pplot 1.1.4

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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):
    """Show how to use 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)
    ###
    dim = 2.25
    fig_obj = pplot.Figure(
        panels=panel_obj,
        indep_var_label="Indep. var.",
        indep_var_units="S",
        log_indep_axis=False,
        fig_width=4 * dim,
        fig_height=3 * dim,
        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, compress=True)

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 2.7, 3.5, 3.6 and 3.7 under Linux (Debian, Ubuntu), Apple macOS 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]:

   $ github_user=myname
   $ git clone --recurse-submodules \
         https://github.com/"${github_user}"/pplot.git
   Cloning into 'pplot'...
   ...
   $ cd pplot || exit 1
   $ export PPLOT_DIR=${PWD}
   $

3. The package uses two sub-modules: a set of custom Pylint plugins to help with some areas of code quality and consistency (under the pylint_plugins directory), and a lightweight package management framework (under the pypkg directory). Additionally, the pre-commit framework is used to perform various pre-commit code quality and consistency checks. To enable the pre-commit hooks:

   $ cd "${PPLOT_DIR}" || exit 1
   $ pre-commit install
   pre-commit installed at .../pplot/.git/hooks/pre-commit
   $

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):

   • Cog (2.5.1 or newer)

   • Coverage (4.5.3 or newer)

   • Decorator (4.4.0 or newer)

   • Docutils (0.14 or newer)

   • Funcsigs (Python 2.x only, 1.0.2 or newer)

   • Imageio (2.5.0 or newer)

   • Inline Syntax Highlight Sphinx Extension (0.2 or newer)

   • Matplotlib (Python 2.x: 2.2.4 or newer, Python 3.x: 3.0.3 or newer)

   • Mock (Python 2.x only, 2.0.0 or newer)

   • Numpy (1.16.2 or newer)

   • Pcsv (1.0.8 or newer)

   • Peng (1.0.9 or newer)

   • Pexdoc (1.1.4 or newer)

   • Pillow (5.4.1 or newer)

   • Pmisc (1.5.8 or newer)

   • Py.test (4.3.1 or newer)

   • PyContracts (1.8.2 or newer)

   • Pydocstyle (3.0.0 or newer)

   • Pylint (Python 2.x: 1.9.4 or newer, Python 3.x: 2.3.1 or newer)

   • Pytest-coverage (2.6.1 or newer)

   • Pytest-pmisc (1.0.7 or newer)

   • Pytest-xdist (optional, 1.26.1 or newer)

   • ReadTheDocs Sphinx theme (0.4.3 or newer)

   • Scipy (1.2.1 or newer)

   • Shellcheck Linter Sphinx Extension (1.0.8 or newer)

   • Six (1.12.0 or newer)

   • Sphinx (1.8.5 or newer)

   • Tox (3.7.0 or newer)

   • Virtualenv (16.4.3 or newer)

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 Pytest:

   $ PKG_NAME=pplot tox
   GLOB sdist-make: .../pplot/setup.py
   py27-pkg create: .../pplot/.tox/py27
   py27-pkg installdeps: -r.../pplot/requirements/tests_py27.pip, -r.../pplot/requirements/docs_py27.pip
   ...
     py27-pkg: commands succeeded
     py35-pkg: commands succeeded
     py36-pkg: commands succeeded
     py37-pkg: commands succeeded
     congratulations :)
   $

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

   $ PKG_NAME=pplot python setup.py tests
   running tests
   running egg_info
   writing pplot.egg-info/PKG-INFO
   writing dependency_links to pplot.egg-info/dependency_links.txt
   writing requirements to pplot.egg-info/requires.txt
   ...
     py27-pkg: commands succeeded
     py35-pkg: commands succeeded
     py36-pkg: commands succeeded
     py37-pkg: commands succeeded
     congratulations :)
   $

   Tox (or Setuptools via Tox) runs with the following default environments: py27-pkg, py35-pkg, py36-pkg and py37-pkg [3]. These use the 2.7, 3.5, 3.6 and 3.7 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 Pytest (the test runner) use a double dash (--) after all the Tox arguments, for example:

   $ PKG_NAME=pplot tox -e py27-pkg -- -n 4
   GLOB sdist-make: .../pplot/setup.py
   py27-pkg inst-nodeps: .../pplot/.tox/.tmp/package/1/pplot-1.1.4.zip
   ...
     py27-pkg: commands succeeded
     congratulations :)
   $

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

   $ PKG_NAME=pplot python setup.py tests -a "-e py27-pkg -- -n 4"
   running tests
   ...
     py27-pkg: commands succeeded
     congratulations :)
   $

   There are other convenience environments defined for Tox [3]:

   • py27-repl, py35-repl, py36-repl and py37-repl run the Python 2.7, 3.5, 3.6 and 3.7 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 (--).

   • py27-test, py35-test, py36-test and py37-test run Pytest using the Python 2.7, 3.5, 3.6 and 3.7 interpreter, respectively, in the appropriate virtual environment. Arguments to pytest can be passed in the command line after a double dash (--) , for example:

     $ PKG_NAME=pplot tox -e py27-test -- -x test_pplot.py
     GLOB sdist-make: .../pplot/setup.py
     py27-pkg inst-nodeps: .../pplot/.tox/.tmp/package/1/pplot-1.1.4.zip
     ...
       py27-pkg: commands succeeded
       congratulations :)
     $

   • py27-test, py35-test, py36-test and py37-test test code and branch coverage using the 2.7, 3.5, 3.6 and 3.7 interpreter, respectively, in the appropriate virtual environment. Arguments to pytest can be passed in the command line after a double dash (--). The report can be found in ${PPLOT_DIR}/.tox/py[PV]/usr/share/ppl ot/tests/htmlcov/index.html where [PV] stands for 2.7, 3.5, 3.6 or 3.7 depending on the interpreter used.

8. Verify that continuous integration tests pass. The package has continuous integration configured for Linux, Apple macOS and Microsoft Windows (all via Azure DevOps).

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

   $ "${PPLOT_DIR}"/pypkg/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 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

[3] (1,2)

Tox configuration largely inspired by Ionel’s codelog

License

The MIT License (MIT)

Copyright (c) 2013-2019 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) 2013-2019 Pablo Acosta-Serafini .. See LICENSE for details

Changelog

• 1.1.4 [2019-03-22]: Documentation and dependencies update

• 1.1.3 [2019-03-16]: Adopted lightweight package management framework

• 1.1.2 [2018-01-16]: Hosted documentation build fixes

• 1.1.1 [2018-01-16]: Corrected PyPI Python interpreter version

• 1.1.0 [2018-01-16]: Added support for specifying independent axis tick labels (ignored for figures with logarithmic independent axis). Improved minimum image size computation. Removed all padding around the exterior of the image. Dropped support for Python interpreter versions 2.6, 3.3 and 3.4. Updated dependencies versions to their current versions

• 1.0.4 [2017-02-25]: Fixed tick marks labels in the [0, 1] range. Closed image tests escapes

• 1.0.3 [2017-02-16]: Python 3.6 support

• 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