This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description


Description

This module can be used to create high-quality, presentation-ready X-Y graphs quickly and easily

Class hierarchy

The properties of the graph (figure in Matplotlib parlance) are defined in an object of the pplot.Figure class.

Each figure can have one or more panels, whose properties are defined by objects of the pplot.Panel class. Panels are arranged vertically in the figure and share the same independent axis. The limits of the independent axis of the figure result from the union of the limits of the independent axis of all the panels. The independent axis is shown by default in the bottom-most panel although it can be configured to be in any panel or panels.

Each panel can have one or more data series, whose properties are defined by objects of the pplot.Series class. A series can be associated with either the primary or secondary dependent axis of the panel. The limits of the primary and secondary dependent axis of the panel result from the union of the primary and secondary dependent data points of all the series associated with each axis. The primary axis is shown on the left of the panel and the secondary axis is shown on the right of the panel. Axes can be linear or logarithmic.

The data for a series is defined by a source. Two data sources are provided: the pplot.BasicSource class provides basic data validation and minimum/maximum independent variable range bounding. The pplot.CsvSource class builds upon the functionality of the pplot.BasicSource class and offers a simple way of accessing data from a comma-separated values (CSV) file. Other data sources can be programmed by inheriting from the pplot.functions.DataSource abstract base class (ABC). The custom data source needs to implement the following methods: __str__, _set_indep_var and _set_dep_var. The latter two methods set the contents of the independent variable (an increasing real Numpy vector) and the dependent variable (a real Numpy vector) of the source, respectively.

Axes tick marks

Axes tick marks are selected so as to create the most readable graph. Two global variables control the actual number of ticks, pplot.constants.MIN_TICKS and pplot.constants.SUGGESTED_MAX_TICKS. In general the number of ticks are between these two bounds; one or two more ticks can be present if a data series uses interpolation and the interpolated curve goes above (below) the largest (smallest) data point. Tick spacing is chosen so as to have the most number of data points “on grid”. Engineering notation (i.e. 1K = 1000, 1m = 0.001, etc.) is used for the axis tick marks.

Example

# plot_example_1.py
from __future__ import print_function
import os, sys, numpy, pplot

def main(fname, no_print):
    """
    Example of how to use the pplot library
    to generate presentation-quality plots
    """
    ###
    # Series definition (Series class)
    ###
    # Extract data from a comma-separated (csv)
    # file using the CsvSource class
    wdir = os.path.dirname(__file__)
    csv_file = os.path.join(wdir, 'data.csv')
    series1_obj = [pplot.Series(
        data_source=pplot.CsvSource(
            fname=csv_file,
            rfilter={'value1':1},
            indep_col_label='value2',
            dep_col_label='value3',
            indep_min=None,
            indep_max=None,
            fproc=series1_proc_func,
            fproc_eargs={'xoffset':1e-3}
        ),
        label='Source 1',
        color='k',
        marker='o',
        interp='CUBIC',
        line_style='-',
        secondary_axis=False
    )]
    # Literal data can be used with the BasicSource class
    series2_obj = [pplot.Series(
        data_source=pplot.BasicSource(
            indep_var=numpy.array([0e-3, 1e-3, 2e-3]),
            dep_var=numpy.array([4, 7, 8]),
        ),
        label='Source 2',
        color='r',
        marker='s',
        interp='STRAIGHT',
        line_style='--',
        secondary_axis=False
    )]
    series3_obj = [pplot.Series(
        data_source=pplot.BasicSource(
            indep_var=numpy.array([0.5e-3, 1e-3, 1.5e-3]),
            dep_var=numpy.array([10, 9, 6]),
        ),
        label='Source 3',
        color='b',
        marker='h',
        interp='STRAIGHT',
        line_style='--',
        secondary_axis=True
    )]
    series4_obj = [pplot.Series(
        data_source=pplot.BasicSource(
            indep_var=numpy.array([0.3e-3, 1.8e-3, 2.5e-3]),
            dep_var=numpy.array([8, 8, 8]),
        ),
        label='Source 4',
        color='g',
        marker='D',
        interp='STRAIGHT',
        line_style=None,
        secondary_axis=True
    )]
    ###
    # Panels definition (Panel class)
    ###
    panel_obj = pplot.Panel(
        series=series1_obj+series2_obj+series3_obj+series4_obj,
        primary_axis_label='Primary axis label',
        primary_axis_units='-',
        secondary_axis_label='Secondary axis label',
        secondary_axis_units='W',
        legend_props={'pos':'lower right', 'cols':1}
    )
    ###
    # Figure definition (Figure class)
    ###
    fig_obj = pplot.Figure(
        panels=panel_obj,
        indep_var_label='Indep. var.',
        indep_var_units='S',
        log_indep_axis=False,
        fig_width=4*2.25,
        fig_height=3*2.25,
        title='Library pplot Example'
    )
    # Save figure
    output_fname = os.path.join(wdir, fname)
    if not no_print:
        print('Saving image to file {0}'.format(output_fname))
    fig_obj.save(output_fname)

def series1_proc_func(indep_var, dep_var, xoffset):
    """ Process data 1 series """
    return (indep_var*1e-3)-xoffset, dep_var

Interpreter

The package has been developed and tested with Python 2.6, 2.7, 3.3, 3.4 and 3.5 under Linux (Debian, Ubuntu), Apple OS X and Microsoft Windows

Installing

$ pip install pplot

Documentation

Available at Read the Docs

Contributing

  1. Abide by the adopted code of conduct

  2. Fork the repository from GitHub and then clone personal copy [1]:

    $ git clone \
          https://github.com/[github-user-name]/pplot.git
    Cloning into 'pplot'...
    ...
    $ cd pplot
    $ export PPLOT_DIR=${PWD}
    
  3. Install the project’s Git hooks and build the documentation. The pre-commit hook does some minor consistency checks, namely trailing whitespace and PEP8 compliance via Pylint. Assuming the directory to which the repository was cloned is in the $PPLOT_DIR shell environment variable:

    $ ${PPLOT_DIR}/sbin/complete-cloning.sh
    Installing Git hooks
    Building pplot package documentation
    ...
    
  4. Ensure that the Python interpreter can find the package modules (update the $PYTHONPATH environment variable, or use sys.paths(), etc.)

    $ export PYTHONPATH=${PYTHONPATH}:${PPLOT_DIR}
    
  5. Install the dependencies (if needed, done automatically by pip):

  6. Implement a new feature or fix a bug

  7. Write a unit test which shows that the contributed code works as expected. Run the package tests to ensure that the bug fix or new feature does not have adverse side effects. If possible achieve 100% code and branch coverage of the contribution. Thorough package validation can be done via Tox and Py.test:

    $ tox
    GLOB sdist-make: .../pplot/setup.py
    py26-pkg inst-nodeps: .../pplot/.tox/dist/pplot-...zip
    

    Setuptools can also be used (Tox is configured as its virtual environment manager) [2]:

    $ python setup.py tests
    running tests
    running egg_info
    writing requirements to pplot.egg-info/requires.txt
    writing pplot.egg-info/PKG-INFO
    ...
    

    Tox (or Setuptools via Tox) runs with the following default environments: py26-pkg, py27-pkg, py33-pkg, py34-pkg and py35-pkg [3]. These use the Python 2.6, 2.7, 3.3, 3.4 and 3.5 interpreters, respectively, to test all code in the documentation (both in Sphinx *.rst source files and in docstrings), run all unit tests, measure test coverage and re-build the exceptions documentation. To pass arguments to Py.test (the test runner) use a double dash (--) after all the Tox arguments, for example:

    $ tox -e py27-pkg -- -n 4
    GLOB sdist-make: .../pplot/setup.py
    py27-pkg inst-nodeps: .../pplot/.tox/dist/pplot-...zip
    ...
    

    Or use the -a Setuptools optional argument followed by a quoted string with the arguments for Py.test. For example:

    $ python setup.py tests -a "-e py27-pkg -- -n 4"
    running tests
    ...
    

    There are other convenience environments defined for Tox [4]:

    • py26-repl, py27-repl, py33-repl, py34-repl and py35-repl run the Python 2.6, 2.7, 3.3, 3.4 or 3.5 REPL, respectively, in the appropriate virtual environment. The pplot package is pip-installed by Tox when the environments are created. Arguments to the interpreter can be passed in the command line after a double dash (--)

    • py26-test, py27-test, py33-test, py34-test and py35-test run py.test using the Python 2.6, 2.7, 3.3, 3.4 or Python 3.5 interpreter, respectively, in the appropriate virtual environment. Arguments to py.test can be passed in the command line after a double dash (--) , for example:

      $ tox -e py34-test -- -x test_pplot.py
      GLOB sdist-make: [...]/pplot/setup.py
      py34-test inst-nodeps: [...]/pplot/.tox/dist/pplot-[...].zip
      py34-test runtests: PYTHONHASHSEED='680528711'
      py34-test runtests: commands[0] | [...]py.test -x test_pplot.py
      ===================== test session starts =====================
      platform linux -- Python 3.4.2 -- py-1.4.30 -- [...]
      ...
      
    • py26-cov, py27-cov, py33-cov, py34-cov and py35-cov test code and branch coverage using the Python 2.6, 2.7, 3.3, 3.4 or 3.5 interpreter, respectively, in the appropriate virtual environment. Arguments to py.test can be passed in the command line after a double dash (--). The report can be found in ${PPLOT_DIR}/.tox/py[PV]/usr/share/pplot/tests/htmlcov/index.html where [PV] stands for 26, 27, 33, 34 or 35 depending on the interpreter used

  8. Verify that continuous integration tests pass. The package has continuous integration configured for Linux (via Travis) and for Microsoft Windows (via Appveyor). Aggregation/cloud code coverage is configured via Codecov. It is assumed that the Codecov repository upload token in the Travis build is stored in the ${CODECOV_TOKEN} environment variable (securely defined in the Travis repository settings page). Travis build artifacts can be transferred to Dropbox using the Dropbox Uploader script (included for convenience in the ${PPLOT_DIR}/sbin directory). For an automatic transfer that does not require manual entering of authentication credentials place the APPKEY, APPSECRET, ACCESS_LEVEL, OAUTH_ACCESS_TOKEN and OAUTH_ACCESS_TOKEN_SECRET values required by Dropbox Uploader in the in the ${DBU_APPKEY}, ${DBU_APPSECRET}, ${DBU_ACCESS_LEVEL}, ${DBU_OAUTH_ACCESS_TOKEN} and ${DBU_OAUTH_ACCESS_TOKEN_SECRET} environment variables, respectively (also securely defined in Travis repository settings page)

  9. Document the new feature or bug fix (if needed). The script ${PPLOT_DIR}/sbin/build_docs.py re-builds the whole package documentation (re-generates images, cogs source files, etc.):

    $ ${PKG_BIN_DIR}/build_docs.py -h
    usage: build_docs.py [-h] [-d DIRECTORY] [-r]
                         [-n NUM_CPUS] [-t]
    
    Build pplot package documentation
    
    optional arguments:
      -h, --help            show this help message and exit
      -d DIRECTORY, --directory DIRECTORY
                            specify source file directory
                            (default ../pplot)
      -r, --rebuild         rebuild exceptions documentation.
                            If no module name is given all
                            modules with auto-generated
                            exceptions documentation are
                            rebuilt
      -n NUM_CPUS, --num-cpus NUM_CPUS
                            number of CPUs to use (default: 1)
      -t, --test            diff original and rebuilt file(s)
                            (exit code 0 indicates file(s) are
                            identical, exit code 1 indicates
                            file(s) are different)
    

Footnotes

[1]All examples are for the bash shell
[2]It appears that Scipy dependencies do not include Numpy (as they should) so running the tests via Setuptools will typically result in an error. The pplot requirement file specifies Numpy before Scipy and this installation order is honored by Tox so running the tests via Tox sidesteps Scipy’s broken dependency problem but requires Tox to be installed before running the tests (Setuptools installs Tox if needed)
[3]It is assumed that all the Python interpreters are in the executables path. Source code for the interpreters can be downloaded from Python’s main site
[4]Tox configuration largely inspired by Ionel’s codelog

License

The MIT License (MIT)

Copyright (c) 2013-2016 Pablo Acosta-Serafini

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. .. CHANGELOG.rst .. Copyright (c) 2013-2016 Pablo Acosta-Serafini .. See LICENSE for details

Changelog

  • 1.0.2 [2016-05-16]: PyPI front page fixes
  • 1.0.1 [2016-05-16]: Documentation build fixes to display README information correctly in repositories and PyPI
  • 1.0.0 [2016-05-16]: Final release of 1.0.0 branch
  • 1.0.0rc1 [2016-05-12]: Initial commit, forked a subset from putil PyPI package
Release History

Release History

1.0.2

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
pplot-1.0.2-py2.py3-none-any.whl (58.9 kB) Copy SHA256 Checksum SHA256 py2.py3 Wheel May 16, 2016
pplot-1.0.2.tar.gz (21.8 MB) Copy SHA256 Checksum SHA256 Source May 16, 2016
pplot-1.0.2.zip (22.0 MB) Copy SHA256 Checksum SHA256 Source May 16, 2016

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS HPE HPE Development Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting