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.
- pandas - on Windows this is part scientific Python distributions like Python(x,y), Anaconda, or Enthought.
pip install plottoolboxconda install -c conda-forge plottoolboxJust 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.
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')