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. Version 0.4.0 only imports Java-NetCDF;
- 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