Metadata-Version: 1.0
Name: brisk
Version: 0.1
Summary: Fast implementation of numerical functions using Numba
Home-page: https://github.com/David-OConnor/brisk
Author: David O'Connor
Author-email: david.alan.oconnor@gmail.com
License: Apache
Description: Brisk: Applied Numba
        ====================
        
        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 <http://numba.pydata.org/>`_
        
        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.
        
Keywords: fast,numba,numerical,optimized
Platform: UNKNOWN
