MDArray ======= MDArray is a multi dimensional array implemented for JRuby inspired by NumPy (www.numpy.org) and Narray (narray.rubyforge.org) by Masahiro Tanaka. MDArray stands on the shoulders of Java-NetCDF and Parallel Colt. NetCDF-Java Library is a Java interface to NetCDF files, as well as to many other types of scientific data formats. It is developed and distributed by Unidata ( http://www.unidata.ucar.edu). Parallel Colt (sites.google.com/site/piotrwendykier/software/parallelcolt) is a multithreaded version of Colt (http://acs.lbl.gov/software/colt/). Colt provides a set of Open Source Libraries for High Performance Scientific and Technical Computing in Java. Scientific and technical computing is characterized by demanding problem sizes and a need for high performance at reasonably small memory footprint. MDArray and SciRuby =================== MDArray subscribes fully to the SciRuby Manifesto (http://sciruby.com/). "Ruby has for some time had no equivalent to the beautifully constructed NumPy, SciPy, and matplotlib libraries for Python. We believe that the time for a Ruby science and visualization package has come. Sometimes when a solution of sugar and water becomes super-saturated, from it precipitates a pure, delicious, and diabetes-inducing crystal of sweetness, induced by no more than the tap of a finger. So is occurring now, we believe, with numeric and visualization libraries for Ruby." Main properties =============== + Homogeneous multidimensional array, a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers; + Easy calculation for large numerical multi dimensional arrays; + Basic types are: boolean, byte, short, int, long, float, double, string, structure; + Based on JRuby, which allows importing Java libraries; + Operator: +,-,*,/,%,**, >, >=, etc. + Functions: abs, ceil, floor, truncate, is_zero, square, cube, fourth; + Binary Operators: &, |, ^, ~ (binary_ones_complement), <<, >>; + Ruby Math functions: acos, acosh, asin, asinh, atan, atan2, atanh, cbrt, cos, erf, exp, gamma, hypot, ldexp, log, log10, log2, sin, sinh, sqrt, tan, tanh, neg; + Boolean operations on boolean arrays: and, or, not; + Fast descriptive statistics from Parallel Colt (complete list found bellow); + Easy manipulation of arrays: reshape, reduce dimension, permute, section, slice, etc. + Reading of two dimensional arrays from CSV files (mainly for debugging and simple testing purposes); + StatList: a list that can grow/shrink and that can compute Parallel Colt descriptive statistics. Descriptive statistics methods ============================== auto_correlation, correlation, covariance, durbin_watson, frequencies, geometric_mean, harmonic_mean, kurtosis, lag1, max, mean, mean_deviation, median, min, moment, moment3, moment4, pooled_mean, pooled_variance, product, quantile, quantile_inverse, rank_interpolated, rms, sample_covariance, sample_kurtosis, sample_kurtosis_standard_error, sample_skew, sample_skew_standard_error, sample_standard_deviation, sample_variance, sample_weighted_variance, skew, split, standard_deviation, standard_error, sum, sum_of_inversions, sum_of_logarithms, sum_of_powers, sum_of_power_deviations, sum_of_squares, sum_of_squared_deviations, trimmed_mean, variance, weighted_mean, weighted_rms, weighted_sums, winsorized_mean. Installation and download ========================= + Install Jruby + jruby -S gem install mdarray Contributors ============ + Contributors are welcome. Homepages ========= + http://rubygems.org/gems/mdarray + https://github.com/rbotafogo/mdarray/wiki HISTORY ======= + 16/05/2013: Version 0.5.0: All loops transfered to Java with over 50% performance improvement. Descriptive statistics from Parallel Colt. + 19/04/2013: Version 0.4.3: Fixes a simple (but fatal bug). No new features + 17/04/2013: Version 0.4.2: Adds simple statistics and boolean operators + 05/05/2013: Version 0.4.0: Initial release

on 2013-05-17 15:21

on 2013-05-17 20:19

Thanks, this is great progress towards permitting choice of using jruby - as opposed to constrained to python. i'm not in a position to be an early adopter but will follow this with keen interest. 2013/5/17 Rodrigo Botafogo <rodrigo@rodrigobotafogo.com>

on 2013-05-17 20:54

Stephen, Thanks for the support. Do you use NumPy. May I ask what your use case is? Cheers, Rodrigo

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