Metadata-Version: 2.0
Name: pycroscopy
Version: 0.0a12
Summary: A suite of Python libraries for high performance scientific computing of microscopy data.
Home-page: http://github.com/pycroscopy/pyCroscopy
Author: S. Somnath, C. Ryan, N. Laanait
Author-email: pycroscopy@gmail.com
License: MIT
Keywords: scientific microscopy data analysis
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Dist: h5py (>=2.6.0)
Requires-Dist: matplotlib
Requires-Dist: numpy (>=1.11.0)
Requires-Dist: numpy-groupies
Requires-Dist: psutil
Requires-Dist: pyqtgraph
Requires-Dist: scikit-image (>=0.12.3)
Requires-Dist: scikit-learn (>=0.17.1)
Requires-Dist: scipy (>=0.17.1)
Requires-Dist: xlrd (>=1.0.0)

pycroscopy
==========

See <https://pycroscopy.github.io/pycroscopy/> for more info.

0. Description
--------------
A python package for image processing and scientific analysis of imaging modalities such as multi-frequency scanning probe microscopy,
scanning tunneling spectroscopy, x-ray diffraction microscopy, and transmission electron microscopy.
Classes implemented here are ported to a high performance computing platform at Oak Ridge National Laboratory (ORNL).

1. Package Structure
--------------------
The package structure is simple, with 4 main modules:
   1. `io`: Input/Output from custom & proprietary microscope formats to HDF5.
   2. `processing`: Multivariate Statistics, Machine Learning, and Filtering.
   3. `analysis`: Model-dependent analysis of image information.
   4. `viz`: Visualization and interactive slicing of high-dimensional data by lightweight Qt viewers.

Once a user converts their microscope's data format into an HDF5 format, by simply extending some of the classes in `io`, the user gains access to the rest of the utilities present in `pycroscopy.*`. 

2. Installation
---------------
Pycroscopy requires the installation of the base python libraries, a C compiler, and preferrably - a development environment such as Spyder from Continuum or PyCharm. 

   1. Install Anaconda 2.5.0 Python 2.7 64-bit:

      a. Mac users: <https://repo.continuum.io/archive/Anaconda2-2.5.0-MacOSX-x86_64.pkg>

      b. Windows users: <https://repo.continuum.io/archive/Anaconda2-2.5.0-Windows-x86_64.exe>

      c. Linux users: <https://repo.continuum.io/archive/Anaconda2-2.5.0-Linux-x86_64.sh>

   2. Install a C compiler - Windows users ONLY:
      
      If you are running on a Windows machine, you will need to load a C compiler, which is required to build certain packages in python including one for parallel processing. Mac Users can skip this step as OS X natively comes with a C compiler.
      
      a. Install Microsoft Visual C++ 2008 SP1 Redistributable Package (x64) from <http://www.microsoft.com/en-us/download/confirmation.aspx?id=2092>

      b. Install Microsoft Visual C++ for Python from <http://aka.ms/vcpython27>

   3. Install multiprocess for parallel computation tasks

      Open a terminal (mac / linux) or command prompt (windows) and type:

         pip install multiprocess

   4. Install pycroscopy:

      Open a terminal (mac / linux) or command prompt (windows) and type:

         pip install pycroscopy

   5. Enjoy pycroscopy!


