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资源说明：Personal programs for fiber optic nanobiosensing simulations.
.. image:: https://github.com/hugadams/PAME/blob/master/screenshots/gui.png
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:width: 200 px
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PAME: Plasmonic Assay Modeling Environment
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Graphical Python application for simulating plasmonic biosensors, particularly fiberoptic biosensors with nanoparticles.
Check out the `PAME preprint`_.
.. _PAME preprint : https://peerj.com/preprints/1085/
Tutorials
=========
IPython Notebooks
-----------------
Some of these are traditional tutorials, others are examples of analyzed data from our lab.
- `Simultation Parsing Tutorial `_
- `Gold Nanoparticle Self-Assembly Compared to Experimental Data `_
- Experimental datasets are preloaded in `scikit-spectra library `_
- `Protein binding to gold nanoparticle film `_
- `Gold and Silver Nanoparticle Combined Layer 1 `_
- `Gold and Silver Nanoparticle Combined Layer 2 `_
Screencasts
-----------
Tutorials are cumulative (eg screencast 2 picks up where 1 ends).
PAME's tutorials are a series of screencasts.
- Screencast 1: `Introduction to PAME- Anti-reflective coatings `_
- Screencast 2: `Introduction to Nanoparticles `_
- Screencast 3: `Nanoparticle film with silica shell `_
- Screencast 4: `Nanoparticle film with protein shell `_
- Screencast 5: `Intro to Fiberoptic Dip Sesnosr `_
- Screencast 6: `Multiplexed Dip Sensor with Gold and Silver Nanoparticles `_
- Screencast 7: `Gold Nanoparticle Fiber Dip Sensor Simulation `_
- Screencast 8: `Dip Sensor with Organosilane Layer `_
- Screencast 9: `Double layer of gold and silver nanoparticles `_
Installation
============
Binaries (ie .exe one-click use files) are `under development `_, but for now, PAME must be installed as a python library and launched through the command line. Anyone interested in helping to develop binaries, please contact.
PAME makes heavy use of the `SciPy Stack (numpy, ipython etc...) `_, and so it has a lot of dependencies. Instead of using a bare python distribution, I'd recommend using a scientific python distribution that comes pre-loaded with the SciPy Stack (eg `canopy `_, do the following.
Download the `PAME sourcecode `_ as a zipfile and unzip. `cd` into the unzipped directory
cd /path/to/PAME
Install from source
python setup.py install
Install the dependencies from pip
pip install -r requirements.txt
You also may need to `install the QT backend ` if it's not already configured.
Conda
-----
I use `anaconda` because it has an excellent virtual environment manager. The advantage is here you can installed a clean working environment only for PAME without altering any of your other packages. For a tutorial on conda virtual environments, `check this out `_. To configure a PAME environment in anaconda, first install anaconda and then do the following:
1. Create a clean virtual environment (mine is named PAMEvenv)
conda create -n PAMEvenv anaconda
This installs several required scientific packages including `numpy`, `pandas` and `ipython`.
2. Activate the environment
source activate PAMEvenv
3. Install pame (download pame source code and unzip, then navigate into directory)
cd /path/to/PAMEdirectory
python setup.py install
4. Conda install/upgrade dependencies
conda install traits traitsui mayavi chaco mpmath PIL
5. To deactivate the virtual environment
source deactivate
Dependencies
------------
The full list of PAME's dependencies is in the `requirements.txt `_ file. You also may need to `install the QT backend `.
Support
=======
Questions? Interested in developing? Message: pame_env@googlegroups.com, or contact me directly (hughesadam87@gmail.com, @hughesadam87)
Web Utilitiles
==============
PAME doesn't run in the browser. Check out these related tools that do!
- `Mie-coefficients `_
- `Film Metrics (Thin Film Solver) `_
- `Mie with shells and other tools `_
License
=======
3-Clause Revised BSD_
.. _BSD : https://github.com/hugadams/PAME/blob/master/LICENSE.txt
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