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scipy.stats.

# truncate

scipy.stats.truncate(X, lb=-inf, ub=inf)

  Truncate the support of a random variable.

  Given a random variable X, `truncate` returns a random variable with support truncated to the interval between lb and ub. The underlying probability density function is normalized accordingly.

  Parameters:
    X ContinuousDistribution

      The random variable to be truncated.

    lb, ub float array-like

      The lower and upper truncation points, respectively. Must be broadcastable with one another and the shape of X.

  Returns:
    X ContinuousDistribution

      The truncated random variable.

  References

  [1]

  “Truncated Distribution”. Wikipedia. https://en.wikipedia.org/wiki/Truncated_distribution

  Examples

  Compare against `scipy.stats.truncnorm`, which truncates a standard normal, then shifts and scales it.

      >>> import numpy as np
      >>> import matplotlib.pyplot as plt
      >>> from scipy import stats
      >>> loc, scale, lb, ub = 1, 2, -2, 2
      >>> X = stats.truncnorm(lb, ub, loc, scale)
      >>> Y = scale * stats.truncate(stats.Normal(), lb, ub) + loc
      >>> x = np.linspace(-3, 5, 300)
      >>> plt.plot(x, X.pdf(x), '-', label='X')
      >>> plt.plot(x, Y.pdf(x), '--', label='Y')
      >>> plt.xlabel('x')
      >>> plt.ylabel('PDF')
      >>> plt.title('Truncated, then Shifted/Scaled Normal')
      >>> plt.legend()
      >>> plt.show()

  However, suppose we wish to shift and scale a normal random variable, then truncate its support to given values. This is straightforward with `truncate`.

      >>> Z = stats.truncate(scale * stats.Normal() + loc, lb, ub)
      >>> Z.plot()
      >>> plt.show()

  Furthermore, `truncate` can be applied to any random variable:

      >>> Rayleigh = stats.make_distribution(stats.rayleigh)
      >>> W = stats.truncate(Rayleigh(), lb=0.5, ub=3)
      >>> W.plot()
      >>> plt.show()