Table of Contents

Dashboard (not a very unique name, I know) is a set of tools to visualize personal data on a, well, some sort of dashboard.

I'm gradually publishing it here and got some old screenshots here.

Ideally I'd like to reuse existing libraries and other people's projects to the maximum extent possible, so we could join forces.


used to visualize #qs data [[qs]]

very similar to #timeline and #memex [[timeline]] [[memex]]

I haven't quite figured out the difference as well yet. Likely will merge in a single tool.

#hpi provides data for my dashboard [[hpi]]

Although in theory you can plug in any Pandas dataframes in it (assuming they fit the schema).

* motivation

why not use something existing?

figuring out core patterns for manipulating and representing this data

Many things are very tedious to redo and reinvent from scratch every time

automatically detecting interesting correlations between data

[2019-08-15] wonder if massive dataframe with literally everything could help

maybe just go over all series? and then generate all pairwise correlations and sort by correlation coefficient + R2? some sort of pareto surface?

what I want to figure out:

very similar to

are 'sleep phases' a thing (or at least the way they are measured in conventional tackers)? how do they impact my waking up, alertness etc? [[sleep]]

[2020-10-27] calculating custom exercise stats

e.g. I have a few traffic lights on my way that might affect my speed/cadence data, and I might want to filter it out

custom/personalized data processing

splitting exercise into cardio and non-cardio

demo of errors: typical mistake is confuising wlog and weight tags (weight gets unrecognized as exercise) [[dashboard]]

* inspiration

[2018-06-06] andreilyskov: qs dashboard example [[qs]]

[2019-04-06] interesting dashboards: spotify, writing and reading. otherwise not that much stuff

[2020-08-22] ok seems that they use similar architecture to mine

[2020-08-22] Flow Dashboard

ok, looks kinda slick..
open source:

Public Github commits
Google Fit - track any activity durations by keyword
Evernote - pull excerpts from specified notebooks
Pocket - Sync stored articles & add notes
Goodreads - Sync currently reading shelf
Track any abstract data via REST API

[2021-02-06] ok, rest api thing is cool, should try connecting it to #hpi [[hpi]]

[2019-11-24] heedy/heedy: An Open-Source Platform for Quantified Self & IoT

Heedy is an open-source aggregator built for storage and analysis of your personal data.
It provides a powerful plugin system, allowing easy integration with various services, as well as deep extensibility.

Extensible: Even a system with fantastic visualizations and powerful analysis has limited utility.
This is because it can only show what the original authors assumed would be useful. Heedy offers a powerful plugin system - plugin writers can add new integrations, plots, or even modify core functionality with a few lines of python or javascript.
A registry is planned, so that users can install plugins with the click of a button.

[2019-05-21] quelf/report.Rmd at master · JakobGM/quelf

jupyter notebooks

Sleep data [sleep as android, sleepcycle]
Study hours [toggl]
Exercise [runkeeper, strong]
Programming [wakatime]

[2019-09-22] markwk/qsledger: Quantified Self Personal Data Aggregator and Data Analysis [[qs]] [[dataliberation]]

interesting, but data exports are not very reusable

[2019-09-28] I guess once I export everything could send a link to that guy?


the dashboard is currently hard-coded in Google Data Studio

[2020-08-22] some good dashboards

workouts, weight, sleep, hr

Juno's Personal Data Exploratory [[pkm]] [[qs]]

[2020-07-02] jeffshek/betterself: Your body's dashboard.

[2020-08-24] demo

[2019-01-13] Sacha Chua: quantified awesome [[qs]]

realtime dashboard

[2019-04-06] ok, looks pretty clean, but might require some tinkering

[2021-02-06] also pretty much only time tracking?

[2020-09-10] quantifiedbob/bob-body-composition-viz: Jupyter Notebook vizualizing 10+ years of my body composition data

ok, but just a plot

[2020-04-04] ammanvedi/quantified-self-server: Aggregate data about myself (workouts, blog posts, music listening history) into a graphql API

Blog posts (markdown from github)
Workout data (Strava API)
Recent music (Tidal API)
into one graphql API that can be called from my personal site.

[2019-04-19] markwk: How to Create a Time Tracking Dashboard using RescueTime, IFTTT and Google Sheets [[qs]]

ok, but too low code for me

[2019-01-08] Personal Dashboard /r/QuantifiedSelf

[2019-04-11] it is not very elaborate though…

minor ideas

keeping query in the address string is pretty clever… can bookmark and easily restart it! [[datasette]]

[2020-08-22] event chart

use tabs within tabs? kinda like in garmin

[2020-10-27] Garmin Connect

pretty nice clean visualizations, I need to borrow these I guess

[2021-01-11] yihong0618/runningpage: Make your own running home page

* implementation

How to actuallly implement it, which software/tools/libraries to use.


keep it modular, I guess

have a core subpackages and various tabs… not sure how to refactor them out later

[2020-09-01] similarly to HPI, it's more of a demonstration how it can be done. Ideally people would be able to reuse core and build their own dashboards

it's very important to benefit from the existing data science infrastructure as much as possible. jupyter, pandas, etc.

[2020-12-10] panel · PyPI

A high-level app and dashboarding solution for Python
Panel works with visualizations from Bokeh, Matplotlib, HoloViews, and many other Python plotting libraries

ok, this is super promising:

Panel can also be used with the separate Param project to create interactively configurable objects with or without associated visualizations, in a fully declarative way. With this approach, you declare your configurable object using the pure-Python, zero-dependency param library, annotating your code with parameter ranges, documentation, and dependencies between parameters and your code. Using this information, you can make all of your domain-specific code be optionally configurable in a GUI, with optional visual displays and debugging information if you like, all with just a few lines of declarations. With this approach, you don’t ever have to decide whether your code will eventually be used in a notebook, in a GUI app, or completely behind the scenes in batch processing, servers, or reports – the same code can support all of these cases equally well, once you declare the associated parameters and constraints.

[2020-12-07] oschuett/appmode: A Jupyter extensions that turns notebooks into web applications.

A Jupyter extensions that turns notebooks into web applications.

[2021-02-06] ok, nice, has 'edit app' button which opens jupyter to edit??

[2020-08-23] ok, #jupyter is the ultimate ad-hoc solution. it preserves state, can be exposed as a frontend and allows for python code

bokeh vs plotly dash

there are couple of example apps here



[2020-09-03] compromise: keep it as framework independent and decoupled as possible. Support both frameworks!

basically have a core library that's imported in all notebooks. move stuff to core as long as possible, but allow quick button to mess with the data in notebook [[toblog]]

try jupyterlab

I think dashboard needs to preserve state somehow, it's annoying to edit code every time.. wonder if dash supports it?

use multiprocessing…

pretty sure pandas frames can be easily serialised?

[2020-09-10] Top 50 matplotlib Visualizations - The Master Plots (w/ Full Python Code) | ML+

39. Time Series Decomposition Plot

ok, this is pretty intersting
re: autocorrelation – what was up with the 'blue region', significance thing??
also good: Seasonal Plot
would be initersting to quickly toggle it in dashboard?

grafana could simply be one of the interfaces

[2021-02-17] apache/superset: Apache Superset is a Data Visualization and Data Exploration Platform [[dashboard]] [[hpi]]

A modern, enterprise-ready business intelligence web application.

whoa looks interesting…

Ugh. it didn't really work – seems to be in the process of deprecation
and still, getting some weird error regardless whether I mount database as read only or not.. and no other logs

2021-04-26 14:19:42,813:WARNING:superset.views.base:[SupersetError(message='(sqlite3.OperationalError) unable to open database file\n(Background on this error at:', error_type=<SupersetErrorType.GENERIC_DB_ENGINE_ERROR: 'GENERIC_DB_ENGINE_ERROR'>, level=<ErrorLevel.ERROR: 'error'>, extra={'engine_name': 'SQLite', 'issue_codes': [{'code': 1002, 'message': 'Issue 1002 - The database returned an unexpected error.'}]})]

a bit finicky overall…
I guess nice that it's possible to separate queries, charts and dashboards…
so one can reuse them in different contexts/for different visualizations

* sleep dashboard/experiments [[sleep]]

Sleep is one of the biggest things I wanna figure out, and I have quite a few ideas of what to test.

Maybe run pca for my sleep data? [[sleep]] [[qs]] [[ml]]

figure out main questions I wanna figure out [[qs]] [[sleep]]

e.g. 'what is the best time for me to go asleep'?

'what is the best sleep duration?'

[2019-08-21] Respiratory rate - Wikipedia [[sleep]]

For humans, the typical respiratory rate for a healthy adult at rest is 12–18 breaths per minute.[

[2020-08-24] add to dataframe

correlate with sliding exponential mean instead? [[sleep]]

add date to tooltip [[sleep]]

def need to highlight holidays on the background (also annotate if it was indeed a day off), e.g. where have I gone [[sleep]]

wonder if coverage is correlated with sleep movement… [[sleep]]

find correlation between bedtime and length of sleep? [[sleep]]

Sleep/exercise correlation: could try different deltas/correlation coeff plot

old sleep logs from taplog? [[sleep]]

check waking up in REM? [[sleep]]

interesting correlation: sleep vs bed time. wonder if that means REM or something?

hmm, sleepy correlates negatively with ex. volume?

hmm. temperature is interesting – looks like it's negatively correlating with HR?? [[qs]]

sleep plot: highlight holidays/weekends as background color?

movements/bed exit in emfit?

[2020-08-25] could plot on the sleep bars plot?

integrate old melatonin experiment, maybe post about it

use ANS balance from emfit?

[2018-06-20] Emfit qs estimated my sleep high, even though i felt a bit like shit. I wonder if it just correlates with sleep length..

temperature during sleep [[qs]] [[sleep]]

different style for sleep length bars? currently red and blue are sort of annoyingly make it hard to spot the pattern? [[emfit]]

Average temperature [[sleep]]

cross correlate with rescuetime activity [[sleep]]

[2020-08-25] could even plot activity bars

figure out alarm wakeup vs 'natural' wakeup, could tell me something about sleep intervals [[qs]]

Emfit recovery – wonder if it correlated with exercise days [[sleep]] [[emfit]]

My daughters sleeping patterns for the first 4 months of her life. One continuous spiral starting on the inside when she was born, each revolution representing a single day. Midnight at the top (24 hour clock). [OC] [[sleep]] [[inspiration]] [[viz]]
Huh that's a nice way to save on space

Melatonin analysis

Zeo sleep self-experiments
in context

wonder what does it mean when morning hrv is higher than evening?? [[qs]] [[hrv]]

I wonder what happened… at some point (around august 2019), coverage has consistently gone up to 100 [[qs]] [[emfit]]

Track pizza dependency? [[qs]] [[sleep]]

Shit wonder if I'm oversleeping because it's too cold? [[qs]] [[sleep]]

pretty strong negative correlation of temp vs avg hr. wonder if that's searsonal or not? post about it soon? [[qs]]

look at HRV peaks and try to see what's happening? [[qs]] [[timeline]]

[2019-11-14] Опыт обращения к сомнологу [[qs]] [[sleep]]

. Никакого алкоголя
. Избегать физнагрузку после 17:00. Нагрузка до 17:00 наоборот улучшает сон

try recommendataions from that post and see how they apply to me?

Compare hr/hrv before and after holidays [[qs]] [[sleep]] [[hpi]]

Compute differences between rem cycles and plot on histogram? [[qs]] [[sleep]]

wow, emfit recovery was really shit last night. why???… seriosly no reason for it [[qs]] [[sleep]]

wip on figuring out if weekdays impact sleep [[qs]]

import my.emfit
by_night = my.emfit.by_night()
data = [{'date':, 'hr': x.measured_hr_avg} for x in by_night.values()]
by_dt = pd.DataFrame(data).set_index('date')
by_dt = by_dt.set_index(pd.to_datetime(by_dt.index))
onday = lambda d: (d == by_dt.index.dayofweek)
we = by_dt[onday(0) | onday(1) | onday(6)]
wd = by_dt[onday(3) | onday(4) | onday(5)]
ax = wd.rolling('30D').mean().plot()
we.rolling('30D').mean().plot(ax=ax, color='red')

plot my emfit sleep, to be fair even two years of plots is quite cool [[publish]] [[qs]]

hmm, my hrv really has fallen down? also sleep hr gone up [[qs]] [[health]]

wonder if it's related to my quality of sleep?

track dependency on meditaion? [[sleep]]

display times in bed definitely… [[sleep]]

would be nice to plot against sunrise/sunset? [[sleep]]

[2020-09-30] Relationships between HRV, sleep and physical activity in personal Oura ring data - Quantified Self / General Health - Quantified Self Forum [[hrv]] [[hr]] [[sleep]] [[exercise]]

nice, pretty similar to my findings?

[2020-09-30] fucking hell! after naples HRV jumped and HR dropped very significantly [[self]] [[sleep]]

correlate dreams with rem/deep etc? [[sleep]]

wtf… without seasons it seems to correlate less?? [[sleep]]

negative correlation of coverage with temperature is pretty weird [[emfit]]

Compare fitbit [[hr]] [[qs]]

For sleep, probably a good idea to log room temperature [[sleep]] [[qs]]

hmm, maybe I need to remove seasonality from the sleep first and then search other correlations?? not sure..

ugh. today is gonna be confusing [[qs]] [[sleep]]

exercise will probably have a lot of impact… but then hr is elevated because of the pizza

[2020-10-18] maybe really need to try two-parameter regression?

[2020-10-18] take into the account past pizza orders? this is gonna have systematic impact on my sundays?

* exercise dashboard/experiments

running dashboard – custom dataframe + add distance + merge with manual notes in a similar way

thinking how to contribute data for the energy plot [[exercise]] [[qs]]

maybe the data is sifted through the elliptical/running/spinning modules and what's left is just taken as is

have some stuff in endomondo comments… need to extract it and use. maybe actually just reuse endoexport, and put in a df [[qs]]

Show distance on speed/hr plot [[qs]]

def need to measure decay to 60 to get more data… [[qs]] [[hr]] [[toblog]]

process google location and guess walks from it [[exercise]] [[qs]]

Detect walks and runs from google data and plot them. Mark velocity with colours. Could also do 'walking intervals' (e.g. if was in a shop inbetween) [[exercise]]

calculate some sort of 'integral' heart rate and also score for interval trainings? [[exercise]]

[2020-09-06] eh? maybe I wanted to break down my interval trainings per activity bursts?

integrate elliptical workouts? Could plot workout specific things and make them easy to compare

filter out specific exercises and plot separately to see the progress [[exercise]]

go through the most common exercise (past) sessions and check them [[exercise]]

[2021-02-06] I guess it's a more general pattern for reviewing data too

exercise provider: trust more notes which got HR data [[qs]]

Look at hr data, its almost 180 all the time. Is it because i was doing cardio less? [[qs]] [[health]] [[exercise]]

* patterns

as in, patterns of working with data, best practices for visualizing things etc

use dynamic sliders for different regression windows

Always plot isolated metrics (temp, hr, etc) – good for debugging [[qs]]

[2020-09-03] also make sure it's easy to do

restrict datapoints 'by experiment'? same way it's done with date sliders?

need to try different date shifts to see it's not correlated

labels: need to display weekday (also on HR nodes?)

would be nice to zoom individual plots..

* error hanling [[errors]]

about importance of error handling when working with personal data [[toblog]]

data is inherently messy and error prone
it's sensible to be defensive and try to relax as much as possible (unless you have time to literally tend to them immediately)
on the other hand, you want to make sure you are aware about the issues in data
example: running dashboard – has a warning about 0 speed
see screenshot at the same time (fix JS table first so duration & starttime have proper rendering)
you can instantly see it on the plot (yellow markers). if you hover, you'll see that the error is about zero speed (and you can also see it in the table)
in the table, you can clearly spot that the problem is the treadmill – indeed, treadmill wasn't connected to the phone in any way, and I'm stationary, so endomondo has no idea how much I moved
however, I did log the distance (along with the training regime) manually
by combining the HR data (recorded by endomondo) and manual data we get the complete representation
(if I add visual highlight on the table, that would be fucking amazing)

error handling – demonstrate on 'multiple sleeps'

demo: running speed/hr plot is a good candidate

demo: unmark one of cardio/non-cardio and demonstrate warnings

document error handling pattern with 'error' [[errors]] [[toblog]]

for correlations, there is overview + zoomed in individual plots showing all the errors, outliers, etc

Each rolling plot can also handle seasonality, residuals etc?

would be cool to scale arbitrarily vertically and horizontally, the whole plot

indicate how recent datapoints are by color? (in the correlations)

corr plot: include influence/leverage etc

would be nice to display data source?

e.g. for blood dashboard

[2019-07-20] links to event sources (e.g. emfit etc)

[2019-04-12] src in exercise volume

add src for jawbone/emfit sleep

link to original event from point? or just 'context'

e.g. endomondo

vertical line tooltip sucks… probably need a single point instead

definitely need to take seasonality (e.g. weekly) into the account, e.g. for multi regression

I guess need seasonality analisys for each metric? maybe have it on a separate pane, e.g. 'debug/insights'? would be nice to do it for all dataframes?

would be nice to include the org file link to the context… then could use mimemacs for it [[promnesia]]

* ideas for more dashboards

rescuetime summarizes my sleep intervals pretty well? could at least have upper bound on sleep during holidays/when not using emfit? [[rescuetime]] [[hpi]]

temperature – probably, seasonality? [[qs]]

display photos as an optional layer along with the map?

[2020-09-06] just need to integrate dashboard with photomap

links into food dashboards.. I guess I need anchors. [[nutrino]]

favsmap should be part of dashboard… also could be the first bit in my public dashboard?

merge google and foursquare, display google labels on top of osm mapS
rationale: various tools for working with maps in one place

shit. if I had food stats that would be just amazing

takeout – contains list of places [[takeout]] [[hpi]]

basically could merge with 4sq? and custom addresses

money? not sure

quantified mind? already have plots

Add check-ins to favsmap

* ideas for experiments

correlate number of commits/lines vs sleep?

glucose and ketone measurements vs food?

[2020-09-06] probably not enough data…

correlate temp sensor with hiking? [[toblog]] [[qs]] [[lifelogging]]

hmm, maybe need to try running/eating pizza on a different day? to make sure the seasonality is not due to running? [[exercise]] [[qs]] [[sleep]]

[2020-10-24] or could check seasonality for months that didn't involve running?

volume vs avg temp is an interesting one to demonstrate the correlation that shouldn't exist

(although again in theory there might be some seasonality)

try to find correlation between sleep and exercise? [[qs]]

in the simplest approach, try to only use cardio and see how it correlates (binary, for instance). although not enough points..

temperature plots: would be interesting to have avg plot for 'indoors' and 'outdoors' periods?

plot spinning power vs heartbeats? [[qs]]

* plotly dash [[plotly]]

For now using #bokeh instead

[2019-10-27] plotly/dash@1.4.0

Dash v1.4.0

[2020-04-10] Re: [plotly/] Importing plotly takes a lot of time (740)

Import time and initialization time should be much improved on Python 3.7 with PR 2368.

[2019-04-10] Dash offline is enabled? plotly/dash

from dash import Dash

app = Dash()

app.css.config.serve_locally = True
app.scripts.config.serve_locally = True

wow. showing points on other plots while rectangle selection is really awesome [[plotly]]

would be really nice to properly render pairplots in plotly

err. seems to always consume cpu in background

[2020-08-26] Re: plotly/ Importing plotly takes a lot of time (740)

Re: [plotly/] Importing plotly takes a lot of time (740

[2019-04-07] Dash User Guide and Documentation - Dash by Plotly

Note that this method has a drawback: it requires that you compute the children property for each individual tab upfront and send all of the tab's content over the network at once. The callback method allows you to compute the tab's content on the fly (that is, when the tab is clicked).

[2019-05-30] ucg8j/awesome-dash: A curated list of awesome Dash (plotly) resources [[viz]]


[2019-07-24] eh, dunno otherwise not so interesting? maybe I'm not really lacking anything, I can think of many things I can add that still need to be implemented..


come up with a better name? just in case of pypi release…

how to detect temporal correlation? [[study]] [[qs]]

[2020-09-01] ok, this lag thing in bokeh could work

[2019-04-19] qsledger/ at master · markwk/qsledger
plotly dash regression example

[2019-10-15] Why you should continuously track your energy level and what I've learned from it. /r/Biohackers [[qs]]

[2019-10-18] about tracker matching subjective score

not sure what mavg is doing. e.g. HR plots of specific exercise – if I didn't exercise during the past month mavg for 14 days should just take value of my last exercise. right??

[2020-09-03] need to test it, just to double check

[2019-04-11] Visualization — pandas 0.24.2 documentation [[viz]]

[2020-09-16] Building a Data Dashboard - Quantified Self / Apps & Tools - Quantified Self Forum

[2020-08-23] All | Search powered by Algolia

Panel, hvPlot, HoloViews, GeoViews, Datashader, Param, Colorcet -- all working together to make Python data visualization easier and more powerful.

[2020-10-11] Accelerating with WebGL — Bokeh 2.2.1 Documentation

[2020-10-11] Accelerating with WebGL — Bokeh 2.2.1 Documentation

Only a subset of Bokeh’s objects are capable of rendering in WebGL. Currently supported are the circle and line glyphs, and many markers: asterisk, circle, square, diamond, triangle, inverted_triangle, cross, circle_cross, square_cross, diamond_cross, x, square_x, and circle_x. You can safely combine multiple glyphs in a plot, even if some are rendered in WebGL, and some are not.

[2020-10-11] hmm, didn't seem to have any effect??

[2020-10-11] FifthHour/correlator: Timeseries correlation in Python, Bokeh Server and on Heroku [[bokeh]]

[2020-11-01] JupyterLab Documentation — JupyterLab 2.3.0a1 documentation

[2020-11-11] raphaelvallat/pingouin: Statistical package in Python based on Pandas

[2020-10-31] How I built a spreadsheet app with Python to make data science easier | Hacker Noon [[spreadsheet]]

[2020-10-28] I just updated my Pandas GUI project to have some sample datasets, here it is working with a Simpsons dataset and proving that the early seasons are the best… /r/compsci

[2019-11-24] Jupyter tools to increase productivity - Towards Data Science [[jupyter]]

Jupyter tools to increase productivity

[2020-05-12] widgets, debugging, toc

[2020-09-10] from IPython.core.debugger import settrace

need to post it somewhere… but not sure how to present to people. could def post some plots to quantifiedself? [[dashboard]] [[toblog]] [[qs]]

[2019-04-09] Short- and long-term effects of a single bout of exercise on heart rate variability: comparison between constant and interval training exercises. - PubMed - NCBI [[hrv]]

R-R intervals, TP, and HF/TP were significantly decreased while LF/TP and LF/HF were significantly increased during the early recovery, when compared with control values. This could be a response to the significant decrease in SAP and DAP at this time. Twenty-four and 48 h after the end of the exercise, HRV parameters were at the same levels as before exercises in the supine posture, but a persistent tachycardia continued to be observed in the upright posture, together with reduced TP values, showing that cardiovascular functions were still disturbed. The short-term HRV recovery seemed dependent on the type of exercise, contrary to the long-term recovery.

[2019-08-17] Plotting with categorical data — seaborn 0.9.0 documentation [[viz]]

The first is the familiar boxplot(). This kind of plot shows the three quartile values of the distribution along with extreme values. The “whiskers” extend to points that lie within 1.5 IQRs of the lower and upper quartile, and then observations that fall outside this range are displayed independently. This means that each value in the boxplot corresponds to an actual observation in the data.

[2020-09-02] not sure if boxplots in particular are usefult to me?

thinking about making it public [[hpi]]

where to run it? it's gotta be sufficiently dynamic?
run from google cloud? suck in the data once? or refresh continuously?
maybe run the original data retrieval? not sure

add more dashboards to web?

[2019-04-10] could add to dashboard?

'Other' workouts i'm using for HR decay – shit, I think I lost hr here after 10th minute. Could detect it automatically I guess? [[qs]] [[hr]]

always keep a tab running on one for my desktops? or pinned

[2019-04-06] shit. influxdb seems to be very unsuitable for the kind of thing I want. also very awkward to render dashboards… [[influxdb]]

[2021-02-13] hmm I guess what I meant is that it's for aggregate data only, and a bit awkwards to explore by-point data?

I really want proper and fast location history.. [[location]]

run with cachew? also integrate with hpi [[cachew]] [[nutrino]]

show my dashboard [[qs]] [[social]]

[2020-09-01] Pandas API — hvPlot 0.6.0 documentation

Lag Plot
Lag plots are used to check if a data set or time series is random. Random data should not exhibit any structure in the lag plot. Non-random structure implies that the underlying data are not random.

mixing in data from taplog [[weight]] [[exercise]] [[qs]]

Dump an org table
Preserve the ids
Join with taplog (maybe even as a df)
Win! Maybe check some similarity score too
Same for weight?

on the hrv plot, diplay as arrow if the trend is up or down [[sleep]]

[2020-11-03] joshlk/dataclassframe: A container for dataclasses with multi-indexing and bulk operations.

hmm.. doesn't have any typing imports at all?? what do they mean 'working nicely with type hints', that they don't crash??

[2021-02-06] ah ok, something added.. maybe reevaluate it later

use for daily dashboard or something? [[remarkable]]

[2020-09-06] bokeh/notebookembed.ipynb at 2.2.1 · bokeh/bokeh [[dashboard]] [[viz]]

A collection of tools for generating data visualizations from browser history data

looks like a heatmap?

[2020-12-30] [[bokeh]]

goddamnit. so frustrating that I can't zoom it properly

Grafana [[timeline]]

eh, I'm not even super sure what I wanna plot…
I guess for beginners would be nice to have sleep plot, maybe estimated from rescuetime
that could also href to timeline slices
plotly plugin
geomap plugin
apparently some people are using influxdb for that stuff? still not worth for me…

uhh. so unclear how to connect my data to grafana.


some issues: unable to hide zeros —> LOTS of 0 points
unable to attach labels?
influx to start terminal client

influxdb on default port… everything is admin/admin

I guess, start with weight for simplicity

then, do sleep

figure out grafana..

post on dataisbeautiful? [[reddit]] [[sleep]]

fix dovpandas warnings

when I release it, make sure it works both against public and private hpi bits [[sleep]]

profile stuff a bit?

[2020-12-05] xkcd: Linear Regression [[toblog]]

[2019-04-15] Python Data Visualization 2018: Why So Many Libraries? - Anaconda

[2019-09-10] try using in dashboard

[2019-04-14] — Bokeh 1.1.0 documentation

[2019-07-23] eh, defaults don't look great. maybe it's faster but unclear

[2020-10-25] Tableau Desktop [[viz]] [[qs]]

-----–— last housekeeping on [2021-02-06] ----

can overlay activity data vs sleep data from different tools? [[dashboard]] [[sleep]] [[hpi]]

[2021-02-12] grafana/worldmap-panel: Worldmap panel plugin for Grafana 3.0 that can be overlaid with circles for data points. [[location]] [[dashboard]]

[2021-02-18] Personal Dashboards for Self-Tracking Data - Quantified Self / Apps & Tools - Quantified Self Forum [[hpi]] [[dashboard]] [[publish]]

some screenshots

could use mybinder for demos? same used by jupyterlab [[dashboard]] [[hpi]]

[2021-04-11] LibrePhotos/librephotos: Self hosted alternative to Google Photos [[photos]] [[memex]]

[2021-04-11] photoprism/photoprism: Personal Photo Management powered by Go and Google TensorFlow [[photos]] [[memex]]

looks good – I guess need to evaluate on the basis of whether I can share it?

[2021-03-05] Introducing Orion: A Powerful Substitute for OwnTracks Recorder - Blog - Kevin Lin [[location]] [[HPI]] [[dashboard]]

orion-web provides a fast, interactive, React-based data visualization frontend for the web, powered by

whoa ok, this is pretty awesome

[2019-09-30] Jupyter notebooks on steroids [[dashboard]] [[ipynb]]

[2019-10-06] try ipywidgets?

[2021-04-29] Personal Dashboards for Self-Tracking Data - Quantified Self / Apps & Tools - Quantified Self Forum [[qs]]

I pretty much 100% agree with your assessment. I personally would also add:

    Automatic backup of data.
    API for simple daily import of data (e.g. from manual or unsupported trackers). To me CSV import is inferior solution, especially for dashboard, where the point is to have the data visualized persistently.
    I would expand the ‘flexible data analysis’ to following specific analysis that I feel are essential (but I haven’t seen anywhere):
    a. cross-correlation across time to uncover temporally delayed relationships between tracked variables
    b. automatic application of the above to all pairs of variables with report of the strongest relationships found
    c. proper reporting of statistical significance of any correlations found
    d. multi-variate regression

motivaion: good points about what's missing from most current solutions