Merge pull request #4348 from juliantaylor/rand-int-check-bug

BUG: handle non integer types for multinomial/dirichlet size

3 files changed, 3296 insertions, 3048 deletions

diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c
index 658cebeac..3548addd3 100644
--- a/numpy/random/mtrand/mtrand.c
+++ b/numpy/random/mtrand/mtrand.c
@@ -1,4 +1,4 @@
-/* Generated by Cython 0.20.1 on Thu Feb 13 19:28:28 2014 */
+/* Generated by Cython 0.20.1post0 on Sat Feb 22 12:24:44 2014 */
 
 #define PY_SSIZE_T_CLEAN
 #ifndef CYTHON_USE_PYLONG_INTERNALS
@@ -19,7 +19,7 @@
 #elif PY_VERSION_HEX < 0x02040000
     #error Cython requires Python 2.4+.
 #else
-#define CYTHON_ABI "0_20_1"
+#define CYTHON_ABI "0_20_1post0"
 #include <stddef.h> /* For offsetof */
 #ifndef offsetof
 #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
@@ -316,6 +316,12 @@ static CYTHON_INLINE float __PYX_NAN() {
   return value;
 }
 #endif
+#ifdef __cplusplus
+template<typename T>
+void __Pyx_call_destructor(T* x) {
+    x->~T();
+}
+#endif
 
 
 #if PY_MAJOR_VERSION >= 3
@@ -621,8 +627,8 @@ typedef long (*__pyx_t_6mtrand_rk_discnmN)(rk_state *, long, long, long);
  */
 typedef long (*__pyx_t_6mtrand_rk_discd)(rk_state *, double);
 
-/* "mtrand.pyx":525
- *     return sum
+/* "mtrand.pyx":535
+ *     return shape
  * 
  * cdef class RandomState:             # <<<<<<<<<<<<<<
  *     """
@@ -725,14 +731,19 @@ static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyOb
 
 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/
 
+static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
+    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
+
 static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/
 
 static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
     PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
     const char* function_name); /*proto*/
 
-static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
-    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
+static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
 
 #define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
     (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
@@ -780,11 +791,6 @@ static CYTHON_INLINE int __Pyx_IterFinish(void); /*proto*/
 
 static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/
 
-static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
-
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-
 static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
 
 #define __Pyx_PyObject_DelSlice(obj, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound) \
@@ -933,6 +939,7 @@ int __pyx_module_is_main_mtrand = 0;
 /* Implementation of 'mtrand' */
 static PyObject *__pyx_builtin_ValueError;
 static PyObject *__pyx_builtin_TypeError;
+static PyObject *__pyx_pf_6mtrand__shape_from_size(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_size, PyObject *__pyx_v_d); /* proto */
 static int __pyx_pf_6mtrand_11RandomState___init__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
 static void __pyx_pf_6mtrand_11RandomState_2__dealloc__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
 static PyObject *__pyx_pf_6mtrand_11RandomState_4seed(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
@@ -989,6 +996,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_104permutation(struct __pyx_obj_
 static PyObject *__pyx_tp_new_6mtrand_RandomState(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
 static char __pyx_k_a[] = "a";
 static char __pyx_k_b[] = "b";
+static char __pyx_k_d[] = "d";
 static char __pyx_k_f[] = "f";
 static char __pyx_k_l[] = "l";
 static char __pyx_k_n[] = "n";
@@ -1073,6 +1081,7 @@ static char __pyx_k_fields[] = "fields";
 static char __pyx_k_gumbel[] = "gumbel";
 static char __pyx_k_import[] = "__import__";
 static char __pyx_k_mean_0[] = "mean <= 0";
+static char __pyx_k_mtrand[] = "mtrand";
 static char __pyx_k_nbad_0[] = "nbad < 0";
 static char __pyx_k_nonc_0[] = "nonc < 0";
 static char __pyx_k_normal[] = "normal";
@@ -1150,6 +1159,7 @@ static char __pyx_k_hypergeometric[] = "hypergeometric";
 static char __pyx_k_standard_gamma[] = "standard_gamma";
 static char __pyx_k_poisson_lam_max[] = "poisson_lam_max";
 static char __pyx_k_random_integers[] = "random_integers";
+static char __pyx_k_shape_from_size[] = "_shape_from_size";
 static char __pyx_k_standard_cauchy[] = "standard_cauchy";
 static char __pyx_k_standard_normal[] = "standard_normal";
 static char __pyx_k_sum_pvals_1_1_0[] = "sum(pvals[:-1]) > 1.0";
@@ -1162,37 +1172,37 @@ static char __pyx_k_multivariate_normal[] = "multivariate_normal";
 static char __pyx_k_noncentral_chisquare[] = "noncentral_chisquare";
 static char __pyx_k_standard_exponential[] = "standard_exponential";
 static char __pyx_k_lam_value_too_large_2[] = "lam value too large.";
-static char __pyx_k_RandomState_f_line_1807[] = "RandomState.f (line 1807)";
+static char __pyx_k_RandomState_f_line_1815[] = "RandomState.f (line 1815)";
 static char __pyx_k_a_must_be_1_dimensional[] = "a must be 1-dimensional";
 static char __pyx_k_p_must_be_1_dimensional[] = "p must be 1-dimensional";
 static char __pyx_k_state_must_be_624_longs[] = "state must be 624 longs";
 static char __pyx_k_a_must_be_greater_than_0[] = "a must be greater than 0";
 static char __pyx_k_algorithm_must_be_MT19937[] = "algorithm must be 'MT19937'";
-static char __pyx_k_RandomState_bytes_line_892[] = "RandomState.bytes (line 892)";
-static char __pyx_k_RandomState_rand_line_1182[] = "RandomState.rand (line 1182)";
-static char __pyx_k_RandomState_wald_line_3237[] = "RandomState.wald (line 3237)";
-static char __pyx_k_RandomState_zipf_line_3685[] = "RandomState.zipf (line 3685)";
+static char __pyx_k_RandomState_bytes_line_900[] = "RandomState.bytes (line 900)";
+static char __pyx_k_RandomState_rand_line_1190[] = "RandomState.rand (line 1190)";
+static char __pyx_k_RandomState_wald_line_3245[] = "RandomState.wald (line 3245)";
+static char __pyx_k_RandomState_zipf_line_3693[] = "RandomState.zipf (line 3693)";
 static char __pyx_k_mean_must_be_1_dimensional[] = "mean must be 1 dimensional";
-static char __pyx_k_RandomState_choice_line_920[] = "RandomState.choice (line 920)";
-static char __pyx_k_RandomState_gamma_line_1716[] = "RandomState.gamma (line 1716)";
-static char __pyx_k_RandomState_power_line_2626[] = "RandomState.power (line 2626)";
-static char __pyx_k_RandomState_randn_line_1226[] = "RandomState.randn (line 1226)";
+static char __pyx_k_RandomState_choice_line_928[] = "RandomState.choice (line 928)";
+static char __pyx_k_RandomState_gamma_line_1724[] = "RandomState.gamma (line 1724)";
+static char __pyx_k_RandomState_power_line_2634[] = "RandomState.power (line 2634)";
+static char __pyx_k_RandomState_randn_line_1234[] = "RandomState.randn (line 1234)";
 static char __pyx_k_a_and_p_must_have_same_size[] = "a and p must have same size";
-static char __pyx_k_RandomState_gumbel_line_2825[] = "RandomState.gumbel (line 2825)";
-static char __pyx_k_RandomState_normal_line_1393[] = "RandomState.normal (line 1393)";
-static char __pyx_k_RandomState_pareto_line_2430[] = "RandomState.pareto (line 2430)";
-static char __pyx_k_RandomState_randint_line_812[] = "RandomState.randint (line 812)";
-static char __pyx_k_RandomState_laplace_line_2735[] = "RandomState.laplace (line 2735)";
-static char __pyx_k_RandomState_poisson_line_3614[] = "RandomState.poisson (line 3614)";
-static char __pyx_k_RandomState_shuffle_line_4395[] = "RandomState.shuffle (line 4395)";
-static char __pyx_k_RandomState_tomaxint_line_765[] = "RandomState.tomaxint (line 765)";
-static char __pyx_k_RandomState_uniform_line_1095[] = "RandomState.uniform (line 1095)";
-static char __pyx_k_RandomState_weibull_line_2526[] = "RandomState.weibull (line 2526)";
+static char __pyx_k_RandomState_gumbel_line_2833[] = "RandomState.gumbel (line 2833)";
+static char __pyx_k_RandomState_normal_line_1401[] = "RandomState.normal (line 1401)";
+static char __pyx_k_RandomState_pareto_line_2438[] = "RandomState.pareto (line 2438)";
+static char __pyx_k_RandomState_randint_line_820[] = "RandomState.randint (line 820)";
+static char __pyx_k_RandomState_laplace_line_2743[] = "RandomState.laplace (line 2743)";
+static char __pyx_k_RandomState_poisson_line_3622[] = "RandomState.poisson (line 3622)";
+static char __pyx_k_RandomState_shuffle_line_4393[] = "RandomState.shuffle (line 4393)";
+static char __pyx_k_RandomState_tomaxint_line_773[] = "RandomState.tomaxint (line 773)";
+static char __pyx_k_RandomState_uniform_line_1103[] = "RandomState.uniform (line 1103)";
+static char __pyx_k_RandomState_weibull_line_2534[] = "RandomState.weibull (line 2534)";
 static char __pyx_k_probabilities_do_not_sum_to_1[] = "probabilities do not sum to 1";
-static char __pyx_k_RandomState_binomial_line_3411[] = "RandomState.binomial (line 3411)";
-static char __pyx_k_RandomState_logistic_line_2956[] = "RandomState.logistic (line 2956)";
-static char __pyx_k_RandomState_rayleigh_line_3165[] = "RandomState.rayleigh (line 3165)";
-static char __pyx_k_RandomState_vonmises_line_2336[] = "RandomState.vonmises (line 2336)";
+static char __pyx_k_RandomState_binomial_line_3419[] = "RandomState.binomial (line 3419)";
+static char __pyx_k_RandomState_logistic_line_2964[] = "RandomState.logistic (line 2964)";
+static char __pyx_k_RandomState_rayleigh_line_3173[] = "RandomState.rayleigh (line 3173)";
+static char __pyx_k_RandomState_vonmises_line_2344[] = "RandomState.vonmises (line 2344)";
 static char __pyx_k_dirichlet_alpha_size_None_Draw[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\n        size : array\n            Number of samples to draw.\n\n        Returns\n        -------\n        samples : ndarray,\n            The drawn samples, of shape (alpha.ndim, size).\n\n        Notes\n        -----\n        .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n        Uses the following property for computation: for each dimension,\n        draw a random sample y_i from a standard gamma generator of shape\n        `alpha_i`, then\n        :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n        Dirichlet distributed.\n\n        References\n        ----------\n        .. [1] David McKay, \"Information Theory, Inference and Learning\n               Algorithms,\" chapter 23,\n               http://www.inference.phy.cam.ac.uk/mackay/\n        .. [2] Wikipedia, \"Dirichlet distribution\",\n               http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n        Examples\n        --------\n        Taking an example cited in Wikipedia, this distribution can be used if\n        one wanted to cut strings (each of initial length 1.0) into K pieces\n        with different lengths, where each piece had, on average, a designated\n        average length, but allowing some variation in the relative sizes of the\n        pieces.\n\n        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n        >>> plt.barh(range(20), s[0])\n        >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n        >>> plt.title(\"Lengths of Strings\")\n\n        ";
 static char __pyx_k_laplace_loc_0_0_scale_1_0_size[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float\n            The position, :math:`\\mu`, of the distribution peak.\n        scale : float\n            :math:`\\lambda`, the exponential decay.\n\n        Notes\n        -----\n        It has the probability density function\n\n        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n        The first law of Laplace, from 1774, states that the frequency of an error\n        can be expressed as an exponential function of the absolute magnitude of\n        the error, which leads to the Laplace distribution. For many problems in\n        Economics and Health sciences, this distribution seems to model the data\n        better than the standard Gaussian distribution\n\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n               Functions with Formulas, Graphs, and Mathematical Tables, 9th\n               printing.  New York: Dover, 1972.\n\n        .. [2] The Laplace distribution and generalizations\n               By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n               Birkhauser, 2001.\n\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n\n        .. [4] Wikipedia, \"Laplace distribution\",\n               http://en.wikipedia.org/wik""i/Laplace_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution\n\n        >>> loc, scale = 0., 1.\n        >>> s = np.random.laplace(loc, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> x = np.arange(-8., 8., .01)\n        >>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)\n        >>> plt.plot(x, pdf)\n\n        Plot Gaussian for comparison:\n\n        >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n        ...      np.exp(-(x - loc)**2 / (2 * scale**2)))\n        >>> plt.plot(x,g)\n\n        ";
 static char __pyx_k_permutation_x_Randomly_permute[] = "\n        permutation(x)\n\n        Randomly permute a sequence, or return a permuted range.\n\n        If `x` is a multi-dimensional array, it is only shuffled along its\n        first index.\n\n        Parameters\n        ----------\n        x : int or array_like\n            If `x` is an integer, randomly permute ``np.arange(x)``.\n            If `x` is an array, make a copy and shuffle the elements\n            randomly.\n\n        Returns\n        -------\n        out : ndarray\n            Permuted sequence or array range.\n\n        Examples\n        --------\n        >>> np.random.permutation(10)\n        array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n        >>> np.random.permutation([1, 4, 9, 12, 15])\n        array([15,  1,  9,  4, 12])\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> np.random.permutation(arr)\n        array([[6, 7, 8],\n               [0, 1, 2],\n               [3, 4, 5]])\n\n        ";
@@ -1204,14 +1214,14 @@ static char __pyx_k_shuffle_x_Modify_a_sequence_in[] = "\n        shuffle(x)\n\n
 static char __pyx_k_standard_exponential_size_None[] = "\n        standard_exponential(size=None)\n\n        Draw samples from the standard exponential distribution.\n\n        `standard_exponential` is identical to the exponential distribution\n        with a scale parameter of 1.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        out : float or ndarray\n            Drawn samples.\n\n        Examples\n        --------\n        Output a 3x8000 array:\n\n        >>> n = np.random.standard_exponential((3, 8000))\n\n        ";
 static char __pyx_k_standard_gamma_shape_size_None[] = "\n        standard_gamma(shape, size=None)\n\n        Draw samples from a Standard Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        shape (sometimes designated \"k\") and scale=1.\n\n        Parameters\n        ----------\n        shape : float\n            Parameter, should be > 0.\n        size : int or tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 1. # mean and width\n        >>> s = np.random.standard_gamma(shape, 1000000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt""\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
 static char __pyx_k_wald_mean_scale_size_None_Draw[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian.\n\n        Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n        this is by no means universal.\n\n        The Inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n        because there is an inverse relationship between the time to cover a unit\n        distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : scalar\n            Distribution mean, should be > 0.\n        scale : scalar\n            Scale parameter, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn sample, all greater than zero.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the Inverse Gaussian distribution first arise from attempts\n        to model Brownian Motion. It is also a competitor to the Weibull for use in\n        reliability modeling and modeling stock returns and interest rate\n        processes.\n\n        References\n        ----------\n        .. [1] Brighton Webs Ltd., Wald Distribution,\n              http://www.brighton-webs.co.uk/distributions/wald.asp\n        .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n              Distribution: Theory : Methodology, and Applications\", CRC Press,\n              1988.\n        .. [3] Wikipedia, \"Wald distribu""tion\"\n              http://en.wikipedia.org/wiki/Wald_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
-static char __pyx_k_RandomState_chisquare_line_2004[] = "RandomState.chisquare (line 2004)";
-static char __pyx_k_RandomState_dirichlet_line_4278[] = "RandomState.dirichlet (line 4278)";
-static char __pyx_k_RandomState_geometric_line_3773[] = "RandomState.geometric (line 3773)";
-static char __pyx_k_RandomState_hypergeometric_line[] = "RandomState.hypergeometric (line 3839)";
-static char __pyx_k_RandomState_lognormal_line_3044[] = "RandomState.lognormal (line 3044)";
-static char __pyx_k_RandomState_logseries_line_3958[] = "RandomState.logseries (line 3958)";
-static char __pyx_k_RandomState_multivariate_normal[] = "RandomState.multivariate_normal (line 4053)";
-static char __pyx_k_RandomState_standard_gamma_line[] = "RandomState.standard_gamma (line 1634)";
+static char __pyx_k_RandomState_chisquare_line_2012[] = "RandomState.chisquare (line 2012)";
+static char __pyx_k_RandomState_dirichlet_line_4281[] = "RandomState.dirichlet (line 4281)";
+static char __pyx_k_RandomState_geometric_line_3781[] = "RandomState.geometric (line 3781)";
+static char __pyx_k_RandomState_hypergeometric_line[] = "RandomState.hypergeometric (line 3847)";
+static char __pyx_k_RandomState_lognormal_line_3052[] = "RandomState.lognormal (line 3052)";
+static char __pyx_k_RandomState_logseries_line_3966[] = "RandomState.logseries (line 3966)";
+static char __pyx_k_RandomState_multivariate_normal[] = "RandomState.multivariate_normal (line 4061)";
+static char __pyx_k_RandomState_standard_gamma_line[] = "RandomState.standard_gamma (line 1642)";
 static char __pyx_k_binomial_n_p_size_None_Draw_sam[] = "\n        binomial(n, p, size=None)\n\n        Draw samples from a binomial distribution.\n\n        Samples are drawn from a Binomial distribution with specified\n        parameters, n trials and p probability of success where\n        n an integer >= 0 and p is in the interval [0,1]. (n may be\n        input as a float, but it is truncated to an integer in use)\n\n        Parameters\n        ----------\n        n : float (but truncated to an integer)\n                parameter, >= 0.\n        p : float\n                parameter, >= 0 and <=1.\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.binom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Binomial distribution is\n\n        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n        where :math:`n` is the number of trials, :math:`p` is the probability\n        of success, and :math:`N` is the number of successes.\n\n        When estimating the standard error of a proportion in a population by\n        using a random sample, the normal distribution works well unless the\n        product p*n <=5, where p = population proportion estimate, and n =\n        number of samples, in which case the binomial distribution is used\n        instead. For example, a sample of 15 people shows 4 who are left\n        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n        so the binomial distribution should be used in this case.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n               Springer-Verlag, 2002.""\n        .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/BinomialDistribution.html\n        .. [5] Wikipedia, \"Binomial-distribution\",\n               http://en.wikipedia.org/wiki/Binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> n, p = 10, .5 # number of trials, probability of each trial\n        >>> s = np.random.binomial(n, p, 1000)\n        # result of flipping a coin 10 times, tested 1000 times.\n\n        A real world example. A company drills 9 wild-cat oil exploration\n        wells, each with an estimated probability of success of 0.1. All nine\n        wells fail. What is the probability of that happening?\n\n        Let's do 20,000 trials of the model, and count the number that\n        generate zero positive results.\n\n        >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n        answer = 0.38885, or 38%.\n\n        ";
 static char __pyx_k_bytes_length_Return_random_byte[] = "\n        bytes(length)\n\n        Return random bytes.\n\n        Parameters\n        ----------\n        length : int\n            Number of random bytes.\n\n        Returns\n        -------\n        out : str\n            String of length `length`.\n\n        Examples\n        --------\n        >>> np.random.bytes(10)\n        ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n        ";
 static char __pyx_k_chisquare_df_size_None_Draw_sam[] = "\n        chisquare(df, size=None)\n\n        Draw samples from a chi-square distribution.\n\n        When `df` independent random variables, each with standard normal\n        distributions (mean 0, variance 1), are squared and summed, the\n        resulting distribution is chi-square (see Notes).  This distribution\n        is often used in hypothesis testing.\n\n        Parameters\n        ----------\n        df : int\n             Number of degrees of freedom.\n        size : tuple of ints, int, optional\n             Size of the returned array.  By default, a scalar is\n             returned.\n\n        Returns\n        -------\n        output : ndarray\n            Samples drawn from the distribution, packed in a `size`-shaped\n            array.\n\n        Raises\n        ------\n        ValueError\n            When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n            is given.\n\n        Notes\n        -----\n        The variable obtained by summing the squares of `df` independent,\n        standard normally distributed random variables:\n\n        .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n\n        is chi-square distributed, denoted\n\n        .. math:: Q \\sim \\chi^2_k.\n\n        The probability density function of the chi-squared distribution is\n\n        .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n                         x^{k/2 - 1} e^{-x/2},\n\n        where :math:`\\Gamma` is the gamma function,\n\n        .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n\n        References\n        ----------\n        `NIST/SEMATECH e-Handbook of Statistical Methods\n        <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_\n\n        Examples\n        --------\n        >>> np.random.chisquare(2,4)\n        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])\n\n        ";
@@ -1220,6 +1230,7 @@ static char __pyx_k_f_dfnum_dfden_size_None_Draw_sa[] = "\n        f(dfnum, dfde
 static char __pyx_k_gamma_shape_scale_1_0_size_None[] = "\n        gamma(shape, scale=1.0, size=None)\n\n        Draw samples from a Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n        \"theta\"), where both parameters are > 0.\n\n        Parameters\n        ----------\n        shape : scalar > 0\n            The shape of the gamma distribution.\n        scale : scalar > 0, optional\n            The scale of the gamma distribution.  Default is equal to 1.\n        size : shape_tuple, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray, float\n            Returns one sample unless `size` parameter is specified.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2.,"" 2. # mean and dispersion\n        >>> s = np.random.gamma(shape, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n        ...                      (sps.gamma(shape)*scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
 static char __pyx_k_geometric_p_size_None_Draw_samp[] = "\n        geometric(p, size=None)\n\n        Draw samples from the geometric distribution.\n\n        Bernoulli trials are experiments with one of two outcomes:\n        success or failure (an example of such an experiment is flipping\n        a coin).  The geometric distribution models the number of trials\n        that must be run in order to achieve success.  It is therefore\n        supported on the positive integers, ``k = 1, 2, ...``.\n\n        The probability mass function of the geometric distribution is\n\n        .. math:: f(k) = (1 - p)^{k - 1} p\n\n        where `p` is the probability of success of an individual trial.\n\n        Parameters\n        ----------\n        p : float\n            The probability of success of an individual trial.\n        size : tuple of ints\n            Number of values to draw from the distribution.  The output\n            is shaped according to `size`.\n\n        Returns\n        -------\n        out : ndarray\n            Samples from the geometric distribution, shaped according to\n            `size`.\n\n        Examples\n        --------\n        Draw ten thousand values from the geometric distribution,\n        with the probability of an individual success equal to 0.35:\n\n        >>> z = np.random.geometric(p=0.35, size=10000)\n\n        How many trials succeeded after a single run?\n\n        >>> (z == 1).sum() / 10000.\n        0.34889999999999999 #random\n\n        ";
 static char __pyx_k_gumbel_loc_0_0_scale_1_0_size_N[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and scale.\n        For more information on the Gumbel distribution, see Notes and References\n        below.\n\n        Parameters\n        ----------\n        loc : float\n            The location of the mode of the distribution.\n        scale : float\n            The scale parameter of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray\n            The samples\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n            probability density function, distribution, or cumulative density\n            function, etc. for each of the above\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n        distributions used in modeling extreme value problems.  The Gumbel is a\n        special case of the Extreme Value Type I distribution for maximums from\n        distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n        the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and rainfall\n        rates.  It is a \"fat-tailed\" distribution - the ""probability of an event in\n        the tail of the distribution is larger than if one used a Gaussian, hence\n        the surprisingly frequent occurrence of 100-year floods. Floods were\n        initially modeled as a Gaussian process, which underestimated the frequency\n        of extreme events.\n\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n        Press, 1958.\n\n        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n        2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_home_jtaylor_prog_numpy_numpy_r[] = "/home/jtaylor/prog/numpy/numpy/random/mtrand/mtrand.pyx";
 static char __pyx_k_hypergeometric_ngood_nbad_nsamp[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : int or array_like\n            Number of ways to make a good selection.  Must be nonnegative.\n        nbad : int or array_like\n            Number of ways to make a bad selection.  Must be nonnegative.\n        nsample : int or array_like\n            Number of items sampled.  Must be at least 1 and at most\n            ``ngood + nbad``.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distrib""ution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn with\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
 static char __pyx_k_logistic_loc_0_0_scale_1_0_size[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Logistic distribution.\n\n        Samples are drawn from a Logistic distribution with specified\n        parameters, loc (location or mean, also median), and scale (>0).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logistic : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Logistic distribution is\n\n        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n        where :math:`\\mu` = location and :math:`s` = scale.\n\n        The Logistic distribution is used in Extreme Value problems where it\n        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n        the World Chess Federation (FIDE) where it is used in the Elo ranking\n        system, assuming the performance of each player is a logistically\n        distributed random variable.\n\n        References\n        ----------\n        .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n               Values, from Insurance, Finance, Hydrology and Other Fields,\n               Birkhauser Verlag, Basel, pp 132-133.\n        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LogisticDistribution.html\n        .. [3] Wikipedia, \"Logistic-distribution\",\n               http://en.wikipedia.org/wiki/Logistic-distribution\n\n        Examples\n   ""     --------\n        Draw samples from the distribution:\n\n        >>> loc, scale = 10, 1\n        >>> s = np.random.logistic(loc, scale, 10000)\n        >>> count, bins, ignored = plt.hist(s, bins=50)\n\n        #   plot against distribution\n\n        >>> def logist(x, loc, scale):\n        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n        ... logist(bins, loc, scale).max())\n        >>> plt.show()\n\n        ";
 static char __pyx_k_lognormal_mean_0_0_sigma_1_0_si[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean,\n        standard deviation, and array shape.  Note that the mean and standard\n        deviation are not the values for the distribution itself, but of the\n        underlying normal distribution it is derived from.\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, > 0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or float\n            The desired samples. An array of the same shape as `size` if given,\n            if `size` is None a float is returned.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.  The probability density function for the log-normal\n        distribution is:\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n        deviation of the normally distributed logarithm of the variable.\n        A log-normal distribution results if a random variable is the *product*\n        of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        Reference""s\n        ----------\n        Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n        across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n        May, 2001.  http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n        Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n        Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale values to be positive\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n        >>> plt.show()\n\n        ";
@@ -1246,18 +1257,18 @@ static char __pyx_k_weibull_a_size_None_Weibull_dis[] = "\n        weibull(a, si
 static char __pyx_k_zipf_a_size_None_Draw_samples_f[] = "\n        zipf(a, size=None)\n\n        Draw samples from a Zipf distribution.\n\n        Samples are drawn from a Zipf distribution with specified parameter\n        `a` > 1.\n\n        The Zipf distribution (also known as the zeta distribution) is a\n        continuous probability distribution that satisfies Zipf's law: the\n        frequency of an item is inversely proportional to its rank in a\n        frequency table.\n\n        Parameters\n        ----------\n        a : float > 1\n            Distribution parameter.\n        size : int or tuple of int, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn; a single integer is equivalent in\n            its result to providing a mono-tuple, i.e., a 1-D array of length\n            *size* is returned.  The default is None, in which case a single\n            scalar is returned.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples are greater than or equal to one.\n\n        See Also\n        --------\n        scipy.stats.distributions.zipf : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Zipf distribution is\n\n        .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n        where :math:`\\zeta` is the Riemann Zeta function.\n\n        It is named for the American linguist George Kingsley Zipf, who noted\n        that the frequency of any word in a sample of a language is inversely\n        proportional to its rank in the frequency table.\n\n        References\n        ----------\n        Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n        in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 2. # parameter\n        >>> s = np.random.zipf""(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        Truncate s values at 50 so plot is interesting\n        >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n        >>> x = np.arange(1., 50.)\n        >>> y = x**(-a)/sps.zetac(a)\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
 static char __pyx_k_Cannot_take_a_larger_sample_than[] = "Cannot take a larger sample than population when 'replace=False'";
 static char __pyx_k_Fewer_non_zero_entries_in_p_than[] = "Fewer non-zero entries in p than size";
-static char __pyx_k_RandomState_multinomial_line_418[] = "RandomState.multinomial (line 4185)";
-static char __pyx_k_RandomState_negative_binomial_li[] = "RandomState.negative_binomial (line 3519)";
-static char __pyx_k_RandomState_noncentral_chisquare[] = "RandomState.noncentral_chisquare (line 2082)";
-static char __pyx_k_RandomState_noncentral_f_line_19[] = "RandomState.noncentral_f (line 1909)";
-static char __pyx_k_RandomState_permutation_line_445[] = "RandomState.permutation (line 4454)";
-static char __pyx_k_RandomState_random_integers_line[] = "RandomState.random_integers (line 1283)";
-static char __pyx_k_RandomState_random_sample_line_7[] = "RandomState.random_sample (line 722)";
-static char __pyx_k_RandomState_standard_cauchy_line[] = "RandomState.standard_cauchy (line 2174)";
-static char __pyx_k_RandomState_standard_exponential[] = "RandomState.standard_exponential (line 1606)";
-static char __pyx_k_RandomState_standard_normal_line[] = "RandomState.standard_normal (line 1361)";
-static char __pyx_k_RandomState_standard_t_line_2235[] = "RandomState.standard_t (line 2235)";
-static char __pyx_k_RandomState_triangular_line_3323[] = "RandomState.triangular (line 3323)";
+static char __pyx_k_RandomState_multinomial_line_419[] = "RandomState.multinomial (line 4193)";
+static char __pyx_k_RandomState_negative_binomial_li[] = "RandomState.negative_binomial (line 3527)";
+static char __pyx_k_RandomState_noncentral_chisquare[] = "RandomState.noncentral_chisquare (line 2090)";
+static char __pyx_k_RandomState_noncentral_f_line_19[] = "RandomState.noncentral_f (line 1917)";
+static char __pyx_k_RandomState_permutation_line_445[] = "RandomState.permutation (line 4452)";
+static char __pyx_k_RandomState_random_integers_line[] = "RandomState.random_integers (line 1291)";
+static char __pyx_k_RandomState_random_sample_line_7[] = "RandomState.random_sample (line 730)";
+static char __pyx_k_RandomState_standard_cauchy_line[] = "RandomState.standard_cauchy (line 2182)";
+static char __pyx_k_RandomState_standard_exponential[] = "RandomState.standard_exponential (line 1614)";
+static char __pyx_k_RandomState_standard_normal_line[] = "RandomState.standard_normal (line 1369)";
+static char __pyx_k_RandomState_standard_t_line_2243[] = "RandomState.standard_t (line 2243)";
+static char __pyx_k_RandomState_triangular_line_3331[] = "RandomState.triangular (line 3331)";
 static char __pyx_k_a_must_be_1_dimensional_or_an_in[] = "a must be 1-dimensional or an integer";
 static char __pyx_k_cov_must_be_2_dimensional_and_sq[] = "cov must be 2 dimensional and square";
 static char __pyx_k_mean_and_cov_must_have_same_leng[] = "mean and cov must have same length";
@@ -1266,50 +1277,50 @@ static char __pyx_k_size_is_not_compatible_with_inpu[] = "size is not compatible
 static PyObject *__pyx_kp_s_Cannot_take_a_larger_sample_than;
 static PyObject *__pyx_kp_s_Fewer_non_zero_entries_in_p_than;
 static PyObject *__pyx_n_s_MT19937;
-static PyObject *__pyx_kp_u_RandomState_binomial_line_3411;
-static PyObject *__pyx_kp_u_RandomState_bytes_line_892;
-static PyObject *__pyx_kp_u_RandomState_chisquare_line_2004;
-static PyObject *__pyx_kp_u_RandomState_choice_line_920;
+static PyObject *__pyx_kp_u_RandomState_binomial_line_3419;
+static PyObject *__pyx_kp_u_RandomState_bytes_line_900;
+static PyObject *__pyx_kp_u_RandomState_chisquare_line_2012;
+static PyObject *__pyx_kp_u_RandomState_choice_line_928;
 static PyObject *__pyx_n_s_RandomState_ctor;
-static PyObject *__pyx_kp_u_RandomState_dirichlet_line_4278;
-static PyObject *__pyx_kp_u_RandomState_f_line_1807;
-static PyObject *__pyx_kp_u_RandomState_gamma_line_1716;
-static PyObject *__pyx_kp_u_RandomState_geometric_line_3773;
-static PyObject *__pyx_kp_u_RandomState_gumbel_line_2825;
+static PyObject *__pyx_kp_u_RandomState_dirichlet_line_4281;
+static PyObject *__pyx_kp_u_RandomState_f_line_1815;
+static PyObject *__pyx_kp_u_RandomState_gamma_line_1724;
+static PyObject *__pyx_kp_u_RandomState_geometric_line_3781;
+static PyObject *__pyx_kp_u_RandomState_gumbel_line_2833;
 static PyObject *__pyx_kp_u_RandomState_hypergeometric_line;
-static PyObject *__pyx_kp_u_RandomState_laplace_line_2735;
-static PyObject *__pyx_kp_u_RandomState_logistic_line_2956;
-static PyObject *__pyx_kp_u_RandomState_lognormal_line_3044;
-static PyObject *__pyx_kp_u_RandomState_logseries_line_3958;
-static PyObject *__pyx_kp_u_RandomState_multinomial_line_418;
+static PyObject *__pyx_kp_u_RandomState_laplace_line_2743;
+static PyObject *__pyx_kp_u_RandomState_logistic_line_2964;
+static PyObject *__pyx_kp_u_RandomState_lognormal_line_3052;
+static PyObject *__pyx_kp_u_RandomState_logseries_line_3966;
+static PyObject *__pyx_kp_u_RandomState_multinomial_line_419;
 static PyObject *__pyx_kp_u_RandomState_multivariate_normal;
 static PyObject *__pyx_kp_u_RandomState_negative_binomial_li;
 static PyObject *__pyx_kp_u_RandomState_noncentral_chisquare;
 static PyObject *__pyx_kp_u_RandomState_noncentral_f_line_19;
-static PyObject *__pyx_kp_u_RandomState_normal_line_1393;
-static PyObject *__pyx_kp_u_RandomState_pareto_line_2430;
+static PyObject *__pyx_kp_u_RandomState_normal_line_1401;
+static PyObject *__pyx_kp_u_RandomState_pareto_line_2438;
 static PyObject *__pyx_kp_u_RandomState_permutation_line_445;
-static PyObject *__pyx_kp_u_RandomState_poisson_line_3614;
-static PyObject *__pyx_kp_u_RandomState_power_line_2626;
-static PyObject *__pyx_kp_u_RandomState_rand_line_1182;
-static PyObject *__pyx_kp_u_RandomState_randint_line_812;
-static PyObject *__pyx_kp_u_RandomState_randn_line_1226;
+static PyObject *__pyx_kp_u_RandomState_poisson_line_3622;
+static PyObject *__pyx_kp_u_RandomState_power_line_2634;
+static PyObject *__pyx_kp_u_RandomState_rand_line_1190;
+static PyObject *__pyx_kp_u_RandomState_randint_line_820;
+static PyObject *__pyx_kp_u_RandomState_randn_line_1234;
 static PyObject *__pyx_kp_u_RandomState_random_integers_line;
 static PyObject *__pyx_kp_u_RandomState_random_sample_line_7;
-static PyObject *__pyx_kp_u_RandomState_rayleigh_line_3165;
-static PyObject *__pyx_kp_u_RandomState_shuffle_line_4395;
+static PyObject *__pyx_kp_u_RandomState_rayleigh_line_3173;
+static PyObject *__pyx_kp_u_RandomState_shuffle_line_4393;
 static PyObject *__pyx_kp_u_RandomState_standard_cauchy_line;
 static PyObject *__pyx_kp_u_RandomState_standard_exponential;
 static PyObject *__pyx_kp_u_RandomState_standard_gamma_line;
 static PyObject *__pyx_kp_u_RandomState_standard_normal_line;
-static PyObject *__pyx_kp_u_RandomState_standard_t_line_2235;
-static PyObject *__pyx_kp_u_RandomState_tomaxint_line_765;
-static PyObject *__pyx_kp_u_RandomState_triangular_line_3323;
-static PyObject *__pyx_kp_u_RandomState_uniform_line_1095;
-static PyObject *__pyx_kp_u_RandomState_vonmises_line_2336;
-static PyObject *__pyx_kp_u_RandomState_wald_line_3237;
-static PyObject *__pyx_kp_u_RandomState_weibull_line_2526;
-static PyObject *__pyx_kp_u_RandomState_zipf_line_3685;
+static PyObject *__pyx_kp_u_RandomState_standard_t_line_2243;
+static PyObject *__pyx_kp_u_RandomState_tomaxint_line_773;
+static PyObject *__pyx_kp_u_RandomState_triangular_line_3331;
+static PyObject *__pyx_kp_u_RandomState_uniform_line_1103;
+static PyObject *__pyx_kp_u_RandomState_vonmises_line_2344;
+static PyObject *__pyx_kp_u_RandomState_wald_line_3245;
+static PyObject *__pyx_kp_u_RandomState_weibull_line_2534;
+static PyObject *__pyx_kp_u_RandomState_zipf_line_3693;
 static PyObject *__pyx_n_s_TypeError;
 static PyObject *__pyx_n_s_ValueError;
 static PyObject *__pyx_n_s_a;
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 static PyObject *__pyx_n_s_cov;
 static PyObject *__pyx_kp_s_cov_must_be_2_dimensional_and_sq;
 static PyObject *__pyx_n_s_cumsum;
+static PyObject *__pyx_n_s_d;
 static PyObject *__pyx_n_s_df;
 static PyObject *__pyx_kp_s_df_0;
 static PyObject *__pyx_kp_s_df_1;
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 static PyObject *__pyx_n_s_gumbel;
 static PyObject *__pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N;
 static PyObject *__pyx_n_s_high;
+static PyObject *__pyx_kp_s_home_jtaylor_prog_numpy_numpy_r;
 static PyObject *__pyx_n_s_hypergeometric;
 static PyObject *__pyx_kp_u_hypergeometric_ngood_nbad_nsamp;
 static PyObject *__pyx_n_s_iinfo;
@@ -1415,6 +1428,7 @@ static PyObject *__pyx_kp_s_mean_and_cov_must_have_same_leng;
 static PyObject *__pyx_kp_s_mean_must_be_1_dimensional;
 static PyObject *__pyx_n_s_mode;
 static PyObject *__pyx_kp_s_mode_right;
+static PyObject *__pyx_n_s_mtrand;
 static PyObject *__pyx_n_s_mu;
 static PyObject *__pyx_n_s_multinomial;
 static PyObject *__pyx_kp_u_multinomial_n_pvals_size_None_D;
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 static PyObject *__pyx_n_s_set_state;
 static PyObject *__pyx_n_s_shape;
 static PyObject *__pyx_kp_s_shape_0;
+static PyObject *__pyx_n_s_shape_from_size;
 static PyObject *__pyx_n_s_shuffle;
 static PyObject *__pyx_kp_u_shuffle_x_Modify_a_sequence_in;
 static PyObject *__pyx_n_s_side;
@@ -1671,7 +1686,9 @@ static PyObject *__pyx_tuple__120;
 static PyObject *__pyx_tuple__121;
 static PyObject *__pyx_tuple__123;
 static PyObject *__pyx_tuple__124;
-static PyObject *__pyx_tuple__125;
+static PyObject *__pyx_tuple__126;
+static PyObject *__pyx_tuple__127;
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 /* "mtrand.pyx":129
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  *         sum = t
  *     return sum             # <<<<<<<<<<<<<<
  * 
- * cdef class RandomState:
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@@ -5283,7 +5300,282 @@ static double __pyx_f_6mtrand_kahan_sum(double *__pyx_v_darr, npy_intp __pyx_v_n
   return __pyx_r;
 }
 
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+  __pyx_t_1 = __Pyx_GetItemInt(__pyx_v_state, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 1); if (unlikely(__pyx_t_1 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
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-  /* "mtrand.pyx":691
+  /* "mtrand.pyx":699
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+  __pyx_t_2 = (__Pyx_PyString_Equals(__pyx_v_algorithm_name, __pyx_n_s_MT19937, Py_NE)); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 699; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (__pyx_t_2) {
 
-    /* "mtrand.pyx":692
+    /* "mtrand.pyx":700
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-    __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__8, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__8, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 700; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __Pyx_Raise(__pyx_t_1, 0, 0, 0);
     __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 700; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   }
 
-  /* "mtrand.pyx":693
+  /* "mtrand.pyx":701
  *         if algorithm_name != 'MT19937':
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  *         key, pos = state[1:3]             # <<<<<<<<<<<<<<
  *         if len(state) == 3:
  *             has_gauss = 0
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-  __pyx_t_1 = __Pyx_PyObject_GetSlice(__pyx_v_state, 1, 3, NULL, NULL, &__pyx_slice__9, 1, 1, 1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = __Pyx_PyObject_GetSlice(__pyx_v_state, 1, 3, NULL, NULL, &__pyx_slice__9, 1, 1, 1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     PyObject* sequence = __pyx_t_1;
@@ -5988,7 +6306,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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       if (size > 2) __Pyx_RaiseTooManyValuesError(2);
       else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -6001,15 +6319,15 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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     __Pyx_INCREF(__pyx_t_4);
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-    __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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+    __pyx_t_5 = PyObject_GetIter(__pyx_t_1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
     __pyx_t_6 = Py_TYPE(__pyx_t_5)->tp_iternext;
@@ -6017,7 +6335,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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     __Pyx_GOTREF(__pyx_t_4);
-    if (__Pyx_IternextUnpackEndCheck(__pyx_t_6(__pyx_t_5), 2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    if (__Pyx_IternextUnpackEndCheck(__pyx_t_6(__pyx_t_5), 2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
     goto __pyx_L5_unpacking_done;
@@ -6025,27 +6343,27 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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     __pyx_t_6 = NULL;
     if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index);
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_t_7 = __Pyx_PyInt_As_int(__pyx_t_4); if (unlikely((__pyx_t_7 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_7 = __Pyx_PyInt_As_int(__pyx_t_4); if (unlikely((__pyx_t_7 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
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   __pyx_t_3 = 0;
   __pyx_v_pos = __pyx_t_7;
 
-  /* "mtrand.pyx":694
+  /* "mtrand.pyx":702
  *             raise ValueError("algorithm must be 'MT19937'")
  *         key, pos = state[1:3]
  *         if len(state) == 3:             # <<<<<<<<<<<<<<
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-  __pyx_t_8 = PyObject_Length(__pyx_v_state); if (unlikely(__pyx_t_8 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 694; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_8 = PyObject_Length(__pyx_v_state); if (unlikely(__pyx_t_8 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 702; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_t_2 = ((__pyx_t_8 == 3) != 0);
   if (__pyx_t_2) {
 
-    /* "mtrand.pyx":695
+    /* "mtrand.pyx":703
  *         key, pos = state[1:3]
  *         if len(state) == 3:
  *             has_gauss = 0             # <<<<<<<<<<<<<<
@@ -6055,7 +6373,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
     __Pyx_INCREF(__pyx_int_0);
     __pyx_v_has_gauss = __pyx_int_0;
 
-    /* "mtrand.pyx":696
+    /* "mtrand.pyx":704
  *         if len(state) == 3:
  *             has_gauss = 0
  *             cached_gaussian = 0.0             # <<<<<<<<<<<<<<
@@ -6068,14 +6386,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
   }
   /*else*/ {
 
-    /* "mtrand.pyx":698
+    /* "mtrand.pyx":706
  *             cached_gaussian = 0.0
  *         else:
  *             has_gauss, cached_gaussian = state[3:5]             # <<<<<<<<<<<<<<
  *         try:
  *             obj = <ndarray>PyArray_ContiguousFromObject(key, NPY_ULONG, 1, 1)
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-    __pyx_t_1 = __Pyx_PyObject_GetSlice(__pyx_v_state, 3, 5, NULL, NULL, &__pyx_slice__10, 1, 1, 1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_1 = __Pyx_PyObject_GetSlice(__pyx_v_state, 3, 5, NULL, NULL, &__pyx_slice__10, 1, 1, 1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __Pyx_GOTREF(__pyx_t_1);
     if ((likely(PyTuple_CheckExact(__pyx_t_1))) || (PyList_CheckExact(__pyx_t_1))) {
       PyObject* sequence = __pyx_t_1;
@@ -6087,7 +6405,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
       if (unlikely(size != 2)) {
         if (size > 2) __Pyx_RaiseTooManyValuesError(2);
         else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
-        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       }
       #if CYTHON_COMPILING_IN_CPYTHON
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@@ -6100,15 +6418,15 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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       __Pyx_INCREF(__pyx_t_3);
       #else
-      __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_3 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_3);
       #endif
       __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
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-      __pyx_t_5 = PyObject_GetIter(__pyx_t_1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_5 = PyObject_GetIter(__pyx_t_1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_5);
       __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
       __pyx_t_6 = Py_TYPE(__pyx_t_5)->tp_iternext;
@@ -6116,7 +6434,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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-      if (__Pyx_IternextUnpackEndCheck(__pyx_t_6(__pyx_t_5), 2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      if (__Pyx_IternextUnpackEndCheck(__pyx_t_6(__pyx_t_5), 2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -6124,7 +6442,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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       __pyx_t_6 = NULL;
       if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index);
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":699
+  /* "mtrand.pyx":707
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@@ -6148,14 +6466,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
     __Pyx_XGOTREF(__pyx_t_11);
     /*try:*/ {
 
-      /* "mtrand.pyx":700
+      /* "mtrand.pyx":708
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-      __pyx_t_1 = PyArray_ContiguousFromObject(__pyx_v_key, NPY_ULONG, 1, 1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 700; __pyx_clineno = __LINE__; goto __pyx_L9_error;}
+      __pyx_t_1 = PyArray_ContiguousFromObject(__pyx_v_key, NPY_ULONG, 1, 1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 708; __pyx_clineno = __LINE__; goto __pyx_L9_error;}
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@@ -6173,7 +6491,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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     __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
 
-    /* "mtrand.pyx":701
+    /* "mtrand.pyx":709
  *         try:
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@@ -6183,19 +6501,19 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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       __Pyx_AddTraceback("mtrand.RandomState.set_state", __pyx_clineno, __pyx_lineno, __pyx_filename);
-      if (__Pyx_GetException(&__pyx_t_3, &__pyx_t_1, &__pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L11_except_error;}
+      if (__Pyx_GetException(&__pyx_t_3, &__pyx_t_1, &__pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 709; __pyx_clineno = __LINE__; goto __pyx_L11_except_error;}
       __Pyx_GOTREF(__pyx_t_3);
       __Pyx_GOTREF(__pyx_t_1);
       __Pyx_GOTREF(__pyx_t_4);
 
-      /* "mtrand.pyx":703
+      /* "mtrand.pyx":711
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-      __pyx_t_5 = PyArray_ContiguousFromObject(__pyx_v_key, NPY_LONG, 1, 1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L11_except_error;}
+      __pyx_t_5 = PyArray_ContiguousFromObject(__pyx_v_key, NPY_LONG, 1, 1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 711; __pyx_clineno = __LINE__; goto __pyx_L11_except_error;}
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@@ -6222,7 +6540,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
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-  /* "mtrand.pyx":704
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@@ -6232,21 +6550,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtr
   __pyx_t_2 = ((PyArray_DIM(arrayObject_obj, 0) != 624) != 0);
   if (__pyx_t_2) {
 
-    /* "mtrand.pyx":705
+    /* "mtrand.pyx":713
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+    __pyx_t_3 = __Pyx_PyInt_As_long(__pyx_v_low); if (unlikely((__pyx_t_3 == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 879; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":873
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+    __pyx_t_3 = __Pyx_PyInt_As_long(__pyx_v_low); if (unlikely((__pyx_t_3 == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 881; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":874
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+    __pyx_t_3 = __Pyx_PyInt_As_long(__pyx_v_high); if (unlikely((__pyx_t_3 == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 882; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 877; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":879
+  /* "mtrand.pyx":887
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-  /* "mtrand.pyx":880
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@@ -6967,7 +7285,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_20randint(struct __pyx_obj_6mtra
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-    /* "mtrand.pyx":881
+    /* "mtrand.pyx":889
  *         diff = <unsigned long>hi - <unsigned long>lo - 1UL
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@@ -6976,7 +7294,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_20randint(struct __pyx_obj_6mtra
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-    /* "mtrand.pyx":882
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+    __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 892; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_6 = __Pyx_PyObject_Call(__pyx_t_5, __pyx_t_4, NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 892; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":885
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-    /* "mtrand.pyx":886
+    /* "mtrand.pyx":894
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-    /* "mtrand.pyx":887
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-      /* "mtrand.pyx":888
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-      /* "mtrand.pyx":889
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-    /* "mtrand.pyx":890
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-  /* "mtrand.pyx":812
+  /* "mtrand.pyx":820
  *         return disc0_array(self.internal_state, rk_long, size)
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  *     def randint(self, low, high=None, size=None):             # <<<<<<<<<<<<<<
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+    __pyx_v_length = __Pyx_PyInt_As_npy_intp(__pyx_arg_length); if (unlikely((__pyx_v_length == (npy_intp)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 900; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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   __pyx_L3_error:;
@@ -7150,19 +7468,19 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_22bytes(struct __pyx_obj_6mtrand
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-  /* "mtrand.pyx":915
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-  __pyx_t_1 = empty_py_bytes(__pyx_v_length, (&__pyx_v_bytes)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 915; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = empty_py_bytes(__pyx_v_length, (&__pyx_v_bytes)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 923; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   __pyx_v_bytestring = __pyx_t_1;
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-  /* "mtrand.pyx":916
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   rk_fill(__pyx_v_bytes, __pyx_v_length, __pyx_v_self->internal_state);
 
-  /* "mtrand.pyx":917
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-  /* "mtrand.pyx":892
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+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "choice") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 928; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+  __Pyx_RaiseArgtupleInvalid("choice", 0, 1, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 928; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -7336,27 +7654,27 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_24choice(struct __pyx_obj_6mtran
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-  /* "mtrand.pyx":998
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1020; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_v_p, __pyx_n_s_size); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1029; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1022; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-        /* "mtrand.pyx":1065
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-        /* "mtrand.pyx":1072
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1490; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1498; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1910; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1911; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_3, __pyx_t_2, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1911; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_5); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1911; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
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-    /* "mtrand.pyx":1904
+    /* "mtrand.pyx":1912
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+    __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__41, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1912; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1904; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1912; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *             raise ValueError("dfden <= 0")
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-  __pyx_t_5 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1905; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_5 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1913; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_5, __pyx_n_s_any); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1913; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_5 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1913; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1913; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1913; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1913; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":1906
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+    __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__42, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1914; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1914; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":1807
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 }
 
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+          __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1917; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+          __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1917; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "noncentral_f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1909; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "noncentral_f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1917; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -11623,7 +11941,7 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_51noncentral_f(PyObject *__pyx_v
   }
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-  __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1909; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1917; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -11655,7 +11973,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
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-  /* "mtrand.pyx":1976
+  /* "mtrand.pyx":1984
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  *         fdfnum = PyFloat_AsDouble(dfnum)             # <<<<<<<<<<<<<<
@@ -11664,7 +11982,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
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-  /* "mtrand.pyx":1977
+  /* "mtrand.pyx":1985
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  *         fdfnum = PyFloat_AsDouble(dfnum)
  *         fdfden = PyFloat_AsDouble(dfden)             # <<<<<<<<<<<<<<
@@ -11673,7 +11991,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
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-  /* "mtrand.pyx":1978
+  /* "mtrand.pyx":1986
  *         fdfnum = PyFloat_AsDouble(dfnum)
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  *         fnonc = PyFloat_AsDouble(nonc)             # <<<<<<<<<<<<<<
@@ -11682,7 +12000,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
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-  /* "mtrand.pyx":1979
+  /* "mtrand.pyx":1987
  *         fdfden = PyFloat_AsDouble(dfden)
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  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
@@ -11692,7 +12010,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
   __pyx_t_1 = ((!(PyErr_Occurred() != 0)) != 0);
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-    /* "mtrand.pyx":1980
+    /* "mtrand.pyx":1988
  *         fnonc = PyFloat_AsDouble(nonc)
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@@ -11702,21 +12020,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
     __pyx_t_1 = ((__pyx_v_fdfnum <= 1.0) != 0);
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-      /* "mtrand.pyx":1981
+      /* "mtrand.pyx":1989
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+      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__43, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1989; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1981; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1989; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":1982
+    /* "mtrand.pyx":1990
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@@ -11726,21 +12044,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
     __pyx_t_1 = ((__pyx_v_fdfden <= 0.0) != 0);
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-      /* "mtrand.pyx":1983
+      /* "mtrand.pyx":1991
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-      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__44, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1983; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__44, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1991; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1983; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1991; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":1984
+    /* "mtrand.pyx":1992
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-      /* "mtrand.pyx":1985
+      /* "mtrand.pyx":1993
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+      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__45, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1993; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1985; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1993; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":1986
+    /* "mtrand.pyx":1994
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-    /* "mtrand.pyx":1987
+    /* "mtrand.pyx":1995
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+    __pyx_t_2 = __pyx_f_6mtrand_cont3_array_sc(__pyx_v_self->internal_state, rk_noncentral_f, __pyx_v_size, __pyx_v_fdfnum, __pyx_v_fdfden, __pyx_v_fnonc); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1994; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":1989
+  /* "mtrand.pyx":1997
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@@ -11796,14 +12114,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
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   PyErr_Clear();
 
-  /* "mtrand.pyx":1991
+  /* "mtrand.pyx":1999
  *         PyErr_Clear()
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  *         odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
  *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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-  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_dfnum, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1991; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_dfnum, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1999; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -11811,14 +12129,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
   __pyx_v_odfnum = ((PyArrayObject *)__pyx_t_3);
   __pyx_t_3 = 0;
 
-  /* "mtrand.pyx":1992
+  /* "mtrand.pyx":2000
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  *         odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
  *         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  * 
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-  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1992; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2000; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -11826,14 +12144,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
   __pyx_v_odfden = ((PyArrayObject *)__pyx_t_2);
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-  /* "mtrand.pyx":1993
+  /* "mtrand.pyx":2001
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  *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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-  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1993; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2001; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":1995
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  *         if np.any(np.less_equal(odfnum, 1.0)):             # <<<<<<<<<<<<<<
  *             raise ValueError("dfnum <= 1")
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+  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2003; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_any); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2003; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2003; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_less_equal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2003; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2003; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2003; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2072; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2080; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":2073
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+    __pyx_t_2 = __pyx_f_6mtrand_cont1_array_sc(__pyx_v_self->internal_state, rk_chisquare, __pyx_v_size, __pyx_v_fdf); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2081; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":2077
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-  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_df, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2077; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_df, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2085; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2086; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2079; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2087; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":2080
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+  __pyx_t_5 = __pyx_f_6mtrand_cont1_array(__pyx_v_self->internal_state, rk_chisquare, __pyx_v_size, __pyx_v_odf); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2088; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":2004
+  /* "mtrand.pyx":2012
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 }
 
-/* "mtrand.pyx":2082
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+          __Pyx_RaiseArgtupleInvalid("noncentral_chisquare", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2090; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-  /* "mtrand.pyx":2154
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2159; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":2172
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+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_mu, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2432; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2435; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":2428
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+  __pyx_t_5 = __pyx_f_6mtrand_cont2_array(__pyx_v_self->internal_state, rk_vonmises, __pyx_v_size, __pyx_v_omu, __pyx_v_okappa); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2436; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_5 = __pyx_f_6mtrand_cont1_array(__pyx_v_self->internal_state, rk_pareto, __pyx_v_size, __pyx_v_oa); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2532; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2822; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__68, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2961; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__70, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3049; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3041; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3049; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3227; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3234; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3242; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_5 = __pyx_f_6mtrand_cont1_array(__pyx_v_self->internal_state, rk_rayleigh, __pyx_v_size, __pyx_v_oscale); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3243; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":3165
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+          __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3245; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-    /* "mtrand.pyx":3316
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+  __pyx_t_4 = __pyx_f_6mtrand_cont2_array(__pyx_v_self->internal_state, rk_wald, __pyx_v_size, __pyx_v_omean, __pyx_v_oscale); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3327; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   __pyx_r = __pyx_t_4;
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-  /* "mtrand.pyx":3237
+  /* "mtrand.pyx":3245
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 }
 
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@@ -16055,12 +16373,12 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_81triangular(PyObject *__pyx_v_s
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+          __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3331; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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         case  2:
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-          __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3323; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+          __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3331; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "triangular") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3331; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -16088,7 +16406,7 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_81triangular(PyObject *__pyx_v_s
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+  __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3331; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-  /* "mtrand.pyx":3385
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-      /* "mtrand.pyx":3390
+      /* "mtrand.pyx":3398
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+      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__80, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3398; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3390; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3398; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      /* "mtrand.pyx":3392
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+      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__81, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3400; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3392; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":3393
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-    /* "mtrand.pyx":3394
+    /* "mtrand.pyx":3402
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+    __pyx_t_2 = __pyx_f_6mtrand_cont3_array_sc(__pyx_v_self->internal_state, rk_triangular, __pyx_v_size, __pyx_v_fleft, __pyx_v_fmode, __pyx_v_fright); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3401; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":3396
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-  /* "mtrand.pyx":3397
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  *         omode = <ndarray>PyArray_FROM_OTF(mode, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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-  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_left, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_left, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3405; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":3398
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+  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_mode, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3406; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":3399
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+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_right, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3407; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":3401
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+  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3409; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3409; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3763; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_2 = __pyx_f_6mtrand_discd_array_sc(__pyx_v_self->internal_state, rk_zipf, __pyx_v_size, __pyx_v_fa); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3772; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__102, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3778; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3770; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3778; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":3771
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+  __pyx_t_5 = __pyx_f_6mtrand_discd_array(__pyx_v_self->internal_state, rk_zipf, __pyx_v_size, __pyx_v_oa); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3779; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *         cdef long lngood, lnbad, lnsample
  * 
  *         lngood = PyInt_AsLong(ngood)             # <<<<<<<<<<<<<<
@@ -18536,7 +18854,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
  */
   __pyx_v_lngood = PyInt_AsLong(__pyx_v_ngood);
 
-  /* "mtrand.pyx":3928
+  /* "mtrand.pyx":3936
  * 
  *         lngood = PyInt_AsLong(ngood)
  *         lnbad = PyInt_AsLong(nbad)             # <<<<<<<<<<<<<<
@@ -18545,7 +18863,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
  */
   __pyx_v_lnbad = PyInt_AsLong(__pyx_v_nbad);
 
-  /* "mtrand.pyx":3929
+  /* "mtrand.pyx":3937
  *         lngood = PyInt_AsLong(ngood)
  *         lnbad = PyInt_AsLong(nbad)
  *         lnsample = PyInt_AsLong(nsample)             # <<<<<<<<<<<<<<
@@ -18554,7 +18872,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
  */
   __pyx_v_lnsample = PyInt_AsLong(__pyx_v_nsample);
 
-  /* "mtrand.pyx":3930
+  /* "mtrand.pyx":3938
  *         lnbad = PyInt_AsLong(nbad)
  *         lnsample = PyInt_AsLong(nsample)
  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
@@ -18564,7 +18882,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
   __pyx_t_1 = ((!(PyErr_Occurred() != 0)) != 0);
   if (__pyx_t_1) {
 
-    /* "mtrand.pyx":3931
+    /* "mtrand.pyx":3939
  *         lnsample = PyInt_AsLong(nsample)
  *         if not PyErr_Occurred():
  *             if lngood < 0:             # <<<<<<<<<<<<<<
@@ -18574,21 +18892,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
     __pyx_t_1 = ((__pyx_v_lngood < 0) != 0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":3932
+      /* "mtrand.pyx":3940
  *         if not PyErr_Occurred():
  *             if lngood < 0:
  *                 raise ValueError("ngood < 0")             # <<<<<<<<<<<<<<
  *             if lnbad < 0:
  *                 raise ValueError("nbad < 0")
  */
-      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__107, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3932; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__107, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3940; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_2);
       __Pyx_Raise(__pyx_t_2, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3932; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3940; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     }
 
-    /* "mtrand.pyx":3933
+    /* "mtrand.pyx":3941
  *             if lngood < 0:
  *                 raise ValueError("ngood < 0")
  *             if lnbad < 0:             # <<<<<<<<<<<<<<
@@ -18598,21 +18916,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
     __pyx_t_1 = ((__pyx_v_lnbad < 0) != 0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":3934
+      /* "mtrand.pyx":3942
  *                 raise ValueError("ngood < 0")
  *             if lnbad < 0:
  *                 raise ValueError("nbad < 0")             # <<<<<<<<<<<<<<
  *             if lnsample < 1:
  *                 raise ValueError("nsample < 1")
  */
-      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__108, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3934; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__108, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3942; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_2);
       __Pyx_Raise(__pyx_t_2, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3934; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3942; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":3935
+    /* "mtrand.pyx":3943
  *             if lnbad < 0:
  *                 raise ValueError("nbad < 0")
  *             if lnsample < 1:             # <<<<<<<<<<<<<<
@@ -18622,21 +18940,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
     __pyx_t_1 = ((__pyx_v_lnsample < 1) != 0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":3936
+      /* "mtrand.pyx":3944
  *                 raise ValueError("nbad < 0")
  *             if lnsample < 1:
  *                 raise ValueError("nsample < 1")             # <<<<<<<<<<<<<<
  *             if lngood + lnbad < lnsample:
  *                 raise ValueError("ngood + nbad < nsample")
  */
-      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__109, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3936; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__109, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3944; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_2);
       __Pyx_Raise(__pyx_t_2, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3936; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3944; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     }
 
-    /* "mtrand.pyx":3937
+    /* "mtrand.pyx":3945
  *             if lnsample < 1:
  *                 raise ValueError("nsample < 1")
  *             if lngood + lnbad < lnsample:             # <<<<<<<<<<<<<<
@@ -18646,21 +18964,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
     __pyx_t_1 = (((__pyx_v_lngood + __pyx_v_lnbad) < __pyx_v_lnsample) != 0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":3938
+      /* "mtrand.pyx":3946
  *                 raise ValueError("nsample < 1")
  *             if lngood + lnbad < lnsample:
  *                 raise ValueError("ngood + nbad < nsample")             # <<<<<<<<<<<<<<
  *             return discnmN_array_sc(self.internal_state, rk_hypergeometric, size,
  *                                     lngood, lnbad, lnsample)
  */
-      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__110, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3938; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__110, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3946; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_2);
       __Pyx_Raise(__pyx_t_2, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3938; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3946; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     }
 
-    /* "mtrand.pyx":3939
+    /* "mtrand.pyx":3947
  *             if lngood + lnbad < lnsample:
  *                 raise ValueError("ngood + nbad < nsample")
  *             return discnmN_array_sc(self.internal_state, rk_hypergeometric, size,             # <<<<<<<<<<<<<<
@@ -18669,21 +18987,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
  */
     __Pyx_XDECREF(__pyx_r);
 
-    /* "mtrand.pyx":3940
+    /* "mtrand.pyx":3948
  *                 raise ValueError("ngood + nbad < nsample")
  *             return discnmN_array_sc(self.internal_state, rk_hypergeometric, size,
  *                                     lngood, lnbad, lnsample)             # <<<<<<<<<<<<<<
  * 
  *         PyErr_Clear()
  */
-    __pyx_t_2 = __pyx_f_6mtrand_discnmN_array_sc(__pyx_v_self->internal_state, rk_hypergeometric, __pyx_v_size, __pyx_v_lngood, __pyx_v_lnbad, __pyx_v_lnsample); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3939; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_2 = __pyx_f_6mtrand_discnmN_array_sc(__pyx_v_self->internal_state, rk_hypergeometric, __pyx_v_size, __pyx_v_lngood, __pyx_v_lnbad, __pyx_v_lnsample); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3947; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __pyx_r = __pyx_t_2;
     __pyx_t_2 = 0;
     goto __pyx_L0;
   }
 
-  /* "mtrand.pyx":3942
+  /* "mtrand.pyx":3950
  *                                     lngood, lnbad, lnsample)
  * 
  *         PyErr_Clear()             # <<<<<<<<<<<<<<
@@ -18692,14 +19010,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
  */
   PyErr_Clear();
 
-  /* "mtrand.pyx":3944
+  /* "mtrand.pyx":3952
  *         PyErr_Clear()
  * 
  *         ongood = <ndarray>PyArray_FROM_OTF(ngood, NPY_LONG, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
  *         onbad = <ndarray>PyArray_FROM_OTF(nbad, NPY_LONG, NPY_ARRAY_ALIGNED)
  *         onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED)
  */
-  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_ngood, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3944; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_ngood, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3952; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_2);
   __pyx_t_3 = __pyx_t_2;
   __Pyx_INCREF(__pyx_t_3);
@@ -18707,14 +19025,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
   __pyx_v_ongood = ((PyArrayObject *)__pyx_t_3);
   __pyx_t_3 = 0;
 
-  /* "mtrand.pyx":3945
+  /* "mtrand.pyx":3953
  * 
  *         ongood = <ndarray>PyArray_FROM_OTF(ngood, NPY_LONG, NPY_ARRAY_ALIGNED)
  *         onbad = <ndarray>PyArray_FROM_OTF(nbad, NPY_LONG, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
  *         onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(ongood, 0)):
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-  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_nbad, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3945; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_nbad, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3953; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   __Pyx_INCREF(__pyx_t_2);
@@ -18722,14 +19040,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
   __pyx_v_onbad = ((PyArrayObject *)__pyx_t_2);
   __pyx_t_2 = 0;
 
-  /* "mtrand.pyx":3946
+  /* "mtrand.pyx":3954
  *         ongood = <ndarray>PyArray_FROM_OTF(ngood, NPY_LONG, NPY_ARRAY_ALIGNED)
  *         onbad = <ndarray>PyArray_FROM_OTF(nbad, NPY_LONG, NPY_ARRAY_ALIGNED)
  *         onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
  *         if np.any(np.less(ongood, 0)):
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-  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_nsample, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3946; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_nsample, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3954; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -18737,24 +19055,24 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
   __pyx_v_onsample = ((PyArrayObject *)__pyx_t_3);
   __pyx_t_3 = 0;
 
-  /* "mtrand.pyx":3947
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  *         onbad = <ndarray>PyArray_FROM_OTF(nbad, NPY_LONG, NPY_ARRAY_ALIGNED)
  *         onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(ongood, 0)):             # <<<<<<<<<<<<<<
  *             raise ValueError("ngood < 0")
  *         if np.any(np.less(onbad, 0)):
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-  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3947; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3955; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_3);
-  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_any); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3947; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_any); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3955; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_2);
   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3947; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3955; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_3);
-  __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_less); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3947; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_less); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3955; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_4);
   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3947; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3955; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_ongood));
@@ -18762,55 +19080,55 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
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+  __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_3, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3955; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
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+  __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3955; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_5);
   __Pyx_GIVEREF(__pyx_t_5);
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+  __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3955; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
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+  __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_5); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3955; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
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-    /* "mtrand.pyx":3948
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   arrayObject_parr = ((PyArrayObject *)__pyx_t_3);
   __pyx_t_3 = 0;
 
-  /* "mtrand.pyx":4246
+  /* "mtrand.pyx":4254
  *         d = len(pvals)
  *         parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1)
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@@ -20229,7 +20546,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
   __pyx_v_pix = ((double *)PyArray_DATA(arrayObject_parr));
 
-  /* "mtrand.pyx":4248
+  /* "mtrand.pyx":4256
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  * 
  *         if kahan_sum(pix, d-1) > (1.0 + 1e-12):             # <<<<<<<<<<<<<<
@@ -20239,146 +20556,86 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
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-    /* "mtrand.pyx":4249
+    /* "mtrand.pyx":4257
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4249; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":4251
+  /* "mtrand.pyx":4259
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-    /* "mtrand.pyx":4252
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+  PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_size);
+  __Pyx_GIVEREF(__pyx_v_size);
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-  /* "mtrand.pyx":4258
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-  PyTuple_SET_ITEM(__pyx_t_3, 1, ((PyObject *)((PyObject*)(&PyInt_Type))));
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   __Pyx_GIVEREF(((PyObject *)((PyObject*)(&PyInt_Type))));
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-  __Pyx_GOTREF(__pyx_t_6);
+  __pyx_t_3 = __Pyx_PyObject_Call(__pyx_t_5, __pyx_t_2, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4261; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
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-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  __pyx_v_multin = __pyx_t_6;
-  __pyx_t_6 = 0;
+  __pyx_v_multin = __pyx_t_3;
+  __pyx_t_3 = 0;
 
-  /* "mtrand.pyx":4259
+  /* "mtrand.pyx":4262
  * 
  *         multin = np.zeros(shape, int)
  *         mnarr = <ndarray>multin             # <<<<<<<<<<<<<<
  *         mnix = <long*>PyArray_DATA(mnarr)
  *         i = 0
  */
-  __pyx_t_6 = __pyx_v_multin;
-  __Pyx_INCREF(__pyx_t_6);
-  arrayObject_mnarr = ((PyArrayObject *)__pyx_t_6);
-  __pyx_t_6 = 0;
+  __pyx_t_3 = __pyx_v_multin;
+  __Pyx_INCREF(__pyx_t_3);
+  arrayObject_mnarr = ((PyArrayObject *)__pyx_t_3);
+  __pyx_t_3 = 0;
 
-  /* "mtrand.pyx":4260
+  /* "mtrand.pyx":4263
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  *         mnix = <long*>PyArray_DATA(mnarr)             # <<<<<<<<<<<<<<
@@ -20387,7 +20644,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
   __pyx_v_mnix = ((long *)PyArray_DATA(arrayObject_mnarr));
 
-  /* "mtrand.pyx":4261
+  /* "mtrand.pyx":4264
  *         mnarr = <ndarray>multin
  *         mnix = <long*>PyArray_DATA(mnarr)
  *         i = 0             # <<<<<<<<<<<<<<
@@ -20396,7 +20653,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
   __pyx_v_i = 0;
 
-  /* "mtrand.pyx":4262
+  /* "mtrand.pyx":4265
  *         mnix = <long*>PyArray_DATA(mnarr)
  *         i = 0
  *         while i < PyArray_SIZE(mnarr):             # <<<<<<<<<<<<<<
@@ -20407,7 +20664,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
     __pyx_t_4 = ((__pyx_v_i < PyArray_SIZE(arrayObject_mnarr)) != 0);
     if (!__pyx_t_4) break;
 
-    /* "mtrand.pyx":4263
+    /* "mtrand.pyx":4266
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@@ -20416,7 +20673,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
     __pyx_v_Sum = 1.0;
 
-    /* "mtrand.pyx":4264
+    /* "mtrand.pyx":4267
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  *             dn = n             # <<<<<<<<<<<<<<
@@ -20425,17 +20682,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
     __pyx_v_dn = __pyx_v_n;
 
-    /* "mtrand.pyx":4265
+    /* "mtrand.pyx":4268
  *             Sum = 1.0
  *             dn = n
  *             for j from 0 <= j < d-1:             # <<<<<<<<<<<<<<
  *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
  *                 dn = dn - mnix[i+j]
  */
-    __pyx_t_7 = (__pyx_v_d - 1);
-    for (__pyx_v_j = 0; __pyx_v_j < __pyx_t_7; __pyx_v_j++) {
+    __pyx_t_6 = (__pyx_v_d - 1);
+    for (__pyx_v_j = 0; __pyx_v_j < __pyx_t_6; __pyx_v_j++) {
 
-      /* "mtrand.pyx":4266
+      /* "mtrand.pyx":4269
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  *             for j from 0 <= j < d-1:
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@@ -20450,11 +20707,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
         #ifdef WITH_THREAD
         PyGILState_Release(__pyx_gilstate_save);
         #endif
-        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4266; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4269; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       }
       (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]) = rk_binomial(__pyx_v_self->internal_state, __pyx_v_dn, ((__pyx_v_pix[__pyx_v_j]) / __pyx_v_Sum));
 
-      /* "mtrand.pyx":4267
+      /* "mtrand.pyx":4270
  *             for j from 0 <= j < d-1:
  *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
  *                 dn = dn - mnix[i+j]             # <<<<<<<<<<<<<<
@@ -20463,7 +20720,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
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-      /* "mtrand.pyx":4268
+      /* "mtrand.pyx":4271
  *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
  *                 dn = dn - mnix[i+j]
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@@ -20473,17 +20730,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
       __pyx_t_4 = ((__pyx_v_dn <= 0) != 0);
       if (__pyx_t_4) {
 
-        /* "mtrand.pyx":4269
+        /* "mtrand.pyx":4272
  *                 dn = dn - mnix[i+j]
  *                 if dn <= 0:
  *                     break             # <<<<<<<<<<<<<<
  *                 Sum = Sum - pix[j]
  *             if dn > 0:
  */
-        goto __pyx_L8_break;
+        goto __pyx_L7_break;
       }
 
-      /* "mtrand.pyx":4270
+      /* "mtrand.pyx":4273
  *                 if dn <= 0:
  *                     break
  *                 Sum = Sum - pix[j]             # <<<<<<<<<<<<<<
@@ -20492,9 +20749,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
       __pyx_v_Sum = (__pyx_v_Sum - (__pyx_v_pix[__pyx_v_j]));
     }
-    __pyx_L8_break:;
+    __pyx_L7_break:;
 
-    /* "mtrand.pyx":4271
+    /* "mtrand.pyx":4274
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  *             if dn > 0:             # <<<<<<<<<<<<<<
@@ -20504,7 +20761,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
     __pyx_t_4 = ((__pyx_v_dn > 0) != 0);
     if (__pyx_t_4) {
 
-      /* "mtrand.pyx":4272
+      /* "mtrand.pyx":4275
  *                 Sum = Sum - pix[j]
  *             if dn > 0:
  *                 mnix[i+d-1] = dn             # <<<<<<<<<<<<<<
@@ -20512,11 +20769,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  *             i = i + d
  */
       (__pyx_v_mnix[((__pyx_v_i + __pyx_v_d) - 1)]) = __pyx_v_dn;
-      goto __pyx_L10;
+      goto __pyx_L9;
     }
-    __pyx_L10:;
+    __pyx_L9:;
 
-    /* "mtrand.pyx":4274
+    /* "mtrand.pyx":4277
  *                 mnix[i+d-1] = dn
  * 
  *             i = i + d             # <<<<<<<<<<<<<<
@@ -20526,7 +20783,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
     __pyx_v_i = (__pyx_v_i + __pyx_v_d);
   }
 
-  /* "mtrand.pyx":4276
+  /* "mtrand.pyx":4279
  *             i = i + d
  * 
  *         return multin             # <<<<<<<<<<<<<<
@@ -20538,7 +20795,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
   __pyx_r = __pyx_v_multin;
   goto __pyx_L0;
 
-  /* "mtrand.pyx":4185
+  /* "mtrand.pyx":4193
  *         return x
  * 
  *     def multinomial(self, npy_intp n, object pvals, size=None):             # <<<<<<<<<<<<<<
@@ -20550,7 +20807,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
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-  __Pyx_XDECREF(__pyx_t_6);
+  __Pyx_XDECREF(__pyx_t_5);
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   {&__pyx_kp_u_RandomState_negative_binomial_li, __pyx_k_RandomState_negative_binomial_li, sizeof(__pyx_k_RandomState_negative_binomial_li), 0, 1, 0, 0},
   {&__pyx_kp_u_RandomState_noncentral_chisquare, __pyx_k_RandomState_noncentral_chisquare, sizeof(__pyx_k_RandomState_noncentral_chisquare), 0, 1, 0, 0},
   {&__pyx_kp_u_RandomState_noncentral_f_line_19, __pyx_k_RandomState_noncentral_f_line_19, sizeof(__pyx_k_RandomState_noncentral_f_line_19), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_normal_line_1393, __pyx_k_RandomState_normal_line_1393, sizeof(__pyx_k_RandomState_normal_line_1393), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_pareto_line_2430, __pyx_k_RandomState_pareto_line_2430, sizeof(__pyx_k_RandomState_pareto_line_2430), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_normal_line_1401, __pyx_k_RandomState_normal_line_1401, sizeof(__pyx_k_RandomState_normal_line_1401), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_pareto_line_2438, __pyx_k_RandomState_pareto_line_2438, sizeof(__pyx_k_RandomState_pareto_line_2438), 0, 1, 0, 0},
   {&__pyx_kp_u_RandomState_permutation_line_445, __pyx_k_RandomState_permutation_line_445, sizeof(__pyx_k_RandomState_permutation_line_445), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_poisson_line_3614, __pyx_k_RandomState_poisson_line_3614, sizeof(__pyx_k_RandomState_poisson_line_3614), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_power_line_2626, __pyx_k_RandomState_power_line_2626, sizeof(__pyx_k_RandomState_power_line_2626), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_rand_line_1182, __pyx_k_RandomState_rand_line_1182, sizeof(__pyx_k_RandomState_rand_line_1182), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_randint_line_812, __pyx_k_RandomState_randint_line_812, sizeof(__pyx_k_RandomState_randint_line_812), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_randn_line_1226, __pyx_k_RandomState_randn_line_1226, sizeof(__pyx_k_RandomState_randn_line_1226), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_poisson_line_3622, __pyx_k_RandomState_poisson_line_3622, sizeof(__pyx_k_RandomState_poisson_line_3622), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_power_line_2634, __pyx_k_RandomState_power_line_2634, sizeof(__pyx_k_RandomState_power_line_2634), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_rand_line_1190, __pyx_k_RandomState_rand_line_1190, sizeof(__pyx_k_RandomState_rand_line_1190), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_randint_line_820, __pyx_k_RandomState_randint_line_820, sizeof(__pyx_k_RandomState_randint_line_820), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_randn_line_1234, __pyx_k_RandomState_randn_line_1234, sizeof(__pyx_k_RandomState_randn_line_1234), 0, 1, 0, 0},
   {&__pyx_kp_u_RandomState_random_integers_line, __pyx_k_RandomState_random_integers_line, sizeof(__pyx_k_RandomState_random_integers_line), 0, 1, 0, 0},
   {&__pyx_kp_u_RandomState_random_sample_line_7, __pyx_k_RandomState_random_sample_line_7, sizeof(__pyx_k_RandomState_random_sample_line_7), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_rayleigh_line_3165, __pyx_k_RandomState_rayleigh_line_3165, sizeof(__pyx_k_RandomState_rayleigh_line_3165), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_shuffle_line_4395, __pyx_k_RandomState_shuffle_line_4395, sizeof(__pyx_k_RandomState_shuffle_line_4395), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_rayleigh_line_3173, __pyx_k_RandomState_rayleigh_line_3173, sizeof(__pyx_k_RandomState_rayleigh_line_3173), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_shuffle_line_4393, __pyx_k_RandomState_shuffle_line_4393, sizeof(__pyx_k_RandomState_shuffle_line_4393), 0, 1, 0, 0},
   {&__pyx_kp_u_RandomState_standard_cauchy_line, __pyx_k_RandomState_standard_cauchy_line, sizeof(__pyx_k_RandomState_standard_cauchy_line), 0, 1, 0, 0},
   {&__pyx_kp_u_RandomState_standard_exponential, __pyx_k_RandomState_standard_exponential, sizeof(__pyx_k_RandomState_standard_exponential), 0, 1, 0, 0},
   {&__pyx_kp_u_RandomState_standard_gamma_line, __pyx_k_RandomState_standard_gamma_line, sizeof(__pyx_k_RandomState_standard_gamma_line), 0, 1, 0, 0},
   {&__pyx_kp_u_RandomState_standard_normal_line, __pyx_k_RandomState_standard_normal_line, sizeof(__pyx_k_RandomState_standard_normal_line), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_standard_t_line_2235, __pyx_k_RandomState_standard_t_line_2235, sizeof(__pyx_k_RandomState_standard_t_line_2235), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_tomaxint_line_765, __pyx_k_RandomState_tomaxint_line_765, sizeof(__pyx_k_RandomState_tomaxint_line_765), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_triangular_line_3323, __pyx_k_RandomState_triangular_line_3323, sizeof(__pyx_k_RandomState_triangular_line_3323), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_uniform_line_1095, __pyx_k_RandomState_uniform_line_1095, sizeof(__pyx_k_RandomState_uniform_line_1095), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_vonmises_line_2336, __pyx_k_RandomState_vonmises_line_2336, sizeof(__pyx_k_RandomState_vonmises_line_2336), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_wald_line_3237, __pyx_k_RandomState_wald_line_3237, sizeof(__pyx_k_RandomState_wald_line_3237), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_weibull_line_2526, __pyx_k_RandomState_weibull_line_2526, sizeof(__pyx_k_RandomState_weibull_line_2526), 0, 1, 0, 0},
-  {&__pyx_kp_u_RandomState_zipf_line_3685, __pyx_k_RandomState_zipf_line_3685, sizeof(__pyx_k_RandomState_zipf_line_3685), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_standard_t_line_2243, __pyx_k_RandomState_standard_t_line_2243, sizeof(__pyx_k_RandomState_standard_t_line_2243), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_tomaxint_line_773, __pyx_k_RandomState_tomaxint_line_773, sizeof(__pyx_k_RandomState_tomaxint_line_773), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_triangular_line_3331, __pyx_k_RandomState_triangular_line_3331, sizeof(__pyx_k_RandomState_triangular_line_3331), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_uniform_line_1103, __pyx_k_RandomState_uniform_line_1103, sizeof(__pyx_k_RandomState_uniform_line_1103), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_vonmises_line_2344, __pyx_k_RandomState_vonmises_line_2344, sizeof(__pyx_k_RandomState_vonmises_line_2344), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_wald_line_3245, __pyx_k_RandomState_wald_line_3245, sizeof(__pyx_k_RandomState_wald_line_3245), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_weibull_line_2534, __pyx_k_RandomState_weibull_line_2534, sizeof(__pyx_k_RandomState_weibull_line_2534), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_zipf_line_3693, __pyx_k_RandomState_zipf_line_3693, sizeof(__pyx_k_RandomState_zipf_line_3693), 0, 1, 0, 0},
   {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1},
   {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1},
   {&__pyx_n_s_a, __pyx_k_a, sizeof(__pyx_k_a), 0, 0, 1, 1},
@@ -21667,6 +21863,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_n_s_cov, __pyx_k_cov, sizeof(__pyx_k_cov), 0, 0, 1, 1},
   {&__pyx_kp_s_cov_must_be_2_dimensional_and_sq, __pyx_k_cov_must_be_2_dimensional_and_sq, sizeof(__pyx_k_cov_must_be_2_dimensional_and_sq), 0, 0, 1, 0},
   {&__pyx_n_s_cumsum, __pyx_k_cumsum, sizeof(__pyx_k_cumsum), 0, 0, 1, 1},
+  {&__pyx_n_s_d, __pyx_k_d, sizeof(__pyx_k_d), 0, 0, 1, 1},
   {&__pyx_n_s_df, __pyx_k_df, sizeof(__pyx_k_df), 0, 0, 1, 1},
   {&__pyx_kp_s_df_0, __pyx_k_df_0, sizeof(__pyx_k_df_0), 0, 0, 1, 0},
   {&__pyx_kp_s_df_1, __pyx_k_df_1, sizeof(__pyx_k_df_1), 0, 0, 1, 0},
@@ -21697,6 +21894,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_n_s_gumbel, __pyx_k_gumbel, sizeof(__pyx_k_gumbel), 0, 0, 1, 1},
   {&__pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N, __pyx_k_gumbel_loc_0_0_scale_1_0_size_N, sizeof(__pyx_k_gumbel_loc_0_0_scale_1_0_size_N), 0, 1, 0, 0},
   {&__pyx_n_s_high, __pyx_k_high, sizeof(__pyx_k_high), 0, 0, 1, 1},
+  {&__pyx_kp_s_home_jtaylor_prog_numpy_numpy_r, __pyx_k_home_jtaylor_prog_numpy_numpy_r, sizeof(__pyx_k_home_jtaylor_prog_numpy_numpy_r), 0, 0, 1, 0},
   {&__pyx_n_s_hypergeometric, __pyx_k_hypergeometric, sizeof(__pyx_k_hypergeometric), 0, 0, 1, 1},
   {&__pyx_kp_u_hypergeometric_ngood_nbad_nsamp, __pyx_k_hypergeometric_ngood_nbad_nsamp, sizeof(__pyx_k_hypergeometric_ngood_nbad_nsamp), 0, 1, 0, 0},
   {&__pyx_n_s_iinfo, __pyx_k_iinfo, sizeof(__pyx_k_iinfo), 0, 0, 1, 1},
@@ -21739,6 +21937,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_kp_s_mean_must_be_1_dimensional, __pyx_k_mean_must_be_1_dimensional, sizeof(__pyx_k_mean_must_be_1_dimensional), 0, 0, 1, 0},
   {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1},
   {&__pyx_kp_s_mode_right, __pyx_k_mode_right, sizeof(__pyx_k_mode_right), 0, 0, 1, 0},
+  {&__pyx_n_s_mtrand, __pyx_k_mtrand, sizeof(__pyx_k_mtrand), 0, 0, 1, 1},
   {&__pyx_n_s_mu, __pyx_k_mu, sizeof(__pyx_k_mu), 0, 0, 1, 1},
   {&__pyx_n_s_multinomial, __pyx_k_multinomial, sizeof(__pyx_k_multinomial), 0, 0, 1, 1},
   {&__pyx_kp_u_multinomial_n_pvals_size_None_D, __pyx_k_multinomial_n_pvals_size_None_D, sizeof(__pyx_k_multinomial_n_pvals_size_None_D), 0, 1, 0, 0},
@@ -21820,6 +22019,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_n_s_set_state, __pyx_k_set_state, sizeof(__pyx_k_set_state), 0, 0, 1, 1},
   {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1},
   {&__pyx_kp_s_shape_0, __pyx_k_shape_0, sizeof(__pyx_k_shape_0), 0, 0, 1, 0},
+  {&__pyx_n_s_shape_from_size, __pyx_k_shape_from_size, sizeof(__pyx_k_shape_from_size), 0, 0, 1, 1},
   {&__pyx_n_s_shuffle, __pyx_k_shuffle, sizeof(__pyx_k_shuffle), 0, 0, 1, 1},
   {&__pyx_kp_u_shuffle_x_Modify_a_sequence_in, __pyx_k_shuffle_x_Modify_a_sequence_in, sizeof(__pyx_k_shuffle_x_Modify_a_sequence_in), 0, 1, 0, 0},
   {&__pyx_n_s_side, __pyx_k_side, sizeof(__pyx_k_side), 0, 0, 1, 1},
@@ -21867,7 +22067,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
 };
 static int __Pyx_InitCachedBuiltins(void) {
   __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   return 0;
   __pyx_L1_error:;
   return -1;
@@ -21954,1295 +22154,1307 @@ static int __Pyx_InitCachedConstants(void) {
   __Pyx_GOTREF(__pyx_tuple__7);
   __Pyx_GIVEREF(__pyx_tuple__7);
 
-  /* "mtrand.pyx":692
+  /* "mtrand.pyx":700
  *         algorithm_name = state[0]
  *         if algorithm_name != 'MT19937':
  *             raise ValueError("algorithm must be 'MT19937'")             # <<<<<<<<<<<<<<
  *         key, pos = state[1:3]
  *         if len(state) == 3:
  */
-  __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_algorithm_must_be_MT19937); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_algorithm_must_be_MT19937); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 700; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__8);
   __Pyx_GIVEREF(__pyx_tuple__8);
 
-  /* "mtrand.pyx":693
+  /* "mtrand.pyx":701
  *         if algorithm_name != 'MT19937':
  *             raise ValueError("algorithm must be 'MT19937'")
  *         key, pos = state[1:3]             # <<<<<<<<<<<<<<
  *         if len(state) == 3:
  *             has_gauss = 0
  */
-  __pyx_slice__9 = PySlice_New(__pyx_int_1, __pyx_int_3, Py_None); if (unlikely(!__pyx_slice__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_slice__9 = PySlice_New(__pyx_int_1, __pyx_int_3, Py_None); if (unlikely(!__pyx_slice__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_slice__9);
   __Pyx_GIVEREF(__pyx_slice__9);
 
-  /* "mtrand.pyx":698
+  /* "mtrand.pyx":706
  *             cached_gaussian = 0.0
  *         else:
  *             has_gauss, cached_gaussian = state[3:5]             # <<<<<<<<<<<<<<
  *         try:
  *             obj = <ndarray>PyArray_ContiguousFromObject(key, NPY_ULONG, 1, 1)
  */
-  __pyx_slice__10 = PySlice_New(__pyx_int_3, __pyx_int_5, Py_None); if (unlikely(!__pyx_slice__10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_slice__10 = PySlice_New(__pyx_int_3, __pyx_int_5, Py_None); if (unlikely(!__pyx_slice__10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 706; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_slice__10);
   __Pyx_GIVEREF(__pyx_slice__10);
 
-  /* "mtrand.pyx":705
+  /* "mtrand.pyx":713
  *             obj = <ndarray>PyArray_ContiguousFromObject(key, NPY_LONG, 1, 1)
  *         if PyArray_DIM(obj, 0) != 624:
  *             raise ValueError("state must be 624 longs")             # <<<<<<<<<<<<<<
  *         memcpy(<void*>(self.internal_state.key), <void*>PyArray_DATA(obj), 624*sizeof(long))
  *         self.internal_state.pos = pos
  */
-  __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_state_must_be_624_longs); if (unlikely(!__pyx_tuple__11)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 705; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_state_must_be_624_longs); if (unlikely(!__pyx_tuple__11)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 713; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__11);
   __Pyx_GIVEREF(__pyx_tuple__11);
 
-  /* "mtrand.pyx":877
+  /* "mtrand.pyx":885
  * 
  *         if lo >= hi :
  *             raise ValueError("low >= high")             # <<<<<<<<<<<<<<
  * 
  *         diff = <unsigned long>hi - <unsigned long>lo - 1UL
  */
-  __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_low_high); if (unlikely(!__pyx_tuple__12)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 877; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_low_high); if (unlikely(!__pyx_tuple__12)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__12);
   __Pyx_GIVEREF(__pyx_tuple__12);
 
-  /* "mtrand.pyx":1004
+  /* "mtrand.pyx":1012
  *                 pop_size = operator.index(a.item())
  *             except TypeError:
  *                 raise ValueError("a must be 1-dimensional or an integer")             # <<<<<<<<<<<<<<
  *             if pop_size <= 0:
  *                 raise ValueError("a must be greater than 0")
  */
-  __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional_or_an_in); if (unlikely(!__pyx_tuple__13)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1004; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional_or_an_in); if (unlikely(!__pyx_tuple__13)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__13);
   __Pyx_GIVEREF(__pyx_tuple__13);
 
-  /* "mtrand.pyx":1006
+  /* "mtrand.pyx":1014
  *                 raise ValueError("a must be 1-dimensional or an integer")
  *             if pop_size <= 0:
  *                 raise ValueError("a must be greater than 0")             # <<<<<<<<<<<<<<
  *         elif a.ndim != 1:
  *             raise ValueError("a must be 1-dimensional")
  */
-  __pyx_tuple__14 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_greater_than_0); if (unlikely(!__pyx_tuple__14)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1006; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__14 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_greater_than_0); if (unlikely(!__pyx_tuple__14)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1014; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__14);
   __Pyx_GIVEREF(__pyx_tuple__14);
 
-  /* "mtrand.pyx":1008
+  /* "mtrand.pyx":1016
  *                 raise ValueError("a must be greater than 0")
  *         elif a.ndim != 1:
  *             raise ValueError("a must be 1-dimensional")             # <<<<<<<<<<<<<<
  *         else:
  *             pop_size = a.shape[0]
  */
-  __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional); if (unlikely(!__pyx_tuple__15)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1008; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional); if (unlikely(!__pyx_tuple__15)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1016; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__15);
   __Pyx_GIVEREF(__pyx_tuple__15);
 
-  /* "mtrand.pyx":1012
+  /* "mtrand.pyx":1020
  *             pop_size = a.shape[0]
  *             if pop_size is 0:
  *                 raise ValueError("a must be non-empty")             # <<<<<<<<<<<<<<
  * 
  *         if None != p:
  */
-  __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_non_empty); if (unlikely(!__pyx_tuple__16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_non_empty); if (unlikely(!__pyx_tuple__16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1020; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__16);
   __Pyx_GIVEREF(__pyx_tuple__16);
 
-  /* "mtrand.pyx":1020
+  /* "mtrand.pyx":1028
  * 
  *             if p.ndim != 1:
  *                 raise ValueError("p must be 1-dimensional")             # <<<<<<<<<<<<<<
  *             if p.size != pop_size:
  *                 raise ValueError("a and p must have same size")
  */
-  __pyx_tuple__17 = PyTuple_Pack(1, __pyx_kp_s_p_must_be_1_dimensional); if (unlikely(!__pyx_tuple__17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1020; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__17 = PyTuple_Pack(1, __pyx_kp_s_p_must_be_1_dimensional); if (unlikely(!__pyx_tuple__17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__17);
   __Pyx_GIVEREF(__pyx_tuple__17);
 
-  /* "mtrand.pyx":1022
+  /* "mtrand.pyx":1030
  *                 raise ValueError("p must be 1-dimensional")
  *             if p.size != pop_size:
  *                 raise ValueError("a and p must have same size")             # <<<<<<<<<<<<<<
  *             if np.logical_or.reduce(p < 0):
  *                 raise ValueError("probabilities are not non-negative")
  */
-  __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_a_and_p_must_have_same_size); if (unlikely(!__pyx_tuple__18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1022; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_a_and_p_must_have_same_size); if (unlikely(!__pyx_tuple__18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1030; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__18);
   __Pyx_GIVEREF(__pyx_tuple__18);
 
-  /* "mtrand.pyx":1024
+  /* "mtrand.pyx":1032
  *                 raise ValueError("a and p must have same size")
  *             if np.logical_or.reduce(p < 0):
  *                 raise ValueError("probabilities are not non-negative")             # <<<<<<<<<<<<<<
  *             if abs(kahan_sum(pix, d) - 1.) > 1e-8:
  *                 raise ValueError("probabilities do not sum to 1")
  */
-  __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_probabilities_are_not_non_negati); if (unlikely(!__pyx_tuple__19)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1024; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_probabilities_are_not_non_negati); if (unlikely(!__pyx_tuple__19)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1032; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__19);
   __Pyx_GIVEREF(__pyx_tuple__19);
 
-  /* "mtrand.pyx":1026
+  /* "mtrand.pyx":1034
  *                 raise ValueError("probabilities are not non-negative")
  *             if abs(kahan_sum(pix, d) - 1.) > 1e-8:
  *                 raise ValueError("probabilities do not sum to 1")             # <<<<<<<<<<<<<<
  * 
  *         shape = size
  */
-  __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_probabilities_do_not_sum_to_1); if (unlikely(!__pyx_tuple__20)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1026; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_probabilities_do_not_sum_to_1); if (unlikely(!__pyx_tuple__20)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1034; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__20);
   __Pyx_GIVEREF(__pyx_tuple__20);
 
-  /* "mtrand.pyx":1046
+  /* "mtrand.pyx":1054
  *         else:
  *             if size > pop_size:
  *                 raise ValueError("Cannot take a larger sample than "             # <<<<<<<<<<<<<<
  *                                  "population when 'replace=False'")
  * 
  */
-  __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_Cannot_take_a_larger_sample_than); if (unlikely(!__pyx_tuple__21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1046; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_Cannot_take_a_larger_sample_than); if (unlikely(!__pyx_tuple__21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1054; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__21);
   __Pyx_GIVEREF(__pyx_tuple__21);
 
-  /* "mtrand.pyx":1051
+  /* "mtrand.pyx":1059
  *             if None != p:
  *                 if np.count_nonzero(p > 0) < size:
  *                     raise ValueError("Fewer non-zero entries in p than size")             # <<<<<<<<<<<<<<
  *                 n_uniq = 0
  *                 p = p.copy()
  */
-  __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_Fewer_non_zero_entries_in_p_than); if (unlikely(!__pyx_tuple__22)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1051; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_Fewer_non_zero_entries_in_p_than); if (unlikely(!__pyx_tuple__22)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1059; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__22);
   __Pyx_GIVEREF(__pyx_tuple__22);
 
-  /* "mtrand.pyx":1076
+  /* "mtrand.pyx":1084
  *         if shape is None and isinstance(idx, np.ndarray):
  *             # In most cases a scalar will have been made an array
  *             idx = idx.item(0)             # <<<<<<<<<<<<<<
  * 
  *         #Use samples as indices for a if a is array-like
  */
-  __pyx_tuple__23 = PyTuple_Pack(1, __pyx_int_0); if (unlikely(!__pyx_tuple__23)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1076; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__23 = PyTuple_Pack(1, __pyx_int_0); if (unlikely(!__pyx_tuple__23)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1084; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__23);
   __Pyx_GIVEREF(__pyx_tuple__23);
 
-  /* "mtrand.pyx":1088
+  /* "mtrand.pyx":1096
  *             # array, taking into account that np.array(item) may not work
  *             # for object arrays.
  *             res = np.empty((), dtype=a.dtype)             # <<<<<<<<<<<<<<
  *             res[()] = a[idx]
  *             return res
  */
-  __pyx_tuple__24 = PyTuple_Pack(1, __pyx_empty_tuple); if (unlikely(!__pyx_tuple__24)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1088; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__24 = PyTuple_Pack(1, __pyx_empty_tuple); if (unlikely(!__pyx_tuple__24)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1096; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__24);
   __Pyx_GIVEREF(__pyx_tuple__24);
 
-  /* "mtrand.pyx":1482
+  /* "mtrand.pyx":1490
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_normal, size, floc, fscale)
  * 
  */
-  __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__25)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1482; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__25)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1490; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__25);
   __Pyx_GIVEREF(__pyx_tuple__25);
 
-  /* "mtrand.pyx":1490
+  /* "mtrand.pyx":1498
  *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_normal, size, oloc, oscale)
  * 
  */
-  __pyx_tuple__26 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__26)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1490; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__26 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__26)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1498; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__26);
   __Pyx_GIVEREF(__pyx_tuple__26);
 
-  /* "mtrand.pyx":1537
+  /* "mtrand.pyx":1545
  *         if not PyErr_Occurred():
  *             if fa <= 0:
  *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *             if fb <= 0:
  *                 raise ValueError("b <= 0")
  */
-  __pyx_tuple__27 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__27)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1537; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__27 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__27)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1545; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__27);
   __Pyx_GIVEREF(__pyx_tuple__27);
 
-  /* "mtrand.pyx":1539
+  /* "mtrand.pyx":1547
  *                 raise ValueError("a <= 0")
  *             if fb <= 0:
  *                 raise ValueError("b <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_beta, size, fa, fb)
  * 
  */
-  __pyx_tuple__28 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__28)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1539; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__28 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__28)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1547; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__28);
   __Pyx_GIVEREF(__pyx_tuple__28);
 
-  /* "mtrand.pyx":1547
+  /* "mtrand.pyx":1555
  *         ob = <ndarray>PyArray_FROM_OTF(b, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 0)):
  *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(ob, 0)):
  *             raise ValueError("b <= 0")
  */
-  __pyx_tuple__29 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__29)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1547; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__29 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__29)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1555; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__29);
   __Pyx_GIVEREF(__pyx_tuple__29);
 
-  /* "mtrand.pyx":1549
+  /* "mtrand.pyx":1557
  *             raise ValueError("a <= 0")
  *         if np.any(np.less_equal(ob, 0)):
  *             raise ValueError("b <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_beta, size, oa, ob)
  * 
  */
-  __pyx_tuple__30 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__30)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1549; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__30 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__30)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1557; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__30);
   __Pyx_GIVEREF(__pyx_tuple__30);
 
-  /* "mtrand.pyx":1596
+  /* "mtrand.pyx":1604
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_exponential, size, fscale)
  * 
  */
-  __pyx_tuple__31 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__31)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1596; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__31 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__31)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1604; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__31);
   __Pyx_GIVEREF(__pyx_tuple__31);
 
-  /* "mtrand.pyx":1603
+  /* "mtrand.pyx":1611
  *         oscale = <ndarray> PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_exponential, size, oscale)
  * 
  */
-  __pyx_tuple__32 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__32)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1603; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__32 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__32)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1611; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__32);
   __Pyx_GIVEREF(__pyx_tuple__32);
 
-  /* "mtrand.pyx":1707
+  /* "mtrand.pyx":1715
  *         if not PyErr_Occurred():
  *             if fshape <= 0:
  *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_standard_gamma, size, fshape)
  * 
  */
-  __pyx_tuple__33 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__33)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1707; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__33 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__33)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1715; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__33);
   __Pyx_GIVEREF(__pyx_tuple__33);
 
-  /* "mtrand.pyx":1713
+  /* "mtrand.pyx":1721
  *         oshape = <ndarray> PyArray_FROM_OTF(shape, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oshape, 0.0)):
  *             raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_standard_gamma, size, oshape)
  * 
  */
-  __pyx_tuple__34 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__34)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1713; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__34 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__34)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1721; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__34);
   __Pyx_GIVEREF(__pyx_tuple__34);
 
-  /* "mtrand.pyx":1793
+  /* "mtrand.pyx":1801
  *         if not PyErr_Occurred():
  *             if fshape <= 0:
  *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")
  */
-  __pyx_tuple__35 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__35)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1793; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__35 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__35)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1801; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__35);
   __Pyx_GIVEREF(__pyx_tuple__35);
 
-  /* "mtrand.pyx":1795
+  /* "mtrand.pyx":1803
  *                 raise ValueError("shape <= 0")
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_gamma, size, fshape, fscale)
  * 
  */
-  __pyx_tuple__36 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1795; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__36 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1803; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__36);
   __Pyx_GIVEREF(__pyx_tuple__36);
 
-  /* "mtrand.pyx":1802
+  /* "mtrand.pyx":1810
  *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oshape, 0.0)):
  *             raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")
  */
-  __pyx_tuple__37 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__37)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1802; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__37 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__37)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1810; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__37);
   __Pyx_GIVEREF(__pyx_tuple__37);
 
-  /* "mtrand.pyx":1804
+  /* "mtrand.pyx":1812
  *             raise ValueError("shape <= 0")
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale)
  * 
  */
-  __pyx_tuple__38 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__38)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1804; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__38 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__38)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1812; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__38);
   __Pyx_GIVEREF(__pyx_tuple__38);
 
-  /* "mtrand.pyx":1894
+  /* "mtrand.pyx":1902
  *         if not PyErr_Occurred():
  *             if fdfnum <= 0:
  *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *             if fdfden <= 0:
  *                 raise ValueError("scale <= 0")
  */
-  __pyx_tuple__39 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__39)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1894; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__39 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__39)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1902; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__39);
   __Pyx_GIVEREF(__pyx_tuple__39);
 
-  /* "mtrand.pyx":1896
+  /* "mtrand.pyx":1904
  *                 raise ValueError("shape <= 0")
  *             if fdfden <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden)
  * 
  */
-  __pyx_tuple__40 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__40)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1896; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__40 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__40)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1904; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__40);
   __Pyx_GIVEREF(__pyx_tuple__40);
 
-  /* "mtrand.pyx":1904
+  /* "mtrand.pyx":1912
  *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(odfnum, 0.0)):
  *             raise ValueError("dfnum <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(odfden, 0.0)):
  *             raise ValueError("dfden <= 0")
  */
-  __pyx_tuple__41 = PyTuple_Pack(1, __pyx_kp_s_dfnum_0); if (unlikely(!__pyx_tuple__41)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1904; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__41 = PyTuple_Pack(1, __pyx_kp_s_dfnum_0); if (unlikely(!__pyx_tuple__41)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1912; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__41);
   __Pyx_GIVEREF(__pyx_tuple__41);
 
-  /* "mtrand.pyx":1906
+  /* "mtrand.pyx":1914
  *             raise ValueError("dfnum <= 0")
  *         if np.any(np.less_equal(odfden, 0.0)):
  *             raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_f, size, odfnum, odfden)
  * 
  */
-  __pyx_tuple__42 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__42)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__42 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__42)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1914; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__42);
   __Pyx_GIVEREF(__pyx_tuple__42);
 
-  /* "mtrand.pyx":1981
+  /* "mtrand.pyx":1989
  *         if not PyErr_Occurred():
  *             if fdfnum <= 1:
  *                 raise ValueError("dfnum <= 1")             # <<<<<<<<<<<<<<
  *             if fdfden <= 0:
  *                 raise ValueError("dfden <= 0")
  */
-  __pyx_tuple__43 = PyTuple_Pack(1, __pyx_kp_s_dfnum_1); if (unlikely(!__pyx_tuple__43)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1981; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__43 = PyTuple_Pack(1, __pyx_kp_s_dfnum_1); if (unlikely(!__pyx_tuple__43)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1989; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__43);
   __Pyx_GIVEREF(__pyx_tuple__43);
 
-  /* "mtrand.pyx":1983
+  /* "mtrand.pyx":1991
  *                 raise ValueError("dfnum <= 1")
  *             if fdfden <= 0:
  *                 raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
  *             if fnonc < 0:
  *                 raise ValueError("nonc < 0")
  */
-  __pyx_tuple__44 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__44)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1983; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__44 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__44)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1991; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__44);
   __Pyx_GIVEREF(__pyx_tuple__44);
 
-  /* "mtrand.pyx":1985
+  /* "mtrand.pyx":1993
  *                 raise ValueError("dfden <= 0")
  *             if fnonc < 0:
  *                 raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
  *             return cont3_array_sc(self.internal_state, rk_noncentral_f, size,
  *                                   fdfnum, fdfden, fnonc)
  */
-  __pyx_tuple__45 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__45)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1985; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__45 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__45)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1993; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__45);
   __Pyx_GIVEREF(__pyx_tuple__45);
 
-  /* "mtrand.pyx":1996
+  /* "mtrand.pyx":2004
  * 
  *         if np.any(np.less_equal(odfnum, 1.0)):
  *             raise ValueError("dfnum <= 1")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(odfden, 0.0)):
  *             raise ValueError("dfden <= 0")
  */
-  __pyx_tuple__46 = PyTuple_Pack(1, __pyx_kp_s_dfnum_1); if (unlikely(!__pyx_tuple__46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1996; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__46 = PyTuple_Pack(1, __pyx_kp_s_dfnum_1); if (unlikely(!__pyx_tuple__46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2004; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__46);
   __Pyx_GIVEREF(__pyx_tuple__46);
 
-  /* "mtrand.pyx":1998
+  /* "mtrand.pyx":2006
  *             raise ValueError("dfnum <= 1")
  *         if np.any(np.less_equal(odfden, 0.0)):
  *             raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(ononc, 0.0)):
  *             raise ValueError("nonc < 0")
  */
-  __pyx_tuple__47 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__47)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1998; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__47 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__47)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2006; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__47);
   __Pyx_GIVEREF(__pyx_tuple__47);
 
-  /* "mtrand.pyx":2000
+  /* "mtrand.pyx":2008
  *             raise ValueError("dfden <= 0")
  *         if np.any(np.less(ononc, 0.0)):
  *             raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
  *         return cont3_array(self.internal_state, rk_noncentral_f, size, odfnum,
  *             odfden, ononc)
  */
-  __pyx_tuple__48 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__48)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2000; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__48 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__48)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2008; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__48);
   __Pyx_GIVEREF(__pyx_tuple__48);
 
-  /* "mtrand.pyx":2072
+  /* "mtrand.pyx":2080
  *         if not PyErr_Occurred():
  *             if fdf <= 0:
  *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_chisquare, size, fdf)
  * 
  */
-  __pyx_tuple__49 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__49)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2072; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__49 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__49)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2080; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__49);
   __Pyx_GIVEREF(__pyx_tuple__49);
 
-  /* "mtrand.pyx":2079
+  /* "mtrand.pyx":2087
  *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(odf, 0.0)):
  *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_chisquare, size, odf)
  * 
  */
-  __pyx_tuple__50 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2079; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__50 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2087; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__50);
   __Pyx_GIVEREF(__pyx_tuple__50);
 
-  /* "mtrand.pyx":2157
+  /* "mtrand.pyx":2165
  *         if not PyErr_Occurred():
  *             if fdf <= 1:
  *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *             if fnonc <= 0:
  *                 raise ValueError("nonc <= 0")
  */
-  __pyx_tuple__51 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__51)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2157; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__51 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__51)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2165; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__51);
   __Pyx_GIVEREF(__pyx_tuple__51);
 
-  /* "mtrand.pyx":2159
+  /* "mtrand.pyx":2167
  *                 raise ValueError("df <= 0")
  *             if fnonc <= 0:
  *                 raise ValueError("nonc <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_noncentral_chisquare,
  *                                   size, fdf, fnonc)
  */
-  __pyx_tuple__52 = PyTuple_Pack(1, __pyx_kp_s_nonc_0_2); if (unlikely(!__pyx_tuple__52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2159; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__52 = PyTuple_Pack(1, __pyx_kp_s_nonc_0_2); if (unlikely(!__pyx_tuple__52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2167; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__52);
   __Pyx_GIVEREF(__pyx_tuple__52);
 
-  /* "mtrand.pyx":2168
+  /* "mtrand.pyx":2176
  *         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(odf, 0.0)):
  *             raise ValueError("df <= 1")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(ononc, 0.0)):
  *             raise ValueError("nonc < 0")
  */
-  __pyx_tuple__53 = PyTuple_Pack(1, __pyx_kp_s_df_1); if (unlikely(!__pyx_tuple__53)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2168; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__53 = PyTuple_Pack(1, __pyx_kp_s_df_1); if (unlikely(!__pyx_tuple__53)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2176; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__53);
   __Pyx_GIVEREF(__pyx_tuple__53);
 
-  /* "mtrand.pyx":2170
+  /* "mtrand.pyx":2178
  *             raise ValueError("df <= 1")
  *         if np.any(np.less_equal(ononc, 0.0)):
  *             raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_noncentral_chisquare, size,
  *             odf, ononc)
  */
-  __pyx_tuple__54 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__54)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__54 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__54)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2178; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__54);
   __Pyx_GIVEREF(__pyx_tuple__54);
 
-  /* "mtrand.pyx":2326
+  /* "mtrand.pyx":2334
  *         if not PyErr_Occurred():
  *             if fdf <= 0:
  *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_standard_t, size, fdf)
  * 
  */
-  __pyx_tuple__55 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__55)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2326; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__55 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__55)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2334; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__55);
   __Pyx_GIVEREF(__pyx_tuple__55);
 
-  /* "mtrand.pyx":2333
+  /* "mtrand.pyx":2341
  *         odf = <ndarray> PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(odf, 0.0)):
  *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_standard_t, size, odf)
  * 
  */
-  __pyx_tuple__56 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__56)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2333; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__56 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__56)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2341; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__56);
   __Pyx_GIVEREF(__pyx_tuple__56);
 
-  /* "mtrand.pyx":2419
+  /* "mtrand.pyx":2427
  *         if not PyErr_Occurred():
  *             if fkappa < 0:
  *                 raise ValueError("kappa < 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_vonmises, size, fmu, fkappa)
  * 
  */
-  __pyx_tuple__57 = PyTuple_Pack(1, __pyx_kp_s_kappa_0); if (unlikely(!__pyx_tuple__57)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2419; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__57 = PyTuple_Pack(1, __pyx_kp_s_kappa_0); if (unlikely(!__pyx_tuple__57)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__57);
   __Pyx_GIVEREF(__pyx_tuple__57);
 
-  /* "mtrand.pyx":2427
+  /* "mtrand.pyx":2435
  *         okappa = <ndarray> PyArray_FROM_OTF(kappa, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(okappa, 0.0)):
  *             raise ValueError("kappa < 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa)
  * 
  */
-  __pyx_tuple__58 = PyTuple_Pack(1, __pyx_kp_s_kappa_0); if (unlikely(!__pyx_tuple__58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__58 = PyTuple_Pack(1, __pyx_kp_s_kappa_0); if (unlikely(!__pyx_tuple__58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2435; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__58);
   __Pyx_GIVEREF(__pyx_tuple__58);
 
-  /* "mtrand.pyx":2516
+  /* "mtrand.pyx":2524
  *         if not PyErr_Occurred():
  *             if fa <= 0:
  *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_pareto, size, fa)
  * 
  */
-  __pyx_tuple__59 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__59)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2516; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__59 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__59)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2524; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__59);
   __Pyx_GIVEREF(__pyx_tuple__59);
 
-  /* "mtrand.pyx":2523
+  /* "mtrand.pyx":2531
  *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 0.0)):
  *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_pareto, size, oa)
  * 
  */
-  __pyx_tuple__60 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__60 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__60);
   __Pyx_GIVEREF(__pyx_tuple__60);
 
-  /* "mtrand.pyx":2616
+  /* "mtrand.pyx":2624
  *         if not PyErr_Occurred():
  *             if fa <= 0:
  *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_weibull, size, fa)
  * 
  */
-  __pyx_tuple__61 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__61)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2616; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__61 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__61)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2624; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__61);
   __Pyx_GIVEREF(__pyx_tuple__61);
 
-  /* "mtrand.pyx":2623
+  /* "mtrand.pyx":2631
  *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 0.0)):
  *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_weibull, size, oa)
  * 
  */
-  __pyx_tuple__62 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2623; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__62 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2631; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__62);
   __Pyx_GIVEREF(__pyx_tuple__62);
 
-  /* "mtrand.pyx":2725
+  /* "mtrand.pyx":2733
  *         if not PyErr_Occurred():
  *             if fa <= 0:
  *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_power, size, fa)
  * 
  */
-  __pyx_tuple__63 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__63)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2725; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__63 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__63)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2733; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__63);
   __Pyx_GIVEREF(__pyx_tuple__63);
 
-  /* "mtrand.pyx":2732
+  /* "mtrand.pyx":2740
  *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 0.0)):
  *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_power, size, oa)
  * 
  */
-  __pyx_tuple__64 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__64)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2732; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__64 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__64)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2740; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__64);
   __Pyx_GIVEREF(__pyx_tuple__64);
 
-  /* "mtrand.pyx":2815
+  /* "mtrand.pyx":2823
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_laplace, size, floc, fscale)
  * 
  */
-  __pyx_tuple__65 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__65)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2815; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__65 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__65)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2823; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__65);
   __Pyx_GIVEREF(__pyx_tuple__65);
 
-  /* "mtrand.pyx":2822
+  /* "mtrand.pyx":2830
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale)
  * 
  */
-  __pyx_tuple__66 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__66)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2822; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__66 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__66)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2830; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__66);
   __Pyx_GIVEREF(__pyx_tuple__66);
 
-  /* "mtrand.pyx":2946
+  /* "mtrand.pyx":2954
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_gumbel, size, floc, fscale)
  * 
  */
-  __pyx_tuple__67 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__67)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2946; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__67 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__67)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2954; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__67);
   __Pyx_GIVEREF(__pyx_tuple__67);
 
-  /* "mtrand.pyx":2953
+  /* "mtrand.pyx":2961
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale)
  * 
  */
-  __pyx_tuple__68 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__68)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2953; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__68 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__68)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2961; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__68);
   __Pyx_GIVEREF(__pyx_tuple__68);
 
-  /* "mtrand.pyx":3034
+  /* "mtrand.pyx":3042
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_logistic, size, floc, fscale)
  * 
  */
-  __pyx_tuple__69 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__69)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3034; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__69 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__69)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3042; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__69);
   __Pyx_GIVEREF(__pyx_tuple__69);
 
-  /* "mtrand.pyx":3041
+  /* "mtrand.pyx":3049
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_logistic, size, oloc, oscale)
  * 
  */
-  __pyx_tuple__70 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__70)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3041; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__70 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__70)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3049; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__70);
   __Pyx_GIVEREF(__pyx_tuple__70);
 
-  /* "mtrand.pyx":3154
+  /* "mtrand.pyx":3162
  *         if not PyErr_Occurred():
  *             if fsigma <= 0:
  *                 raise ValueError("sigma <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma)
  * 
  */
-  __pyx_tuple__71 = PyTuple_Pack(1, __pyx_kp_s_sigma_0); if (unlikely(!__pyx_tuple__71)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3154; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__71 = PyTuple_Pack(1, __pyx_kp_s_sigma_0); if (unlikely(!__pyx_tuple__71)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3162; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__71);
   __Pyx_GIVEREF(__pyx_tuple__71);
 
-  /* "mtrand.pyx":3162
+  /* "mtrand.pyx":3170
  *         osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(osigma, 0.0)):
  *             raise ValueError("sigma <= 0.0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_lognormal, size, omean, osigma)
  * 
  */
-  __pyx_tuple__72 = PyTuple_Pack(1, __pyx_kp_s_sigma_0_0); if (unlikely(!__pyx_tuple__72)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3162; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__72 = PyTuple_Pack(1, __pyx_kp_s_sigma_0_0); if (unlikely(!__pyx_tuple__72)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__72);
   __Pyx_GIVEREF(__pyx_tuple__72);
 
-  /* "mtrand.pyx":3227
+  /* "mtrand.pyx":3235
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_rayleigh, size, fscale)
  * 
  */
-  __pyx_tuple__73 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__73)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3227; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__73 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__73)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3235; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__73);
   __Pyx_GIVEREF(__pyx_tuple__73);
 
-  /* "mtrand.pyx":3234
+  /* "mtrand.pyx":3242
  *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0.0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_rayleigh, size, oscale)
  * 
  */
-  __pyx_tuple__74 = PyTuple_Pack(1, __pyx_kp_s_scale_0_0); if (unlikely(!__pyx_tuple__74)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3234; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__74 = PyTuple_Pack(1, __pyx_kp_s_scale_0_0); if (unlikely(!__pyx_tuple__74)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3242; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__74);
   __Pyx_GIVEREF(__pyx_tuple__74);
 
-  /* "mtrand.pyx":3307
+  /* "mtrand.pyx":3315
  *         if not PyErr_Occurred():
  *             if fmean <= 0:
  *                 raise ValueError("mean <= 0")             # <<<<<<<<<<<<<<
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")
  */
-  __pyx_tuple__75 = PyTuple_Pack(1, __pyx_kp_s_mean_0); if (unlikely(!__pyx_tuple__75)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3307; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__75 = PyTuple_Pack(1, __pyx_kp_s_mean_0); if (unlikely(!__pyx_tuple__75)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3315; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__75);
   __Pyx_GIVEREF(__pyx_tuple__75);
 
-  /* "mtrand.pyx":3309
+  /* "mtrand.pyx":3317
  *                 raise ValueError("mean <= 0")
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale)
  * 
  */
-  __pyx_tuple__76 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__76)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3309; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__76 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__76)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3317; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__76);
   __Pyx_GIVEREF(__pyx_tuple__76);
 
-  /* "mtrand.pyx":3316
+  /* "mtrand.pyx":3324
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(omean,0.0)):
  *             raise ValueError("mean <= 0.0")             # <<<<<<<<<<<<<<
  *         elif np.any(np.less_equal(oscale,0.0)):
  *             raise ValueError("scale <= 0.0")
  */
-  __pyx_tuple__77 = PyTuple_Pack(1, __pyx_kp_s_mean_0_0); if (unlikely(!__pyx_tuple__77)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3316; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__77 = PyTuple_Pack(1, __pyx_kp_s_mean_0_0); if (unlikely(!__pyx_tuple__77)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3324; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__77);
   __Pyx_GIVEREF(__pyx_tuple__77);
 
-  /* "mtrand.pyx":3318
+  /* "mtrand.pyx":3326
  *             raise ValueError("mean <= 0.0")
  *         elif np.any(np.less_equal(oscale,0.0)):
  *             raise ValueError("scale <= 0.0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_wald, size, omean, oscale)
  * 
  */
-  __pyx_tuple__78 = PyTuple_Pack(1, __pyx_kp_s_scale_0_0); if (unlikely(!__pyx_tuple__78)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3318; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__78 = PyTuple_Pack(1, __pyx_kp_s_scale_0_0); if (unlikely(!__pyx_tuple__78)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3326; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__78);
   __Pyx_GIVEREF(__pyx_tuple__78);
 
-  /* "mtrand.pyx":3388
+  /* "mtrand.pyx":3396
  *         if not PyErr_Occurred():
  *             if fleft > fmode:
  *                 raise ValueError("left > mode")             # <<<<<<<<<<<<<<
  *             if fmode > fright:
  *                 raise ValueError("mode > right")
  */
-  __pyx_tuple__79 = PyTuple_Pack(1, __pyx_kp_s_left_mode); if (unlikely(!__pyx_tuple__79)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3388; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__79 = PyTuple_Pack(1, __pyx_kp_s_left_mode); if (unlikely(!__pyx_tuple__79)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3396; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__79);
   __Pyx_GIVEREF(__pyx_tuple__79);
 
-  /* "mtrand.pyx":3390
+  /* "mtrand.pyx":3398
  *                 raise ValueError("left > mode")
  *             if fmode > fright:
  *                 raise ValueError("mode > right")             # <<<<<<<<<<<<<<
  *             if fleft == fright:
  *                 raise ValueError("left == right")
  */
-  __pyx_tuple__80 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__80)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3390; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__80 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__80)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3398; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__80);
   __Pyx_GIVEREF(__pyx_tuple__80);
 
-  /* "mtrand.pyx":3392
+  /* "mtrand.pyx":3400
  *                 raise ValueError("mode > right")
  *             if fleft == fright:
  *                 raise ValueError("left == right")             # <<<<<<<<<<<<<<
  *             return cont3_array_sc(self.internal_state, rk_triangular, size, fleft,
  *                                   fmode, fright)
  */
-  __pyx_tuple__81 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__81)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3392; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__81 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__81)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3400; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__81);
   __Pyx_GIVEREF(__pyx_tuple__81);
 
-  /* "mtrand.pyx":3402
+  /* "mtrand.pyx":3410
  * 
  *         if np.any(np.greater(oleft, omode)):
  *             raise ValueError("left > mode")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(omode, oright)):
  *             raise ValueError("mode > right")
  */
-  __pyx_tuple__82 = PyTuple_Pack(1, __pyx_kp_s_left_mode); if (unlikely(!__pyx_tuple__82)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3402; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__82 = PyTuple_Pack(1, __pyx_kp_s_left_mode); if (unlikely(!__pyx_tuple__82)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3410; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__82);
   __Pyx_GIVEREF(__pyx_tuple__82);
 
-  /* "mtrand.pyx":3404
+  /* "mtrand.pyx":3412
  *             raise ValueError("left > mode")
  *         if np.any(np.greater(omode, oright)):
  *             raise ValueError("mode > right")             # <<<<<<<<<<<<<<
  *         if np.any(np.equal(oleft, oright)):
  *             raise ValueError("left == right")
  */
-  __pyx_tuple__83 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__83)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3404; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__83 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__83)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3412; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__83);
   __Pyx_GIVEREF(__pyx_tuple__83);
 
-  /* "mtrand.pyx":3406
+  /* "mtrand.pyx":3414
  *             raise ValueError("mode > right")
  *         if np.any(np.equal(oleft, oright)):
  *             raise ValueError("left == right")             # <<<<<<<<<<<<<<
  *         return cont3_array(self.internal_state, rk_triangular, size, oleft,
  *             omode, oright)
  */
-  __pyx_tuple__84 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__84)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3406; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__84 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__84)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3414; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__84);
   __Pyx_GIVEREF(__pyx_tuple__84);
 
-  /* "mtrand.pyx":3500
+  /* "mtrand.pyx":3508
  *         if not PyErr_Occurred():
  *             if ln < 0:
  *                 raise ValueError("n < 0")             # <<<<<<<<<<<<<<
  *             if fp < 0:
  *                 raise ValueError("p < 0")
  */
-  __pyx_tuple__85 = PyTuple_Pack(1, __pyx_kp_s_n_0); if (unlikely(!__pyx_tuple__85)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3500; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__85 = PyTuple_Pack(1, __pyx_kp_s_n_0); if (unlikely(!__pyx_tuple__85)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3508; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__85);
   __Pyx_GIVEREF(__pyx_tuple__85);
 
-  /* "mtrand.pyx":3502
+  /* "mtrand.pyx":3510
  *                 raise ValueError("n < 0")
  *             if fp < 0:
  *                 raise ValueError("p < 0")             # <<<<<<<<<<<<<<
  *             elif fp > 1:
  *                 raise ValueError("p > 1")
  */
-  __pyx_tuple__86 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__86)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3502; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__86 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__86)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3510; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__86);
   __Pyx_GIVEREF(__pyx_tuple__86);
 
-  /* "mtrand.pyx":3504
+  /* "mtrand.pyx":3512
  *                 raise ValueError("p < 0")
  *             elif fp > 1:
  *                 raise ValueError("p > 1")             # <<<<<<<<<<<<<<
  *             return discnp_array_sc(self.internal_state, rk_binomial, size, ln, fp)
  * 
  */
-  __pyx_tuple__87 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__87)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3504; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__87 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__87)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3512; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__87);
   __Pyx_GIVEREF(__pyx_tuple__87);
 
-  /* "mtrand.pyx":3512
+  /* "mtrand.pyx":3520
  *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(n, 0)):
  *             raise ValueError("n < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(p, 0)):
  *             raise ValueError("p < 0")
  */
-  __pyx_tuple__88 = PyTuple_Pack(1, __pyx_kp_s_n_0); if (unlikely(!__pyx_tuple__88)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3512; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__88 = PyTuple_Pack(1, __pyx_kp_s_n_0); if (unlikely(!__pyx_tuple__88)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3520; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__88);
   __Pyx_GIVEREF(__pyx_tuple__88);
 
-  /* "mtrand.pyx":3514
+  /* "mtrand.pyx":3522
  *             raise ValueError("n < 0")
  *         if np.any(np.less(p, 0)):
  *             raise ValueError("p < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(p, 1)):
  *             raise ValueError("p > 1")
  */
-  __pyx_tuple__89 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__89)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__89 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__89)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3522; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__89);
   __Pyx_GIVEREF(__pyx_tuple__89);
 
-  /* "mtrand.pyx":3516
+  /* "mtrand.pyx":3524
  *             raise ValueError("p < 0")
  *         if np.any(np.greater(p, 1)):
  *             raise ValueError("p > 1")             # <<<<<<<<<<<<<<
  *         return discnp_array(self.internal_state, rk_binomial, size, on, op)
  * 
  */
-  __pyx_tuple__90 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__90)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3516; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__90 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__90)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3524; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__90);
   __Pyx_GIVEREF(__pyx_tuple__90);
 
-  /* "mtrand.pyx":3593
+  /* "mtrand.pyx":3601
  *         if not PyErr_Occurred():
  *             if fn <= 0:
  *                 raise ValueError("n <= 0")             # <<<<<<<<<<<<<<
  *             if fp < 0:
  *                 raise ValueError("p < 0")
  */
-  __pyx_tuple__91 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__91)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3593; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__91 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__91)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3601; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__91);
   __Pyx_GIVEREF(__pyx_tuple__91);
 
-  /* "mtrand.pyx":3595
+  /* "mtrand.pyx":3603
  *                 raise ValueError("n <= 0")
  *             if fp < 0:
  *                 raise ValueError("p < 0")             # <<<<<<<<<<<<<<
  *             elif fp > 1:
  *                 raise ValueError("p > 1")
  */
-  __pyx_tuple__92 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3595; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__92 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3603; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__92);
   __Pyx_GIVEREF(__pyx_tuple__92);
 
-  /* "mtrand.pyx":3597
+  /* "mtrand.pyx":3605
  *                 raise ValueError("p < 0")
  *             elif fp > 1:
  *                 raise ValueError("p > 1")             # <<<<<<<<<<<<<<
  *             return discdd_array_sc(self.internal_state, rk_negative_binomial,
  *                                    size, fn, fp)
  */
-  __pyx_tuple__93 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__93)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3597; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__93 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__93)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3605; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__93);
   __Pyx_GIVEREF(__pyx_tuple__93);
 
-  /* "mtrand.pyx":3606
+  /* "mtrand.pyx":3614
  *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(n, 0)):
  *             raise ValueError("n <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(p, 0)):
  *             raise ValueError("p < 0")
  */
-  __pyx_tuple__94 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__94)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3606; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__94 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__94)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__94);
   __Pyx_GIVEREF(__pyx_tuple__94);
 
-  /* "mtrand.pyx":3608
+  /* "mtrand.pyx":3616
  *             raise ValueError("n <= 0")
  *         if np.any(np.less(p, 0)):
  *             raise ValueError("p < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(p, 1)):
  *             raise ValueError("p > 1")
  */
-  __pyx_tuple__95 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__95)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3608; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__95 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__95)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3616; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__95);
   __Pyx_GIVEREF(__pyx_tuple__95);
 
-  /* "mtrand.pyx":3610
+  /* "mtrand.pyx":3618
  *             raise ValueError("p < 0")
  *         if np.any(np.greater(p, 1)):
  *             raise ValueError("p > 1")             # <<<<<<<<<<<<<<
  *         return discdd_array(self.internal_state, rk_negative_binomial, size,
  *                             on, op)
  */
-  __pyx_tuple__96 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__96)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3610; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__96 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__96)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3618; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__96);
   __Pyx_GIVEREF(__pyx_tuple__96);
 
-  /* "mtrand.pyx":3671
+  /* "mtrand.pyx":3679
  *         if not PyErr_Occurred():
  *             if lam < 0:
  *                 raise ValueError("lam < 0")             # <<<<<<<<<<<<<<
  *             if lam > self.poisson_lam_max:
  *                 raise ValueError("lam value too large")
  */
-  __pyx_tuple__97 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__97)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3671; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__97 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__97)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3679; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__97);
   __Pyx_GIVEREF(__pyx_tuple__97);
 
-  /* "mtrand.pyx":3673
+  /* "mtrand.pyx":3681
  *                 raise ValueError("lam < 0")
  *             if lam > self.poisson_lam_max:
  *                 raise ValueError("lam value too large")             # <<<<<<<<<<<<<<
  *             return discd_array_sc(self.internal_state, rk_poisson, size, flam)
  * 
  */
-  __pyx_tuple__98 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large); if (unlikely(!__pyx_tuple__98)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3673; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__98 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large); if (unlikely(!__pyx_tuple__98)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3681; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__98);
   __Pyx_GIVEREF(__pyx_tuple__98);
 
-  /* "mtrand.pyx":3680
+  /* "mtrand.pyx":3688
  *         olam = <ndarray>PyArray_FROM_OTF(lam, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(olam, 0)):
  *             raise ValueError("lam < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(olam, self.poisson_lam_max)):
  *             raise ValueError("lam value too large.")
  */
-  __pyx_tuple__99 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__99)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3680; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__99 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__99)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3688; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__99);
   __Pyx_GIVEREF(__pyx_tuple__99);
 
-  /* "mtrand.pyx":3682
+  /* "mtrand.pyx":3690
  *             raise ValueError("lam < 0")
  *         if np.any(np.greater(olam, self.poisson_lam_max)):
  *             raise ValueError("lam value too large.")             # <<<<<<<<<<<<<<
  *         return discd_array(self.internal_state, rk_poisson, size, olam)
  * 
  */
-  __pyx_tuple__100 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large_2); if (unlikely(!__pyx_tuple__100)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3682; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__100 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large_2); if (unlikely(!__pyx_tuple__100)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__100);
   __Pyx_GIVEREF(__pyx_tuple__100);
 
-  /* "mtrand.pyx":3763
+  /* "mtrand.pyx":3771
  *         if not PyErr_Occurred():
  *             if fa <= 1.0:
  *                 raise ValueError("a <= 1.0")             # <<<<<<<<<<<<<<
  *             return discd_array_sc(self.internal_state, rk_zipf, size, fa)
  * 
  */
-  __pyx_tuple__101 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__101)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3763; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__101 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__101)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3771; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__101);
   __Pyx_GIVEREF(__pyx_tuple__101);
 
-  /* "mtrand.pyx":3770
+  /* "mtrand.pyx":3778
  *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 1.0)):
  *             raise ValueError("a <= 1.0")             # <<<<<<<<<<<<<<
  *         return discd_array(self.internal_state, rk_zipf, size, oa)
  * 
  */
-  __pyx_tuple__102 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__102)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3770; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__102 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__102)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3778; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__102);
   __Pyx_GIVEREF(__pyx_tuple__102);
 
-  /* "mtrand.pyx":3824
+  /* "mtrand.pyx":3832
  *         if not PyErr_Occurred():
  *             if fp < 0.0:
  *                 raise ValueError("p < 0.0")             # <<<<<<<<<<<<<<
  *             if fp > 1.0:
  *                 raise ValueError("p > 1.0")
  */
-  __pyx_tuple__103 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__103)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3824; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__103 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__103)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3832; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__103);
   __Pyx_GIVEREF(__pyx_tuple__103);
 
-  /* "mtrand.pyx":3826
+  /* "mtrand.pyx":3834
  *                 raise ValueError("p < 0.0")
  *             if fp > 1.0:
  *                 raise ValueError("p > 1.0")             # <<<<<<<<<<<<<<
  *             return discd_array_sc(self.internal_state, rk_geometric, size, fp)
  * 
  */
-  __pyx_tuple__104 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__104)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3826; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__104 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__104)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__104);
   __Pyx_GIVEREF(__pyx_tuple__104);
 
-  /* "mtrand.pyx":3834
+  /* "mtrand.pyx":3842
  *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(op, 0.0)):
  *             raise ValueError("p < 0.0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(op, 1.0)):
  *             raise ValueError("p > 1.0")
  */
-  __pyx_tuple__105 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__105)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__105 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__105)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3842; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__105);
   __Pyx_GIVEREF(__pyx_tuple__105);
 
-  /* "mtrand.pyx":3836
+  /* "mtrand.pyx":3844
  *             raise ValueError("p < 0.0")
  *         if np.any(np.greater(op, 1.0)):
  *             raise ValueError("p > 1.0")             # <<<<<<<<<<<<<<
  *         return discd_array(self.internal_state, rk_geometric, size, op)
  * 
  */
-  __pyx_tuple__106 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__106)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__106 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__106)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3844; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__106);
   __Pyx_GIVEREF(__pyx_tuple__106);
 
-  /* "mtrand.pyx":3932
+  /* "mtrand.pyx":3940
  *         if not PyErr_Occurred():
  *             if lngood < 0:
  *                 raise ValueError("ngood < 0")             # <<<<<<<<<<<<<<
  *             if lnbad < 0:
  *                 raise ValueError("nbad < 0")
  */
-  __pyx_tuple__107 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__107)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3932; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__107 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__107)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3940; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__107);
   __Pyx_GIVEREF(__pyx_tuple__107);
 
-  /* "mtrand.pyx":3934
+  /* "mtrand.pyx":3942
  *                 raise ValueError("ngood < 0")
  *             if lnbad < 0:
  *                 raise ValueError("nbad < 0")             # <<<<<<<<<<<<<<
  *             if lnsample < 1:
  *                 raise ValueError("nsample < 1")
  */
-  __pyx_tuple__108 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__108)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3934; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__108 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__108)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3942; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__108);
   __Pyx_GIVEREF(__pyx_tuple__108);
 
-  /* "mtrand.pyx":3936
+  /* "mtrand.pyx":3944
  *                 raise ValueError("nbad < 0")
  *             if lnsample < 1:
  *                 raise ValueError("nsample < 1")             # <<<<<<<<<<<<<<
  *             if lngood + lnbad < lnsample:
  *                 raise ValueError("ngood + nbad < nsample")
  */
-  __pyx_tuple__109 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__109)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3936; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__109 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__109)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3944; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__109);
   __Pyx_GIVEREF(__pyx_tuple__109);
 
-  /* "mtrand.pyx":3938
+  /* "mtrand.pyx":3946
  *                 raise ValueError("nsample < 1")
  *             if lngood + lnbad < lnsample:
  *                 raise ValueError("ngood + nbad < nsample")             # <<<<<<<<<<<<<<
  *             return discnmN_array_sc(self.internal_state, rk_hypergeometric, size,
  *                                     lngood, lnbad, lnsample)
  */
-  __pyx_tuple__110 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__110)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3938; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__110 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__110)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3946; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__110);
   __Pyx_GIVEREF(__pyx_tuple__110);
 
-  /* "mtrand.pyx":3948
+  /* "mtrand.pyx":3956
  *         onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(ongood, 0)):
  *             raise ValueError("ngood < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(onbad, 0)):
  *             raise ValueError("nbad < 0")
  */
-  __pyx_tuple__111 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__111)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3948; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__111 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__111)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3956; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__111);
   __Pyx_GIVEREF(__pyx_tuple__111);
 
-  /* "mtrand.pyx":3950
+  /* "mtrand.pyx":3958
  *             raise ValueError("ngood < 0")
  *         if np.any(np.less(onbad, 0)):
  *             raise ValueError("nbad < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(onsample, 1)):
  *             raise ValueError("nsample < 1")
  */
-  __pyx_tuple__112 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__112)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3950; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__112 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__112)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3958; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__112);
   __Pyx_GIVEREF(__pyx_tuple__112);
 
-  /* "mtrand.pyx":3952
+  /* "mtrand.pyx":3960
  *             raise ValueError("nbad < 0")
  *         if np.any(np.less(onsample, 1)):
  *             raise ValueError("nsample < 1")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(np.add(ongood, onbad),onsample)):
  *             raise ValueError("ngood + nbad < nsample")
  */
-  __pyx_tuple__113 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__113)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3952; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__113 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__113)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3960; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__113);
   __Pyx_GIVEREF(__pyx_tuple__113);
 
-  /* "mtrand.pyx":3954
+  /* "mtrand.pyx":3962
  *             raise ValueError("nsample < 1")
  *         if np.any(np.less(np.add(ongood, onbad),onsample)):
  *             raise ValueError("ngood + nbad < nsample")             # <<<<<<<<<<<<<<
  *         return discnmN_array(self.internal_state, rk_hypergeometric, size,
  *             ongood, onbad, onsample)
  */
-  __pyx_tuple__114 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__114)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3954; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__114 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__114)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3962; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__114);
   __Pyx_GIVEREF(__pyx_tuple__114);
 
-  /* "mtrand.pyx":4038
+  /* "mtrand.pyx":4046
  *         if not PyErr_Occurred():
  *             if fp <= 0.0:
  *                 raise ValueError("p <= 0.0")             # <<<<<<<<<<<<<<
  *             if fp >= 1.0:
  *                 raise ValueError("p >= 1.0")
  */
-  __pyx_tuple__115 = PyTuple_Pack(1, __pyx_kp_s_p_0_0_2); if (unlikely(!__pyx_tuple__115)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4038; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__115 = PyTuple_Pack(1, __pyx_kp_s_p_0_0_2); if (unlikely(!__pyx_tuple__115)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4046; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__115);
   __Pyx_GIVEREF(__pyx_tuple__115);
 
-  /* "mtrand.pyx":4040
+  /* "mtrand.pyx":4048
  *                 raise ValueError("p <= 0.0")
  *             if fp >= 1.0:
  *                 raise ValueError("p >= 1.0")             # <<<<<<<<<<<<<<
  *             return discd_array_sc(self.internal_state, rk_logseries, size, fp)
  * 
  */
-  __pyx_tuple__116 = PyTuple_Pack(1, __pyx_kp_s_p_1_0_2); if (unlikely(!__pyx_tuple__116)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4040; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__116 = PyTuple_Pack(1, __pyx_kp_s_p_1_0_2); if (unlikely(!__pyx_tuple__116)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4048; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__116);
   __Pyx_GIVEREF(__pyx_tuple__116);
 
-  /* "mtrand.pyx":4047
+  /* "mtrand.pyx":4055
  *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(op, 0.0)):
  *             raise ValueError("p <= 0.0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater_equal(op, 1.0)):
  *             raise ValueError("p >= 1.0")
  */
-  __pyx_tuple__117 = PyTuple_Pack(1, __pyx_kp_s_p_0_0_2); if (unlikely(!__pyx_tuple__117)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4047; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__117 = PyTuple_Pack(1, __pyx_kp_s_p_0_0_2); if (unlikely(!__pyx_tuple__117)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4055; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__117);
   __Pyx_GIVEREF(__pyx_tuple__117);
 
-  /* "mtrand.pyx":4049
+  /* "mtrand.pyx":4057
  *             raise ValueError("p <= 0.0")
  *         if np.any(np.greater_equal(op, 1.0)):
  *             raise ValueError("p >= 1.0")             # <<<<<<<<<<<<<<
  *         return discd_array(self.internal_state, rk_logseries, size, op)
  * 
  */
-  __pyx_tuple__118 = PyTuple_Pack(1, __pyx_kp_s_p_1_0_2); if (unlikely(!__pyx_tuple__118)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4049; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__118 = PyTuple_Pack(1, __pyx_kp_s_p_1_0_2); if (unlikely(!__pyx_tuple__118)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4057; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__118);
   __Pyx_GIVEREF(__pyx_tuple__118);
 
-  /* "mtrand.pyx":4152
+  /* "mtrand.pyx":4160
  *             shape = size
  *         if len(mean.shape) != 1:
  *                raise ValueError("mean must be 1 dimensional")             # <<<<<<<<<<<<<<
  *         if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]):
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  */
-  __pyx_tuple__119 = PyTuple_Pack(1, __pyx_kp_s_mean_must_be_1_dimensional); if (unlikely(!__pyx_tuple__119)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4152; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__119 = PyTuple_Pack(1, __pyx_kp_s_mean_must_be_1_dimensional); if (unlikely(!__pyx_tuple__119)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4160; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__119);
   __Pyx_GIVEREF(__pyx_tuple__119);
 
-  /* "mtrand.pyx":4154
+  /* "mtrand.pyx":4162
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  *         if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]):
  *                raise ValueError("cov must be 2 dimensional and square")             # <<<<<<<<<<<<<<
  *         if mean.shape[0] != cov.shape[0]:
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-  __pyx_tuple__120 = PyTuple_Pack(1, __pyx_kp_s_cov_must_be_2_dimensional_and_sq); if (unlikely(!__pyx_tuple__120)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4154; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__120 = PyTuple_Pack(1, __pyx_kp_s_cov_must_be_2_dimensional_and_sq); if (unlikely(!__pyx_tuple__120)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4162; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__120);
   __Pyx_GIVEREF(__pyx_tuple__120);
 
-  /* "mtrand.pyx":4156
+  /* "mtrand.pyx":4164
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  *         if mean.shape[0] != cov.shape[0]:
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-  __pyx_tuple__121 = PyTuple_Pack(1, __pyx_kp_s_mean_and_cov_must_have_same_leng); if (unlikely(!__pyx_tuple__121)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4156; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__121 = PyTuple_Pack(1, __pyx_kp_s_mean_and_cov_must_have_same_leng); if (unlikely(!__pyx_tuple__121)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4164; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__121);
   __Pyx_GIVEREF(__pyx_tuple__121);
 
-  /* "mtrand.pyx":4160
+  /* "mtrand.pyx":4168
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  *         final_shape = list(shape[:])             # <<<<<<<<<<<<<<
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+  __pyx_slice__122 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__122)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4168; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_slice__122);
   __Pyx_GIVEREF(__pyx_slice__122);
 
-  /* "mtrand.pyx":4249
+  /* "mtrand.pyx":4257
  * 
  *         if kahan_sum(pix, d-1) > (1.0 + 1e-12):
  *             raise ValueError("sum(pvals[:-1]) > 1.0")             # <<<<<<<<<<<<<<
  * 
- *         if size is None:
+ *         shape = _shape_from_size(size, d)
  */
-  __pyx_tuple__123 = PyTuple_Pack(1, __pyx_kp_s_sum_pvals_1_1_0); if (unlikely(!__pyx_tuple__123)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4249; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__123 = PyTuple_Pack(1, __pyx_kp_s_sum_pvals_1_1_0); if (unlikely(!__pyx_tuple__123)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__123);
   __Pyx_GIVEREF(__pyx_tuple__123);
 
-  /* "mtrand.pyx":559
+  /* "mtrand.pyx":525
+ *     return sum
+ * 
+ * def _shape_from_size(size, d):             # <<<<<<<<<<<<<<
+ *     if size is None:
+ *         shape = (d,)
+ */
+  __pyx_tuple__124 = PyTuple_Pack(3, __pyx_n_s_size, __pyx_n_s_d, __pyx_n_s_shape); if (unlikely(!__pyx_tuple__124)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 525; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__124);
+  __Pyx_GIVEREF(__pyx_tuple__124);
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+
+  /* "mtrand.pyx":569
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-  __Pyx_GIVEREF(__pyx_tuple__124);
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-  __Pyx_GOTREF(__pyx_tuple__125);
-  __Pyx_GIVEREF(__pyx_tuple__125);
+  __pyx_tuple__126 = PyTuple_Pack(1, __pyx_n_s_l); if (unlikely(!__pyx_tuple__126)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __Pyx_GIVEREF(__pyx_tuple__126);
+  __pyx_tuple__127 = PyTuple_Pack(1, __pyx_n_s_l); if (unlikely(!__pyx_tuple__127)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -23352,9 +23564,9 @@ PyMODINIT_FUNC PyInit_mtrand(void)
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-  if (PyType_Ready(&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 525; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyType_Ready(&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 535; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -23394,772 +23606,784 @@ PyMODINIT_FUNC PyInit_mtrand(void)
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-  /* "mtrand.pyx":559
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+  __pyx_t_1 = __Pyx_GetModuleGlobalName(__pyx_n_s_rand_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4515; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_4 = __Pyx_GetModuleGlobalName(__pyx_n_s_rand_2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4516; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_1 = __Pyx_GetModuleGlobalName(__pyx_n_s_rand_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_pareto); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_4 = __Pyx_GetModuleGlobalName(__pyx_n_s_rand_2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4522; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_weibull); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4522; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_1 = __Pyx_GetModuleGlobalName(__pyx_n_s_rand_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4534; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_bytes_line_900, __pyx_kp_u_bytes_length_Return_random_byte) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_choice_line_928, __pyx_kp_u_choice_a_size_None_replace_True) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_uniform_line_1103, __pyx_kp_u_uniform_low_0_0_high_1_0_size_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_rand_line_1190, __pyx_kp_u_rand_d0_d1_dn_Random_values_in) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_randn_line_1234, __pyx_kp_u_randn_d0_d1_dn_Return_a_sample) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_random_integers_line, __pyx_kp_u_random_integers_low_high_None_s) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_normal_line, __pyx_kp_u_standard_normal_size_None_Retur) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_normal_line_1393, __pyx_kp_u_normal_loc_0_0_scale_1_0_size_N) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_normal_line_1401, __pyx_kp_u_normal_loc_0_0_scale_1_0_size_N) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_exponential, __pyx_kp_u_standard_exponential_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_gamma_line, __pyx_kp_u_standard_gamma_shape_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_gamma_line_1716, __pyx_kp_u_gamma_shape_scale_1_0_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_f_line_1807, __pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_gamma_line_1724, __pyx_kp_u_gamma_shape_scale_1_0_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_f_line_1815, __pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_noncentral_f_line_19, __pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_chisquare_line_2004, __pyx_kp_u_chisquare_df_size_None_Draw_sam) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_chisquare_line_2012, __pyx_kp_u_chisquare_df_size_None_Draw_sam) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_noncentral_chisquare, __pyx_kp_u_noncentral_chisquare_df_nonc_si) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_cauchy_line, __pyx_kp_u_standard_cauchy_size_None_Stand) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_t_line_2235, __pyx_kp_u_standard_t_df_size_None_Standar) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_vonmises_line_2336, __pyx_kp_u_vonmises_mu_kappa_size_None_Dra) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_pareto_line_2430, __pyx_kp_u_pareto_a_size_None_Draw_samples) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_weibull_line_2526, __pyx_kp_u_weibull_a_size_None_Weibull_dis) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_power_line_2626, __pyx_kp_u_power_a_size_None_Draws_samples) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_laplace_line_2735, __pyx_kp_u_laplace_loc_0_0_scale_1_0_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_gumbel_line_2825, __pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_logistic_line_2956, __pyx_kp_u_logistic_loc_0_0_scale_1_0_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_lognormal_line_3044, __pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_rayleigh_line_3165, __pyx_kp_u_rayleigh_scale_1_0_size_None_Dr) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_wald_line_3237, __pyx_kp_u_wald_mean_scale_size_None_Draw) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_triangular_line_3323, __pyx_kp_u_triangular_left_mode_right_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_binomial_line_3411, __pyx_kp_u_binomial_n_p_size_None_Draw_sam) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_t_line_2243, __pyx_kp_u_standard_t_df_size_None_Standar) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_vonmises_line_2344, __pyx_kp_u_vonmises_mu_kappa_size_None_Dra) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_pareto_line_2438, __pyx_kp_u_pareto_a_size_None_Draw_samples) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_weibull_line_2534, __pyx_kp_u_weibull_a_size_None_Weibull_dis) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_power_line_2634, __pyx_kp_u_power_a_size_None_Draws_samples) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_laplace_line_2743, __pyx_kp_u_laplace_loc_0_0_scale_1_0_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_gumbel_line_2833, __pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_logistic_line_2964, __pyx_kp_u_logistic_loc_0_0_scale_1_0_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_lognormal_line_3052, __pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_rayleigh_line_3173, __pyx_kp_u_rayleigh_scale_1_0_size_None_Dr) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_wald_line_3245, __pyx_kp_u_wald_mean_scale_size_None_Draw) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_triangular_line_3331, __pyx_kp_u_triangular_left_mode_right_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_binomial_line_3419, __pyx_kp_u_binomial_n_p_size_None_Draw_sam) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_negative_binomial_li, __pyx_kp_u_negative_binomial_n_p_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_poisson_line_3614, __pyx_kp_u_poisson_lam_1_0_size_None_Draw) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_zipf_line_3685, __pyx_kp_u_zipf_a_size_None_Draw_samples_f) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_geometric_line_3773, __pyx_kp_u_geometric_p_size_None_Draw_samp) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_poisson_line_3622, __pyx_kp_u_poisson_lam_1_0_size_None_Draw) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_zipf_line_3693, __pyx_kp_u_zipf_a_size_None_Draw_samples_f) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_geometric_line_3781, __pyx_kp_u_geometric_p_size_None_Draw_samp) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_hypergeometric_line, __pyx_kp_u_hypergeometric_ngood_nbad_nsamp) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_logseries_line_3958, __pyx_kp_u_logseries_p_size_None_Draw_samp) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_logseries_line_3966, __pyx_kp_u_logseries_p_size_None_Draw_samp) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_multivariate_normal, __pyx_kp_u_multivariate_normal_mean_cov_si) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_multinomial_line_418, __pyx_kp_u_multinomial_n_pvals_size_None_D) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_dirichlet_line_4278, __pyx_kp_u_dirichlet_alpha_size_None_Draw) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_shuffle_line_4395, __pyx_kp_u_shuffle_x_Modify_a_sequence_in) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_multinomial_line_419, __pyx_kp_u_multinomial_n_pvals_size_None_D) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_dirichlet_line_4281, __pyx_kp_u_dirichlet_alpha_size_None_Draw) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_shuffle_line_4393, __pyx_kp_u_shuffle_x_Modify_a_sequence_in) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_permutation_line_445, __pyx_kp_u_permutation_x_Randomly_permute) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
@@ -24501,6 +24725,31 @@ bad:
 }
 #endif
 
+static void __Pyx_RaiseArgtupleInvalid(
+    const char* func_name,
+    int exact,
+    Py_ssize_t num_min,
+    Py_ssize_t num_max,
+    Py_ssize_t num_found)
+{
+    Py_ssize_t num_expected;
+    const char *more_or_less;
+    if (num_found < num_min) {
+        num_expected = num_min;
+        more_or_less = "at least";
+    } else {
+        num_expected = num_max;
+        more_or_less = "at most";
+    }
+    if (exact) {
+        more_or_less = "exactly";
+    }
+    PyErr_Format(PyExc_TypeError,
+                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                 func_name, more_or_less, num_expected,
+                 (num_expected == 1) ? "" : "s", num_found);
+}
+
 static void __Pyx_RaiseDoubleKeywordsError(
     const char* func_name,
     PyObject* kw_name)
@@ -24615,29 +24864,92 @@ bad:
     return -1;
 }
 
-static void __Pyx_RaiseArgtupleInvalid(
-    const char* func_name,
-    int exact,
-    Py_ssize_t num_min,
-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
-    Py_ssize_t num_expected;
-    const char *more_or_less;
-    if (num_found < num_min) {
-        num_expected = num_min;
-        more_or_less = "at least";
-    } else {
-        num_expected = num_max;
-        more_or_less = "at most";
-    }
-    if (exact) {
-        more_or_less = "exactly";
+static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyThreadState *tstate = PyThreadState_GET();
+    *type = tstate->exc_type;
+    *value = tstate->exc_value;
+    *tb = tstate->exc_traceback;
+    Py_XINCREF(*type);
+    Py_XINCREF(*value);
+    Py_XINCREF(*tb);
+#else
+    PyErr_GetExcInfo(type, value, tb);
+#endif
+}
+static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    PyThreadState *tstate = PyThreadState_GET();
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = type;
+    tstate->exc_value = value;
+    tstate->exc_traceback = tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+#else
+    PyErr_SetExcInfo(type, value, tb);
+#endif
+}
+
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
+    PyObject *local_type, *local_value, *local_tb;
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    PyThreadState *tstate = PyThreadState_GET();
+    local_type = tstate->curexc_type;
+    local_value = tstate->curexc_value;
+    local_tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+#else
+    PyErr_Fetch(&local_type, &local_value, &local_tb);
+#endif
+    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
+#if CYTHON_COMPILING_IN_CPYTHON
+    if (unlikely(tstate->curexc_type))
+#else
+    if (unlikely(PyErr_Occurred()))
+#endif
+        goto bad;
+    #if PY_MAJOR_VERSION >= 3
+    if (local_tb) {
+        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
+            goto bad;
     }
-    PyErr_Format(PyExc_TypeError,
-                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                 func_name, more_or_less, num_expected,
-                 (num_expected == 1) ? "" : "s", num_found);
+    #endif
+    Py_XINCREF(local_tb);
+    Py_XINCREF(local_type);
+    Py_XINCREF(local_value);
+    *type = local_type;
+    *value = local_value;
+    *tb = local_tb;
+#if CYTHON_COMPILING_IN_CPYTHON
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = local_type;
+    tstate->exc_value = local_value;
+    tstate->exc_traceback = local_tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+#else
+    PyErr_SetExcInfo(local_type, local_value, local_tb);
+#endif
+    return 0;
+bad:
+    *type = 0;
+    *value = 0;
+    *tb = 0;
+    Py_XDECREF(local_type);
+    Py_XDECREF(local_value);
+    Py_XDECREF(local_tb);
+    return -1;
 }
 
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
@@ -24993,94 +25305,6 @@ static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
     return 0;
 }
 
-static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyThreadState *tstate = PyThreadState_GET();
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-#else
-    PyErr_GetExcInfo(type, value, tb);
-#endif
-}
-static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyThreadState *tstate = PyThreadState_GET();
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(type, value, tb);
-#endif
-}
-
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *local_type, *local_value, *local_tb;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyThreadState *tstate = PyThreadState_GET();
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_COMPILING_IN_CPYTHON
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
 static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
     if (unlikely(!type)) {
         PyErr_SetString(PyExc_SystemError, "Missing type object");
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index 0f6d453c7..27689a9e1 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -522,6 +522,16 @@ cdef double kahan_sum(double *darr, npy_intp n):
         sum = t
     return sum
 
+def _shape_from_size(size, d):
+    if size is None:
+        shape = (d,)
+    else:
+        try:
+           shape = (operator.index(size), d)
+        except TypeError:
+           shape = tuple(size) + (d,)
+    return shape
+
 cdef class RandomState:
     """
     RandomState(seed=None)
@@ -589,14 +599,12 @@ cdef class RandomState:
         """
         cdef rk_error errcode
         cdef ndarray obj "arrayObject_obj"
-        if seed is None:
-            errcode = rk_randomseed(self.internal_state)
-        elif type(seed) is int:
-            rk_seed(seed, self.internal_state)
-        elif isinstance(seed, np.integer):
-            iseed = int(seed)
-            rk_seed(iseed, self.internal_state)
-        else:
+        try:
+            if seed is None:
+                errcode = rk_randomseed(self.internal_state)
+            else:
+                rk_seed(operator.index(seed), self.internal_state)
+        except TypeError:
             obj = <ndarray>PyArray_ContiguousFromObject(seed, NPY_LONG, 1, 1)
             init_by_array(self.internal_state, <unsigned long *>PyArray_DATA(obj),
                 PyArray_DIM(obj, 0))
@@ -4248,12 +4256,7 @@ cdef class RandomState:
         if kahan_sum(pix, d-1) > (1.0 + 1e-12):
             raise ValueError("sum(pvals[:-1]) > 1.0")
 
-        if size is None:
-            shape = (d,)
-        elif type(size) is int:
-            shape = (size, d)
-        else:
-            shape = size + (d,)
+        shape = _shape_from_size(size, d)
 
         multin = np.zeros(shape, int)
         mnarr = <ndarray>multin
@@ -4366,12 +4369,7 @@ cdef class RandomState:
         alpha_arr   = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1)
         alpha_data  = <double*>PyArray_DATA(alpha_arr)
 
-        if size is None:
-            shape = (k,)
-        elif type(size) is int:
-            shape = (size, k)
-        else:
-            shape = size + (k,)
+        shape = _shape_from_size(size, k)
 
         diric   = np.zeros(shape, np.float64)
         val_arr = <ndarray>diric
diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py
index fb44722bb..1b18713cf 100644
--- a/numpy/random/tests/test_random.py
+++ b/numpy/random/tests/test_random.py
@@ -1,7 +1,7 @@
 from __future__ import division, absolute_import, print_function
 
 from numpy.testing import TestCase, run_module_suite, assert_,\
-        assert_raises
+        assert_raises, assert_equal
 from numpy import random
 from numpy.compat import asbytes
 import numpy as np
@@ -31,6 +31,20 @@ class TestMultinomial(TestCase):
         assert_(np.all(-5 <= x))
         assert_(np.all(x < -1))
 
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(np.random.multinomial(1 ,p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1 ,p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1 ,p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1 ,p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(np.random.multinomial(1 ,p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(np.random.multinomial(1 ,p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, np.random.multinomial, 1 , p,
+                      np.float(1))
+
 
 class TestSetState(TestCase):
     def setUp(self):
@@ -289,6 +303,18 @@ class TestRandomDist(TestCase):
                              [ 0.56974431743975207,  0.43025568256024799]]])
         np.testing.assert_array_almost_equal(actual, desired, decimal=15)
 
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162,  39.74494232180943953])
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(np.random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(np.random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, np.random.dirichlet, p, np.float(1))
+
     def test_exponential(self):
         np.random.seed(self.seed)
         actual = np.random.exponential(1.1234, size=(3, 2))