This repository consist of some example to get started with python (examples and materials to begin with python is taken from Kristin Thyng's course on python in geosciences) and focus will be on the Geophysical Data Analysis course lectures, taught by Professor John Wilkin and Professor Bob Chant at Rutgers University.
There are several ways to create random sets in numpy and other libraries, including:
numpy.random.rand: generates arrays of random values between 0 and 1 from a uniform distribution.
numpy.random.randn: generates arrays of random values from a standard normal distribution with mean 0 and standard deviation 1.
numpy.random.randint: generates arrays of random integers between a specified minimum and maximum value, inclusive.
numpy.random.choice: generates arrays of random values chosen from a specified 1-dimensional array or list.
numpy.random.permutation: generates a random permutation of a specified 1-dimensional array or list.
In addition to these functions, there are many other random number generation functions available in numpy and other libraries such as random and scipy.stats that allow you to generate random sets for various distributions.
In summary, there are several ways to create random sets in numpy, including using functions like numpy.random.rand, numpy.random.randn, numpy.random.randint, numpy.random.choice, and numpy.random.permutation, among others.
There are several ways to create random sets in numpy and other libraries, including:
numpy.random.rand: generates arrays of random values between 0 and 1 from a uniform distribution.numpy.random.randn: generates arrays of random values from a standard normal distribution with mean 0 and standard deviation 1.numpy.random.randint: generates arrays of random integers between a specified minimum and maximum value, inclusive.numpy.random.choice: generates arrays of random values chosen from a specified 1-dimensional array or list.numpy.random.permutation: generates a random permutation of a specified 1-dimensional array or list. In addition to these functions, there are many other random number generation functions available in numpy and other libraries such as random andscipy.statsthat allow you to generate random sets for various distributions.In summary, there are several ways to create random sets in numpy, including using functions like
numpy.random.rand,numpy.random.randn,numpy.random.randint,numpy.random.choice, andnumpy.random.permutation, among others.