About NumPy#

ReLife is built using NumPy, a fundamental Python library for numerical computing. While you don’t need to be a NumPy expert, understanding its basics will help since ReLife often requires data input of np.ndarray type.

There are 3 standard representations of data in ReLife :

  • If you want to pass a scalar value, use float built-in type (np.float64 is accepted but not required).

  • If you want to pass a vector of \(\mathbb{R}^n\), e.g. \(n\) values for one asset, use a np.ndarray of shape (n,).

  • If you want to pass a matrix of \(\mathbb{R}^{m\times n}\), i.e. \(n\) values for \(m\) assets, use a np.ndarray of shape (m, n)

Broadcasting examples

>>> from relife.lifetime_model import Weibull
>>> weibull = Weibull(3.47, 0.012)
>>> weibull.sf(40.)
np.float64(0.9246627462729304)

The output has the same number of dimension than the input. To compute \(P(T > 40)\), but also \(P(T > 50)\) and \(P(T > 60)\), we can benefit from broadcasting and compute three survival function evaluations in parallel.

>>> import numpy as np
>>> weibull.sf(np.array([40., 50., 60.])) # 1d array of shape (3,)
array([0.92466275, 0.84375201, 0.72625935])

This logic is extended until two dimensions. For instance, it is sometimes usefull to pass several values per assets.

>>> weibull.sf(np.array([[40., 50., 60.], [42., 55., 68.]])) # 2d array of shape (2, 3)
array([[0.92466275, 0.84375201, 0.72625935],
       [0.91139796, 0.78939177, 0.61029328]])

Each row encodes a vector of values for each asset.

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