API Reference¶
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mcrand.distribution(pdf, bounds, max_sample=100000, *args)¶ Generator of random numbers following the given probability density function.
Parameters: - pdf : func
the probability density function.
- Input parameters:
- x : float
- the evaluation point
- *args : tuple
- extra parameters
- Output parameters:
- y : float
- bounds : tuple of floats
lower and upper limit
- max_sample : int
the quantity of random numbers to return
- *args : args
extra arguments for the pdf
Returns: - random_numbers : numpy.ndarray
collection of random numbers following the given pdf.
Notes
pdf is expected to be a probability density function therefore it must be positively defined in the range specified.
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mcrand.sample(pdf, x0, xf, shape, *args)¶ Generator of random numbers following the given probability density function.
Parameters: - pdf : func
the probability density function
- x0 : float
the lower limit
- xf : float
the upper limit
- shape : int or tuple of ints
the shape of the generated random numbers array
- *args : args
extra arguments for the pdf
Returns: - random_numbers : numpy.ndarray
collection of random numbers following the given pdf with the specified shape.
See also
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mcrand.uniform_integration(f, limits, N, *args)¶ MonteCarlo integration of multidimensional functions.
Parameters: - f : func
integration function. input parameters: * x : numpy.ndarray
the evaluation point, n-dimensional vector
- *args : tuple
- extra parameters
output parameters: * y : float
- limits : list of tuple
a list containing the lower and upper limits of integration for each dimension as tuple with two ints (low, high).
- N : int
number of points used
- *args : args
extra arguments for the evaluation function
Returns: - integral : float
the result of the integral
- error : float
the standard deviation