distilling-flask 0.0.1a1

Personal running data analysis and visualization, powered by Flask, Dash, and Pandas.

distilling-flask

Personal running data display and analysis app, powered by Flask/Dash/Pandas.

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Table of Contents

• Introduction
• Dependencies and Installation
• Running the App
• Testing
• Contact
• License

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Introduction

Given a valid client id and client secret for your Strava API app, this app talks to Strava's API and displays running activities in a variety of dashboards using plotly Dash.

I built this app so I could pick apart my raw Strava data, which includes data streams for elevation, grade, and moving/stopped periods. I think Strava presents data in an unrealistically favorable way in its app ecosystem, and I wanted to work with the raw feeds. There is power in looking at the reality of things.

If you have a [ersponal Strava API application, you can view any of your Strava runs in a dashboard powered by Plotly Dash. From there, you can save each run to a database, and view the long-term effects of training in a training log dashboard.

A demo app is available to view on my website.

See the Running the App below to see how everything works.

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Dependencies and Installation

Check out the requirements file to see all dependencies.

Python IDE

Clone the repo:

git clone https://github.com/aaron-schroeder/distilling-flask.git

Change into the new directory and start a virtual environment. Then, install the requirements:

pip install -r requirements.txt

You should be able to run the app now. See Examples below for more info.

Docker container

Create an image by running the following command in the same dir as Dockerfile:

docker build -t distillingflask:latest .

Create and start a container from the image with

docker run --name distillingflask  \
    -e MODULE_NAME=application.app  \
    -e VARIABLE_NAME=server  \
    -e STRAVA_CLIENT_ID=<client id>  \
    -e STRAVA_CLIENT_SECRET=<client secret>  \
    -e PASSWORD=<password>  \
    -d  \
    -p 5000:80  \
    --rm  \
    distillingflask:latest

Running the App

Locally

Strava-connected, with your Strava app client id and client secret

This option pretty much gets you the full-blown app running on your local machine. You can now authorize the app to use the data from your Strava account.

To do this, you must:

• Create your own API application on Strava's website
• Within the "My API application" section of your Strava settings:
  • Set the authorization callback domain for your app to localhost
  • Copy your app's "client ID" and "client secret" somewhere secure

To run the app using Flask's CLI:

STRAVA_CLIENT_ID=${STRAVA_CLIENT_ID}  \
STRAVA_CLIENT_SECRET=${STRAVA_CLIENT_SECRET}  \
PASSWORD=super_secret_password  \
flask --app application

Strava-disconnected, allowing a subset of features.

You don't need to set your app up with Strava to access some of its features like the file upload analysis dashboard. The command to run this configuration of the app is simpler.

Flask CLI:

PASSWORD=super_secret_password  \
flask --app 'application:create_app("dummy")'

distilling-flask CLI: To use df (the distilling-flask CLI), first install the package locally with pip install -e .

PASSWORD=super_secret_password  \
df rundummy

In a python script:

import os

import application


# Choose the password for this app. Ideally don't use your Strava password.
os.environ['PASSWORD'] = 'super_secret_password'
app = application.create_app()
app.run()

There are a number of optional settings that control the behavior of the simulated Strava client. They can all be set with environment variables, a .env file, or (in the case of the distilling-flask CLI) arguments to the command.

• MOCK_STRAVALIB_ACTIVITY_COUNT
• MOCK_STRAVALIB_SHORT_LIMIT
• MOCK_STRAVALIB_LONG_LIMIT
• MOCK_STRAVALIB_SHORT_USAGE
• MOCK_STRAVALIB_LONG_USAGE

Filetypes accepted by the upload-to-analyze dashboard:

• fit file (requires fitparse and dateutil)
• tcx file (requires activereader)
• gpx file (requires activereader)
• csv file (requires that headers adhere to the naming convention defined by the application)

Production

To create an instance of the app with production-oriented settings, call create_app(config_name='prod').

Like the development configuration, the production configuration of defaults to using an on-disk SQLite database, or any database specified by the DATABASE_URL environment variable (including in-memory SQLite with the url sqlite://)

The production configuration also allows for the use of a PostgreSQL database if the right environment variables (starting with POSTGRES_) are set.

SECRET_KEY=random_secret_key  \
STRAVA_CLIENT_ID=00000  \
STRAVA_CLIENT_SECRET=gobbledygoop  \
POSTGRES_DB=db_name  \
POSTGRES_PORT=5432  \
POSTGRES_USER=user  \
POSTGRES_PW=password  \
POSTGRES_URL=dburl.example.com  \
flask --app "application:create_app('prod')"

StreamLabel and custom accessors for pandas objects

Create a DataFrame where each row represents a record, and each column represents a data stream with a unique (field, source) id.

import pandas as pd
from application.labels import StreamLabel

df = pd.DataFrame.from_dict({
    StreamLabel('time', 'strava'): [0, 1, 2, 3, 4, 5],
    StreamLabel('speed', 'strava'): [3.0, 3.2, 3.4, 3.6, 3.8, 3.6],
    StreamLabel('speed', 'garmin'): [2.9, 3.1, 3.3, 3.5, 3.7, 3.8],
})

Use the custom accessor to work with this specifically-formatted DataFrame.

>>> df.sl.has_source('strava')
True

>>> df.sl.has_source('device')
False

>>> df.sl.source('strava')

   time (strava)  speed (strava)
0              0             3.0
1              1             3.2
2              2             3.4
3              3             3.6
4              4             3.8
5              5             3.6

>>> df.sl.has_fields('speed', 'time')
True

>>> df.sl.field('speed')

   speed (strava)  speed (garmin)
0             3.0             2.9
1             3.2             3.1
2             3.4             3.3
3             3.6             3.5
4             3.8             3.7
5             3.6             3.8

>>> StreamLabel.from_str('speed~new_src')
speed (new_src)

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Testing

Functional testing

This requires user-supplied files in the following locations:

• client_secrets.json
• tests/functional_tests/strava_credentials.json

pip install -r requirements_dev.txt
python -m unittest discover -p test_*.py tests.functional_tests

Unit testing

python -m unittest discover -p test_*.py tests.unit_tests

Project Status

Current Activities

The Flask app is becoming a full-fledged training log. Strava activities can be viewed in a dashboard and saved to a database, and soon uploaded files will be saveable too. The long-term effects of training can be visualized in a training log dashboard, which is still evolving.

Future Work

Coming up, I'd like to set up pipelines that take running activity data from a variety of sources and filetypes, and displays the time series in a common interface. To that end, I've created a class to be used as column labels in pandas.DataFrame. StreamLabel keeps track of both the field name and the source of data streams. This facilitates a common, recognizable labeling system for data streams stored in DataFrame columns. I've created custom accessors for pandas.DataFrame and pandas.Index to work with StreamLabel.

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Contact

You can get in touch with me at one of the following places:

• GitHub: github.com/aaron-schroeder
• LinkedIn: linkedin.com/in/aarondschroeder
• Twitter: @trailzealot
• Instagram: @trailzealot

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License

This project is licensed under the MIT License. See LICENSE file for details.