{ "info": { "author": "Liza Wood and Levannia Lildhar", "author_email": "liza.4.bc@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "# `myfitness`: Tools to Analyze Apple Health Data\n\n---\n\nBuild_Stamp [![Build Status](https://travis-ci.org/LevanniaLildhar/DATA533_lab4_Liza_Levannia.svg?branch=master)](https://travis-ci.org/LevanniaLildhar/DATA533_lab4_Liza_Levannia)\n\n---\n\nThe Apple Health Fitness Tracker was a collaborative project by Liza Wood and Levannia Lildhar.\n\nThe `myfitness` package provides some basic tools to analyze the health data in a CSV file downloaded from Apple Health. These tools could be used to analyze and compare the data from multiple people.\n\n`healthdata`\n- contains two modules\n 1. `data` - contains the name, age and gender of the person (the \"superclass\") and a method to read in the CSV health data file using pandas\n 2. `chart` - uses pygal to provide an interactive bar chart to the user\n\n`summary`\n- contains two modules\n 1. `table` - returns a summarized dataframe of average steps per month\n 2. `maxmin`- contains functions to calculate the maximum and minimum number of steps\n\n## Package Structure\n\n`myfitness` --> package\n\n - `healthdata` --> sub-package\n\n - `data` --> module\n - `chart` -->module\n\n - `summary` --> sub-package\n\n - `table` --> module\n - `maxmin` --> module\n\n## Package Details\n\nThe package functions of `myfitness` are described below. The use of the package is also demonstrated in the test file included in this repositry.\n\n### `healthdata`\n\nThis subpackage provides users a method of importing data as well as viewing the data interactively.\n\nDetailed descriptions of the `data` module in the `healthdata` subpackage is shown below: \n\n| Class/Function | Description | Parameters | Return |\n| ------------- |:------------------------------------------------------------------: | :----------------:|:-----------------------:|\n| `Person` | Create an object of class Person() to be used in further analysis. The 'display' function displays the name, age and gender of a Person() object | name, age, gender | An object of class Person with name, age and gender attributes |\n| `healthdata` | Create a object of class healthdata() this inherits from the superclass Person()| name, age, gender, file (downloaded from Apple Health, as CSV) | Display of healthdata object attributes name, age, gender and dataframe containing healthdata() object file |\n\nDetailed descriptions of the `chart` function in the `healthdata` subpackage is shown below: \n\n| Function | Description | Parameters | Return |\n| ------------- |:------------------------------------------------------------------: | :----------------:|:-----------------------:|\n| `chart` | Creates an interactive bar graph using pygal | columnX as list of strings, columnY as list of values, xlabel as string, ylabel as string, filename | .svg file with xlabel, ylabel, title, and filename |\n\n### `summary`\n\nThis subpackage provides users with some basic statistical analysis tools to view their data extracted from Apple Health.\n\nDetailed descriptions of the `table` function in the `summary` subpackage is shown below: \n\n|Function | Description | Parameters | Return |\n| ------------- |:------------------------------------------------------------------: | :----------------:|:-----------------------:|\n| `table` | Function to summarize the average number of steps taken per month using the pandas package | data: Apple Health .csv file imported as a Pandas DataFrame | A Pandas dataframe, summarizing the average number of steps taken by month, indicated by the last date of the month. |\n\nDetailed descriptions of the `maxMin` function in the `summary` subpackage is shown below: \n\n| Function | Description | Parameters | Return |\n| ------------- |:------------------------------------------------------------------: | :----------------:|:-----------------------:|\n| `getMax` | Find the maximum number of steps in the data and the date it was achieved. | data: Pandas DataFrame containing Apple Health data imported from a CSV file.|The row of values for when the maximum number of steps were achieved:Start date, Finish date,Distance(mi), Steps (count) |\n| `getMin` | Find the maximum number of steps in the data and the date it was achieved.| data: Pandas DataFrame containing Apple Health data imported from a CSV file. | The row of values for when the maximum number of steps were achieved:Start date, Finish date, Distance(mi), Steps (count) |\n\n## Testing\n\n`myfitness_tests` contains the necessary test suite and classes to verify that the package is working correctly. There are a total of four classes that conduct unit testing as well as the suite.\n\nTest Suite Coverge Report ![Coverage Report](https://github.com/lizawood/Apple-Health-Fitness-Tracker/blob/master/Package/myfitness_tests/Test_Coverage_Report_Screenshot.png)\n\n## Requirements\n\nThis package requires the following Python modules:\n\n- numpy\n- pandas\n- pygal\n- IPython\n- CairoSVG", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/lizawood/Apple-Health-Fitness-Tracker", "keywords": "", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "myFitness", "package_url": "https://pypi.org/project/myFitness/", "platform": "", "project_url": "https://pypi.org/project/myFitness/", "project_urls": { "Homepage": "https://github.com/lizawood/Apple-Health-Fitness-Tracker" }, "release_url": "https://pypi.org/project/myFitness/0.3/", "requires_dist": null, "requires_python": "", "summary": "This package provides some basic tools to analyze the health data in a CSV file downloaded from Apple Health.", "version": "0.3" }, "last_serial": 5487979, "releases": { "0.1": [ { "comment_text": "", "digests": { "md5": "7ec874dc3f65b0b7582c012443f411af", "sha256": "a8a7eba147ede02f893b678f08aca69b1b6168c22da8a6bf2afa51b752aafec9" }, "downloads": -1, "filename": "myFitness-0.1.tar.gz", "has_sig": false, "md5_digest": "7ec874dc3f65b0b7582c012443f411af", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 3997, "upload_time": "2018-12-12T18:46:40", "url": "https://files.pythonhosted.org/packages/eb/6b/4f96a13eda7b9e0e07014a7241084bc5b9d8b516d326784cfe74a3ba75f1/myFitness-0.1.tar.gz" } ], "0.2": [ { "comment_text": "", "digests": { "md5": "7df713ca6e96e57ad394b439f41f1c18", "sha256": "992b2fd50e1dc279f025fa3d589c921792d118e13d79c25f8d783e13781525fc" }, "downloads": -1, "filename": "myFitness-0.2.tar.gz", "has_sig": false, "md5_digest": "7df713ca6e96e57ad394b439f41f1c18", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 3978, "upload_time": "2019-07-04T00:31:52", "url": "https://files.pythonhosted.org/packages/69/65/730ba89bbced9cef001b54a3f5abb47f90ae690be35cda764985fd70e1e9/myFitness-0.2.tar.gz" } ], "0.3": [ { "comment_text": "", "digests": { "md5": "6981ce5138a8137d88fd8b752ab31c18", "sha256": "f0acfa7fc05bd9d9ded25cb5ec406df0314dc070444ae28281007bc8510d4f55" }, "downloads": -1, "filename": "myFitness-0.3.tar.gz", "has_sig": false, "md5_digest": "6981ce5138a8137d88fd8b752ab31c18", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 4392, "upload_time": "2019-07-04T18:48:40", "url": "https://files.pythonhosted.org/packages/0e/51/859a93df4242e8415726f94b4efd0f3370d325ba976a055d58bd78048b08/myFitness-0.3.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "6981ce5138a8137d88fd8b752ab31c18", "sha256": "f0acfa7fc05bd9d9ded25cb5ec406df0314dc070444ae28281007bc8510d4f55" }, "downloads": -1, "filename": "myFitness-0.3.tar.gz", "has_sig": false, "md5_digest": "6981ce5138a8137d88fd8b752ab31c18", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 4392, "upload_time": "2019-07-04T18:48:40", "url": "https://files.pythonhosted.org/packages/0e/51/859a93df4242e8415726f94b4efd0f3370d325ba976a055d58bd78048b08/myFitness-0.3.tar.gz" } ] }