pyparty 0.3.0

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==========
What's New
==========
Check out `Object Hunter`_, a ``pyparty`` script for identifying, summarizing and plotting
groups of objects in an image.

.. _`Object Hunter` : http://nbviewer.ipython.org/urls/raw.github.com/hugadams/pyparty/master/examples/Notebooks/objecthunt_tutorial.ipynb?create=1


======================================
pyparty: Python (py) particles (party)
======================================

``pyparty`` is a small library for drawing, labeling, patterning and manipulating
particles in 2d images. ``pyparty`` was built primarily over the excellent
image processing library, scikit-image_.

.. _scikit-image: http://scikit-image.org


Getting Started
===============

The current documentation (and in-a-pinch test suite) is a series of example notebooks
(`IPython Notebook`_), which cover most of the basics. These have been linked below:

- **TUTORIALS**:
- `Intro to Canvas: Basic Operations`_
- `Intro to Shapes`_
- `Intro to Grids`_
- `Intro to MultiCanvas`_

- **LABELS FROM IMAGES**:
- `Intro to Labeling`_
- `Labeling Nanoparticle Species`_

- **MISCELLANEOUS**:
- `Matplotlib Color Maps`_
- `Watershedding Example Adapted`_

- **ARTIFICIAL IMAGES**:
- `Basic Artificial SEM Images and Noise`_
- `Simple Images and Labels for JORS`_

- **SCRIPTS**:
- `Object Hunter`_

- **COMING SOON**:
- *Advanced artificial SEM/TEM images*

.. _`Intro to Canvas: Basic Operations`: http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/basictests.ipynb?create=1
.. _`Intro to Shapes`: http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/shapes.ipynb?create=1
.. _`Intro to Grids` : http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/grids.ipynb?create=1
.. _`Intro to MultiCanvas` : http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/multi_tutorial.ipynb?create=1
.. _`Intro to Labeling`: http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/Analyze_Particles.ipynb?create=1
.. _`Labeling Nanoparticle Species` : http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/groups_of_labels.ipynb?create=1
.. _`Basic Artificial SEM Images and Noise` : http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/making_noise.ipynb?create=1
.. _`Matplotlib Color Maps` : http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/gwu_maps.ipynb?create=1
.. _`Watershedding Example Adapted` : http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/watershed.ipynb?create=1
.. _`Simple Images and Labels for JORS` : http://nbviewer.ipython.org/github/hugadams/pyparty/blob/master/examples/Notebooks/JORS_data.ipynb?create=1
.. _`Object Hunter` : http://nbviewer.ipython.org/urls/raw.github.com/hugadams/pyparty/master/examples/Notebooks/objecthunt_tutorial.ipynb?create=1

Notebooks were initialized with ``pylab``:

ipython notebook --pylab=inline

Having trouble viewing/editing notebooks? Consider using `Enthought
Canopy`_, which has a notebook kernel builtin, as well as a graphical package manager.
For simple viewing, paste the github url of each notebook into the IPython Notebook viewer_.

.. _documentation: http://hugadams.github.com/pyparty/
.. _`IPython Notebook`: http://ipython.org/notebook.html?utm_content=buffer83c2c&utm_source=buffer&utm_medium=twitter&utm_campaign=Buffer
.. _`Enthought Canopy`: https://www.enthought.com/products/canopy/
.. _viewer: http://nbviewer.ipython.org/

**These notebooks are free for redistribution. If referencing in publication, please cite as:**
- Hughes, A. (2012). `A Computational Framework for Plasmonic Nanobiosensing`_. Python in Science Conference [SCIPY].

.. _`A Computational Framework for Plasmonic Nanobiosensing`: https://www.researchgate.net/publication/236672995_A_Computational_Framework_for_Plasmonic_Nanobiosensing:

Overview and Features
=====================

``pyparty`` provides a simple API for particle analysis in 2d images, while streamlining
common operations in the image processing pipeline.

*Some key features include*:

1. Pythonic **ParticleManager** for storing and manipulating particles from image
labels OR builtin shapes. Some highlights of **Particles** include:
- A common datastructure for array operations like rotations and
translations.
- ``skimage`` descriptors_ as primary attributes on all particles.
- Filtering and mapping based with numpy logical indexing syntax.

2. A **Grid** system for patterning particles, as well as mesh utilities for creating
image backgrounds.

3. A **Canvas** to easily integrate *Grids*, *Particles* and flexible *Backgrounds*.
In addition, Canvas also provides simplified interfaces for:
- binarization / thresholding
- plotting
- slicing and other pythonic container operations

4. A plotting API based on matplotlib.imshow() that generally supports
rasterizaztions AND `matplotlib patches`_.

5. Flexible color designations ('red', (1,0,0), 00FF00), and strict typing
to ensure consistency in data and plots.

6. General ndarray operations such as rotations and translations supported by ALL particle types.

7. API for adding **Noise** to images.

.. _descriptors : http://scikit-image.org/docs/dev/api/skimage.measure.html#regionprops
.. _`matplotlib patches` : http://matplotlib.org/examples/api/patch_collection.html

What are some use cases, and will pyparty help to me?
=====================================================

Tasked well-suited for ``pyparty`` include:

1. Filtering and characterization of cells based on descriptors like
eccentricit and area.

2. Patterning a grid of particles over a shadowed background to compare performance
of thresholding algorithms.

3. Manipulating particles in a *pythonic* manner:

- delete all particles that have area > 50 pixels.
- sort and color ellipses in order of increasing eccentricity.
- dilate all particles appearing in bottom half of an image

4. Scripting without leaving Python.

5. Plot particles as rasterizations or matplotlib patches side-by-side.

In short, you may consider using ``pyparty`` if you are doing image analysis and find
generating, managing or labeling particles as a bottleneck.

.. _patchcollection : http://matplotlib.org/examples/api/patch_collection.html

History
=======
``pyparty`` originally began at the George Washington University (2013) in an
effort to generate test data for SEM and AFM images of gold nanoparticles on glass substrates.
We really enjoyed scikit-image_ for image processing and sought to implement it in generating test data.
We sought to provide an API for managing labeled particles from real images. Scikit-image draw and measure
modules laid the groundwork to the core functionality that ``pyparty`` attempts to streamline.

I should also note that some of the inspiration came from the excellent ``Analyze Particles`` features
in ImageJ_.

.. _ImageJ : http://rsbweb.nih.gov/ij/

License
=======

3-Clause Revised BSD_

.. _BSD : https://github.com/hugadams/pyparty/blob/master/LICENSE.txt

Dependencies
============
``pyparty`` requires ``scikit-image``, ``Traits`` and their dependencies, which
include many core packages such as ``numpy`` and ``matplotlib``. If you are new
to Python for scientific computing, consider downloading a packaged distribution_.

.. _distribution : https://www.enthought.com/products/canopy/

``pyparty`` uses Traits_ because it is well-suited for writing clean, type-checked
object-oriented classes. You will not need to understand or use ``Traits``
unless you develop for ``pyparty``; *it is not used in the public API*, and may be
removed in future installments after the core functionality is stable.

.. _Traits : http://code.enthought.com/projects/traits/

Installation
============

I would recommend using `Enthought Canopy`_ and installing ``Traits`` and
``scikit-image`` through the package manager; however, ``pyparty`` is also
registered on PyPi_.

.. _PyPi : https://pypi.python.org/pypi/pyparty

Pip Install
-----------

Make sure you have pip installed:

sudo apt-get install python-pip

Then:

pip install pyparty

To install all of the dependencies, download ``pyparty`` from github, navigate
to the base directory and type:

pip install -r requirements.txt


Installation from source
------------------------

In the ``pyparty`` base directory run:

python setup.py install

The developmental version can be cloned from github:

git clone https://github.com/hugadams/pyparty.git

This will not install any dependencies.


Related Libraries
=================
Interested in the Python ecosystem? Check out some of these related libraries:

- NumPy_ (Fundamental vectorized numerics in Python)
- SciPy_ (Collection of core, numpy-based scientific libraries)
- scikit-image_ (Scipy image processing suite)
- matplotlib_ (De facto static plotting in Python)
- pandas_ (Data analysis library : inspired ``pyparty`` ParticleManager API)
- ilastik_ (Interactive Learning and Segmentation Tool)
- Pillow_ (Python Image Library)

.. _Pillow: http://python-imaging.github.io/
.. _NumPy: http://www.numpy.org/
.. _pandas: http://pandas.pydata.org/
.. _SciPy: http://scipy.org/
.. _matplotlib : http://matplotlib.org/
.. _ilastik : http://www.ilastik.org/

Coming Soon
===========
- More multi-particle types.
- Better control of color shading of labels.
- More examples.

Have a feature request, or want to report a bug? Please fill out a github
issue_ with the appropriate label.

.. _issue : https://github.com/hugadams/pyparty/issues

About the Author
================

I'm a PhD student at GWU (check me out on researchgate_, Linkedin_ or twitter_)
and former Enthought intern. I work in biomolecule sensing and plasmonics.
Like any PhD student, my time is stretched across many projects. As such,
the ``pyparty`` source code may is messy in places, and a proper nosetests
platform is still under development. Batch running the IPython notebook tutorials
serves as a basic regression test platform.

.. _researchgate : https://www.researchgate.net/profile/Adam_Hughes2/?ev=hdr_xprf
.. _Linkedin : http://www.linkedin.com/profile/view?id=121484744&goback=%2Enmp_*1_*1_*1_*1_*1_*1_*1_*1_*1_*1_*1&trk=spm_pic
.. _twitter : https://twitter.com/hughesadam87

Acknowledgements
================
Thank you scikit-image team for their patience and assistance with us on the
mailing list, and for putting together a great library for the community.

Thank you countless developers who have patiently answered hundreds of
my questions on too many mailing lists and sites to list.

Thank you `Zhaowen Liu`_ for all of your help with this project, our
other projects and for your unwaivering encouragement (and for the panda).

.. _`Zhaowen Liu` : https://github.com/EvelynLiu77