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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.

Release History

This version
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Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
(5.8 kB) Copy SHA256 Hash SHA256
Wheel py2.py3 Apr 9, 2015
(4.1 kB) Copy SHA256 Hash SHA256
Source None Apr 9, 2015

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