Global Climate Strike
Debugging Web APIs with session based authentication in Postman
- Open the web app in Chrome or Firefox and open the Developer Tools (F12 or Ctrl+Shift+I).
- Login and look for an API call in the Network Tab of the Developer Tools.
- Right click the request and select Copy -> Copy as cURL
- Open Postman and use the Import Button -> Paste Raw Text
- You can now execute the same request as done in the browser.
- Start modifying it and happy hacking.
Lessons learnt: First Hackathon
- Be prepared. In my case the material was sent out a week beforehand. Use the time to setup your machine(s) and code base so you can start directly.
- Do research on the topic. I've not looked into state of the art algorithms for the problem beforehand. This cost me a lot of time in the Hackathon, to understand the algorithm we used.
- Have compute resources ready. I could have used 2 further machines, but forgot to setup ssh, Teamviewer or any other remote control on them.
- Do whatever you know best. Some of my team members had far more experience on the topic than me. I've tried to follow there example, which wasn't successful. I think I would have been more successful using more basic approaches (which I have enough knowledge about) than trying to follow the "state of the art" approach. At the end I've tried to support them as best as I could (mostly doing evaluations).
- Push for more organization. We quickly came to the conclusion that only 1 or 2 approaches (transfer learning on common models) would be feasible in the short time of a hackathon. This led to us being rather unorganized. Everybody tried to get the models running as fast as possible and tweaking them the rest of the time. I think we could have produced more insight on the topic with regular "stand ups".
AutoTexture
I've built a small tool to create tileable, seamless textures.
Commute to Work
[Helsinki] View on City
[Helsinki] Serenity
[Helsinki] Pond on Island
[Helsinki] Squirrel
Visualizing Likert Scale Survey Data
I'm currently helping to evaluating a large market research survey, which ueses Likert Scales. To visualize the data I've tried several plots. The plots below where created with artificially created data to expose the strengths and weaknesses of different plot types.
Data distribution:
You can find the code for all plots here!
Bar Plot: Mean Values
Pros:
- Easy to create
- Simple to read
- Q3 – Q5 distinguishable
Cons:
- Hides a lot complexity
- Doesn't show spread
- Creates high confidence in shown values
Bar Plot: Mean values and Standard Deviation
Extension of first plot.
Pros:
- Still simple
- Introduces skepticism into shown data
- Hints at spread in data
Cons:
- Hides a lot complexity
- Still doesn't show distribution
Violin Plot
Pros:
- High Resolution
- Shows distribution of data
Cons:
- Harder to read
- Noisy on small sample sizes
- Shows data as being continuous
Vertical Histograms
Pros:
- High Resolution
- Shows distribution of data
Cons:
- Harder to read
- Shows data as being continuous
Scatter Plot
Pros:
- High Resolution
- Actually shows complete data
- Shows distribution of data
Cons:
- Hard to read
- Introduced noise and overdraw can distort data
Scaled Dots
Pros:
- High Resolution
- Shows distribution of data
- Shows data as discrete values
Cons:
- Visual distortion of proportions
- Humans can't easily compare circle sizes
Scaled Dots
Pros:
- Looks scientific
- Contains Median, Quantiles
- Shows outliers
Cons:
- Not designed for discrete data
- Doesn't show distribution correctly (e.g. Q4 + Q5)
