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        "author": "Matthew Czech",
        "author_email": "czech1117@gmail.com",
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        "description": "# GaitPy\nRead and process raw vertical accelerometry data from a sensor on the lower back during gait; calculate clinical gait characteristics. \n\n[![status](https://joss.theoj.org/papers/a2233c9e27db0b6625dc56a3f7363875/status.svg)](https://joss.theoj.org/papers/a2233c9e27db0b6625dc56a3f7363875)\n\n[![Build Status](https://travis-ci.com/matt002/GaitPy.svg?branch=master)](https://travis-ci.com/matt002/GaitPy)\n\n## Overview\nGaitPy provides python functions to read accelerometry data from a single lumbar-mounted sensor and estimate clinical \ncharacteristics of gait. \n\n- Device location: lower back/lumbar\n- Sensing modality: Accelerometer\n- Sensor data: Vertical acceleration\n- Minimum sampling rate: 50Hz\n\n[DOCUMENTATION](https://matt002.github.io/GaitPy/html/index.html)\n\n[COMMUNITY GUIDELINES](https://github.com/matt002/GaitPy/blob/master/CONTRIBUTING.md)\n\n## Installation\nGaitPy is compatible with python v3.6 on MacOSX, Windows, and Linux and is available through [Anaconda](https://www.anaconda.com/distribution/).\n\n**Installation via Anaconda (recommended):**\n\nOnce you have Anaconda installed, open a terminal window and create a new environment using the following command.\n```sh\nconda create --name my_env python=3.6\n```\nThen, activate your environment using the following command for Mac and Linux.\n```sh\nsource activate my_env\n```\nFor Windows use the following.\n```sh\nactivate my_env\n```\nLastly, to install GaitPy, use the following command.\n```sh\nconda install gaitpy\n```\n\n**Alternatively, you can install via pip:**\n```sh\npip install gaitpy\n```\n\n**You can also install GaitPy from source:**\n```sh\ngit clone https://github.com/matt002/gaitpy\ncd ~/gaitpy\npython setup.py install\n```\n\n## Basic usage\nGaitPy consists of the following 3 functions:\n1. classify_bouts: If your data consists of gait and non-gait data, run the classify_bouts function to first \nclassify bouts of gait. If your data is solely during gait, this function is not necessary to use. \n2. extract_features: Extract initial contact (IC) and final contact (FC) events from your data and estimate \nvarious temporal and spatial gait features.\n3. plot_contacts: Plot the resulting bout detections and IC/FC events alongside your raw accelerometer signal. \n\nGaitPy accepts a csv file or pandas dataframe that includes a column containing unix timestamps and a column containing\nvertical acceleration from a lumbar-mounted sensor. GaitPy makes three assumptions by default:\n1. Timestamps and vertical acceleration columns are labeled 'timestamps' and 'y' respectively, however \nthis can be changed using the 'v_acc_col_name' and 'ts_col_name' arguments respectively. \n2. Timestamps are in Unix milliseconds and data is in meters per second squared, however this can be be changed\n using the 'ts_units' and 'v_acc_units' arguments respectively.  \n3. Baseline vertical acceleration data is -9.8m/s^2 or -1g. If your baseline data is +9.8m/s^2 or +1g, set the 'flip' \nargument to True.\n\nAdditionally, the sample rate of your device (at least 50Hz) and height of the subject must be provided. \n\nMore details about the inputs and ouputs of each of these functions can be found in the [documentation](https://matt002.github.io/GaitPy/html/index.html)  and Czech et al. 2019.[![status](https://joss.theoj.org/papers/a2233c9e27db0b6625dc56a3f7363875/status.svg)](https://joss.theoj.org/papers/a2233c9e27db0b6625dc56a3f7363875)\n\n```sh\nfrom gaitpy.gait import Gaitpy\n\nraw_data = 'raw-data-path or pandas dataframe'\nsample_rate = 128 # hertz\nsubject_height = 170 # centimeters\n\n#### Create an instance of Gaitpy ####\ngaitpy = Gaitpy(raw_data,                           # Raw data consisting of vertical acceleration from lumbar location and unix timestamps\n                sample_rate,                        # Sample rate of raw data (in Hertz)\n                v_acc_col_name='y',                 # Vertical acceleration column name\n                ts_col_name='timestamps',           # Timestamp column name\n                v_acc_units='m/s^2',                # Units of vertical acceleration\n                ts_units='ms',                      # Units of timestamps\n                flip=False)                         # If baseline data is at +1g or +9.8m/s^2, set flip=True\n\n#### Classify bouts of gait - Optional (use if your data consists of gait and non-gait periods)####\ngait_bouts = gaitpy.classify_bouts(result_file='/my/folder/classified_gait.h5')     # File to save results to (None by default)\n\n#### Extract gait characteristics ####\ngait_features = gaitpy.extract_features(subject_height,                               # Subject height\n                                        subject_height_units='centimeter',            # Units of subject height\n                                        result_file='/my/folder/gait_features.csv',   # File to save results to (None by default)\n                                        classified_gait=gait_bouts)                   # Pandas Dataframe or .h5 file results of classify_bouts function (None by default)\n\n#### Plot results of gait feature extraction ####\ngaitpy.plot_contacts(gait_features,                                     # Pandas Dataframe or .csv file results of extract_features function\n                     result_file='/my/folder/plot_contacts.html)',      # File to save results to (None by default)\n                     show_plot=True)                                    # Specify whether to display plot upon completion (True by default)\n\n```\n\n## Running the demo\n\nThe demo file provided lets you to test whether GaitPy outputs the expected results on your system. \n\nYou may run the demo directly from a terminal window:\n\n```sh\ncd gaitpy/demo\npython demo.py\n```\n\nYou may also run the demo via a python interpreter. In a terminal window start python by typing:\n\n```sh\npython\n```\n\nIn the interpreter window you can then import and run the demo with the following two commands:\n\n```sh\nfrom gaitpy.demo import demo\ndemo.run_demo()\n```\n\nThe demo script will prompt you to type in a results directory. Following the run, results will be saved in the provided \nresults directory (less than 250kB of data will be saved). Running the demo should take less than a minute, though this \nmay vary depending on your machine. \n\n## Contributing to the project\nPlease help contribute to the project! See the [CONTRIBUTING.md](https://github.com/matt002/GaitPy/blob/master/CONTRIBUTING.md) file for details.\n\n## Acknowledgements\nThe Digital Medicine & Translational Imaging group at Pfizer, Inc supported the development of this package.\n\n## Author\nMatthew Czech\n\n## License\nGaitPy is under the MIT license\n\n\n",
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