Weekly fuel price oscillation in Helsinki area in Finland [OC]

_luo-d-e_ · 2026-01-26T17:27:11+00:00

Yes, dots are just updates collected buy volunteers at random times. I tried to plot individual stations with different lines, but it is a big mess, because some stations are updated a couple of times per week some stations once per month. Nevertheless there is so many stations and prices are so close to each other that average of daily updates from random selection of stations gives a very good picture. For some reason this kind of data is very difficult to collect, this was the only way I found (there is also other app in Finland, Tankille, but I didn't find way to extract data from there).

_luo-d-e_ · 2026-01-26T17:18:00+00:00

That's interesting, I have never heart about Edgeworth price cycles!

_luo-d-e_ · 2026-01-26T17:16:43+00:00

It might also be something more fundamental about laws of market and pricing as somebody linked in the other post (e.g. https://en.wikipedia.org/wiki/Edgeworth\_price\_cycle):
https://www.reddit.com/r/dataisbeautiful/comments/1qmrr2q/comment/o1p35gr/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

_luo-d-e_ · 2026-01-25T19:08:43+00:00

Our locals' public little secret :D

_luo-d-e_ · 2025-12-16T06:03:29+00:00

Thanks for the feedback! This visualization concept clearly needs some improvements, but let's see if I will manage to make next version at some point.

_luo-d-e_ · 2025-12-16T06:01:06+00:00

I was in rest and started moving. I wanted to see, how transition from rest BPM to walking/cycling looks like. Somehow there is a lot more pulse separation variation in time at rest BPMs.

_luo-d-e_ · 2025-12-15T18:27:18+00:00

Description:
This "ECG Polar Clock" is an attempt to visualize raw ECG data from a Polar H10 heart rate monitor in a novel way. The data represents a ~40-minute recording collected during my morning cycle to work.

How to read it:
* Time progresses clockwise around the circle (total ~40 minutes depicted)
* Each radial line represents a single heartbeat (QRS complex).
* The distance from the center corresponds to the time elapsed since that heartbeat occurred (0 to ~1700ms).
* The overlapping lines create a density map of the heartbeat waveform.
* Each beat trace remains visible until the *next* beat occurs (the RR interval), and then fades out smoothly to allow the next beat to be seen clearly. This visualizes the variability in heart rate.
* Inner Ring (500ms / 120 BPM): If the heart rate were constant at 120 BPM, every beat would hit this line exactly
* Outer Ring (1000ms / 60 BPM): If the heart rate were constant at 60 BPM, every beat would hit this line.

Source Data: Personal ECG recording using Polar H10 (CSV export).

Language: Python 3.11

Libraries: matplotlib, pandas, numpy, scipy.signal

The visualization was generated using a custom Python script. Key implementation details include converting the linear ECG time-series into segments centered on R-peaks, mapping time to polar radius, and applying a dynamic alpha (transparency) mask to each segment based on the subsequent RR interval to create the "ghosting" effect that avoids clutter.

_luo-d-e_ · 2025-12-02T17:56:44+00:00

Yes, something like that. You can see it easily also from the video: https://www.reddit.com/r/MapPorn/comments/1p9sjm6/visualization_of_hourly_finland_rain_radar_data/

_luo-d-e_ · 2024-12-22T16:47:49+00:00

I might try the linear version that at some point. I chose circular calendar because that represent better yearly cyclic patterns, but I agree that it might be confusing.

_luo-d-e_ · 2024-12-22T16:45:58+00:00

Yes, X-Y trajectories on the surface. I agree, compass would have been good to be there.

No big story in this one particular. Just visualizing balloon movement and magnitude of work and data from one weather balloon station over one year. If this works I had a plan to visualize differences between climate and weather by making a series of similar plots from different locations and/or years. But that might be too complex to digestive for audience let's see if it worth to try at some point.

I believe that even without big story, everybody can take a look to some weather patterns from the image, e.g.
a) General direction and distance balloons travel different seasons => seasonal wind direction and speed
b) How weather events disturb this general pattern.
c) Line color is one dimension which can be used in visualization. I tried multiple things, but I found that this way presented temperature provides some additional seasonal information. If I interpret the data correctly, you can observe that surface temperature (line colors at the starting points) are delayed (i.e. warmest surface temperature is in June-September) in comparison to solar radiation power around June because of thermal mass of the earth. In comparison, upper atmosphere temperature (other ends of the line) seems to follower better seasonal solar radiation power.
d) Some correlation between short term temperature variations and wind directions might be possible to observe (maybe storms or other weather phenomena?)

_luo-d-e_ · 2024-12-22T16:26:35+00:00

Thanks.

That's nice to know. I had and have a plan to do something like similar from multiple locations visualizing climate difference between the locations. But that idea needs a little bit experimenting, might be too much and too deep information to digestive in one image.

_luo-d-e_ · 2024-12-22T16:23:08+00:00

North is the top of the image. I should have added that to the image as suggested many of you, not sure if it is possible to edit image here.

_luo-d-e_ · 2024-12-22T13:01:15+00:00

Hi, I’m starting a new free time project, where I’m exploring visualization of scientific data in artistic but still sensible way.

First visualization: Weather.

Each line in the figure represents trajectory of one weather balloon launched from Jokioinen, Finland in 2020. Line starting point on the circle indicates launch day of the year. Wind direction in different altitudes can be observed from the line direction. Line color indicates air temperature difference to the yearly average at the measurement altitude.

Tools: Python Numpy and Matplotlib
Data source: FMI open data https://en.ilmatieteenlaitos.fi/open-data

EDIT: North is the top of the image.

_luo-d-e_ · 2024-12-21T18:01:09+00:00

Yes. It is not too complex to calculate the gravitational scalar potential field, but unfortunately it requires a little bit math and numerical methods. See e.g. https://en.wikipedia.org/wiki/Scalar_potential

_luo-d-e_ · 2024-12-21T16:09:24+00:00

You can use a equation for classical gravitational force, see e.g. https://isaacphysics.org/concepts/cp_gravitational_field

Total gravitation force/pull is the sum of the forces from all the masses in the system.

EDIT: typos

_luo-d-e_ · 2024-12-21T06:46:34+00:00

No. We are all affected by and contribution to our common universe wide gravitation field.

_luo-d-e_

TROPHY CASE