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        "description": "\ufeff========================\n Monte Carlo integrator\n========================\n\nThis package provides a Monte Carlo integrator which can be used to evaluate\nmulti-dimensional integrals. The results are numerical approximations which are\ndependent on the use of random number generation.\n\nExample 1\n=========\n\nIn this example we compute :math:`\\int_0^1 x^2 dx`::\n\n    import mcint\n    import random\n    \n    def integrand(x): # Describe the function being integrated\n        return (x**2)\n    \n    def sampler(): # Describe how Monte Carlo samples are taken\n        while True:\n            yield random.random()\n    \n    result, error = mcint.integrate(integrand, sampler(), measure=1.0, n=100)\n    \n    print \"The integral of x**2 between 0 and 1 is approximately\", result\n\nThe second argument to the integrate() function should be an iterable\nexpression, in this case it is a generator. We could do away with this sampler\nusing the following::\n\n    result, error = mcint.integrate(integrand, iter(random.random, -1), measure=1.0, n=100)\n\nThis creates an iterable object from the random.random() function which will\ncontinuously call random.random() until it returns -1 (which it will never do as\nit returns values between 0.0 and 1.0.\n\nExample 2\n=========\n\nIn this example we compute :math:`\\int_0^1 \\int_0^\\sqrt{1-y^2} x^2+y^2 dx dy`::\n\n    import mcint\n    import random\n    import math\n    \n    def integrand(x):\n        return (x[0]**2 + x[1]**2)\n    \n    def sampler():\n        while True:\n            y = random.random()\n            x = random.random()\n            if x**2+y**2 <= 1:\n                yield (x,y)\n    \n    result, error = mcint.integrate(integrand, sampler(), measure=math.pi/4)",
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