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diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c index 8f451eee2..9f82736b4 100644 --- a/numpy/random/mtrand/mtrand.c +++ b/numpy/random/mtrand/mtrand.c @@ -1,4 +1,4 @@ -/* Generated by Cython 0.15 on Sat Mar 3 18:15:37 2012 */ +/* Generated by Cython 0.15 on Sat Mar 3 21:58:53 2012 */ #define PY_SSIZE_T_CLEAN #include "Python.h" @@ -802,36 +802,36 @@ static char __pyx_k_244[] = "\n gumbel(loc=0.0, scale=1.0, size=None)\n\n static char __pyx_k_245[] = "RandomState.logistic (line 2921)"; static char __pyx_k_246[] = "\n logistic(loc=0.0, scale=1.0, size=None)\n\n Draw samples from a Logistic distribution.\n\n Samples are drawn from a Logistic distribution with specified\n parameters, loc (location or mean, also median), and scale (>0).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logistic : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Logistic distribution is\n\n .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n where :math:`\\mu` = location and :math:`s` = scale.\n\n The Logistic distribution is used in Extreme Value problems where it\n can act as a mixture of Gumbel distributions, in Epidemiology, and by\n the World Chess Federation (FIDE) where it is used in the Elo ranking\n system, assuming the performance of each player is a logistically\n distributed random variable.\n\n References\n ----------\n .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel, pp 132-133.\n .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LogisticDistribution.html\n .. [3] Wikipedia, \"Logistic-distribution\",\n http://en.wikipedia.org/wiki/Logistic-distribution\n\n Examples\n "" --------\n Draw samples from the distribution:\n\n >>> loc, scale = 10, 1\n >>> s = np.random.logistic(loc, scale, 10000)\n >>> count, bins, ignored = plt.hist(s, bins=50)\n\n # plot against distribution\n\n >>> def logist(x, loc, scale):\n ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n ... logist(bins, loc, scale).max())\n >>> plt.show()\n\n "; static char __pyx_k_247[] = "RandomState.lognormal (line 3009)"; -static char __pyx_k_248[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean, standard\n deviation, and shape. Note that the mean and standard deviation are not the\n values for the distribution itself, but of the underlying normal\n distribution it is derived from.\n\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, >0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed.\n\n The probability density function for the log-normal distribution is\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n of the normally distributed logarithm of the variable.\n\n A log-normal distribution results if a random variable is the *product* of\n a large number of independent, identically-distributed variables in the\n same way that a normal distribution results if the variable is the *sum*\n of a large number of independent, identically-distributed variables\n (see the last example). It is one of the so-called \"fat-tailed\"\n distributions.\n\n The log-normal distribution is commonly used to model the lifespan of units\n with fatigue-stress failure modes. Since thi""s includes\n most mechanical systems, the log-normal distribution has widespread\n application.\n\n It is also commonly used to model oil field sizes, species abundance, and\n latent periods of infectious diseases.\n\n References\n ----------\n .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n Distributions across the Sciences: Keys and Clues\", May 2001\n Vol. 51 No. 5 BioScience\n http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 31-32.\n .. [3] Wikipedia, \"Lognormal distribution\",\n http://en.wikipedia.org/wiki/Lognormal_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b"" = np.array(b) / np.min(b) # scale values to be positive\n\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, color='r', linewidth=2)\n >>> plt.show()\n\n "; -static char __pyx_k_249[] = "RandomState.rayleigh (line 3140)"; +static char __pyx_k_248[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean,\n standard deviation, and array shape. Note that the mean and standard\n deviation are not the values for the distribution itself, but of the\n underlying normal distribution it is derived from.\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, > 0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or float\n The desired samples. An array of the same shape as `size` if given,\n if `size` is None a float is returned.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed. The probability density function for the log-normal\n distribution is:\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n deviation of the normally distributed logarithm of the variable.\n A log-normal distribution results if a random variable is the *product*\n of a large number of independent, identically-distributed variables in\n the same way that a normal distribution results if the variable is the\n *sum* of a large number of independent, identically-distributed\n variables.\n\n Reference""s\n ----------\n Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n May, 2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b = np.array(b) / np.min(b) # scale values to be positive\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n >>> plt.show()\n\n "; +static char __pyx_k_249[] = "RandomState.rayleigh (line 3130)"; static char __pyx_k_250[] = "\n rayleigh(scale=1.0, size=None)\n\n Draw samples from a Rayleigh distribution.\n\n The :math:`\\chi` and Weibull distributions are generalizations of the\n Rayleigh.\n\n Parameters\n ----------\n scale : scalar\n Scale, also equals the mode. Should be >= 0.\n size : int or tuple of ints, optional\n Shape of the output. Default is None, in which case a single\n value is returned.\n\n Notes\n -----\n The probability density function for the Rayleigh distribution is\n\n .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n The Rayleigh distribution arises if the wind speed and wind direction are\n both gaussian variables, then the vector wind velocity forms a Rayleigh\n distribution. The Rayleigh distribution is used to model the expected\n output from wind turbines.\n\n References\n ----------\n ..[1] Brighton Webs Ltd., Rayleigh Distribution,\n http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n ..[2] Wikipedia, \"Rayleigh distribution\"\n http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n Wave heights tend to follow a Rayleigh distribution. If the mean wave\n height is 1 meter, what fraction of waves are likely to be larger than 3\n meters?\n\n >>> meanvalue = 1\n >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n >>> s = np.random.rayleigh(modevalue, 1000000)\n\n The percentage of waves larger than 3 meters is:\n\n >>> 100.*sum(s>3)/1000000.\n 0.087300000000000003\n\n "; -static char __pyx_k_251[] = "RandomState.wald (line 3212)"; +static char __pyx_k_251[] = "RandomState.wald (line 3202)"; static char __pyx_k_252[] = "\n wald(mean, scale, size=None)\n\n Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n As the scale approaches infinity, the distribution becomes more like a\n Gaussian.\n\n Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n this is by no means universal.\n\n The Inverse Gaussian distribution was first studied in relationship to\n Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n because there is an inverse relationship between the time to cover a unit\n distance and distance covered in unit time.\n\n Parameters\n ----------\n mean : scalar\n Distribution mean, should be > 0.\n scale : scalar\n Scale parameter, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn sample, all greater than zero.\n\n Notes\n -----\n The probability density function for the Wald distribution is\n\n .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n As noted above the Inverse Gaussian distribution first arise from attempts\n to model Brownian Motion. It is also a competitor to the Weibull for use in\n reliability modeling and modeling stock returns and interest rate\n processes.\n\n References\n ----------\n ..[1] Brighton Webs Ltd., Wald Distribution,\n http://www.brighton-webs.co.uk/distributions/wald.asp\n ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n Distribution: Theory : Methodology, and Applications\", CRC Press,\n 1988.\n ..[3] Wikipedia, \"Wald distributio""n\"\n http://en.wikipedia.org/wiki/Wald_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n >>> plt.show()\n\n "; -static char __pyx_k_253[] = "RandomState.triangular (line 3298)"; +static char __pyx_k_253[] = "RandomState.triangular (line 3288)"; static char __pyx_k_254[] = "\n triangular(left, mode, right, size=None)\n\n Draw samples from the triangular distribution.\n\n The triangular distribution is a continuous probability distribution with\n lower limit left, peak at mode, and upper limit right. Unlike the other\n distributions, these parameters directly define the shape of the pdf.\n\n Parameters\n ----------\n left : scalar\n Lower limit.\n mode : scalar\n The value where the peak of the distribution occurs.\n The value should fulfill the condition ``left <= mode <= right``.\n right : scalar\n Upper limit, should be larger than `left`.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n The returned samples all lie in the interval [left, right].\n\n Notes\n -----\n The probability density function for the Triangular distribution is\n\n .. math:: P(x;l, m, r) = \\begin{cases}\n \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n 0& \\text{otherwise}.\n \\end{cases}\n\n The triangular distribution is often used in ill-defined problems where the\n underlying distribution is not known, but some knowledge of the limits and\n mode exists. Often it is used in simulations.\n\n References\n ----------\n ..[1] Wikipedia, \"Triangular distribution\"\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=2""00,\n ... normed=True)\n >>> plt.show()\n\n "; -static char __pyx_k_255[] = "RandomState.binomial (line 3386)"; +static char __pyx_k_255[] = "RandomState.binomial (line 3376)"; static char __pyx_k_256[] = "\n binomial(n, p, size=None)\n\n Draw samples from a binomial distribution.\n\n Samples are drawn from a Binomial distribution with specified\n parameters, n trials and p probability of success where\n n an integer > 0 and p is in the interval [0,1]. (n may be\n input as a float, but it is truncated to an integer in use)\n\n Parameters\n ----------\n n : float (but truncated to an integer)\n parameter, > 0.\n p : float\n parameter, >= 0 and <=1.\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.binom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Binomial distribution is\n\n .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n where :math:`n` is the number of trials, :math:`p` is the probability\n of success, and :math:`N` is the number of successes.\n\n When estimating the standard error of a proportion in a population by\n using a random sample, the normal distribution works well unless the\n product p*n <=5, where p = population proportion estimate, and n =\n number of samples, in which case the binomial distribution is used\n instead. For example, a sample of 15 people shows 4 who are left\n handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n so the binomial distribution should be used in this case.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n Springer-Verlag, 2002.\n "" .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.\n .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/BinomialDistribution.html\n .. [5] Wikipedia, \"Binomial-distribution\",\n http://en.wikipedia.org/wiki/Binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> n, p = 10, .5 # number of trials, probability of each trial\n >>> s = np.random.binomial(n, p, 1000)\n # result of flipping a coin 10 times, tested 1000 times.\n\n A real world example. A company drills 9 wild-cat oil exploration\n wells, each with an estimated probability of success of 0.1. All nine\n wells fail. What is the probability of that happening?\n\n Let's do 20,000 trials of the model, and count the number that\n generate zero positive results.\n\n >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n answer = 0.38885, or 38%.\n\n "; -static char __pyx_k_257[] = "RandomState.negative_binomial (line 3494)"; +static char __pyx_k_257[] = "RandomState.negative_binomial (line 3484)"; static char __pyx_k_258[] = "\n negative_binomial(n, p, size=None)\n\n Draw samples from a negative_binomial distribution.\n\n Samples are drawn from a negative_Binomial distribution with specified\n parameters, `n` trials and `p` probability of success where `n` is an\n integer > 0 and `p` is in the interval [0, 1].\n\n Parameters\n ----------\n n : int\n Parameter, > 0.\n p : float\n Parameter, >= 0 and <=1.\n size : int or tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : int or ndarray of ints\n Drawn samples.\n\n Notes\n -----\n The probability density for the Negative Binomial distribution is\n\n .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n where :math:`n-1` is the number of successes, :math:`p` is the probability\n of success, and :math:`N+n-1` is the number of trials.\n\n The negative binomial distribution gives the probability of n-1 successes\n and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n If one throws a die repeatedly until the third time a \"1\" appears, then the\n probability distribution of the number of non-\"1\"s that appear before the\n third \"1\" is a negative binomial distribution.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n .. [2] Wikipedia, \"Negative binomial distribution\",\n http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n A real world example. A company drills wild-cat oil exploration well""s, each\n with an estimated probability of success of 0.1. What is the probability\n of having one success for each successive well, that is what is the\n probability of a single success after drilling 5 wells, after 6 wells,\n etc.?\n\n >>> s = np.random.negative_binomial(1, 0.1, 100000)\n >>> for i in range(1, 11):\n ... probability = sum(s<i) / 100000.\n ... print i, \"wells drilled, probability of one success =\", probability\n\n "; -static char __pyx_k_259[] = "RandomState.poisson (line 3589)"; +static char __pyx_k_259[] = "RandomState.poisson (line 3579)"; static char __pyx_k_260[] = "\n poisson(lam=1.0, size=None)\n\n Draw samples from a Poisson distribution.\n\n The Poisson distribution is the limit of the Binomial\n distribution for large N.\n\n Parameters\n ----------\n lam : float\n Expectation of interval, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Notes\n -----\n The Poisson distribution\n\n .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n For events with an expected separation :math:`\\lambda` the Poisson\n distribution :math:`f(k; \\lambda)` describes the probability of\n :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n Because the output is limited to the range of the C long type, a\n ValueError is raised when `lam` is within 10 sigma of the maximum\n representable value.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n .. [2] Wikipedia, \"Poisson distribution\",\n http://en.wikipedia.org/wiki/Poisson_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> import numpy as np\n >>> s = np.random.poisson(5, 10000)\n\n Display histogram of the sample:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n >>> plt.show()\n\n "; -static char __pyx_k_261[] = "RandomState.zipf (line 3660)"; +static char __pyx_k_261[] = "RandomState.zipf (line 3650)"; static char __pyx_k_262[] = "\n zipf(a, size=None)\n\n Draw samples from a Zipf distribution.\n\n Samples are drawn from a Zipf distribution with specified parameter\n `a` > 1.\n\n The Zipf distribution (also known as the zeta distribution) is a\n continuous probability distribution that satisfies Zipf's law: the\n frequency of an item is inversely proportional to its rank in a\n frequency table.\n\n Parameters\n ----------\n a : float > 1\n Distribution parameter.\n size : int or tuple of int, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn; a single integer is equivalent in\n its result to providing a mono-tuple, i.e., a 1-D array of length\n *size* is returned. The default is None, in which case a single\n scalar is returned.\n\n Returns\n -------\n samples : scalar or ndarray\n The returned samples are greater than or equal to one.\n\n See Also\n --------\n scipy.stats.distributions.zipf : probability density function,\n distribution, or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Zipf distribution is\n\n .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n where :math:`\\zeta` is the Riemann Zeta function.\n\n It is named for the American linguist George Kingsley Zipf, who noted\n that the frequency of any word in a sample of a language is inversely\n proportional to its rank in the frequency table.\n\n References\n ----------\n Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 2. # parameter\n >>> s = np.random.zipf""(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n Truncate s values at 50 so plot is interesting\n >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n >>> x = np.arange(1., 50.)\n >>> y = x**(-a)/sps.zetac(a)\n >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n >>> plt.show()\n\n "; -static char __pyx_k_263[] = "RandomState.geometric (line 3748)"; +static char __pyx_k_263[] = "RandomState.geometric (line 3738)"; static char __pyx_k_264[] = "\n geometric(p, size=None)\n\n Draw samples from the geometric distribution.\n\n Bernoulli trials are experiments with one of two outcomes:\n success or failure (an example of such an experiment is flipping\n a coin). The geometric distribution models the number of trials\n that must be run in order to achieve success. It is therefore\n supported on the positive integers, ``k = 1, 2, ...``.\n\n The probability mass function of the geometric distribution is\n\n .. math:: f(k) = (1 - p)^{k - 1} p\n\n where `p` is the probability of success of an individual trial.\n\n Parameters\n ----------\n p : float\n The probability of success of an individual trial.\n size : tuple of ints\n Number of values to draw from the distribution. The output\n is shaped according to `size`.\n\n Returns\n -------\n out : ndarray\n Samples from the geometric distribution, shaped according to\n `size`.\n\n Examples\n --------\n Draw ten thousand values from the geometric distribution,\n with the probability of an individual success equal to 0.35:\n\n >>> z = np.random.geometric(p=0.35, size=10000)\n\n How many trials succeeded after a single run?\n\n >>> (z == 1).sum() / 10000.\n 0.34889999999999999 #random\n\n "; -static char __pyx_k_265[] = "RandomState.hypergeometric (line 3814)"; +static char __pyx_k_265[] = "RandomState.hypergeometric (line 3804)"; static char __pyx_k_266[] = "\n hypergeometric(ngood, nbad, nsample, size=None)\n\n Draw samples from a Hypergeometric distribution.\n\n Samples are drawn from a Hypergeometric distribution with specified\n parameters, ngood (ways to make a good selection), nbad (ways to make\n a bad selection), and nsample = number of items sampled, which is less\n than or equal to the sum ngood + nbad.\n\n Parameters\n ----------\n ngood : float (but truncated to an integer)\n parameter, > 0.\n nbad : float\n parameter, >= 0.\n nsample : float\n parameter, > 0 and <= ngood+nbad\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.hypergeom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Hypergeometric distribution is\n\n .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n for P(x) the probability of x successes, n = ngood, m = nbad, and\n N = number of samples.\n\n Consider an urn with black and white marbles in it, ngood of them\n black and nbad are white. If you draw nsample balls without\n replacement, then the Hypergeometric distribution describes the\n distribution of black balls in the drawn sample.\n\n Note that this distribution is very similar to the Binomial\n distribution, except that in this case, samples are drawn without\n replacement, whereas in the Binomial case samples are drawn wit""h\n replacement (or the sample space is infinite). As the sample space\n becomes large, this distribution approaches the Binomial.\n\n References\n ----------\n .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/HypergeometricDistribution.html\n .. [3] Wikipedia, \"Hypergeometric-distribution\",\n http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> ngood, nbad, nsamp = 100, 2, 10\n # number of good, number of bad, and number of samples\n >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n >>> hist(s)\n # note that it is very unlikely to grab both bad items\n\n Suppose you have an urn with 15 white and 15 black marbles.\n If you pull 15 marbles at random, how likely is it that\n 12 or more of them are one color?\n\n >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n # answer = 0.003 ... pretty unlikely!\n\n "; -static char __pyx_k_267[] = "RandomState.logseries (line 3933)"; +static char __pyx_k_267[] = "RandomState.logseries (line 3923)"; static char __pyx_k_268[] = "\n logseries(p, size=None)\n\n Draw samples from a Logarithmic Series distribution.\n\n Samples are drawn from a Log Series distribution with specified\n parameter, p (probability, 0 < p < 1).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logser : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Log Series distribution is\n\n .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n where p = probability.\n\n The Log Series distribution is frequently used to represent species\n richness and occurrence, first proposed by Fisher, Corbet, and\n Williams in 1943 [2]. It may also be used to model the numbers of\n occupants seen in cars [3].\n\n References\n ----------\n .. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional\n species diversity through the log series distribution of\n occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n Volume 5, Number 5, September 1999 , pp. 187-195(9).\n .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n relation between the number of species and the number of\n individuals in a random sample of an animal population.\n Journal of Animal Ecology, 12:42-58.\n .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n Data Sets, CRC Press, 1994.\n .. [4] Wikipedia, \"Log""arithmic-distribution\",\n http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = .6\n >>> s = np.random.logseries(a, 10000)\n >>> count, bins, ignored = plt.hist(s)\n\n # plot against distribution\n\n >>> def logseries(k, p):\n ... return -p**k/(k*log(1-p))\n >>> plt.plot(bins, logseries(bins, a)*count.max()/\n logseries(bins, a).max(), 'r')\n >>> plt.show()\n\n "; -static char __pyx_k_269[] = "RandomState.multivariate_normal (line 4028)"; +static char __pyx_k_269[] = "RandomState.multivariate_normal (line 4018)"; static char __pyx_k_270[] = "\n multivariate_normal(mean, cov[, size])\n\n Draw random samples from a multivariate normal distribution.\n\n The multivariate normal, multinormal or Gaussian distribution is a\n generalization of the one-dimensional normal distribution to higher\n dimensions. Such a distribution is specified by its mean and\n covariance matrix. These parameters are analogous to the mean\n (average or \"center\") and variance (standard deviation, or \"width,\"\n squared) of the one-dimensional normal distribution.\n\n Parameters\n ----------\n mean : 1-D array_like, of length N\n Mean of the N-dimensional distribution.\n cov : 2-D array_like, of shape (N, N)\n Covariance matrix of the distribution. Must be symmetric and\n positive semi-definite for \"physically meaningful\" results.\n size : tuple of ints, optional\n Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because\n each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n If no shape is specified, a single (`N`-D) sample is returned.\n\n Returns\n -------\n out : ndarray\n The drawn samples, of shape *size*, if that was provided. If not,\n the shape is ``(N,)``.\n\n In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n value drawn from the distribution.\n\n Notes\n -----\n The mean is a coordinate in N-dimensional space, which represents the\n location where samples are most likely to be generated. This is\n analogous to the peak of the bell curve for the one-dimensional or\n univariate normal distribution.\n\n Covariance indicates the level to which two variables vary together.\n From the multivariate normal distribution, we draw ""N-dimensional\n samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix\n element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n \"spread\").\n\n Instead of specifying the full covariance matrix, popular\n approximations include:\n\n - Spherical covariance (*cov* is a multiple of the identity matrix)\n - Diagonal covariance (*cov* has non-negative elements, and only on\n the diagonal)\n\n This geometrical property can be seen in two dimensions by plotting\n generated data-points:\n\n >>> mean = [0,0]\n >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n >>> import matplotlib.pyplot as plt\n >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n Note that the covariance matrix must be non-negative definite.\n\n References\n ----------\n Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n 3rd ed., New York: McGraw-Hill, 1991.\n\n Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n 2nd ed., New York: Wiley, 2001.\n\n Examples\n --------\n >>> mean = (1,2)\n >>> cov = [[1,0],[1,0]]\n >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n >>> x.shape\n (3, 3, 2)\n\n The following is probably true, given that 0.6 is roughly twice the\n standard deviation:\n\n >>> print list( (x[0,0,:] - mean) < 0.6 )\n [True, True]\n\n "; -static char __pyx_k_271[] = "RandomState.multinomial (line 4160)"; +static char __pyx_k_271[] = "RandomState.multinomial (line 4150)"; static char __pyx_k_272[] = "\n multinomial(n, pvals, size=None)\n\n Draw samples from a multinomial distribution.\n\n The multinomial distribution is a multivariate generalisation of the\n binomial distribution. Take an experiment with one of ``p``\n possible outcomes. An example of such an experiment is throwing a dice,\n where the outcome can be 1 through 6. Each sample drawn from the\n distribution represents `n` such experiments. Its values,\n ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n was ``i``.\n\n Parameters\n ----------\n n : int\n Number of experiments.\n pvals : sequence of floats, length p\n Probabilities of each of the ``p`` different outcomes. These\n should sum to 1 (however, the last element is always assumed to\n account for the remaining probability, as long as\n ``sum(pvals[:-1]) <= 1)``.\n size : tuple of ints\n Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n and the output shape becomes ``(M, N, K, p)``, since each sample\n has shape ``(p,)``.\n\n Examples\n --------\n Throw a dice 20 times:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=1)\n array([[4, 1, 7, 5, 2, 1]])\n\n It landed 4 times on 1, once on 2, etc.\n\n Now, throw the dice 20 times, and 20 times again:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=2)\n array([[3, 4, 3, 3, 4, 3],\n [2, 4, 3, 4, 0, 7]])\n\n For the first run, we threw 3 times 1, 4 times 2, etc. For the second,\n we threw 2 times 1, 4 times 2, etc.\n\n A loaded dice is more likely to land on number 6:\n\n >>> np.random.multinomial(100, [1/7.]*5)\n array([13, 16, 13, 16, 42])\n\n "; -static char __pyx_k_273[] = "RandomState.dirichlet (line 4253)"; +static char __pyx_k_273[] = "RandomState.dirichlet (line 4243)"; static char __pyx_k_274[] = "\n dirichlet(alpha, size=None)\n\n Draw samples from the Dirichlet distribution.\n\n Draw `size` samples of dimension k from a Dirichlet distribution. A\n Dirichlet-distributed random variable can be seen as a multivariate\n generalization of a Beta distribution. Dirichlet pdf is the conjugate\n prior of a multinomial in Bayesian inference.\n\n Parameters\n ----------\n alpha : array\n Parameter of the distribution (k dimension for sample of\n dimension k).\n size : array\n Number of samples to draw.\n\n Returns\n -------\n samples : ndarray,\n The drawn samples, of shape (alpha.ndim, size).\n\n Notes\n -----\n .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n Uses the following property for computation: for each dimension,\n draw a random sample y_i from a standard gamma generator of shape\n `alpha_i`, then\n :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n Dirichlet distributed.\n\n References\n ----------\n .. [1] David McKay, \"Information Theory, Inference and Learning\n Algorithms,\" chapter 23,\n http://www.inference.phy.cam.ac.uk/mackay/\n .. [2] Wikipedia, \"Dirichlet distribution\",\n http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n Examples\n --------\n Taking an example cited in Wikipedia, this distribution can be used if\n one wanted to cut strings (each of initial length 1.0) into K pieces\n with different lengths, where each piece had, on average, a designated\n average length, but allowing some variation in the relative sizes of the\n pieces.\n\n >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n >>> plt.barh(range(20), s[0])\n >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n >>> plt.title(\"Lengths of Strings\")\n\n "; -static char __pyx_k_275[] = "RandomState.shuffle (line 4369)"; +static char __pyx_k_275[] = "RandomState.shuffle (line 4359)"; static char __pyx_k_276[] = "\n shuffle(x)\n\n Modify a sequence in-place by shuffling its contents.\n\n Parameters\n ----------\n x : array_like\n The array or list to be shuffled.\n\n Returns\n -------\n None\n\n Examples\n --------\n >>> arr = np.arange(10)\n >>> np.random.shuffle(arr)\n >>> arr\n [1 7 5 2 9 4 3 6 0 8]\n\n This function only shuffles the array along the first index of a\n multi-dimensional array:\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.shuffle(arr)\n >>> arr\n array([[3, 4, 5],\n [6, 7, 8],\n [0, 1, 2]])\n\n "; -static char __pyx_k_277[] = "RandomState.permutation (line 4431)"; +static char __pyx_k_277[] = "RandomState.permutation (line 4421)"; static char __pyx_k_278[] = "\n permutation(x)\n\n Randomly permute a sequence, or return a permuted range.\n\n If `x` is a multi-dimensional array, it is only shuffled along its\n first index.\n\n Parameters\n ----------\n x : int or array_like\n If `x` is an integer, randomly permute ``np.arange(x)``.\n If `x` is an array, make a copy and shuffle the elements\n randomly.\n\n Returns\n -------\n out : ndarray\n Permuted sequence or array range.\n\n Examples\n --------\n >>> np.random.permutation(10)\n array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n >>> np.random.permutation([1, 4, 9, 12, 15])\n array([15, 1, 9, 4, 12])\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.permutation(arr)\n array([[6, 7, 8],\n [0, 1, 2],\n [3, 4, 5]])\n\n "; static char __pyx_k__df[] = "df"; static char __pyx_k__mu[] = "mu"; @@ -13493,7 +13493,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_36logistic(PyObject *__pyx_v_sel */ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_37lognormal[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean, standard\n deviation, and shape. Note that the mean and standard deviation are not the\n values for the distribution itself, but of the underlying normal\n distribution it is derived from.\n\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, >0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed.\n\n The probability density function for the log-normal distribution is\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n of the normally distributed logarithm of the variable.\n\n A log-normal distribution results if a random variable is the *product* of\n a large number of independent, identically-distributed variables in the\n same way that a normal distribution results if the variable is the *sum*\n of a large number of independent, identically-distributed variables\n (see the last example). It is one of the so-called \"fat-tailed\"\n distributions.\n\n The log-normal distribution is commonly used to model the lifespan of units\n with fatigue-stress failure modes. Since thi""s includes\n most mechanical systems, the log-normal distribution has widespread\n application.\n\n It is also commonly used to model oil field sizes, species abundance, and\n latent periods of infectious diseases.\n\n References\n ----------\n .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n Distributions across the Sciences: Keys and Clues\", May 2001\n Vol. 51 No. 5 BioScience\n http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 31-32.\n .. [3] Wikipedia, \"Lognormal distribution\",\n http://en.wikipedia.org/wiki/Lognormal_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b"" = np.array(b) / np.min(b) # scale values to be positive\n\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, color='r', linewidth=2)\n >>> plt.show()\n\n "; +static char __pyx_doc_6mtrand_11RandomState_37lognormal[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean,\n standard deviation, and array shape. Note that the mean and standard\n deviation are not the values for the distribution itself, but of the\n underlying normal distribution it is derived from.\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, > 0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or float\n The desired samples. An array of the same shape as `size` if given,\n if `size` is None a float is returned.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed. The probability density function for the log-normal\n distribution is:\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n deviation of the normally distributed logarithm of the variable.\n A log-normal distribution results if a random variable is the *product*\n of a large number of independent, identically-distributed variables in\n the same way that a normal distribution results if the variable is the\n *sum* of a large number of independent, identically-distributed\n variables.\n\n Reference""s\n ----------\n Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n May, 2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b = np.array(b) / np.min(b) # scale values to be positive\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n >>> plt.show()\n\n "; static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_mean = 0; PyObject *__pyx_v_sigma = 0; @@ -13571,7 +13571,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se return NULL; __pyx_L4_argument_unpacking_done:; - /* "mtrand.pyx":3124 + /* "mtrand.pyx":3114 * cdef double fmean, fsigma * * fmean = PyFloat_AsDouble(mean) # <<<<<<<<<<<<<< @@ -13580,7 +13580,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se */ __pyx_v_fmean = PyFloat_AsDouble(__pyx_v_mean); - /* "mtrand.pyx":3125 + /* "mtrand.pyx":3115 * * fmean = PyFloat_AsDouble(mean) * fsigma = PyFloat_AsDouble(sigma) # <<<<<<<<<<<<<< @@ -13589,7 +13589,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se */ __pyx_v_fsigma = PyFloat_AsDouble(__pyx_v_sigma); - /* "mtrand.pyx":3127 + /* "mtrand.pyx":3117 * fsigma = PyFloat_AsDouble(sigma) * * if not PyErr_Occurred(): # <<<<<<<<<<<<<< @@ -13599,7 +13599,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se __pyx_t_1 = (!PyErr_Occurred()); 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3202; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_112), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3192; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3202; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3192; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } __pyx_L7:; - /* "mtrand.pyx":3203 + /* "mtrand.pyx":3193 * if fscale <= 0: * raise ValueError("scale <= 0") * return cont1_array_sc(self.internal_state, rk_rayleigh, size, fscale) # <<<<<<<<<<<<<< @@ -13895,7 +13895,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_38rayleigh(PyObject *__pyx_v_sel * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3208; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3208; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3208; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_5)); __Pyx_INCREF(((PyObject *)__pyx_v_oscale)); PyTuple_SET_ITEM(__pyx_t_5, 0, ((PyObject *)__pyx_v_oscale)); @@ -13953,40 +13953,40 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_38rayleigh(PyObject *__pyx_v_sel PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3208; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_5)); 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if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3208; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; if (__pyx_t_1) { - /* "mtrand.pyx":3209 + /* "mtrand.pyx":3199 * oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) * if np.any(np.less_equal(oscale, 0.0)): * raise ValueError("scale <= 0.0") # <<<<<<<<<<<<<< * return cont1_array(self.internal_state, rk_rayleigh, size, oscale) * */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_114), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3209; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_114), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3199; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3209; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3199; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } __pyx_L8:; - /* "mtrand.pyx":3210 + /* "mtrand.pyx":3200 * if np.any(np.less_equal(oscale, 0.0)): * raise ValueError("scale <= 0.0") * return cont1_array(self.internal_state, rk_rayleigh, size, oscale) # <<<<<<<<<<<<<< @@ -13994,7 +13994,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_38rayleigh(PyObject *__pyx_v_sel * def wald(self, mean, scale, size=None): */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_rayleigh, __pyx_v_size, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3210; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_rayleigh, __pyx_v_size, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3200; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; @@ -14016,7 +14016,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_38rayleigh(PyObject *__pyx_v_sel return __pyx_r; } -/* "mtrand.pyx":3212 +/* "mtrand.pyx":3202 * return cont1_array(self.internal_state, rk_rayleigh, size, oscale) * * def wald(self, mean, scale, size=None): # <<<<<<<<<<<<<< @@ -14066,7 +14066,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__scale); if (likely(values[1])) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3212; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3202; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } case 2: if (kw_args > 0) { @@ -14075,7 +14075,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P } } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "wald") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3212; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "wald") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3202; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_mean = values[0]; __pyx_v_scale = values[1]; @@ -14094,14 +14094,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3212; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3202; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.wald", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - /* "mtrand.pyx":3278 + /* "mtrand.pyx":3268 * cdef double fmean, fscale * * fmean = PyFloat_AsDouble(mean) # <<<<<<<<<<<<<< @@ -14110,7 +14110,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P */ __pyx_v_fmean = PyFloat_AsDouble(__pyx_v_mean); - /* "mtrand.pyx":3279 + /* "mtrand.pyx":3269 * * fmean = PyFloat_AsDouble(mean) * fscale = PyFloat_AsDouble(scale) # <<<<<<<<<<<<<< @@ -14119,7 +14119,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P */ __pyx_v_fscale = PyFloat_AsDouble(__pyx_v_scale); - /* "mtrand.pyx":3280 + /* "mtrand.pyx":3270 * fmean = PyFloat_AsDouble(mean) * fscale = PyFloat_AsDouble(scale) * if not PyErr_Occurred(): # <<<<<<<<<<<<<< @@ -14129,7 +14129,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P __pyx_t_1 = (!PyErr_Occurred()); if (__pyx_t_1) { - /* "mtrand.pyx":3281 + /* "mtrand.pyx":3271 * fscale = PyFloat_AsDouble(scale) * if not PyErr_Occurred(): * if fmean <= 0: # <<<<<<<<<<<<<< @@ -14139,23 +14139,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P __pyx_t_1 = (__pyx_v_fmean <= 0.0); if (__pyx_t_1) { - /* "mtrand.pyx":3282 + /* "mtrand.pyx":3272 * if not PyErr_Occurred(): * if fmean <= 0: * raise ValueError("mean <= 0") # <<<<<<<<<<<<<< * if fscale <= 0: * raise ValueError("scale <= 0") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_116), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3282; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_116), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3272; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3282; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3272; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } __pyx_L7:; - /* "mtrand.pyx":3283 + /* "mtrand.pyx":3273 * if fmean <= 0: * raise ValueError("mean <= 0") * if fscale <= 0: # <<<<<<<<<<<<<< @@ -14165,23 +14165,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P __pyx_t_1 = (__pyx_v_fscale <= 0.0); if (__pyx_t_1) { - /* "mtrand.pyx":3284 + /* "mtrand.pyx":3274 * raise ValueError("mean <= 0") * if fscale <= 0: * raise ValueError("scale <= 0") # <<<<<<<<<<<<<< * return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale) * */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_117), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3284; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_117), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3274; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3284; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3274; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } __pyx_L8:; - /* "mtrand.pyx":3285 + /* "mtrand.pyx":3275 * if fscale <= 0: * raise ValueError("scale <= 0") * return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale) # <<<<<<<<<<<<<< @@ -14189,7 +14189,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); 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- /* "mtrand.pyx":3288 + /* "mtrand.pyx":3278 * * PyErr_Clear() * omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED) # <<<<<<<<<<<<<< * oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) * if np.any(np.less_equal(omean,0.0)): */ - __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_mean, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3288; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_mean, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3278; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3288; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3278; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_v_omean = ((PyArrayObject *)__pyx_t_2); __pyx_t_2 = 0; - /* "mtrand.pyx":3289 + /* "mtrand.pyx":3279 * PyErr_Clear() * omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED) * oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) # <<<<<<<<<<<<<< * if np.any(np.less_equal(omean,0.0)): * raise ValueError("mean <= 0.0") */ - __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_scale, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3289; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_scale, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3279; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3289; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3279; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_v_oscale = ((PyArrayObject *)__pyx_t_2); __pyx_t_2 = 0; - /* "mtrand.pyx":3290 + /* "mtrand.pyx":3280 * omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED) * oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED) * if np.any(np.less_equal(omean,0.0)): # <<<<<<<<<<<<<< * raise ValueError("mean <= 0.0") * elif np.any(np.less_equal(oscale,0.0)): */ - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3290; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); 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if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3290; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3290; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_5)); __Pyx_INCREF(((PyObject *)__pyx_v_omean)); PyTuple_SET_ITEM(__pyx_t_5, 0, ((PyObject *)__pyx_v_omean)); @@ -14260,58 +14260,58 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_2); 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3292; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3282; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_5 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3292; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3282; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3292; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3282; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); __Pyx_INCREF(((PyObject *)__pyx_v_oscale)); PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)__pyx_v_oscale)); @@ -14319,40 +14319,40 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3292; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3282; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); 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if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3292; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3282; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; if (__pyx_t_1) { - /* "mtrand.pyx":3293 + /* "mtrand.pyx":3283 * raise ValueError("mean <= 0.0") * elif np.any(np.less_equal(oscale,0.0)): * raise ValueError("scale <= 0.0") # <<<<<<<<<<<<<< * return cont2_array(self.internal_state, rk_wald, size, omean, oscale) * */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_120), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3293; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_120), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3283; 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3284; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; @@ -14383,7 +14383,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P return __pyx_r; } -/* "mtrand.pyx":3298 +/* "mtrand.pyx":3288 * * * def triangular(self, left, mode, right, size=None): # <<<<<<<<<<<<<< @@ -14437,13 +14437,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__mode); if (likely(values[1])) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3298; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3288; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } case 2: values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__right); 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__pyx_v_mode = values[1]; @@ -14473,14 +14473,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3298; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3288; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.triangular", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - /* "mtrand.pyx":3358 + /* "mtrand.pyx":3348 * cdef double fleft, fmode, fright * * fleft = PyFloat_AsDouble(left) # <<<<<<<<<<<<<< @@ -14489,7 +14489,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s */ __pyx_v_fleft = PyFloat_AsDouble(__pyx_v_left); 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if (__pyx_t_1) { - /* "mtrand.pyx":3363 + /* "mtrand.pyx":3353 * if not PyErr_Occurred(): * if fleft > fmode: * raise ValueError("left > mode") # <<<<<<<<<<<<<< * if fmode > fright: * raise ValueError("mode > right") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_122), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3363; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_122), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3353; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3363; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3353; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } __pyx_L7:; - /* "mtrand.pyx":3364 + /* "mtrand.pyx":3354 * if fleft > fmode: * raise ValueError("left > mode") * if fmode > fright: # <<<<<<<<<<<<<< @@ -14553,23 +14553,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s __pyx_t_1 = (__pyx_v_fmode > __pyx_v_fright); if (__pyx_t_1) { - /* "mtrand.pyx":3365 + /* "mtrand.pyx":3355 * raise ValueError("left > mode") * if fmode > fright: * raise ValueError("mode > right") # <<<<<<<<<<<<<< * if fleft == fright: * raise ValueError("left == right") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_124), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3365; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_124), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3365; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } __pyx_L8:; - /* "mtrand.pyx":3366 + /* "mtrand.pyx":3356 * if fmode > fright: * raise ValueError("mode > right") * if fleft == fright: # <<<<<<<<<<<<<< @@ -14579,23 +14579,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s __pyx_t_1 = (__pyx_v_fleft == __pyx_v_fright); 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if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3380; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3370; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_5 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__any); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3380; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__any); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3370; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3380; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; 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__pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "binomial") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3376; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_n = values[0]; __pyx_v_p = values[1]; @@ -14960,14 +14960,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_41binomial(PyObject *__pyx_v_sel } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("binomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3386; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("binomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3376; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.binomial", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); 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__pyx_lineno = 3486; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__less_equal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3476; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3486; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3476; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_2)); __Pyx_INCREF(__pyx_v_n); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_n); @@ -15149,57 +15149,57 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_41binomial(PyObject *__pyx_v_sel __Pyx_INCREF(__pyx_int_0); PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_int_0); __Pyx_GIVEREF(__pyx_int_0); - __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3486; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3476; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; - __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3486; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3476; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_2)); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); __pyx_t_5 = 0; - __pyx_t_5 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3486; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3476; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_5); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3486; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_5); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3476; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; if (__pyx_t_1) { - /* "mtrand.pyx":3487 + /* "mtrand.pyx":3477 * op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED) * if np.any(np.less_equal(n, 0)): * raise ValueError("n <= 0") # <<<<<<<<<<<<<< * if np.any(np.less(p, 0)): * raise ValueError("p < 0") */ - __pyx_t_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_136), NULL); 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PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_int_0); __Pyx_GIVEREF(__pyx_int_0); - __pyx_t_4 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3488; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3478; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; - __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3488; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3478; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_5)); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_4); 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if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3491; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_138), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3481; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3491; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3481; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L11; } __pyx_L11:; - /* "mtrand.pyx":3492 + /* "mtrand.pyx":3482 * if np.any(np.greater(p, 1)): * raise ValueError("p > 1") * return discnp_array(self.internal_state, rk_binomial, size, on, op) # <<<<<<<<<<<<<< @@ -15306,7 +15306,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_41binomial(PyObject *__pyx_v_sel * def negative_binomial(self, n, p, size=None): */ __Pyx_XDECREF(__pyx_r); 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__pyx_v_p = values[1]; @@ -15407,14 +15407,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("negative_binomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3494; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("negative_binomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3484; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.negative_binomial", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - /* "mtrand.pyx":3564 + /* "mtrand.pyx":3554 * cdef double fp * * fp = PyFloat_AsDouble(p) # <<<<<<<<<<<<<< @@ -15423,7 +15423,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ */ __pyx_v_fp = PyFloat_AsDouble(__pyx_v_p); 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3578; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_n, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_2))); __pyx_v_on = ((PyArrayObject *)__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - /* "mtrand.pyx":3579 + /* "mtrand.pyx":3569 * * on = <ndarray>PyArray_FROM_OTF(n, NPY_DOUBLE, NPY_ALIGNED) * op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED) # <<<<<<<<<<<<<< * if np.any(np.less_equal(n, 0)): * raise ValueError("n <= 0") */ - __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_p, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3579; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_p, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_2))); __pyx_v_op = ((PyArrayObject *)__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - /* "mtrand.pyx":3580 + /* "mtrand.pyx":3570 * on = <ndarray>PyArray_FROM_OTF(n, NPY_DOUBLE, NPY_ALIGNED) * op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED) * if np.any(np.less_equal(n, 0)): # <<<<<<<<<<<<<< * raise ValueError("n <= 0") * if np.any(np.less(p, 0)): */ - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3580; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3580; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3580; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__less_equal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3580; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__less_equal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; 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PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_int_1); __Pyx_GIVEREF(__pyx_int_1); - __pyx_t_3 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; - __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); 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if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3585; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_144), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3575; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3585; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3575; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L11; } __pyx_L11:; - /* "mtrand.pyx":3586 + /* "mtrand.pyx":3576 * if np.any(np.greater(p, 1)): * raise ValueError("p > 1") * return discdd_array(self.internal_state, rk_negative_binomial, size, # <<<<<<<<<<<<<< @@ -15762,14 +15762,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ */ __Pyx_XDECREF(__pyx_r); 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{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3589; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("poisson", 0, 0, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3579; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.poisson", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - /* "mtrand.pyx":3643 + /* "mtrand.pyx":3633 * cdef ndarray olam * cdef double flam * flam = PyFloat_AsDouble(lam) # <<<<<<<<<<<<<< @@ -15875,7 +15875,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self */ __pyx_v_flam = PyFloat_AsDouble(__pyx_v_lam); - /* "mtrand.pyx":3644 + /* "mtrand.pyx":3634 * cdef double flam * flam = PyFloat_AsDouble(lam) * if not PyErr_Occurred(): # <<<<<<<<<<<<<< @@ -15885,68 +15885,68 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self __pyx_t_1 = (!PyErr_Occurred()); 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__pyx_lineno = 3649; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __pyx_f_6mtrand_discd_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_poisson, __pyx_v_size, __pyx_v_flam); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; @@ -15963,7 +15963,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self } __pyx_L6:; - /* "mtrand.pyx":3651 + /* "mtrand.pyx":3641 * return discd_array_sc(self.internal_state, rk_poisson, size, flam) * * PyErr_Clear() # <<<<<<<<<<<<<< @@ -15972,37 +15972,37 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self */ PyErr_Clear(); - /* "mtrand.pyx":3653 + /* "mtrand.pyx":3643 * PyErr_Clear() * * olam = <ndarray>PyArray_FROM_OTF(lam, NPY_DOUBLE, NPY_ALIGNED) # <<<<<<<<<<<<<< * if np.any(np.less(olam, 0)): * raise ValueError("lam < 0") */ - __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_lam, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3653; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_lam, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3643; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3))); __pyx_v_olam = ((PyArrayObject *)__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - /* "mtrand.pyx":3654 + /* "mtrand.pyx":3644 * * olam = <ndarray>PyArray_FROM_OTF(lam, NPY_DOUBLE, NPY_ALIGNED) * if np.any(np.less(olam, 0)): # <<<<<<<<<<<<<< * raise ValueError("lam < 0") * if np.any(np.greater(olam, self.poisson_lam_max)): */ - __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3654; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; 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goto __pyx_L1_error;} + __pyx_t_5 = __pyx_f_6mtrand_discd_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_poisson, __pyx_v_size, __pyx_v_olam); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3648; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __pyx_r = __pyx_t_5; __pyx_t_5 = 0; @@ -16133,7 +16133,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self return __pyx_r; } -/* "mtrand.pyx":3660 +/* "mtrand.pyx":3650 * return discd_array(self.internal_state, rk_poisson, size, olam) * * def zipf(self, a, size=None): # <<<<<<<<<<<<<< @@ -16182,7 +16182,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_44zipf(PyObject *__pyx_v_self, P } } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "zipf") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3660; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "zipf") < 0)) {__pyx_filename = __pyx_f[0]; 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__pyx_lineno = 3810; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3800; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3810; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3800; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); __Pyx_INCREF(((PyObject *)__pyx_v_op)); PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)__pyx_v_op)); @@ -16646,40 +16646,40 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_45geometric(PyObject *__pyx_v_se PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; 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__pyx_lineno = 3814; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "hypergeometric") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3804; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_ngood = values[0]; __pyx_v_nbad = values[1]; @@ -16800,14 +16800,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("hypergeometric", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3814; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("hypergeometric", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3804; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.hypergeometric", __pyx_clineno, __pyx_lineno, __pyx_filename); 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__pyx_t_4 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; - __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3922; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3912; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_3)); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); __pyx_t_5 = 0; - __pyx_t_5 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3922; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3912; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_5); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3922; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_5); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3912; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; if (__pyx_t_1) { - /* "mtrand.pyx":3923 + /* "mtrand.pyx":3913 * onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ALIGNED) * if np.any(np.less(ongood, 1)): * raise ValueError("ngood < 1") # <<<<<<<<<<<<<< * if np.any(np.less(onbad, 1)): * raise ValueError("nbad < 1") */ - __pyx_t_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_170), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3923; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_170), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3913; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_Raise(__pyx_t_5, 0, 0, 0); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3923; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3913; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L11; } __pyx_L11:; - /* "mtrand.pyx":3924 + /* "mtrand.pyx":3914 * if np.any(np.less(ongood, 1)): * raise ValueError("ngood < 1") * if np.any(np.less(onbad, 1)): # <<<<<<<<<<<<<< * raise ValueError("nbad < 1") * if np.any(np.less(onsample, 1)): */ - __pyx_t_5 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3924; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); - __pyx_t_3 = PyObject_GetAttr(__pyx_t_5, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3924; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(__pyx_t_5, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - __pyx_t_5 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3924; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); - __pyx_t_2 = PyObject_GetAttr(__pyx_t_5, __pyx_n_s__less); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3924; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(__pyx_t_5, __pyx_n_s__less); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3924; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_5)); __Pyx_INCREF(((PyObject *)__pyx_v_onbad)); PyTuple_SET_ITEM(__pyx_t_5, 0, ((PyObject *)__pyx_v_onbad)); @@ -17119,57 +17119,57 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx __Pyx_INCREF(__pyx_int_1); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_int_1); __Pyx_GIVEREF(__pyx_int_1); - __pyx_t_4 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3924; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; - __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3924; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_5)); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_4); __pyx_t_4 = 0; - __pyx_t_4 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3924; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3924; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_1) { - /* "mtrand.pyx":3925 + /* "mtrand.pyx":3915 * raise ValueError("ngood < 1") * if np.any(np.less(onbad, 1)): * raise ValueError("nbad < 1") # <<<<<<<<<<<<<< * if np.any(np.less(onsample, 1)): * raise ValueError("nsample < 1") */ - __pyx_t_4 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_171), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3925; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_171), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3915; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_Raise(__pyx_t_4, 0, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3925; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3915; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L12; } __pyx_L12:; - /* "mtrand.pyx":3926 + /* "mtrand.pyx":3916 * if np.any(np.less(onbad, 1)): * raise ValueError("nbad < 1") * if np.any(np.less(onsample, 1)): # <<<<<<<<<<<<<< * raise ValueError("nsample < 1") * if np.any(np.less(np.add(ongood, onbad),onsample)): */ - __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3926; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_5 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__any); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3926; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__any); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3926; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_3 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__less); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3926; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__less); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3926; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); __Pyx_INCREF(((PyObject *)__pyx_v_onsample)); PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)__pyx_v_onsample)); @@ -17177,62 +17177,62 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx __Pyx_INCREF(__pyx_int_1); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_int_1); __Pyx_GIVEREF(__pyx_int_1); - __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3926; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; - __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3926; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); 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__pyx_lineno = 3928; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__add); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3918; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3928; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3918; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_2)); __Pyx_INCREF(((PyObject *)__pyx_v_ongood)); PyTuple_SET_ITEM(__pyx_t_2, 0, ((PyObject *)__pyx_v_ongood)); @@ -17240,11 +17240,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx __Pyx_INCREF(((PyObject *)__pyx_v_onbad)); PyTuple_SET_ITEM(__pyx_t_2, 1, ((PyObject *)__pyx_v_onbad)); __Pyx_GIVEREF(((PyObject *)__pyx_v_onbad)); 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} -/* "mtrand.pyx":3933 +/* "mtrand.pyx":3923 * ongood, onbad, onsample) * * def logseries(self, p, size=None): # <<<<<<<<<<<<<< @@ -17375,7 +17375,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se } } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "logseries") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3933; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "logseries") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3923; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_p = values[0]; __pyx_v_size = values[1]; @@ -17390,14 +17390,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("logseries", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3933; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("logseries", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3923; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.logseries", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - /* "mtrand.pyx":4010 + /* "mtrand.pyx":4000 * cdef double fp * * fp = PyFloat_AsDouble(p) # <<<<<<<<<<<<<< @@ -17406,7 +17406,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se */ __pyx_v_fp = PyFloat_AsDouble(__pyx_v_p); - /* "mtrand.pyx":4011 + /* "mtrand.pyx":4001 * * fp = PyFloat_AsDouble(p) * if not PyErr_Occurred(): # <<<<<<<<<<<<<< @@ -17416,7 +17416,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se __pyx_t_1 = (!PyErr_Occurred()); if (__pyx_t_1) { - /* "mtrand.pyx":4012 + /* "mtrand.pyx":4002 * fp = PyFloat_AsDouble(p) * if not PyErr_Occurred(): * if fp <= 0.0: # <<<<<<<<<<<<<< @@ -17426,23 +17426,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se __pyx_t_1 = (__pyx_v_fp <= 0.0); if (__pyx_t_1) { - /* "mtrand.pyx":4013 + /* "mtrand.pyx":4003 * if not PyErr_Occurred(): * if fp <= 0.0: * raise ValueError("p <= 0.0") # <<<<<<<<<<<<<< * if fp >= 1.0: * raise ValueError("p >= 1.0") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_175), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_175), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4003; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4003; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } __pyx_L7:; - /* "mtrand.pyx":4014 + /* "mtrand.pyx":4004 * if fp <= 0.0: * raise ValueError("p <= 0.0") * if fp >= 1.0: # <<<<<<<<<<<<<< @@ -17452,23 +17452,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se __pyx_t_1 = (__pyx_v_fp >= 1.0); 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4021; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4021; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4021; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; 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if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4021; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; if (__pyx_t_1) { - /* "mtrand.pyx":4022 + /* "mtrand.pyx":4012 * op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED) * if np.any(np.less_equal(op, 0.0)): * raise ValueError("p <= 0.0") # <<<<<<<<<<<<<< * if np.any(np.greater_equal(op, 1.0)): * raise ValueError("p >= 1.0") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_178), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4022; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_178), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4012; 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if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); __Pyx_INCREF(((PyObject *)__pyx_v_op)); PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)__pyx_v_op)); @@ -17594,40 +17594,40 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; - __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyObject_Call(__pyx_t_5, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_t_5, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; if (__pyx_t_1) { - /* "mtrand.pyx":4024 + /* "mtrand.pyx":4014 * raise ValueError("p <= 0.0") * if np.any(np.greater_equal(op, 1.0)): * raise ValueError("p >= 1.0") # <<<<<<<<<<<<<< * return discd_array(self.internal_state, rk_logseries, size, op) * */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_179), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4024; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_179), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4014; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4024; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4014; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L10; } __pyx_L10:; - /* "mtrand.pyx":4025 + /* "mtrand.pyx":4015 * if np.any(np.greater_equal(op, 1.0)): * raise ValueError("p >= 1.0") * return discd_array(self.internal_state, rk_logseries, size, op) # <<<<<<<<<<<<<< @@ -17635,7 +17635,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se * # Multivariate distributions: */ __Pyx_XDECREF(__pyx_r); 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if (likely(values[1])) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("multivariate_normal", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4028; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("multivariate_normal", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4018; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } case 2: if (kw_args > 0) { @@ -17725,7 +17725,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * } } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "multivariate_normal") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4028; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "multivariate_normal") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4018; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_mean = values[0]; __pyx_v_cov = values[1]; @@ -17744,7 +17744,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("multivariate_normal", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4028; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("multivariate_normal", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4018; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.multivariate_normal", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); @@ -17753,24 +17753,24 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __Pyx_INCREF(__pyx_v_mean); __Pyx_INCREF(__pyx_v_cov); - /* "mtrand.pyx":4120 + /* "mtrand.pyx":4110 * """ * # Check preconditions on arguments * mean = np.array(mean) # <<<<<<<<<<<<<< * cov = np.array(cov) * if size is None: */ - __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4120; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4110; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - __pyx_t_2 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__array); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4120; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__array); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4110; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4120; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4110; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_1)); __Pyx_INCREF(__pyx_v_mean); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v_mean); __Pyx_GIVEREF(__pyx_v_mean); - __pyx_t_3 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_1), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4120; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_1), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4110; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; @@ -17778,24 +17778,24 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __pyx_v_mean = __pyx_t_3; __pyx_t_3 = 0; - /* "mtrand.pyx":4121 + /* "mtrand.pyx":4111 * # Check preconditions on arguments * mean = np.array(mean) * cov = np.array(cov) # <<<<<<<<<<<<<< * if size is None: * shape = [] */ - __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4121; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4111; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __pyx_t_1 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__array); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4121; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__array); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4111; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4121; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4111; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_3)); __Pyx_INCREF(__pyx_v_cov); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_cov); __Pyx_GIVEREF(__pyx_v_cov); - __pyx_t_2 = PyObject_Call(__pyx_t_1, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4121; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_t_1, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4111; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; @@ -17803,7 +17803,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __pyx_v_cov = __pyx_t_2; __pyx_t_2 = 0; - /* "mtrand.pyx":4122 + /* "mtrand.pyx":4112 * mean = np.array(mean) * cov = np.array(cov) * if size is None: # <<<<<<<<<<<<<< @@ -17813,14 +17813,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __pyx_t_4 = (__pyx_v_size == Py_None); if (__pyx_t_4) { - /* "mtrand.pyx":4123 + /* "mtrand.pyx":4113 * cov = np.array(cov) * if size is None: * shape = [] # <<<<<<<<<<<<<< * else: * shape = size */ - __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4123; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4113; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_2)); __pyx_v_shape = ((PyObject *)__pyx_t_2); __pyx_t_2 = 0; @@ -17828,7 +17828,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * } /*else*/ { - /* "mtrand.pyx":4125 + /* "mtrand.pyx":4115 * shape = [] * else: * shape = size # <<<<<<<<<<<<<< @@ -17840,64 +17840,64 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * } __pyx_L6:; - /* "mtrand.pyx":4126 + /* "mtrand.pyx":4116 * else: * shape = size * if len(mean.shape) != 1: # <<<<<<<<<<<<<< * raise ValueError("mean must be 1 dimensional") * if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]): */ - __pyx_t_2 = PyObject_GetAttr(__pyx_v_mean, __pyx_n_s__shape); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4126; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(__pyx_v_mean, __pyx_n_s__shape); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4116; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_5 = PyObject_Length(__pyx_t_2); if (unlikely(__pyx_t_5 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4126; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Length(__pyx_t_2); if (unlikely(__pyx_t_5 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4116; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_4 = (__pyx_t_5 != 1); if (__pyx_t_4) { - /* "mtrand.pyx":4127 + /* "mtrand.pyx":4117 * shape = size * if len(mean.shape) != 1: * raise ValueError("mean must be 1 dimensional") # <<<<<<<<<<<<<< * if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]): * raise ValueError("cov must be 2 dimensional and square") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_181), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4127; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_181), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4117; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4127; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4117; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } __pyx_L7:; - /* "mtrand.pyx":4128 + /* "mtrand.pyx":4118 * if len(mean.shape) != 1: * raise ValueError("mean must be 1 dimensional") * if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]): # <<<<<<<<<<<<<< * raise ValueError("cov must be 2 dimensional and square") * if mean.shape[0] != cov.shape[0]: */ - __pyx_t_2 = PyObject_GetAttr(__pyx_v_cov, __pyx_n_s__shape); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(__pyx_v_cov, __pyx_n_s__shape); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4118; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_5 = PyObject_Length(__pyx_t_2); if (unlikely(__pyx_t_5 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Length(__pyx_t_2); if (unlikely(__pyx_t_5 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4118; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_4 = (__pyx_t_5 != 2); if (!__pyx_t_4) { - __pyx_t_2 = PyObject_GetAttr(__pyx_v_cov, __pyx_n_s__shape); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(__pyx_v_cov, __pyx_n_s__shape); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4118; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = __Pyx_GetItemInt(__pyx_t_2, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_GetItemInt(__pyx_t_2, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4118; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyObject_GetAttr(__pyx_v_cov, __pyx_n_s__shape); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(__pyx_v_cov, __pyx_n_s__shape); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4118; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_1 = __Pyx_GetItemInt(__pyx_t_2, 1, sizeof(long), PyInt_FromLong); if (!__pyx_t_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_GetItemInt(__pyx_t_2, 1, sizeof(long), PyInt_FromLong); if (!__pyx_t_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4118; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyObject_RichCompare(__pyx_t_3, __pyx_t_1, Py_NE); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_RichCompare(__pyx_t_3, __pyx_t_1, Py_NE); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4118; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4118; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_7 = __pyx_t_6; } else { @@ -17905,64 +17905,64 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * } if (__pyx_t_7) { - /* "mtrand.pyx":4129 + /* "mtrand.pyx":4119 * raise ValueError("mean must be 1 dimensional") * if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]): * raise ValueError("cov must be 2 dimensional and square") # <<<<<<<<<<<<<< * if mean.shape[0] != cov.shape[0]: * raise ValueError("mean and cov must have same length") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_183), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4129; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_183), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4119; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4129; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4119; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } __pyx_L8:; - /* "mtrand.pyx":4130 + /* "mtrand.pyx":4120 * if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]): * raise ValueError("cov must be 2 dimensional and square") * if mean.shape[0] != cov.shape[0]: # <<<<<<<<<<<<<< * raise ValueError("mean and cov must have same length") * # Compute shape of output */ - __pyx_t_2 = PyObject_GetAttr(__pyx_v_mean, __pyx_n_s__shape); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4130; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(__pyx_v_mean, __pyx_n_s__shape); 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__pyx_t_9 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; - /* "mtrand.pyx":4142 + /* "mtrand.pyx":4132 * x = self.standard_normal(np.multiply.reduce(final_shape)) * x.shape = (np.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. */ - __pyx_t_3 = PyObject_GetAttr(__pyx_v_mean, __pyx_n_s__shape); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4142; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(__pyx_v_mean, __pyx_n_s__shape); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4132; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __pyx_t_9 = __Pyx_GetItemInt(__pyx_t_3, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_9) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4142; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_9 = __Pyx_GetItemInt(__pyx_t_3, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_9) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4132; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4141; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4131; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_3)); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); @@ -18122,51 +18122,51 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __pyx_t_1 = 0; __pyx_t_9 = 0; - /* "mtrand.pyx":4141 + /* "mtrand.pyx":4131 * # many rows are necessary to form a matrix of shape final_shape. * x = self.standard_normal(np.multiply.reduce(final_shape)) * x.shape = (np.multiply.reduce(final_shape[0:len(final_shape)-1]), # <<<<<<<<<<<<<< * mean.shape[0]) * # Transform matrix of standard normals into matrix where each row */ - if (PyObject_SetAttr(__pyx_v_x, __pyx_n_s__shape, ((PyObject *)__pyx_t_3)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4141; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_v_x, __pyx_n_s__shape, ((PyObject *)__pyx_t_3)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4131; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; - /* "mtrand.pyx":4150 + /* "mtrand.pyx":4140 * # decomposition of cov is such an A. * * from numpy.dual import svd # <<<<<<<<<<<<<< * # XXX: we really should be doing this by Cholesky decomposition * (u,s,v) = svd(cov) */ - __pyx_t_3 = PyList_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4150; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyList_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4140; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_3)); __Pyx_INCREF(((PyObject *)__pyx_n_s__svd)); PyList_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_n_s__svd)); __Pyx_GIVEREF(((PyObject *)__pyx_n_s__svd)); - __pyx_t_9 = __Pyx_Import(((PyObject *)__pyx_n_s_186), ((PyObject *)__pyx_t_3), -1); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4150; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_9 = __Pyx_Import(((PyObject *)__pyx_n_s_186), ((PyObject *)__pyx_t_3), -1); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4140; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; - __pyx_t_3 = PyObject_GetAttr(__pyx_t_9, __pyx_n_s__svd); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4150; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(__pyx_t_9, __pyx_n_s__svd); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4140; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_t_3); __pyx_v_svd = __pyx_t_3; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; - /* "mtrand.pyx":4152 + /* "mtrand.pyx":4142 * from numpy.dual import svd * # XXX: we really should be doing this by Cholesky decomposition * (u,s,v) = svd(cov) # <<<<<<<<<<<<<< * x = np.dot(x*np.sqrt(s),v) * # The rows of x now have the correct covariance but mean 0. 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- if (PyObject_SetAttr(__pyx_v_x, __pyx_n_s__shape, ((PyObject *)__pyx_t_2)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4157; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_v_x, __pyx_n_s__shape, ((PyObject *)__pyx_t_2)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4147; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; - /* "mtrand.pyx":4158 + /* "mtrand.pyx":4148 * np.add(mean,x,x) * x.shape = tuple(final_shape) * return x # <<<<<<<<<<<<<< @@ -18349,7 +18349,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * return __pyx_r; } -/* "mtrand.pyx":4160 +/* "mtrand.pyx":4150 * return x * * def multinomial(self, long n, object pvals, size=None): # <<<<<<<<<<<<<< @@ -18407,7 +18407,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__pvals); if (likely(values[1])) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("multinomial", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4160; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("multinomial", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4150; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } case 2: if (kw_args > 0) { @@ -18416,9 +18416,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ } } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "multinomial") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4160; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "multinomial") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4150; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } - __pyx_v_n = __Pyx_PyInt_AsLong(values[0]); if (unlikely((__pyx_v_n == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4160; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __pyx_v_n = __Pyx_PyInt_AsLong(values[0]); if (unlikely((__pyx_v_n == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4150; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_v_pvals = values[1]; __pyx_v_size = values[2]; } else { @@ -18428,44 +18428,44 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2); case 2: __pyx_v_pvals = PyTuple_GET_ITEM(__pyx_args, 1); - __pyx_v_n = __Pyx_PyInt_AsLong(PyTuple_GET_ITEM(__pyx_args, 0)); if (unlikely((__pyx_v_n == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4160; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __pyx_v_n = __Pyx_PyInt_AsLong(PyTuple_GET_ITEM(__pyx_args, 0)); if (unlikely((__pyx_v_n == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4150; __pyx_clineno = __LINE__; goto __pyx_L3_error;} break; default: goto __pyx_L5_argtuple_error; } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("multinomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4160; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("multinomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4150; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.multinomial", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - /* "mtrand.pyx":4219 + /* "mtrand.pyx":4209 * cdef double Sum * * d = len(pvals) # <<<<<<<<<<<<<< * parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1) * pix = <double*>parr.data */ - __pyx_t_1 = PyObject_Length(__pyx_v_pvals); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_Length(__pyx_v_pvals); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4209; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_v_d = __pyx_t_1; - /* "mtrand.pyx":4220 + /* "mtrand.pyx":4210 * * d = len(pvals) * parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1) # <<<<<<<<<<<<<< * pix = <double*>parr.data * */ - __pyx_t_2 = PyArray_ContiguousFromObject(__pyx_v_pvals, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyArray_ContiguousFromObject(__pyx_v_pvals, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4210; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_2))); arrayObject_parr = ((PyArrayObject *)__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - /* "mtrand.pyx":4221 + /* "mtrand.pyx":4211 * d = len(pvals) * parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1) * pix = <double*>parr.data # <<<<<<<<<<<<<< @@ -18474,7 +18474,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ */ __pyx_v_pix = ((double *)arrayObject_parr->data); - /* "mtrand.pyx":4223 + /* "mtrand.pyx":4213 * pix = <double*>parr.data * * if kahan_sum(pix, d-1) > (1.0 + 1e-12): # <<<<<<<<<<<<<< @@ -18484,23 +18484,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ __pyx_t_3 = (__pyx_f_6mtrand_kahan_sum(__pyx_v_pix, (__pyx_v_d - 1)) > (1.0 + 1e-12)); if (__pyx_t_3) { - /* "mtrand.pyx":4224 + /* "mtrand.pyx":4214 * * if kahan_sum(pix, d-1) > (1.0 + 1e-12): * raise ValueError("sum(pvals[:-1]) > 1.0") # <<<<<<<<<<<<<< * * if size is None: */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_188), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4224; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_188), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4224; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L6; } __pyx_L6:; - /* "mtrand.pyx":4226 + /* "mtrand.pyx":4216 * raise ValueError("sum(pvals[:-1]) > 1.0") * * if size is None: # <<<<<<<<<<<<<< @@ -18510,16 +18510,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ __pyx_t_3 = (__pyx_v_size == Py_None); if (__pyx_t_3) { - /* "mtrand.pyx":4227 + /* "mtrand.pyx":4217 * * if size is None: * shape = (d,) # <<<<<<<<<<<<<< * elif type(size) is int: * shape = (size, d) */ - __pyx_t_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4227; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4217; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4227; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4217; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); @@ -18529,7 +18529,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ goto __pyx_L7; } - /* "mtrand.pyx":4228 + /* "mtrand.pyx":4218 * if size is None: * shape = (d,) * elif type(size) is int: # <<<<<<<<<<<<<< @@ -18539,16 +18539,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ __pyx_t_3 = (((PyObject *)Py_TYPE(__pyx_v_size)) == ((PyObject *)((PyObject*)(&PyInt_Type)))); if (__pyx_t_3) { - /* "mtrand.pyx":4229 + /* "mtrand.pyx":4219 * shape = (d,) * elif type(size) is int: * shape = (size, d) # <<<<<<<<<<<<<< * else: * shape = size + (d,) */ - __pyx_t_4 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4229; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4229; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_2)); __Pyx_INCREF(__pyx_v_size); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_size); @@ -18562,21 +18562,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ } /*else*/ { - /* "mtrand.pyx":4231 + /* "mtrand.pyx":4221 * shape = (size, d) * else: * shape = size + (d,) # <<<<<<<<<<<<<< * * multin = np.zeros(shape, int) */ - __pyx_t_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4231; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4231; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyNumber_Add(__pyx_v_size, ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4231; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyNumber_Add(__pyx_v_size, ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; __pyx_v_shape = __pyx_t_2; @@ -18584,19 +18584,19 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ } __pyx_L7:; - /* "mtrand.pyx":4233 + /* "mtrand.pyx":4223 * shape = size + (d,) * * multin = np.zeros(shape, int) # <<<<<<<<<<<<<< * mnarr = <ndarray>multin * mnix = <long*>mnarr.data */ - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4233; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__zeros); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4233; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__zeros); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4233; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_2)); __Pyx_INCREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape); @@ -18604,14 +18604,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ __Pyx_INCREF(((PyObject *)((PyObject*)(&PyInt_Type)))); PyTuple_SET_ITEM(__pyx_t_2, 1, ((PyObject *)((PyObject*)(&PyInt_Type)))); __Pyx_GIVEREF(((PyObject *)((PyObject*)(&PyInt_Type)))); - __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4233; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; __pyx_v_multin = __pyx_t_5; __pyx_t_5 = 0; - /* "mtrand.pyx":4234 + /* "mtrand.pyx":4224 * * multin = np.zeros(shape, int) * mnarr = <ndarray>multin # <<<<<<<<<<<<<< @@ -18621,7 +18621,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_multin))); arrayObject_mnarr = ((PyArrayObject *)__pyx_v_multin); - /* "mtrand.pyx":4235 + /* "mtrand.pyx":4225 * multin = np.zeros(shape, int) * mnarr = <ndarray>multin * mnix = <long*>mnarr.data # <<<<<<<<<<<<<< @@ -18630,7 +18630,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ */ __pyx_v_mnix = ((long *)arrayObject_mnarr->data); 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- /* "mtrand.pyx":4242 + /* "mtrand.pyx":4232 * for j from 0 <= j < d-1: * mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum) * dn = dn - mnix[i+j] # <<<<<<<<<<<<<< @@ -18700,7 +18700,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ */ __pyx_v_dn = (__pyx_v_dn - (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)])); - /* "mtrand.pyx":4243 + /* "mtrand.pyx":4233 * mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum) * dn = dn - mnix[i+j] * if dn <= 0: # <<<<<<<<<<<<<< @@ -18710,7 +18710,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ __pyx_t_3 = (__pyx_v_dn <= 0); if (__pyx_t_3) { - /* "mtrand.pyx":4244 + /* "mtrand.pyx":4234 * dn = dn - mnix[i+j] * if dn <= 0: * break # <<<<<<<<<<<<<< @@ -18722,7 +18722,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ } __pyx_L12:; - /* "mtrand.pyx":4245 + /* "mtrand.pyx":4235 * if dn <= 0: * break * Sum = Sum - pix[j] # <<<<<<<<<<<<<< @@ -18733,7 +18733,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ } __pyx_L11_break:; 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__pyx_lineno = 4253; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "dirichlet") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4243; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_alpha = values[0]; __pyx_v_size = values[1]; @@ -18870,37 +18870,37 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("dirichlet", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4253; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("dirichlet", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4243; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("mtrand.RandomState.dirichlet", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - /* "mtrand.pyx":4339 + /* "mtrand.pyx":4329 * cdef double acc, invacc * * k = len(alpha) # <<<<<<<<<<<<<< * alpha_arr = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1) * alpha_data = <double*>alpha_arr.data */ - __pyx_t_1 = PyObject_Length(__pyx_v_alpha); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4339; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_Length(__pyx_v_alpha); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4329; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_v_k = __pyx_t_1; - /* "mtrand.pyx":4340 + /* "mtrand.pyx":4330 * * k = len(alpha) * alpha_arr = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1) # <<<<<<<<<<<<<< * alpha_data = <double*>alpha_arr.data * */ - __pyx_t_2 = PyArray_ContiguousFromObject(__pyx_v_alpha, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyArray_ContiguousFromObject(__pyx_v_alpha, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4330; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_2))); __pyx_v_alpha_arr = ((PyArrayObject *)__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - /* "mtrand.pyx":4341 + /* "mtrand.pyx":4331 * k = len(alpha) * alpha_arr = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1) * alpha_data = <double*>alpha_arr.data # <<<<<<<<<<<<<< @@ -18909,7 +18909,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se */ __pyx_v_alpha_data = ((double *)__pyx_v_alpha_arr->data); - /* "mtrand.pyx":4343 + /* "mtrand.pyx":4333 * alpha_data = <double*>alpha_arr.data * * if size is None: # <<<<<<<<<<<<<< @@ -18919,16 +18919,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se __pyx_t_3 = (__pyx_v_size == Py_None); if (__pyx_t_3) { - /* "mtrand.pyx":4344 + /* "mtrand.pyx":4334 * * if size is None: * shape = (k,) # <<<<<<<<<<<<<< * elif type(size) is int: * shape = (size, k) */ - __pyx_t_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4344; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4334; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4344; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4334; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); @@ -18938,7 +18938,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se goto __pyx_L6; } - /* "mtrand.pyx":4345 + /* "mtrand.pyx":4335 * if size is None: * shape = (k,) * elif type(size) is int: # <<<<<<<<<<<<<< @@ -18948,16 +18948,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se __pyx_t_3 = (((PyObject *)Py_TYPE(__pyx_v_size)) == ((PyObject *)((PyObject*)(&PyInt_Type)))); if (__pyx_t_3) { - /* "mtrand.pyx":4346 + /* "mtrand.pyx":4336 * shape = (k,) * elif type(size) is int: * shape = (size, k) # <<<<<<<<<<<<<< * else: * shape = size + (k,) */ - __pyx_t_4 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4346; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4336; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4346; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4336; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_2)); __Pyx_INCREF(__pyx_v_size); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_size); @@ -18971,21 +18971,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se } /*else*/ { - /* "mtrand.pyx":4348 + /* "mtrand.pyx":4338 * shape = (size, k) * else: * shape = size + (k,) # <<<<<<<<<<<<<< * * diric = np.zeros(shape, np.float64) */ - __pyx_t_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4348; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4338; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4348; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4338; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_4)); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyNumber_Add(__pyx_v_size, ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4348; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyNumber_Add(__pyx_v_size, ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4338; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; __pyx_v_shape = __pyx_t_2; @@ -18993,24 +18993,24 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se } __pyx_L6:; - /* "mtrand.pyx":4350 + /* "mtrand.pyx":4340 * shape = size + (k,) * * diric = np.zeros(shape, np.float64) # <<<<<<<<<<<<<< * val_arr = <ndarray>diric * val_data= <double*>val_arr.data */ - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4350; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__zeros); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4350; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__zeros); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4350; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __pyx_t_5 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__float64); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4350; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__float64); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4350; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_2)); __Pyx_INCREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape); @@ -19018,14 +19018,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); __pyx_t_5 = 0; - __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4350; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; __pyx_v_diric = __pyx_t_5; __pyx_t_5 = 0; - /* "mtrand.pyx":4351 + /* "mtrand.pyx":4341 * * diric = np.zeros(shape, np.float64) * val_arr = <ndarray>diric # <<<<<<<<<<<<<< @@ -19035,7 +19035,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_diric))); __pyx_v_val_arr = ((PyArrayObject *)__pyx_v_diric); - /* "mtrand.pyx":4352 + /* "mtrand.pyx":4342 * diric = np.zeros(shape, np.float64) * val_arr = <ndarray>diric * val_data= <double*>val_arr.data # <<<<<<<<<<<<<< @@ -19044,7 +19044,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se */ __pyx_v_val_data = ((double *)__pyx_v_val_arr->data); 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} - /* "mtrand.pyx":4361 + /* "mtrand.pyx":4351 * val_data[i+j] = rk_standard_gamma(self.internal_state, alpha_data[j]) * acc = acc + val_data[i+j] * invacc = 1/acc # <<<<<<<<<<<<<< @@ -19120,11 +19120,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se */ if (unlikely(__pyx_v_acc == 0)) { PyErr_Format(PyExc_ZeroDivisionError, "float division"); - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4361; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4351; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } __pyx_v_invacc = (1.0 / __pyx_v_acc); - /* "mtrand.pyx":4362 + /* "mtrand.pyx":4352 * acc = acc + val_data[i+j] * invacc = 1/acc * for j from 0 <= j < k: # <<<<<<<<<<<<<< @@ -19134,7 +19134,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se __pyx_t_6 = __pyx_v_k; for (__pyx_v_j = 0; __pyx_v_j < __pyx_t_6; __pyx_v_j++) { - /* "mtrand.pyx":4363 + /* "mtrand.pyx":4353 * invacc = 1/acc * for j from 0 <= j < k: * val_data[i+j] = val_data[i+j] * invacc # <<<<<<<<<<<<<< @@ -19144,7 +19144,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se (__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) = ((__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) * __pyx_v_invacc); } - /* "mtrand.pyx":4364 + /* "mtrand.pyx":4354 * for j from 0 <= j < k: * val_data[i+j] = val_data[i+j] * invacc * i = i + k # <<<<<<<<<<<<<< @@ -19154,7 +19154,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se __pyx_v_i = (__pyx_v_i + __pyx_v_k); } - /* "mtrand.pyx":4366 + /* "mtrand.pyx":4356 * i = i + k * * return diric # <<<<<<<<<<<<<< @@ -19184,7 +19184,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se return __pyx_r; } -/* "mtrand.pyx":4369 +/* "mtrand.pyx":4359 * * # Shuffling and permutations: * def shuffle(self, object x): # <<<<<<<<<<<<<< @@ -19213,17 +19213,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_51shuffle(PyObject *__pyx_v_self int __pyx_clineno = 0; __Pyx_RefNannySetupContext("shuffle"); - /* "mtrand.pyx":4405 + /* "mtrand.pyx":4395 * cdef int copy * * i = len(x) - 1 # <<<<<<<<<<<<<< * try: * j = len(x[0]) */ - __pyx_t_1 = PyObject_Length(__pyx_v_x); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4405; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_Length(__pyx_v_x); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4395; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_v_i = (__pyx_t_1 - 1); - /* "mtrand.pyx":4406 + /* "mtrand.pyx":4396 * * i = len(x) - 1 * try: # <<<<<<<<<<<<<< @@ -19237,16 +19237,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_51shuffle(PyObject *__pyx_v_self __Pyx_XGOTREF(__pyx_t_4); /*try:*/ { - /* "mtrand.pyx":4407 + /* "mtrand.pyx":4397 * i = len(x) - 1 * try: * j = len(x[0]) # <<<<<<<<<<<<<< * except: * j = 0 */ - __pyx_t_5 = __Pyx_GetItemInt(__pyx_v_x, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4407; __pyx_clineno = __LINE__; goto __pyx_L5_error;} + __pyx_t_5 = __Pyx_GetItemInt(__pyx_v_x, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4397; __pyx_clineno = __LINE__; goto __pyx_L5_error;} __Pyx_GOTREF(__pyx_t_5); - __pyx_t_1 = PyObject_Length(__pyx_t_5); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4407; __pyx_clineno = __LINE__; goto __pyx_L5_error;} + __pyx_t_1 = PyObject_Length(__pyx_t_5); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4397; __pyx_clineno = __LINE__; goto __pyx_L5_error;} __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_j = __pyx_t_1; } @@ -19257,7 +19257,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_51shuffle(PyObject *__pyx_v_self __pyx_L5_error:; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; - /* "mtrand.pyx":4408 + /* "mtrand.pyx":4398 * try: * j = len(x[0]) * except: # <<<<<<<<<<<<<< @@ -19266,12 +19266,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_51shuffle(PyObject *__pyx_v_self */ /*except:*/ { __Pyx_AddTraceback("mtrand.RandomState.shuffle", __pyx_clineno, __pyx_lineno, __pyx_filename); - if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4408; 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if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4506; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4496; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - __pyx_t_4 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__lognormal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4506; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__lognormal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4496; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - if (PyObject_SetAttr(__pyx_m, __pyx_n_s__lognormal, __pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4506; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_n_s__lognormal, __pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; 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if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4509; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4499; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__triangular); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4509; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__triangular); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4499; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - if (PyObject_SetAttr(__pyx_m, __pyx_n_s__triangular, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4509; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_n_s__triangular, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; 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if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4520; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4510; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - __pyx_t_4 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__multinomial); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4520; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__multinomial); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4510; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - if (PyObject_SetAttr(__pyx_m, __pyx_n_s__multinomial, __pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4520; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_n_s__multinomial, __pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4510; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - /* "mtrand.pyx":4521 + /* "mtrand.pyx":4511 * multivariate_normal = _rand.multivariate_normal * multinomial = _rand.multinomial * dirichlet = _rand.dirichlet # <<<<<<<<<<<<<< * * shuffle = _rand.shuffle */ - __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4521; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4511; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__dirichlet); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4521; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__dirichlet); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; 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