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    "info": {
        "author": "Kendrick Shaw, Jeffrey Gill",
        "author_email": "kms15@case.edu, jeffrey.p.gill@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "axographio\n==========\n\n.. image:: https://img.shields.io/pypi/v/axographio.svg\n    :target: PyPI_\n    :alt: PyPI project\n\n.. image:: https://img.shields.io/badge/github-source_code-blue.svg\n    :target: GitHub_\n    :alt: GitHub source code\n\n.. image:: https://img.shields.io/badge/binder-launch_demo-e66581.svg\n    :target: Binder_\n    :alt: Launch a demo in Binder\n\n**axographio** is a Python package that makes it easy to read and write binary\ndata files in the AxoGraph file format.\n\nAxoGraph_ is a commercial software package for data acquisition and analysis\nthat is widely used in electrophysiological research. Although it can read and\nwrite files in text format, its binary format is much smaller and faster to load\nand save; thus many users preferentially use this format. The company\ndistributes the details of the file format along with sample C++ code for\nreading and writing to these files using third-party software, such as this\nPython package.\n\nPython_ is a powerful and easy to use general purpose programming language.\nThere are many useful Python libraries available for scientific data analysis\nand data visualization such as SciPy, Matplotlib, and Mayavi.\n\nThis package provides a simple interface for loading AxoGraph data files into\na Python program or interactive session. If you want to analyze data you\nrecorded in AxoGraph using Python-based tools, this package provides the glue\ncode you'll need. You can also write data to the AxoGraph binary format so that\nit can be viewed and analyzed within AxoGraph.\n\n.. _PyPI:       https://pypi.org/project/axographio/\n.. _GitHub:     https://github.com/CWRUChielLab/axographio\n.. _Binder:     https://mybinder.org/v2/gh/CWRUChielLab/axographio/master?filepath=examples%2Fbasic-demo.ipynb\n.. _AxoGraph:   https://axograph.com\n.. _Python:     https://python.org\n\nGetting axographio\n------------------\n\n**axographio** is compatible with both Python 2 and Python 3.\n\nThe easiest way to get **axographio** is to install the latest stable version\nusing ``pip``, but you can alternatively build it from the source code.\n\nInstalling the latest stable version\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nRequirements for installing and running **axographio**:\n\n* The NumPy package (``pip install numpy``)\n\nThe **axographio** package contains C++ code that must be compiled. PyPI_ stores\npre-compiled copies of the package for common platforms (e.g., Python 3 on\n64-bit Windows), and these can be installed using ``pip``.\n\nTo install the latest stable version, try the following::\n\n    pip install axographio\n\nIf a pre-compiled package is available for your platform on PyPI_, ``pip``\nshould quickly download and install it. If not, ``pip`` will automatically\nattempt to build the package from source code. Building the package has\nadditional requirements. If ``pip`` fails during building, keep reading.\n\nBuilding from source code\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIf you need to build the package because a pre-compiled version is not already\navailable for your platform on PyPI_, or if you just want to try building from\nthe source code, you will need to meet additional requirements.\n\nRequirements for building **axographio** from source code:\n\n* The NumPy package (``pip install numpy``)\n* The Cython package, version 0.19 or later (``pip install cython>=0.19``)\n* A C++ compiler  (e.g., Visual C++ Build Tools from Microsoft on Windows\n  systems, or Xcode on Mac systems)\n\nIf ``pip`` failed while trying to build from source code, make sure you meet\nthese requirements and try again.\n\nIf you would like to build and install using the latest development source code\nfrom GitHub_, try the following::\n\n    pip install git+https://github.com/CWRUChielLab/axographio\n\nThis command requires ``git``. If you don't have ``git``, you can instead\nmanually download the source from GitHub_ and install from your local\ndirectory::\n\n    pip install C:\\wherever-you-put-the-source-code\n\nUsage\n-----\n\nTry out the Binder_ demo for an interactive Python session that requires no\ninstallation or fuss. You can start hacking *right now!*\n\nLoading a data file is as easy as calling ``read``:\n\n>>> import axographio\n>>>\n>>> f = axographio.read('AxoGraph X File.axgx')\n\nAt this point the variable ``f`` will contain a ``file_contents`` object with\nthe column names and data from the file.  For example, you could now plot the\nfirst two columns using Matplotlib:\n\n>>> import matplotlib.pyplot as plt\n>>>\n>>> plt.plot(f.data[0], f.data[1])\n>>> plt.xlabel(f.names[0])\n>>> plt.ylabel(f.names[1])\n>>> plt.show()  # may be optional depending on your OS\n\nOf course, you probably have grander plans than just plotting the data.  The\ncolumn data supports the standard sequence interfaces (i.e., indexing,\niteration, etc.) and can be converted to a NumPy or SciPy array using the\n``asarray`` functions in these packages, e.g.:\n\n>>> import numpy as np\n>>>\n>>> times = np.asarray(f.data[0])\n\nWriting files is also relatively easy.  You simply create a new\n``file_contents`` object (or use one you loaded earlier), and then call\n``write``.  For example, the following code creates a file in the current\ndirectory called 'my60Hz.axgx' with two channels with 60 Hz sine waves:\n\n>>> import axographio\n>>> import numpy as np\n>>>\n>>> times = np.arange(0, 10, 0.0001)\n>>> column1 = np.sin(2*np.pi * 60 * times)\n>>> column2 = np.cos(2*np.pi * 60 * times)\n>>> f = axographio.file_contents(\n...    ['time (s)', 'my recording (V)', 'your recording (V)'],\n...    [times, column1, column2])\n>>> f.write('my60Hz.axgx') # created in the current directory\n\nQuestions and Support\n---------------------\n\nPlease post any questions, problems, comments, or suggestions in the `GitHub\nissue tracker <https://github.com/CWRUChielLab/axographio/issues>`_.\n\nChanges\n-------\n\n0.3.1\n~~~~~\n\n* Modify NumPy's global print settings only when running tests\n\n0.3.0\n~~~~~\n\n* Package test suite can be run using ``axographio.tests.run()``\n* Package version can be accessed using ``axographio.__version__``\n* Added example Jupyter notebook to source repository (not included with\n  installation)\n* Updated installation instructions\n* Improved documentation\n* Reorganized source code file structure\n* Fixed doctests for NumPy < 1.14\n\n0.2.0\n~~~~~\n\n* Added compatibility with Python 3\n\n0.1.1\n~~~~~\n\n* Fixed a rounding error that could create one extra data point in the time\n  column\n\n0.1.0\n~~~~~\n\n* First release\n\nAcknowledgments\n---------------\n\nThis initial version of this project was written in the\nChiel Laboratory at Case Western Reserve University, with support from NIH\ngrant NS047073, an Ohio Innovation Incentive Award Fellowship, and the\nCase Western Reserve MSTP (NIH T32 GM007250).  This project builds on a\nnumber of other open source projects, including Python, C++ AxoGraph file\ninput/output code from AxoGraph Scientific (placed in the public domain; a\nmodified version is included with the project source code), Cython, and many\nothers.  Thanks also to Dr. Hillel Chiel for providing testing and helpful\nsuggestions.\n\n\n",
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        "keywords": "physiology,electrophysiology,axograph",
        "license": "BSD License",
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