plottoolbox 104.2.0

Command line script and Python library to make plots from data files.

plottoolbox - Quick Guide

The plottoolbox is a Python script to manipulate time-series on the command line or by function calls within Python. Uses pandas (http://pandas.pydata.org/) or numpy (http://numpy.scipy.org) for any heavy lifting.

Requirements

• pandas - on Windows this is part scientific Python distributions like Python(x,y), Anaconda, or Enthought.

Installation

Should be as easy as running pip install plottoolbox or easy_install plottoolbox at any command line. Not sure on Windows whether this will bring in pandas, but as mentioned above, if you start with scientific Python distribution then you shouldn’t have a problem.

Usage - Command Line

Just run ‘plottoolbox –help’ to get a list of subcommands:

usage: plottoolbox [-h]
                   {autocorrelation, bar, bar_stacked, barh, barh_stacked,
                   bootstrap, boxplot, double_mass, heatmap, histogram,
                   kde, kde_time, lag_plot, lognorm_xaxis, lognorm_yaxis,
                   norm_xaxis, norm_yaxis, probability_density,
                   scatter_matrix, target, taylor, time, weibull_xaxis,
                   weibull_yaxis, xy, about} ...

positional arguments:
  {autocorrelation, bar, bar_stacked, barh, barh_stacked, bootstrap,
  boxplot, double_mass, heatmap, histogram, kde, kde_time, lag_plot,
  lognorm_xaxis, lognorm_yaxis, norm_xaxis, norm_yaxis,
  probability_density, scatter_matrix, target, taylor, time, weibull_xaxis,
  weibull_yaxis, xy, about}

autocorrelation
    Autocorrelation plot.
bar
    Bar plot, sometimes called a "column" plot.
bar_stacked
    Stacked vertical bar, sometimes called a stacked column plot.
barh
    Bar plot, sometimes called a "column" plot.
barh_stacked
    Horizontal stacked bar plot.
bootstrap
    Bootstrap plot randomly selects a subset of the imput time-series.
boxplot
    Box and whiskers plot.
double_mass
    Double mass curve - cumulative sum of x against cumulative sum of y.
heatmap
    2D heatmap of daily data.
histogram
    Histogram.
kde
    Kernel density estimation of probability density function.
kde_time
    A time-series plot with a kernel density estimation (KDE) plot.
lag_plot
    Lag plot.
lognorm_xaxis
    Log-normal x-axis.
lognorm_yaxis
    Log-normal y-axis.
norm_xaxis
    Normal x-axis.
norm_yaxis
    Normal y-axis.
probability_density
    Probability plot.
scatter_matrix
    Plots all columns against each other in matrix of plots.
target
    Creates a "target" diagram to plot goodness of fit.
taylor
    Taylor diagram to plot goodness of fit.
time
    Time-series plot.
weibull_xaxis
    Weibull x-axis.
weibull_yaxis
    Weibull y-axis.
xy
    Creates an 'x,y' plot, also known as a scatter plot.
about
    Display version number and system information.

optional arguments:
  -h, --help            show this help message and exit

The default for all of the subcommands is to accept data from stdin (typically a pipe). If a subcommand accepts an input file for an argument, you can use “–input_ts=input_file_name.csv”, or to explicitly specify from stdin (the default) “–input_ts=’-‘”.

For the subcommands that output data it is printed to the screen and you can then redirect to a file.

Usage - API

You can use all of the command line subcommands as functions. The function signature is identical to the command line subcommands. The return is always a PANDAS DataFrame. Input can be a CSV or TAB separated file, or a PANDAS DataFrame and is supplied to the function via the ‘input_ts’ keyword.

Simply import plottoolbox:

from plottoolbox import plottoolbox

# Then you could call the functions
plt = plottoolbox.time(input_ts='tests/test_fill_01.csv')