Command line script to and Python library to create plots.
Project description
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.
mando - command line parser
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')
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