Some visualizations for exploring data exported from wearable devices.
Data is not stored here, this will behave strangely without data.
Basic startup:
pip install -r requirements.txt
python -m hmcdashboard
# localhost:5000Currently this assumes data for each user is exported from the main
database into a series of .csv files.
- Caching into flat files makes later loading slightly faster over sqlite and doesn't have the overhead of maintaining the sqlite paging, etc.
- Since the main aim here is visualization rather than analysis,
we can cut the size of the cached files in half (6GB β 3GB) but produce
identical visualizations (some notes on doing this are in my
dev log in the
docs/directory, and use thecleanup.pyscript).
So first-time-setup will require building a copy of the metadata
from the data/ directory:
$ ls data
blood_oxygenation heart_rate sleep stress
$ python build_user_table.py
Building sleep
100%|βββββββββββββββββββββββββββββββββββββββ| 308/308 [00:00<00:00, 4146.80it/s]
Building heart_rate
100%|βββββββββββββββββββββββββββββββββββββββββ| 354/354 [00:08<00:00, 40.28it/s]
Building blood_oxygenation
100%|βββββββββββββββββββββββββββββββββββββββββββ| 81/81 [00:00<00:00, 89.98it/s]
Building stress
100%|βββββββββββββββββββββββββββββββββββββββββ| 359/359 [00:13<00:00, 27.20it/s]
Writing to user_metadata.csvThis app can be redistributed under the terms of the MIT License.
- Backend:
Flask,Pandas - Plotting:
plotly.js,plotly.py - Frontend:
jQuery,CoreUI UI Kit,CoreUI Bootstrap Admin Template,CoreUI Linear Icons
I learned a trick for switching out plotly visualizations between the front and back from the Hepta Analytics blog post by Yvonne Wambui (https://blog.heptanalytics.com/flask-plotly-dashboard/), which is adapted under the terms of the MIT License.
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