridfn_types[10]
+cdef char *ufunc_ncfdtridfn_doc = (
+    "ncfdtridfn(p, dfd, nc, f, out=None)\n"
+    "\n"
+    "Calculate degrees of freedom (numerator) for the noncentral F-distribution.\n"
+    "\n"
+    "This is the inverse with respect to `dfn` of `ncfdtr`.\n"
+    "See `ncfdtr` for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    Value of the cumulative distribution function. Must be in the\n"
+    "    range [0, 1].\n"
+    "dfd : array_like\n"
+    "    Degrees of freedom of the denominator sum of squares. Range (0, inf).\n"
+    "nc : array_like\n"
+    "    Noncentrality parameter.  Should be in range (0, 1e4).\n"
+    "f : float\n"
+    "    Quantiles, i.e., the upper limit of integration.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "dfn : scalar or ndarray\n"
+    "    Degrees of freedom of the numerator sum of squares.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ncfdtr : CDF of the non-central F distribution.\n"
+    "ncfdtri : Quantile function; inverse of `ncfdtr` with respect to `f`.\n"
+    "ncfdtridfd : Inverse of `ncfdtr` with respect to `dfd`.\n"
+    "ncfdtrinc : Inverse of `ncfdtr` with respect to `nc`.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The value of the cumulative noncentral F distribution is not necessarily\n"
+    "monotone in either degrees of freedom. There thus may be two values that\n"
+    "provide a given CDF value. This routine assumes monotonicity and will\n"
+    "find an arbitrary one of the two values.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import ncfdtr, ncfdtridfn\n"
+    "\n"
+    "Compute the CDF for several values of `dfn`:\n"
+    "\n"
+    ">>> dfn = [1, 2, 3]\n"
+    ">>> p = ncfdtr(dfn, 2, 0.25, 15)\n"
+    ">>> p\n"
+    "array([ 0.92562363,  0.93020416,  0.93188394])\n"
+    "\n"
+    "Compute the inverse. We recover the values of `dfn`, as expected:\n"
+    "\n"
+    ">>> ncfdtridfn(p, 2, 0.25, 15)\n"
+    "array([ 1.,  2.,  3.])")
+ufunc_ncfdtridfn_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd__As_ffff_f
+ufunc_ncfdtridfn_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd__As_dddd_d
+ufunc_ncfdtridfn_types[0] = <char>NPY_FLOAT
+ufunc_ncfdtridfn_types[1] = <char>NPY_FLOAT
+ufunc_ncfdtridfn_types[2] = <char>NPY_FLOAT
+ufunc_ncfdtridfn_types[3] = <char>NPY_FLOAT
+ufunc_ncfdtridfn_types[4] = <char>NPY_FLOAT
+ufunc_ncfdtridfn_types[5] = <char>NPY_DOUBLE
+ufunc_ncfdtridfn_types[6] = <char>NPY_DOUBLE
+ufunc_ncfdtridfn_types[7] = <char>NPY_DOUBLE
+ufunc_ncfdtridfn_types[8] = <char>NPY_DOUBLE
+ufunc_ncfdtridfn_types[9] = <char>NPY_DOUBLE
+ufunc_ncfdtridfn_ptr[2*0] = <void*>_func_ncfdtridfn
+ufunc_ncfdtridfn_ptr[2*0+1] = <void*>(<char*>"ncfdtridfn")
+ufunc_ncfdtridfn_ptr[2*1] = <void*>_func_ncfdtridfn
+ufunc_ncfdtridfn_ptr[2*1+1] = <void*>(<char*>"ncfdtridfn")
+ufunc_ncfdtridfn_data[0] = &ufunc_ncfdtridfn_ptr[2*0]
+ufunc_ncfdtridfn_data[1] = &ufunc_ncfdtridfn_ptr[2*1]
+ncfdtridfn = np.PyUFunc_FromFuncAndData(ufunc_ncfdtridfn_loops, ufunc_ncfdtridfn_data, ufunc_ncfdtridfn_types, 2, 4, 1, 0, "ncfdtridfn", ufunc_ncfdtridfn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ncfdtrinc_loops[2]
+cdef void *ufunc_ncfdtrinc_ptr[4]
+cdef void *ufunc_ncfdtrinc_data[2]
+cdef char ufunc_ncfdtrinc_types[10]
+cdef char *ufunc_ncfdtrinc_doc = (
+    "ncfdtrinc(dfn, dfd, p, f, out=None)\n"
+    "\n"
+    "Calculate non-centrality parameter for non-central F distribution.\n"
+    "\n"
+    "This is the inverse with respect to `nc` of `ncfdtr`.\n"
+    "See `ncfdtr` for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "dfn : array_like\n"
+    "    Degrees of freedom of the numerator sum of squares. Range (0, inf).\n"
+    "dfd : array_like\n"
+    "    Degrees of freedom of the denominator sum of squares. Range (0, inf).\n"
+    "p : array_like\n"
+    "    Value of the cumulative distribution function. Must be in the\n"
+    "    range [0, 1].\n"
+    "f : array_like\n"
+    "    Quantiles, i.e., the upper limit of integration.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "nc : scalar or ndarray\n"
+    "    Noncentrality parameter.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ncfdtr : CDF of the non-central F distribution.\n"
+    "ncfdtri : Quantile function; inverse of `ncfdtr` with respect to `f`.\n"
+    "ncfdtridfd : Inverse of `ncfdtr` with respect to `dfd`.\n"
+    "ncfdtridfn : Inverse of `ncfdtr` with respect to `dfn`.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import ncfdtr, ncfdtrinc\n"
+    "\n"
+    "Compute the CDF for several values of `nc`:\n"
+    "\n"
+    ">>> nc = [0.5, 1.5, 2.0]\n"
+    ">>> p = ncfdtr(2, 3, nc, 15)\n"
+    ">>> p\n"
+    "array([ 0.96309246,  0.94327955,  0.93304098])\n"
+    "\n"
+    "Compute the inverse. We recover the values of `nc`, as expected:\n"
+    "\n"
+    ">>> ncfdtrinc(2, 3, p, 15)\n"
+    "array([ 0.5,  1.5,  2. ])")
+ufunc_ncfdtrinc_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd__As_ffff_f
+ufunc_ncfdtrinc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd__As_dddd_d
+ufunc_ncfdtrinc_types[0] = <char>NPY_FLOAT
+ufunc_ncfdtrinc_types[1] = <char>NPY_FLOAT
+ufunc_ncfdtrinc_types[2] = <char>NPY_FLOAT
+ufunc_ncfdtrinc_types[3] = <char>NPY_FLOAT
+ufunc_ncfdtrinc_types[4] = <char>NPY_FLOAT
+ufunc_ncfdtrinc_types[5] = <char>NPY_DOUBLE
+ufunc_ncfdtrinc_types[6] = <char>NPY_DOUBLE
+ufunc_ncfdtrinc_types[7] = <char>NPY_DOUBLE
+ufunc_ncfdtrinc_types[8] = <char>NPY_DOUBLE
+ufunc_ncfdtrinc_types[9] = <char>NPY_DOUBLE
+ufunc_ncfdtrinc_ptr[2*0] = <void*>_func_ncfdtrinc
+ufunc_ncfdtrinc_ptr[2*0+1] = <void*>(<char*>"ncfdtrinc")
+ufunc_ncfdtrinc_ptr[2*1] = <void*>_func_ncfdtrinc
+ufunc_ncfdtrinc_ptr[2*1+1] = <void*>(<char*>"ncfdtrinc")
+ufunc_ncfdtrinc_data[0] = &ufunc_ncfdtrinc_ptr[2*0]
+ufunc_ncfdtrinc_data[1] = &ufunc_ncfdtrinc_ptr[2*1]
+ncfdtrinc = np.PyUFunc_FromFuncAndData(ufunc_ncfdtrinc_loops, ufunc_ncfdtrinc_data, ufunc_ncfdtrinc_types, 2, 4, 1, 0, "ncfdtrinc", ufunc_ncfdtrinc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nctdtr_loops[2]
+cdef void *ufunc_nctdtr_ptr[4]
+cdef void *ufunc_nctdtr_data[2]
+cdef char ufunc_nctdtr_types[8]
+cdef char *ufunc_nctdtr_doc = (
+    "nctdtr(df, nc, t, out=None)\n"
+    "\n"
+    "Cumulative distribution function of the non-central `t` distribution.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "df : array_like\n"
+    "    Degrees of freedom of the distribution. Should be in range (0, inf).\n"
+    "nc : array_like\n"
+    "    Noncentrality parameter. Should be in range (-1e6, 1e6).\n"
+    "t : array_like\n"
+    "    Quantiles, i.e., the upper limit of integration.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "cdf : scalar or ndarray\n"
+    "    The calculated CDF. If all inputs are scalar, the return will be a\n"
+    "    float. Otherwise, it will be an array.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "nctdtrit : Inverse CDF (iCDF) of the non-central t distribution.\n"
+    "nctdtridf : Calculate degrees of freedom, given CDF and iCDF values.\n"
+    "nctdtrinc : Calculate non-centrality parameter, given CDF iCDF values.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    ">>> from scipy import stats\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    "\n"
+    "Plot the CDF of the non-central t distribution, for nc=0. Compare with the\n"
+    "t-distribution from scipy.stats:\n"
+    "\n"
+    ">>> x = np.linspace(-5, 5, num=500)\n"
+    ">>> df = 3\n"
+    ">>> nct_stats = stats.t.cdf(x, df)\n"
+    ">>> nct_special = special.nctdtr(df, 0, x)\n"
+    "\n"
+    ">>> fig = plt.figure()\n"
+    ">>> ax = fig.add_subplot(111)\n"
+    ">>> ax.plot(x, nct_stats, 'b-', lw=3)\n"
+    ">>> ax.plot(x, nct_special, 'r-')\n"
+    ">>> plt.show()")
+ufunc_nctdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nctdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nctdtr_types[0] = <char>NPY_FLOAT
+ufunc_nctdtr_types[1] = <char>NPY_FLOAT
+ufunc_nctdtr_types[2] = <char>NPY_FLOAT
+ufunc_nctdtr_types[3] = <char>NPY_FLOAT
+ufunc_nctdtr_types[4] = <char>NPY_DOUBLE
+ufunc_nctdtr_types[5] = <char>NPY_DOUBLE
+ufunc_nctdtr_types[6] = <char>NPY_DOUBLE
+ufunc_nctdtr_types[7] = <char>NPY_DOUBLE
+ufunc_nctdtr_ptr[2*0] = <void*>_func_nctdtr
+ufunc_nctdtr_ptr[2*0+1] = <void*>(<char*>"nctdtr")
+ufunc_nctdtr_ptr[2*1] = <void*>_func_nctdtr
+ufunc_nctdtr_ptr[2*1+1] = <void*>(<char*>"nctdtr")
+ufunc_nctdtr_data[0] = &ufunc_nctdtr_ptr[2*0]
+ufunc_nctdtr_data[1] = &ufunc_nctdtr_ptr[2*1]
+nctdtr = np.PyUFunc_FromFuncAndData(ufunc_nctdtr_loops, ufunc_nctdtr_data, ufunc_nctdtr_types, 2, 3, 1, 0, "nctdtr", ufunc_nctdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nctdtridf_loops[2]
+cdef void *ufunc_nctdtridf_ptr[4]
+cdef void *ufunc_nctdtridf_data[2]
+cdef char ufunc_nctdtridf_types[8]
+cdef char *ufunc_nctdtridf_doc = (
+    "nctdtridf(p, nc, t, out=None)\n"
+    "\n"
+    "Calculate degrees of freedom for non-central t distribution.\n"
+    "\n"
+    "See `nctdtr` for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    CDF values, in range (0, 1].\n"
+    "nc : array_like\n"
+    "    Noncentrality parameter. Should be in range (-1e6, 1e6).\n"
+    "t : array_like\n"
+    "    Quantiles, i.e., the upper limit of integration.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "df : scalar or ndarray\n"
+    "    The degrees of freedom. If all inputs are scalar, the return will be a\n"
+    "    float. Otherwise, it will be an array.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "nctdtr :  CDF of the non-central `t` distribution.\n"
+    "nctdtrit : Inverse CDF (iCDF) of the non-central t distribution.\n"
+    "nctdtrinc : Calculate non-centrality parameter, given CDF iCDF values.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import nctdtr, nctdtridf\n"
+    "\n"
+    "Compute the CDF for several values of `df`:\n"
+    "\n"
+    ">>> df = [1, 2, 3]\n"
+    ">>> p = nctdtr(df, 0.25, 1)\n"
+    ">>> p\n"
+    "array([0.67491974, 0.716464  , 0.73349456])\n"
+    "\n"
+    "Compute the inverse. We recover the values of `df`, as expected:\n"
+    "\n"
+    ">>> nctdtridf(p, 0.25, 1)\n"
+    "array([1., 2., 3.])")
+ufunc_nctdtridf_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nctdtridf_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nctdtridf_types[0] = <char>NPY_FLOAT
+ufunc_nctdtridf_types[1] = <char>NPY_FLOAT
+ufunc_nctdtridf_types[2] = <char>NPY_FLOAT
+ufunc_nctdtridf_types[3] = <char>NPY_FLOAT
+ufunc_nctdtridf_types[4] = <char>NPY_DOUBLE
+ufunc_nctdtridf_types[5] = <char>NPY_DOUBLE
+ufunc_nctdtridf_types[6] = <char>NPY_DOUBLE
+ufunc_nctdtridf_types[7] = <char>NPY_DOUBLE
+ufunc_nctdtridf_ptr[2*0] = <void*>_func_nctdtridf
+ufunc_nctdtridf_ptr[2*0+1] = <void*>(<char*>"nctdtridf")
+ufunc_nctdtridf_ptr[2*1] = <void*>_func_nctdtridf
+ufunc_nctdtridf_ptr[2*1+1] = <void*>(<char*>"nctdtridf")
+ufunc_nctdtridf_data[0] = &ufunc_nctdtridf_ptr[2*0]
+ufunc_nctdtridf_data[1] = &ufunc_nctdtridf_ptr[2*1]
+nctdtridf = np.PyUFunc_FromFuncAndData(ufunc_nctdtridf_loops, ufunc_nctdtridf_data, ufunc_nctdtridf_types, 2, 3, 1, 0, "nctdtridf", ufunc_nctdtridf_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nctdtrinc_loops[2]
+cdef void *ufunc_nctdtrinc_ptr[4]
+cdef void *ufunc_nctdtrinc_data[2]
+cdef char ufunc_nctdtrinc_types[8]
+cdef char *ufunc_nctdtrinc_doc = (
+    "nctdtrinc(df, p, t, out=None)\n"
+    "\n"
+    "Calculate non-centrality parameter for non-central t distribution.\n"
+    "\n"
+    "See `nctdtr` for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "df : array_like\n"
+    "    Degrees of freedom of the distribution. Should be in range (0, inf).\n"
+    "p : array_like\n"
+    "    CDF values, in range (0, 1].\n"
+    "t : array_like\n"
+    "    Quantiles, i.e., the upper limit of integration.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "nc : scalar or ndarray\n"
+    "    Noncentrality parameter\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "nctdtr :  CDF of the non-central `t` distribution.\n"
+    "nctdtrit : Inverse CDF (iCDF) of the non-central t distribution.\n"
+    "nctdtridf : Calculate degrees of freedom, given CDF and iCDF values.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import nctdtr, nctdtrinc\n"
+    "\n"
+    "Compute the CDF for several values of `nc`:\n"
+    "\n"
+    ">>> nc = [0.5, 1.5, 2.5]\n"
+    ">>> p = nctdtr(3, nc, 1.5)\n"
+    ">>> p\n"
+    "array([0.77569497, 0.45524533, 0.1668691 ])\n"
+    "\n"
+    "Compute the inverse. We recover the values of `nc`, as expected:\n"
+    "\n"
+    ">>> nctdtrinc(3, p, 1.5)\n"
+    "array([0.5, 1.5, 2.5])")
+ufunc_nctdtrinc_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nctdtrinc_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nctdtrinc_types[0] = <char>NPY_FLOAT
+ufunc_nctdtrinc_types[1] = <char>NPY_FLOAT
+ufunc_nctdtrinc_types[2] = <char>NPY_FLOAT
+ufunc_nctdtrinc_types[3] = <char>NPY_FLOAT
+ufunc_nctdtrinc_types[4] = <char>NPY_DOUBLE
+ufunc_nctdtrinc_types[5] = <char>NPY_DOUBLE
+ufunc_nctdtrinc_types[6] = <char>NPY_DOUBLE
+ufunc_nctdtrinc_types[7] = <char>NPY_DOUBLE
+ufunc_nctdtrinc_ptr[2*0] = <void*>_func_nctdtrinc
+ufunc_nctdtrinc_ptr[2*0+1] = <void*>(<char*>"nctdtrinc")
+ufunc_nctdtrinc_ptr[2*1] = <void*>_func_nctdtrinc
+ufunc_nctdtrinc_ptr[2*1+1] = <void*>(<char*>"nctdtrinc")
+ufunc_nctdtrinc_data[0] = &ufunc_nctdtrinc_ptr[2*0]
+ufunc_nctdtrinc_data[1] = &ufunc_nctdtrinc_ptr[2*1]
+nctdtrinc = np.PyUFunc_FromFuncAndData(ufunc_nctdtrinc_loops, ufunc_nctdtrinc_data, ufunc_nctdtrinc_types, 2, 3, 1, 0, "nctdtrinc", ufunc_nctdtrinc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nctdtrit_loops[2]
+cdef void *ufunc_nctdtrit_ptr[4]
+cdef void *ufunc_nctdtrit_data[2]
+cdef char ufunc_nctdtrit_types[8]
+cdef char *ufunc_nctdtrit_doc = (
+    "nctdtrit(df, nc, p, out=None)\n"
+    "\n"
+    "Inverse cumulative distribution function of the non-central t distribution.\n"
+    "\n"
+    "See `nctdtr` for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "df : array_like\n"
+    "    Degrees of freedom of the distribution. Should be in range (0, inf).\n"
+    "nc : array_like\n"
+    "    Noncentrality parameter. Should be in range (-1e6, 1e6).\n"
+    "p : array_like\n"
+    "    CDF values, in range (0, 1].\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "t : scalar or ndarray\n"
+    "    Quantiles\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "nctdtr :  CDF of the non-central `t` distribution.\n"
+    "nctdtridf : Calculate degrees of freedom, given CDF and iCDF values.\n"
+    "nctdtrinc : Calculate non-centrality parameter, given CDF iCDF values.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import nctdtr, nctdtrit\n"
+    "\n"
+    "Compute the CDF for several values of `t`:\n"
+    "\n"
+    ">>> t = [0.5, 1, 1.5]\n"
+    ">>> p = nctdtr(3, 1, t)\n"
+    ">>> p\n"
+    "array([0.29811049, 0.46922687, 0.6257559 ])\n"
+    "\n"
+    "Compute the inverse. We recover the values of `t`, as expected:\n"
+    "\n"
+    ">>> nctdtrit(3, 1, p)\n"
+    "array([0.5, 1. , 1.5])")
+ufunc_nctdtrit_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nctdtrit_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nctdtrit_types[0] = <char>NPY_FLOAT
+ufunc_nctdtrit_types[1] = <char>NPY_FLOAT
+ufunc_nctdtrit_types[2] = <char>NPY_FLOAT
+ufunc_nctdtrit_types[3] = <char>NPY_FLOAT
+ufunc_nctdtrit_types[4] = <char>NPY_DOUBLE
+ufunc_nctdtrit_types[5] = <char>NPY_DOUBLE
+ufunc_nctdtrit_types[6] = <char>NPY_DOUBLE
+ufunc_nctdtrit_types[7] = <char>NPY_DOUBLE
+ufunc_nctdtrit_ptr[2*0] = <void*>_func_nctdtrit
+ufunc_nctdtrit_ptr[2*0+1] = <void*>(<char*>"nctdtrit")
+ufunc_nctdtrit_ptr[2*1] = <void*>_func_nctdtrit
+ufunc_nctdtrit_ptr[2*1+1] = <void*>(<char*>"nctdtrit")
+ufunc_nctdtrit_data[0] = &ufunc_nctdtrit_ptr[2*0]
+ufunc_nctdtrit_data[1] = &ufunc_nctdtrit_ptr[2*1]
+nctdtrit = np.PyUFunc_FromFuncAndData(ufunc_nctdtrit_loops, ufunc_nctdtrit_data, ufunc_nctdtrit_types, 2, 3, 1, 0, "nctdtrit", ufunc_nctdtrit_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ndtr_loops[4]
+cdef void *ufunc_ndtr_ptr[8]
+cdef void *ufunc_ndtr_data[4]
+cdef char ufunc_ndtr_types[8]
+cdef char *ufunc_ndtr_doc = (
+    "ndtr(x, out=None)\n"
+    "\n"
+    "Cumulative distribution of the standard normal distribution.\n"
+    "\n"
+    "Returns the area under the standard Gaussian probability\n"
+    "density function, integrated from minus infinity to `x`\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   \\frac{1}{\\sqrt{2\\pi}} \\int_{-\\infty}^x \\exp(-t^2/2) dt\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like, real or complex\n"
+    "    Argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The value of the normal CDF evaluated at `x`\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "log_ndtr : Logarithm of ndtr\n"
+    "ndtri : Inverse of ndtr, standard normal percentile function\n"
+    "erf : Error function\n"
+    "erfc : 1 - erf\n"
+    "scipy.stats.norm : Normal distribution\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate `ndtr` at one point.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import ndtr\n"
+    ">>> ndtr(0.5)\n"
+    "0.6914624612740131\n"
+    "\n"
+    "Evaluate the function at several points by providing a NumPy array\n"
+    "or list for `x`.\n"
+    "\n"
+    ">>> ndtr([0, 0.5, 2])\n"
+    "array([0.5       , 0.69146246, 0.97724987])\n"
+    "\n"
+    "Plot the function.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(-5, 5, 100)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> ax.plot(x, ndtr(x))\n"
+    ">>> ax.set_title(r\"Standard normal cumulative distribution function $\\Phi$\")\n"
+    ">>> plt.show()")
+ufunc_ndtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_ndtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_ndtr_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_ndtr_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_ndtr_types[0] = <char>NPY_FLOAT
+ufunc_ndtr_types[1] = <char>NPY_FLOAT
+ufunc_ndtr_types[2] = <char>NPY_DOUBLE
+ufunc_ndtr_types[3] = <char>NPY_DOUBLE
+ufunc_ndtr_types[4] = <char>NPY_CFLOAT
+ufunc_ndtr_types[5] = <char>NPY_CFLOAT
+ufunc_ndtr_types[6] = <char>NPY_CDOUBLE
+ufunc_ndtr_types[7] = <char>NPY_CDOUBLE
+ufunc_ndtr_ptr[2*0] = <void*>_func_ndtr
+ufunc_ndtr_ptr[2*0+1] = <void*>(<char*>"ndtr")
+ufunc_ndtr_ptr[2*1] = <void*>_func_ndtr
+ufunc_ndtr_ptr[2*1+1] = <void*>(<char*>"ndtr")
+ufunc_ndtr_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_ndtr
+ufunc_ndtr_ptr[2*2+1] = <void*>(<char*>"ndtr")
+ufunc_ndtr_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_ndtr
+ufunc_ndtr_ptr[2*3+1] = <void*>(<char*>"ndtr")
+ufunc_ndtr_data[0] = &ufunc_ndtr_ptr[2*0]
+ufunc_ndtr_data[1] = &ufunc_ndtr_ptr[2*1]
+ufunc_ndtr_data[2] = &ufunc_ndtr_ptr[2*2]
+ufunc_ndtr_data[3] = &ufunc_ndtr_ptr[2*3]
+ndtr = np.PyUFunc_FromFuncAndData(ufunc_ndtr_loops, ufunc_ndtr_data, ufunc_ndtr_types, 4, 1, 1, 0, "ndtr", ufunc_ndtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ndtri_loops[2]
+cdef void *ufunc_ndtri_ptr[4]
+cdef void *ufunc_ndtri_data[2]
+cdef char ufunc_ndtri_types[4]
+cdef char *ufunc_ndtri_doc = (
+    "ndtri(y, out=None)\n"
+    "\n"
+    "Inverse of `ndtr` vs x\n"
+    "\n"
+    "Returns the argument x for which the area under the standard normal\n"
+    "probability density function (integrated from minus infinity to `x`)\n"
+    "is equal to y.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    Probability\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "x : scalar or ndarray\n"
+    "    Value of x such that ``ndtr(x) == p``.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "ndtr : Standard normal cumulative probability distribution\n"
+    "ndtri_exp : Inverse of log_ndtr\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "`ndtri` is the percentile function of the standard normal distribution.\n"
+    "This means it returns the inverse of the cumulative density `ndtr`. First,\n"
+    "let us compute a cumulative density value.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import ndtri, ndtr\n"
+    ">>> cdf_val = ndtr(2)\n"
+    ">>> cdf_val\n"
+    "0.9772498680518208\n"
+    "\n"
+    "Verify that `ndtri` yields the original value for `x` up to floating point\n"
+    "errors.\n"
+    "\n"
+    ">>> ndtri(cdf_val)\n"
+    "2.0000000000000004\n"
+    "\n"
+    "Plot the function. For that purpose, we provide a NumPy array as argument.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> x = np.linspace(0.01, 1, 200)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> ax.plot(x, ndtri(x))\n"
+    ">>> ax.set_title(\"Standard normal percentile function\")\n"
+    ">>> plt.show()")
+ufunc_ndtri_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_ndtri_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_ndtri_types[0] = <char>NPY_FLOAT
+ufunc_ndtri_types[1] = <char>NPY_FLOAT
+ufunc_ndtri_types[2] = <char>NPY_DOUBLE
+ufunc_ndtri_types[3] = <char>NPY_DOUBLE
+ufunc_ndtri_ptr[2*0] = <void*>_func_ndtri
+ufunc_ndtri_ptr[2*0+1] = <void*>(<char*>"ndtri")
+ufunc_ndtri_ptr[2*1] = <void*>_func_ndtri
+ufunc_ndtri_ptr[2*1+1] = <void*>(<char*>"ndtri")
+ufunc_ndtri_data[0] = &ufunc_ndtri_ptr[2*0]
+ufunc_ndtri_data[1] = &ufunc_ndtri_ptr[2*1]
+ndtri = np.PyUFunc_FromFuncAndData(ufunc_ndtri_loops, ufunc_ndtri_data, ufunc_ndtri_types, 2, 1, 1, 0, "ndtri", ufunc_ndtri_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_ndtri_exp_loops[2]
+cdef void *ufunc_ndtri_exp_ptr[4]
+cdef void *ufunc_ndtri_exp_data[2]
+cdef char ufunc_ndtri_exp_types[4]
+cdef char *ufunc_ndtri_exp_doc = (
+    "ndtri_exp(y, out=None)\n"
+    "\n"
+    "Inverse of `log_ndtr` vs x. Allows for greater precision than\n"
+    "`ndtri` composed with `numpy.exp` for very small values of y and for\n"
+    "y close to 0.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "y : array_like of float\n"
+    "    Function argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Inverse of the log CDF of the standard normal distribution, evaluated\n"
+    "    at y.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "log_ndtr : log of the standard normal cumulative distribution function\n"
+    "ndtr : standard normal cumulative distribution function\n"
+    "ndtri : standard normal percentile function\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "`ndtri_exp` agrees with the naive implementation when the latter does\n"
+    "not suffer from underflow.\n"
+    "\n"
+    ">>> sc.ndtri_exp(-1)\n"
+    "-0.33747496376420244\n"
+    ">>> sc.ndtri(np.exp(-1))\n"
+    "-0.33747496376420244\n"
+    "\n"
+    "For extreme values of y, the naive approach fails\n"
+    "\n"
+    ">>> sc.ndtri(np.exp(-800))\n"
+    "-inf\n"
+    ">>> sc.ndtri(np.exp(-1e-20))\n"
+    "inf\n"
+    "\n"
+    "whereas `ndtri_exp` is still able to compute the result to high precision.\n"
+    "\n"
+    ">>> sc.ndtri_exp(-800)\n"
+    "-39.88469483825668\n"
+    ">>> sc.ndtri_exp(-1e-20)\n"
+    "9.262340089798409")
+ufunc_ndtri_exp_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_ndtri_exp_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_ndtri_exp_types[0] = <char>NPY_FLOAT
+ufunc_ndtri_exp_types[1] = <char>NPY_FLOAT
+ufunc_ndtri_exp_types[2] = <char>NPY_DOUBLE
+ufunc_ndtri_exp_types[3] = <char>NPY_DOUBLE
+ufunc_ndtri_exp_ptr[2*0] = <void*>_func_ndtri_exp
+ufunc_ndtri_exp_ptr[2*0+1] = <void*>(<char*>"ndtri_exp")
+ufunc_ndtri_exp_ptr[2*1] = <void*>_func_ndtri_exp
+ufunc_ndtri_exp_ptr[2*1+1] = <void*>(<char*>"ndtri_exp")
+ufunc_ndtri_exp_data[0] = &ufunc_ndtri_exp_ptr[2*0]
+ufunc_ndtri_exp_data[1] = &ufunc_ndtri_exp_ptr[2*1]
+ndtri_exp = np.PyUFunc_FromFuncAndData(ufunc_ndtri_exp_loops, ufunc_ndtri_exp_data, ufunc_ndtri_exp_types, 2, 1, 1, 0, "ndtri_exp", ufunc_ndtri_exp_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nrdtrimn_loops[2]
+cdef void *ufunc_nrdtrimn_ptr[4]
+cdef void *ufunc_nrdtrimn_data[2]
+cdef char ufunc_nrdtrimn_types[8]
+cdef char *ufunc_nrdtrimn_doc = (
+    "nrdtrimn(p, std, x, out=None)\n"
+    "\n"
+    "Calculate mean of normal distribution given other params.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    CDF values, in range (0, 1].\n"
+    "std : array_like\n"
+    "    Standard deviation.\n"
+    "x : array_like\n"
+    "    Quantiles, i.e. the upper limit of integration.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "mn : scalar or ndarray\n"
+    "    The mean of the normal distribution.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "scipy.stats.norm : Normal distribution\n"
+    "ndtr : Standard normal cumulative probability distribution\n"
+    "ndtri : Inverse of standard normal CDF with respect to quantile\n"
+    "nrdtrisd : Inverse of normal distribution CDF with respect to\n"
+    "           standard deviation\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "`nrdtrimn` can be used to recover the mean of a normal distribution\n"
+    "if we know the CDF value `p` for a given quantile `x` and the\n"
+    "standard deviation `std`. First, we calculate\n"
+    "the normal distribution CDF for an exemplary parameter set.\n"
+    "\n"
+    ">>> from scipy.stats import norm\n"
+    ">>> mean = 3.\n"
+    ">>> std = 2.\n"
+    ">>> x = 6.\n"
+    ">>> p = norm.cdf(x, loc=mean, scale=std)\n"
+    ">>> p\n"
+    "0.9331927987311419\n"
+    "\n"
+    "Verify that `nrdtrimn` returns the original value for `mean`.\n"
+    "\n"
+    ">>> from scipy.special import nrdtrimn\n"
+    ">>> nrdtrimn(p, std, x)\n"
+    "3.0000000000000004")
+ufunc_nrdtrimn_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nrdtrimn_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nrdtrimn_types[0] = <char>NPY_FLOAT
+ufunc_nrdtrimn_types[1] = <char>NPY_FLOAT
+ufunc_nrdtrimn_types[2] = <char>NPY_FLOAT
+ufunc_nrdtrimn_types[3] = <char>NPY_FLOAT
+ufunc_nrdtrimn_types[4] = <char>NPY_DOUBLE
+ufunc_nrdtrimn_types[5] = <char>NPY_DOUBLE
+ufunc_nrdtrimn_types[6] = <char>NPY_DOUBLE
+ufunc_nrdtrimn_types[7] = <char>NPY_DOUBLE
+ufunc_nrdtrimn_ptr[2*0] = <void*>_func_nrdtrimn
+ufunc_nrdtrimn_ptr[2*0+1] = <void*>(<char*>"nrdtrimn")
+ufunc_nrdtrimn_ptr[2*1] = <void*>_func_nrdtrimn
+ufunc_nrdtrimn_ptr[2*1+1] = <void*>(<char*>"nrdtrimn")
+ufunc_nrdtrimn_data[0] = &ufunc_nrdtrimn_ptr[2*0]
+ufunc_nrdtrimn_data[1] = &ufunc_nrdtrimn_ptr[2*1]
+nrdtrimn = np.PyUFunc_FromFuncAndData(ufunc_nrdtrimn_loops, ufunc_nrdtrimn_data, ufunc_nrdtrimn_types, 2, 3, 1, 0, "nrdtrimn", ufunc_nrdtrimn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_nrdtrisd_loops[2]
+cdef void *ufunc_nrdtrisd_ptr[4]
+cdef void *ufunc_nrdtrisd_data[2]
+cdef char ufunc_nrdtrisd_types[8]
+cdef char *ufunc_nrdtrisd_doc = (
+    "nrdtrisd(mn, p, x, out=None)\n"
+    "\n"
+    "Calculate standard deviation of normal distribution given other params.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "mn : scalar or ndarray\n"
+    "    The mean of the normal distribution.\n"
+    "p : array_like\n"
+    "    CDF values, in range (0, 1].\n"
+    "x : array_like\n"
+    "    Quantiles, i.e. the upper limit of integration.\n"
+    "\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "std : scalar or ndarray\n"
+    "    Standard deviation.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "scipy.stats.norm : Normal distribution\n"
+    "ndtr : Standard normal cumulative probability distribution\n"
+    "ndtri : Inverse of standard normal CDF with respect to quantile\n"
+    "nrdtrimn : Inverse of normal distribution CDF with respect to\n"
+    "           mean\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "`nrdtrisd` can be used to recover the standard deviation of a normal\n"
+    "distribution if we know the CDF value `p` for a given quantile `x` and\n"
+    "the mean `mn`. First, we calculate the normal distribution CDF for an\n"
+    "exemplary parameter set.\n"
+    "\n"
+    ">>> from scipy.stats import norm\n"
+    ">>> mean = 3.\n"
+    ">>> std = 2.\n"
+    ">>> x = 6.\n"
+    ">>> p = norm.cdf(x, loc=mean, scale=std)\n"
+    ">>> p\n"
+    "0.9331927987311419\n"
+    "\n"
+    "Verify that `nrdtrisd` returns the original value for `std`.\n"
+    "\n"
+    ">>> from scipy.special import nrdtrisd\n"
+    ">>> nrdtrisd(mean, p, x)\n"
+    "2.0000000000000004")
+ufunc_nrdtrisd_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_nrdtrisd_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_nrdtrisd_types[0] = <char>NPY_FLOAT
+ufunc_nrdtrisd_types[1] = <char>NPY_FLOAT
+ufunc_nrdtrisd_types[2] = <char>NPY_FLOAT
+ufunc_nrdtrisd_types[3] = <char>NPY_FLOAT
+ufunc_nrdtrisd_types[4] = <char>NPY_DOUBLE
+ufunc_nrdtrisd_types[5] = <char>NPY_DOUBLE
+ufunc_nrdtrisd_types[6] = <char>NPY_DOUBLE
+ufunc_nrdtrisd_types[7] = <char>NPY_DOUBLE
+ufunc_nrdtrisd_ptr[2*0] = <void*>_func_nrdtrisd
+ufunc_nrdtrisd_ptr[2*0+1] = <void*>(<char*>"nrdtrisd")
+ufunc_nrdtrisd_ptr[2*1] = <void*>_func_nrdtrisd
+ufunc_nrdtrisd_ptr[2*1+1] = <void*>(<char*>"nrdtrisd")
+ufunc_nrdtrisd_data[0] = &ufunc_nrdtrisd_ptr[2*0]
+ufunc_nrdtrisd_data[1] = &ufunc_nrdtrisd_ptr[2*1]
+nrdtrisd = np.PyUFunc_FromFuncAndData(ufunc_nrdtrisd_loops, ufunc_nrdtrisd_data, ufunc_nrdtrisd_types, 2, 3, 1, 0, "nrdtrisd", ufunc_nrdtrisd_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_obl_ang1_loops[2]
+cdef void *ufunc_obl_ang1_ptr[4]
+cdef void *ufunc_obl_ang1_data[2]
+cdef char ufunc_obl_ang1_types[12]
+cdef char *ufunc_obl_ang1_doc = (
+    "obl_ang1(m, n, c, x, out=None)\n"
+    "\n"
+    "Oblate spheroidal angular function of the first kind and its derivative\n"
+    "\n"
+    "Computes the oblate spheroidal angular function of the first kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Mode parameter m (nonnegative)\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "x : array_like\n"
+    "    Parameter x (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "obl_ang1_cv")
+ufunc_obl_ang1_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_ffff_ff
+ufunc_obl_ang1_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_dddd_dd
+ufunc_obl_ang1_types[0] = <char>NPY_FLOAT
+ufunc_obl_ang1_types[1] = <char>NPY_FLOAT
+ufunc_obl_ang1_types[2] = <char>NPY_FLOAT
+ufunc_obl_ang1_types[3] = <char>NPY_FLOAT
+ufunc_obl_ang1_types[4] = <char>NPY_FLOAT
+ufunc_obl_ang1_types[5] = <char>NPY_FLOAT
+ufunc_obl_ang1_types[6] = <char>NPY_DOUBLE
+ufunc_obl_ang1_types[7] = <char>NPY_DOUBLE
+ufunc_obl_ang1_types[8] = <char>NPY_DOUBLE
+ufunc_obl_ang1_types[9] = <char>NPY_DOUBLE
+ufunc_obl_ang1_types[10] = <char>NPY_DOUBLE
+ufunc_obl_ang1_types[11] = <char>NPY_DOUBLE
+ufunc_obl_ang1_ptr[2*0] = <void*>_func_oblate_aswfa_nocv_wrap
+ufunc_obl_ang1_ptr[2*0+1] = <void*>(<char*>"obl_ang1")
+ufunc_obl_ang1_ptr[2*1] = <void*>_func_oblate_aswfa_nocv_wrap
+ufunc_obl_ang1_ptr[2*1+1] = <void*>(<char*>"obl_ang1")
+ufunc_obl_ang1_data[0] = &ufunc_obl_ang1_ptr[2*0]
+ufunc_obl_ang1_data[1] = &ufunc_obl_ang1_ptr[2*1]
+obl_ang1 = np.PyUFunc_FromFuncAndData(ufunc_obl_ang1_loops, ufunc_obl_ang1_data, ufunc_obl_ang1_types, 2, 4, 2, 0, "obl_ang1", ufunc_obl_ang1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_obl_ang1_cv_loops[2]
+cdef void *ufunc_obl_ang1_cv_ptr[4]
+cdef void *ufunc_obl_ang1_cv_data[2]
+cdef char ufunc_obl_ang1_cv_types[14]
+cdef char *ufunc_obl_ang1_cv_doc = (
+    "obl_ang1_cv(m, n, c, cv, x, out=None)\n"
+    "\n"
+    "Oblate spheroidal angular function obl_ang1 for precomputed characteristic value\n"
+    "\n"
+    "Computes the oblate spheroidal angular function of the first kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\n"
+    "pre-computed characteristic value.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Mode parameter m (nonnegative)\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "cv : array_like\n"
+    "    Characteristic value\n"
+    "x : array_like\n"
+    "    Parameter x (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "obl_ang1")
+ufunc_obl_ang1_cv_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_fffff_ff
+ufunc_obl_ang1_cv_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_ddddd_dd
+ufunc_obl_ang1_cv_types[0] = <char>NPY_FLOAT
+ufunc_obl_ang1_cv_types[1] = <char>NPY_FLOAT
+ufunc_obl_ang1_cv_types[2] = <char>NPY_FLOAT
+ufunc_obl_ang1_cv_types[3] = <char>NPY_FLOAT
+ufunc_obl_ang1_cv_types[4] = <char>NPY_FLOAT
+ufunc_obl_ang1_cv_types[5] = <char>NPY_FLOAT
+ufunc_obl_ang1_cv_types[6] = <char>NPY_FLOAT
+ufunc_obl_ang1_cv_types[7] = <char>NPY_DOUBLE
+ufunc_obl_ang1_cv_types[8] = <char>NPY_DOUBLE
+ufunc_obl_ang1_cv_types[9] = <char>NPY_DOUBLE
+ufunc_obl_ang1_cv_types[10] = <char>NPY_DOUBLE
+ufunc_obl_ang1_cv_types[11] = <char>NPY_DOUBLE
+ufunc_obl_ang1_cv_types[12] = <char>NPY_DOUBLE
+ufunc_obl_ang1_cv_types[13] = <char>NPY_DOUBLE
+ufunc_obl_ang1_cv_ptr[2*0] = <void*>_func_oblate_aswfa_wrap
+ufunc_obl_ang1_cv_ptr[2*0+1] = <void*>(<char*>"obl_ang1_cv")
+ufunc_obl_ang1_cv_ptr[2*1] = <void*>_func_oblate_aswfa_wrap
+ufunc_obl_ang1_cv_ptr[2*1+1] = <void*>(<char*>"obl_ang1_cv")
+ufunc_obl_ang1_cv_data[0] = &ufunc_obl_ang1_cv_ptr[2*0]
+ufunc_obl_ang1_cv_data[1] = &ufunc_obl_ang1_cv_ptr[2*1]
+obl_ang1_cv = np.PyUFunc_FromFuncAndData(ufunc_obl_ang1_cv_loops, ufunc_obl_ang1_cv_data, ufunc_obl_ang1_cv_types, 2, 5, 2, 0, "obl_ang1_cv", ufunc_obl_ang1_cv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_obl_cv_loops[2]
+cdef void *ufunc_obl_cv_ptr[4]
+cdef void *ufunc_obl_cv_data[2]
+cdef char ufunc_obl_cv_types[8]
+cdef char *ufunc_obl_cv_doc = (
+    "obl_cv(m, n, c, out=None)\n"
+    "\n"
+    "Characteristic value of oblate spheroidal function\n"
+    "\n"
+    "Computes the characteristic value of oblate spheroidal wave\n"
+    "functions of order `m`, `n` (n>=m) and spheroidal parameter `c`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Mode parameter m (nonnegative)\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "cv : scalar or ndarray\n"
+    "    Characteristic value")
+ufunc_obl_cv_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_obl_cv_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_obl_cv_types[0] = <char>NPY_FLOAT
+ufunc_obl_cv_types[1] = <char>NPY_FLOAT
+ufunc_obl_cv_types[2] = <char>NPY_FLOAT
+ufunc_obl_cv_types[3] = <char>NPY_FLOAT
+ufunc_obl_cv_types[4] = <char>NPY_DOUBLE
+ufunc_obl_cv_types[5] = <char>NPY_DOUBLE
+ufunc_obl_cv_types[6] = <char>NPY_DOUBLE
+ufunc_obl_cv_types[7] = <char>NPY_DOUBLE
+ufunc_obl_cv_ptr[2*0] = <void*>_func_oblate_segv_wrap
+ufunc_obl_cv_ptr[2*0+1] = <void*>(<char*>"obl_cv")
+ufunc_obl_cv_ptr[2*1] = <void*>_func_oblate_segv_wrap
+ufunc_obl_cv_ptr[2*1+1] = <void*>(<char*>"obl_cv")
+ufunc_obl_cv_data[0] = &ufunc_obl_cv_ptr[2*0]
+ufunc_obl_cv_data[1] = &ufunc_obl_cv_ptr[2*1]
+obl_cv = np.PyUFunc_FromFuncAndData(ufunc_obl_cv_loops, ufunc_obl_cv_data, ufunc_obl_cv_types, 2, 3, 1, 0, "obl_cv", ufunc_obl_cv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_obl_rad1_loops[2]
+cdef void *ufunc_obl_rad1_ptr[4]
+cdef void *ufunc_obl_rad1_data[2]
+cdef char ufunc_obl_rad1_types[12]
+cdef char *ufunc_obl_rad1_doc = (
+    "obl_rad1(m, n, c, x, out=None)\n"
+    "\n"
+    "Oblate spheroidal radial function of the first kind and its derivative\n"
+    "\n"
+    "Computes the oblate spheroidal radial function of the first kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Mode parameter m (nonnegative)\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "x : array_like\n"
+    "    Parameter x (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "obl_rad1_cv")
+ufunc_obl_rad1_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_ffff_ff
+ufunc_obl_rad1_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_dddd_dd
+ufunc_obl_rad1_types[0] = <char>NPY_FLOAT
+ufunc_obl_rad1_types[1] = <char>NPY_FLOAT
+ufunc_obl_rad1_types[2] = <char>NPY_FLOAT
+ufunc_obl_rad1_types[3] = <char>NPY_FLOAT
+ufunc_obl_rad1_types[4] = <char>NPY_FLOAT
+ufunc_obl_rad1_types[5] = <char>NPY_FLOAT
+ufunc_obl_rad1_types[6] = <char>NPY_DOUBLE
+ufunc_obl_rad1_types[7] = <char>NPY_DOUBLE
+ufunc_obl_rad1_types[8] = <char>NPY_DOUBLE
+ufunc_obl_rad1_types[9] = <char>NPY_DOUBLE
+ufunc_obl_rad1_types[10] = <char>NPY_DOUBLE
+ufunc_obl_rad1_types[11] = <char>NPY_DOUBLE
+ufunc_obl_rad1_ptr[2*0] = <void*>_func_oblate_radial1_nocv_wrap
+ufunc_obl_rad1_ptr[2*0+1] = <void*>(<char*>"obl_rad1")
+ufunc_obl_rad1_ptr[2*1] = <void*>_func_oblate_radial1_nocv_wrap
+ufunc_obl_rad1_ptr[2*1+1] = <void*>(<char*>"obl_rad1")
+ufunc_obl_rad1_data[0] = &ufunc_obl_rad1_ptr[2*0]
+ufunc_obl_rad1_data[1] = &ufunc_obl_rad1_ptr[2*1]
+obl_rad1 = np.PyUFunc_FromFuncAndData(ufunc_obl_rad1_loops, ufunc_obl_rad1_data, ufunc_obl_rad1_types, 2, 4, 2, 0, "obl_rad1", ufunc_obl_rad1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_obl_rad1_cv_loops[2]
+cdef void *ufunc_obl_rad1_cv_ptr[4]
+cdef void *ufunc_obl_rad1_cv_data[2]
+cdef char ufunc_obl_rad1_cv_types[14]
+cdef char *ufunc_obl_rad1_cv_doc = (
+    "obl_rad1_cv(m, n, c, cv, x, out=None)\n"
+    "\n"
+    "Oblate spheroidal radial function obl_rad1 for precomputed characteristic value\n"
+    "\n"
+    "Computes the oblate spheroidal radial function of the first kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\n"
+    "pre-computed characteristic value.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Mode parameter m (nonnegative)\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "cv : array_like\n"
+    "    Characteristic value\n"
+    "x : array_like\n"
+    "    Parameter x (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "obl_rad1")
+ufunc_obl_rad1_cv_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_fffff_ff
+ufunc_obl_rad1_cv_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_ddddd_dd
+ufunc_obl_rad1_cv_types[0] = <char>NPY_FLOAT
+ufunc_obl_rad1_cv_types[1] = <char>NPY_FLOAT
+ufunc_obl_rad1_cv_types[2] = <char>NPY_FLOAT
+ufunc_obl_rad1_cv_types[3] = <char>NPY_FLOAT
+ufunc_obl_rad1_cv_types[4] = <char>NPY_FLOAT
+ufunc_obl_rad1_cv_types[5] = <char>NPY_FLOAT
+ufunc_obl_rad1_cv_types[6] = <char>NPY_FLOAT
+ufunc_obl_rad1_cv_types[7] = <char>NPY_DOUBLE
+ufunc_obl_rad1_cv_types[8] = <char>NPY_DOUBLE
+ufunc_obl_rad1_cv_types[9] = <char>NPY_DOUBLE
+ufunc_obl_rad1_cv_types[10] = <char>NPY_DOUBLE
+ufunc_obl_rad1_cv_types[11] = <char>NPY_DOUBLE
+ufunc_obl_rad1_cv_types[12] = <char>NPY_DOUBLE
+ufunc_obl_rad1_cv_types[13] = <char>NPY_DOUBLE
+ufunc_obl_rad1_cv_ptr[2*0] = <void*>_func_oblate_radial1_wrap
+ufunc_obl_rad1_cv_ptr[2*0+1] = <void*>(<char*>"obl_rad1_cv")
+ufunc_obl_rad1_cv_ptr[2*1] = <void*>_func_oblate_radial1_wrap
+ufunc_obl_rad1_cv_ptr[2*1+1] = <void*>(<char*>"obl_rad1_cv")
+ufunc_obl_rad1_cv_data[0] = &ufunc_obl_rad1_cv_ptr[2*0]
+ufunc_obl_rad1_cv_data[1] = &ufunc_obl_rad1_cv_ptr[2*1]
+obl_rad1_cv = np.PyUFunc_FromFuncAndData(ufunc_obl_rad1_cv_loops, ufunc_obl_rad1_cv_data, ufunc_obl_rad1_cv_types, 2, 5, 2, 0, "obl_rad1_cv", ufunc_obl_rad1_cv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_obl_rad2_loops[2]
+cdef void *ufunc_obl_rad2_ptr[4]
+cdef void *ufunc_obl_rad2_data[2]
+cdef char ufunc_obl_rad2_types[12]
+cdef char *ufunc_obl_rad2_doc = (
+    "obl_rad2(m, n, c, x, out=None)\n"
+    "\n"
+    "Oblate spheroidal radial function of the second kind and its derivative.\n"
+    "\n"
+    "Computes the oblate spheroidal radial function of the second kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Mode parameter m (nonnegative)\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "x : array_like\n"
+    "    Parameter x (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "obl_rad2_cv")
+ufunc_obl_rad2_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_ffff_ff
+ufunc_obl_rad2_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_dddd_dd
+ufunc_obl_rad2_types[0] = <char>NPY_FLOAT
+ufunc_obl_rad2_types[1] = <char>NPY_FLOAT
+ufunc_obl_rad2_types[2] = <char>NPY_FLOAT
+ufunc_obl_rad2_types[3] = <char>NPY_FLOAT
+ufunc_obl_rad2_types[4] = <char>NPY_FLOAT
+ufunc_obl_rad2_types[5] = <char>NPY_FLOAT
+ufunc_obl_rad2_types[6] = <char>NPY_DOUBLE
+ufunc_obl_rad2_types[7] = <char>NPY_DOUBLE
+ufunc_obl_rad2_types[8] = <char>NPY_DOUBLE
+ufunc_obl_rad2_types[9] = <char>NPY_DOUBLE
+ufunc_obl_rad2_types[10] = <char>NPY_DOUBLE
+ufunc_obl_rad2_types[11] = <char>NPY_DOUBLE
+ufunc_obl_rad2_ptr[2*0] = <void*>_func_oblate_radial2_nocv_wrap
+ufunc_obl_rad2_ptr[2*0+1] = <void*>(<char*>"obl_rad2")
+ufunc_obl_rad2_ptr[2*1] = <void*>_func_oblate_radial2_nocv_wrap
+ufunc_obl_rad2_ptr[2*1+1] = <void*>(<char*>"obl_rad2")
+ufunc_obl_rad2_data[0] = &ufunc_obl_rad2_ptr[2*0]
+ufunc_obl_rad2_data[1] = &ufunc_obl_rad2_ptr[2*1]
+obl_rad2 = np.PyUFunc_FromFuncAndData(ufunc_obl_rad2_loops, ufunc_obl_rad2_data, ufunc_obl_rad2_types, 2, 4, 2, 0, "obl_rad2", ufunc_obl_rad2_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_obl_rad2_cv_loops[2]
+cdef void *ufunc_obl_rad2_cv_ptr[4]
+cdef void *ufunc_obl_rad2_cv_data[2]
+cdef char ufunc_obl_rad2_cv_types[14]
+cdef char *ufunc_obl_rad2_cv_doc = (
+    "obl_rad2_cv(m, n, c, cv, x, out=None)\n"
+    "\n"
+    "Oblate spheroidal radial function obl_rad2 for precomputed characteristic value\n"
+    "\n"
+    "Computes the oblate spheroidal radial function of the second kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\n"
+    "pre-computed characteristic value.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Mode parameter m (nonnegative)\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "cv : array_like\n"
+    "    Characteristic value\n"
+    "x : array_like\n"
+    "    Parameter x (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "obl_rad2")
+ufunc_obl_rad2_cv_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_fffff_ff
+ufunc_obl_rad2_cv_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_ddddd_dd
+ufunc_obl_rad2_cv_types[0] = <char>NPY_FLOAT
+ufunc_obl_rad2_cv_types[1] = <char>NPY_FLOAT
+ufunc_obl_rad2_cv_types[2] = <char>NPY_FLOAT
+ufunc_obl_rad2_cv_types[3] = <char>NPY_FLOAT
+ufunc_obl_rad2_cv_types[4] = <char>NPY_FLOAT
+ufunc_obl_rad2_cv_types[5] = <char>NPY_FLOAT
+ufunc_obl_rad2_cv_types[6] = <char>NPY_FLOAT
+ufunc_obl_rad2_cv_types[7] = <char>NPY_DOUBLE
+ufunc_obl_rad2_cv_types[8] = <char>NPY_DOUBLE
+ufunc_obl_rad2_cv_types[9] = <char>NPY_DOUBLE
+ufunc_obl_rad2_cv_types[10] = <char>NPY_DOUBLE
+ufunc_obl_rad2_cv_types[11] = <char>NPY_DOUBLE
+ufunc_obl_rad2_cv_types[12] = <char>NPY_DOUBLE
+ufunc_obl_rad2_cv_types[13] = <char>NPY_DOUBLE
+ufunc_obl_rad2_cv_ptr[2*0] = <void*>_func_oblate_radial2_wrap
+ufunc_obl_rad2_cv_ptr[2*0+1] = <void*>(<char*>"obl_rad2_cv")
+ufunc_obl_rad2_cv_ptr[2*1] = <void*>_func_oblate_radial2_wrap
+ufunc_obl_rad2_cv_ptr[2*1+1] = <void*>(<char*>"obl_rad2_cv")
+ufunc_obl_rad2_cv_data[0] = &ufunc_obl_rad2_cv_ptr[2*0]
+ufunc_obl_rad2_cv_data[1] = &ufunc_obl_rad2_cv_ptr[2*1]
+obl_rad2_cv = np.PyUFunc_FromFuncAndData(ufunc_obl_rad2_cv_loops, ufunc_obl_rad2_cv_data, ufunc_obl_rad2_cv_types, 2, 5, 2, 0, "obl_rad2_cv", ufunc_obl_rad2_cv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_owens_t_loops[2]
+cdef void *ufunc_owens_t_ptr[4]
+cdef void *ufunc_owens_t_data[2]
+cdef char ufunc_owens_t_types[6]
+cdef char *ufunc_owens_t_doc = (
+    "owens_t(h, a, out=None)\n"
+    "\n"
+    "Owen's T Function.\n"
+    "\n"
+    "The function T(h, a) gives the probability of the event\n"
+    "(X > h and 0 < Y < a * X) where X and Y are independent\n"
+    "standard normal random variables.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "h: array_like\n"
+    "    Input value.\n"
+    "a: array_like\n"
+    "    Input value.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "t: scalar or ndarray\n"
+    "    Probability of the event (X > h and 0 < Y < a * X),\n"
+    "    where X and Y are independent standard normal random variables.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] M. Patefield and D. Tandy, \"Fast and accurate calculation of\n"
+    "       Owen's T Function\", Statistical Software vol. 5, pp. 1-25, 2000.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy import special\n"
+    ">>> a = 3.5\n"
+    ">>> h = 0.78\n"
+    ">>> special.owens_t(h, a)\n"
+    "0.10877216734852274")
+ufunc_owens_t_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_owens_t_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_owens_t_types[0] = <char>NPY_FLOAT
+ufunc_owens_t_types[1] = <char>NPY_FLOAT
+ufunc_owens_t_types[2] = <char>NPY_FLOAT
+ufunc_owens_t_types[3] = <char>NPY_DOUBLE
+ufunc_owens_t_types[4] = <char>NPY_DOUBLE
+ufunc_owens_t_types[5] = <char>NPY_DOUBLE
+ufunc_owens_t_ptr[2*0] = <void*>_func_owens_t
+ufunc_owens_t_ptr[2*0+1] = <void*>(<char*>"owens_t")
+ufunc_owens_t_ptr[2*1] = <void*>_func_owens_t
+ufunc_owens_t_ptr[2*1+1] = <void*>(<char*>"owens_t")
+ufunc_owens_t_data[0] = &ufunc_owens_t_ptr[2*0]
+ufunc_owens_t_data[1] = &ufunc_owens_t_ptr[2*1]
+owens_t = np.PyUFunc_FromFuncAndData(ufunc_owens_t_loops, ufunc_owens_t_data, ufunc_owens_t_types, 2, 2, 1, 0, "owens_t", ufunc_owens_t_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pbdv_loops[2]
+cdef void *ufunc_pbdv_ptr[4]
+cdef void *ufunc_pbdv_data[2]
+cdef char ufunc_pbdv_types[8]
+cdef char *ufunc_pbdv_doc = (
+    "pbdv(v, x, out=None)\n"
+    "\n"
+    "Parabolic cylinder function D\n"
+    "\n"
+    "Returns (d, dp) the parabolic cylinder function Dv(x) in d and the\n"
+    "derivative, Dv'(x) in dp.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Real parameter\n"
+    "x : array_like\n"
+    "    Real argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "d : scalar or ndarray\n"
+    "    Value of the function\n"
+    "dp : scalar or ndarray\n"
+    "    Value of the derivative vs x")
+ufunc_pbdv_loops[0] = <np.PyUFuncGenericFunction>loop_i_dd_dd_As_ff_ff
+ufunc_pbdv_loops[1] = <np.PyUFuncGenericFunction>loop_i_dd_dd_As_dd_dd
+ufunc_pbdv_types[0] = <char>NPY_FLOAT
+ufunc_pbdv_types[1] = <char>NPY_FLOAT
+ufunc_pbdv_types[2] = <char>NPY_FLOAT
+ufunc_pbdv_types[3] = <char>NPY_FLOAT
+ufunc_pbdv_types[4] = <char>NPY_DOUBLE
+ufunc_pbdv_types[5] = <char>NPY_DOUBLE
+ufunc_pbdv_types[6] = <char>NPY_DOUBLE
+ufunc_pbdv_types[7] = <char>NPY_DOUBLE
+ufunc_pbdv_ptr[2*0] = <void*>_func_pbdv_wrap
+ufunc_pbdv_ptr[2*0+1] = <void*>(<char*>"pbdv")
+ufunc_pbdv_ptr[2*1] = <void*>_func_pbdv_wrap
+ufunc_pbdv_ptr[2*1+1] = <void*>(<char*>"pbdv")
+ufunc_pbdv_data[0] = &ufunc_pbdv_ptr[2*0]
+ufunc_pbdv_data[1] = &ufunc_pbdv_ptr[2*1]
+pbdv = np.PyUFunc_FromFuncAndData(ufunc_pbdv_loops, ufunc_pbdv_data, ufunc_pbdv_types, 2, 2, 2, 0, "pbdv", ufunc_pbdv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pbvv_loops[2]
+cdef void *ufunc_pbvv_ptr[4]
+cdef void *ufunc_pbvv_data[2]
+cdef char ufunc_pbvv_types[8]
+cdef char *ufunc_pbvv_doc = (
+    "pbvv(v, x, out=None)\n"
+    "\n"
+    "Parabolic cylinder function V\n"
+    "\n"
+    "Returns the parabolic cylinder function Vv(x) in v and the\n"
+    "derivative, Vv'(x) in vp.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Real parameter\n"
+    "x : array_like\n"
+    "    Real argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "v : scalar or ndarray\n"
+    "    Value of the function\n"
+    "vp : scalar or ndarray\n"
+    "    Value of the derivative vs x")
+ufunc_pbvv_loops[0] = <np.PyUFuncGenericFunction>loop_i_dd_dd_As_ff_ff
+ufunc_pbvv_loops[1] = <np.PyUFuncGenericFunction>loop_i_dd_dd_As_dd_dd
+ufunc_pbvv_types[0] = <char>NPY_FLOAT
+ufunc_pbvv_types[1] = <char>NPY_FLOAT
+ufunc_pbvv_types[2] = <char>NPY_FLOAT
+ufunc_pbvv_types[3] = <char>NPY_FLOAT
+ufunc_pbvv_types[4] = <char>NPY_DOUBLE
+ufunc_pbvv_types[5] = <char>NPY_DOUBLE
+ufunc_pbvv_types[6] = <char>NPY_DOUBLE
+ufunc_pbvv_types[7] = <char>NPY_DOUBLE
+ufunc_pbvv_ptr[2*0] = <void*>_func_pbvv_wrap
+ufunc_pbvv_ptr[2*0+1] = <void*>(<char*>"pbvv")
+ufunc_pbvv_ptr[2*1] = <void*>_func_pbvv_wrap
+ufunc_pbvv_ptr[2*1+1] = <void*>(<char*>"pbvv")
+ufunc_pbvv_data[0] = &ufunc_pbvv_ptr[2*0]
+ufunc_pbvv_data[1] = &ufunc_pbvv_ptr[2*1]
+pbvv = np.PyUFunc_FromFuncAndData(ufunc_pbvv_loops, ufunc_pbvv_data, ufunc_pbvv_types, 2, 2, 2, 0, "pbvv", ufunc_pbvv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pbwa_loops[2]
+cdef void *ufunc_pbwa_ptr[4]
+cdef void *ufunc_pbwa_data[2]
+cdef char ufunc_pbwa_types[8]
+cdef char *ufunc_pbwa_doc = (
+    "pbwa(a, x, out=None)\n"
+    "\n"
+    "Parabolic cylinder function W.\n"
+    "\n"
+    "The function is a particular solution to the differential equation\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    y'' + \\left(\\frac{1}{4}x^2 - a\\right)y = 0,\n"
+    "\n"
+    "for a full definition see section 12.14 in [1]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like\n"
+    "    Real parameter\n"
+    "x : array_like\n"
+    "    Real argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "w : scalar or ndarray\n"
+    "    Value of the function\n"
+    "wp : scalar or ndarray\n"
+    "    Value of the derivative in x\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The function is a wrapper for a Fortran routine by Zhang and Jin\n"
+    "[2]_. The implementation is accurate only for ``|a|, |x| < 5`` and\n"
+    "returns NaN outside that range.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Digital Library of Mathematical Functions, 14.30.\n"
+    "       https://dlmf.nist.gov/14.30\n"
+    ".. [2] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n"
+    "       Functions\", John Wiley and Sons, 1996.\n"
+    "       https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html")
+ufunc_pbwa_loops[0] = <np.PyUFuncGenericFunction>loop_i_dd_dd_As_ff_ff
+ufunc_pbwa_loops[1] = <np.PyUFuncGenericFunction>loop_i_dd_dd_As_dd_dd
+ufunc_pbwa_types[0] = <char>NPY_FLOAT
+ufunc_pbwa_types[1] = <char>NPY_FLOAT
+ufunc_pbwa_types[2] = <char>NPY_FLOAT
+ufunc_pbwa_types[3] = <char>NPY_FLOAT
+ufunc_pbwa_types[4] = <char>NPY_DOUBLE
+ufunc_pbwa_types[5] = <char>NPY_DOUBLE
+ufunc_pbwa_types[6] = <char>NPY_DOUBLE
+ufunc_pbwa_types[7] = <char>NPY_DOUBLE
+ufunc_pbwa_ptr[2*0] = <void*>_func_pbwa_wrap
+ufunc_pbwa_ptr[2*0+1] = <void*>(<char*>"pbwa")
+ufunc_pbwa_ptr[2*1] = <void*>_func_pbwa_wrap
+ufunc_pbwa_ptr[2*1+1] = <void*>(<char*>"pbwa")
+ufunc_pbwa_data[0] = &ufunc_pbwa_ptr[2*0]
+ufunc_pbwa_data[1] = &ufunc_pbwa_ptr[2*1]
+pbwa = np.PyUFunc_FromFuncAndData(ufunc_pbwa_loops, ufunc_pbwa_data, ufunc_pbwa_types, 2, 2, 2, 0, "pbwa", ufunc_pbwa_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pdtr_loops[2]
+cdef void *ufunc_pdtr_ptr[4]
+cdef void *ufunc_pdtr_data[2]
+cdef char ufunc_pdtr_types[6]
+cdef char *ufunc_pdtr_doc = (
+    "pdtr(k, m, out=None)\n"
+    "\n"
+    "Poisson cumulative distribution function.\n"
+    "\n"
+    "Defined as the probability that a Poisson-distributed random\n"
+    "variable with event rate :math:`m` is less than or equal to\n"
+    ":math:`k`. More concretely, this works out to be [1]_\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "   \\exp(-m) \\sum_{j = 0}^{\\lfloor{k}\\rfloor} \\frac{m^j}{j!}.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    Number of occurrences (nonnegative, real)\n"
+    "m : array_like\n"
+    "    Shape parameter (nonnegative, real)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the Poisson cumulative distribution function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "pdtrc : Poisson survival function\n"
+    "pdtrik : inverse of `pdtr` with respect to `k`\n"
+    "pdtri : inverse of `pdtr` with respect to `m`\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] https://en.wikipedia.org/wiki/Poisson_distribution\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is a cumulative distribution function, so it converges to 1\n"
+    "monotonically as `k` goes to infinity.\n"
+    "\n"
+    ">>> sc.pdtr([1, 10, 100, np.inf], 1)\n"
+    "array([0.73575888, 0.99999999, 1.        , 1.        ])\n"
+    "\n"
+    "It is discontinuous at integers and constant between integers.\n"
+    "\n"
+    ">>> sc.pdtr([1, 1.5, 1.9, 2], 1)\n"
+    "array([0.73575888, 0.73575888, 0.73575888, 0.9196986 ])")
+ufunc_pdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_pdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_pdtr_types[0] = <char>NPY_FLOAT
+ufunc_pdtr_types[1] = <char>NPY_FLOAT
+ufunc_pdtr_types[2] = <char>NPY_FLOAT
+ufunc_pdtr_types[3] = <char>NPY_DOUBLE
+ufunc_pdtr_types[4] = <char>NPY_DOUBLE
+ufunc_pdtr_types[5] = <char>NPY_DOUBLE
+ufunc_pdtr_ptr[2*0] = <void*>_func_pdtr
+ufunc_pdtr_ptr[2*0+1] = <void*>(<char*>"pdtr")
+ufunc_pdtr_ptr[2*1] = <void*>_func_pdtr
+ufunc_pdtr_ptr[2*1+1] = <void*>(<char*>"pdtr")
+ufunc_pdtr_data[0] = &ufunc_pdtr_ptr[2*0]
+ufunc_pdtr_data[1] = &ufunc_pdtr_ptr[2*1]
+pdtr = np.PyUFunc_FromFuncAndData(ufunc_pdtr_loops, ufunc_pdtr_data, ufunc_pdtr_types, 2, 2, 1, 0, "pdtr", ufunc_pdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pdtrc_loops[2]
+cdef void *ufunc_pdtrc_ptr[4]
+cdef void *ufunc_pdtrc_data[2]
+cdef char ufunc_pdtrc_types[6]
+cdef char *ufunc_pdtrc_doc = (
+    "pdtrc(k, m, out=None)\n"
+    "\n"
+    "Poisson survival function\n"
+    "\n"
+    "Returns the sum of the terms from k+1 to infinity of the Poisson\n"
+    "distribution: sum(exp(-m) * m**j / j!, j=k+1..inf) = gammainc(\n"
+    "k+1, m). Arguments must both be non-negative doubles.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    Number of occurrences (nonnegative, real)\n"
+    "m : array_like\n"
+    "    Shape parameter (nonnegative, real)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the Poisson survival function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "pdtr : Poisson cumulative distribution function\n"
+    "pdtrik : inverse of `pdtr` with respect to `k`\n"
+    "pdtri : inverse of `pdtr` with respect to `m`")
+ufunc_pdtrc_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_pdtrc_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_pdtrc_types[0] = <char>NPY_FLOAT
+ufunc_pdtrc_types[1] = <char>NPY_FLOAT
+ufunc_pdtrc_types[2] = <char>NPY_FLOAT
+ufunc_pdtrc_types[3] = <char>NPY_DOUBLE
+ufunc_pdtrc_types[4] = <char>NPY_DOUBLE
+ufunc_pdtrc_types[5] = <char>NPY_DOUBLE
+ufunc_pdtrc_ptr[2*0] = <void*>_func_pdtrc
+ufunc_pdtrc_ptr[2*0+1] = <void*>(<char*>"pdtrc")
+ufunc_pdtrc_ptr[2*1] = <void*>_func_pdtrc
+ufunc_pdtrc_ptr[2*1+1] = <void*>(<char*>"pdtrc")
+ufunc_pdtrc_data[0] = &ufunc_pdtrc_ptr[2*0]
+ufunc_pdtrc_data[1] = &ufunc_pdtrc_ptr[2*1]
+pdtrc = np.PyUFunc_FromFuncAndData(ufunc_pdtrc_loops, ufunc_pdtrc_data, ufunc_pdtrc_types, 2, 2, 1, 0, "pdtrc", ufunc_pdtrc_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pdtri_loops[3]
+cdef void *ufunc_pdtri_ptr[6]
+cdef void *ufunc_pdtri_data[3]
+cdef char ufunc_pdtri_types[9]
+cdef char *ufunc_pdtri_doc = (
+    "pdtri(k, y, out=None)\n"
+    "\n"
+    "Inverse to `pdtr` vs m\n"
+    "\n"
+    "Returns the Poisson variable `m` such that the sum from 0 to `k` of\n"
+    "the Poisson density is equal to the given probability `y`:\n"
+    "calculated by ``gammaincinv(k + 1, y)``. `k` must be a nonnegative\n"
+    "integer and `y` between 0 and 1.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "k : array_like\n"
+    "    Number of occurrences (nonnegative, real)\n"
+    "y : array_like\n"
+    "    Probability\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the shape parameter `m` such that ``pdtr(k, m) = p``\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "pdtr : Poisson cumulative distribution function\n"
+    "pdtrc : Poisson survival function\n"
+    "pdtrik : inverse of `pdtr` with respect to `k`")
+ufunc_pdtri_loops[0] = <np.PyUFuncGenericFunction>loop_d_id__As_ld_d
+ufunc_pdtri_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_pdtri_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_pdtri_types[0] = <char>NPY_LONG
+ufunc_pdtri_types[1] = <char>NPY_DOUBLE
+ufunc_pdtri_types[2] = <char>NPY_DOUBLE
+ufunc_pdtri_types[3] = <char>NPY_FLOAT
+ufunc_pdtri_types[4] = <char>NPY_FLOAT
+ufunc_pdtri_types[5] = <char>NPY_FLOAT
+ufunc_pdtri_types[6] = <char>NPY_DOUBLE
+ufunc_pdtri_types[7] = <char>NPY_DOUBLE
+ufunc_pdtri_types[8] = <char>NPY_DOUBLE
+ufunc_pdtri_ptr[2*0] = <void*>_func_pdtri
+ufunc_pdtri_ptr[2*0+1] = <void*>(<char*>"pdtri")
+ufunc_pdtri_ptr[2*1] = <void*>_func_pdtri_unsafe
+ufunc_pdtri_ptr[2*1+1] = <void*>(<char*>"pdtri")
+ufunc_pdtri_ptr[2*2] = <void*>_func_pdtri_unsafe
+ufunc_pdtri_ptr[2*2+1] = <void*>(<char*>"pdtri")
+ufunc_pdtri_data[0] = &ufunc_pdtri_ptr[2*0]
+ufunc_pdtri_data[1] = &ufunc_pdtri_ptr[2*1]
+ufunc_pdtri_data[2] = &ufunc_pdtri_ptr[2*2]
+pdtri = np.PyUFunc_FromFuncAndData(ufunc_pdtri_loops, ufunc_pdtri_data, ufunc_pdtri_types, 3, 2, 1, 0, "pdtri", ufunc_pdtri_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pdtrik_loops[2]
+cdef void *ufunc_pdtrik_ptr[4]
+cdef void *ufunc_pdtrik_data[2]
+cdef char ufunc_pdtrik_types[6]
+cdef char *ufunc_pdtrik_doc = (
+    "pdtrik(p, m, out=None)\n"
+    "\n"
+    "Inverse to `pdtr` vs `m`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Shape parameter (nonnegative, real)\n"
+    "p : array_like\n"
+    "    Probability\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The number of occurrences `k` such that ``pdtr(k, m) = p``\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "pdtr : Poisson cumulative distribution function\n"
+    "pdtrc : Poisson survival function\n"
+    "pdtri : inverse of `pdtr` with respect to `m`")
+ufunc_pdtrik_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_pdtrik_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_pdtrik_types[0] = <char>NPY_FLOAT
+ufunc_pdtrik_types[1] = <char>NPY_FLOAT
+ufunc_pdtrik_types[2] = <char>NPY_FLOAT
+ufunc_pdtrik_types[3] = <char>NPY_DOUBLE
+ufunc_pdtrik_types[4] = <char>NPY_DOUBLE
+ufunc_pdtrik_types[5] = <char>NPY_DOUBLE
+ufunc_pdtrik_ptr[2*0] = <void*>_func_pdtrik
+ufunc_pdtrik_ptr[2*0+1] = <void*>(<char*>"pdtrik")
+ufunc_pdtrik_ptr[2*1] = <void*>_func_pdtrik
+ufunc_pdtrik_ptr[2*1+1] = <void*>(<char*>"pdtrik")
+ufunc_pdtrik_data[0] = &ufunc_pdtrik_ptr[2*0]
+ufunc_pdtrik_data[1] = &ufunc_pdtrik_ptr[2*1]
+pdtrik = np.PyUFunc_FromFuncAndData(ufunc_pdtrik_loops, ufunc_pdtrik_data, ufunc_pdtrik_types, 2, 2, 1, 0, "pdtrik", ufunc_pdtrik_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_poch_loops[2]
+cdef void *ufunc_poch_ptr[4]
+cdef void *ufunc_poch_data[2]
+cdef char ufunc_poch_types[6]
+cdef char *ufunc_poch_doc = (
+    "poch(z, m, out=None)\n"
+    "\n"
+    "Pochhammer symbol.\n"
+    "\n"
+    "The Pochhammer symbol (rising factorial) is defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    (z)_m = \\frac{\\Gamma(z + m)}{\\Gamma(z)}\n"
+    "\n"
+    "For positive integer `m` it reads\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    (z)_m = z (z + 1) ... (z + m - 1)\n"
+    "\n"
+    "See [dlmf]_ for more details.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z, m : array_like\n"
+    "    Real-valued arguments.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The value of the function.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] Nist, Digital Library of Mathematical Functions\n"
+    "    https://dlmf.nist.gov/5.2#iii\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is 1 when m is 0.\n"
+    "\n"
+    ">>> sc.poch([1, 2, 3, 4], 0)\n"
+    "array([1., 1., 1., 1.])\n"
+    "\n"
+    "For z equal to 1 it reduces to the factorial function.\n"
+    "\n"
+    ">>> sc.poch(1, 5)\n"
+    "120.0\n"
+    ">>> 1 * 2 * 3 * 4 * 5\n"
+    "120\n"
+    "\n"
+    "It can be expressed in terms of the gamma function.\n"
+    "\n"
+    ">>> z, m = 3.7, 2.1\n"
+    ">>> sc.poch(z, m)\n"
+    "20.529581933776953\n"
+    ">>> sc.gamma(z + m) / sc.gamma(z)\n"
+    "20.52958193377696")
+ufunc_poch_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_poch_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_poch_types[0] = <char>NPY_FLOAT
+ufunc_poch_types[1] = <char>NPY_FLOAT
+ufunc_poch_types[2] = <char>NPY_FLOAT
+ufunc_poch_types[3] = <char>NPY_DOUBLE
+ufunc_poch_types[4] = <char>NPY_DOUBLE
+ufunc_poch_types[5] = <char>NPY_DOUBLE
+ufunc_poch_ptr[2*0] = <void*>_func_poch
+ufunc_poch_ptr[2*0+1] = <void*>(<char*>"poch")
+ufunc_poch_ptr[2*1] = <void*>_func_poch
+ufunc_poch_ptr[2*1+1] = <void*>(<char*>"poch")
+ufunc_poch_data[0] = &ufunc_poch_ptr[2*0]
+ufunc_poch_data[1] = &ufunc_poch_ptr[2*1]
+poch = np.PyUFunc_FromFuncAndData(ufunc_poch_loops, ufunc_poch_data, ufunc_poch_types, 2, 2, 1, 0, "poch", ufunc_poch_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_powm1_loops[2]
+cdef void *ufunc_powm1_ptr[4]
+cdef void *ufunc_powm1_data[2]
+cdef char ufunc_powm1_types[6]
+cdef char *ufunc_powm1_doc = (
+    "powm1(x, y, out=None)\n"
+    "\n"
+    "Computes ``x**y - 1``.\n"
+    "\n"
+    "This function is useful when `y` is near 0, or when `x` is near 1.\n"
+    "\n"
+    "The function is implemented for real types only (unlike ``numpy.power``,\n"
+    "which accepts complex inputs).\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    The base. Must be a real type (i.e. integer or float, not complex).\n"
+    "y : array_like\n"
+    "    The exponent. Must be a real type (i.e. integer or float, not complex).\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "array_like\n"
+    "    Result of the calculation\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    ".. versionadded:: 1.10.0\n"
+    "\n"
+    "The underlying code is implemented for single precision and double\n"
+    "precision floats only.  Unlike `numpy.power`, integer inputs to\n"
+    "`powm1` are converted to floating point, and complex inputs are\n"
+    "not accepted.\n"
+    "\n"
+    "Note the following edge cases:\n"
+    "\n"
+    "* ``powm1(x, 0)`` returns 0 for any ``x``, including 0, ``inf``\n"
+    "  and ``nan``.\n"
+    "* ``powm1(1, y)`` returns 0 for any ``y``, including ``nan``\n"
+    "  and ``inf``.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import powm1\n"
+    "\n"
+    ">>> x = np.array([1.2, 10.0, 0.9999999975])\n"
+    ">>> y = np.array([1e-9, 1e-11, 0.1875])\n"
+    ">>> powm1(x, y)\n"
+    "array([ 1.82321557e-10,  2.30258509e-11, -4.68749998e-10])\n"
+    "\n"
+    "It can be verified that the relative errors in those results\n"
+    "are less than 2.5e-16.\n"
+    "\n"
+    "Compare that to the result of ``x**y - 1``, where the\n"
+    "relative errors are all larger than 8e-8:\n"
+    "\n"
+    ">>> x**y - 1\n"
+    "array([ 1.82321491e-10,  2.30258035e-11, -4.68750039e-10])")
+ufunc_powm1_loops[0] = <np.PyUFuncGenericFunction>loop_f_ff__As_ff_f
+ufunc_powm1_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_powm1_types[0] = <char>NPY_FLOAT
+ufunc_powm1_types[1] = <char>NPY_FLOAT
+ufunc_powm1_types[2] = <char>NPY_FLOAT
+ufunc_powm1_types[3] = <char>NPY_DOUBLE
+ufunc_powm1_types[4] = <char>NPY_DOUBLE
+ufunc_powm1_types[5] = <char>NPY_DOUBLE
+ufunc_powm1_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_powm1_float
+ufunc_powm1_ptr[2*0+1] = <void*>(<char*>"powm1")
+ufunc_powm1_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_powm1_double
+ufunc_powm1_ptr[2*1+1] = <void*>(<char*>"powm1")
+ufunc_powm1_data[0] = &ufunc_powm1_ptr[2*0]
+ufunc_powm1_data[1] = &ufunc_powm1_ptr[2*1]
+powm1 = np.PyUFunc_FromFuncAndData(ufunc_powm1_loops, ufunc_powm1_data, ufunc_powm1_types, 2, 2, 1, 0, "powm1", ufunc_powm1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pro_ang1_loops[2]
+cdef void *ufunc_pro_ang1_ptr[4]
+cdef void *ufunc_pro_ang1_data[2]
+cdef char ufunc_pro_ang1_types[12]
+cdef char *ufunc_pro_ang1_doc = (
+    "pro_ang1(m, n, c, x, out=None)\n"
+    "\n"
+    "Prolate spheroidal angular function of the first kind and its derivative\n"
+    "\n"
+    "Computes the prolate spheroidal angular function of the first kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Nonnegative mode parameter m\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "x : array_like\n"
+    "    Real parameter (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x")
+ufunc_pro_ang1_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_ffff_ff
+ufunc_pro_ang1_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_dddd_dd
+ufunc_pro_ang1_types[0] = <char>NPY_FLOAT
+ufunc_pro_ang1_types[1] = <char>NPY_FLOAT
+ufunc_pro_ang1_types[2] = <char>NPY_FLOAT
+ufunc_pro_ang1_types[3] = <char>NPY_FLOAT
+ufunc_pro_ang1_types[4] = <char>NPY_FLOAT
+ufunc_pro_ang1_types[5] = <char>NPY_FLOAT
+ufunc_pro_ang1_types[6] = <char>NPY_DOUBLE
+ufunc_pro_ang1_types[7] = <char>NPY_DOUBLE
+ufunc_pro_ang1_types[8] = <char>NPY_DOUBLE
+ufunc_pro_ang1_types[9] = <char>NPY_DOUBLE
+ufunc_pro_ang1_types[10] = <char>NPY_DOUBLE
+ufunc_pro_ang1_types[11] = <char>NPY_DOUBLE
+ufunc_pro_ang1_ptr[2*0] = <void*>_func_prolate_aswfa_nocv_wrap
+ufunc_pro_ang1_ptr[2*0+1] = <void*>(<char*>"pro_ang1")
+ufunc_pro_ang1_ptr[2*1] = <void*>_func_prolate_aswfa_nocv_wrap
+ufunc_pro_ang1_ptr[2*1+1] = <void*>(<char*>"pro_ang1")
+ufunc_pro_ang1_data[0] = &ufunc_pro_ang1_ptr[2*0]
+ufunc_pro_ang1_data[1] = &ufunc_pro_ang1_ptr[2*1]
+pro_ang1 = np.PyUFunc_FromFuncAndData(ufunc_pro_ang1_loops, ufunc_pro_ang1_data, ufunc_pro_ang1_types, 2, 4, 2, 0, "pro_ang1", ufunc_pro_ang1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pro_ang1_cv_loops[2]
+cdef void *ufunc_pro_ang1_cv_ptr[4]
+cdef void *ufunc_pro_ang1_cv_data[2]
+cdef char ufunc_pro_ang1_cv_types[14]
+cdef char *ufunc_pro_ang1_cv_doc = (
+    "pro_ang1_cv(m, n, c, cv, x, out=None)\n"
+    "\n"
+    "Prolate spheroidal angular function pro_ang1 for precomputed characteristic value\n"
+    "\n"
+    "Computes the prolate spheroidal angular function of the first kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\n"
+    "pre-computed characteristic value.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Nonnegative mode parameter m\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "cv : array_like\n"
+    "    Characteristic value\n"
+    "x : array_like\n"
+    "    Real parameter (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x")
+ufunc_pro_ang1_cv_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_fffff_ff
+ufunc_pro_ang1_cv_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_ddddd_dd
+ufunc_pro_ang1_cv_types[0] = <char>NPY_FLOAT
+ufunc_pro_ang1_cv_types[1] = <char>NPY_FLOAT
+ufunc_pro_ang1_cv_types[2] = <char>NPY_FLOAT
+ufunc_pro_ang1_cv_types[3] = <char>NPY_FLOAT
+ufunc_pro_ang1_cv_types[4] = <char>NPY_FLOAT
+ufunc_pro_ang1_cv_types[5] = <char>NPY_FLOAT
+ufunc_pro_ang1_cv_types[6] = <char>NPY_FLOAT
+ufunc_pro_ang1_cv_types[7] = <char>NPY_DOUBLE
+ufunc_pro_ang1_cv_types[8] = <char>NPY_DOUBLE
+ufunc_pro_ang1_cv_types[9] = <char>NPY_DOUBLE
+ufunc_pro_ang1_cv_types[10] = <char>NPY_DOUBLE
+ufunc_pro_ang1_cv_types[11] = <char>NPY_DOUBLE
+ufunc_pro_ang1_cv_types[12] = <char>NPY_DOUBLE
+ufunc_pro_ang1_cv_types[13] = <char>NPY_DOUBLE
+ufunc_pro_ang1_cv_ptr[2*0] = <void*>_func_prolate_aswfa_wrap
+ufunc_pro_ang1_cv_ptr[2*0+1] = <void*>(<char*>"pro_ang1_cv")
+ufunc_pro_ang1_cv_ptr[2*1] = <void*>_func_prolate_aswfa_wrap
+ufunc_pro_ang1_cv_ptr[2*1+1] = <void*>(<char*>"pro_ang1_cv")
+ufunc_pro_ang1_cv_data[0] = &ufunc_pro_ang1_cv_ptr[2*0]
+ufunc_pro_ang1_cv_data[1] = &ufunc_pro_ang1_cv_ptr[2*1]
+pro_ang1_cv = np.PyUFunc_FromFuncAndData(ufunc_pro_ang1_cv_loops, ufunc_pro_ang1_cv_data, ufunc_pro_ang1_cv_types, 2, 5, 2, 0, "pro_ang1_cv", ufunc_pro_ang1_cv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pro_cv_loops[2]
+cdef void *ufunc_pro_cv_ptr[4]
+cdef void *ufunc_pro_cv_data[2]
+cdef char ufunc_pro_cv_types[8]
+cdef char *ufunc_pro_cv_doc = (
+    "pro_cv(m, n, c, out=None)\n"
+    "\n"
+    "Characteristic value of prolate spheroidal function\n"
+    "\n"
+    "Computes the characteristic value of prolate spheroidal wave\n"
+    "functions of order `m`, `n` (n>=m) and spheroidal parameter `c`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Nonnegative mode parameter m\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "cv : scalar or ndarray\n"
+    "    Characteristic value")
+ufunc_pro_cv_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_pro_cv_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_pro_cv_types[0] = <char>NPY_FLOAT
+ufunc_pro_cv_types[1] = <char>NPY_FLOAT
+ufunc_pro_cv_types[2] = <char>NPY_FLOAT
+ufunc_pro_cv_types[3] = <char>NPY_FLOAT
+ufunc_pro_cv_types[4] = <char>NPY_DOUBLE
+ufunc_pro_cv_types[5] = <char>NPY_DOUBLE
+ufunc_pro_cv_types[6] = <char>NPY_DOUBLE
+ufunc_pro_cv_types[7] = <char>NPY_DOUBLE
+ufunc_pro_cv_ptr[2*0] = <void*>_func_prolate_segv_wrap
+ufunc_pro_cv_ptr[2*0+1] = <void*>(<char*>"pro_cv")
+ufunc_pro_cv_ptr[2*1] = <void*>_func_prolate_segv_wrap
+ufunc_pro_cv_ptr[2*1+1] = <void*>(<char*>"pro_cv")
+ufunc_pro_cv_data[0] = &ufunc_pro_cv_ptr[2*0]
+ufunc_pro_cv_data[1] = &ufunc_pro_cv_ptr[2*1]
+pro_cv = np.PyUFunc_FromFuncAndData(ufunc_pro_cv_loops, ufunc_pro_cv_data, ufunc_pro_cv_types, 2, 3, 1, 0, "pro_cv", ufunc_pro_cv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pro_rad1_loops[2]
+cdef void *ufunc_pro_rad1_ptr[4]
+cdef void *ufunc_pro_rad1_data[2]
+cdef char ufunc_pro_rad1_types[12]
+cdef char *ufunc_pro_rad1_doc = (
+    "pro_rad1(m, n, c, x, out=None)\n"
+    "\n"
+    "Prolate spheroidal radial function of the first kind and its derivative\n"
+    "\n"
+    "Computes the prolate spheroidal radial function of the first kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Nonnegative mode parameter m\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "x : array_like\n"
+    "    Real parameter (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x")
+ufunc_pro_rad1_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_ffff_ff
+ufunc_pro_rad1_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_dddd_dd
+ufunc_pro_rad1_types[0] = <char>NPY_FLOAT
+ufunc_pro_rad1_types[1] = <char>NPY_FLOAT
+ufunc_pro_rad1_types[2] = <char>NPY_FLOAT
+ufunc_pro_rad1_types[3] = <char>NPY_FLOAT
+ufunc_pro_rad1_types[4] = <char>NPY_FLOAT
+ufunc_pro_rad1_types[5] = <char>NPY_FLOAT
+ufunc_pro_rad1_types[6] = <char>NPY_DOUBLE
+ufunc_pro_rad1_types[7] = <char>NPY_DOUBLE
+ufunc_pro_rad1_types[8] = <char>NPY_DOUBLE
+ufunc_pro_rad1_types[9] = <char>NPY_DOUBLE
+ufunc_pro_rad1_types[10] = <char>NPY_DOUBLE
+ufunc_pro_rad1_types[11] = <char>NPY_DOUBLE
+ufunc_pro_rad1_ptr[2*0] = <void*>_func_prolate_radial1_nocv_wrap
+ufunc_pro_rad1_ptr[2*0+1] = <void*>(<char*>"pro_rad1")
+ufunc_pro_rad1_ptr[2*1] = <void*>_func_prolate_radial1_nocv_wrap
+ufunc_pro_rad1_ptr[2*1+1] = <void*>(<char*>"pro_rad1")
+ufunc_pro_rad1_data[0] = &ufunc_pro_rad1_ptr[2*0]
+ufunc_pro_rad1_data[1] = &ufunc_pro_rad1_ptr[2*1]
+pro_rad1 = np.PyUFunc_FromFuncAndData(ufunc_pro_rad1_loops, ufunc_pro_rad1_data, ufunc_pro_rad1_types, 2, 4, 2, 0, "pro_rad1", ufunc_pro_rad1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pro_rad1_cv_loops[2]
+cdef void *ufunc_pro_rad1_cv_ptr[4]
+cdef void *ufunc_pro_rad1_cv_data[2]
+cdef char ufunc_pro_rad1_cv_types[14]
+cdef char *ufunc_pro_rad1_cv_doc = (
+    "pro_rad1_cv(m, n, c, cv, x, out=None)\n"
+    "\n"
+    "Prolate spheroidal radial function pro_rad1 for precomputed characteristic value\n"
+    "\n"
+    "Computes the prolate spheroidal radial function of the first kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\n"
+    "pre-computed characteristic value.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Nonnegative mode parameter m\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "cv : array_like\n"
+    "    Characteristic value\n"
+    "x : array_like\n"
+    "    Real parameter (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x")
+ufunc_pro_rad1_cv_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_fffff_ff
+ufunc_pro_rad1_cv_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_ddddd_dd
+ufunc_pro_rad1_cv_types[0] = <char>NPY_FLOAT
+ufunc_pro_rad1_cv_types[1] = <char>NPY_FLOAT
+ufunc_pro_rad1_cv_types[2] = <char>NPY_FLOAT
+ufunc_pro_rad1_cv_types[3] = <char>NPY_FLOAT
+ufunc_pro_rad1_cv_types[4] = <char>NPY_FLOAT
+ufunc_pro_rad1_cv_types[5] = <char>NPY_FLOAT
+ufunc_pro_rad1_cv_types[6] = <char>NPY_FLOAT
+ufunc_pro_rad1_cv_types[7] = <char>NPY_DOUBLE
+ufunc_pro_rad1_cv_types[8] = <char>NPY_DOUBLE
+ufunc_pro_rad1_cv_types[9] = <char>NPY_DOUBLE
+ufunc_pro_rad1_cv_types[10] = <char>NPY_DOUBLE
+ufunc_pro_rad1_cv_types[11] = <char>NPY_DOUBLE
+ufunc_pro_rad1_cv_types[12] = <char>NPY_DOUBLE
+ufunc_pro_rad1_cv_types[13] = <char>NPY_DOUBLE
+ufunc_pro_rad1_cv_ptr[2*0] = <void*>_func_prolate_radial1_wrap
+ufunc_pro_rad1_cv_ptr[2*0+1] = <void*>(<char*>"pro_rad1_cv")
+ufunc_pro_rad1_cv_ptr[2*1] = <void*>_func_prolate_radial1_wrap
+ufunc_pro_rad1_cv_ptr[2*1+1] = <void*>(<char*>"pro_rad1_cv")
+ufunc_pro_rad1_cv_data[0] = &ufunc_pro_rad1_cv_ptr[2*0]
+ufunc_pro_rad1_cv_data[1] = &ufunc_pro_rad1_cv_ptr[2*1]
+pro_rad1_cv = np.PyUFunc_FromFuncAndData(ufunc_pro_rad1_cv_loops, ufunc_pro_rad1_cv_data, ufunc_pro_rad1_cv_types, 2, 5, 2, 0, "pro_rad1_cv", ufunc_pro_rad1_cv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pro_rad2_loops[2]
+cdef void *ufunc_pro_rad2_ptr[4]
+cdef void *ufunc_pro_rad2_data[2]
+cdef char ufunc_pro_rad2_types[12]
+cdef char *ufunc_pro_rad2_doc = (
+    "pro_rad2(m, n, c, x, out=None)\n"
+    "\n"
+    "Prolate spheroidal radial function of the second kind and its derivative\n"
+    "\n"
+    "Computes the prolate spheroidal radial function of the second kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Nonnegative mode parameter m\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "cv : array_like\n"
+    "    Characteristic value\n"
+    "x : array_like\n"
+    "    Real parameter (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x")
+ufunc_pro_rad2_loops[0] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_ffff_ff
+ufunc_pro_rad2_loops[1] = <np.PyUFuncGenericFunction>loop_d_dddd_d_As_dddd_dd
+ufunc_pro_rad2_types[0] = <char>NPY_FLOAT
+ufunc_pro_rad2_types[1] = <char>NPY_FLOAT
+ufunc_pro_rad2_types[2] = <char>NPY_FLOAT
+ufunc_pro_rad2_types[3] = <char>NPY_FLOAT
+ufunc_pro_rad2_types[4] = <char>NPY_FLOAT
+ufunc_pro_rad2_types[5] = <char>NPY_FLOAT
+ufunc_pro_rad2_types[6] = <char>NPY_DOUBLE
+ufunc_pro_rad2_types[7] = <char>NPY_DOUBLE
+ufunc_pro_rad2_types[8] = <char>NPY_DOUBLE
+ufunc_pro_rad2_types[9] = <char>NPY_DOUBLE
+ufunc_pro_rad2_types[10] = <char>NPY_DOUBLE
+ufunc_pro_rad2_types[11] = <char>NPY_DOUBLE
+ufunc_pro_rad2_ptr[2*0] = <void*>_func_prolate_radial2_nocv_wrap
+ufunc_pro_rad2_ptr[2*0+1] = <void*>(<char*>"pro_rad2")
+ufunc_pro_rad2_ptr[2*1] = <void*>_func_prolate_radial2_nocv_wrap
+ufunc_pro_rad2_ptr[2*1+1] = <void*>(<char*>"pro_rad2")
+ufunc_pro_rad2_data[0] = &ufunc_pro_rad2_ptr[2*0]
+ufunc_pro_rad2_data[1] = &ufunc_pro_rad2_ptr[2*1]
+pro_rad2 = np.PyUFunc_FromFuncAndData(ufunc_pro_rad2_loops, ufunc_pro_rad2_data, ufunc_pro_rad2_types, 2, 4, 2, 0, "pro_rad2", ufunc_pro_rad2_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pro_rad2_cv_loops[2]
+cdef void *ufunc_pro_rad2_cv_ptr[4]
+cdef void *ufunc_pro_rad2_cv_data[2]
+cdef char ufunc_pro_rad2_cv_types[14]
+cdef char *ufunc_pro_rad2_cv_doc = (
+    "pro_rad2_cv(m, n, c, cv, x, out=None)\n"
+    "\n"
+    "Prolate spheroidal radial function pro_rad2 for precomputed characteristic value\n"
+    "\n"
+    "Computes the prolate spheroidal radial function of the second kind\n"
+    "and its derivative (with respect to `x`) for mode parameters m>=0\n"
+    "and n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\n"
+    "pre-computed characteristic value.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Nonnegative mode parameter m\n"
+    "n : array_like\n"
+    "    Mode parameter n (>= m)\n"
+    "c : array_like\n"
+    "    Spheroidal parameter\n"
+    "cv : array_like\n"
+    "    Characteristic value\n"
+    "x : array_like\n"
+    "    Real parameter (``|x| < 1.0``)\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Value of the function\n"
+    "sp : scalar or ndarray\n"
+    "    Value of the derivative vs x")
+ufunc_pro_rad2_cv_loops[0] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_fffff_ff
+ufunc_pro_rad2_cv_loops[1] = <np.PyUFuncGenericFunction>loop_i_ddddd_dd_As_ddddd_dd
+ufunc_pro_rad2_cv_types[0] = <char>NPY_FLOAT
+ufunc_pro_rad2_cv_types[1] = <char>NPY_FLOAT
+ufunc_pro_rad2_cv_types[2] = <char>NPY_FLOAT
+ufunc_pro_rad2_cv_types[3] = <char>NPY_FLOAT
+ufunc_pro_rad2_cv_types[4] = <char>NPY_FLOAT
+ufunc_pro_rad2_cv_types[5] = <char>NPY_FLOAT
+ufunc_pro_rad2_cv_types[6] = <char>NPY_FLOAT
+ufunc_pro_rad2_cv_types[7] = <char>NPY_DOUBLE
+ufunc_pro_rad2_cv_types[8] = <char>NPY_DOUBLE
+ufunc_pro_rad2_cv_types[9] = <char>NPY_DOUBLE
+ufunc_pro_rad2_cv_types[10] = <char>NPY_DOUBLE
+ufunc_pro_rad2_cv_types[11] = <char>NPY_DOUBLE
+ufunc_pro_rad2_cv_types[12] = <char>NPY_DOUBLE
+ufunc_pro_rad2_cv_types[13] = <char>NPY_DOUBLE
+ufunc_pro_rad2_cv_ptr[2*0] = <void*>_func_prolate_radial2_wrap
+ufunc_pro_rad2_cv_ptr[2*0+1] = <void*>(<char*>"pro_rad2_cv")
+ufunc_pro_rad2_cv_ptr[2*1] = <void*>_func_prolate_radial2_wrap
+ufunc_pro_rad2_cv_ptr[2*1+1] = <void*>(<char*>"pro_rad2_cv")
+ufunc_pro_rad2_cv_data[0] = &ufunc_pro_rad2_cv_ptr[2*0]
+ufunc_pro_rad2_cv_data[1] = &ufunc_pro_rad2_cv_ptr[2*1]
+pro_rad2_cv = np.PyUFunc_FromFuncAndData(ufunc_pro_rad2_cv_loops, ufunc_pro_rad2_cv_data, ufunc_pro_rad2_cv_types, 2, 5, 2, 0, "pro_rad2_cv", ufunc_pro_rad2_cv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_pseudo_huber_loops[2]
+cdef void *ufunc_pseudo_huber_ptr[4]
+cdef void *ufunc_pseudo_huber_data[2]
+cdef char ufunc_pseudo_huber_types[6]
+cdef char *ufunc_pseudo_huber_doc = (
+    "pseudo_huber(delta, r, out=None)\n"
+    "\n"
+    "Pseudo-Huber loss function.\n"
+    "\n"
+    ".. math:: \\mathrm{pseudo\\_huber}(\\delta, r) =\n"
+    "          \\delta^2 \\left( \\sqrt{ 1 + \\left( \\frac{r}{\\delta} \\right)^2 } - 1 \\right)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "delta : array_like\n"
+    "    Input array, indicating the soft quadratic vs. linear loss changepoint.\n"
+    "r : array_like\n"
+    "    Input array, possibly representing residuals.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "res : scalar or ndarray\n"
+    "    The computed Pseudo-Huber loss function values.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "huber: Similar function which this function approximates\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Like `huber`, `pseudo_huber` often serves as a robust loss function\n"
+    "in statistics or machine learning to reduce the influence of outliers.\n"
+    "Unlike `huber`, `pseudo_huber` is smooth.\n"
+    "\n"
+    "Typically, `r` represents residuals, the difference\n"
+    "between a model prediction and data. Then, for :math:`|r|\\leq\\delta`,\n"
+    "`pseudo_huber` resembles the squared error and for :math:`|r|>\\delta` the\n"
+    "absolute error. This way, the Pseudo-Huber loss often achieves\n"
+    "a fast convergence in model fitting for small residuals like the squared\n"
+    "error loss function and still reduces the influence of outliers\n"
+    "(:math:`|r|>\\delta`) like the absolute error loss. As :math:`\\delta` is\n"
+    "the cutoff between squared and absolute error regimes, it has\n"
+    "to be tuned carefully for each problem. `pseudo_huber` is also\n"
+    "convex, making it suitable for gradient based optimization. [1]_ [2]_\n"
+    "\n"
+    ".. versionadded:: 0.15.0\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Hartley, Zisserman, \"Multiple View Geometry in Computer Vision\".\n"
+    "       2003. Cambridge University Press. p. 619\n"
+    ".. [2] Charbonnier et al. \"Deterministic edge-preserving regularization\n"
+    "       in computed imaging\". 1997. IEEE Trans. Image Processing.\n"
+    "       6 (2): 298 - 311.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Import all necessary modules.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import pseudo_huber, huber\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    "\n"
+    "Calculate the function for ``delta=1`` at ``r=2``.\n"
+    "\n"
+    ">>> pseudo_huber(1., 2.)\n"
+    "1.2360679774997898\n"
+    "\n"
+    "Calculate the function at ``r=2`` for different `delta` by providing\n"
+    "a list or NumPy array for `delta`.\n"
+    "\n"
+    ">>> pseudo_huber([1., 2., 4.], 3.)\n"
+    "array([2.16227766, 3.21110255, 4.        ])\n"
+    "\n"
+    "Calculate the function for ``delta=1`` at several points by providing\n"
+    "a list or NumPy array for `r`.\n"
+    "\n"
+    ">>> pseudo_huber(2., np.array([1., 1.5, 3., 4.]))\n"
+    "array([0.47213595, 1.        , 3.21110255, 4.94427191])\n"
+    "\n"
+    "The function can be calculated for different `delta` and `r` by\n"
+    "providing arrays for both with compatible shapes for broadcasting.\n"
+    "\n"
+    ">>> r = np.array([1., 2.5, 8., 10.])\n"
+    ">>> deltas = np.array([[1.], [5.], [9.]])\n"
+    ">>> print(r.shape, deltas.shape)\n"
+    "(4,) (3, 1)\n"
+    "\n"
+    ">>> pseudo_huber(deltas, r)\n"
+    "array([[ 0.41421356,  1.6925824 ,  7.06225775,  9.04987562],\n"
+    "       [ 0.49509757,  2.95084972, 22.16990566, 30.90169944],\n"
+    "       [ 0.49846624,  3.06693762, 27.37435121, 40.08261642]])\n"
+    "\n"
+    "Plot the function for different `delta`.\n"
+    "\n"
+    ">>> x = np.linspace(-4, 4, 500)\n"
+    ">>> deltas = [1, 2, 3]\n"
+    ">>> linestyles = [\"dashed\", \"dotted\", \"dashdot\"]\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> combined_plot_parameters = list(zip(deltas, linestyles))\n"
+    ">>> for delta, style in combined_plot_parameters:\n"
+    "...     ax.plot(x, pseudo_huber(delta, x), label=rf\"$\\delta={delta}$\",\n"
+    "...             ls=style)\n"
+    ">>> ax.legend(loc=\"upper center\")\n"
+    ">>> ax.set_xlabel(\"$x$\")\n"
+    ">>> ax.set_title(r\"Pseudo-Huber loss function $h_{\\delta}(x)$\")\n"
+    ">>> ax.set_xlim(-4, 4)\n"
+    ">>> ax.set_ylim(0, 8)\n"
+    ">>> plt.show()\n"
+    "\n"
+    "Finally, illustrate the difference between `huber` and `pseudo_huber` by\n"
+    "plotting them and their gradients with respect to `r`. The plot shows\n"
+    "that `pseudo_huber` is continuously differentiable while `huber` is not\n"
+    "at the points :math:`\\pm\\delta`.\n"
+    "\n"
+    ">>> def huber_grad(delta, x):\n"
+    "...     grad = np.copy(x)\n"
+    "...     linear_area = np.argwhere(np.abs(x) > delta)\n"
+    "...     grad[linear_area]=delta*np.sign(x[linear_area])\n"
+    "...     return grad\n"
+    ">>> def pseudo_huber_grad(delta, x):\n"
+    "...     return x* (1+(x/delta)**2)**(-0.5)\n"
+    ">>> x=np.linspace(-3, 3, 500)\n"
+    ">>> delta = 1.\n"
+    ">>> fig, ax = plt.subplots(figsize=(7, 7))\n"
+    ">>> ax.plot(x, huber(delta, x), label=\"Huber\", ls=\"dashed\")\n"
+    ">>> ax.plot(x, huber_grad(delta, x), label=\"Huber Gradient\", ls=\"dashdot\")\n"
+    ">>> ax.plot(x, pseudo_huber(delta, x), label=\"Pseudo-Huber\", ls=\"dotted\")\n"
+    ">>> ax.plot(x, pseudo_huber_grad(delta, x), label=\"Pseudo-Huber Gradient\",\n"
+    "...         ls=\"solid\")\n"
+    ">>> ax.legend(loc=\"upper center\")\n"
+    ">>> plt.show()")
+ufunc_pseudo_huber_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_pseudo_huber_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_pseudo_huber_types[0] = <char>NPY_FLOAT
+ufunc_pseudo_huber_types[1] = <char>NPY_FLOAT
+ufunc_pseudo_huber_types[2] = <char>NPY_FLOAT
+ufunc_pseudo_huber_types[3] = <char>NPY_DOUBLE
+ufunc_pseudo_huber_types[4] = <char>NPY_DOUBLE
+ufunc_pseudo_huber_types[5] = <char>NPY_DOUBLE
+ufunc_pseudo_huber_ptr[2*0] = <void*>_func_pseudo_huber
+ufunc_pseudo_huber_ptr[2*0+1] = <void*>(<char*>"pseudo_huber")
+ufunc_pseudo_huber_ptr[2*1] = <void*>_func_pseudo_huber
+ufunc_pseudo_huber_ptr[2*1+1] = <void*>(<char*>"pseudo_huber")
+ufunc_pseudo_huber_data[0] = &ufunc_pseudo_huber_ptr[2*0]
+ufunc_pseudo_huber_data[1] = &ufunc_pseudo_huber_ptr[2*1]
+pseudo_huber = np.PyUFunc_FromFuncAndData(ufunc_pseudo_huber_loops, ufunc_pseudo_huber_data, ufunc_pseudo_huber_types, 2, 2, 1, 0, "pseudo_huber", ufunc_pseudo_huber_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_psi_loops[4]
+cdef void *ufunc_psi_ptr[8]
+cdef void *ufunc_psi_data[4]
+cdef char ufunc_psi_types[8]
+cdef char *ufunc_psi_doc = (
+    "psi(z, out=None)\n"
+    "\n"
+    "The digamma function.\n"
+    "\n"
+    "The logarithmic derivative of the gamma function evaluated at ``z``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Real or complex argument.\n"
+    "out : ndarray, optional\n"
+    "    Array for the computed values of ``psi``.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "digamma : scalar or ndarray\n"
+    "    Computed values of ``psi``.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For large values not close to the negative real axis, ``psi`` is\n"
+    "computed using the asymptotic series (5.11.2) from [1]_. For small\n"
+    "arguments not close to the negative real axis, the recurrence\n"
+    "relation (5.5.2) from [1]_ is used until the argument is large\n"
+    "enough to use the asymptotic series. For values close to the\n"
+    "negative real axis, the reflection formula (5.5.4) from [1]_ is\n"
+    "used first. Note that ``psi`` has a family of zeros on the\n"
+    "negative real axis which occur between the poles at nonpositive\n"
+    "integers. Around the zeros the reflection formula suffers from\n"
+    "cancellation and the implementation loses precision. The sole\n"
+    "positive zero and the first negative zero, however, are handled\n"
+    "separately by precomputing series expansions using [2]_, so the\n"
+    "function should maintain full accuracy around the origin.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] NIST Digital Library of Mathematical Functions\n"
+    "       https://dlmf.nist.gov/5\n"
+    ".. [2] Fredrik Johansson and others.\n"
+    "       \"mpmath: a Python library for arbitrary-precision floating-point arithmetic\"\n"
+    "       (Version 0.19) http://mpmath.org/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import psi\n"
+    ">>> z = 3 + 4j\n"
+    ">>> psi(z)\n"
+    "(1.55035981733341+1.0105022091860445j)\n"
+    "\n"
+    "Verify psi(z) = psi(z + 1) - 1/z:\n"
+    "\n"
+    ">>> psi(z + 1) - 1/z\n"
+    "(1.55035981733341+1.0105022091860445j)")
+ufunc_psi_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_psi_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_psi_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_psi_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_psi_types[0] = <char>NPY_FLOAT
+ufunc_psi_types[1] = <char>NPY_FLOAT
+ufunc_psi_types[2] = <char>NPY_DOUBLE
+ufunc_psi_types[3] = <char>NPY_DOUBLE
+ufunc_psi_types[4] = <char>NPY_CFLOAT
+ufunc_psi_types[5] = <char>NPY_CFLOAT
+ufunc_psi_types[6] = <char>NPY_CDOUBLE
+ufunc_psi_types[7] = <char>NPY_CDOUBLE
+ufunc_psi_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_digamma
+ufunc_psi_ptr[2*0+1] = <void*>(<char*>"psi")
+ufunc_psi_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_digamma
+ufunc_psi_ptr[2*1+1] = <void*>(<char*>"psi")
+ufunc_psi_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_cdigamma
+ufunc_psi_ptr[2*2+1] = <void*>(<char*>"psi")
+ufunc_psi_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_cdigamma
+ufunc_psi_ptr[2*3+1] = <void*>(<char*>"psi")
+ufunc_psi_data[0] = &ufunc_psi_ptr[2*0]
+ufunc_psi_data[1] = &ufunc_psi_ptr[2*1]
+ufunc_psi_data[2] = &ufunc_psi_ptr[2*2]
+ufunc_psi_data[3] = &ufunc_psi_ptr[2*3]
+psi = np.PyUFunc_FromFuncAndData(ufunc_psi_loops, ufunc_psi_data, ufunc_psi_types, 4, 1, 1, 0, "psi", ufunc_psi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_radian_loops[2]
+cdef void *ufunc_radian_ptr[4]
+cdef void *ufunc_radian_data[2]
+cdef char ufunc_radian_types[8]
+cdef char *ufunc_radian_doc = (
+    "radian(d, m, s, out=None)\n"
+    "\n"
+    "Convert from degrees to radians.\n"
+    "\n"
+    "Returns the angle given in (d)egrees, (m)inutes, and (s)econds in\n"
+    "radians.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "d : array_like\n"
+    "    Degrees, can be real-valued.\n"
+    "m : array_like\n"
+    "    Minutes, can be real-valued.\n"
+    "s : array_like\n"
+    "    Seconds, can be real-valued.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of the inputs in radians.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "There are many ways to specify an angle.\n"
+    "\n"
+    ">>> sc.radian(90, 0, 0)\n"
+    "1.5707963267948966\n"
+    ">>> sc.radian(0, 60 * 90, 0)\n"
+    "1.5707963267948966\n"
+    ">>> sc.radian(0, 0, 60**2 * 90)\n"
+    "1.5707963267948966\n"
+    "\n"
+    "The inputs can be real-valued.\n"
+    "\n"
+    ">>> sc.radian(1.5, 0, 0)\n"
+    "0.02617993877991494\n"
+    ">>> sc.radian(1, 30, 0)\n"
+    "0.02617993877991494")
+ufunc_radian_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_radian_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_radian_types[0] = <char>NPY_FLOAT
+ufunc_radian_types[1] = <char>NPY_FLOAT
+ufunc_radian_types[2] = <char>NPY_FLOAT
+ufunc_radian_types[3] = <char>NPY_FLOAT
+ufunc_radian_types[4] = <char>NPY_DOUBLE
+ufunc_radian_types[5] = <char>NPY_DOUBLE
+ufunc_radian_types[6] = <char>NPY_DOUBLE
+ufunc_radian_types[7] = <char>NPY_DOUBLE
+ufunc_radian_ptr[2*0] = <void*>_func_radian
+ufunc_radian_ptr[2*0+1] = <void*>(<char*>"radian")
+ufunc_radian_ptr[2*1] = <void*>_func_radian
+ufunc_radian_ptr[2*1+1] = <void*>(<char*>"radian")
+ufunc_radian_data[0] = &ufunc_radian_ptr[2*0]
+ufunc_radian_data[1] = &ufunc_radian_ptr[2*1]
+radian = np.PyUFunc_FromFuncAndData(ufunc_radian_loops, ufunc_radian_data, ufunc_radian_types, 2, 3, 1, 0, "radian", ufunc_radian_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_rel_entr_loops[2]
+cdef void *ufunc_rel_entr_ptr[4]
+cdef void *ufunc_rel_entr_data[2]
+cdef char ufunc_rel_entr_types[6]
+cdef char *ufunc_rel_entr_doc = (
+    "rel_entr(x, y, out=None)\n"
+    "\n"
+    "Elementwise function for computing relative entropy.\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\mathrm{rel\\_entr}(x, y) =\n"
+    "        \\begin{cases}\n"
+    "            x \\log(x / y) & x > 0, y > 0 \\\\\n"
+    "            0 & x = 0, y \\ge 0 \\\\\n"
+    "            \\infty & \\text{otherwise}\n"
+    "        \\end{cases}\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x, y : array_like\n"
+    "    Input arrays\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Relative entropy of the inputs\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "entr, kl_div, scipy.stats.entropy\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    ".. versionadded:: 0.15.0\n"
+    "\n"
+    "This function is jointly convex in x and y.\n"
+    "\n"
+    "The origin of this function is in convex programming; see\n"
+    "[1]_. Given two discrete probability distributions :math:`p_1,\n"
+    "\\ldots, p_n` and :math:`q_1, \\ldots, q_n`, the definition of relative\n"
+    "entropy in the context of *information theory* is\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\sum_{i = 1}^n \\mathrm{rel\\_entr}(p_i, q_i).\n"
+    "\n"
+    "To compute the latter quantity, use `scipy.stats.entropy`.\n"
+    "\n"
+    "See [2]_ for details.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Boyd, Stephen and Lieven Vandenberghe. *Convex optimization*.\n"
+    "       Cambridge University Press, 2004.\n"
+    "       :doi:`https://doi.org/10.1017/CBO9780511804441`\n"
+    ".. [2] Kullback-Leibler divergence,\n"
+    "       https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence")
+ufunc_rel_entr_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_rel_entr_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_rel_entr_types[0] = <char>NPY_FLOAT
+ufunc_rel_entr_types[1] = <char>NPY_FLOAT
+ufunc_rel_entr_types[2] = <char>NPY_FLOAT
+ufunc_rel_entr_types[3] = <char>NPY_DOUBLE
+ufunc_rel_entr_types[4] = <char>NPY_DOUBLE
+ufunc_rel_entr_types[5] = <char>NPY_DOUBLE
+ufunc_rel_entr_ptr[2*0] = <void*>_func_rel_entr
+ufunc_rel_entr_ptr[2*0+1] = <void*>(<char*>"rel_entr")
+ufunc_rel_entr_ptr[2*1] = <void*>_func_rel_entr
+ufunc_rel_entr_ptr[2*1+1] = <void*>(<char*>"rel_entr")
+ufunc_rel_entr_data[0] = &ufunc_rel_entr_ptr[2*0]
+ufunc_rel_entr_data[1] = &ufunc_rel_entr_ptr[2*1]
+rel_entr = np.PyUFunc_FromFuncAndData(ufunc_rel_entr_loops, ufunc_rel_entr_data, ufunc_rel_entr_types, 2, 2, 1, 0, "rel_entr", ufunc_rel_entr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_rgamma_loops[4]
+cdef void *ufunc_rgamma_ptr[8]
+cdef void *ufunc_rgamma_data[4]
+cdef char ufunc_rgamma_types[8]
+cdef char *ufunc_rgamma_doc = (
+    "rgamma(z, out=None)\n"
+    "\n"
+    "Reciprocal of the gamma function.\n"
+    "\n"
+    "Defined as :math:`1 / \\Gamma(z)`, where :math:`\\Gamma` is the\n"
+    "gamma function. For more on the gamma function see `gamma`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Real or complex valued input\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Function results\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "gamma, gammaln, loggamma\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The gamma function has no zeros and has simple poles at\n"
+    "nonpositive integers, so `rgamma` is an entire function with zeros\n"
+    "at the nonpositive integers. See the discussion in [dlmf]_ for\n"
+    "more details.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] Nist, Digital Library of Mathematical functions,\n"
+    "    https://dlmf.nist.gov/5.2#i\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is the reciprocal of the gamma function.\n"
+    "\n"
+    ">>> sc.rgamma([1, 2, 3, 4])\n"
+    "array([1.        , 1.        , 0.5       , 0.16666667])\n"
+    ">>> 1 / sc.gamma([1, 2, 3, 4])\n"
+    "array([1.        , 1.        , 0.5       , 0.16666667])\n"
+    "\n"
+    "It is zero at nonpositive integers.\n"
+    "\n"
+    ">>> sc.rgamma([0, -1, -2, -3])\n"
+    "array([0., 0., 0., 0.])\n"
+    "\n"
+    "It rapidly underflows to zero along the positive real axis.\n"
+    "\n"
+    ">>> sc.rgamma([10, 100, 179])\n"
+    "array([2.75573192e-006, 1.07151029e-156, 0.00000000e+000])")
+ufunc_rgamma_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_rgamma_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_rgamma_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_rgamma_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_rgamma_types[0] = <char>NPY_FLOAT
+ufunc_rgamma_types[1] = <char>NPY_FLOAT
+ufunc_rgamma_types[2] = <char>NPY_DOUBLE
+ufunc_rgamma_types[3] = <char>NPY_DOUBLE
+ufunc_rgamma_types[4] = <char>NPY_CFLOAT
+ufunc_rgamma_types[5] = <char>NPY_CFLOAT
+ufunc_rgamma_types[6] = <char>NPY_CDOUBLE
+ufunc_rgamma_types[7] = <char>NPY_CDOUBLE
+ufunc_rgamma_ptr[2*0] = <void*>_func_rgamma
+ufunc_rgamma_ptr[2*0+1] = <void*>(<char*>"rgamma")
+ufunc_rgamma_ptr[2*1] = <void*>_func_rgamma
+ufunc_rgamma_ptr[2*1+1] = <void*>(<char*>"rgamma")
+ufunc_rgamma_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_crgamma
+ufunc_rgamma_ptr[2*2+1] = <void*>(<char*>"rgamma")
+ufunc_rgamma_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_crgamma
+ufunc_rgamma_ptr[2*3+1] = <void*>(<char*>"rgamma")
+ufunc_rgamma_data[0] = &ufunc_rgamma_ptr[2*0]
+ufunc_rgamma_data[1] = &ufunc_rgamma_ptr[2*1]
+ufunc_rgamma_data[2] = &ufunc_rgamma_ptr[2*2]
+ufunc_rgamma_data[3] = &ufunc_rgamma_ptr[2*3]
+rgamma = np.PyUFunc_FromFuncAndData(ufunc_rgamma_loops, ufunc_rgamma_data, ufunc_rgamma_types, 4, 1, 1, 0, "rgamma", ufunc_rgamma_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_round_loops[2]
+cdef void *ufunc_round_ptr[4]
+cdef void *ufunc_round_data[2]
+cdef char ufunc_round_types[4]
+cdef char *ufunc_round_doc = (
+    "round(x, out=None)\n"
+    "\n"
+    "Round to the nearest integer.\n"
+    "\n"
+    "Returns the nearest integer to `x`.  If `x` ends in 0.5 exactly,\n"
+    "the nearest even integer is chosen.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real valued input.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The nearest integers to the elements of `x`. The result is of\n"
+    "    floating type, not integer type.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It rounds to even.\n"
+    "\n"
+    ">>> sc.round([0.5, 1.5])\n"
+    "array([0., 2.])")
+ufunc_round_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_round_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_round_types[0] = <char>NPY_FLOAT
+ufunc_round_types[1] = <char>NPY_FLOAT
+ufunc_round_types[2] = <char>NPY_DOUBLE
+ufunc_round_types[3] = <char>NPY_DOUBLE
+ufunc_round_ptr[2*0] = <void*>_func_round
+ufunc_round_ptr[2*0+1] = <void*>(<char*>"round")
+ufunc_round_ptr[2*1] = <void*>_func_round
+ufunc_round_ptr[2*1+1] = <void*>(<char*>"round")
+ufunc_round_data[0] = &ufunc_round_ptr[2*0]
+ufunc_round_data[1] = &ufunc_round_ptr[2*1]
+round = np.PyUFunc_FromFuncAndData(ufunc_round_loops, ufunc_round_data, ufunc_round_types, 2, 1, 1, 0, "round", ufunc_round_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_shichi_loops[4]
+cdef void *ufunc_shichi_ptr[8]
+cdef void *ufunc_shichi_data[4]
+cdef char ufunc_shichi_types[12]
+cdef char *ufunc_shichi_doc = (
+    "shichi(x, out=None)\n"
+    "\n"
+    "Hyperbolic sine and cosine integrals.\n"
+    "\n"
+    "The hyperbolic sine integral is\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "  \\int_0^x \\frac{\\sinh{t}}{t}dt\n"
+    "\n"
+    "and the hyperbolic cosine integral is\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "  \\gamma + \\log(x) + \\int_0^x \\frac{\\cosh{t} - 1}{t} dt\n"
+    "\n"
+    "where :math:`\\gamma` is Euler's constant and :math:`\\log` is the\n"
+    "principal branch of the logarithm [1]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real or complex points at which to compute the hyperbolic sine\n"
+    "    and cosine integrals.\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "si : scalar or ndarray\n"
+    "    Hyperbolic sine integral at ``x``\n"
+    "ci : scalar or ndarray\n"
+    "    Hyperbolic cosine integral at ``x``\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "sici : Sine and cosine integrals.\n"
+    "exp1 : Exponential integral E1.\n"
+    "expi : Exponential integral Ei.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For real arguments with ``x < 0``, ``chi`` is the real part of the\n"
+    "hyperbolic cosine integral. For such points ``chi(x)`` and ``chi(x\n"
+    "+ 0j)`` differ by a factor of ``1j*pi``.\n"
+    "\n"
+    "For real arguments the function is computed by calling Cephes'\n"
+    "[2]_ *shichi* routine. For complex arguments the algorithm is based\n"
+    "on Mpmath's [3]_ *shi* and *chi* routines.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    "       (See Section 5.2.)\n"
+    ".. [2] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    ".. [3] Fredrik Johansson and others.\n"
+    "       \"mpmath: a Python library for arbitrary-precision floating-point\n"
+    "       arithmetic\" (Version 0.19) http://mpmath.org/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> from scipy.special import shichi, sici\n"
+    "\n"
+    "`shichi` accepts real or complex input:\n"
+    "\n"
+    ">>> shichi(0.5)\n"
+    "(0.5069967498196671, -0.05277684495649357)\n"
+    ">>> shichi(0.5 + 2.5j)\n"
+    "((0.11772029666668238+1.831091777729851j),\n"
+    " (0.29912435887648825+1.7395351121166562j))\n"
+    "\n"
+    "The hyperbolic sine and cosine integrals Shi(z) and Chi(z) are\n"
+    "related to the sine and cosine integrals Si(z) and Ci(z) by\n"
+    "\n"
+    "* Shi(z) = -i*Si(i*z)\n"
+    "* Chi(z) = Ci(-i*z) + i*pi/2\n"
+    "\n"
+    ">>> z = 0.25 + 5j\n"
+    ">>> shi, chi = shichi(z)\n"
+    ">>> shi, -1j*sici(1j*z)[0]            # Should be the same.\n"
+    "((-0.04834719325101729+1.5469354086921228j),\n"
+    " (-0.04834719325101729+1.5469354086921228j))\n"
+    ">>> chi, sici(-1j*z)[1] + 1j*np.pi/2  # Should be the same.\n"
+    "((-0.19568708973868087+1.556276312103824j),\n"
+    " (-0.19568708973868087+1.556276312103824j))\n"
+    "\n"
+    "Plot the functions evaluated on the real axis:\n"
+    "\n"
+    ">>> xp = np.geomspace(1e-8, 4.0, 250)\n"
+    ">>> x = np.concatenate((-xp[::-1], xp))\n"
+    ">>> shi, chi = shichi(x)\n"
+    "\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> ax.plot(x, shi, label='Shi(x)')\n"
+    ">>> ax.plot(x, chi, '--', label='Chi(x)')\n"
+    ">>> ax.set_xlabel('x')\n"
+    ">>> ax.set_title('Hyperbolic Sine and Cosine Integrals')\n"
+    ">>> ax.legend(shadow=True, framealpha=1, loc='lower right')\n"
+    ">>> ax.grid(True)\n"
+    ">>> plt.show()")
+ufunc_shichi_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_f_ff
+ufunc_shichi_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_d_dd
+ufunc_shichi_loops[2] = <np.PyUFuncGenericFunction>loop_i_D_DD_As_F_FF
+ufunc_shichi_loops[3] = <np.PyUFuncGenericFunction>loop_i_D_DD_As_D_DD
+ufunc_shichi_types[0] = <char>NPY_FLOAT
+ufunc_shichi_types[1] = <char>NPY_FLOAT
+ufunc_shichi_types[2] = <char>NPY_FLOAT
+ufunc_shichi_types[3] = <char>NPY_DOUBLE
+ufunc_shichi_types[4] = <char>NPY_DOUBLE
+ufunc_shichi_types[5] = <char>NPY_DOUBLE
+ufunc_shichi_types[6] = <char>NPY_CFLOAT
+ufunc_shichi_types[7] = <char>NPY_CFLOAT
+ufunc_shichi_types[8] = <char>NPY_CFLOAT
+ufunc_shichi_types[9] = <char>NPY_CDOUBLE
+ufunc_shichi_types[10] = <char>NPY_CDOUBLE
+ufunc_shichi_types[11] = <char>NPY_CDOUBLE
+ufunc_shichi_ptr[2*0] = <void*>_func_shichi
+ufunc_shichi_ptr[2*0+1] = <void*>(<char*>"shichi")
+ufunc_shichi_ptr[2*1] = <void*>_func_shichi
+ufunc_shichi_ptr[2*1+1] = <void*>(<char*>"shichi")
+ufunc_shichi_ptr[2*2] = <void*>_func_cshichi
+ufunc_shichi_ptr[2*2+1] = <void*>(<char*>"shichi")
+ufunc_shichi_ptr[2*3] = <void*>_func_cshichi
+ufunc_shichi_ptr[2*3+1] = <void*>(<char*>"shichi")
+ufunc_shichi_data[0] = &ufunc_shichi_ptr[2*0]
+ufunc_shichi_data[1] = &ufunc_shichi_ptr[2*1]
+ufunc_shichi_data[2] = &ufunc_shichi_ptr[2*2]
+ufunc_shichi_data[3] = &ufunc_shichi_ptr[2*3]
+shichi = np.PyUFunc_FromFuncAndData(ufunc_shichi_loops, ufunc_shichi_data, ufunc_shichi_types, 4, 1, 2, 0, "shichi", ufunc_shichi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_sici_loops[4]
+cdef void *ufunc_sici_ptr[8]
+cdef void *ufunc_sici_data[4]
+cdef char ufunc_sici_types[12]
+cdef char *ufunc_sici_doc = (
+    "sici(x, out=None)\n"
+    "\n"
+    "Sine and cosine integrals.\n"
+    "\n"
+    "The sine integral is\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "  \\int_0^x \\frac{\\sin{t}}{t}dt\n"
+    "\n"
+    "and the cosine integral is\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "  \\gamma + \\log(x) + \\int_0^x \\frac{\\cos{t} - 1}{t}dt\n"
+    "\n"
+    "where :math:`\\gamma` is Euler's constant and :math:`\\log` is the\n"
+    "principal branch of the logarithm [1]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real or complex points at which to compute the sine and cosine\n"
+    "    integrals.\n"
+    "out : tuple of ndarray, optional\n"
+    "    Optional output arrays for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "si : scalar or ndarray\n"
+    "    Sine integral at ``x``\n"
+    "ci : scalar or ndarray\n"
+    "    Cosine integral at ``x``\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "shichi : Hyperbolic sine and cosine integrals.\n"
+    "exp1 : Exponential integral E1.\n"
+    "expi : Exponential integral Ei.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For real arguments with ``x < 0``, ``ci`` is the real part of the\n"
+    "cosine integral. For such points ``ci(x)`` and ``ci(x + 0j)``\n"
+    "differ by a factor of ``1j*pi``.\n"
+    "\n"
+    "For real arguments the function is computed by calling Cephes'\n"
+    "[2]_ *sici* routine. For complex arguments the algorithm is based\n"
+    "on Mpmath's [3]_ *si* and *ci* routines.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Milton Abramowitz and Irene A. Stegun, eds.\n"
+    "       Handbook of Mathematical Functions with Formulas,\n"
+    "       Graphs, and Mathematical Tables. New York: Dover, 1972.\n"
+    "       (See Section 5.2.)\n"
+    ".. [2] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    ".. [3] Fredrik Johansson and others.\n"
+    "       \"mpmath: a Python library for arbitrary-precision floating-point\n"
+    "       arithmetic\" (Version 0.19) http://mpmath.org/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> from scipy.special import sici, exp1\n"
+    "\n"
+    "`sici` accepts real or complex input:\n"
+    "\n"
+    ">>> sici(2.5)\n"
+    "(1.7785201734438267, 0.2858711963653835)\n"
+    ">>> sici(2.5 + 3j)\n"
+    "((4.505735874563953+0.06863305018999577j),\n"
+    "(0.0793644206906966-2.935510262937543j))\n"
+    "\n"
+    "For z in the right half plane, the sine and cosine integrals are\n"
+    "related to the exponential integral E1 (implemented in SciPy as\n"
+    "`scipy.special.exp1`) by\n"
+    "\n"
+    "* Si(z) = (E1(i*z) - E1(-i*z))/2i + pi/2\n"
+    "* Ci(z) = -(E1(i*z) + E1(-i*z))/2\n"
+    "\n"
+    "See [1]_ (equations 5.2.21 and 5.2.23).\n"
+    "\n"
+    "We can verify these relations:\n"
+    "\n"
+    ">>> z = 2 - 3j\n"
+    ">>> sici(z)\n"
+    "((4.54751388956229-1.3991965806460565j),\n"
+    "(1.408292501520851+2.9836177420296055j))\n"
+    "\n"
+    ">>> (exp1(1j*z) - exp1(-1j*z))/2j + np.pi/2  # Same as sine integral\n"
+    "(4.54751388956229-1.3991965806460565j)\n"
+    "\n"
+    ">>> -(exp1(1j*z) + exp1(-1j*z))/2            # Same as cosine integral\n"
+    "(1.408292501520851+2.9836177420296055j)\n"
+    "\n"
+    "Plot the functions evaluated on the real axis; the dotted horizontal\n"
+    "lines are at pi/2 and -pi/2:\n"
+    "\n"
+    ">>> x = np.linspace(-16, 16, 150)\n"
+    ">>> si, ci = sici(x)\n"
+    "\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> ax.plot(x, si, label='Si(x)')\n"
+    ">>> ax.plot(x, ci, '--', label='Ci(x)')\n"
+    ">>> ax.legend(shadow=True, framealpha=1, loc='upper left')\n"
+    ">>> ax.set_xlabel('x')\n"
+    ">>> ax.set_title('Sine and Cosine Integrals')\n"
+    ">>> ax.axhline(np.pi/2, linestyle=':', alpha=0.5, color='k')\n"
+    ">>> ax.axhline(-np.pi/2, linestyle=':', alpha=0.5, color='k')\n"
+    ">>> ax.grid(True)\n"
+    ">>> plt.show()")
+ufunc_sici_loops[0] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_f_ff
+ufunc_sici_loops[1] = <np.PyUFuncGenericFunction>loop_i_d_dd_As_d_dd
+ufunc_sici_loops[2] = <np.PyUFuncGenericFunction>loop_i_D_DD_As_F_FF
+ufunc_sici_loops[3] = <np.PyUFuncGenericFunction>loop_i_D_DD_As_D_DD
+ufunc_sici_types[0] = <char>NPY_FLOAT
+ufunc_sici_types[1] = <char>NPY_FLOAT
+ufunc_sici_types[2] = <char>NPY_FLOAT
+ufunc_sici_types[3] = <char>NPY_DOUBLE
+ufunc_sici_types[4] = <char>NPY_DOUBLE
+ufunc_sici_types[5] = <char>NPY_DOUBLE
+ufunc_sici_types[6] = <char>NPY_CFLOAT
+ufunc_sici_types[7] = <char>NPY_CFLOAT
+ufunc_sici_types[8] = <char>NPY_CFLOAT
+ufunc_sici_types[9] = <char>NPY_CDOUBLE
+ufunc_sici_types[10] = <char>NPY_CDOUBLE
+ufunc_sici_types[11] = <char>NPY_CDOUBLE
+ufunc_sici_ptr[2*0] = <void*>_func_sici
+ufunc_sici_ptr[2*0+1] = <void*>(<char*>"sici")
+ufunc_sici_ptr[2*1] = <void*>_func_sici
+ufunc_sici_ptr[2*1+1] = <void*>(<char*>"sici")
+ufunc_sici_ptr[2*2] = <void*>_func_csici
+ufunc_sici_ptr[2*2+1] = <void*>(<char*>"sici")
+ufunc_sici_ptr[2*3] = <void*>_func_csici
+ufunc_sici_ptr[2*3+1] = <void*>(<char*>"sici")
+ufunc_sici_data[0] = &ufunc_sici_ptr[2*0]
+ufunc_sici_data[1] = &ufunc_sici_ptr[2*1]
+ufunc_sici_data[2] = &ufunc_sici_ptr[2*2]
+ufunc_sici_data[3] = &ufunc_sici_ptr[2*3]
+sici = np.PyUFunc_FromFuncAndData(ufunc_sici_loops, ufunc_sici_data, ufunc_sici_types, 4, 1, 2, 0, "sici", ufunc_sici_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_sindg_loops[2]
+cdef void *ufunc_sindg_ptr[4]
+cdef void *ufunc_sindg_data[2]
+cdef char ufunc_sindg_types[4]
+cdef char *ufunc_sindg_doc = (
+    "sindg(x, out=None)\n"
+    "\n"
+    "Sine of the angle `x` given in degrees.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Angle, given in degrees.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Sine at the input.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "cosdg, tandg, cotdg\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is more accurate than using sine directly.\n"
+    "\n"
+    ">>> x = 180 * np.arange(3)\n"
+    ">>> sc.sindg(x)\n"
+    "array([ 0., -0.,  0.])\n"
+    ">>> np.sin(x * np.pi / 180)\n"
+    "array([ 0.0000000e+00,  1.2246468e-16, -2.4492936e-16])")
+ufunc_sindg_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_sindg_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_sindg_types[0] = <char>NPY_FLOAT
+ufunc_sindg_types[1] = <char>NPY_FLOAT
+ufunc_sindg_types[2] = <char>NPY_DOUBLE
+ufunc_sindg_types[3] = <char>NPY_DOUBLE
+ufunc_sindg_ptr[2*0] = <void*>_func_sindg
+ufunc_sindg_ptr[2*0+1] = <void*>(<char*>"sindg")
+ufunc_sindg_ptr[2*1] = <void*>_func_sindg
+ufunc_sindg_ptr[2*1+1] = <void*>(<char*>"sindg")
+ufunc_sindg_data[0] = &ufunc_sindg_ptr[2*0]
+ufunc_sindg_data[1] = &ufunc_sindg_ptr[2*1]
+sindg = np.PyUFunc_FromFuncAndData(ufunc_sindg_loops, ufunc_sindg_data, ufunc_sindg_types, 2, 1, 1, 0, "sindg", ufunc_sindg_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_smirnov_loops[3]
+cdef void *ufunc_smirnov_ptr[6]
+cdef void *ufunc_smirnov_data[3]
+cdef char ufunc_smirnov_types[9]
+cdef char *ufunc_smirnov_doc = (
+    "smirnov(n, d, out=None)\n"
+    "\n"
+    "Kolmogorov-Smirnov complementary cumulative distribution function\n"
+    "\n"
+    "Returns the exact Kolmogorov-Smirnov complementary cumulative\n"
+    "distribution function,(aka the Survival Function) of Dn+ (or Dn-)\n"
+    "for a one-sided test of equality between an empirical and a\n"
+    "theoretical distribution. It is equal to the probability that the\n"
+    "maximum difference between a theoretical distribution and an empirical\n"
+    "one based on `n` samples is greater than d.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : int\n"
+    "  Number of samples\n"
+    "d : float array_like\n"
+    "  Deviation between the Empirical CDF (ECDF) and the target CDF.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The value(s) of smirnov(n, d), Prob(Dn+ >= d) (Also Prob(Dn- >= d))\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "smirnovi : The Inverse Survival Function for the distribution\n"
+    "scipy.stats.ksone : Provides the functionality as a continuous distribution\n"
+    "kolmogorov, kolmogi : Functions for the two-sided distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "`smirnov` is used by `stats.kstest` in the application of the\n"
+    "Kolmogorov-Smirnov Goodness of Fit test. For historical reasons this\n"
+    "function is exposed in `scpy.special`, but the recommended way to achieve\n"
+    "the most accurate CDF/SF/PDF/PPF/ISF computations is to use the\n"
+    "`stats.ksone` distribution.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import smirnov\n"
+    ">>> from scipy.stats import norm\n"
+    "\n"
+    "Show the probability of a gap at least as big as 0, 0.5 and 1.0 for a\n"
+    "sample of size 5.\n"
+    "\n"
+    ">>> smirnov(5, [0, 0.5, 1.0])\n"
+    "array([ 1.   ,  0.056,  0.   ])\n"
+    "\n"
+    "Compare a sample of size 5 against N(0, 1), the standard normal\n"
+    "distribution with mean 0 and standard deviation 1.\n"
+    "\n"
+    "`x` is the sample.\n"
+    "\n"
+    ">>> x = np.array([-1.392, -0.135, 0.114, 0.190, 1.82])\n"
+    "\n"
+    ">>> target = norm(0, 1)\n"
+    ">>> cdfs = target.cdf(x)\n"
+    ">>> cdfs\n"
+    "array([0.0819612 , 0.44630594, 0.5453811 , 0.57534543, 0.9656205 ])\n"
+    "\n"
+    "Construct the empirical CDF and the K-S statistics (Dn+, Dn-, Dn).\n"
+    "\n"
+    ">>> n = len(x)\n"
+    ">>> ecdfs = np.arange(n+1, dtype=float)/n\n"
+    ">>> cols = np.column_stack([x, ecdfs[1:], cdfs, cdfs - ecdfs[:n],\n"
+    "...                        ecdfs[1:] - cdfs])\n"
+    ">>> with np.printoptions(precision=3):\n"
+    "...    print(cols)\n"
+    "[[-1.392  0.2    0.082  0.082  0.118]\n"
+    " [-0.135  0.4    0.446  0.246 -0.046]\n"
+    " [ 0.114  0.6    0.545  0.145  0.055]\n"
+    " [ 0.19   0.8    0.575 -0.025  0.225]\n"
+    " [ 1.82   1.     0.966  0.166  0.034]]\n"
+    ">>> gaps = cols[:, -2:]\n"
+    ">>> Dnpm = np.max(gaps, axis=0)\n"
+    ">>> print(f'Dn-={Dnpm[0]:f}, Dn+={Dnpm[1]:f}')\n"
+    "Dn-=0.246306, Dn+=0.224655\n"
+    ">>> probs = smirnov(n, Dnpm)\n"
+    ">>> print(f'For a sample of size {n} drawn from N(0, 1):',\n"
+    "...       f' Smirnov n={n}: Prob(Dn- >= {Dnpm[0]:f}) = {probs[0]:.4f}',\n"
+    "...       f' Smirnov n={n}: Prob(Dn+ >= {Dnpm[1]:f}) = {probs[1]:.4f}',\n"
+    "...       sep='\\n')\n"
+    "For a sample of size 5 drawn from N(0, 1):\n"
+    " Smirnov n=5: Prob(Dn- >= 0.246306) = 0.4711\n"
+    " Smirnov n=5: Prob(Dn+ >= 0.224655) = 0.5245\n"
+    "\n"
+    "Plot the empirical CDF and the standard normal CDF.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> plt.step(np.concatenate(([-2.5], x, [2.5])),\n"
+    "...          np.concatenate((ecdfs, [1])),\n"
+    "...          where='post', label='Empirical CDF')\n"
+    ">>> xx = np.linspace(-2.5, 2.5, 100)\n"
+    ">>> plt.plot(xx, target.cdf(xx), '--', label='CDF for N(0, 1)')\n"
+    "\n"
+    "Add vertical lines marking Dn+ and Dn-.\n"
+    "\n"
+    ">>> iminus, iplus = np.argmax(gaps, axis=0)\n"
+    ">>> plt.vlines([x[iminus]], ecdfs[iminus], cdfs[iminus], color='r',\n"
+    "...            alpha=0.5, lw=4)\n"
+    ">>> plt.vlines([x[iplus]], cdfs[iplus], ecdfs[iplus+1], color='m',\n"
+    "...            alpha=0.5, lw=4)\n"
+    "\n"
+    ">>> plt.grid(True)\n"
+    ">>> plt.legend(framealpha=1, shadow=True)\n"
+    ">>> plt.show()")
+ufunc_smirnov_loops[0] = <np.PyUFuncGenericFunction>loop_d_id__As_ld_d
+ufunc_smirnov_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_smirnov_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_smirnov_types[0] = <char>NPY_LONG
+ufunc_smirnov_types[1] = <char>NPY_DOUBLE
+ufunc_smirnov_types[2] = <char>NPY_DOUBLE
+ufunc_smirnov_types[3] = <char>NPY_FLOAT
+ufunc_smirnov_types[4] = <char>NPY_FLOAT
+ufunc_smirnov_types[5] = <char>NPY_FLOAT
+ufunc_smirnov_types[6] = <char>NPY_DOUBLE
+ufunc_smirnov_types[7] = <char>NPY_DOUBLE
+ufunc_smirnov_types[8] = <char>NPY_DOUBLE
+ufunc_smirnov_ptr[2*0] = <void*>_func_smirnov
+ufunc_smirnov_ptr[2*0+1] = <void*>(<char*>"smirnov")
+ufunc_smirnov_ptr[2*1] = <void*>_func_smirnov_unsafe
+ufunc_smirnov_ptr[2*1+1] = <void*>(<char*>"smirnov")
+ufunc_smirnov_ptr[2*2] = <void*>_func_smirnov_unsafe
+ufunc_smirnov_ptr[2*2+1] = <void*>(<char*>"smirnov")
+ufunc_smirnov_data[0] = &ufunc_smirnov_ptr[2*0]
+ufunc_smirnov_data[1] = &ufunc_smirnov_ptr[2*1]
+ufunc_smirnov_data[2] = &ufunc_smirnov_ptr[2*2]
+smirnov = np.PyUFunc_FromFuncAndData(ufunc_smirnov_loops, ufunc_smirnov_data, ufunc_smirnov_types, 3, 2, 1, 0, "smirnov", ufunc_smirnov_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_smirnovi_loops[3]
+cdef void *ufunc_smirnovi_ptr[6]
+cdef void *ufunc_smirnovi_data[3]
+cdef char ufunc_smirnovi_types[9]
+cdef char *ufunc_smirnovi_doc = (
+    "smirnovi(n, p, out=None)\n"
+    "\n"
+    "Inverse to `smirnov`\n"
+    "\n"
+    "Returns `d` such that ``smirnov(n, d) == p``, the critical value\n"
+    "corresponding to `p`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : int\n"
+    "  Number of samples\n"
+    "p : float array_like\n"
+    "    Probability\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The value(s) of smirnovi(n, p), the critical values.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "smirnov : The Survival Function (SF) for the distribution\n"
+    "scipy.stats.ksone : Provides the functionality as a continuous distribution\n"
+    "kolmogorov, kolmogi : Functions for the two-sided distribution\n"
+    "scipy.stats.kstwobign : Two-sided Kolmogorov-Smirnov distribution, large n\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "`smirnov` is used by `stats.kstest` in the application of the\n"
+    "Kolmogorov-Smirnov Goodness of Fit test. For historical reasons this\n"
+    "function is exposed in `scpy.special`, but the recommended way to achieve\n"
+    "the most accurate CDF/SF/PDF/PPF/ISF computations is to use the\n"
+    "`stats.ksone` distribution.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import smirnovi, smirnov\n"
+    "\n"
+    ">>> n = 24\n"
+    ">>> deviations = [0.1, 0.2, 0.3]\n"
+    "\n"
+    "Use `smirnov` to compute the complementary CDF of the Smirnov\n"
+    "distribution for the given number of samples and deviations.\n"
+    "\n"
+    ">>> p = smirnov(n, deviations)\n"
+    ">>> p\n"
+    "array([0.58105083, 0.12826832, 0.01032231])\n"
+    "\n"
+    "The inverse function ``smirnovi(n, p)`` returns ``deviations``.\n"
+    "\n"
+    ">>> smirnovi(n, p)\n"
+    "array([0.1, 0.2, 0.3])")
+ufunc_smirnovi_loops[0] = <np.PyUFuncGenericFunction>loop_d_id__As_ld_d
+ufunc_smirnovi_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_smirnovi_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_smirnovi_types[0] = <char>NPY_LONG
+ufunc_smirnovi_types[1] = <char>NPY_DOUBLE
+ufunc_smirnovi_types[2] = <char>NPY_DOUBLE
+ufunc_smirnovi_types[3] = <char>NPY_FLOAT
+ufunc_smirnovi_types[4] = <char>NPY_FLOAT
+ufunc_smirnovi_types[5] = <char>NPY_FLOAT
+ufunc_smirnovi_types[6] = <char>NPY_DOUBLE
+ufunc_smirnovi_types[7] = <char>NPY_DOUBLE
+ufunc_smirnovi_types[8] = <char>NPY_DOUBLE
+ufunc_smirnovi_ptr[2*0] = <void*>_func_smirnovi
+ufunc_smirnovi_ptr[2*0+1] = <void*>(<char*>"smirnovi")
+ufunc_smirnovi_ptr[2*1] = <void*>_func_smirnovi_unsafe
+ufunc_smirnovi_ptr[2*1+1] = <void*>(<char*>"smirnovi")
+ufunc_smirnovi_ptr[2*2] = <void*>_func_smirnovi_unsafe
+ufunc_smirnovi_ptr[2*2+1] = <void*>(<char*>"smirnovi")
+ufunc_smirnovi_data[0] = &ufunc_smirnovi_ptr[2*0]
+ufunc_smirnovi_data[1] = &ufunc_smirnovi_ptr[2*1]
+ufunc_smirnovi_data[2] = &ufunc_smirnovi_ptr[2*2]
+smirnovi = np.PyUFunc_FromFuncAndData(ufunc_smirnovi_loops, ufunc_smirnovi_data, ufunc_smirnovi_types, 3, 2, 1, 0, "smirnovi", ufunc_smirnovi_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_spence_loops[4]
+cdef void *ufunc_spence_ptr[8]
+cdef void *ufunc_spence_data[4]
+cdef char ufunc_spence_types[8]
+cdef char *ufunc_spence_doc = (
+    "spence(z, out=None)\n"
+    "\n"
+    "Spence's function, also known as the dilogarithm.\n"
+    "\n"
+    "It is defined to be\n"
+    "\n"
+    ".. math::\n"
+    "  \\int_1^z \\frac{\\log(t)}{1 - t}dt\n"
+    "\n"
+    "for complex :math:`z`, where the contour of integration is taken\n"
+    "to avoid the branch cut of the logarithm. Spence's function is\n"
+    "analytic everywhere except the negative real axis where it has a\n"
+    "branch cut.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Points at which to evaluate Spence's function\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "s : scalar or ndarray\n"
+    "    Computed values of Spence's function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "There is a different convention which defines Spence's function by\n"
+    "the integral\n"
+    "\n"
+    ".. math::\n"
+    "  -\\int_0^z \\frac{\\log(1 - t)}{t}dt;\n"
+    "\n"
+    "this is our ``spence(1 - z)``.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import spence\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    "\n"
+    "The function is defined for complex inputs:\n"
+    "\n"
+    ">>> spence([1-1j, 1.5+2j, 3j, -10-5j])\n"
+    "array([-0.20561676+0.91596559j, -0.86766909-1.39560134j,\n"
+    "       -0.59422064-2.49129918j, -1.14044398+6.80075924j])\n"
+    "\n"
+    "For complex inputs on the branch cut, which is the negative real axis,\n"
+    "the function returns the limit for ``z`` with positive imaginary part.\n"
+    "For example, in the following, note the sign change of the imaginary\n"
+    "part of the output for ``z = -2`` and ``z = -2 - 1e-8j``:\n"
+    "\n"
+    ">>> spence([-2 + 1e-8j, -2, -2 - 1e-8j])\n"
+    "array([2.32018041-3.45139229j, 2.32018042-3.4513923j ,\n"
+    "       2.32018041+3.45139229j])\n"
+    "\n"
+    "The function returns ``nan`` for real inputs on the branch cut:\n"
+    "\n"
+    ">>> spence(-1.5)\n"
+    "nan\n"
+    "\n"
+    "Verify some particular values: ``spence(0) = pi**2/6``,\n"
+    "``spence(1) = 0`` and ``spence(2) = -pi**2/12``.\n"
+    "\n"
+    ">>> spence([0, 1, 2])\n"
+    "array([ 1.64493407,  0.        , -0.82246703])\n"
+    ">>> np.pi**2/6, -np.pi**2/12\n"
+    "(1.6449340668482264, -0.8224670334241132)\n"
+    "\n"
+    "Verify the identity::\n"
+    "\n"
+    "    spence(z) + spence(1 - z) = pi**2/6 - log(z)*log(1 - z)\n"
+    "\n"
+    ">>> z = 3 + 4j\n"
+    ">>> spence(z) + spence(1 - z)\n"
+    "(-2.6523186143876067+1.8853470951513935j)\n"
+    ">>> np.pi**2/6 - np.log(z)*np.log(1 - z)\n"
+    "(-2.652318614387606+1.885347095151394j)\n"
+    "\n"
+    "Plot the function for positive real input.\n"
+    "\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0, 6, 400)\n"
+    ">>> ax.plot(x, spence(x))\n"
+    ">>> ax.grid()\n"
+    ">>> ax.set_xlabel('x')\n"
+    ">>> ax.set_title('spence(x)')\n"
+    ">>> plt.show()")
+ufunc_spence_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_spence_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_spence_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_spence_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_spence_types[0] = <char>NPY_FLOAT
+ufunc_spence_types[1] = <char>NPY_FLOAT
+ufunc_spence_types[2] = <char>NPY_DOUBLE
+ufunc_spence_types[3] = <char>NPY_DOUBLE
+ufunc_spence_types[4] = <char>NPY_CFLOAT
+ufunc_spence_types[5] = <char>NPY_CFLOAT
+ufunc_spence_types[6] = <char>NPY_CDOUBLE
+ufunc_spence_types[7] = <char>NPY_CDOUBLE
+ufunc_spence_ptr[2*0] = <void*>_func_spence
+ufunc_spence_ptr[2*0+1] = <void*>(<char*>"spence")
+ufunc_spence_ptr[2*1] = <void*>_func_spence
+ufunc_spence_ptr[2*1+1] = <void*>(<char*>"spence")
+ufunc_spence_ptr[2*2] = <void*>_func_cspence
+ufunc_spence_ptr[2*2+1] = <void*>(<char*>"spence")
+ufunc_spence_ptr[2*3] = <void*>_func_cspence
+ufunc_spence_ptr[2*3+1] = <void*>(<char*>"spence")
+ufunc_spence_data[0] = &ufunc_spence_ptr[2*0]
+ufunc_spence_data[1] = &ufunc_spence_ptr[2*1]
+ufunc_spence_data[2] = &ufunc_spence_ptr[2*2]
+ufunc_spence_data[3] = &ufunc_spence_ptr[2*3]
+spence = np.PyUFunc_FromFuncAndData(ufunc_spence_loops, ufunc_spence_data, ufunc_spence_types, 4, 1, 1, 0, "spence", ufunc_spence_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_sph_harm_loops[3]
+cdef void *ufunc_sph_harm_ptr[6]
+cdef void *ufunc_sph_harm_data[3]
+cdef char ufunc_sph_harm_types[15]
+cdef char *ufunc_sph_harm_doc = (
+    "sph_harm(m, n, theta, phi, out=None)\n"
+    "\n"
+    "Compute spherical harmonics.\n"
+    "\n"
+    "The spherical harmonics are defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    Y^m_n(\\theta,\\phi) = \\sqrt{\\frac{2n+1}{4\\pi} \\frac{(n-m)!}{(n+m)!}}\n"
+    "      e^{i m \\theta} P^m_n(\\cos(\\phi))\n"
+    "\n"
+    "where :math:`P_n^m` are the associated Legendre functions; see `lpmv`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "m : array_like\n"
+    "    Order of the harmonic (int); must have ``|m| <= n``.\n"
+    "n : array_like\n"
+    "   Degree of the harmonic (int); must have ``n >= 0``. This is\n"
+    "   often denoted by ``l`` (lower case L) in descriptions of\n"
+    "   spherical harmonics.\n"
+    "theta : array_like\n"
+    "   Azimuthal (longitudinal) coordinate; must be in ``[0, 2*pi]``.\n"
+    "phi : array_like\n"
+    "   Polar (colatitudinal) coordinate; must be in ``[0, pi]``.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "y_mn : complex scalar or ndarray\n"
+    "   The harmonic :math:`Y^m_n` sampled at ``theta`` and ``phi``.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "There are different conventions for the meanings of the input\n"
+    "arguments ``theta`` and ``phi``. In SciPy ``theta`` is the\n"
+    "azimuthal angle and ``phi`` is the polar angle. It is common to\n"
+    "see the opposite convention, that is, ``theta`` as the polar angle\n"
+    "and ``phi`` as the azimuthal angle.\n"
+    "\n"
+    "Note that SciPy's spherical harmonics include the Condon-Shortley\n"
+    "phase [2]_ because it is part of `lpmv`.\n"
+    "\n"
+    "With SciPy's conventions, the first several spherical harmonics\n"
+    "are\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    Y_0^0(\\theta, \\phi) &= \\frac{1}{2} \\sqrt{\\frac{1}{\\pi}} \\\\\n"
+    "    Y_1^{-1}(\\theta, \\phi) &= \\frac{1}{2} \\sqrt{\\frac{3}{2\\pi}}\n"
+    "                                e^{-i\\theta} \\sin(\\phi) \\\\\n"
+    "    Y_1^0(\\theta, \\phi) &= \\frac{1}{2} \\sqrt{\\frac{3}{\\pi}}\n"
+    "                             \\cos(\\phi) \\\\\n"
+    "    Y_1^1(\\theta, \\phi) &= -\\frac{1}{2} \\sqrt{\\frac{3}{2\\pi}}\n"
+    "                             e^{i\\theta} \\sin(\\phi).\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Digital Library of Mathematical Functions, 14.30.\n"
+    "       https://dlmf.nist.gov/14.30\n"
+    ".. [2] https://en.wikipedia.org/wiki/Spherical_harmonics#Condon.E2.80.93Shortley_phase")
+ufunc_sph_harm_loops[0] = <np.PyUFuncGenericFunction>loop_D_iidd__As_lldd_D
+ufunc_sph_harm_loops[1] = <np.PyUFuncGenericFunction>loop_D_dddd__As_ffff_F
+ufunc_sph_harm_loops[2] = <np.PyUFuncGenericFunction>loop_D_dddd__As_dddd_D
+ufunc_sph_harm_types[0] = <char>NPY_LONG
+ufunc_sph_harm_types[1] = <char>NPY_LONG
+ufunc_sph_harm_types[2] = <char>NPY_DOUBLE
+ufunc_sph_harm_types[3] = <char>NPY_DOUBLE
+ufunc_sph_harm_types[4] = <char>NPY_CDOUBLE
+ufunc_sph_harm_types[5] = <char>NPY_FLOAT
+ufunc_sph_harm_types[6] = <char>NPY_FLOAT
+ufunc_sph_harm_types[7] = <char>NPY_FLOAT
+ufunc_sph_harm_types[8] = <char>NPY_FLOAT
+ufunc_sph_harm_types[9] = <char>NPY_CFLOAT
+ufunc_sph_harm_types[10] = <char>NPY_DOUBLE
+ufunc_sph_harm_types[11] = <char>NPY_DOUBLE
+ufunc_sph_harm_types[12] = <char>NPY_DOUBLE
+ufunc_sph_harm_types[13] = <char>NPY_DOUBLE
+ufunc_sph_harm_types[14] = <char>NPY_CDOUBLE
+ufunc_sph_harm_ptr[2*0] = <void*>_func_sph_harmonic
+ufunc_sph_harm_ptr[2*0+1] = <void*>(<char*>"sph_harm")
+ufunc_sph_harm_ptr[2*1] = <void*>_func_sph_harmonic_unsafe
+ufunc_sph_harm_ptr[2*1+1] = <void*>(<char*>"sph_harm")
+ufunc_sph_harm_ptr[2*2] = <void*>_func_sph_harmonic_unsafe
+ufunc_sph_harm_ptr[2*2+1] = <void*>(<char*>"sph_harm")
+ufunc_sph_harm_data[0] = &ufunc_sph_harm_ptr[2*0]
+ufunc_sph_harm_data[1] = &ufunc_sph_harm_ptr[2*1]
+ufunc_sph_harm_data[2] = &ufunc_sph_harm_ptr[2*2]
+sph_harm = np.PyUFunc_FromFuncAndData(ufunc_sph_harm_loops, ufunc_sph_harm_data, ufunc_sph_harm_types, 3, 4, 1, 0, "sph_harm", ufunc_sph_harm_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_stdtr_loops[2]
+cdef void *ufunc_stdtr_ptr[4]
+cdef void *ufunc_stdtr_data[2]
+cdef char ufunc_stdtr_types[6]
+cdef char *ufunc_stdtr_doc = (
+    "stdtr(df, t, out=None)\n"
+    "\n"
+    "Student t distribution cumulative distribution function\n"
+    "\n"
+    "Returns the integral:\n"
+    "\n"
+    ".. math::\n"
+    "    \\frac{\\Gamma((df+1)/2)}{\\sqrt{\\pi df} \\Gamma(df/2)}\n"
+    "    \\int_{-\\infty}^t (1+x^2/df)^{-(df+1)/2}\\, dx\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "df : array_like\n"
+    "    Degrees of freedom\n"
+    "t : array_like\n"
+    "    Upper bound of the integral\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the Student t CDF at t\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "stdtridf : inverse of stdtr with respect to `df`\n"
+    "stdtrit : inverse of stdtr with respect to `t`\n"
+    "scipy.stats.t : student t distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The student t distribution is also available as `scipy.stats.t`.\n"
+    "Calling `stdtr` directly can improve performance compared to the\n"
+    "``cdf`` method of `scipy.stats.t` (see last example below).\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function for ``df=3`` at ``t=1``.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import stdtr\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> stdtr(3, 1)\n"
+    "0.8044988905221148\n"
+    "\n"
+    "Plot the function for three different degrees of freedom.\n"
+    "\n"
+    ">>> x = np.linspace(-10, 10, 1000)\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> parameters = [(1, \"solid\"), (3, \"dashed\"), (10, \"dotted\")]\n"
+    ">>> for (df, linestyle) in parameters:\n"
+    "...     ax.plot(x, stdtr(df, x), ls=linestyle, label=f\"$df={df}$\")\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_title(\"Student t distribution cumulative distribution function\")\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The function can be computed for several degrees of freedom at the same\n"
+    "time by providing a NumPy array or list for `df`:\n"
+    "\n"
+    ">>> stdtr([1, 2, 3], 1)\n"
+    "array([0.75      , 0.78867513, 0.80449889])\n"
+    "\n"
+    "It is possible to calculate the function at several points for several\n"
+    "different degrees of freedom simultaneously by providing arrays for `df`\n"
+    "and `t` with shapes compatible for broadcasting. Compute `stdtr` at\n"
+    "4 points for 3 degrees of freedom resulting in an array of shape 3x4.\n"
+    "\n"
+    ">>> dfs = np.array([[1], [2], [3]])\n"
+    ">>> t = np.array([2, 4, 6, 8])\n"
+    ">>> dfs.shape, t.shape\n"
+    "((3, 1), (4,))\n"
+    "\n"
+    ">>> stdtr(dfs, t)\n"
+    "array([[0.85241638, 0.92202087, 0.94743154, 0.96041658],\n"
+    "       [0.90824829, 0.97140452, 0.98666426, 0.99236596],\n"
+    "       [0.93033702, 0.98599577, 0.99536364, 0.99796171]])\n"
+    "\n"
+    "The t distribution is also available as `scipy.stats.t`. Calling `stdtr`\n"
+    "directly can be much faster than calling the ``cdf`` method of\n"
+    "`scipy.stats.t`. To get the same results, one must use the following\n"
+    "parametrization: ``scipy.stats.t(df).cdf(x) = stdtr(df, x)``.\n"
+    "\n"
+    ">>> from scipy.stats import t\n"
+    ">>> df, x = 3, 1\n"
+    ">>> stdtr_result = stdtr(df, x)  # this can be faster than below\n"
+    ">>> stats_result = t(df).cdf(x)\n"
+    ">>> stats_result == stdtr_result  # test that results are equal\n"
+    "True")
+ufunc_stdtr_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_stdtr_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_stdtr_types[0] = <char>NPY_FLOAT
+ufunc_stdtr_types[1] = <char>NPY_FLOAT
+ufunc_stdtr_types[2] = <char>NPY_FLOAT
+ufunc_stdtr_types[3] = <char>NPY_DOUBLE
+ufunc_stdtr_types[4] = <char>NPY_DOUBLE
+ufunc_stdtr_types[5] = <char>NPY_DOUBLE
+ufunc_stdtr_ptr[2*0] = <void*>_func_stdtr
+ufunc_stdtr_ptr[2*0+1] = <void*>(<char*>"stdtr")
+ufunc_stdtr_ptr[2*1] = <void*>_func_stdtr
+ufunc_stdtr_ptr[2*1+1] = <void*>(<char*>"stdtr")
+ufunc_stdtr_data[0] = &ufunc_stdtr_ptr[2*0]
+ufunc_stdtr_data[1] = &ufunc_stdtr_ptr[2*1]
+stdtr = np.PyUFunc_FromFuncAndData(ufunc_stdtr_loops, ufunc_stdtr_data, ufunc_stdtr_types, 2, 2, 1, 0, "stdtr", ufunc_stdtr_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_stdtridf_loops[2]
+cdef void *ufunc_stdtridf_ptr[4]
+cdef void *ufunc_stdtridf_data[2]
+cdef char ufunc_stdtridf_types[6]
+cdef char *ufunc_stdtridf_doc = (
+    "stdtridf(p, t, out=None)\n"
+    "\n"
+    "Inverse of `stdtr` vs df\n"
+    "\n"
+    "Returns the argument df such that stdtr(df, t) is equal to `p`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "p : array_like\n"
+    "    Probability\n"
+    "t : array_like\n"
+    "    Upper bound of the integral\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "df : scalar or ndarray\n"
+    "    Value of `df` such that ``stdtr(df, t) == p``\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "stdtr : Student t CDF\n"
+    "stdtrit : inverse of stdtr with respect to `t`\n"
+    "scipy.stats.t : Student t distribution\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compute the student t cumulative distribution function for one\n"
+    "parameter set.\n"
+    "\n"
+    ">>> from scipy.special import stdtr, stdtridf\n"
+    ">>> df, x = 5, 2\n"
+    ">>> cdf_value = stdtr(df, x)\n"
+    ">>> cdf_value\n"
+    "0.9490302605850709\n"
+    "\n"
+    "Verify that `stdtridf` recovers the original value for `df` given\n"
+    "the CDF value and `x`.\n"
+    "\n"
+    ">>> stdtridf(cdf_value, x)\n"
+    "5.0")
+ufunc_stdtridf_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_stdtridf_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_stdtridf_types[0] = <char>NPY_FLOAT
+ufunc_stdtridf_types[1] = <char>NPY_FLOAT
+ufunc_stdtridf_types[2] = <char>NPY_FLOAT
+ufunc_stdtridf_types[3] = <char>NPY_DOUBLE
+ufunc_stdtridf_types[4] = <char>NPY_DOUBLE
+ufunc_stdtridf_types[5] = <char>NPY_DOUBLE
+ufunc_stdtridf_ptr[2*0] = <void*>_func_stdtridf
+ufunc_stdtridf_ptr[2*0+1] = <void*>(<char*>"stdtridf")
+ufunc_stdtridf_ptr[2*1] = <void*>_func_stdtridf
+ufunc_stdtridf_ptr[2*1+1] = <void*>(<char*>"stdtridf")
+ufunc_stdtridf_data[0] = &ufunc_stdtridf_ptr[2*0]
+ufunc_stdtridf_data[1] = &ufunc_stdtridf_ptr[2*1]
+stdtridf = np.PyUFunc_FromFuncAndData(ufunc_stdtridf_loops, ufunc_stdtridf_data, ufunc_stdtridf_types, 2, 2, 1, 0, "stdtridf", ufunc_stdtridf_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_stdtrit_loops[2]
+cdef void *ufunc_stdtrit_ptr[4]
+cdef void *ufunc_stdtrit_data[2]
+cdef char ufunc_stdtrit_types[6]
+cdef char *ufunc_stdtrit_doc = (
+    "stdtrit(df, p, out=None)\n"
+    "\n"
+    "The `p`-th quantile of the student t distribution.\n"
+    "\n"
+    "This function is the inverse of the student t distribution cumulative\n"
+    "distribution function (CDF), returning `t` such that `stdtr(df, t) = p`.\n"
+    "\n"
+    "Returns the argument `t` such that stdtr(df, t) is equal to `p`.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "df : array_like\n"
+    "    Degrees of freedom\n"
+    "p : array_like\n"
+    "    Probability\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "t : scalar or ndarray\n"
+    "    Value of `t` such that ``stdtr(df, t) == p``\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "stdtr : Student t CDF\n"
+    "stdtridf : inverse of stdtr with respect to `df`\n"
+    "scipy.stats.t : Student t distribution\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The student t distribution is also available as `scipy.stats.t`. Calling\n"
+    "`stdtrit` directly can improve performance compared to the ``ppf``\n"
+    "method of `scipy.stats.t` (see last example below).\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "`stdtrit` represents the inverse of the student t distribution CDF which\n"
+    "is available as `stdtr`. Here, we calculate the CDF for ``df`` at\n"
+    "``x=1``. `stdtrit` then returns ``1`` up to floating point errors\n"
+    "given the same value for `df` and the computed CDF value.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import stdtr, stdtrit\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> df = 3\n"
+    ">>> x = 1\n"
+    ">>> cdf_value = stdtr(df, x)\n"
+    ">>> stdtrit(df, cdf_value)\n"
+    "0.9999999994418539\n"
+    "\n"
+    "Plot the function for three different degrees of freedom.\n"
+    "\n"
+    ">>> x = np.linspace(0, 1, 1000)\n"
+    ">>> parameters = [(1, \"solid\"), (2, \"dashed\"), (5, \"dotted\")]\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> for (df, linestyle) in parameters:\n"
+    "...     ax.plot(x, stdtrit(df, x), ls=linestyle, label=f\"$df={df}$\")\n"
+    ">>> ax.legend()\n"
+    ">>> ax.set_ylim(-10, 10)\n"
+    ">>> ax.set_title(\"Student t distribution quantile function\")\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The function can be computed for several degrees of freedom at the same\n"
+    "time by providing a NumPy array or list for `df`:\n"
+    "\n"
+    ">>> stdtrit([1, 2, 3], 0.7)\n"
+    "array([0.72654253, 0.6172134 , 0.58438973])\n"
+    "\n"
+    "It is possible to calculate the function at several points for several\n"
+    "different degrees of freedom simultaneously by providing arrays for `df`\n"
+    "and `p` with shapes compatible for broadcasting. Compute `stdtrit` at\n"
+    "4 points for 3 degrees of freedom resulting in an array of shape 3x4.\n"
+    "\n"
+    ">>> dfs = np.array([[1], [2], [3]])\n"
+    ">>> p = np.array([0.2, 0.4, 0.7, 0.8])\n"
+    ">>> dfs.shape, p.shape\n"
+    "((3, 1), (4,))\n"
+    "\n"
+    ">>> stdtrit(dfs, p)\n"
+    "array([[-1.37638192, -0.3249197 ,  0.72654253,  1.37638192],\n"
+    "       [-1.06066017, -0.28867513,  0.6172134 ,  1.06066017],\n"
+    "       [-0.97847231, -0.27667066,  0.58438973,  0.97847231]])\n"
+    "\n"
+    "The t distribution is also available as `scipy.stats.t`. Calling `stdtrit`\n"
+    "directly can be much faster than calling the ``ppf`` method of\n"
+    "`scipy.stats.t`. To get the same results, one must use the following\n"
+    "parametrization: ``scipy.stats.t(df).ppf(x) = stdtrit(df, x)``.\n"
+    "\n"
+    ">>> from scipy.stats import t\n"
+    ">>> df, x = 3, 0.5\n"
+    ">>> stdtrit_result = stdtrit(df, x)  # this can be faster than below\n"
+    ">>> stats_result = t(df).ppf(x)\n"
+    ">>> stats_result == stdtrit_result  # test that results are equal\n"
+    "True")
+ufunc_stdtrit_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_stdtrit_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_stdtrit_types[0] = <char>NPY_FLOAT
+ufunc_stdtrit_types[1] = <char>NPY_FLOAT
+ufunc_stdtrit_types[2] = <char>NPY_FLOAT
+ufunc_stdtrit_types[3] = <char>NPY_DOUBLE
+ufunc_stdtrit_types[4] = <char>NPY_DOUBLE
+ufunc_stdtrit_types[5] = <char>NPY_DOUBLE
+ufunc_stdtrit_ptr[2*0] = <void*>_func_stdtrit
+ufunc_stdtrit_ptr[2*0+1] = <void*>(<char*>"stdtrit")
+ufunc_stdtrit_ptr[2*1] = <void*>_func_stdtrit
+ufunc_stdtrit_ptr[2*1+1] = <void*>(<char*>"stdtrit")
+ufunc_stdtrit_data[0] = &ufunc_stdtrit_ptr[2*0]
+ufunc_stdtrit_data[1] = &ufunc_stdtrit_ptr[2*1]
+stdtrit = np.PyUFunc_FromFuncAndData(ufunc_stdtrit_loops, ufunc_stdtrit_data, ufunc_stdtrit_types, 2, 2, 1, 0, "stdtrit", ufunc_stdtrit_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_struve_loops[2]
+cdef void *ufunc_struve_ptr[4]
+cdef void *ufunc_struve_data[2]
+cdef char ufunc_struve_types[6]
+cdef char *ufunc_struve_doc = (
+    "struve(v, x, out=None)\n"
+    "\n"
+    "Struve function.\n"
+    "\n"
+    "Return the value of the Struve function of order `v` at `x`.  The Struve\n"
+    "function is defined as,\n"
+    "\n"
+    ".. math::\n"
+    "    H_v(x) = (z/2)^{v + 1} \\sum_{n=0}^\\infty\n"
+    "    \\frac{(-1)^n (z/2)^{2n}}{\\Gamma(n + \\frac{3}{2}) \\Gamma(n + v + \\frac{3}{2})},\n"
+    "\n"
+    "where :math:`\\Gamma` is the gamma function.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order of the Struve function (float).\n"
+    "x : array_like\n"
+    "    Argument of the Struve function (float; must be positive unless `v` is\n"
+    "    an integer).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "H : scalar or ndarray\n"
+    "    Value of the Struve function of order `v` at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "modstruve: Modified Struve function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Three methods discussed in [1]_ are used to evaluate the Struve function:\n"
+    "\n"
+    "- power series\n"
+    "- expansion in Bessel functions (if :math:`|z| < |v| + 20`)\n"
+    "- asymptotic large-z expansion (if :math:`z \\geq 0.7v + 12`)\n"
+    "\n"
+    "Rounding errors are estimated based on the largest terms in the sums, and\n"
+    "the result associated with the smallest error is returned.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] NIST Digital Library of Mathematical Functions\n"
+    "       https://dlmf.nist.gov/11\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the Struve function of order 1 at 2.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import struve\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> struve(1, 2.)\n"
+    "0.6467637282835622\n"
+    "\n"
+    "Calculate the Struve function at 2 for orders 1, 2 and 3 by providing\n"
+    "a list for the order parameter `v`.\n"
+    "\n"
+    ">>> struve([1, 2, 3], 2.)\n"
+    "array([0.64676373, 0.28031806, 0.08363767])\n"
+    "\n"
+    "Calculate the Struve function of order 1 for several points by providing\n"
+    "an array for `x`.\n"
+    "\n"
+    ">>> points = np.array([2., 5., 8.])\n"
+    ">>> struve(1, points)\n"
+    "array([0.64676373, 0.80781195, 0.48811605])\n"
+    "\n"
+    "Compute the Struve function for several orders at several points by\n"
+    "providing arrays for `v` and `z`. The arrays have to be broadcastable\n"
+    "to the correct shapes.\n"
+    "\n"
+    ">>> orders = np.array([[1], [2], [3]])\n"
+    ">>> points.shape, orders.shape\n"
+    "((3,), (3, 1))\n"
+    "\n"
+    ">>> struve(orders, points)\n"
+    "array([[0.64676373, 0.80781195, 0.48811605],\n"
+    "       [0.28031806, 1.56937455, 1.51769363],\n"
+    "       [0.08363767, 1.50872065, 2.98697513]])\n"
+    "\n"
+    "Plot the Struve functions of order 0 to 3 from -10 to 10.\n"
+    "\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-10., 10., 1000)\n"
+    ">>> for i in range(4):\n"
+    "...     ax.plot(x, struve(i, x), label=f'$H_{i!r}$')\n"
+    ">>> ax.legend(ncol=2)\n"
+    ">>> ax.set_xlim(-10, 10)\n"
+    ">>> ax.set_title(r\"Struve functions $H_{\\nu}$\")\n"
+    ">>> plt.show()")
+ufunc_struve_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_struve_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_struve_types[0] = <char>NPY_FLOAT
+ufunc_struve_types[1] = <char>NPY_FLOAT
+ufunc_struve_types[2] = <char>NPY_FLOAT
+ufunc_struve_types[3] = <char>NPY_DOUBLE
+ufunc_struve_types[4] = <char>NPY_DOUBLE
+ufunc_struve_types[5] = <char>NPY_DOUBLE
+ufunc_struve_ptr[2*0] = <void*>_func_struve_h
+ufunc_struve_ptr[2*0+1] = <void*>(<char*>"struve")
+ufunc_struve_ptr[2*1] = <void*>_func_struve_h
+ufunc_struve_ptr[2*1+1] = <void*>(<char*>"struve")
+ufunc_struve_data[0] = &ufunc_struve_ptr[2*0]
+ufunc_struve_data[1] = &ufunc_struve_ptr[2*1]
+struve = np.PyUFunc_FromFuncAndData(ufunc_struve_loops, ufunc_struve_data, ufunc_struve_types, 2, 2, 1, 0, "struve", ufunc_struve_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_tandg_loops[2]
+cdef void *ufunc_tandg_ptr[4]
+cdef void *ufunc_tandg_data[2]
+cdef char ufunc_tandg_types[4]
+cdef char *ufunc_tandg_doc = (
+    "tandg(x, out=None)\n"
+    "\n"
+    "Tangent of angle `x` given in degrees.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Angle, given in degrees.\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results.\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Tangent at the input.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "sindg, cosdg, cotdg\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import scipy.special as sc\n"
+    "\n"
+    "It is more accurate than using tangent directly.\n"
+    "\n"
+    ">>> x = 180 * np.arange(3)\n"
+    ">>> sc.tandg(x)\n"
+    "array([0., 0., 0.])\n"
+    ">>> np.tan(x * np.pi / 180)\n"
+    "array([ 0.0000000e+00, -1.2246468e-16, -2.4492936e-16])")
+ufunc_tandg_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_tandg_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_tandg_types[0] = <char>NPY_FLOAT
+ufunc_tandg_types[1] = <char>NPY_FLOAT
+ufunc_tandg_types[2] = <char>NPY_DOUBLE
+ufunc_tandg_types[3] = <char>NPY_DOUBLE
+ufunc_tandg_ptr[2*0] = <void*>_func_tandg
+ufunc_tandg_ptr[2*0+1] = <void*>(<char*>"tandg")
+ufunc_tandg_ptr[2*1] = <void*>_func_tandg
+ufunc_tandg_ptr[2*1+1] = <void*>(<char*>"tandg")
+ufunc_tandg_data[0] = &ufunc_tandg_ptr[2*0]
+ufunc_tandg_data[1] = &ufunc_tandg_ptr[2*1]
+tandg = np.PyUFunc_FromFuncAndData(ufunc_tandg_loops, ufunc_tandg_data, ufunc_tandg_types, 2, 1, 1, 0, "tandg", ufunc_tandg_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_tklmbda_loops[2]
+cdef void *ufunc_tklmbda_ptr[4]
+cdef void *ufunc_tklmbda_data[2]
+cdef char ufunc_tklmbda_types[6]
+cdef char *ufunc_tklmbda_doc = (
+    "tklmbda(x, lmbda, out=None)\n"
+    "\n"
+    "Cumulative distribution function of the Tukey lambda distribution.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x, lmbda : array_like\n"
+    "    Parameters\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "cdf : scalar or ndarray\n"
+    "    Value of the Tukey lambda CDF\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "scipy.stats.tukeylambda : Tukey lambda distribution\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> from scipy.special import tklmbda, expit\n"
+    "\n"
+    "Compute the cumulative distribution function (CDF) of the Tukey lambda\n"
+    "distribution at several ``x`` values for `lmbda` = -1.5.\n"
+    "\n"
+    ">>> x = np.linspace(-2, 2, 9)\n"
+    ">>> x\n"
+    "array([-2. , -1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ])\n"
+    ">>> tklmbda(x, -1.5)\n"
+    "array([0.34688734, 0.3786554 , 0.41528805, 0.45629737, 0.5       ,\n"
+    "       0.54370263, 0.58471195, 0.6213446 , 0.65311266])\n"
+    "\n"
+    "When `lmbda` is 0, the function is the logistic sigmoid function,\n"
+    "which is implemented in `scipy.special` as `expit`.\n"
+    "\n"
+    ">>> tklmbda(x, 0)\n"
+    "array([0.11920292, 0.18242552, 0.26894142, 0.37754067, 0.5       ,\n"
+    "       0.62245933, 0.73105858, 0.81757448, 0.88079708])\n"
+    ">>> expit(x)\n"
+    "array([0.11920292, 0.18242552, 0.26894142, 0.37754067, 0.5       ,\n"
+    "       0.62245933, 0.73105858, 0.81757448, 0.88079708])\n"
+    "\n"
+    "When `lmbda` is 1, the Tukey lambda distribution is uniform on the\n"
+    "interval [-1, 1], so the CDF increases linearly.\n"
+    "\n"
+    ">>> t = np.linspace(-1, 1, 9)\n"
+    ">>> tklmbda(t, 1)\n"
+    "array([0.   , 0.125, 0.25 , 0.375, 0.5  , 0.625, 0.75 , 0.875, 1.   ])\n"
+    "\n"
+    "In the following, we generate plots for several values of `lmbda`.\n"
+    "\n"
+    "The first figure shows graphs for `lmbda` <= 0.\n"
+    "\n"
+    ">>> styles = ['-', '-.', '--', ':']\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-12, 12, 500)\n"
+    ">>> for k, lmbda in enumerate([-1.0, -0.5, 0.0]):\n"
+    "...     y = tklmbda(x, lmbda)\n"
+    "...     ax.plot(x, y, styles[k], label=rf'$\\lambda$ = {lmbda:-4.1f}')\n"
+    "\n"
+    ">>> ax.set_title(r'tklmbda(x, $\\lambda$)')\n"
+    ">>> ax.set_label('x')\n"
+    ">>> ax.legend(framealpha=1, shadow=True)\n"
+    ">>> ax.grid(True)\n"
+    "\n"
+    "The second figure shows graphs for `lmbda` > 0.  The dots in the\n"
+    "graphs show the bounds of the support of the distribution.\n"
+    "\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(-4.2, 4.2, 500)\n"
+    ">>> lmbdas = [0.25, 0.5, 1.0, 1.5]\n"
+    ">>> for k, lmbda in enumerate(lmbdas):\n"
+    "...     y = tklmbda(x, lmbda)\n"
+    "...     ax.plot(x, y, styles[k], label=fr'$\\lambda$ = {lmbda}')\n"
+    "\n"
+    ">>> ax.set_prop_cycle(None)\n"
+    ">>> for lmbda in lmbdas:\n"
+    "...     ax.plot([-1/lmbda, 1/lmbda], [0, 1], '.', ms=8)\n"
+    "\n"
+    ">>> ax.set_title(r'tklmbda(x, $\\lambda$)')\n"
+    ">>> ax.set_xlabel('x')\n"
+    ">>> ax.legend(framealpha=1, shadow=True)\n"
+    ">>> ax.grid(True)\n"
+    "\n"
+    ">>> plt.tight_layout()\n"
+    ">>> plt.show()\n"
+    "\n"
+    "The CDF of the Tukey lambda distribution is also implemented as the\n"
+    "``cdf`` method of `scipy.stats.tukeylambda`.  In the following,\n"
+    "``tukeylambda.cdf(x, -0.5)`` and ``tklmbda(x, -0.5)`` compute the\n"
+    "same values:\n"
+    "\n"
+    ">>> from scipy.stats import tukeylambda\n"
+    ">>> x = np.linspace(-2, 2, 9)\n"
+    "\n"
+    ">>> tukeylambda.cdf(x, -0.5)\n"
+    "array([0.21995157, 0.27093858, 0.33541677, 0.41328161, 0.5       ,\n"
+    "       0.58671839, 0.66458323, 0.72906142, 0.78004843])\n"
+    "\n"
+    ">>> tklmbda(x, -0.5)\n"
+    "array([0.21995157, 0.27093858, 0.33541677, 0.41328161, 0.5       ,\n"
+    "       0.58671839, 0.66458323, 0.72906142, 0.78004843])\n"
+    "\n"
+    "The implementation in ``tukeylambda`` also provides location and scale\n"
+    "parameters, and other methods such as ``pdf()`` (the probability\n"
+    "density function) and ``ppf()`` (the inverse of the CDF), so for\n"
+    "working with the Tukey lambda distribution, ``tukeylambda`` is more\n"
+    "generally useful.  The primary advantage of ``tklmbda`` is that it is\n"
+    "significantly faster than ``tukeylambda.cdf``.")
+ufunc_tklmbda_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_tklmbda_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_tklmbda_types[0] = <char>NPY_FLOAT
+ufunc_tklmbda_types[1] = <char>NPY_FLOAT
+ufunc_tklmbda_types[2] = <char>NPY_FLOAT
+ufunc_tklmbda_types[3] = <char>NPY_DOUBLE
+ufunc_tklmbda_types[4] = <char>NPY_DOUBLE
+ufunc_tklmbda_types[5] = <char>NPY_DOUBLE
+ufunc_tklmbda_ptr[2*0] = <void*>_func_tukeylambdacdf
+ufunc_tklmbda_ptr[2*0+1] = <void*>(<char*>"tklmbda")
+ufunc_tklmbda_ptr[2*1] = <void*>_func_tukeylambdacdf
+ufunc_tklmbda_ptr[2*1+1] = <void*>(<char*>"tklmbda")
+ufunc_tklmbda_data[0] = &ufunc_tklmbda_ptr[2*0]
+ufunc_tklmbda_data[1] = &ufunc_tklmbda_ptr[2*1]
+tklmbda = np.PyUFunc_FromFuncAndData(ufunc_tklmbda_loops, ufunc_tklmbda_data, ufunc_tklmbda_types, 2, 2, 1, 0, "tklmbda", ufunc_tklmbda_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_voigt_profile_loops[2]
+cdef void *ufunc_voigt_profile_ptr[4]
+cdef void *ufunc_voigt_profile_data[2]
+cdef char ufunc_voigt_profile_types[8]
+cdef char *ufunc_voigt_profile_doc = (
+    "voigt_profile(x, sigma, gamma, out=None)\n"
+    "\n"
+    "Voigt profile.\n"
+    "\n"
+    "The Voigt profile is a convolution of a 1-D Normal distribution with\n"
+    "standard deviation ``sigma`` and a 1-D Cauchy distribution with half-width at\n"
+    "half-maximum ``gamma``.\n"
+    "\n"
+    "If ``sigma = 0``, PDF of Cauchy distribution is returned.\n"
+    "Conversely, if ``gamma = 0``, PDF of Normal distribution is returned.\n"
+    "If ``sigma = gamma = 0``, the return value is ``Inf`` for ``x = 0``,\n"
+    "and ``0`` for all other ``x``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Real argument\n"
+    "sigma : array_like\n"
+    "    The standard deviation of the Normal distribution part\n"
+    "gamma : array_like\n"
+    "    The half-width at half-maximum of the Cauchy distribution part\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    The Voigt profile at the given arguments\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "wofz : Faddeeva function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "It can be expressed in terms of Faddeeva function\n"
+    "\n"
+    ".. math:: V(x; \\sigma, \\gamma) = \\frac{Re[w(z)]}{\\sigma\\sqrt{2\\pi}},\n"
+    ".. math:: z = \\frac{x + i\\gamma}{\\sqrt{2}\\sigma}\n"
+    "\n"
+    "where :math:`w(z)` is the Faddeeva function.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] https://en.wikipedia.org/wiki/Voigt_profile\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function at point 2 for ``sigma=1`` and ``gamma=1``.\n"
+    "\n"
+    ">>> from scipy.special import voigt_profile\n"
+    ">>> import numpy as np\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> voigt_profile(2, 1., 1.)\n"
+    "0.09071519942627544\n"
+    "\n"
+    "Calculate the function at several points by providing a NumPy array\n"
+    "for `x`.\n"
+    "\n"
+    ">>> values = np.array([-2., 0., 5])\n"
+    ">>> voigt_profile(values, 1., 1.)\n"
+    "array([0.0907152 , 0.20870928, 0.01388492])\n"
+    "\n"
+    "Plot the function for different parameter sets.\n"
+    "\n"
+    ">>> fig, ax = plt.subplots(figsize=(8, 8))\n"
+    ">>> x = np.linspace(-10, 10, 500)\n"
+    ">>> parameters_list = [(1.5, 0., \"solid\"), (1.3, 0.5, \"dashed\"),\n"
+    "...                    (0., 1.8, \"dotted\"), (1., 1., \"dashdot\")]\n"
+    ">>> for params in parameters_list:\n"
+    "...     sigma, gamma, linestyle = params\n"
+    "...     voigt = voigt_profile(x, sigma, gamma)\n"
+    "...     ax.plot(x, voigt, label=rf\"$\\sigma={sigma},\\, \\gamma={gamma}$\",\n"
+    "...             ls=linestyle)\n"
+    ">>> ax.legend()\n"
+    ">>> plt.show()\n"
+    "\n"
+    "Verify visually that the Voigt profile indeed arises as the convolution\n"
+    "of a normal and a Cauchy distribution.\n"
+    "\n"
+    ">>> from scipy.signal import convolve\n"
+    ">>> x, dx = np.linspace(-10, 10, 500, retstep=True)\n"
+    ">>> def gaussian(x, sigma):\n"
+    "...     return np.exp(-0.5 * x**2/sigma**2)/(sigma * np.sqrt(2*np.pi))\n"
+    ">>> def cauchy(x, gamma):\n"
+    "...     return gamma/(np.pi * (np.square(x)+gamma**2))\n"
+    ">>> sigma = 2\n"
+    ">>> gamma = 1\n"
+    ">>> gauss_profile = gaussian(x, sigma)\n"
+    ">>> cauchy_profile = cauchy(x, gamma)\n"
+    ">>> convolved = dx * convolve(cauchy_profile, gauss_profile, mode=\"same\")\n"
+    ">>> voigt = voigt_profile(x, sigma, gamma)\n"
+    ">>> fig, ax = plt.subplots(figsize=(8, 8))\n"
+    ">>> ax.plot(x, gauss_profile, label=\"Gauss: $G$\", c='b')\n"
+    ">>> ax.plot(x, cauchy_profile, label=\"Cauchy: $C$\", c='y', ls=\"dashed\")\n"
+    ">>> xx = 0.5*(x[1:] + x[:-1])  # midpoints\n"
+    ">>> ax.plot(xx, convolved[1:], label=\"Convolution: $G * C$\", ls='dashdot',\n"
+    "...         c='k')\n"
+    ">>> ax.plot(x, voigt, label=\"Voigt\", ls='dotted', c='r')\n"
+    ">>> ax.legend()\n"
+    ">>> plt.show()")
+ufunc_voigt_profile_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_voigt_profile_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_voigt_profile_types[0] = <char>NPY_FLOAT
+ufunc_voigt_profile_types[1] = <char>NPY_FLOAT
+ufunc_voigt_profile_types[2] = <char>NPY_FLOAT
+ufunc_voigt_profile_types[3] = <char>NPY_FLOAT
+ufunc_voigt_profile_types[4] = <char>NPY_DOUBLE
+ufunc_voigt_profile_types[5] = <char>NPY_DOUBLE
+ufunc_voigt_profile_types[6] = <char>NPY_DOUBLE
+ufunc_voigt_profile_types[7] = <char>NPY_DOUBLE
+ufunc_voigt_profile_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_voigt_profile
+ufunc_voigt_profile_ptr[2*0+1] = <void*>(<char*>"voigt_profile")
+ufunc_voigt_profile_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_voigt_profile
+ufunc_voigt_profile_ptr[2*1+1] = <void*>(<char*>"voigt_profile")
+ufunc_voigt_profile_data[0] = &ufunc_voigt_profile_ptr[2*0]
+ufunc_voigt_profile_data[1] = &ufunc_voigt_profile_ptr[2*1]
+voigt_profile = np.PyUFunc_FromFuncAndData(ufunc_voigt_profile_loops, ufunc_voigt_profile_data, ufunc_voigt_profile_types, 2, 3, 1, 0, "voigt_profile", ufunc_voigt_profile_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_wofz_loops[2]
+cdef void *ufunc_wofz_ptr[4]
+cdef void *ufunc_wofz_data[2]
+cdef char ufunc_wofz_types[4]
+cdef char *ufunc_wofz_doc = (
+    "wofz(z, out=None)\n"
+    "\n"
+    "Faddeeva function\n"
+    "\n"
+    "Returns the value of the Faddeeva function for complex argument::\n"
+    "\n"
+    "    exp(-z**2) * erfc(-i*z)\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    complex argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the Faddeeva function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "dawsn, erf, erfc, erfcx, erfi\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Steven G. Johnson, Faddeeva W function implementation.\n"
+    "   http://ab-initio.mit.edu/Faddeeva\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy import special\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    "\n"
+    ">>> x = np.linspace(-3, 3)\n"
+    ">>> z = special.wofz(x)\n"
+    "\n"
+    ">>> plt.plot(x, z.real, label='wofz(x).real')\n"
+    ">>> plt.plot(x, z.imag, label='wofz(x).imag')\n"
+    ">>> plt.xlabel('$x$')\n"
+    ">>> plt.legend(framealpha=1, shadow=True)\n"
+    ">>> plt.grid(alpha=0.25)\n"
+    ">>> plt.show()")
+ufunc_wofz_loops[0] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_wofz_loops[1] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_wofz_types[0] = <char>NPY_CFLOAT
+ufunc_wofz_types[1] = <char>NPY_CFLOAT
+ufunc_wofz_types[2] = <char>NPY_CDOUBLE
+ufunc_wofz_types[3] = <char>NPY_CDOUBLE
+ufunc_wofz_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_w
+ufunc_wofz_ptr[2*0+1] = <void*>(<char*>"wofz")
+ufunc_wofz_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_faddeeva_w
+ufunc_wofz_ptr[2*1+1] = <void*>(<char*>"wofz")
+ufunc_wofz_data[0] = &ufunc_wofz_ptr[2*0]
+ufunc_wofz_data[1] = &ufunc_wofz_ptr[2*1]
+wofz = np.PyUFunc_FromFuncAndData(ufunc_wofz_loops, ufunc_wofz_data, ufunc_wofz_types, 2, 1, 1, 0, "wofz", ufunc_wofz_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_wright_bessel_loops[2]
+cdef void *ufunc_wright_bessel_ptr[4]
+cdef void *ufunc_wright_bessel_data[2]
+cdef char ufunc_wright_bessel_types[8]
+cdef char *ufunc_wright_bessel_doc = (
+    "wright_bessel(a, b, x, out=None)\n"
+    "\n"
+    "Wright's generalized Bessel function.\n"
+    "\n"
+    "Wright's generalized Bessel function is an entire function and defined as\n"
+    "\n"
+    ".. math:: \\Phi(a, b; x) = \\sum_{k=0}^\\infty \\frac{x^k}{k! \\Gamma(a k + b)}\n"
+    "\n"
+    "See Also [1].\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "a : array_like of float\n"
+    "    a >= 0\n"
+    "b : array_like of float\n"
+    "    b >= 0\n"
+    "x : array_like of float\n"
+    "    x >= 0\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Value of the Wright's generalized Bessel function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Due to the complexity of the function with its three parameters, only\n"
+    "non-negative arguments are implemented.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Digital Library of Mathematical Functions, 10.46.\n"
+    "       https://dlmf.nist.gov/10.46.E1\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> from scipy.special import wright_bessel\n"
+    ">>> a, b, x = 1.5, 1.1, 2.5\n"
+    ">>> wright_bessel(a, b-1, x)\n"
+    "4.5314465939443025\n"
+    "\n"
+    "Now, let us verify the relation\n"
+    "\n"
+    ".. math:: \\Phi(a, b-1; x) = a x \\Phi(a, b+a; x) + (b-1) \\Phi(a, b; x)\n"
+    "\n"
+    ">>> a * x * wright_bessel(a, b+a, x) + (b-1) * wright_bessel(a, b, x)\n"
+    "4.5314465939443025")
+ufunc_wright_bessel_loops[0] = <np.PyUFuncGenericFunction>loop_d_ddd__As_fff_f
+ufunc_wright_bessel_loops[1] = <np.PyUFuncGenericFunction>loop_d_ddd__As_ddd_d
+ufunc_wright_bessel_types[0] = <char>NPY_FLOAT
+ufunc_wright_bessel_types[1] = <char>NPY_FLOAT
+ufunc_wright_bessel_types[2] = <char>NPY_FLOAT
+ufunc_wright_bessel_types[3] = <char>NPY_FLOAT
+ufunc_wright_bessel_types[4] = <char>NPY_DOUBLE
+ufunc_wright_bessel_types[5] = <char>NPY_DOUBLE
+ufunc_wright_bessel_types[6] = <char>NPY_DOUBLE
+ufunc_wright_bessel_types[7] = <char>NPY_DOUBLE
+ufunc_wright_bessel_ptr[2*0] = <void*>_func_wright_bessel_scalar
+ufunc_wright_bessel_ptr[2*0+1] = <void*>(<char*>"wright_bessel")
+ufunc_wright_bessel_ptr[2*1] = <void*>_func_wright_bessel_scalar
+ufunc_wright_bessel_ptr[2*1+1] = <void*>(<char*>"wright_bessel")
+ufunc_wright_bessel_data[0] = &ufunc_wright_bessel_ptr[2*0]
+ufunc_wright_bessel_data[1] = &ufunc_wright_bessel_ptr[2*1]
+wright_bessel = np.PyUFunc_FromFuncAndData(ufunc_wright_bessel_loops, ufunc_wright_bessel_data, ufunc_wright_bessel_types, 2, 3, 1, 0, "wright_bessel", ufunc_wright_bessel_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_wrightomega_loops[4]
+cdef void *ufunc_wrightomega_ptr[8]
+cdef void *ufunc_wrightomega_data[4]
+cdef char ufunc_wrightomega_types[8]
+cdef char *ufunc_wrightomega_doc = (
+    "wrightomega(z, out=None)\n"
+    "\n"
+    "Wright Omega function.\n"
+    "\n"
+    "Defined as the solution to\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\omega + \\log(\\omega) = z\n"
+    "\n"
+    "where :math:`\\log` is the principal branch of the complex logarithm.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "z : array_like\n"
+    "    Points at which to evaluate the Wright Omega function\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function values\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "omega : scalar or ndarray\n"
+    "    Values of the Wright Omega function\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "lambertw : The Lambert W function\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    ".. versionadded:: 0.19.0\n"
+    "\n"
+    "The function can also be defined as\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    \\omega(z) = W_{K(z)}(e^z)\n"
+    "\n"
+    "where :math:`K(z) = \\lceil (\\Im(z) - \\pi)/(2\\pi) \\rceil` is the\n"
+    "unwinding number and :math:`W` is the Lambert W function.\n"
+    "\n"
+    "The implementation here is taken from [1]_.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Lawrence, Corless, and Jeffrey, \"Algorithm 917: Complex\n"
+    "       Double-Precision Evaluation of the Wright :math:`\\omega`\n"
+    "       Function.\" ACM Transactions on Mathematical Software,\n"
+    "       2012. :doi:`10.1145/2168773.2168779`.\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import wrightomega, lambertw\n"
+    "\n"
+    ">>> wrightomega([-2, -1, 0, 1, 2])\n"
+    "array([0.12002824, 0.27846454, 0.56714329, 1.        , 1.5571456 ])\n"
+    "\n"
+    "Complex input:\n"
+    "\n"
+    ">>> wrightomega(3 + 5j)\n"
+    "(1.5804428632097158+3.8213626783287937j)\n"
+    "\n"
+    "Verify that ``wrightomega(z)`` satisfies ``w + log(w) = z``:\n"
+    "\n"
+    ">>> w = -5 + 4j\n"
+    ">>> wrightomega(w + np.log(w))\n"
+    "(-5+4j)\n"
+    "\n"
+    "Verify the connection to ``lambertw``:\n"
+    "\n"
+    ">>> z = 0.5 + 3j\n"
+    ">>> wrightomega(z)\n"
+    "(0.0966015889280649+1.4937828458191993j)\n"
+    ">>> lambertw(np.exp(z))\n"
+    "(0.09660158892806493+1.4937828458191993j)\n"
+    "\n"
+    ">>> z = 0.5 + 4j\n"
+    ">>> wrightomega(z)\n"
+    "(-0.3362123489037213+2.282986001579032j)\n"
+    ">>> lambertw(np.exp(z), k=1)\n"
+    "(-0.33621234890372115+2.282986001579032j)")
+ufunc_wrightomega_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_wrightomega_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_wrightomega_loops[2] = <np.PyUFuncGenericFunction>loop_D_D__As_F_F
+ufunc_wrightomega_loops[3] = <np.PyUFuncGenericFunction>loop_D_D__As_D_D
+ufunc_wrightomega_types[0] = <char>NPY_FLOAT
+ufunc_wrightomega_types[1] = <char>NPY_FLOAT
+ufunc_wrightomega_types[2] = <char>NPY_DOUBLE
+ufunc_wrightomega_types[3] = <char>NPY_DOUBLE
+ufunc_wrightomega_types[4] = <char>NPY_CFLOAT
+ufunc_wrightomega_types[5] = <char>NPY_CFLOAT
+ufunc_wrightomega_types[6] = <char>NPY_CDOUBLE
+ufunc_wrightomega_types[7] = <char>NPY_CDOUBLE
+ufunc_wrightomega_ptr[2*0] = <void*>scipy.special._ufuncs_cxx._export_wrightomega_real
+ufunc_wrightomega_ptr[2*0+1] = <void*>(<char*>"wrightomega")
+ufunc_wrightomega_ptr[2*1] = <void*>scipy.special._ufuncs_cxx._export_wrightomega_real
+ufunc_wrightomega_ptr[2*1+1] = <void*>(<char*>"wrightomega")
+ufunc_wrightomega_ptr[2*2] = <void*>scipy.special._ufuncs_cxx._export_wrightomega
+ufunc_wrightomega_ptr[2*2+1] = <void*>(<char*>"wrightomega")
+ufunc_wrightomega_ptr[2*3] = <void*>scipy.special._ufuncs_cxx._export_wrightomega
+ufunc_wrightomega_ptr[2*3+1] = <void*>(<char*>"wrightomega")
+ufunc_wrightomega_data[0] = &ufunc_wrightomega_ptr[2*0]
+ufunc_wrightomega_data[1] = &ufunc_wrightomega_ptr[2*1]
+ufunc_wrightomega_data[2] = &ufunc_wrightomega_ptr[2*2]
+ufunc_wrightomega_data[3] = &ufunc_wrightomega_ptr[2*3]
+wrightomega = np.PyUFunc_FromFuncAndData(ufunc_wrightomega_loops, ufunc_wrightomega_data, ufunc_wrightomega_types, 4, 1, 1, 0, "wrightomega", ufunc_wrightomega_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_xlog1py_loops[4]
+cdef void *ufunc_xlog1py_ptr[8]
+cdef void *ufunc_xlog1py_data[4]
+cdef char ufunc_xlog1py_types[12]
+cdef char *ufunc_xlog1py_doc = (
+    "xlog1py(x, y, out=None)\n"
+    "\n"
+    "Compute ``x*log1p(y)`` so that the result is 0 if ``x = 0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Multiplier\n"
+    "y : array_like\n"
+    "    Argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "z : scalar or ndarray\n"
+    "    Computed x*log1p(y)\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "\n"
+    ".. versionadded:: 0.13.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "This example shows how the function can be used to calculate the log of\n"
+    "the probability mass function for a geometric discrete random variable.\n"
+    "The probability mass function of the geometric distribution is defined\n"
+    "as follows:\n"
+    "\n"
+    ".. math:: f(k) = (1-p)^{k-1} p\n"
+    "\n"
+    "where :math:`p` is the probability of a single success\n"
+    "and :math:`1-p` is the probability of a single failure\n"
+    "and :math:`k` is the number of trials to get the first success.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import xlog1py\n"
+    ">>> p = 0.5\n"
+    ">>> k = 100\n"
+    ">>> _pmf = np.power(1 - p, k - 1) * p\n"
+    ">>> _pmf\n"
+    "7.888609052210118e-31\n"
+    "\n"
+    "If we take k as a relatively large number the value of the probability\n"
+    "mass function can become very low. In such cases taking the log of the\n"
+    "pmf would be more suitable as the log function can change the values\n"
+    "to a scale that is more appropriate to work with.\n"
+    "\n"
+    ">>> _log_pmf = xlog1py(k - 1, -p) + np.log(p)\n"
+    ">>> _log_pmf\n"
+    "-69.31471805599453\n"
+    "\n"
+    "We can confirm that we get a value close to the original pmf value by\n"
+    "taking the exponential of the log pmf.\n"
+    "\n"
+    ">>> _orig_pmf = np.exp(_log_pmf)\n"
+    ">>> np.isclose(_pmf, _orig_pmf)\n"
+    "True")
+ufunc_xlog1py_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_xlog1py_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_xlog1py_loops[2] = <np.PyUFuncGenericFunction>loop_D_DD__As_FF_F
+ufunc_xlog1py_loops[3] = <np.PyUFuncGenericFunction>loop_D_DD__As_DD_D
+ufunc_xlog1py_types[0] = <char>NPY_FLOAT
+ufunc_xlog1py_types[1] = <char>NPY_FLOAT
+ufunc_xlog1py_types[2] = <char>NPY_FLOAT
+ufunc_xlog1py_types[3] = <char>NPY_DOUBLE
+ufunc_xlog1py_types[4] = <char>NPY_DOUBLE
+ufunc_xlog1py_types[5] = <char>NPY_DOUBLE
+ufunc_xlog1py_types[6] = <char>NPY_CFLOAT
+ufunc_xlog1py_types[7] = <char>NPY_CFLOAT
+ufunc_xlog1py_types[8] = <char>NPY_CFLOAT
+ufunc_xlog1py_types[9] = <char>NPY_CDOUBLE
+ufunc_xlog1py_types[10] = <char>NPY_CDOUBLE
+ufunc_xlog1py_types[11] = <char>NPY_CDOUBLE
+ufunc_xlog1py_ptr[2*0] = <void*>_func_xlog1py[double]
+ufunc_xlog1py_ptr[2*0+1] = <void*>(<char*>"xlog1py")
+ufunc_xlog1py_ptr[2*1] = <void*>_func_xlog1py[double]
+ufunc_xlog1py_ptr[2*1+1] = <void*>(<char*>"xlog1py")
+ufunc_xlog1py_ptr[2*2] = <void*>_func_xlog1py[double_complex]
+ufunc_xlog1py_ptr[2*2+1] = <void*>(<char*>"xlog1py")
+ufunc_xlog1py_ptr[2*3] = <void*>_func_xlog1py[double_complex]
+ufunc_xlog1py_ptr[2*3+1] = <void*>(<char*>"xlog1py")
+ufunc_xlog1py_data[0] = &ufunc_xlog1py_ptr[2*0]
+ufunc_xlog1py_data[1] = &ufunc_xlog1py_ptr[2*1]
+ufunc_xlog1py_data[2] = &ufunc_xlog1py_ptr[2*2]
+ufunc_xlog1py_data[3] = &ufunc_xlog1py_ptr[2*3]
+xlog1py = np.PyUFunc_FromFuncAndData(ufunc_xlog1py_loops, ufunc_xlog1py_data, ufunc_xlog1py_types, 4, 2, 1, 0, "xlog1py", ufunc_xlog1py_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_xlogy_loops[4]
+cdef void *ufunc_xlogy_ptr[8]
+cdef void *ufunc_xlogy_data[4]
+cdef char ufunc_xlogy_types[12]
+cdef char *ufunc_xlogy_doc = (
+    "xlogy(x, y, out=None)\n"
+    "\n"
+    "Compute ``x*log(y)`` so that the result is 0 if ``x = 0``.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Multiplier\n"
+    "y : array_like\n"
+    "    Argument\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "z : scalar or ndarray\n"
+    "    Computed x*log(y)\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The log function used in the computation is the natural log.\n"
+    "\n"
+    ".. versionadded:: 0.13.0\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "We can use this function to calculate the binary logistic loss also\n"
+    "known as the binary cross entropy. This loss function is used for\n"
+    "binary classification problems and is defined as:\n"
+    "\n"
+    ".. math::\n"
+    "    L = 1/n * \\sum_{i=0}^n -(y_i*log(y\\_pred_i) + (1-y_i)*log(1-y\\_pred_i))\n"
+    "\n"
+    "We can define the parameters `x` and `y` as y and y_pred respectively.\n"
+    "y is the array of the actual labels which over here can be either 0 or 1.\n"
+    "y_pred is the array of the predicted probabilities with respect to\n"
+    "the positive class (1).\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import xlogy\n"
+    ">>> y = np.array([0, 1, 0, 1, 1, 0])\n"
+    ">>> y_pred = np.array([0.3, 0.8, 0.4, 0.7, 0.9, 0.2])\n"
+    ">>> n = len(y)\n"
+    ">>> loss = -(xlogy(y, y_pred) + xlogy(1 - y, 1 - y_pred)).sum()\n"
+    ">>> loss /= n\n"
+    ">>> loss\n"
+    "0.29597052165495025\n"
+    "\n"
+    "A lower loss is usually better as it indicates that the predictions are\n"
+    "similar to the actual labels. In this example since our predicted\n"
+    "probabilities are close to the actual labels, we get an overall loss\n"
+    "that is reasonably low and appropriate.")
+ufunc_xlogy_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_xlogy_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_xlogy_loops[2] = <np.PyUFuncGenericFunction>loop_D_DD__As_FF_F
+ufunc_xlogy_loops[3] = <np.PyUFuncGenericFunction>loop_D_DD__As_DD_D
+ufunc_xlogy_types[0] = <char>NPY_FLOAT
+ufunc_xlogy_types[1] = <char>NPY_FLOAT
+ufunc_xlogy_types[2] = <char>NPY_FLOAT
+ufunc_xlogy_types[3] = <char>NPY_DOUBLE
+ufunc_xlogy_types[4] = <char>NPY_DOUBLE
+ufunc_xlogy_types[5] = <char>NPY_DOUBLE
+ufunc_xlogy_types[6] = <char>NPY_CFLOAT
+ufunc_xlogy_types[7] = <char>NPY_CFLOAT
+ufunc_xlogy_types[8] = <char>NPY_CFLOAT
+ufunc_xlogy_types[9] = <char>NPY_CDOUBLE
+ufunc_xlogy_types[10] = <char>NPY_CDOUBLE
+ufunc_xlogy_types[11] = <char>NPY_CDOUBLE
+ufunc_xlogy_ptr[2*0] = <void*>_func_xlogy[double]
+ufunc_xlogy_ptr[2*0+1] = <void*>(<char*>"xlogy")
+ufunc_xlogy_ptr[2*1] = <void*>_func_xlogy[double]
+ufunc_xlogy_ptr[2*1+1] = <void*>(<char*>"xlogy")
+ufunc_xlogy_ptr[2*2] = <void*>_func_xlogy[double_complex]
+ufunc_xlogy_ptr[2*2+1] = <void*>(<char*>"xlogy")
+ufunc_xlogy_ptr[2*3] = <void*>_func_xlogy[double_complex]
+ufunc_xlogy_ptr[2*3+1] = <void*>(<char*>"xlogy")
+ufunc_xlogy_data[0] = &ufunc_xlogy_ptr[2*0]
+ufunc_xlogy_data[1] = &ufunc_xlogy_ptr[2*1]
+ufunc_xlogy_data[2] = &ufunc_xlogy_ptr[2*2]
+ufunc_xlogy_data[3] = &ufunc_xlogy_ptr[2*3]
+xlogy = np.PyUFunc_FromFuncAndData(ufunc_xlogy_loops, ufunc_xlogy_data, ufunc_xlogy_types, 4, 2, 1, 0, "xlogy", ufunc_xlogy_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_y0_loops[2]
+cdef void *ufunc_y0_ptr[4]
+cdef void *ufunc_y0_data[2]
+cdef char ufunc_y0_types[4]
+cdef char *ufunc_y0_doc = (
+    "y0(x, out=None)\n"
+    "\n"
+    "Bessel function of the second kind of order 0.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "Y : scalar or ndarray\n"
+    "    Value of the Bessel function of the second kind of order 0 at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "j0: Bessel function of the first kind of order 0\n"
+    "yv: Bessel function of the first kind\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The domain is divided into the intervals [0, 5] and (5, infinity). In the\n"
+    "first interval a rational approximation :math:`R(x)` is employed to\n"
+    "compute,\n"
+    "\n"
+    ".. math::\n"
+    "\n"
+    "    Y_0(x) = R(x) + \\frac{2 \\log(x) J_0(x)}{\\pi},\n"
+    "\n"
+    "where :math:`J_0` is the Bessel function of the first kind of order 0.\n"
+    "\n"
+    "In the second interval, the Hankel asymptotic expansion is employed with\n"
+    "two rational functions of degree 6/6 and 7/7.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `y0`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function at one point:\n"
+    "\n"
+    ">>> from scipy.special import y0\n"
+    ">>> y0(1.)\n"
+    "0.08825696421567697\n"
+    "\n"
+    "Calculate at several points:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> y0(np.array([0.5, 2., 3.]))\n"
+    "array([-0.44451873,  0.51037567,  0.37685001])\n"
+    "\n"
+    "Plot the function from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> y = y0(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_y0_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_y0_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_y0_types[0] = <char>NPY_FLOAT
+ufunc_y0_types[1] = <char>NPY_FLOAT
+ufunc_y0_types[2] = <char>NPY_DOUBLE
+ufunc_y0_types[3] = <char>NPY_DOUBLE
+ufunc_y0_ptr[2*0] = <void*>_func_y0
+ufunc_y0_ptr[2*0+1] = <void*>(<char*>"y0")
+ufunc_y0_ptr[2*1] = <void*>_func_y0
+ufunc_y0_ptr[2*1+1] = <void*>(<char*>"y0")
+ufunc_y0_data[0] = &ufunc_y0_ptr[2*0]
+ufunc_y0_data[1] = &ufunc_y0_ptr[2*1]
+y0 = np.PyUFunc_FromFuncAndData(ufunc_y0_loops, ufunc_y0_data, ufunc_y0_types, 2, 1, 1, 0, "y0", ufunc_y0_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_y1_loops[2]
+cdef void *ufunc_y1_ptr[4]
+cdef void *ufunc_y1_data[2]
+cdef char ufunc_y1_types[4]
+cdef char *ufunc_y1_doc = (
+    "y1(x, out=None)\n"
+    "\n"
+    "Bessel function of the second kind of order 1.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like\n"
+    "    Argument (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "Y : scalar or ndarray\n"
+    "    Value of the Bessel function of the second kind of order 1 at `x`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "j1: Bessel function of the first kind of order 1\n"
+    "yn: Bessel function of the second kind\n"
+    "yv: Bessel function of the second kind\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "The domain is divided into the intervals [0, 8] and (8, infinity). In the\n"
+    "first interval a 25 term Chebyshev expansion is used, and computing\n"
+    ":math:`J_1` (the Bessel function of the first kind) is required. In the\n"
+    "second, the asymptotic trigonometric representation is employed using two\n"
+    "rational functions of degree 5/5.\n"
+    "\n"
+    "This function is a wrapper for the Cephes [1]_ routine `y1`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Calculate the function at one point:\n"
+    "\n"
+    ">>> from scipy.special import y1\n"
+    ">>> y1(1.)\n"
+    "-0.7812128213002888\n"
+    "\n"
+    "Calculate at several points:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> y1(np.array([0.5, 2., 3.]))\n"
+    "array([-1.47147239, -0.10703243,  0.32467442])\n"
+    "\n"
+    "Plot the function from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> y = y1(x)\n"
+    ">>> ax.plot(x, y)\n"
+    ">>> plt.show()")
+ufunc_y1_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_y1_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_y1_types[0] = <char>NPY_FLOAT
+ufunc_y1_types[1] = <char>NPY_FLOAT
+ufunc_y1_types[2] = <char>NPY_DOUBLE
+ufunc_y1_types[3] = <char>NPY_DOUBLE
+ufunc_y1_ptr[2*0] = <void*>_func_y1
+ufunc_y1_ptr[2*0+1] = <void*>(<char*>"y1")
+ufunc_y1_ptr[2*1] = <void*>_func_y1
+ufunc_y1_ptr[2*1+1] = <void*>(<char*>"y1")
+ufunc_y1_data[0] = &ufunc_y1_ptr[2*0]
+ufunc_y1_data[1] = &ufunc_y1_ptr[2*1]
+y1 = np.PyUFunc_FromFuncAndData(ufunc_y1_loops, ufunc_y1_data, ufunc_y1_types, 2, 1, 1, 0, "y1", ufunc_y1_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_yn_loops[3]
+cdef void *ufunc_yn_ptr[6]
+cdef void *ufunc_yn_data[3]
+cdef char ufunc_yn_types[9]
+cdef char *ufunc_yn_doc = (
+    "yn(n, x, out=None)\n"
+    "\n"
+    "Bessel function of the second kind of integer order and real argument.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "n : array_like\n"
+    "    Order (integer).\n"
+    "x : array_like\n"
+    "    Argument (float).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "Y : scalar or ndarray\n"
+    "    Value of the Bessel function, :math:`Y_n(x)`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "yv : For real order and real or complex argument.\n"
+    "y0: faster implementation of this function for order 0\n"
+    "y1: faster implementation of this function for order 1\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "Wrapper for the Cephes [1]_ routine `yn`.\n"
+    "\n"
+    "The function is evaluated by forward recurrence on `n`, starting with\n"
+    "values computed by the Cephes routines `y0` and `y1`. If `n = 0` or 1,\n"
+    "the routine for `y0` or `y1` is called directly.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Cephes Mathematical Functions Library,\n"
+    "       http://www.netlib.org/cephes/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the function of order 0 at one point.\n"
+    "\n"
+    ">>> from scipy.special import yn\n"
+    ">>> yn(0, 1.)\n"
+    "0.08825696421567697\n"
+    "\n"
+    "Evaluate the function at one point for different orders.\n"
+    "\n"
+    ">>> yn(0, 1.), yn(1, 1.), yn(2, 1.)\n"
+    "(0.08825696421567697, -0.7812128213002888, -1.6506826068162546)\n"
+    "\n"
+    "The evaluation for different orders can be carried out in one call by\n"
+    "providing a list or NumPy array as argument for the `v` parameter:\n"
+    "\n"
+    ">>> yn([0, 1, 2], 1.)\n"
+    "array([ 0.08825696, -0.78121282, -1.65068261])\n"
+    "\n"
+    "Evaluate the function at several points for order 0 by providing an\n"
+    "array for `z`.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> points = np.array([0.5, 3., 8.])\n"
+    ">>> yn(0, points)\n"
+    "array([-0.44451873,  0.37685001,  0.22352149])\n"
+    "\n"
+    "If `z` is an array, the order parameter `v` must be broadcastable to\n"
+    "the correct shape if different orders shall be computed in one call.\n"
+    "To calculate the orders 0 and 1 for an 1D array:\n"
+    "\n"
+    ">>> orders = np.array([[0], [1]])\n"
+    ">>> orders.shape\n"
+    "(2, 1)\n"
+    "\n"
+    ">>> yn(orders, points)\n"
+    "array([[-0.44451873,  0.37685001,  0.22352149],\n"
+    "       [-1.47147239,  0.32467442, -0.15806046]])\n"
+    "\n"
+    "Plot the functions of order 0 to 3 from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> for i in range(4):\n"
+    "...     ax.plot(x, yn(i, x), label=f'$Y_{i!r}$')\n"
+    ">>> ax.set_ylim(-3, 1)\n"
+    ">>> ax.legend()\n"
+    ">>> plt.show()")
+ufunc_yn_loops[0] = <np.PyUFuncGenericFunction>loop_d_id__As_ld_d
+ufunc_yn_loops[1] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_yn_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_yn_types[0] = <char>NPY_LONG
+ufunc_yn_types[1] = <char>NPY_DOUBLE
+ufunc_yn_types[2] = <char>NPY_DOUBLE
+ufunc_yn_types[3] = <char>NPY_FLOAT
+ufunc_yn_types[4] = <char>NPY_FLOAT
+ufunc_yn_types[5] = <char>NPY_FLOAT
+ufunc_yn_types[6] = <char>NPY_DOUBLE
+ufunc_yn_types[7] = <char>NPY_DOUBLE
+ufunc_yn_types[8] = <char>NPY_DOUBLE
+ufunc_yn_ptr[2*0] = <void*>_func_yn
+ufunc_yn_ptr[2*0+1] = <void*>(<char*>"yn")
+ufunc_yn_ptr[2*1] = <void*>_func_yn_unsafe
+ufunc_yn_ptr[2*1+1] = <void*>(<char*>"yn")
+ufunc_yn_ptr[2*2] = <void*>_func_yn_unsafe
+ufunc_yn_ptr[2*2+1] = <void*>(<char*>"yn")
+ufunc_yn_data[0] = &ufunc_yn_ptr[2*0]
+ufunc_yn_data[1] = &ufunc_yn_ptr[2*1]
+ufunc_yn_data[2] = &ufunc_yn_ptr[2*2]
+yn = np.PyUFunc_FromFuncAndData(ufunc_yn_loops, ufunc_yn_data, ufunc_yn_types, 3, 2, 1, 0, "yn", ufunc_yn_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_yv_loops[4]
+cdef void *ufunc_yv_ptr[8]
+cdef void *ufunc_yv_data[4]
+cdef char ufunc_yv_types[12]
+cdef char *ufunc_yv_doc = (
+    "yv(v, z, out=None)\n"
+    "\n"
+    "Bessel function of the second kind of real order and complex argument.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order (float).\n"
+    "z : array_like\n"
+    "    Argument (float or complex).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "Y : scalar or ndarray\n"
+    "    Value of the Bessel function of the second kind, :math:`Y_v(x)`.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "yve : :math:`Y_v` with leading exponential behavior stripped off.\n"
+    "y0: faster implementation of this function for order 0\n"
+    "y1: faster implementation of this function for order 1\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For positive `v` values, the computation is carried out using the\n"
+    "AMOS [1]_ `zbesy` routine, which exploits the connection to the Hankel\n"
+    "Bessel functions :math:`H_v^{(1)}` and :math:`H_v^{(2)}`,\n"
+    "\n"
+    ".. math:: Y_v(z) = \\frac{1}{2\\imath} (H_v^{(1)} - H_v^{(2)}).\n"
+    "\n"
+    "For negative `v` values the formula,\n"
+    "\n"
+    ".. math:: Y_{-v}(z) = Y_v(z) \\cos(\\pi v) + J_v(z) \\sin(\\pi v)\n"
+    "\n"
+    "is used, where :math:`J_v(z)` is the Bessel function of the first kind,\n"
+    "computed using the AMOS routine `zbesj`.  Note that the second term is\n"
+    "exactly zero for integer `v`; to improve accuracy the second term is\n"
+    "explicitly omitted for `v` values such that `v = floor(v)`.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Evaluate the function of order 0 at one point.\n"
+    "\n"
+    ">>> from scipy.special import yv\n"
+    ">>> yv(0, 1.)\n"
+    "0.088256964215677\n"
+    "\n"
+    "Evaluate the function at one point for different orders.\n"
+    "\n"
+    ">>> yv(0, 1.), yv(1, 1.), yv(1.5, 1.)\n"
+    "(0.088256964215677, -0.7812128213002889, -1.102495575160179)\n"
+    "\n"
+    "The evaluation for different orders can be carried out in one call by\n"
+    "providing a list or NumPy array as argument for the `v` parameter:\n"
+    "\n"
+    ">>> yv([0, 1, 1.5], 1.)\n"
+    "array([ 0.08825696, -0.78121282, -1.10249558])\n"
+    "\n"
+    "Evaluate the function at several points for order 0 by providing an\n"
+    "array for `z`.\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> points = np.array([0.5, 3., 8.])\n"
+    ">>> yv(0, points)\n"
+    "array([-0.44451873,  0.37685001,  0.22352149])\n"
+    "\n"
+    "If `z` is an array, the order parameter `v` must be broadcastable to\n"
+    "the correct shape if different orders shall be computed in one call.\n"
+    "To calculate the orders 0 and 1 for an 1D array:\n"
+    "\n"
+    ">>> orders = np.array([[0], [1]])\n"
+    ">>> orders.shape\n"
+    "(2, 1)\n"
+    "\n"
+    ">>> yv(orders, points)\n"
+    "array([[-0.44451873,  0.37685001,  0.22352149],\n"
+    "       [-1.47147239,  0.32467442, -0.15806046]])\n"
+    "\n"
+    "Plot the functions of order 0 to 3 from 0 to 10.\n"
+    "\n"
+    ">>> import matplotlib.pyplot as plt\n"
+    ">>> fig, ax = plt.subplots()\n"
+    ">>> x = np.linspace(0., 10., 1000)\n"
+    ">>> for i in range(4):\n"
+    "...     ax.plot(x, yv(i, x), label=f'$Y_{i!r}$')\n"
+    ">>> ax.set_ylim(-3, 1)\n"
+    ">>> ax.legend()\n"
+    ">>> plt.show()")
+ufunc_yv_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_yv_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_yv_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_yv_loops[3] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_yv_types[0] = <char>NPY_FLOAT
+ufunc_yv_types[1] = <char>NPY_FLOAT
+ufunc_yv_types[2] = <char>NPY_FLOAT
+ufunc_yv_types[3] = <char>NPY_FLOAT
+ufunc_yv_types[4] = <char>NPY_CFLOAT
+ufunc_yv_types[5] = <char>NPY_CFLOAT
+ufunc_yv_types[6] = <char>NPY_DOUBLE
+ufunc_yv_types[7] = <char>NPY_DOUBLE
+ufunc_yv_types[8] = <char>NPY_DOUBLE
+ufunc_yv_types[9] = <char>NPY_DOUBLE
+ufunc_yv_types[10] = <char>NPY_CDOUBLE
+ufunc_yv_types[11] = <char>NPY_CDOUBLE
+ufunc_yv_ptr[2*0] = <void*>_func_cbesy_wrap_real
+ufunc_yv_ptr[2*0+1] = <void*>(<char*>"yv")
+ufunc_yv_ptr[2*1] = <void*>_func_cbesy_wrap
+ufunc_yv_ptr[2*1+1] = <void*>(<char*>"yv")
+ufunc_yv_ptr[2*2] = <void*>_func_cbesy_wrap_real
+ufunc_yv_ptr[2*2+1] = <void*>(<char*>"yv")
+ufunc_yv_ptr[2*3] = <void*>_func_cbesy_wrap
+ufunc_yv_ptr[2*3+1] = <void*>(<char*>"yv")
+ufunc_yv_data[0] = &ufunc_yv_ptr[2*0]
+ufunc_yv_data[1] = &ufunc_yv_ptr[2*1]
+ufunc_yv_data[2] = &ufunc_yv_ptr[2*2]
+ufunc_yv_data[3] = &ufunc_yv_ptr[2*3]
+yv = np.PyUFunc_FromFuncAndData(ufunc_yv_loops, ufunc_yv_data, ufunc_yv_types, 4, 2, 1, 0, "yv", ufunc_yv_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_yve_loops[4]
+cdef void *ufunc_yve_ptr[8]
+cdef void *ufunc_yve_data[4]
+cdef char ufunc_yve_types[12]
+cdef char *ufunc_yve_doc = (
+    "yve(v, z, out=None)\n"
+    "\n"
+    "Exponentially scaled Bessel function of the second kind of real order.\n"
+    "\n"
+    "Returns the exponentially scaled Bessel function of the second\n"
+    "kind of real order `v` at complex `z`::\n"
+    "\n"
+    "    yve(v, z) = yv(v, z) * exp(-abs(z.imag))\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "v : array_like\n"
+    "    Order (float).\n"
+    "z : array_like\n"
+    "    Argument (float or complex).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "Y : scalar or ndarray\n"
+    "    Value of the exponentially scaled Bessel function.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "yv: Unscaled Bessel function of the second kind of real order.\n"
+    "\n"
+    "Notes\n"
+    "-----\n"
+    "For positive `v` values, the computation is carried out using the\n"
+    "AMOS [1]_ `zbesy` routine, which exploits the connection to the Hankel\n"
+    "Bessel functions :math:`H_v^{(1)}` and :math:`H_v^{(2)}`,\n"
+    "\n"
+    ".. math:: Y_v(z) = \\frac{1}{2\\imath} (H_v^{(1)} - H_v^{(2)}).\n"
+    "\n"
+    "For negative `v` values the formula,\n"
+    "\n"
+    ".. math:: Y_{-v}(z) = Y_v(z) \\cos(\\pi v) + J_v(z) \\sin(\\pi v)\n"
+    "\n"
+    "is used, where :math:`J_v(z)` is the Bessel function of the first kind,\n"
+    "computed using the AMOS routine `zbesj`.  Note that the second term is\n"
+    "exactly zero for integer `v`; to improve accuracy the second term is\n"
+    "explicitly omitted for `v` values such that `v = floor(v)`.\n"
+    "\n"
+    "Exponentially scaled Bessel functions are useful for large `z`:\n"
+    "for these, the unscaled Bessel functions can easily under-or overflow.\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n"
+    "       of a Complex Argument and Nonnegative Order\",\n"
+    "       http://netlib.org/amos/\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    "Compare the output of `yv` and `yve` for large complex arguments for `z`\n"
+    "by computing their values for order ``v=1`` at ``z=1000j``. We see that\n"
+    "`yv` returns nan but `yve` returns a finite number:\n"
+    "\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import yv, yve\n"
+    ">>> v = 1\n"
+    ">>> z = 1000j\n"
+    ">>> yv(v, z), yve(v, z)\n"
+    "((nan+nanj), (-0.012610930256928629+7.721967686709076e-19j))\n"
+    "\n"
+    "For real arguments for `z`, `yve` returns the same as `yv` up to\n"
+    "floating point errors.\n"
+    "\n"
+    ">>> v, z = 1, 1000\n"
+    ">>> yv(v, z), yve(v, z)\n"
+    "(-0.02478433129235178, -0.02478433129235179)\n"
+    "\n"
+    "The function can be evaluated for several orders at the same time by\n"
+    "providing a list or NumPy array for `v`:\n"
+    "\n"
+    ">>> yve([1, 2, 3], 1j)\n"
+    "array([-0.20791042+0.14096627j,  0.38053618-0.04993878j,\n"
+    "       0.00815531-1.66311097j])\n"
+    "\n"
+    "In the same way, the function can be evaluated at several points in one\n"
+    "call by providing a list or NumPy array for `z`:\n"
+    "\n"
+    ">>> yve(1, np.array([1j, 2j, 3j]))\n"
+    "array([-0.20791042+0.14096627j, -0.21526929+0.01205044j,\n"
+    "       -0.19682671+0.00127278j])\n"
+    "\n"
+    "It is also possible to evaluate several orders at several points\n"
+    "at the same time by providing arrays for `v` and `z` with\n"
+    "broadcasting compatible shapes. Compute `yve` for two different orders\n"
+    "`v` and three points `z` resulting in a 2x3 array.\n"
+    "\n"
+    ">>> v = np.array([[1], [2]])\n"
+    ">>> z = np.array([3j, 4j, 5j])\n"
+    ">>> v.shape, z.shape\n"
+    "((2, 1), (3,))\n"
+    "\n"
+    ">>> yve(v, z)\n"
+    "array([[-1.96826713e-01+1.27277544e-03j, -1.78750840e-01+1.45558819e-04j,\n"
+    "        -1.63972267e-01+1.73494110e-05j],\n"
+    "       [1.94960056e-03-1.11782545e-01j,  2.02902325e-04-1.17626501e-01j,\n"
+    "        2.27727687e-05-1.17951906e-01j]])")
+ufunc_yve_loops[0] = <np.PyUFuncGenericFunction>loop_d_dd__As_ff_f
+ufunc_yve_loops[1] = <np.PyUFuncGenericFunction>loop_D_dD__As_fF_F
+ufunc_yve_loops[2] = <np.PyUFuncGenericFunction>loop_d_dd__As_dd_d
+ufunc_yve_loops[3] = <np.PyUFuncGenericFunction>loop_D_dD__As_dD_D
+ufunc_yve_types[0] = <char>NPY_FLOAT
+ufunc_yve_types[1] = <char>NPY_FLOAT
+ufunc_yve_types[2] = <char>NPY_FLOAT
+ufunc_yve_types[3] = <char>NPY_FLOAT
+ufunc_yve_types[4] = <char>NPY_CFLOAT
+ufunc_yve_types[5] = <char>NPY_CFLOAT
+ufunc_yve_types[6] = <char>NPY_DOUBLE
+ufunc_yve_types[7] = <char>NPY_DOUBLE
+ufunc_yve_types[8] = <char>NPY_DOUBLE
+ufunc_yve_types[9] = <char>NPY_DOUBLE
+ufunc_yve_types[10] = <char>NPY_CDOUBLE
+ufunc_yve_types[11] = <char>NPY_CDOUBLE
+ufunc_yve_ptr[2*0] = <void*>_func_cbesy_wrap_e_real
+ufunc_yve_ptr[2*0+1] = <void*>(<char*>"yve")
+ufunc_yve_ptr[2*1] = <void*>_func_cbesy_wrap_e
+ufunc_yve_ptr[2*1+1] = <void*>(<char*>"yve")
+ufunc_yve_ptr[2*2] = <void*>_func_cbesy_wrap_e_real
+ufunc_yve_ptr[2*2+1] = <void*>(<char*>"yve")
+ufunc_yve_ptr[2*3] = <void*>_func_cbesy_wrap_e
+ufunc_yve_ptr[2*3+1] = <void*>(<char*>"yve")
+ufunc_yve_data[0] = &ufunc_yve_ptr[2*0]
+ufunc_yve_data[1] = &ufunc_yve_ptr[2*1]
+ufunc_yve_data[2] = &ufunc_yve_ptr[2*2]
+ufunc_yve_data[3] = &ufunc_yve_ptr[2*3]
+yve = np.PyUFunc_FromFuncAndData(ufunc_yve_loops, ufunc_yve_data, ufunc_yve_types, 4, 2, 1, 0, "yve", ufunc_yve_doc, 0)
+
+cdef np.PyUFuncGenericFunction ufunc_zetac_loops[2]
+cdef void *ufunc_zetac_ptr[4]
+cdef void *ufunc_zetac_data[2]
+cdef char ufunc_zetac_types[4]
+cdef char *ufunc_zetac_doc = (
+    "zetac(x, out=None)\n"
+    "\n"
+    "Riemann zeta function minus 1.\n"
+    "\n"
+    "This function is defined as\n"
+    "\n"
+    ".. math:: \\zeta(x) = \\sum_{k=2}^{\\infty} 1 / k^x,\n"
+    "\n"
+    "where ``x > 1``.  For ``x < 1`` the analytic continuation is\n"
+    "computed. For more information on the Riemann zeta function, see\n"
+    "[dlmf]_.\n"
+    "\n"
+    "Parameters\n"
+    "----------\n"
+    "x : array_like of float\n"
+    "    Values at which to compute zeta(x) - 1 (must be real).\n"
+    "out : ndarray, optional\n"
+    "    Optional output array for the function results\n"
+    "\n"
+    "Returns\n"
+    "-------\n"
+    "scalar or ndarray\n"
+    "    Values of zeta(x) - 1.\n"
+    "\n"
+    "See Also\n"
+    "--------\n"
+    "zeta\n"
+    "\n"
+    "References\n"
+    "----------\n"
+    ".. [dlmf] NIST Digital Library of Mathematical Functions\n"
+    "          https://dlmf.nist.gov/25\n"
+    "\n"
+    "Examples\n"
+    "--------\n"
+    ">>> import numpy as np\n"
+    ">>> from scipy.special import zetac, zeta\n"
+    "\n"
+    "Some special values:\n"
+    "\n"
+    ">>> zetac(2), np.pi**2/6 - 1\n"
+    "(0.64493406684822641, 0.6449340668482264)\n"
+    "\n"
+    ">>> zetac(-1), -1.0/12 - 1\n"
+    "(-1.0833333333333333, -1.0833333333333333)\n"
+    "\n"
+    "Compare ``zetac(x)`` to ``zeta(x) - 1`` for large `x`:\n"
+    "\n"
+    ">>> zetac(60), zeta(60) - 1\n"
+    "(8.673617380119933e-19, 0.0)")
+ufunc_zetac_loops[0] = <np.PyUFuncGenericFunction>loop_d_d__As_f_f
+ufunc_zetac_loops[1] = <np.PyUFuncGenericFunction>loop_d_d__As_d_d
+ufunc_zetac_types[0] = <char>NPY_FLOAT
+ufunc_zetac_types[1] = <char>NPY_FLOAT
+ufunc_zetac_types[2] = <char>NPY_DOUBLE
+ufunc_zetac_types[3] = <char>NPY_DOUBLE
+ufunc_zetac_ptr[2*0] = <void*>_func_zetac
+ufunc_zetac_ptr[2*0+1] = <void*>(<char*>"zetac")
+ufunc_zetac_ptr[2*1] = <void*>_func_zetac
+ufunc_zetac_ptr[2*1+1] = <void*>(<char*>"zetac")
+ufunc_zetac_data[0] = &ufunc_zetac_ptr[2*0]
+ufunc_zetac_data[1] = &ufunc_zetac_ptr[2*1]
+zetac = np.PyUFunc_FromFuncAndData(ufunc_zetac_loops, ufunc_zetac_data, ufunc_zetac_types, 2, 1, 1, 0, "zetac", ufunc_zetac_doc, 0)
+
+#
+# Aliases
+#
+jn = jv