.. image:: https://github.com/timcera/plottoolbox/actions/workflows/python-package.yml/badge.svg
:alt: Tests
:target: https://github.com/timcera/plottoolbox/actions/workflows/python-package.yml
:height: 20
.. image:: https://img.shields.io/coveralls/github/timcera/plottoolbox
:alt: Test Coverage
:target: https://coveralls.io/r/timcera/plottoolbox?branch=master
:height: 20
.. image:: https://img.shields.io/pypi/v/plottoolbox.svg
:alt: Latest release
:target: https://pypi.python.org/pypi/plottoolbox/
:height: 20
.. image:: https://img.shields.io/pypi/l/plottoolbox.svg
:alt: BSD-3 clause license
:target: https://pypi.python.org/pypi/plottoolbox/
:height: 20
.. image:: https://img.shields.io/pypi/dd/plottoolbox.svg
:alt: plottoolbox downloads
:target: https://pypi.python.org/pypi/plottoolbox/
:height: 20
.. image:: https://img.shields.io/pypi/pyversions/plottoolbox
:alt: PyPI - Python Version
:target: https://pypi.org/project/plottoolbox/
:height: 20
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
~~~
.. code-block:: bash
pip install plottoolbox
conda
~~~~~
.. code-block:: bash
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')
Raw data
{
"_id": null,
"home_page": null,
"name": "plottoolbox",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "time-series, cli-app, aggregate, fill, filter",
"author": null,
"author_email": "Tim Cera <tim@cerazone.net>",
"download_url": "https://files.pythonhosted.org/packages/cd/f6/1a9afc4e05b3c329ab28e7965c978f7559440a9c98e073708be9312cbeaa/plottoolbox-105.0.2.tar.gz",
"platform": null,
"description": ".. image:: https://github.com/timcera/plottoolbox/actions/workflows/python-package.yml/badge.svg\n :alt: Tests\n :target: https://github.com/timcera/plottoolbox/actions/workflows/python-package.yml\n :height: 20\n\n.. image:: https://img.shields.io/coveralls/github/timcera/plottoolbox\n :alt: Test Coverage\n :target: https://coveralls.io/r/timcera/plottoolbox?branch=master\n :height: 20\n\n.. image:: https://img.shields.io/pypi/v/plottoolbox.svg\n :alt: Latest release\n :target: https://pypi.python.org/pypi/plottoolbox/\n :height: 20\n\n.. image:: https://img.shields.io/pypi/l/plottoolbox.svg\n :alt: BSD-3 clause license\n :target: https://pypi.python.org/pypi/plottoolbox/\n :height: 20\n\n.. image:: https://img.shields.io/pypi/dd/plottoolbox.svg\n :alt: plottoolbox downloads\n :target: https://pypi.python.org/pypi/plottoolbox/\n :height: 20\n\n.. image:: https://img.shields.io/pypi/pyversions/plottoolbox\n :alt: PyPI - Python Version\n :target: https://pypi.org/project/plottoolbox/\n :height: 20\n\nplottoolbox - Quick Guide\n=========================\nThe plottoolbox is a Python script to manipulate time-series on the command line\nor by function calls within Python. Uses pandas (http://pandas.pydata.org/)\nor numpy (http://numpy.scipy.org) for any heavy lifting.\n\nRequirements\n------------\n* pandas - on Windows this is part scientific Python distributions like\n Python(x,y), Anaconda, or Enthought.\n\nInstallation\n------------\npip\n~~~\n.. code-block:: bash\n\n pip install plottoolbox\n\nconda\n~~~~~\n.. code-block:: bash\n\n conda install -c conda-forge plottoolbox\n\n\nUsage - Command Line\n--------------------\nJust run 'plottoolbox --help' to get a list of subcommands::\n\n usage: plottoolbox [-h]\n {autocorrelation, bar, bar_stacked, barh, barh_stacked,\n bootstrap, boxplot, double_mass, heatmap, histogram,\n kde, kde_time, lag_plot, lognorm_xaxis, lognorm_yaxis,\n norm_xaxis, norm_yaxis, probability_density,\n scatter_matrix, target, taylor, time, weibull_xaxis,\n weibull_yaxis, xy, about} ...\n\n positional arguments:\n {autocorrelation, bar, bar_stacked, barh, barh_stacked, bootstrap,\n boxplot, double_mass, heatmap, histogram, kde, kde_time, lag_plot,\n lognorm_xaxis, lognorm_yaxis, norm_xaxis, norm_yaxis,\n probability_density, scatter_matrix, target, taylor, time, weibull_xaxis,\n weibull_yaxis, xy, about}\n\n autocorrelation\n Autocorrelation plot.\n bar\n Bar plot, sometimes called a \"column\" plot.\n bar_stacked\n Stacked vertical bar, sometimes called a stacked column plot.\n barh\n Bar plot, sometimes called a \"column\" plot.\n barh_stacked\n Horizontal stacked bar plot.\n bootstrap\n Bootstrap plot randomly selects a subset of the imput time-series.\n boxplot\n Box and whiskers plot.\n double_mass\n Double mass curve - cumulative sum of x against cumulative sum of y.\n heatmap\n 2D heatmap of daily data.\n histogram\n Histogram.\n kde\n Kernel density estimation of probability density function.\n kde_time\n A time-series plot with a kernel density estimation (KDE) plot.\n lag_plot\n Lag plot.\n lognorm_xaxis\n Log-normal x-axis.\n lognorm_yaxis\n Log-normal y-axis.\n norm_xaxis\n Normal x-axis.\n norm_yaxis\n Normal y-axis.\n probability_density\n Probability plot.\n scatter_matrix\n Plots all columns against each other in matrix of plots.\n target\n Creates a \"target\" diagram to plot goodness of fit.\n taylor\n Taylor diagram to plot goodness of fit.\n time\n Time-series plot.\n weibull_xaxis\n Weibull x-axis.\n weibull_yaxis\n Weibull y-axis.\n xy\n Creates an 'x,y' plot, also known as a scatter plot.\n about\n Display version number and system information.\n\n optional arguments:\n -h, --help show this help message and exit\n\nThe default for all of the subcommands is to accept data from stdin (typically\na pipe). If a subcommand accepts an input file for an argument, you can use\n\"--input_ts=input_file_name.csv\", or to explicitly specify from stdin (the\ndefault) \"--input_ts='-'\".\n\nFor the subcommands that output data it is printed to the screen and you can\nthen redirect to a file.\n\nUsage - API\n-----------\nYou can use all of the command line subcommands as functions. The function\nsignature is identical to the command line subcommands. The return is always\na PANDAS DataFrame. Input can be a CSV or TAB separated file, or a PANDAS\nDataFrame and is supplied to the function via the 'input_ts' keyword.\n\nSimply import plottoolbox::\n\n from plottoolbox import plottoolbox\n\n # Then you could call the functions\n plt = plottoolbox.time(input_ts='tests/test_fill_01.csv')\n",
"bugtrack_url": null,
"license": "BSD-3-Clause",
"summary": "Command line script and Python library to make plots from data files.",
"version": "105.0.2",
"project_urls": {
"bitbucket": "https://bitbucket.org/timcera/plottoolbox/src/main/",
"documentation": "https://timcera.bitbucket.io/plottoolbox/docs/index.html#plottoolbox-documentation",
"github": "https://github.com/timcera/plottoolbox"
},
"split_keywords": [
"time-series",
" cli-app",
" aggregate",
" fill",
" filter"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "ff035fa0e65b635c660d9d07a0e2de2efa778551166df636681a5829c7b0b25a",
"md5": "c17ee812434b157b72ab6b17333eb59f",
"sha256": "c11a6b19673e1315cd1869056cb94e2102743112d14d4da55e7a19141c0a8675"
},
"downloads": -1,
"filename": "plottoolbox-105.0.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "c17ee812434b157b72ab6b17333eb59f",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 296803,
"upload_time": "2024-03-31T20:40:26",
"upload_time_iso_8601": "2024-03-31T20:40:26.305345Z",
"url": "https://files.pythonhosted.org/packages/ff/03/5fa0e65b635c660d9d07a0e2de2efa778551166df636681a5829c7b0b25a/plottoolbox-105.0.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "cdf61a9afc4e05b3c329ab28e7965c978f7559440a9c98e073708be9312cbeaa",
"md5": "8a8f7ad4a521031d4ef12a0ef2aea03c",
"sha256": "d493e95afaa938b0b7fcf68d1a9873509fbe0e72745b37fd3f178d7fdfdf2442"
},
"downloads": -1,
"filename": "plottoolbox-105.0.2.tar.gz",
"has_sig": false,
"md5_digest": "8a8f7ad4a521031d4ef12a0ef2aea03c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 4486291,
"upload_time": "2024-03-31T20:40:22",
"upload_time_iso_8601": "2024-03-31T20:40:22.882087Z",
"url": "https://files.pythonhosted.org/packages/cd/f6/1a9afc4e05b3c329ab28e7965c978f7559440a9c98e073708be9312cbeaa/plottoolbox-105.0.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-03-31 20:40:22",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "timcera",
"github_project": "plottoolbox",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "plottoolbox"
}
JSON
