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

pip

pip install plottoolbox

conda

conda install -c conda-forge plottoolbox

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')