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DATA PRODUCTIVITY TOOLKIT Description -------------------------------------------------------------------------------- The Data Productivity Toolkit is a collection of linux command-line tools designed to facilitate the analysis of text-based data sets. Modeled after the general linux pipeline tools such as awk, grep, and sed, the kit provides powerfull tools for selecting/combining data, performing statistics, and visualizing results. The tools are all written in python and in many instances provide a command-line API to basic python and numpy/scipy/matplotlib routines. Prerequisites -------------------------------------------------------------------------------- The Data Productivity Toolkit is written completely in python. It does, however, require that the following third-party python modules be installed. - numpy - scipy - matplotlib - mpl-toolkits.basemap - mpl_toolkits.natgrid - jinja2 - django Installation -------------------------------------------------------------------------------- 1) Copy all files into a directory. 2) Add that directory to your path. 3) In that directory, create a symbolic link with the name ppython. It should point to the python install on your system that contains the modules listed above. (Note: it is a good idea to use a python install created by the utitity virtualenv. This will allow good flexibility for maintaining a version of python best suited to run the toolkit. Note that the package ships with a ppython symlink to /usr/bin/python. 4) Make sure your install of matplotlib is capable of sending plots to the screen. You may have to set your matplotlib graphics back-end appropriately. List of tools (run with -h option for documentaion) -------------------------------------------------------------------------------- Creates bar charts p.binit Assigns data to 2 dimensional bin structure Rearrages columnar data into key,x,y format p.catToTable Create a table from data in key,x,y format p.cdf Plots the cumulative distribution An awk-like math utility p.color Makes color scatter plots p.cumsum Computes the cumulative sum of inputs p.datetime Converts text-based time stamps to seconds from an epoch p.dedup Removes duplicate keys p.distribute Distribute jobs across computers efficiently p.exec Sequentially run commands read from stdin p.gps2utc Convert gps time to utc time p.grab Grab columns from a file with python-like indexing p.grabHeader Extract the commented header from a file p.groupStat Perform statistics over keyed subgroups of input p.hist Plots a histogram p.htmlWrap Create an html wrapper for images in a directory p.interp Does polynomial interpolation p.join Join two files on specified key columns Link to files based on specified key columns p.linspace Generate a linear spaced sequence of numbers Plot points on a map p.medianFilter Runs data through a median filter p.minMax Find min/max values in specified data column p.multiJoin Join multiple files together based on key p.normalize Normalizes input data p.parallel Run commands in parallel p.parallelSSH Run commands in parallel across several machines p.plot Plot points on a graph p.quadAdd Add all columns from stdin in quadrature p.quantiles Compute quantiles from input data p.rand Generate a sequence of random numbers p.rex Bring python rex to the command line p.scat Make a scatter plot of input data p.sed A sed-like utility with python syntax p.shuffle Randomly shuffle rows of data p.smooth Smooth data p.sort Sort data based on specified keys p.split Split data based on a supplied delimeter p.strip Remove comments and/or nans from rows p.tableFormat Nicely format input columns in a table format p.template Bring jinja templates to the command line p.utc2gps Convert utc time to gps time p.utc2local Convert utc time to local time given a lon
Source: README, updated 2011-12-05