Moved scipy directory to numpy

14 files changed, 8887 insertions, 0 deletions

diff --git a/numpy/random/__init__.py b/numpy/random/__init__.py
new file mode 100644
index 000000000..39607f993
--- /dev/null
+++ b/numpy/random/__init__.py
@@ -0,0 +1,17 @@
+# To get sub-modules
+from info import __doc__, __all__
+from mtrand import *
+
+# Some aliases:
+ranf = random = sample = random_sample
+__all__.extend(['ranf','random','sample'])
+
+def __RandomState_ctor():
+    """Return a RandomState instance.
+    
+    This function exists solely to assist (un)pickling.
+    """
+    return RandomState()
+
+from scipy.testing import ScipyTest 
+test = ScipyTest().test
diff --git a/numpy/random/info.py b/numpy/random/info.py
new file mode 100644
index 000000000..44864495e
--- /dev/null
+++ b/numpy/random/info.py
@@ -0,0 +1,56 @@
+"""\
+Core Random Tools
+=================
+
+"""
+
+depends = ['base']
+global_symbols = ['rand','randn']
+
+__all__ = [
+    'beta',
+    'binomial',
+    'bytes',
+    'chisquare',
+    'exponential',
+    'f',
+    'gamma',
+    'geometric',
+    'get_state',
+    'gumbel',
+    'hypergeometric',
+    'laplace',
+    'logistic',
+    'lognormal',
+    'logseries',
+    'multinomial',
+    'multivariate_normal',
+    'negative_binomial',
+    'noncentral_chisquare',
+    'noncentral_f',
+    'normal',
+    'pareto',
+    'permutation',
+    'poisson',
+    'power',
+    'rand',
+    'randint',
+    'randn',
+    'random_integers',
+    'random_sample',
+    'rayleigh',
+    'seed',
+    'set_state',
+    'shuffle',
+    'standard_cauchy',
+    'standard_exponential',
+    'standard_gamma',
+    'standard_normal',
+    'standard_t',
+    'triangular',
+    'uniform',
+    'vonmises',
+    'wald',
+    'weibull',
+    'zipf'
+]
diff --git a/numpy/random/mtrand/Python.pxi b/numpy/random/mtrand/Python.pxi
new file mode 100644
index 000000000..d82b6dc4f
--- /dev/null
+++ b/numpy/random/mtrand/Python.pxi
@@ -0,0 +1,24 @@
+# :Author:    Robert Kern
+# :Copyright: 2004, Enthought, Inc.
+# :License:   BSD Style
+
+
+cdef extern from "Python.h":
+    ctypedef int size_t
+    char* PyString_AsString(object string)
+    object PyString_FromString(char* c_string)
+
+    void* PyMem_Malloc(size_t n)
+    void* PyMem_Realloc(void* buf, size_t n)
+    void PyMem_Free(void* buf)
+
+    void Py_DECREF(object obj)
+    void Py_XDECREF(object obj)
+    void Py_INCREF(object obj)
+    void Py_XINCREF(object obj)
+   
+cdef extern from "string.h":
+    void *memcpy(void *s1, void *s2, int n)
+
+cdef extern from "math.h":
+    double fabs(double x)
diff --git a/numpy/random/mtrand/distributions.c b/numpy/random/mtrand/distributions.c
new file mode 100644
index 000000000..3f5ff2355
--- /dev/null
+++ b/numpy/random/mtrand/distributions.c
@@ -0,0 +1,845 @@
+/* Copyright 2005 Robert Kern (robert.kern@gmail.com)
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the
+ * "Software"), to deal in the Software without restriction, including
+ * without limitation the rights to use, copy, modify, merge, publish,
+ * distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to
+ * the following conditions:
+ * 
+ * The above copyright notice and this permission notice shall be included
+ * in all copies or substantial portions of the Software.
+ * 
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+/* The implementations of rk_hypergeometric_hyp(), rk_hypergeometric_hrua(),
+ * and rk_triangular() were adapted from Ivan Frohne's rv.py which has this 
+ * license:
+ * 
+ *            Copyright 1998 by Ivan Frohne; Wasilla, Alaska, U.S.A.
+ *                            All Rights Reserved
+ *
+ * Permission to use, copy, modify and distribute this software and its
+ * documentation for any purpose, free of charge, is granted subject to the
+ * following conditions:
+ *   The above copyright notice and this permission notice shall be included in
+ *   all copies or substantial portions of the software.
+ *
+ *   THE SOFTWARE AND DOCUMENTATION IS PROVIDED WITHOUT WARRANTY OF ANY KIND,
+ *   EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO MERCHANTABILITY, FITNESS
+ *   FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE AUTHOR
+ *   OR COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM OR DAMAGES IN A CONTRACT
+ *   ACTION, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ *   SOFTWARE OR ITS DOCUMENTATION.
+ */
+
+#include <math.h>
+#include "distributions.h"
+#include <stdio.h>
+
+#ifndef min
+#define min(x,y) ((x<y)?x:y)
+#define max(x,y) ((x>y)?x:y)
+#endif
+
+/* log-gamma function to support some of these distributions. The 
+ * algorithm comes from SPECFUN by Shanjie Zhang and Jianming Jin and their
+ * book "Computation of Special Functions", 1996, John Wiley & Sons, Inc.
+ */
+extern double loggam(double x);
+double loggam(double x)
+{
+    double x0, x2, xp, gl, gl0;
+    long k, n;
+    
+    static double a[10] = {8.333333333333333e-02,-2.777777777777778e-03,
+         7.936507936507937e-04,-5.952380952380952e-04,
+         8.417508417508418e-04,-1.917526917526918e-03,
+         6.410256410256410e-03,-2.955065359477124e-02,
+         1.796443723688307e-01,-1.39243221690590e+00};
+    x0 = x;
+    n = 0;
+    if ((x == 1.0) || (x == 2.0))
+    {
+        return 0.0;
+    }
+    else if (x <= 7.0)
+    {
+        n = (long)(7 - x);
+        x0 = x + n;
+    }
+    x2 = 1.0/(x0*x0);
+    xp = 2*M_PI;
+    gl0 = a[9];
+    for (k=8; k>=0; k--)
+    {
+        gl0 *= x2;
+        gl0 += a[k];
+    }
+    gl = gl0/x0 + 0.5*log(xp) + (x0-0.5)*log(x0) - x0;
+    if (x <= 7.0)
+    {
+        for (k=1; k<=n; k++)
+        {
+            gl -= log(x0-1.0);
+            x0 -= 1.0;
+        }
+    }
+    return gl;
+}
+
+double rk_normal(rk_state *state, double loc, double scale)
+{
+    return loc + scale*rk_gauss(state);
+}
+
+double rk_standard_exponential(rk_state *state)
+{
+    /* We use -log(1-U) since U is [0, 1) */
+    return -log(1.0 - rk_double(state));
+}
+
+double rk_exponential(rk_state *state, double scale)
+{
+    return scale * rk_standard_exponential(state);
+}
+
+double rk_uniform(rk_state *state, double loc, double scale)
+{
+    return loc + scale*rk_double(state);
+}
+
+double rk_standard_gamma(rk_state *state, double shape)
+{
+    double b, c;
+    double U, V, X, Y;
+
+    if (shape == 1.0)
+    {
+        return rk_standard_exponential(state);
+    }
+    else if (shape < 1.0)
+    {
+        for (;;)
+        {
+            U = rk_double(state);
+            V = rk_standard_exponential(state);
+            if (U <= 1.0 - shape)
+            {
+                X = pow(U, 1./shape);
+                if (X <= V)
+                {
+                    return X;
+                }
+            }
+            else
+            {
+                Y = -log((1-U)/shape);
+                X = pow(1.0 - shape + shape*Y, 1./shape);
+                if (X <= (V + Y))
+                {
+                    return X;
+                }
+            }
+        }
+    }
+    else
+    {
+        b = shape - 1./3.;
+        c = 1./sqrt(9*b);
+        for (;;)
+        {
+            do
+            {
+                X = rk_gauss(state); 
+                V = 1.0 + c*X;
+            } while (V <= 0.0);
+
+            V = V*V*V;
+            U = rk_double(state);
+            if (U < 1.0 - 0.0331*(X*X)*(X*X)) return (b*V);
+            if (log(U) < 0.5*X*X + b*(1. - V + log(V))) return (b*V);
+        }
+    }
+}
+
+double rk_gamma(rk_state *state, double shape, double scale)
+{
+    return scale * rk_standard_gamma(state, shape);
+}
+
+double rk_beta(rk_state *state, double a, double b)
+{
+    double Ga, Gb;
+
+    if ((a <= 1.0) && (b <= 1.0))
+    {
+        double U, V, X, Y;
+        /* Use Jonk's algorithm */
+
+        while (1)
+        {
+            U = rk_double(state);
+            V = rk_double(state);
+            X = pow(U, 1.0/a);
+            Y = pow(V, 1.0/b);
+
+            if ((X + Y) <= 1.0)
+            {
+                return X;
+            }
+        }
+    }
+    else
+    {
+        Ga = rk_standard_gamma(state, a);
+        Gb = rk_standard_gamma(state, b);
+        return Ga/(Ga + Gb);
+    }
+}
+
+double rk_chisquare(rk_state *state, double df)
+{
+    return 2.0*rk_standard_gamma(state, df/2.0);
+}
+
+double rk_noncentral_chisquare(rk_state *state, double df, double nonc)
+{
+    double Chi2, N;
+
+    Chi2 = rk_chisquare(state, df-1);
+    N = rk_gauss(state) + sqrt(nonc);
+    return Chi2 + N*N;
+}
+
+double rk_f(rk_state *state, double dfnum, double dfden)
+{
+    return rk_chisquare(state, dfnum) / rk_chisquare(state, dfden);
+}
+
+double rk_noncentral_f(rk_state *state, double dfnum, double dfden, double nonc)
+{
+    return ((rk_noncentral_chisquare(state, dfnum, nonc)*dfden) /
+            (rk_chisquare(state, dfden)*dfnum));
+}
+
+long rk_binomial_btpe(rk_state *state, long n, double p)
+{
+    double r,q,fm,p1,xm,xl,xr,c,laml,lamr,p2,p3,p4;
+    double a,u,v,s,F,rho,t,A,nrq,x1,x2,f1,f2,z,z2,w,w2,x;
+    long m,y,k,i;
+
+    if (!(state->has_binomial) || 
+         (state->nsave != n) ||
+         (state->psave != p))
+    {
+        /* initialize */
+        state->nsave = n;
+        state->psave = p;
+        state->has_binomial = 1;
+        state->r = r = min(p, 1.0-p);
+        state->q = q = 1.0 - r;
+        state->fm = fm = n*r+r;
+        state->m = m = (long)floor(state->fm);
+        state->p1 = p1 = floor(2.195*sqrt(n*r*q)-4.6*q) + 0.5;
+        state->xm = xm = m + 0.5;
+        state->xl = xl = xm - p1;
+        state->xr = xr = xm + p1;
+        state->c = c = 0.134 + 20.5/(15.3 + m);
+        a = (fm - xl)/(fm-xl*r);
+        state->laml = laml = a*(1.0 + a/2.0);
+        a = (xr - fm)/(xr*q);
+        state->lamr = lamr = a*(1.0 + a/2.0);
+        state->p2 = p2 = p1*(1.0 + 2.0*c);
+        state->p3 = p3 = p2 + c/laml;
+        state->p4 = p4 = p3 + c/lamr;
+    }
+    else
+    {
+        r = state->r;
+        q = state->q;
+        fm = state->fm;
+        m = state->m;
+        p1 = state->p1;
+        xm = state->xm;
+        xl = state->xl;
+        xr = state->xr;
+        c = state->c;
+        laml = state->laml;
+        lamr = state->lamr;
+        p2 = state->p2;
+        p3 = state->p3;
+        p4 = state->p4;
+    }
+
+  /* sigh ... */
+  Step10:
+    nrq = n*r*q;
+    u = rk_double(state)*p4;
+    v = rk_double(state);
+    if (u > p1) goto Step20;
+    y = (long)floor(xm - p1*v + u);
+    goto Step60;
+
+  Step20:
+    if (u > p2) goto Step30;
+    x = xl + (u - p1)/c;
+    v = v*c + 1.0 - fabs(m - x + 0.5)/p1;
+    if (v > 1.0) goto Step10;
+    y = (long)floor(x);
+    goto Step50;
+
+  Step30:
+    if (u > p3) goto Step40;
+    y = (long)floor(xl + log(v)/laml);
+    if (y < 0) goto Step10;
+    v = v*(u-p2)*laml;
+    goto Step50;
+
+  Step40:
+    y = (int)floor(xr - log(v)/lamr);
+    if (y > n) goto Step10;
+    v = v*(u-p3)*lamr;
+
+  Step50:
+    k = fabs(y - m);
+    if ((k > 20) && (k < ((nrq)/2.0 - 1))) goto Step52;
+
+    s = r/q;
+    a = s*(n+1);
+    F = 1.0;
+    if (m < y)
+    {
+        for (i=m; i<=y; i++)
+        {
+            F *= (a/i - s);
+        }
+    }
+    else if (m > y)
+    {
+        for (i=y; i<=m; i++)
+        {
+            F /= (a/i - s);
+        }
+    }
+    else
+    {
+        if (v > F) goto Step10;
+        goto Step60;
+    }
+
+    Step52:
+    rho = (k/(nrq))*((k*(k/3.0 + 0.625) + 0.16666666666666666)/nrq + 0.5);
+    t = -k*k/(2*nrq);
+    A = log(v);
+    if (A < (t - rho)) goto Step60;
+    if (A > (t + rho)) goto Step10;
+
+    x1 = y+1;
+    f1 = m+1;
+    z = n+1-m;
+    w = n-y+1;
+    x2 = x1*x1;
+    f2 = f1*f1;
+    z2 = z*z;
+    w2 = w*w;
+    if (A > (xm*log(f1/x1)
+           + (n-m+0.5)*log(z/w)
+           + (y-m)*log(w*r/(x1*q))
+           + (13680.-(462.-(132.-(99.-140./f2)/f2)/f2)/f2)/f1/166320.
+           + (13680.-(462.-(132.-(99.-140./z2)/z2)/z2)/z2)/z/166320.
+           + (13680.-(462.-(132.-(99.-140./x2)/x2)/x2)/x2)/x1/166320.
+           + (13680.-(462.-(132.-(99.-140./w2)/w2)/w2)/w2)/w/166320.))
+    {
+        goto Step10;
+    }
+
+  Step60:
+    if (p > 0.5)
+    {
+        y = n - y;
+    }
+
+    return y;
+}
+
+long rk_binomial_inversion(rk_state *state, long n, double p)
+{
+    double q, qn, np, px, U;
+    long X, bound;
+
+    if (!(state->has_binomial) || 
+         (state->nsave != n) ||
+         (state->psave != p))
+    {
+        state->nsave = n;
+        state->psave = p;
+        state->has_binomial = 1;
+        state->q = q = 1.0 - p;
+        state->r = qn = exp(n * log(q));
+        state->c = np = n*p;
+        state->m = bound = min(n, np + 10.0*sqrt(np));
+    } else
+    {
+        q = state->q;
+        qn = state->r;
+        np = state->c;
+        bound = state->m;
+    }
+    X = 0;
+    px = qn;
+    U = rk_double(state);
+    while (U > px)
+    {
+        X++;
+        if (X > bound) 
+        {
+            X = 0;
+            px = qn;
+            U = rk_double(state);
+        } else 
+        {
+            U -= px;
+            px  = ((n-X+1) * p * px)/(X*q);
+        }
+    }
+    return X;
+}
+
+long rk_binomial(rk_state *state, long n, double p)
+{
+    double q;
+
+    if (p <= 0.5)
+    {
+        if (p*n <= 30.0)
+        {
+            return rk_binomial_inversion(state, n, p);
+        }
+        else
+        {
+            return rk_binomial_btpe(state, n, p);
+        }
+    }
+    else
+    {
+        q = 1.0-p;
+        if (q*n <= 30.0)
+        {
+            return n - rk_binomial_inversion(state, n, q);
+        }
+        else
+        {
+            return n - rk_binomial_btpe(state, n, q);
+        }
+    }
+
+}
+
+long rk_negative_binomial(rk_state *state, long n, double p)
+{
+    double Y;
+
+    Y = rk_gamma(state, n, (1-p)/p);
+    return rk_poisson(state, Y);
+}
+
+long rk_poisson_mult(rk_state *state, double lam)
+{
+    long X;
+    double prod, U, enlam;
+
+    enlam = exp(-lam);
+    X = 0;
+    prod = 1.0;
+    while (1)
+    {
+        U = rk_double(state);
+        prod *= U;
+        if (prod > enlam)
+        {
+            X += 1;
+        }
+        else
+        {
+            return X;
+        }
+    }
+}
+
+#define LS2PI 0.91893853320467267
+#define TWELFTH 0.083333333333333333333333
+long rk_poisson_ptrs(rk_state *state, double lam)
+{
+    long k;
+    double U, V, slam, loglam, a, b, invalpha, vr, us;
+
+    slam = sqrt(lam);
+    loglam = log(lam);
+    b = 0.931 + 2.53*slam;
+    a = -0.059 + 0.02483*b;
+    invalpha = 1.1239 + 1.1328/(b-3.4);
+    vr = 0.9277 - 3.6224/(b-2);
+
+    while (1)
+    {
+        U = rk_double(state) - 0.5;
+        V = rk_double(state);
+        us = 0.5 - fabs(U);
+        k = (long)floor((2*a/us + b)*U + lam + 0.43);
+        if ((us >= 0.07) && (V <= vr))
+        {
+            return k;
+        }
+        if ((k < 0) ||
+            ((us < 0.013) && (V > us)))
+        {
+            continue;
+        }
+        if ((log(V) + log(invalpha) - log(a/(us*us)+b)) <=
+            (-lam + k*loglam - loggam(k+1)))
+        {
+            return k;
+        }
+
+        
+    }
+
+}
+
+long rk_poisson(rk_state *state, double lam)
+{
+    if (lam >= 10)
+    {
+        return rk_poisson_ptrs(state, lam);
+    }
+    else
+    {
+        return rk_poisson_mult(state, lam);
+    }
+}
+
+double rk_standard_cauchy(rk_state *state)
+{
+    return rk_gauss(state) / rk_gauss(state);
+}
+
+double rk_standard_t(rk_state *state, double df)
+{
+    double N, G, X;
+
+    N = rk_gauss(state);
+    G = rk_standard_gamma(state, df/2);
+    X = sqrt(df/2)*N/sqrt(G);
+    return X;
+}
+
+double rk_vonmises(rk_state *state, double mu, double kappa)
+{
+    double r, rho, s;
+    double U, V, W, Y, Z;
+    double result, mod;
+
+    if (kappa < 1e-8)
+    {
+        return M_PI * (2*rk_double(state)-1);
+    }
+    else
+    {
+        r = 1 + sqrt(1 + 4*kappa*kappa);
+        rho = (r - sqrt(2*r))/(2*kappa);
+        s = (1 + rho*rho)/(2*rho);
+
+        while (1)
+        {
+            U = 2*rk_double(state) - 1;
+            V = 2*rk_double(state) - 1;
+            Z = cos(M_PI*U);
+            W = (1 + s*Z)/(s + Z);
+            Y = kappa * (s - W);
+            if ((Y*(2-Y) - V >= 0) || (log(Y/V)+1 - Y >= 0))
+            {
+                break;
+            }
+        }
+
+        if (U < 0)
+        {
+            result = acos(W);
+        }
+        else
+        {
+            result = -acos(W);
+        }
+        result += mu + M_PI;
+        mod = fmod(result, 2*M_PI);
+        if (mod && (mod < 0))
+        {
+            mod += 2*M_PI;
+        }
+        return mod - M_PI;
+    }
+}
+
+double rk_pareto(rk_state *state, double a)
+{
+    return exp(rk_standard_exponential(state)/a) - 1;
+}
+
+double rk_weibull(rk_state *state, double a)
+{
+    return pow(rk_standard_exponential(state), 1./a);
+}
+
+double rk_power(rk_state *state, double a)
+{
+    return pow(1 - exp(-rk_standard_exponential(state)), 1./a);
+}
+
+double rk_laplace(rk_state *state, double loc, double scale)
+{
+    double U;
+    
+    U = rk_double(state);
+    if (U < 0.5)
+    {
+        U = loc + scale * log(U + U);
+    } else 
+    {
+        U = loc - scale * log(2.0 - U - U);
+    }
+    return U;
+}
+
+double rk_gumbel(rk_state *state, double loc, double scale)
+{
+    double U;
+    
+    U = 1.0 - rk_double(state);
+    return loc - scale * log(-log(U));
+}
+
+double rk_logistic(rk_state *state, double loc, double scale)
+{
+    double U;
+    
+    U = rk_double(state);
+    return loc + scale * log(U/(1.0 - U));
+}
+
+double rk_lognormal(rk_state *state, double mean, double sigma)
+{
+    return exp(rk_normal(state, mean, sigma));
+}
+
+double rk_rayleigh(rk_state *state, double mode)
+{
+    return mode*sqrt(-2.0 * log(1.0 - rk_double(state)));
+}
+
+double rk_wald(rk_state *state, double mean, double scale)
+{
+    double U, X, Y;
+    double mu_2l;
+    
+    mu_2l = mean / (2*scale);
+    Y = rk_gauss(state);
+    Y = mean*Y*Y;
+    X = mean + mu_2l*(Y - sqrt(4*scale*Y + Y*Y));
+    U = rk_double(state);
+    if (U <= mean/(mean+X))
+    {
+        return X;
+    } else
+    {
+        return mean*mean/X;
+    }
+}
+
+long rk_zipf(rk_state *state, double a)
+{
+    double T, U, V;
+    long X;
+    double b;
+    
+    b = pow(2.0, a-1.0);
+    do
+    {
+        U = rk_double(state);
+        V = rk_double(state);
+        X = (long)floor(pow(U, -1.0/(a-1.0)));
+        T = pow(1.0 + 1.0/X, a-1.0);  
+    } while ((V *X*(T-1.0)/(b-1.0)) > (T/b));
+    return X;
+}
+
+long rk_geometric_search(rk_state *state, double p)
+{
+    double U;
+    long X;
+    double sum, prod, q;
+    
+    X = 1;
+    sum = prod = p;
+    q = 1.0 - p;
+    U = rk_double(state);
+    while (U > sum)
+    {
+        prod *= q;
+        sum += prod;
+        X++;
+    }
+    return X;
+}
+
+long rk_geometric_inversion(rk_state *state, double p)
+{
+    return (long)ceil(log(1.0-rk_double(state))/log(1.0-p));
+}
+
+long rk_geometric(rk_state *state, double p)
+{
+    if (p >= 0.333333333333333333333333)
+    {
+        return rk_geometric_search(state, p);
+    } else
+    {
+        return rk_geometric_inversion(state, p);
+    }
+}
+
+long rk_hypergeometric_hyp(rk_state *state, long good, long bad, long sample)
+{
+    long d1, K, Z;
+    double d2, U, Y;
+    
+    d1 = bad + good - sample;
+    d2 = (double)min(bad, good);
+    
+    Y = d2;
+    K = sample;
+    while (Y > 0.0)
+    {
+        U = rk_double(state);
+        Y -= (long)floor(U + Y/(d1 + K));
+        K--;
+        if (K == 0) break;
+    }
+    Z = (long)(d2 - Y);
+    if (bad > good) Z = sample - Z;
+    return Z;
+}
+
+/* D1 = 2*sqrt(2/e) */
+/* D2 = 3 - 2*sqrt(3/e) */
+#define D1 1.7155277699214135
+#define D2 0.8989161620588988
+long rk_hypergeometric_hrua(rk_state *state, long good, long bad, long sample)
+{
+    long mingoodbad, maxgoodbad, popsize, m, d9;
+    double d4, d5, d6, d7, d8, d10, d11;
+    long Z;
+    double T, W, X, Y;
+    
+    mingoodbad = min(good, bad);
+    popsize = good + bad;
+    maxgoodbad = max(good, bad);
+    m = min(sample, popsize - sample);
+    d4 = ((double)mingoodbad) / popsize;
+    d5 = 1.0 - d4;
+    d6 = m*d4 + 0.5;
+    d7 = sqrt((popsize - m) * sample * d4 *d5 / (popsize-1) + 0.5);
+    d8 = D1*d7 + D2;
+    d9 = (long)floor((double)((m+1)*(mingoodbad+1))/(popsize+2));
+    d10 = (loggam(d9+1) + loggam(mingoodbad-d9+1) + loggam(m-d9+1) + 
+           loggam(maxgoodbad-m+d9+1));
+    d11 = min(min(m, mingoodbad)+1.0, floor(d6+16*d7));
+    /* 16 for 16-decimal-digit precision in D1 and D2 */
+    
+    while (1)
+    {
+        X = rk_double(state);
+        Y = rk_double(state);
+        W = d6 + d8*(Y- 0.5)/X;
+        
+        /* fast rejection: */
+        if ((W < 0.0) || (W >= d11)) continue;
+        
+        Z = (long)floor(W);
+        T = d10 - (loggam(Z+1) + loggam(mingoodbad-Z+1) + loggam(m-Z+1) +
+                   loggam(maxgoodbad-m+Z+1));
+        
+        /* fast acceptance: */
+        if ((X*(4.0-X)-3.0) <= T) break;
+        
+        /* fast rejection: */
+        if (X*(X-T) >= 1) continue;
+
+        if (2.0*log(X) <= T) break;  /* acceptance */
+    }
+    
+    /* this is a correction to HRUA* by Ivan Frohne in rv.py */
+    if (bad > good) Z = m - Z;
+    
+    /* another fix from rv.py to allow sample to exceed popsize/2 */
+    if (m < sample) Z = bad - Z;
+    
+    return Z;
+}
+#undef D1
+#undef D2
+
+long rk_hypergeometric(rk_state *state, long good, long bad, long sample)
+{
+    if (sample > 10)
+    {
+        return rk_hypergeometric_hrua(state, good, bad, sample);
+    } else
+    {
+        return rk_hypergeometric_hyp(state, good, bad, sample);
+    }
+}
+
+double rk_triangular(rk_state *state, double left, double mode, double right)
+{
+    double base, leftbase, ratio, leftprod, rightprod;
+    double U;
+    
+    base = right - left;
+    leftbase = mode - left;
+    ratio = leftbase / base;
+    leftprod = leftbase*base;
+    rightprod = (right - mode)*base;
+    
+    U = rk_double(state);
+    if (U <= ratio)
+    {
+        return left + sqrt(U*leftprod);
+    } else 
+    {
+      return right - sqrt((1.0 - U) * rightprod);  
+    }
+}
+
+long rk_logseries(rk_state *state, double p)
+{
+    double q, r, U, V;
+    
+    r = log(1.0 - p);
+    
+    V = rk_double(state);
+    if (V >= p) return 1;
+    U = rk_double(state);
+    q = 1.0 - exp(r*U);
+    if (V <= q*q) return (long)floor(1 + log(V)/log(q));
+    if (V <= q) return 1;
+    return 2;
+}
diff --git a/numpy/random/mtrand/distributions.h b/numpy/random/mtrand/distributions.h
new file mode 100644
index 000000000..0d6de639d
--- /dev/null
+++ b/numpy/random/mtrand/distributions.h
@@ -0,0 +1,185 @@
+/* Copyright 2005 Robert Kern (robert.kern@gmail.com)
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the
+ * "Software"), to deal in the Software without restriction, including
+ * without limitation the rights to use, copy, modify, merge, publish,
+ * distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to
+ * the following conditions:
+ * 
+ * The above copyright notice and this permission notice shall be included
+ * in all copies or substantial portions of the Software.
+ * 
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+#ifndef _RK_DISTR_
+#define _RK_DISTR_
+
+#include "randomkit.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/* References:
+ * 
+ * Devroye, Luc. _Non-Uniform Random Variate Generation_.
+ *  Springer-Verlag, New York, 1986.
+ *  http://cgm.cs.mcgill.ca/~luc/rnbookindex.html
+ * 
+ * Kachitvichyanukul, V. and Schmeiser, B. W. Binomial Random Variate
+ *  Generation. Communications of the ACM, 31, 2 (February, 1988) 216.
+ *
+ * Hoermann, W. The Transformed Rejection Method for Generating Poisson Random
+ *  Variables. Insurance: Mathematics and Economics, (to appear)
+ *  http://citeseer.csail.mit.edu/151115.html
+ *
+ * Marsaglia, G. and Tsang, W. W. A Simple Method for Generating Gamma 
+ * Variables. ACM Transactions on Mathematical Software, Vol. 26, No. 3,
+ * September 2000, Pages 363–372.
+ */
+ 
+/* Normal distribution with mean=loc and standard deviation=scale. */
+extern double rk_normal(rk_state *state, double loc, double scale);
+
+/* Standard exponential distribution (mean=1) computed by inversion of the 
+ * CDF. */
+extern double rk_standard_exponential(rk_state *state);
+
+/* Exponential distribution with mean=scale. */
+extern double rk_exponential(rk_state *state, double scale);
+
+/* Uniform distribution on interval [loc, loc+scale). */
+extern double rk_uniform(rk_state *state, double loc, double scale);
+
+/* Standard gamma distribution with shape parameter. 
+ * When shape < 1, the algorithm given by (Devroye p. 304) is used.
+ * When shape == 1, a Exponential variate is generated.
+ * When shape > 1, the small and fast method of (Marsaglia and Tsang 2000) 
+ * is used.
+ */
+extern double rk_standard_gamma(rk_state *state, double shape);
+
+/* Gamma distribution with shape and scale. */
+extern double rk_gamma(rk_state *state, double shape, double scale);
+
+/* Beta distribution computed by combining two gamma variates (Devroye p. 432).
+ */
+extern double rk_beta(rk_state *state, double a, double b);
+
+/* Chi^2 distribution computed by transforming a gamma variate (it being a
+ * special case Gamma(df/2, 2)). */
+extern double rk_chisquare(rk_state *state, double df);
+
+/* Noncentral Chi^2 distribution computed by modifying a Chi^2 variate. */
+extern double rk_noncentral_chisquare(rk_state *state, double df, double nonc);
+
+/* F distribution computed by taking the ratio of two Chi^2 variates. */
+extern double rk_f(rk_state *state, double dfnum, double dfden);
+
+/* Noncentral F distribution computed by taking the ratio of a noncentral Chi^2
+ * and a Chi^2 variate. */
+extern double rk_noncentral_f(rk_state *state, double dfnum, double dfden, double nonc);
+
+/* Binomial distribution with n Bernoulli trials with success probability p.
+ * When n*p <= 30, the "Second waiting time method" given by (Devroye p. 525) is
+ * used. Otherwise, the BTPE algorithm of (Kachitvichyanukul and Schmeiser 1988)
+ * is used. */
+extern long rk_binomial(rk_state *state, long n, double p);
+
+/* Binomial distribution using BTPE. */
+extern long rk_binomial_btpe(rk_state *state, long n, double p);
+
+/* Binomial distribution using inversion and chop-down */
+extern long rk_binomial_inversion(rk_state *state, long n, double p);
+
+/* Negative binomial distribution computed by generating a Gamma(n, (1-p)/p)
+ * variate Y and returning a Poisson(Y) variate (Devroye p. 543). */
+extern long rk_negative_binomial(rk_state *state, long n, double p);
+
+/* Poisson distribution with mean=lam.
+ * When lam < 10, a basic algorithm using repeated multiplications of uniform
+ * variates is used (Devroye p. 504).
+ * When lam >= 10, algorithm PTRS from (Hoermann 1992) is used.
+ */
+extern long rk_poisson(rk_state *state, double lam);
+
+/* Poisson distribution computed by repeated multiplication of uniform variates.
+ */
+extern long rk_poisson_mult(rk_state *state, double lam);
+
+/* Poisson distribution computer by the PTRS algorithm. */
+extern long rk_poisson_ptrs(rk_state *state, double lam);
+
+/* Standard Cauchy distribution computed by dividing standard gaussians 
+ * (Devroye p. 451). */
+extern double rk_standard_cauchy(rk_state *state);
+
+/* Standard t-distribution with df degrees of freedom (Devroye p. 445 as
+ * corrected in the Errata). */
+extern double rk_standard_t(rk_state *state, double df);
+
+/* von Mises circular distribution with center mu and shape kappa on [-pi,pi]
+ * (Devroye p. 476 as corrected in the Errata). */
+extern double rk_vonmises(rk_state *state, double mu, double kappa);
+
+/* Pareto distribution via inversion (Devroye p. 262) */
+extern double rk_pareto(rk_state *state, double a);
+
+/* Weibull distribution via inversion (Devroye p. 262) */
+extern double rk_weibull(rk_state *state, double a);
+
+/* Power distribution via inversion (Devroye p. 262) */
+extern double rk_power(rk_state *state, double a);
+
+/* Laplace distribution */
+extern double rk_laplace(rk_state *state, double loc, double scale);
+
+/* Gumbel distribution */
+extern double rk_gumbel(rk_state *state, double loc, double scale);
+
+/* Logistic distribution */
+extern double rk_logistic(rk_state *state, double loc, double scale);
+
+/* Log-normal distribution */
+extern double rk_lognormal(rk_state *state, double mean, double sigma);
+
+/* Rayleigh distribution */
+extern double rk_rayleigh(rk_state *state, double mode);
+
+/* Wald distribution */
+extern double rk_wald(rk_state *state, double mean, double scale);
+
+/* Zipf distribution */
+extern long rk_zipf(rk_state *state, double a);
+
+/* Geometric distribution */
+extern long rk_geometric(rk_state *state, double p);
+extern long rk_geometric_search(rk_state *state, double p);
+extern long rk_geometric_inversion(rk_state *state, double p);
+
+/* Hypergeometric distribution */
+extern long rk_hypergeometric(rk_state *state, long good, long bad, long sample);
+extern long rk_hypergeometric_hyp(rk_state *state, long good, long bad, long sample);
+extern long rk_hypergeometric_hrua(rk_state *state, long good, long bad, long sample);
+
+/* Triangular distribution */
+extern double rk_triangular(rk_state *state, double left, double mode, double right);
+
+/* Logarithmic series distribution */
+extern long rk_logseries(rk_state *state, double p);
+
+#ifdef __cplusplus
+}
+#endif
+
+
+#endif /* _RK_DISTR_ */
diff --git a/numpy/random/mtrand/generate_mtrand_c.py b/numpy/random/mtrand/generate_mtrand_c.py
new file mode 100644
index 000000000..3f440985f
--- /dev/null
+++ b/numpy/random/mtrand/generate_mtrand_c.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+import sys
+import re
+import os
+
+unused_internal_funcs = ['__Pyx_PrintItem',
+                         '__Pyx_PrintNewline',
+                         '__Pyx_ReRaise',
+                         '__Pyx_GetExcValue',
+                         '__Pyx_ArgTypeTest',
+                         '__Pyx_TypeTest',
+                         '__Pyx_SetVtable',
+                         '__Pyx_GetVtable',
+                         '__Pyx_CreateClass']
+
+if __name__ == '__main__':
+    os.system('pyrexc mtrand.pyx')
+    mtrand_c = open('mtrand.c', 'r')
+    processed = open('mtrand_pp.c', 'w')
+    unused_funcs_str = '(' + '|'.join(unused_internal_funcs) + ')'
+    uifpat = re.compile(r'static \w+ \*?'+unused_funcs_str+r'.*/\*proto\*/')
+    for linenum, line in enumerate(mtrand_c):
+        m = re.match(r'^(\s+arrayObject\w*\s*=\s*[(])[(]PyObject\s*[*][)]',
+                     line)
+        if m:
+            line = '%s(PyArrayObject *)%s' % (m.group(1), line[m.end():])
+        m = uifpat.match(line)
+        if m:
+            line = ''
+        m = re.search(unused_funcs_str, line)
+        if m:
+            print >>sys.stderr, \
+                "%s was declared unused, but is used at line %d" % (m.group(),
+                                                                    linenum+1)
+        processed.write(line)
+    mtrand_c.close()
+    processed.close()
+    os.rename('mtrand_pp.c', 'mtrand.c')
diff --git a/numpy/random/mtrand/initarray.c b/numpy/random/mtrand/initarray.c
new file mode 100644
index 000000000..9bf0ef286
--- /dev/null
+++ b/numpy/random/mtrand/initarray.c
@@ -0,0 +1,134 @@
+/* These function have been adapted from Python 2.4.1's _randommodule.c
+
+  The following changes have been made to it in 2005 by Robert Kern:
+
+    * init_by_array has been declared extern, has a void return, and uses the
+      rk_state structure to hold its data.
+
+   The original file has the following verbatim comments: 
+
+   ------------------------------------------------------------------
+   The code in this module was based on a download from:
+	  http://www.math.keio.ac.jp/~matumoto/MT2002/emt19937ar.html
+
+   It was modified in 2002 by Raymond Hettinger as follows:
+
+	* the principal computational lines untouched except for tabbing.
+
+	* renamed genrand_res53() to random_random() and wrapped
+	  in python calling/return code.
+
+	* genrand_int32() and the helper functions, init_genrand()
+	  and init_by_array(), were declared static, wrapped in
+	  Python calling/return code.  also, their global data
+	  references were replaced with structure references.
+
+	* unused functions from the original were deleted.
+	  new, original C python code was added to implement the
+	  Random() interface.
+
+   The following are the verbatim comments from the original code:
+
+   A C-program for MT19937, with initialization improved 2002/1/26.
+   Coded by Takuji Nishimura and Makoto Matsumoto.
+
+   Before using, initialize the state by using init_genrand(seed)
+   or init_by_array(init_key, key_length).
+
+   Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
+   All rights reserved.
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+     1. Redistributions of source code must retain the above copyright
+	notice, this list of conditions and the following disclaimer.
+
+     2. Redistributions in binary form must reproduce the above copyright
+	notice, this list of conditions and the following disclaimer in the
+	documentation and/or other materials provided with the distribution.
+
+     3. The names of its contributors may not be used to endorse or promote
+	products derived from this software without specific prior written
+	permission.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+   Any feedback is very welcome.
+   http://www.math.keio.ac.jp/matumoto/emt.html
+   email: matumoto@math.keio.ac.jp
+*/
+
+#include "initarray.h"
+
+static void
+init_genrand(rk_state *self, unsigned long s);
+
+/* initializes mt[RK_STATE_LEN] with a seed */
+static void
+init_genrand(rk_state *self, unsigned long s)
+{
+	int mti;
+	unsigned long *mt;
+
+	mt = self->key;
+	mt[0]= s & 0xffffffffUL;
+	for (mti=1; mti<RK_STATE_LEN; mti++) {
+		mt[mti] =
+		(1812433253UL * (mt[mti-1] ^ (mt[mti-1] >> 30)) + mti);
+		/* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */
+		/* In the previous versions, MSBs of the seed affect   */
+		/* only MSBs of the array mt[]. 		       */
+		/* 2002/01/09 modified by Makoto Matsumoto	       */
+		mt[mti] &= 0xffffffffUL;
+		/* for >32 bit machines */
+	}
+	self->pos = mti;
+	return;
+}
+
+
+/* initialize by an array with array-length */
+/* init_key is the array for initializing keys */
+/* key_length is its length */
+extern void
+init_by_array(rk_state *self, unsigned long init_key[], unsigned long key_length)
+{
+	unsigned int i, j, k;	/* was signed in the original code. RDH 12/16/2002 */
+	unsigned long *mt;
+
+	mt = self->key;
+	init_genrand(self, 19650218UL);
+	i=1; j=0;
+	k = (RK_STATE_LEN>key_length ? RK_STATE_LEN : key_length);
+	for (; k; k--) {
+		mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1664525UL))
+			 + init_key[j] + j; /* non linear */
+		mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */
+		i++; j++;
+		if (i>=RK_STATE_LEN) { mt[0] = mt[RK_STATE_LEN-1]; i=1; }
+		if (j>=key_length) j=0;
+	}
+	for (k=RK_STATE_LEN-1; k; k--) {
+		mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1566083941UL))
+			 - i; /* non linear */
+		mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */
+		i++;
+		if (i>=RK_STATE_LEN) { mt[0] = mt[RK_STATE_LEN-1]; i=1; }
+	}
+
+    mt[0] = 0x80000000UL; /* MSB is 1; assuring non-zero initial array */
+}
+
diff --git a/numpy/random/mtrand/initarray.h b/numpy/random/mtrand/initarray.h
new file mode 100644
index 000000000..a4ac210f4
--- /dev/null
+++ b/numpy/random/mtrand/initarray.h
@@ -0,0 +1,6 @@
+#include "randomkit.h"
+
+extern void
+init_by_array(rk_state *self, unsigned long init_key[], 
+              unsigned long key_length);
+
diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c
new file mode 100644
index 000000000..995e4871d
--- /dev/null
+++ b/numpy/random/mtrand/mtrand.c
@@ -0,0 +1,5990 @@
+/* Generated by Pyrex 0.9.3.1 on Sun Dec 25 23:14:18 2005 */
+
+#include "Python.h"
+#include "structmember.h"
+#ifndef PY_LONG_LONG
+  #define PY_LONG_LONG LONG_LONG
+#endif
+#include "string.h"
+#include "math.h"
+#include "scipy/arrayobject.h"
+#include "randomkit.h"
+#include "distributions.h"
+#include "initarray.h"
+
+
+typedef struct {PyObject **p; char *s;} __Pyx_InternTabEntry; /*proto*/
+typedef struct {PyObject **p; char *s; long n;} __Pyx_StringTabEntry; /*proto*/
+static PyObject *__Pyx_UnpackItem(PyObject *, int); /*proto*/
+static int __Pyx_EndUnpack(PyObject *, int); /*proto*/
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/
+static int __Pyx_GetStarArgs(PyObject **args, PyObject **kwds, char *kwd_list[], int nargs, PyObject **args2, PyObject **kwds2); /*proto*/
+static void __Pyx_WriteUnraisable(char *name); /*proto*/
+static void __Pyx_AddTraceback(char *funcname); /*proto*/
+static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, long size);  /*proto*/
+static int __Pyx_InternStrings(__Pyx_InternTabEntry *t); /*proto*/
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
+static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
+
+static PyObject *__pyx_m;
+static PyObject *__pyx_b;
+static int __pyx_lineno;
+static char *__pyx_filename;
+staticforward char **__pyx_f;
+
+/* Declarations from mtrand */
+
+staticforward PyTypeObject __pyx_type_6mtrand_RandomState;
+
+struct __pyx_obj_6mtrand_RandomState {
+  PyObject_HEAD
+  rk_state (*internal_state);
+};
+
+static PyTypeObject *__pyx_ptype_6mtrand_dtypedescr = 0;
+static PyTypeObject *__pyx_ptype_6mtrand_ndarray = 0;
+static PyTypeObject *__pyx_ptype_6mtrand_RandomState = 0;
+static PyObject *__pyx_k2;
+static PyObject *__pyx_k3;
+static PyObject *__pyx_k4;
+static PyObject *__pyx_k5;
+static PyObject *__pyx_k6;
+static PyObject *__pyx_k7;
+static double __pyx_k8;
+static double __pyx_k9;
+static PyObject *__pyx_k10;
+static PyObject *__pyx_k11;
+static PyObject *__pyx_k12;
+static PyObject *__pyx_k13;
+static double __pyx_k14;
+static double __pyx_k15;
+static PyObject *__pyx_k16;
+static PyObject *__pyx_k17;
+static double __pyx_k18;
+static PyObject *__pyx_k19;
+static PyObject *__pyx_k20;
+static PyObject *__pyx_k21;
+static double __pyx_k22;
+static PyObject *__pyx_k23;
+static PyObject *__pyx_k24;
+static PyObject *__pyx_k25;
+static PyObject *__pyx_k26;
+static PyObject *__pyx_k27;
+static PyObject *__pyx_k28;
+static PyObject *__pyx_k29;
+static PyObject *__pyx_k30;
+static PyObject *__pyx_k31;
+static PyObject *__pyx_k32;
+static PyObject *__pyx_k33;
+static double __pyx_k34;
+static double __pyx_k35;
+static PyObject *__pyx_k36;
+static double __pyx_k37;
+static double __pyx_k38;
+static PyObject *__pyx_k39;
+static double __pyx_k40;
+static double __pyx_k41;
+static PyObject *__pyx_k42;
+static double __pyx_k43;
+static double __pyx_k44;
+static PyObject *__pyx_k45;
+static double __pyx_k46;
+static PyObject *__pyx_k47;
+static PyObject *__pyx_k48;
+static PyObject *__pyx_k49;
+static PyObject *__pyx_k50;
+static PyObject *__pyx_k51;
+static double __pyx_k52;
+static PyObject *__pyx_k53;
+static PyObject *__pyx_k54;
+static PyObject *__pyx_k55;
+static PyObject *__pyx_k56;
+static PyObject *__pyx_k57;
+static PyObject *__pyx_k58;
+static PyObject *__pyx_k59;
+static PyObject *(__pyx_f_6mtrand_cont0_array(rk_state (*),double ((*)(rk_state (*))),PyObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_cont1_array(rk_state (*),double ((*)(rk_state (*),double )),PyObject *,double )); /*proto*/
+static PyObject *(__pyx_f_6mtrand_cont2_array(rk_state (*),double ((*)(rk_state (*),double ,double )),PyObject *,double ,double )); /*proto*/
+static PyObject *(__pyx_f_6mtrand_cont3_array(rk_state (*),double ((*)(rk_state (*),double ,double ,double )),PyObject *,double ,double ,double )); /*proto*/
+static PyObject *(__pyx_f_6mtrand_disc0_array(rk_state (*),long ((*)(rk_state (*))),PyObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_discnp_array(rk_state (*),long ((*)(rk_state (*),long ,double )),PyObject *,long ,double )); /*proto*/
+static PyObject *(__pyx_f_6mtrand_discnmN_array(rk_state (*),long ((*)(rk_state (*),long ,long ,long )),PyObject *,long ,long ,long )); /*proto*/
+static PyObject *(__pyx_f_6mtrand_discd_array(rk_state (*),long ((*)(rk_state (*),double )),PyObject *,double )); /*proto*/
+static double (__pyx_f_6mtrand_kahan_sum(double (*),long )); /*proto*/
+
+/* Implementation of mtrand */
+
+
+static PyObject *__pyx_n__sp;
+static PyObject *__pyx_n__rand;
+static PyObject *__pyx_n_seed;
+static PyObject *__pyx_n_get_state;
+static PyObject *__pyx_n_set_state;
+static PyObject *__pyx_n_random_sample;
+static PyObject *__pyx_n_randint;
+static PyObject *__pyx_n_bytes;
+static PyObject *__pyx_n_uniform;
+static PyObject *__pyx_n_rand;
+static PyObject *__pyx_n_randn;
+static PyObject *__pyx_n_random_integers;
+static PyObject *__pyx_n_standard_normal;
+static PyObject *__pyx_n_normal;
+static PyObject *__pyx_n_beta;
+static PyObject *__pyx_n_exponential;
+static PyObject *__pyx_n_standard_exponential;
+static PyObject *__pyx_n_standard_gamma;
+static PyObject *__pyx_n_gamma;
+static PyObject *__pyx_n_f;
+static PyObject *__pyx_n_noncentral_f;
+static PyObject *__pyx_n_chisquare;
+static PyObject *__pyx_n_noncentral_chisquare;
+static PyObject *__pyx_n_standard_cauchy;
+static PyObject *__pyx_n_standard_t;
+static PyObject *__pyx_n_vonmises;
+static PyObject *__pyx_n_pareto;
+static PyObject *__pyx_n_weibull;
+static PyObject *__pyx_n_power;
+static PyObject *__pyx_n_laplace;
+static PyObject *__pyx_n_gumbel;
+static PyObject *__pyx_n_logistic;
+static PyObject *__pyx_n_lognormal;
+static PyObject *__pyx_n_rayleigh;
+static PyObject *__pyx_n_wald;
+static PyObject *__pyx_n_triangular;
+static PyObject *__pyx_n_binomial;
+static PyObject *__pyx_n_negative_binomial;
+static PyObject *__pyx_n_poisson;
+static PyObject *__pyx_n_zipf;
+static PyObject *__pyx_n_geometric;
+static PyObject *__pyx_n_hypergeometric;
+static PyObject *__pyx_n_logseries;
+static PyObject *__pyx_n_multivariate_normal;
+static PyObject *__pyx_n_multinomial;
+static PyObject *__pyx_n_shuffle;
+static PyObject *__pyx_n_permutation;
+static PyObject *__pyx_n_scipy;
+
+static PyObject *__pyx_n_empty;
+static PyObject *__pyx_n_Float64;
+
+static PyObject *__pyx_f_6mtrand_cont0_array(rk_state (*__pyx_v_state),double ((*__pyx_v_func)(rk_state (*))),PyObject *__pyx_v_size) {
+  double (*__pyx_v_array_data);
+  PyArrayObject *arrayObject;
+  long __pyx_v_length;
+  long __pyx_v_i;
+  PyObject *__pyx_r;
+  int __pyx_1;
+  PyObject *__pyx_2 = 0;
+  PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
+  Py_INCREF(__pyx_v_size);
+  arrayObject = (void *)Py_None; Py_INCREF((PyObject *) arrayObject);
+
+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":128 */
+  __pyx_1 = __pyx_v_size == Py_None;
+  if (__pyx_1) {
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":129 */
+    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 129; goto __pyx_L1;}
+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
+    goto __pyx_L0;
+    goto __pyx_L2;
+  }
+  /*else*/ {
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":131 */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 131; goto __pyx_L1;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 131; goto __pyx_L1;}
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 131; goto __pyx_L1;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_Float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 131; goto __pyx_L1;}
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 131; goto __pyx_L1;}
+    Py_INCREF(__pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
+    __pyx_4 = 0;
+    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 131; goto __pyx_L1;}
+    Py_DECREF(__pyx_3); __pyx_3 = 0;
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_INCREF(((PyObject *)__pyx_4));
+    Py_DECREF(((PyObject *)arrayObject));
+    arrayObject = ((PyArrayObject *)__pyx_4);
+    Py_DECREF(__pyx_4); __pyx_4 = 0;
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":132 */
+    __pyx_v_length = PyArray_SIZE(arrayObject);
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":133 */
+    __pyx_v_array_data = ((double (*))arrayObject->data);
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":134 */
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+
+      /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":135 */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state);
+    }
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":136 */
+    Py_INCREF(((PyObject *)arrayObject));
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+  }
+  __pyx_L2:;
+
+  __pyx_r = Py_None; Py_INCREF(__pyx_r);
+  goto __pyx_L0;
+  __pyx_L1:;
+  Py_XDECREF(__pyx_2);
+  Py_XDECREF(__pyx_3);
+  Py_XDECREF(__pyx_4);
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+  __pyx_r = 0;
+  __pyx_L0:;
+  Py_DECREF(arrayObject);
+  Py_DECREF(__pyx_v_size);
+  return __pyx_r;
+}
+
+static PyObject *__pyx_f_6mtrand_cont1_array(rk_state (*__pyx_v_state),double ((*__pyx_v_func)(rk_state (*),double )),PyObject *__pyx_v_size,double __pyx_v_a) {
+  double (*__pyx_v_array_data);
+  PyArrayObject *arrayObject;
+  long __pyx_v_length;
+  long __pyx_v_i;
+  PyObject *__pyx_r;
+  int __pyx_1;
+  PyObject *__pyx_2 = 0;
+  PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
+  Py_INCREF(__pyx_v_size);
+  arrayObject = (void *)Py_None; Py_INCREF((PyObject *) arrayObject);
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+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":144 */
+  __pyx_1 = __pyx_v_size == Py_None;
+  if (__pyx_1) {
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":145 */
+    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state,__pyx_v_a)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 145; goto __pyx_L1;}
+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
+    goto __pyx_L0;
+    goto __pyx_L2;
+  }
+  /*else*/ {
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":147 */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 147; goto __pyx_L1;}
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+    Py_DECREF(__pyx_3); __pyx_3 = 0;
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_INCREF(((PyObject *)__pyx_4));
+    Py_DECREF(((PyObject *)arrayObject));
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+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":149 */
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+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":150 */
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+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":152 */
+    Py_INCREF(((PyObject *)arrayObject));
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+  __pyx_r = Py_None; Py_INCREF(__pyx_r);
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+  Py_XDECREF(__pyx_3);
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+}
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+static PyObject *__pyx_f_6mtrand_cont2_array(rk_state (*__pyx_v_state),double ((*__pyx_v_func)(rk_state (*),double ,double )),PyObject *__pyx_v_size,double __pyx_v_a,double __pyx_v_b) {
+  double (*__pyx_v_array_data);
+  PyArrayObject *arrayObject;
+  long __pyx_v_length;
+  long __pyx_v_i;
+  PyObject *__pyx_r;
+  int __pyx_1;
+  PyObject *__pyx_2 = 0;
+  PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
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+    goto __pyx_L2;
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+}
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+static PyObject *__pyx_f_6mtrand_cont3_array(rk_state (*__pyx_v_state),double ((*__pyx_v_func)(rk_state (*),double ,double ,double )),PyObject *__pyx_v_size,double __pyx_v_a,double __pyx_v_b,double __pyx_v_c) {
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+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a);
+    }
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":252 */
+    Py_INCREF(((PyObject *)arrayObject));
+    __pyx_r = ((PyObject *)arrayObject);
+    goto __pyx_L0;
+  }
+  __pyx_L2:;
+
+  __pyx_r = Py_None; Py_INCREF(__pyx_r);
+  goto __pyx_L0;
+  __pyx_L1:;
+  Py_XDECREF(__pyx_2);
+  Py_XDECREF(__pyx_3);
+  Py_XDECREF(__pyx_4);
+  __Pyx_AddTraceback("mtrand.discd_array");
+  __pyx_r = 0;
+  __pyx_L0:;
+  Py_DECREF(arrayObject);
+  Py_DECREF(__pyx_v_size);
+  return __pyx_r;
+}
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+static double __pyx_f_6mtrand_kahan_sum(double (*__pyx_v_darr),long __pyx_v_n) {
+  double __pyx_v_c;
+  double __pyx_v_y;
+  double __pyx_v_t;
+  double __pyx_v_sum;
+  long __pyx_v_i;
+  double __pyx_r;
+
+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":257 */
+  __pyx_v_sum = (__pyx_v_darr[0]);
+
+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":258 */
+  __pyx_v_c = 0.0;
+
+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":259 */
+  for (__pyx_v_i = 1; __pyx_v_i < __pyx_v_n; ++__pyx_v_i) {
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":260 */
+    __pyx_v_y = ((__pyx_v_darr[__pyx_v_i]) - __pyx_v_c);
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":261 */
+    __pyx_v_t = (__pyx_v_sum + __pyx_v_y);
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":262 */
+    __pyx_v_c = ((__pyx_v_t - __pyx_v_sum) - __pyx_v_y);
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":263 */
+    __pyx_v_sum = __pyx_v_t;
+  }
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+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":264 */
+  __pyx_r = __pyx_v_sum;
+  goto __pyx_L0;
+
+  __pyx_r = 0;
+  goto __pyx_L0;
+  __Pyx_WriteUnraisable("mtrand.kahan_sum");
+  __pyx_L0:;
+  return __pyx_r;
+}
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+static int __pyx_f_6mtrand_11RandomState___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+  PyObject *__pyx_v_seed = 0;
+  int __pyx_r;
+  PyObject *__pyx_1 = 0;
+  PyObject *__pyx_2 = 0;
+  PyObject *__pyx_3 = 0;
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+  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|O", __pyx_argnames, &__pyx_v_seed)) return -1;
+  Py_INCREF(__pyx_v_self);
+  Py_INCREF(__pyx_v_seed);
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+  Py_INCREF(__pyx_v_seed);
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+  Py_DECREF(__pyx_1); __pyx_1 = 0;
+  Py_DECREF(__pyx_2); __pyx_2 = 0;
+  Py_DECREF(__pyx_3); __pyx_3 = 0;
+
+  __pyx_r = 0;
+  goto __pyx_L0;
+  __pyx_L1:;
+  Py_XDECREF(__pyx_1);
+  Py_XDECREF(__pyx_2);
+  Py_XDECREF(__pyx_3);
+  __Pyx_AddTraceback("mtrand.RandomState.__init__");
+  __pyx_r = -1;
+  __pyx_L0:;
+  Py_DECREF(__pyx_v_self);
+  Py_DECREF(__pyx_v_seed);
+  return __pyx_r;
+}
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+static void __pyx_f_6mtrand_11RandomState___dealloc__(PyObject *__pyx_v_self); /*proto*/
+static void __pyx_f_6mtrand_11RandomState___dealloc__(PyObject *__pyx_v_self) {
+  int __pyx_1;
+  Py_INCREF(__pyx_v_self);
+
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+  __pyx_1 = (((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state != 0);
+  if (__pyx_1) {
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+    PyMem_Free(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
+    goto __pyx_L2;
+  }
+  __pyx_L2:;
+
+  goto __pyx_L0;
+  __Pyx_AddTraceback("mtrand.RandomState.__dealloc__");
+  __pyx_L0:;
+  Py_DECREF(__pyx_v_self);
+}
+
+static PyObject *__pyx_n_type;
+static PyObject *__pyx_n_int;
+
+static PyObject *__pyx_f_6mtrand_11RandomState_seed(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_seed[] = "Seed the generator.\n\n        seed(seed=None)\n\n        seed can be an integer, an array (or other sequence) of integers of any\n        length, or None. If seed is None, then RandomState will try to read data\n        from /dev/urandom (or the Windows analogue) if available or seed from\n        the clock otherwise.\n        ";
+static PyObject *__pyx_f_6mtrand_11RandomState_seed(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+  rk_error __pyx_v_errcode;
+  PyArrayObject *arrayObject_obj;
+  PyObject *__pyx_r;
+  int __pyx_1;
+  PyObject *__pyx_2 = 0;
+  PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
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+  Py_INCREF(__pyx_v_self);
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+  __pyx_1 = __pyx_v_seed == Py_None;
+  if (__pyx_1) {
+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":308 */
+    __pyx_v_errcode = rk_randomseed(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
+    goto __pyx_L2;
+  }
+  __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_type); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 309; goto __pyx_L1;}
+  __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 309; goto __pyx_L1;}
+  Py_INCREF(__pyx_v_seed);
+  PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_seed);
+  __pyx_4 = PyObject_CallObject(__pyx_2, __pyx_3); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 309; goto __pyx_L1;}
+  Py_DECREF(__pyx_2); __pyx_2 = 0;
+  Py_DECREF(__pyx_3); __pyx_3 = 0;
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+  __pyx_1 = __pyx_4 == __pyx_2;
+  Py_DECREF(__pyx_4); __pyx_4 = 0;
+  Py_DECREF(__pyx_2); __pyx_2 = 0;
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+
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+    __pyx_5 = PyLong_AsUnsignedLong(__pyx_v_seed); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 310; goto __pyx_L1;}
+    rk_seed(__pyx_5,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
+    goto __pyx_L2;
+  }
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+
+    /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":312 */
+    __pyx_3 = ((PyObject *)PyArray_ContiguousFromObject(__pyx_v_seed,PyArray_LONG,1,1)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 312; goto __pyx_L1;}
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+    Py_DECREF(((PyObject *)arrayObject_obj));
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+
+  __pyx_r = Py_None; Py_INCREF(__pyx_r);
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+  __pyx_L1:;
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+  Py_XDECREF(__pyx_3);
+  Py_XDECREF(__pyx_4);
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+  Py_DECREF(__pyx_v_seed);
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+}
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+static PyObject *__pyx_n_MT19937;
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+
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+static PyObject *__pyx_f_6mtrand_11RandomState_get_state(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+  PyObject *__pyx_r;
+  PyObject *__pyx_1 = 0;
+  PyObject *__pyx_2 = 0;
+  PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
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+  Py_INCREF(__pyx_v_self);
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+  Py_INCREF(((PyObject *)arrayObject_state));
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+  __pyx_r = Py_None; Py_INCREF(__pyx_r);
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+  Py_XDECREF(__pyx_2);
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+}
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+static PyObject *__pyx_n_ValueError;
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+static PyObject *__pyx_k62p;
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+  PyObject *__pyx_v_algorithm_name;
+  PyObject *__pyx_v_key;
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+  PyObject *__pyx_2 = 0;
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+  Py_INCREF(__pyx_v_self);
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+  __pyx_v_key = Py_None; Py_INCREF(__pyx_v_key);
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+  __pyx_1 = PyInt_FromLong(0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 335; goto __pyx_L1;}
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+  Py_DECREF(__pyx_1); __pyx_1 = 0;
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+static PyObject *__pyx_f_6mtrand_11RandomState_standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+  __pyx_r = Py_None; Py_INCREF(__pyx_r);
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+  double __pyx_v_df;
+  PyObject *__pyx_v_size = 0;
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+  double __pyx_v_kappa;
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+  {&__pyx_n___RandomState_ctor, "__RandomState_ctor"},
+  {&__pyx_n__rand, "_rand"},
+  {&__pyx_n__sp, "_sp"},
+  {&__pyx_n_add, "add"},
+  {&__pyx_n_append, "append"},
+  {&__pyx_n_arange, "arange"},
+  {&__pyx_n_array, "array"},
+  {&__pyx_n_beta, "beta"},
+  {&__pyx_n_binomial, "binomial"},
+  {&__pyx_n_bytes, "bytes"},
+  {&__pyx_n_chisquare, "chisquare"},
+  {&__pyx_n_empty, "empty"},
+  {&__pyx_n_exponential, "exponential"},
+  {&__pyx_n_f, "f"},
+  {&__pyx_n_gamma, "gamma"},
+  {&__pyx_n_geometric, "geometric"},
+  {&__pyx_n_get_state, "get_state"},
+  {&__pyx_n_gumbel, "gumbel"},
+  {&__pyx_n_hypergeometric, "hypergeometric"},
+  {&__pyx_n_int, "int"},
+  {&__pyx_n_isinstance, "isinstance"},
+  {&__pyx_n_laplace, "laplace"},
+  {&__pyx_n_len, "len"},
+  {&__pyx_n_list, "list"},
+  {&__pyx_n_logistic, "logistic"},
+  {&__pyx_n_lognormal, "lognormal"},
+  {&__pyx_n_logseries, "logseries"},
+  {&__pyx_n_matrixmultiply, "matrixmultiply"},
+  {&__pyx_n_multinomial, "multinomial"},
+  {&__pyx_n_multiply, "multiply"},
+  {&__pyx_n_multivariate_normal, "multivariate_normal"},
+  {&__pyx_n_negative_binomial, "negative_binomial"},
+  {&__pyx_n_noncentral_chisquare, "noncentral_chisquare"},
+  {&__pyx_n_noncentral_f, "noncentral_f"},
+  {&__pyx_n_normal, "normal"},
+  {&__pyx_n_pareto, "pareto"},
+  {&__pyx_n_permutation, "permutation"},
+  {&__pyx_n_poisson, "poisson"},
+  {&__pyx_n_power, "power"},
+  {&__pyx_n_rand, "rand"},
+  {&__pyx_n_randint, "randint"},
+  {&__pyx_n_randn, "randn"},
+  {&__pyx_n_random, "random"},
+  {&__pyx_n_random_integers, "random_integers"},
+  {&__pyx_n_random_sample, "random_sample"},
+  {&__pyx_n_rayleigh, "rayleigh"},
+  {&__pyx_n_reduce, "reduce"},
+  {&__pyx_n_scipy, "scipy"},
+  {&__pyx_n_seed, "seed"},
+  {&__pyx_n_set_state, "set_state"},
+  {&__pyx_n_shape, "shape"},
+  {&__pyx_n_shuffle, "shuffle"},
+  {&__pyx_n_size, "size"},
+  {&__pyx_n_sqrt, "sqrt"},
+  {&__pyx_n_standard_cauchy, "standard_cauchy"},
+  {&__pyx_n_standard_exponential, "standard_exponential"},
+  {&__pyx_n_standard_gamma, "standard_gamma"},
+  {&__pyx_n_standard_normal, "standard_normal"},
+  {&__pyx_n_standard_t, "standard_t"},
+  {&__pyx_n_svd, "svd"},
+  {&__pyx_n_triangular, "triangular"},
+  {&__pyx_n_tuple, "tuple"},
+  {&__pyx_n_type, "type"},
+  {&__pyx_n_uniform, "uniform"},
+  {&__pyx_n_vonmises, "vonmises"},
+  {&__pyx_n_wald, "wald"},
+  {&__pyx_n_weibull, "weibull"},
+  {&__pyx_n_zeros, "zeros"},
+  {&__pyx_n_zipf, "zipf"},
+  {0, 0}
+};
+
+static __Pyx_StringTabEntry __pyx_string_tab[] = {
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+
+static PyObject *__pyx_tp_new_6mtrand_RandomState(PyTypeObject *t, PyObject *a, PyObject *k) {
+  PyObject *o = (*t->tp_alloc)(t, 0);
+  struct __pyx_obj_6mtrand_RandomState *p = (struct __pyx_obj_6mtrand_RandomState *)o;
+  return o;
+}
+
+static void __pyx_tp_dealloc_6mtrand_RandomState(PyObject *o) {
+  {
+    PyObject *etype, *eval, *etb;
+    PyErr_Fetch(&etype, &eval, &etb);
+    ++o->ob_refcnt;
+    __pyx_f_6mtrand_11RandomState___dealloc__(o);
+    if (PyErr_Occurred()) PyErr_WriteUnraisable(o);
+    --o->ob_refcnt;
+    PyErr_Restore(etype, eval, etb);
+  }
+  (*o->ob_type->tp_free)(o);
+}
+
+static int __pyx_tp_traverse_6mtrand_RandomState(PyObject *o, visitproc v, void *a) {
+  return 0;
+}
+
+static int __pyx_tp_clear_6mtrand_RandomState(PyObject *o) {
+  return 0;
+}
+
+static struct PyMethodDef __pyx_methods_6mtrand_RandomState[] = {
+  {"seed", (PyCFunction)__pyx_f_6mtrand_11RandomState_seed, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_seed},
+  {"get_state", (PyCFunction)__pyx_f_6mtrand_11RandomState_get_state, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_get_state},
+  {"set_state", (PyCFunction)__pyx_f_6mtrand_11RandomState_set_state, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_set_state},
+  {"__getstate__", (PyCFunction)__pyx_f_6mtrand_11RandomState___getstate__, METH_VARARGS|METH_KEYWORDS, 0},
+  {"__setstate__", (PyCFunction)__pyx_f_6mtrand_11RandomState___setstate__, METH_VARARGS|METH_KEYWORDS, 0},
+  {"__reduce__", (PyCFunction)__pyx_f_6mtrand_11RandomState___reduce__, METH_VARARGS|METH_KEYWORDS, 0},
+  {"random_sample", (PyCFunction)__pyx_f_6mtrand_11RandomState_random_sample, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_random_sample},
+  {"tomaxint", (PyCFunction)__pyx_f_6mtrand_11RandomState_tomaxint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_tomaxint},
+  {"randint", (PyCFunction)__pyx_f_6mtrand_11RandomState_randint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_randint},
+  {"bytes", (PyCFunction)__pyx_f_6mtrand_11RandomState_bytes, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_bytes},
+  {"uniform", (PyCFunction)__pyx_f_6mtrand_11RandomState_uniform, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_uniform},
+  {"rand", (PyCFunction)__pyx_f_6mtrand_11RandomState_rand, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_rand},
+  {"randn", (PyCFunction)__pyx_f_6mtrand_11RandomState_randn, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_randn},
+  {"random_integers", (PyCFunction)__pyx_f_6mtrand_11RandomState_random_integers, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_random_integers},
+  {"standard_normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_normal},
+  {"normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_normal},
+  {"beta", (PyCFunction)__pyx_f_6mtrand_11RandomState_beta, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_beta},
+  {"exponential", (PyCFunction)__pyx_f_6mtrand_11RandomState_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_exponential},
+  {"standard_exponential", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_exponential},
+  {"standard_gamma", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_gamma},
+  {"gamma", (PyCFunction)__pyx_f_6mtrand_11RandomState_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_gamma},
+  {"f", (PyCFunction)__pyx_f_6mtrand_11RandomState_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_f},
+  {"noncentral_f", (PyCFunction)__pyx_f_6mtrand_11RandomState_noncentral_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_noncentral_f},
+  {"chisquare", (PyCFunction)__pyx_f_6mtrand_11RandomState_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_chisquare},
+  {"noncentral_chisquare", (PyCFunction)__pyx_f_6mtrand_11RandomState_noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_noncentral_chisquare},
+  {"standard_cauchy", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_cauchy, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_cauchy},
+  {"standard_t", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_t, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_t},
+  {"vonmises", (PyCFunction)__pyx_f_6mtrand_11RandomState_vonmises, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_vonmises},
+  {"pareto", (PyCFunction)__pyx_f_6mtrand_11RandomState_pareto, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_pareto},
+  {"weibull", (PyCFunction)__pyx_f_6mtrand_11RandomState_weibull, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_weibull},
+  {"power", (PyCFunction)__pyx_f_6mtrand_11RandomState_power, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_power},
+  {"laplace", (PyCFunction)__pyx_f_6mtrand_11RandomState_laplace, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_laplace},
+  {"gumbel", (PyCFunction)__pyx_f_6mtrand_11RandomState_gumbel, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_gumbel},
+  {"logistic", (PyCFunction)__pyx_f_6mtrand_11RandomState_logistic, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logistic},
+  {"lognormal", (PyCFunction)__pyx_f_6mtrand_11RandomState_lognormal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_lognormal},
+  {"rayleigh", (PyCFunction)__pyx_f_6mtrand_11RandomState_rayleigh, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_rayleigh},
+  {"wald", (PyCFunction)__pyx_f_6mtrand_11RandomState_wald, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_wald},
+  {"triangular", (PyCFunction)__pyx_f_6mtrand_11RandomState_triangular, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_triangular},
+  {"binomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_binomial},
+  {"negative_binomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_negative_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_negative_binomial},
+  {"poisson", (PyCFunction)__pyx_f_6mtrand_11RandomState_poisson, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_poisson},
+  {"zipf", (PyCFunction)__pyx_f_6mtrand_11RandomState_zipf, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_zipf},
+  {"geometric", (PyCFunction)__pyx_f_6mtrand_11RandomState_geometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_geometric},
+  {"hypergeometric", (PyCFunction)__pyx_f_6mtrand_11RandomState_hypergeometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_hypergeometric},
+  {"logseries", (PyCFunction)__pyx_f_6mtrand_11RandomState_logseries, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logseries},
+  {"multivariate_normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_multivariate_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multivariate_normal},
+  {"multinomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_multinomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multinomial},
+  {"shuffle", (PyCFunction)__pyx_f_6mtrand_11RandomState_shuffle, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_shuffle},
+  {"permutation", (PyCFunction)__pyx_f_6mtrand_11RandomState_permutation, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_permutation},
+  {0, 0, 0, 0}
+};
+
+static PyNumberMethods __pyx_tp_as_number_RandomState = {
+  0, /*nb_add*/
+  0, /*nb_subtract*/
+  0, /*nb_multiply*/
+  0, /*nb_divide*/
+  0, /*nb_remainder*/
+  0, /*nb_divmod*/
+  0, /*nb_power*/
+  0, /*nb_negative*/
+  0, /*nb_positive*/
+  0, /*nb_absolute*/
+  0, /*nb_nonzero*/
+  0, /*nb_invert*/
+  0, /*nb_lshift*/
+  0, /*nb_rshift*/
+  0, /*nb_and*/
+  0, /*nb_xor*/
+  0, /*nb_or*/
+  0, /*nb_coerce*/
+  0, /*nb_int*/
+  0, /*nb_long*/
+  0, /*nb_float*/
+  0, /*nb_oct*/
+  0, /*nb_hex*/
+  0, /*nb_inplace_add*/
+  0, /*nb_inplace_subtract*/
+  0, /*nb_inplace_multiply*/
+  0, /*nb_inplace_divide*/
+  0, /*nb_inplace_remainder*/
+  0, /*nb_inplace_power*/
+  0, /*nb_inplace_lshift*/
+  0, /*nb_inplace_rshift*/
+  0, /*nb_inplace_and*/
+  0, /*nb_inplace_xor*/
+  0, /*nb_inplace_or*/
+  0, /*nb_floor_divide*/
+  0, /*nb_true_divide*/
+  0, /*nb_inplace_floor_divide*/
+  0, /*nb_inplace_true_divide*/
+};
+
+static PySequenceMethods __pyx_tp_as_sequence_RandomState = {
+  0, /*sq_length*/
+  0, /*sq_concat*/
+  0, /*sq_repeat*/
+  0, /*sq_item*/
+  0, /*sq_slice*/
+  0, /*sq_ass_item*/
+  0, /*sq_ass_slice*/
+  0, /*sq_contains*/
+  0, /*sq_inplace_concat*/
+  0, /*sq_inplace_repeat*/
+};
+
+static PyMappingMethods __pyx_tp_as_mapping_RandomState = {
+  0, /*mp_length*/
+  0, /*mp_subscript*/
+  0, /*mp_ass_subscript*/
+};
+
+static PyBufferProcs __pyx_tp_as_buffer_RandomState = {
+  0, /*bf_getreadbuffer*/
+  0, /*bf_getwritebuffer*/
+  0, /*bf_getsegcount*/
+  0, /*bf_getcharbuffer*/
+};
+
+statichere PyTypeObject __pyx_type_6mtrand_RandomState = {
+  PyObject_HEAD_INIT(0)
+  0, /*ob_size*/
+  "mtrand.RandomState", /*tp_name*/
+  sizeof(struct __pyx_obj_6mtrand_RandomState), /*tp_basicsize*/
+  0, /*tp_itemsize*/
+  __pyx_tp_dealloc_6mtrand_RandomState, /*tp_dealloc*/
+  0, /*tp_print*/
+  0, /*tp_getattr*/
+  0, /*tp_setattr*/
+  0, /*tp_compare*/
+  0, /*tp_repr*/
+  &__pyx_tp_as_number_RandomState, /*tp_as_number*/
+  &__pyx_tp_as_sequence_RandomState, /*tp_as_sequence*/
+  &__pyx_tp_as_mapping_RandomState, /*tp_as_mapping*/
+  0, /*tp_hash*/
+  0, /*tp_call*/
+  0, /*tp_str*/
+  0, /*tp_getattro*/
+  0, /*tp_setattro*/
+  &__pyx_tp_as_buffer_RandomState, /*tp_as_buffer*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE, /*tp_flags*/
+  "Container for the Mersenne Twister PRNG.\n\n    Constructor\n    -----------\n    RandomState(seed=None): initializes the PRNG with the given seed. See the\n        seed() method for details.\n\n    Distribution Methods\n    -----------------\n    RandomState exposes a number of methods for generating random numbers drawn\n    from a variety of probability distributions. In addition to the\n    distribution-specific arguments, each method takes a keyword argument\n    size=None. If size is None, then a single value is generated and returned.\n    If size is an integer, then a 1-D scipy array filled with generated values\n    is returned. If size is a tuple, then a scipy array with that shape is\n    filled and returned.\n    ", /*tp_doc*/
+  __pyx_tp_traverse_6mtrand_RandomState, /*tp_traverse*/
+  __pyx_tp_clear_6mtrand_RandomState, /*tp_clear*/
+  0, /*tp_richcompare*/
+  0, /*tp_weaklistoffset*/
+  0, /*tp_iter*/
+  0, /*tp_iternext*/
+  __pyx_methods_6mtrand_RandomState, /*tp_methods*/
+  0, /*tp_members*/
+  0, /*tp_getset*/
+  0, /*tp_base*/
+  0, /*tp_dict*/
+  0, /*tp_descr_get*/
+  0, /*tp_descr_set*/
+  0, /*tp_dictoffset*/
+  __pyx_f_6mtrand_11RandomState___init__, /*tp_init*/
+  0, /*tp_alloc*/
+  __pyx_tp_new_6mtrand_RandomState, /*tp_new*/
+  0, /*tp_free*/
+  0, /*tp_is_gc*/
+  0, /*tp_bases*/
+  0, /*tp_mro*/
+  0, /*tp_cache*/
+  0, /*tp_subclasses*/
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_logseries, __pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 966; goto __pyx_L1;}
+  Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":968 */
+  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 968; goto __pyx_L1;}
+  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_multivariate_normal); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 968; goto __pyx_L1;}
+  Py_DECREF(__pyx_1); __pyx_1 = 0;
+  if (PyObject_SetAttr(__pyx_m, __pyx_n_multivariate_normal, __pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 968; goto __pyx_L1;}
+  Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":969 */
+  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 969; goto __pyx_L1;}
+  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_multinomial); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 969; goto __pyx_L1;}
+  Py_DECREF(__pyx_1); __pyx_1 = 0;
+  if (PyObject_SetAttr(__pyx_m, __pyx_n_multinomial, __pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 969; goto __pyx_L1;}
+  Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":971 */
+  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 971; goto __pyx_L1;}
+  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_shuffle); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 971; goto __pyx_L1;}
+  Py_DECREF(__pyx_1); __pyx_1 = 0;
+  if (PyObject_SetAttr(__pyx_m, __pyx_n_shuffle, __pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 971; goto __pyx_L1;}
+  Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+  /* "/home/oliphant/core/scipy/lib/mtrand/mtrand.pyx":972 */
+  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 972; goto __pyx_L1;}
+  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_permutation); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 972; goto __pyx_L1;}
+  Py_DECREF(__pyx_1); __pyx_1 = 0;
+  if (PyObject_SetAttr(__pyx_m, __pyx_n_permutation, __pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 972; goto __pyx_L1;}
+  Py_DECREF(__pyx_2); __pyx_2 = 0;
+  return;
+  __pyx_L1:;
+  Py_XDECREF(__pyx_1);
+  Py_XDECREF(__pyx_2);
+  __Pyx_AddTraceback("mtrand");
+}
+
+static char *__pyx_filenames[] = {
+  "mtrand.pyx",
+  "scipy.pxi",
+};
+statichere char **__pyx_f = __pyx_filenames;
+
+/* Runtime support code */
+
+static int __Pyx_GetStarArgs(
+    PyObject **args, 
+    PyObject **kwds,
+    char *kwd_list[], 
+    int nargs,
+    PyObject **args2, 
+    PyObject **kwds2)
+{
+    PyObject *x = 0, *args1 = 0, *kwds1 = 0;
+    
+    if (args2)
+        *args2 = 0;
+    if (kwds2)
+        *kwds2 = 0;
+    
+    if (args2) {
+        args1 = PyTuple_GetSlice(*args, 0, nargs);
+        if (!args1)
+            goto bad;
+        *args2 = PyTuple_GetSlice(*args, nargs, PyTuple_Size(*args));
+        if (!*args2)
+            goto bad;
+    }
+    else {
+        args1 = *args;
+        Py_INCREF(args1);
+    }
+    
+    if (kwds2) {
+        if (*kwds) {
+            char **p;
+            kwds1 = PyDict_New();
+            if (!kwds)
+                goto bad;
+            *kwds2 = PyDict_Copy(*kwds);
+            if (!*kwds2)
+                goto bad;
+            for (p = kwd_list; *p; p++) {
+                x = PyDict_GetItemString(*kwds, *p);
+                if (x) {
+                    if (PyDict_SetItemString(kwds1, *p, x) < 0)
+                        goto bad;
+                    if (PyDict_DelItemString(*kwds2, *p) < 0)
+                        goto bad;
+                }
+            }
+        }
+        else {
+            *kwds2 = PyDict_New();
+            if (!*kwds2)
+                goto bad;
+        }
+    }
+    else {
+        kwds1 = *kwds;
+        Py_XINCREF(kwds1);
+    }
+    
+    *args = args1;
+    *kwds = kwds1;
+    return 0;
+bad:
+    Py_XDECREF(args1);
+    Py_XDECREF(kwds1);
+    Py_XDECREF(*args2);
+    Py_XDECREF(*kwds2);
+    return -1;
+}
+
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) {
+    PyObject *__import__ = 0;
+    PyObject *empty_list = 0;
+    PyObject *module = 0;
+    PyObject *global_dict = 0;
+    PyObject *empty_dict = 0;
+    PyObject *list;
+    __import__ = PyObject_GetAttrString(__pyx_b, "__import__");
+    if (!__import__)
+        goto bad;
+    if (from_list)
+        list = from_list;
+    else {
+        empty_list = PyList_New(0);
+        if (!empty_list)
+            goto bad;
+        list = empty_list;
+    }
+    global_dict = PyModule_GetDict(__pyx_m);
+    if (!global_dict)
+        goto bad;
+    empty_dict = PyDict_New();
+    if (!empty_dict)
+        goto bad;
+    module = PyObject_CallFunction(__import__, "OOOO",
+        name, global_dict, empty_dict, list);
+bad:
+    Py_XDECREF(empty_list);
+    Py_XDECREF(__import__);
+    Py_XDECREF(empty_dict);
+    return module;
+}
+
+static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) {
+    PyObject *result;
+    result = PyObject_GetAttr(dict, name);
+    if (!result)
+        PyErr_SetObject(PyExc_NameError, name);
+    return result;
+}
+
+static void __Pyx_WriteUnraisable(char *name) {
+    PyObject *old_exc, *old_val, *old_tb;
+    PyObject *ctx;
+    PyErr_Fetch(&old_exc, &old_val, &old_tb);
+    ctx = PyString_FromString(name);
+    PyErr_Restore(old_exc, old_val, old_tb);
+    if (!ctx)
+        ctx = Py_None;
+    PyErr_WriteUnraisable(ctx);
+}
+
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
+    Py_XINCREF(type);
+    Py_XINCREF(value);
+    Py_XINCREF(tb);
+    /* First, check the traceback argument, replacing None with NULL. */
+    if (tb == Py_None) {
+        Py_DECREF(tb);
+        tb = 0;
+    }
+    else if (tb != NULL && !PyTraceBack_Check(tb)) {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: arg 3 must be a traceback or None");
+        goto raise_error;
+    }
+    /* Next, replace a missing value with None */
+    if (value == NULL) {
+        value = Py_None;
+        Py_INCREF(value);
+    }
+    /* Next, repeatedly, replace a tuple exception with its first item */
+    while (PyTuple_Check(type) && PyTuple_Size(type) > 0) {
+        PyObject *tmp = type;
+        type = PyTuple_GET_ITEM(type, 0);
+        Py_INCREF(type);
+        Py_DECREF(tmp);
+    }
+    if (PyString_Check(type))
+        ;
+    else if (PyClass_Check(type))
+        ; /*PyErr_NormalizeException(&type, &value, &tb);*/
+    else if (PyInstance_Check(type)) {
+        /* Raising an instance.  The value should be a dummy. */
+        if (value != Py_None) {
+            PyErr_SetString(PyExc_TypeError,
+              "instance exception may not have a separate value");
+            goto raise_error;
+        }
+        else {
+            /* Normalize to raise <class>, <instance> */
+            Py_DECREF(value);
+            value = type;
+            type = (PyObject*) ((PyInstanceObject*)type)->in_class;
+            Py_INCREF(type);
+        }
+    }
+    else {
+        /* Not something you can raise.  You get an exception
+           anyway, just not what you specified :-) */
+        PyErr_Format(PyExc_TypeError,
+                 "exceptions must be strings, classes, or "
+                 "instances, not %s", type->ob_type->tp_name);
+        goto raise_error;
+    }
+    PyErr_Restore(type, value, tb);
+    return;
+raise_error:
+    Py_XDECREF(value);
+    Py_XDECREF(type);
+    Py_XDECREF(tb);
+    return;
+}
+
+static void __Pyx_UnpackError(void) {
+    PyErr_SetString(PyExc_ValueError, "unpack sequence of wrong size");
+}
+
+static PyObject *__Pyx_UnpackItem(PyObject *seq, int i) {
+  PyObject *item = PySequence_GetItem(seq, i);
+  if (!item) {
+    if (PyErr_ExceptionMatches(PyExc_IndexError))
+      __Pyx_UnpackError();
+  }
+  return item;
+}
+
+static int __Pyx_EndUnpack(PyObject *seq, int i) {
+  PyObject *item = PySequence_GetItem(seq, i);
+  if (item) {
+    Py_DECREF(item);
+    __Pyx_UnpackError();
+    return -1;
+  }
+  PyErr_Clear();
+  return 0;
+}
+
+static int __Pyx_InternStrings(__Pyx_InternTabEntry *t) {
+    while (t->p) {
+        *t->p = PyString_InternFromString(t->s);
+        if (!*t->p)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+    while (t->p) {
+        *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
+        if (!*t->p)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, 
+    long size) 
+{
+    PyObject *py_module_name = 0;
+    PyObject *py_class_name = 0;
+    PyObject *py_name_list = 0;
+    PyObject *py_module = 0;
+    PyObject *result = 0;
+    
+    py_module_name = PyString_FromString(module_name);
+    if (!py_module_name)
+        goto bad;
+    py_class_name = PyString_FromString(class_name);
+    if (!py_class_name)
+        goto bad;
+    py_name_list = PyList_New(1);
+    if (!py_name_list)
+        goto bad;
+    Py_INCREF(py_class_name);
+    if (PyList_SetItem(py_name_list, 0, py_class_name) < 0)
+        goto bad;
+    py_module = __Pyx_Import(py_module_name, py_name_list);
+    if (!py_module)
+        goto bad;
+    result = PyObject_GetAttr(py_module, py_class_name);
+    if (!result)
+        goto bad;
+    if (!PyType_Check(result)) {
+        PyErr_Format(PyExc_TypeError, 
+            "%s.%s is not a type object",
+            module_name, class_name);
+        goto bad;
+    }
+    if (((PyTypeObject *)result)->tp_basicsize != size) {
+        PyErr_Format(PyExc_ValueError, 
+            "%s.%s does not appear to be the correct type object",
+            module_name, class_name);
+        goto bad;
+    }
+    goto done;
+bad:
+    Py_XDECREF(result);
+    result = 0;
+done:
+    Py_XDECREF(py_module_name);
+    Py_XDECREF(py_class_name);
+    Py_XDECREF(py_name_list);
+    return (PyTypeObject *)result;
+}
+
+#include "compile.h"
+#include "frameobject.h"
+#include "traceback.h"
+
+static void __Pyx_AddTraceback(char *funcname) {
+    PyObject *py_srcfile = 0;
+    PyObject *py_funcname = 0;
+    PyObject *py_globals = 0;
+    PyObject *empty_tuple = 0;
+    PyObject *empty_string = 0;
+    PyCodeObject *py_code = 0;
+    PyFrameObject *py_frame = 0;
+    
+    py_srcfile = PyString_FromString(__pyx_filename);
+    if (!py_srcfile) goto bad;
+    py_funcname = PyString_FromString(funcname);
+    if (!py_funcname) goto bad;
+    py_globals = PyModule_GetDict(__pyx_m);
+    if (!py_globals) goto bad;
+    empty_tuple = PyTuple_New(0);
+    if (!empty_tuple) goto bad;
+    empty_string = PyString_FromString("");
+    if (!empty_string) goto bad;
+    py_code = PyCode_New(
+        0,            /*int argcount,*/
+        0,            /*int nlocals,*/
+        0,            /*int stacksize,*/
+        0,            /*int flags,*/
+        empty_string, /*PyObject *code,*/
+        empty_tuple,  /*PyObject *consts,*/
+        empty_tuple,  /*PyObject *names,*/
+        empty_tuple,  /*PyObject *varnames,*/
+        empty_tuple,  /*PyObject *freevars,*/
+        empty_tuple,  /*PyObject *cellvars,*/
+        py_srcfile,   /*PyObject *filename,*/
+        py_funcname,  /*PyObject *name,*/
+        __pyx_lineno,   /*int firstlineno,*/
+        empty_string  /*PyObject *lnotab*/
+    );
+    if (!py_code) goto bad;
+    py_frame = PyFrame_New(
+        PyThreadState_Get(), /*PyThreadState *tstate,*/
+        py_code,             /*PyCodeObject *code,*/
+        py_globals,          /*PyObject *globals,*/
+        0                    /*PyObject *locals*/
+    );
+    if (!py_frame) goto bad;
+    py_frame->f_lineno = __pyx_lineno;
+    PyTraceBack_Here(py_frame);
+bad:
+    Py_XDECREF(py_srcfile);
+    Py_XDECREF(py_funcname);
+    Py_XDECREF(empty_tuple);
+    Py_XDECREF(empty_string);
+    Py_XDECREF(py_code);
+    Py_XDECREF(py_frame);
+}
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
new file mode 100644
index 000000000..bf20ac913
--- /dev/null
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -0,0 +1,972 @@
+# mtrand.pyx -- A Pyrex wrapper of Jean-Sebastien Roy's RandomKit
+#
+# Copyright 2005 Robert Kern (robert.kern@gmail.com)
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+# 
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+# 
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+include "Python.pxi"
+include "scipy.pxi"
+
+cdef extern from "math.h":
+    double exp(double x)
+    double log(double x)
+    double floor(double x)
+    double sin(double x)
+    double cos(double x)
+
+cdef extern from "randomkit.h":
+
+    ctypedef struct rk_state:
+        unsigned long key[624]
+        int pos
+
+    ctypedef enum rk_error:
+        RK_NOERR = 0
+        RK_ENODEV = 1
+        RK_ERR_MAX = 2
+
+    char *rk_strerror[2]
+    
+    # 0xFFFFFFFFUL
+    unsigned long RK_MAX
+
+    void rk_seed(unsigned long seed, rk_state *state)
+    rk_error rk_randomseed(rk_state *state)
+    unsigned long rk_random(rk_state *state)
+    long rk_long(rk_state *state)
+    unsigned long rk_ulong(rk_state *state)
+    unsigned long rk_interval(unsigned long max, rk_state *state)
+    double rk_double(rk_state *state)
+    void rk_fill(void *buffer, size_t size, rk_state *state)
+    rk_error rk_devfill(void *buffer, size_t size, int strong)
+    rk_error rk_altfill(void *buffer, size_t size, int strong,
+            rk_state *state)
+    double rk_gauss(rk_state *state)
+
+cdef extern from "distributions.h":
+    
+    double rk_normal(rk_state *state, double loc, double scale)
+    double rk_standard_exponential(rk_state *state)
+    double rk_exponential(rk_state *state, double scale)
+    double rk_uniform(rk_state *state, double loc, double scale)
+    double rk_standard_gamma(rk_state *state, double shape)
+    double rk_gamma(rk_state *state, double shape, double scale)
+    double rk_beta(rk_state *state, double a, double b)
+    double rk_chisquare(rk_state *state, double df)
+    double rk_noncentral_chisquare(rk_state *state, double df, double nonc)
+    double rk_f(rk_state *state, double dfnum, double dfden)
+    double rk_noncentral_f(rk_state *state, double dfnum, double dfden, double nonc)
+    double rk_standard_cauchy(rk_state *state)
+    double rk_standard_t(rk_state *state, double df)
+    double rk_vonmises(rk_state *state, double mu, double kappa)
+    double rk_pareto(rk_state *state, double a)
+    double rk_weibull(rk_state *state, double a)
+    double rk_power(rk_state *state, double a)
+    double rk_laplace(rk_state *state, double loc, double scale)
+    double rk_gumbel(rk_state *state, double loc, double scale)
+    double rk_logistic(rk_state *state, double loc, double scale)
+    double rk_lognormal(rk_state *state, double mode, double sigma)
+    double rk_rayleigh(rk_state *state, double mode)
+    double rk_wald(rk_state *state, double mean, double scale)
+    double rk_triangular(rk_state *state, double left, double mode, double right)
+    
+    long rk_binomial(rk_state *state, long n, double p)
+    long rk_binomial_btpe(rk_state *state, long n, double p)
+    long rk_binomial_inversion(rk_state *state, long n, double p)
+    long rk_negative_binomial(rk_state *state, long n, double p)
+    long rk_poisson(rk_state *state, double lam)
+    long rk_poisson_mult(rk_state *state, double lam)
+    long rk_poisson_ptrs(rk_state *state, double lam)
+    long rk_zipf(rk_state *state, double a)
+    long rk_geometric(rk_state *state, double p)
+    long rk_hypergeometric(rk_state *state, long good, long bad, long sample)
+    long rk_logseries(rk_state *state, double p)
+
+ctypedef double (* rk_cont0)(rk_state *state)
+ctypedef double (* rk_cont1)(rk_state *state, double a)
+ctypedef double (* rk_cont2)(rk_state *state, double a, double b)
+ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c)
+
+ctypedef long (* rk_disc0)(rk_state *state)
+ctypedef long (* rk_discnp)(rk_state *state, long n, double p)
+ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N)
+ctypedef long (* rk_discd)(rk_state *state, double a)
+
+
+cdef extern from "initarray.h":
+   void init_by_array(rk_state *self, unsigned long *init_key, 
+                      unsigned long key_length)
+
+# Initialize scipy
+import_array()
+
+import scipy as _sp
+
+cdef object cont0_array(rk_state *state, rk_cont0 func, object size):
+    cdef double *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Float64)
+        length = PyArray_SIZE(array)
+        array_data = <double *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state)
+        return array
+
+cdef object cont1_array(rk_state *state, rk_cont1 func, object size, double a):
+    cdef double *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state, a)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Float64)
+        length = PyArray_SIZE(array)
+        array_data = <double *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, a)
+        return array
+
+cdef object cont2_array(rk_state *state, rk_cont2 func, object size, double a, 
+    double b):
+    cdef double *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state, a, b)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Float64)
+        length = PyArray_SIZE(array)
+        array_data = <double *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, a, b)
+        return array
+
+cdef object cont3_array(rk_state *state, rk_cont3 func, object size, double a, 
+    double b, double c):
+
+    cdef double *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+    
+    if size is None:
+        return func(state, a, b, c)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Float64)
+        length = PyArray_SIZE(array)
+        array_data = <double *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, a, b, c)
+        return array
+
+cdef object disc0_array(rk_state *state, rk_disc0 func, object size):
+    cdef long *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Int)
+        length = PyArray_SIZE(array)
+        array_data = <long *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state)
+        return array
+
+cdef object discnp_array(rk_state *state, rk_discnp func, object size, long n, double p):
+    cdef long *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state, n, p)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Int)
+        length = PyArray_SIZE(array)
+        array_data = <long *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, n, p)
+        return array
+
+cdef object discnmN_array(rk_state *state, rk_discnmN func, object size, 
+    long n, long m, long N):
+    cdef long *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state, n, m, N)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Int)
+        length = PyArray_SIZE(array)
+        array_data = <long *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, n, m, N)
+        return array
+
+cdef object discd_array(rk_state *state, rk_discd func, object size, double a):
+    cdef long *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state, a)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Int)
+        length = PyArray_SIZE(array)
+        array_data = <long *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, a)
+        return array
+
+cdef double kahan_sum(double *darr, long n):
+    cdef double c, y, t, sum
+    cdef long i
+    sum = darr[0]
+    c = 0.0
+    for i from 1 <= i < n:
+        y = darr[i] - c
+        t = sum + y
+        c = (t-sum) - y
+        sum = t
+    return sum
+
+cdef class RandomState:
+    """Container for the Mersenne Twister PRNG.
+
+    Constructor
+    -----------
+    RandomState(seed=None): initializes the PRNG with the given seed. See the
+        seed() method for details.
+
+    Distribution Methods
+    -----------------
+    RandomState exposes a number of methods for generating random numbers drawn
+    from a variety of probability distributions. In addition to the
+    distribution-specific arguments, each method takes a keyword argument
+    size=None. If size is None, then a single value is generated and returned.
+    If size is an integer, then a 1-D scipy array filled with generated values
+    is returned. If size is a tuple, then a scipy array with that shape is
+    filled and returned.
+    """
+    cdef rk_state *internal_state
+
+    def __init__(self, seed=None):
+        self.internal_state = <rk_state*>PyMem_Malloc(sizeof(rk_state))
+
+        self.seed(seed)
+
+    def __dealloc__(self):
+        if self.internal_state != NULL:
+            PyMem_Free(self.internal_state)
+
+    def seed(self, seed=None):
+        """Seed the generator.
+
+        seed(seed=None)
+
+        seed can be an integer, an array (or other sequence) of integers of any
+        length, or None. If seed is None, then RandomState will try to read data
+        from /dev/urandom (or the Windows analogue) if available or seed from
+        the clock otherwise.
+        """
+        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)
+        else:
+            obj = <ndarray>PyArray_ContiguousFromObject(seed, PyArray_LONG, 1, 1)
+            init_by_array(self.internal_state, <unsigned long *>(obj.data),
+                obj.dimensions[0])
+
+    def get_state(self):
+        """Return a tuple representing the internal state of the generator.
+
+        get_state() -> ('MT19937', int key[624], int pos)
+        """
+        cdef ndarray state "arrayObject_state"
+        state = <ndarray>_sp.empty(624, _sp.Int)
+        memcpy(<void*>(state.data), self.internal_state.key, 624*sizeof(long))
+        return ('MT19937', state, self.internal_state.pos)
+        
+    def set_state(self, state):
+        """Set the state from a tuple.
+        
+        state = ('MT19937', int key[624], int pos)
+        
+        set_state(state)
+        """
+        cdef ndarray obj "arrayObject_obj"
+        cdef int pos
+        algorithm_name = state[0]
+        if algorithm_name != 'MT19937':
+            raise ValueError("algorithm must be 'MT19937'")
+        key, pos = state[1:]
+        obj = <ndarray>PyArray_ContiguousFromObject(key, PyArray_LONG, 1, 1)
+        if obj.dimensions[0] != 624:
+            raise ValueError("state must be 624 longs")
+        memcpy(self.internal_state.key, <void*>(obj.data), 624*sizeof(long))
+        self.internal_state.pos = pos
+    
+    # Pickling support:
+    def __getstate__(self):
+        return self.get_state()
+
+    def __setstate__(self, state):
+        self.set_state(state)
+
+    def __reduce__(self):
+        return (_sp.random.__RandomState_ctor, (), self.get_state())
+
+    # Basic distributions:
+    def random_sample(self, size=None):
+        """Return random floats in the half-open interval [0.0, 1.0).
+
+        random_sample(size=None) -> random values
+        """
+        return cont0_array(self.internal_state, rk_double, size)
+
+    def tomaxint(self, size=None):
+        """Returns random integers x such that 0 <= x <= sys.maxint.
+
+        tomaxint(size=None) -> random values
+        """
+        return disc0_array(self.internal_state, rk_long, size)
+
+    def randint(self, low, high=None, size=None):
+        """Return random integers x such that low <= x < high.
+
+        randint(low, high=None, size=None) -> random values
+
+        If high is None, then 0 <= x < low.
+        """
+        cdef long lo, hi, diff
+        cdef long *array_data
+        cdef ndarray array "arrayObject"
+        cdef long length
+        cdef long i
+
+        if high is None:
+            lo = 0
+            hi = low
+        else:
+            lo = low
+            hi = high
+
+        diff = hi - lo - 1
+        if diff < 0:
+            raise ValueError("low >= high")
+    
+        if size is None:
+            return rk_interval(diff, self.internal_state)
+        else:
+            array = <ndarray>_sp.empty(size, _sp.Int)
+            length = PyArray_SIZE(array)
+            array_data = <long *>array.data
+            for i from 0 <= i < length:
+                array_data[i] = lo + <long>rk_interval(diff, self.internal_state)
+            return array
+
+    def bytes(self, unsigned int length):
+        """Return random bytes.
+
+        bytes(length) -> str
+        """
+        cdef void *bytes
+        bytes = PyMem_Malloc(length)
+        rk_fill(bytes, length, self.internal_state)
+        bytestring = PyString_FromString(<char*>bytes)
+        PyMem_Free(bytes)
+        return bytestring
+
+    def uniform(self, double low=0.0, double high=1.0, size=None):
+        """Uniform distribution over [low, high).
+
+        uniform(low=0.0, high=1.0, size=None) -> random values
+        """
+        return cont2_array(self.internal_state, rk_uniform, size, low, 
+            high-low)
+
+    def rand(self, *args):
+        """Return an array of the given dimensions which is initialized to 
+        random numbers from a uniform distribution in the range [0,1).
+
+        rand(d0, d1, ..., dn) -> random values
+        """
+        if len(args) == 0:
+            return self.random_sample()
+        else:
+            return self.random_sample(size=args)
+
+    def randn(self, *args):
+        """Returns zero-mean, unit-variance Gaussian random numbers in an 
+        array of shape (d0, d1, ..., dn).
+
+        randn(d0, d1, ..., dn) -> random values
+        """
+        if len(args) == 0:
+            return self.standard_normal()
+        else:
+            return self.standard_normal(args)
+
+    def random_integers(self, low, high=None, size=None):
+        """Return random integers x such that low <= x <= high.
+
+        random_integers(low, high=None, size=None) -> random values.
+
+        If high is None, then 1 <= x <= low.
+        """
+        if high is None:
+            high = low
+            low = 1
+        return self.randint(low, high+1, size)
+
+    # Complicated, continuous distributions:
+    def standard_normal(self, size=None):
+        """Standard Normal distribution (mean=0, stdev=1).
+
+        standard_normal(size=None) -> random values
+        """
+        return cont0_array(self.internal_state, rk_gauss, size)
+
+    def normal(self, double loc=0.0, double scale=1.0, size=None):
+        """Normal distribution (mean=loc, stdev=scale).
+
+        normal(loc=0.0, scale=1.0, size=None) -> random values
+        """
+        if scale <= 0:
+            raise ValueError("scale <= 0")
+        return cont2_array(self.internal_state, rk_normal, size, loc, scale)
+
+    def beta(self, double a, double b, size=None):
+        """Beta distribution over [0, 1].
+
+        beta(a, b, size=None) -> random values
+        """
+        if a <= 0:
+            raise ValueError("a <= 0")
+        elif b <= 0:
+            raise ValueError("b <= 0")
+        return cont2_array(self.internal_state, rk_beta, size, a, b)
+
+    def exponential(self, double scale=1.0, size=None):
+        """Exponential distribution.
+
+        exponential(scale=1.0, size=None) -> random values
+        """
+        if scale <= 0:
+            raise ValueError("scale <= 0")
+        return cont1_array(self.internal_state, rk_exponential, size, scale)
+
+    def standard_exponential(self, size=None):
+        """Standard exponential distribution (scale=1).
+
+        standard_exponential(size=None) -> random values
+        """
+        return cont0_array(self.internal_state, rk_standard_exponential, size)
+
+    def standard_gamma(self, double shape, size=None):
+        """Standard Gamma distribution.
+
+        standard_gamma(shape, size=None) -> random values
+        """
+        if shape <= 0:
+            raise ValueError("shape <= 0")
+        return cont1_array(self.internal_state, rk_standard_gamma, size, shape)
+
+    def gamma(self, double shape, double scale=1.0, size=None):
+        """Gamma distribution.
+
+        gamma(shape, scale=1.0, size=None) -> random values
+        """
+        if shape <= 0:
+            raise ValueError("shape <= 0")
+        elif scale <= 0:
+            raise ValueError("scale <= 0")
+        return cont2_array(self.internal_state, rk_gamma, size, shape, scale)
+
+    def f(self, double dfnum, double dfden, size=None):
+        """F distribution.
+
+        f(dfnum, dfden, size=None) -> random values
+        """
+        if dfnum <= 0:
+            raise ValueError("dfnum <= 0")
+        elif dfden <= 0:
+            raise ValueError("dfden <= 0")
+        return cont2_array(self.internal_state, rk_f, size, dfnum, dfden)
+
+    def noncentral_f(self, double dfnum, double dfden, double nonc, size=None):
+        """Noncentral F distribution.
+
+        noncentral_f(dfnum, dfden, nonc, size=None) -> random values
+        """
+        if dfnum <= 1:
+            raise ValueError("dfnum <= 1")
+        elif dfden <= 0:
+            raise ValueError("dfden <= 0")
+        elif nonc < 0:
+            raise ValueError("nonc < 0")
+        return cont3_array(self.internal_state, rk_noncentral_f, size, dfnum,
+            dfden, nonc)
+
+    def chisquare(self, double df, size=None):
+        """Chi^2 distribution.
+
+        chisquare(df, size=None) -> random values
+        """
+        if df <= 0:
+            raise ValueError("df <= 0")
+        return cont1_array(self.internal_state, rk_chisquare, size, df)
+
+    def noncentral_chisquare(self, double df, double nonc, size=None):
+        """Noncentral Chi^2 distribution.
+
+        noncentral_chisquare(df, nonc, size=None) -> random values
+        """
+        if df <= 1:
+            raise ValueError("df <= 1")
+        elif nonc < 0:
+            raise ValueError("nonc < 0")
+        return cont2_array(self.internal_state, rk_noncentral_chisquare, size,
+            df, nonc)
+    
+    def standard_cauchy(self, size=None):
+        """Standard Cauchy with mode=0.
+
+        standard_cauchy(size=None)
+        """
+        return cont0_array(self.internal_state, rk_standard_cauchy, size)
+
+    def standard_t(self, double df, size=None):
+        """Standard Student's t distribution with df degrees of freedom.
+
+        standard_t(df, size=None)
+        """
+        if df <= 0:
+            raise ValueError("df <= 0")
+        return cont1_array(self.internal_state, rk_standard_t, size, df)
+
+    def vonmises(self, double mu, double kappa, size=None):
+        """von Mises circular distribution with mode mu and dispersion parameter
+        kappa on [-pi, pi].
+
+        vonmises(mu, kappa, size=None)
+        """
+        if kappa < 0:
+            raise ValueError("kappa < 0")
+        return cont2_array(self.internal_state, rk_vonmises, size, mu, kappa)
+
+    def pareto(self, double a, size=None):
+        """Pareto distribution.
+
+        pareto(a, size=None)
+        """
+        if a <= 0:
+            raise ValueError("a <= 0")
+        return cont1_array(self.internal_state, rk_pareto, size, a)
+
+    def weibull(self, double a, size=None):
+        """Weibull distribution.
+
+        weibull(a, size=None)
+        """
+        if a <= 0:
+            raise ValueError("a <= 0")
+        return cont1_array(self.internal_state, rk_weibull, size, a)
+
+    def power(self, double a, size=None):
+        """Power distribution.
+
+        power(a, size=None)
+        """
+        if a <= 0:
+            raise ValueError("a <= 0")
+        return cont1_array(self.internal_state, rk_power, size, a)
+
+    def laplace(self, double loc=0.0, double scale=1.0, size=None):
+        """Laplace distribution.
+        
+        laplace(loc=0.0, scale=1.0, size=None)
+        """
+        if scale <= 0.0:
+            raise ValueError("scale <= 0.0")
+        return cont2_array(self.internal_state, rk_laplace, size, loc, scale)
+    
+    def gumbel(self, double loc=0.0, double scale=1.0, size=None):
+        """Gumbel distribution.
+        
+        gumbel(loc=0.0, scale=1.0, size=None)
+        """
+        if scale <= 0.0:
+            raise ValueError("scale <= 0.0")
+        return cont2_array(self.internal_state, rk_gumbel, size, loc, scale)
+    
+    def logistic(self, double loc=0.0, double scale=1.0, size=None):
+        """Logistic distribution.
+        
+        logistic(loc=0.0, scale=1.0, size=None)
+        """
+        if scale <= 0.0:
+            raise ValueError("scale <= 0.0")
+        return cont2_array(self.internal_state, rk_logistic, size, loc, scale)
+
+    def lognormal(self, double mean=0.0, double sigma=1.0, size=None):
+        """Log-normal distribution.
+        
+        Note that the mean parameter is not the mean of this distribution, but 
+        the underlying normal distribution.
+        
+            lognormal(mean, sigma) <=> exp(normal(mean, sigma))
+        
+        lognormal(mean=0.0, sigma=1.0, size=None)
+        """
+        if sigma <= 0.0:
+            raise ValueError("sigma <= 0.0")
+        return cont2_array(self.internal_state, rk_lognormal, size, mean, sigma)
+    
+    def rayleigh(self, double scale=1.0, size=None):
+        """Rayleigh distribution.
+        
+        rayleigh(scale=1.0, size=None)
+        """
+        if scale <= 0.0:
+            raise ValueError("scale <= 0.0")
+        return cont1_array(self.internal_state, rk_rayleigh, size, scale)
+    
+    def wald(self, double mean, double scale, size=None):
+        """Wald (inverse Gaussian) distribution.
+        
+        wald(mean, scale, size=None)
+        """
+        if mean <= 0.0:
+            raise ValueError("mean <= 0.0")
+        elif scale <= 0.0:
+            raise ValueError("scale <= 0.0")
+        return cont2_array(self.internal_state, rk_wald, size, mean, scale)
+
+    def triangular(self, double left, double mode, double right, size=None):
+        """Triangular distribution starting at left, peaking at mode, and 
+        ending at right (left <= mode <= right).
+        
+        triangular(left, mode, right, size=None)
+        """
+        if left > mode:
+            raise ValueError("left > mode")
+        elif mode > right:
+            raise ValueError("mode > right")
+        elif left == right:
+            raise ValueError("left == right")
+        return cont3_array(self.internal_state, rk_triangular, size, left, 
+            mode, right)
+
+    # Complicated, discrete distributions:
+    def binomial(self, long n, double p, size=None):
+        """Binomial distribution of n trials and p probability of success.
+
+        binomial(n, p, size=None) -> random values
+        """
+        if n <= 0:
+            raise ValueError("n <= 0")
+        elif p < 0:
+            raise ValueError("p < 0")
+        elif p > 1:
+            raise ValueError("p > 1")
+        return discnp_array(self.internal_state, rk_binomial, size, n, p)
+
+    def negative_binomial(self, long n, double p, size=None):
+        """Negative Binomial distribution.
+
+        negative_binomial(n, p, size=None) -> random values
+        """
+        if n <= 0:
+            raise ValueError("n <= 0")
+        elif p < 0:
+            raise ValueError("p < 0")
+        elif p > 1:
+            raise ValueError("p > 1")
+        return discnp_array(self.internal_state, rk_negative_binomial, size, n,
+            p)
+
+    def poisson(self, double lam=1.0, size=None):
+        """Poisson distribution.
+
+        poisson(lam=1.0, size=None) -> random values
+        """
+        if lam <= 0:
+            raise ValueError("lam <= 0")
+        return discd_array(self.internal_state, rk_poisson, size, lam)
+
+    def zipf(self, double a, size=None):
+        """Zipf distribution.
+        
+        zipf(a, size=None)
+        """
+        if a <= 1.0:
+            raise ValueError("a <= 1.0")
+        return discd_array(self.internal_state, rk_zipf, size, a)
+    
+    def geometric(self, double p, size=None):
+        """Geometric distribution with p being the probability of "success" on
+        an individual trial.
+        
+        geometric(p, size=None)
+        """
+        if p < 0.0:
+            raise ValueError("p < 0.0")
+        elif p > 1.0:
+            raise ValueError("p > 1.0")
+        return discd_array(self.internal_state, rk_geometric, size, p)
+    
+    def hypergeometric(self, long ngood, long nbad, long nsample, size=None):
+        """Hypergeometric distribution.
+        
+        Consider an urn with ngood "good" balls and nbad "bad" balls. If one 
+        were to draw nsample balls from the urn without replacement, then 
+        the hypergeometric distribution describes the distribution of "good" 
+        balls in the sample.
+        
+        hypergeometric(ngood, nbad, nsample, size=None)        
+        """
+        if ngood < 1:
+            raise ValueError("ngood < 1")
+        elif nbad < 1:
+            raise ValueError("nbad < 1")
+        elif ngood + nbad < nsample:
+            raise ValueError("ngood + nbad < nsample")
+        elif nsample < 1:
+            raise ValueError("nsample < 1")
+        return discnmN_array(self.internal_state, rk_hypergeometric, size,
+            ngood, nbad, nsample)
+
+    def logseries(self, double p, size=None):
+        """Logarithmic series distribution.
+        
+        logseries(p, size=None)
+        """
+        if p < 0:
+            raise ValueError("p < 0")
+        elif p > 1:
+            raise ValueError("p > 1")
+        return discd_array(self.internal_state, rk_logseries, size, p)
+
+    # Multivariate distributions:
+    def multivariate_normal(self, mean, cov, size=None):
+        """Return an array containing multivariate normally distributed random numbers
+        with specified mean and covariance.
+
+        multivariate_normal(mean, cov) -> random values
+        multivariate_normal(mean, cov, [m, n, ...]) -> random values
+
+        mean must be a 1 dimensional array. cov must be a square two dimensional
+        array with the same number of rows and columns as mean has elements.
+
+        The first form returns a single 1-D array containing a multivariate
+        normal.
+
+        The second form returns an array of shape (m, n, ..., cov.shape[0]).
+        In this case, output[i,j,...,:] is a 1-D array containing a multivariate
+        normal.
+        """
+        # Check preconditions on arguments
+        mean = _sp.array(mean)
+        cov = _sp.array(cov)
+        if size is None:
+            shape = []
+        else:
+            shape = size
+        if len(mean.shape) != 1:
+               raise ArgumentError("mean must be 1 dimensional")
+        if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]):
+               raise ArgumentError("cov must be 2 dimensional and square")
+        if mean.shape[0] != cov.shape[0]:
+               raise ArgumentError("mean and cov must have same length")
+        # Compute shape of output
+        if isinstance(shape, int):
+            shape = [shape]
+        final_shape = list(shape[:])
+        final_shape.append(mean.shape[0])
+        # Create a matrix of independent standard normally distributed random
+        # numbers. The matrix has rows with the same length as mean and as
+        # many rows are necessary to form a matrix of shape final_shape.
+        x = standard_normal(_sp.multiply.reduce(final_shape))
+        x.shape = (_sp.multiply.reduce(final_shape[0:len(final_shape)-1]),
+                   mean.shape[0])
+        # Transform matrix of standard normals into matrix where each row
+        # contains multivariate normals with the desired covariance.
+        # Compute A such that matrixmultiply(transpose(A),A) == cov.
+        # Then the matrix products of the rows of x and A has the desired
+        # covariance. Note that sqrt(s)*v where (u,s,v) is the singular value
+        # decomposition of cov is such an A.
+        
+        from scipy.corelinalg import svd
+        # XXX: we really should be doing this by Cholesky decomposition
+        (u,s,v) = svd(cov)
+        x = _sp.matrixmultiply(x*_sp.sqrt(s),v)
+        # The rows of x now have the correct covariance but mean 0. Add
+        # mean to each row. Then each row will have mean mean.
+        _sp.add(mean,x,x)
+        x.shape = tuple(final_shape)
+        return x
+
+    def multinomial(self, long n, object pvals, size=None):
+        """Multinomial distribution.
+        
+        multinomial(n, pvals, size=None) -> random values
+
+        pvals is a sequence of probabilities that should sum to 1 (however, the
+        last element is always assumed to account for the remaining probability
+        as long as sum(pvals[:-1]) <= 1).
+        """
+        cdef long d
+        cdef ndarray parr "arrayObject_parr", mnarr "arrayObject_mnarr"
+        cdef double *pix
+        cdef long *mnix
+        cdef long i, j, dn
+        cdef double Sum
+
+        d = len(pvals)
+        parr = <ndarray>PyArray_ContiguousFromObject(pvals, PyArray_DOUBLE, 1, 1)
+        pix = <double*>parr.data
+
+        if kahan_sum(pix, d-1) > 1.0:
+            raise ValueError("sum(pvals) > 1.0")
+
+        if size is None:
+            shape = (d,)
+        elif type(size) is int:
+            shape = (size, d)
+        else:
+            shape = size + (d,)
+
+        multin = _sp.zeros(shape, _sp.Int)
+        mnarr = <ndarray>multin
+        mnix = <long*>mnarr.data
+        i = 0
+        while i < PyArray_SIZE(mnarr):
+            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]
+                if dn <= 0:
+                    break
+                Sum = Sum - pix[j]
+            if dn > 0:
+                mnix[i+d-1] = dn
+
+            i = i + d
+
+        return multin
+
+    # Shuffling and permutations:
+    def shuffle(self, object x):
+        """Modify a sequence in-place by shuffling its contents.
+        
+        shuffle(x)
+        """
+        cdef long i, j
+
+        # adaptation of random.shuffle()
+        i = len(x) - 1
+        while i > 0:
+            j = rk_interval(i, self.internal_state)
+            x[i], x[j] = x[j], x[i]
+            i = i - 1
+                
+    def permutation(self, object x):
+        """Given an integer, return a shuffled sequence of integers >= 0 and 
+        < x; given a sequence, return a shuffled array copy.
+
+        permutation(x)
+        """
+        if type(x) is int:
+            arr = _sp.arange(x)
+        else:
+            arr = _sp.array(x)
+        self.shuffle(arr)
+        return arr    
+
+_rand = RandomState()
+seed = _rand.seed
+get_state = _rand.get_state
+set_state = _rand.set_state
+random_sample = _rand.random_sample
+randint = _rand.randint
+bytes = _rand.bytes
+uniform = _rand.uniform
+rand = _rand.rand
+randn = _rand.randn
+random_integers = _rand.random_integers
+standard_normal = _rand.standard_normal
+normal = _rand.normal
+beta = _rand.beta
+exponential = _rand.exponential
+standard_exponential = _rand.standard_exponential
+standard_gamma = _rand.standard_gamma
+gamma = _rand.gamma
+f = _rand.f
+noncentral_f = _rand.noncentral_f
+chisquare = _rand.chisquare
+noncentral_chisquare = _rand.noncentral_chisquare
+standard_cauchy = _rand.standard_cauchy
+standard_t = _rand.standard_t
+vonmises = _rand.vonmises
+pareto = _rand.pareto
+weibull = _rand.weibull
+power = _rand.power
+laplace = _rand.laplace
+gumbel = _rand.gumbel
+logistic = _rand.logistic
+lognormal = _rand.lognormal
+rayleigh = _rand.rayleigh
+wald = _rand.wald
+triangular = _rand.triangular
+
+binomial = _rand.binomial
+negative_binomial = _rand.negative_binomial
+poisson = _rand.poisson
+zipf = _rand.zipf
+geometric = _rand.geometric
+hypergeometric = _rand.hypergeometric
+logseries = _rand.logseries
+
+multivariate_normal = _rand.multivariate_normal
+multinomial = _rand.multinomial
+
+shuffle = _rand.shuffle
+permutation = _rand.permutation
diff --git a/numpy/random/mtrand/randomkit.c b/numpy/random/mtrand/randomkit.c
new file mode 100644
index 000000000..6a58f2dba
--- /dev/null
+++ b/numpy/random/mtrand/randomkit.c
@@ -0,0 +1,355 @@
+/* Random kit 1.3 */
+
+/*
+ * Copyright (c) 2003-2005, Jean-Sebastien Roy (js@jeannot.org)
+ *
+ * The rk_random and rk_seed functions algorithms and the original design of 
+ * the Mersenne Twister RNG:
+ *
+ *   Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
+ *   All rights reserved.
+ *
+ *   Redistribution and use in source and binary forms, with or without
+ *   modification, are permitted provided that the following conditions
+ *   are met:
+ * 
+ *   1. Redistributions of source code must retain the above copyright
+ *   notice, this list of conditions and the following disclaimer.
+ *
+ *   2. Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ *   3. The names of its contributors may not be used to endorse or promote
+ *   products derived from this software without specific prior written
+ *   permission.
+ *
+ *   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ *   "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ *   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ *   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ *   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ *   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ *   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ *   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ *   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ *   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ *   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ * 
+ * Original algorithm for the implementation of rk_interval function from
+ * Richard J. Wagner's implementation of the Mersenne Twister RNG, optimised by
+ * Magnus Jonsson.
+ *
+ * Constants used in the rk_double implementation by Isaku Wada.
+ * 
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the
+ * "Software"), to deal in the Software without restriction, including
+ * without limitation the rights to use, copy, modify, merge, publish,
+ * distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to
+ * the following conditions:
+ * 
+ * The above copyright notice and this permission notice shall be included
+ * in all copies or substantial portions of the Software.
+ * 
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+/* static char const rcsid[] =
+  "@(#) $Jeannot: randomkit.c,v 1.28 2005/07/21 22:14:09 js Exp $"; */
+
+#include <stddef.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <errno.h>
+#include <time.h>
+#include <limits.h>
+#include <math.h>
+
+#ifdef _WIN32
+/* Windows */
+#include <sys/timeb.h>
+#ifndef RK_NO_WINCRYPT
+/* Windows crypto */
+#ifndef _WIN32_WINNT
+#define _WIN32_WINNT 0x0400
+#endif
+#include <windows.h>
+#include <wincrypt.h>
+#endif
+#else
+/* Unix */
+#include <sys/time.h>
+#include <unistd.h>
+#endif
+
+#include "randomkit.h"
+
+#ifndef RK_DEV_URANDOM
+#define RK_DEV_URANDOM "/dev/urandom"
+#endif
+
+#ifndef RK_DEV_RANDOM
+#define RK_DEV_RANDOM "/dev/random"
+#endif
+
+char *rk_strerror[RK_ERR_MAX] =
+{
+	"no error",
+	"random device unvavailable"
+};
+
+/* static functions */
+static unsigned long rk_hash(unsigned long key);
+
+void rk_seed(unsigned long seed, rk_state *state)
+{
+	int pos;
+	seed &= 0xffffffffUL;
+
+	/* Knuth's PRNG as used in the Mersenne Twister reference implementation */
+	for (pos=0; pos<RK_STATE_LEN; pos++)
+	{
+		state->key[pos] = seed;
+		seed = (1812433253UL * (seed ^ (seed >> 30)) + pos + 1) & 0xffffffffUL;
+	}
+
+	state->pos = RK_STATE_LEN;
+	state->has_gauss = 0;
+}
+
+/* Thomas Wang 32 bits integer hash function */
+unsigned long rk_hash(unsigned long key)
+{
+  key += ~(key << 15);
+  key ^=  (key >> 10);
+  key +=  (key << 3);
+  key ^=  (key >> 6);
+  key += ~(key << 11);
+  key ^=  (key >> 16);
+  return key;
+}
+
+rk_error rk_randomseed(rk_state *state)
+{
+#ifndef _WIN32
+	struct timeval tv;
+#else
+	struct _timeb	tv;
+#endif
+        int i;
+
+	if(rk_devfill(state->key, sizeof(state->key), 0) == RK_NOERR)
+	{
+		state->key[0] |= 0x80000000UL; /* ensures non-zero key */
+		state->pos = RK_STATE_LEN;
+		state->has_gauss = 0;
+
+                for (i=0; i<624; i++)
+                {
+                    state->key[i] &= 0xffffffffUL;
+                }
+
+		return RK_NOERR;
+	}
+
+#ifndef _WIN32
+	gettimeofday(&tv, NULL);
+	rk_seed(rk_hash(getpid()) ^ rk_hash(tv.tv_sec) ^ rk_hash(tv.tv_usec)
+		^ rk_hash(clock()), state);
+#else
+	_ftime(&tv);
+	rk_seed(rk_hash(tv.time) ^ rk_hash(tv.millitm) ^ rk_hash(clock()), state);
+#endif
+
+	return RK_ENODEV;
+}
+
+/* Magic Mersenne Twister constants */
+#define N 624
+#define M 397
+#define MATRIX_A 0x9908b0dfUL
+#define UPPER_MASK 0x80000000UL
+#define LOWER_MASK 0x7fffffffUL
+
+/* Slightly optimised reference implementation of the Mersenne Twister */
+unsigned long rk_random(rk_state *state)
+{
+  unsigned long y;
+
+  if (state->pos == RK_STATE_LEN)
+	{
+    int i;
+
+    for (i=0;i<N-M;i++)
+		{
+      y = (state->key[i] & UPPER_MASK) | (state->key[i+1] & LOWER_MASK);
+      state->key[i] = state->key[i+M] ^ (y>>1) ^ (-(y & 1) & MATRIX_A);
+    }
+    for (;i<N-1;i++)
+		{
+      y = (state->key[i] & UPPER_MASK) | (state->key[i+1] & LOWER_MASK);
+      state->key[i] = state->key[i+(M-N)] ^ (y>>1) ^ (-(y & 1) & MATRIX_A);
+    }
+    y = (state->key[N-1] & UPPER_MASK) | (state->key[0] & LOWER_MASK);
+    state->key[N-1] = state->key[M-1] ^ (y>>1) ^ (-(y & 1) & MATRIX_A);
+
+    state->pos = 0;
+  }
+  
+  y = state->key[state->pos++];
+
+  /* Tempering */
+  y ^= (y >> 11);
+  y ^= (y << 7) & 0x9d2c5680UL;
+  y ^= (y << 15) & 0xefc60000UL;
+  y ^= (y >> 18);
+
+  return y;
+}
+
+long rk_long(rk_state *state)
+{
+	return rk_ulong(state) >> 1;
+}
+
+unsigned long rk_ulong(rk_state *state)
+{
+#if ULONG_MAX <= 0xffffffffUL
+	return rk_random(state);
+#else
+	return (rk_random(state) << 32) | (rk_random(state));
+#endif
+}
+
+unsigned long rk_interval(unsigned long max, rk_state *state)
+{
+	unsigned long mask = max, value;
+
+	if (max == 0) return 0;
+
+	/* Smallest bit mask >= max */
+	mask |= mask >> 1;
+	mask |= mask >> 2;
+	mask |= mask >> 4;
+	mask |= mask >> 8;
+	mask |= mask >> 16;
+#if ULONG_MAX > 0xffffffffUL
+	mask |= mask >> 32;
+#endif
+
+	/* Search a random value in [0..mask] <= max */
+	while ((value = (rk_ulong(state) & mask)) > max);
+
+	return value;
+}
+
+double rk_double(rk_state *state)
+{
+	/* shifts : 67108864 = 0x4000000, 9007199254740992 = 0x20000000000000 */
+	long a = rk_random(state) >> 5, b = rk_random(state) >> 6;
+	return (a * 67108864.0 + b) / 9007199254740992.0;
+}
+
+void rk_fill(void *buffer, size_t size, rk_state *state)
+{
+	unsigned long r;
+	unsigned char *buf = buffer;
+	
+	for (; size >= 4; size -= 4)
+	{
+		r = rk_random(state);
+		*(buf++) = r & 0xFF;
+		*(buf++) = (r >> 8) & 0xFF;
+		*(buf++) = (r >> 16) & 0xFF;
+		*(buf++) = (r >> 24) & 0xFF;
+	}
+	
+	if (!size) return;
+
+	r = rk_random(state);
+
+	for (; size; r >>= 8, size --)
+		*(buf++) = (unsigned char)(r & 0xFF);
+}
+
+rk_error rk_devfill(void *buffer, size_t size, int strong)
+{
+#ifndef _WIN32
+	FILE *rfile;
+	int done;
+
+	if (strong)
+		rfile = fopen(RK_DEV_RANDOM, "rb");
+	else
+		rfile = fopen(RK_DEV_URANDOM, "rb");
+	if (rfile == NULL)
+		return RK_ENODEV;
+	done = fread(buffer, size, 1, rfile);
+	fclose(rfile);
+	if (done)
+		return RK_NOERR;
+#else
+
+#ifndef RK_NO_WINCRYPT
+	HCRYPTPROV hCryptProv;
+	BOOL done;
+
+	if (!CryptAcquireContext(&hCryptProv, NULL, NULL, PROV_RSA_FULL,
+		CRYPT_VERIFYCONTEXT) || !hCryptProv)
+		return RK_ENODEV;
+	done = CryptGenRandom(hCryptProv, size, (unsigned char *)buffer);
+	CryptReleaseContext(hCryptProv, 0);
+	if (done)
+		return RK_NOERR;
+#endif
+
+#endif
+
+	return RK_ENODEV;
+}
+
+rk_error rk_altfill(void *buffer, size_t size, int strong, rk_state *state)
+{
+	rk_error err;
+
+	err = rk_devfill(buffer, size, strong);
+	if (err)
+		rk_fill(buffer, size, state);
+
+	return err;
+}
+
+double rk_gauss(rk_state *state)
+{
+	if (state->has_gauss)
+	{
+		state->has_gauss = 0;
+		return state->gauss;
+	}
+	else
+	{
+		double f, x1, x2, r2;
+		do
+		{
+			x1 = 2.0*rk_double(state) - 1.0;
+			x2 = 2.0*rk_double(state) - 1.0;
+			r2 = x1*x1 + x2*x2;
+		}
+		while (r2 >= 1.0 || r2 == 0.0);
+		
+		f = sqrt(-2.0*log(r2)/r2); /* Box-Muller transform */
+		state->has_gauss = 1;
+		state->gauss = f*x1; /* Keep for next call */
+		return f*x2;
+	}
+}
+
+
diff --git a/numpy/random/mtrand/randomkit.h b/numpy/random/mtrand/randomkit.h
new file mode 100644
index 000000000..389666854
--- /dev/null
+++ b/numpy/random/mtrand/randomkit.h
@@ -0,0 +1,189 @@
+/* Random kit 1.3 */
+
+/*
+ * Copyright (c) 2003-2005, Jean-Sebastien Roy (js@jeannot.org)
+ * 
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the
+ * "Software"), to deal in the Software without restriction, including
+ * without limitation the rights to use, copy, modify, merge, publish,
+ * distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to
+ * the following conditions:
+ * 
+ * The above copyright notice and this permission notice shall be included
+ * in all copies or substantial portions of the Software.
+ * 
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+/* @(#) $Jeannot: randomkit.h,v 1.24 2005/07/21 22:14:09 js Exp $ */
+
+/*
+ * Typical use:
+ *
+ * {
+ * 	rk_state state;
+ * 	unsigned long seed = 1, random_value;
+ * 	
+ * 	rk_seed(seed, &state); // Initialize the RNG
+ * 	...
+ * 	random_value = rk_random(&state); // Generate random values in [0..RK_MAX]
+ * }
+ * 
+ * Instead of rk_seed, you can use rk_randomseed which will get a random seed
+ * from /dev/urandom (or the clock, if /dev/urandom is unavailable):
+ *
+ * {
+ * 	rk_state state;
+ * 	unsigned long random_value;
+ * 	
+ * 	rk_randomseed(&state); // Initialize the RNG with a random seed
+ * 	...
+ * 	random_value = rk_random(&state); // Generate random values in [0..RK_MAX]
+ * }
+ */
+ 
+/*
+ * Useful macro:
+ * 	RK_DEV_RANDOM: the device used for random seeding.
+ *                 defaults to "/dev/urandom"
+ */
+
+#include <stddef.h>
+
+#ifndef _RANDOMKIT_
+#define _RANDOMKIT_
+
+#define	RK_STATE_LEN 624
+
+typedef struct rk_state_
+{
+	unsigned long key[RK_STATE_LEN];
+	int pos;
+	int has_gauss; /* !=0: gauss contains a gaussian deviate */
+	double gauss;
+
+    /* The rk_state structure has been extended to store the following
+     * information for the binomial generator. If the input values of n or p
+     * are different than nsave and psave, then the other parameters will be
+     * recomputed. RTK 2005-09-02 */
+
+    int has_binomial; /* !=0: following parameters initialized for 
+                              binomial */
+    double psave;
+    long nsave;
+    double r;
+    double q;
+    double fm;
+    long m;
+    double p1;
+    double xm;
+    double xl;
+    double xr;
+    double c;
+    double laml;
+    double lamr;
+    double p2;
+    double p3;
+    double p4;
+
+}
+rk_state;
+
+typedef enum {
+	RK_NOERR = 0, /* no error */
+	RK_ENODEV = 1, /* no RK_DEV_RANDOM device */
+	RK_ERR_MAX = 2
+} rk_error;
+
+/* error strings */
+extern char *rk_strerror[RK_ERR_MAX];
+
+/* Maximum generated random value */
+#define RK_MAX 0xFFFFFFFFUL
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/*
+ * Initialize the RNG state using the given seed.
+ */
+extern void rk_seed(unsigned long seed, rk_state *state);
+
+/*
+ * Initialize the RNG state using a random seed.
+ * Uses /dev/random or, when unavailable, the clock (see randomkit.c).
+ * Returns RK_NOERR when no errors occurs.
+ * Returns RK_ENODEV when the use of RK_DEV_RANDOM failed (for example because
+ * there is no such device). In this case, the RNG was initialized using the
+ * clock.
+ */
+extern rk_error rk_randomseed(rk_state *state);
+
+/*
+ * Returns a random unsigned long between 0 and RK_MAX inclusive
+ */
+extern unsigned long rk_random(rk_state *state);
+
+/*
+ * Returns a random long between 0 and LONG_MAX inclusive
+ */
+extern long rk_long(rk_state *state);
+
+/*
+ * Returns a random unsigned long between 0 and ULONG_MAX inclusive
+ */
+extern unsigned long rk_ulong(rk_state *state);
+
+/*
+ * Returns a random unsigned long between 0 and max inclusive.
+ */
+extern unsigned long rk_interval(unsigned long max, rk_state *state);
+
+/*
+ * Returns a random double between 0.0 and 1.0, 1.0 excluded.
+ */
+extern double rk_double(rk_state *state);
+
+/*
+ * fill the buffer with size random bytes
+ */
+extern void rk_fill(void *buffer, size_t size, rk_state *state);
+
+/*
+ * fill the buffer with randombytes from the random device
+ * Returns RK_ENODEV if the device is unavailable, or RK_NOERR if it is
+ * On Unix, if strong is defined, RK_DEV_RANDOM is used. If not, RK_DEV_URANDOM
+ * is used instead. This parameter has no effect on Windows.
+ * Warning: on most unixes RK_DEV_RANDOM will wait for enough entropy to answer
+ * which can take a very long time on quiet systems.
+ */
+extern rk_error rk_devfill(void *buffer, size_t size, int strong);
+
+/*
+ * fill the buffer using rk_devfill if the random device is available and using
+ * rk_fill if is is not
+ * parameters have the same meaning as rk_fill and rk_devfill
+ * Returns RK_ENODEV if the device is unavailable, or RK_NOERR if it is
+ */
+extern rk_error rk_altfill(void *buffer, size_t size, int strong,
+	rk_state *state);
+
+/*
+ * return a random gaussian deviate with variance unity and zero mean.
+ */
+extern double rk_gauss(rk_state *state);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* _RANDOMKIT_ */
diff --git a/numpy/random/mtrand/scipy.pxi b/numpy/random/mtrand/scipy.pxi
new file mode 100644
index 000000000..9c7b4d01c
--- /dev/null
+++ b/numpy/random/mtrand/scipy.pxi
@@ -0,0 +1,52 @@
+# :Author:    Robert Kern
+# :Copyright: 2004, Enthought, Inc.
+# :License:   BSD Style
+
+
+cdef extern from "scipy/arrayobject.h":
+    ctypedef enum PyArray_TYPES:
+        PyArray_BOOL
+        PyArray_BYTE
+        PyArray_UBYTE
+        PyArray_SHORT
+        PyArray_USHORT 
+        PyArray_INT
+        PyArray_UINT 
+        PyArray_LONG
+        PyArray_ULONG
+        PyArray_FLOAT
+        PyArray_DOUBLE 
+        PyArray_LONGDOUBLE
+        PyArray_CFLOAT
+        PyArray_CDOUBLE
+        PyArray_CLONGDOUBLE
+        PyArray_OBJECT
+        PyArray_STRING
+        PyArray_UNICODE
+        PyArray_VOID
+        PyArray_NTYPES
+        PyArray_NOTYPE
+
+    ctypedef int intp 
+
+    ctypedef extern class scipy.dtypedescr [object PyArray_Descr]:
+        cdef int type_num, elsize
+        cdef char type
+
+    ctypedef extern class scipy.ndarray [object PyArrayObject]:
+        cdef char *data
+        cdef int nd
+        cdef intp *dimensions
+        cdef intp *strides
+        cdef object base
+        cdef dtypedescr descr
+        cdef int flags
+
+    ndarray PyArray_SimpleNew(int ndims, intp* dims, int item_type)
+    int PyArray_Check(object obj)
+    ndarray PyArray_ContiguousFromObject(object obj, PyArray_TYPES type, 
+        int mindim, int maxdim)
+    intp PyArray_SIZE(ndarray arr)
+    void *PyArray_DATA(ndarray arr)
+
+    void import_array()
diff --git a/numpy/random/setup.py b/numpy/random/setup.py
new file mode 100644
index 000000000..17e5e0133
--- /dev/null
+++ b/numpy/random/setup.py
@@ -0,0 +1,24 @@
+
+from os.path import join
+
+def configuration(parent_package='',top_path=None):
+    from scipy.distutils.misc_util import Configuration
+    config = Configuration('random',parent_package,top_path)
+
+    # Configure mtrand
+    config.add_extension('mtrand',
+                         sources=[join('mtrand', x) for x in 
+                                  ['mtrand.c', 'randomkit.c', 'initarray.c',
+                                   'distributions.c']],
+                         libraries=['m'],
+                         depends = [join('mtrand','*.h'),
+                                    join('mtrand','*.pyx'),
+                                    join('mtrand','*.pxi'),
+                                    ]
+                        )
+            
+    return config
+
+if __name__ == '__main__':
+    from scipy.distutils.core import setup
+    setup(**configuration(top_path='').todict())