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Fast implementation of numerical functions using Numba

Project description

Optimized numerical computation using Continuum’s Numba. Intended as a drop-in replacement for numerical functions in numpy, scipy, or builtins. Provides strong performance boosts.

Numba website

Inputs use numpy arrays, not lists. Rough/early release - Open to suggestions and bug reports.

Included functions

  • sum: Similar to builtin sum, or numpy.sum
  • mean: Similar to numpy.mean
  • var: Variance test, similar to numpy.var
  • cov: Covariance estimation, similar to numpy.cov
  • std: Standard deviation, similar to numpy.std
  • corr: Pearson correlation test, similar to scipy.stats.pearsonr
  • bisect: Similar to standard library bisect.bisect
  • bisect_left: Similar to standard library builtin.bisect_left
  • interp: Linear interpoliation, similar to numpy.interp. x is an array.
  • interp_one: Linear interpolation, similar to numpy.interp. x is a single value.
  • detrend: Similar to scipy.signal.detrend. Linear or constant trend.
  • ols: Simple Ordinary Least Squares regression for two data sets.
  • ols_single: Simple Ordinary Least Squares regression for one data set.
  • lin_resids: Residuals calculation from a linear regression with two data sets
  • lin_resids_single: Residuals calculation from a linear regression with one data set.

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