axographio 0.3.2

Description:

axographio 0.3.2

axographio is a Python package that makes it easy to read and write binary
data files in the AxoGraph file format.
AxoGraph is a commercial software package for data acquisition and analysis
that is widely used in electrophysiological research. Although it can read and
write files in text format, its binary format is much smaller and faster to load
and save; thus many users preferentially use this format. The company
distributes the details of the file format along with sample C++ code for
reading and writing to these files using third-party software, such as this
Python package.
Python is a powerful and easy to use general purpose programming language.
There are many useful Python libraries available for scientific data analysis
and data visualization such as SciPy, Matplotlib, and Mayavi.
This package provides a simple interface for loading AxoGraph data files into
a Python program or interactive session. If you want to analyze data you
recorded in AxoGraph using Python-based tools, this package provides the glue
code you’ll need. You can also write data to the AxoGraph binary format so that
it can be viewed and analyzed within AxoGraph.

Getting axographio
axographio is compatible with both Python 2 and Python 3.
The easiest way to get axographio is to install the latest stable version
using pip, but you can alternatively build it from the source code.

Installing the latest stable version
Requirements for installing and running axographio:

The NumPy package (pip install numpy)

The axographio package contains C++ code that must be compiled. PyPI stores
pre-compiled copies of the package for common platforms (e.g., Python 3 on
64-bit Windows), and these can be installed using pip.
To install the latest stable version, try the following:
pip install axographio
If a pre-compiled package is available for your platform on PyPI, pip
should quickly download and install it. If not, pip will automatically
attempt to build the package from source code. Building the package has
additional requirements. If pip fails during building, keep reading.


Building from source code
If you need to build the package because a pre-compiled version is not already
available for your platform on PyPI, or if you just want to try building from
the source code, you will need to meet additional requirements.
Requirements for building axographio from source code:

The NumPy package (pip install numpy)
The Cython package, version 0.19 or later (pip install cython>=0.19)
A C++ compiler (e.g., Visual C++ Build Tools from Microsoft on Windows
systems, or Xcode on Mac systems)

If pip failed while trying to build from source code, make sure you meet
these requirements and try again.
If you would like to build and install using the latest development source code
from GitHub, try the following:
pip install git+https://github.com/CWRUChielLab/axographio
This command requires git. If you don’t have git, you can instead
manually download the source from GitHub and install from your local
directory:
pip install C:\wherever-you-put-the-source-code



Usage
Try out the Binder demo for an interactive Python session that requires no
installation or fuss. You can start hacking right now!
Loading a data file is as easy as calling read:
>>> import axographio
>>>
>>> f = axographio.read('AxoGraph X File.axgx')

At this point the variable f will contain a file_contents object with
the column names and data from the file. For example, you could now plot the
first two columns using Matplotlib:
>>> import matplotlib.pyplot as plt
>>>
>>> plt.plot(f.data[0], f.data[1])
>>> plt.xlabel(f.names[0])
>>> plt.ylabel(f.names[1])
>>> plt.show() # may be optional depending on your OS

Of course, you probably have grander plans than just plotting the data. The
column data supports the standard sequence interfaces (i.e., indexing,
iteration, etc.) and can be converted to a NumPy or SciPy array using the
asarray functions in these packages, e.g.:
>>> import numpy as np
>>>
>>> times = np.asarray(f.data[0])

Writing files is also relatively easy. You simply create a new
file_contents object (or use one you loaded earlier), and then call
write. For example, the following code creates a file in the current
directory called ‘my60Hz.axgx’ with two channels with 60 Hz sine waves:
>>> import axographio
>>> import numpy as np
>>>
>>> times = np.arange(0, 10, 0.0001)
>>> column1 = np.sin(2*np.pi * 60 * times)
>>> column2 = np.cos(2*np.pi * 60 * times)
>>> f = axographio.file_contents(
... ['time (s)', 'my recording (V)', 'your recording (V)'],
... [times, column1, column2])
>>> f.write('my60Hz.axgx') # created in the current directory



Questions and Support
Please post any questions, problems, comments, or suggestions in the GitHub
issue tracker.


Changes

0.3.2

Re-release of 0.3.1 with version bump to address issue #8



0.3.1

Modify NumPy’s global print settings only when running tests



0.3.0

Package test suite can be run using axographio.tests.run()
Package version can be accessed using axographio.__version__
Added example Jupyter notebook to source repository (not included with
installation)
Updated installation instructions
Improved documentation
Reorganized source code file structure
Fixed doctests for NumPy < 1.14



0.2.0

Added compatibility with Python 3



0.1.1

Fixed a rounding error that could create one extra data point in the time
column



0.1.0

First release




Acknowledgments
This initial version of this project was written in the
Chiel Laboratory at Case Western Reserve University, with support from NIH
grant NS047073, an Ohio Innovation Incentive Award Fellowship, and the
Case Western Reserve MSTP (NIH T32 GM007250). This project builds on a
number of other open source projects, including Python, C++ AxoGraph file
input/output code from AxoGraph Scientific (placed in the public domain; a
modified version is included with the project source code), Cython, and many
others. Thanks also to Dr. Hillel Chiel for providing testing and helpful
suggestions.