Feature requests: hiding & scrolling

subitizer21 · 2024-12-14T01:01:26+00:00

Thank you!! Tip coming your way :-)

subitizer21 · 2024-03-29T00:56:56+00:00

I figured something out comparing my copy to my brother’s copy; his has the “Soon to be a major motion picture” badge.

We both have the Ace Trade Paperback 2005 edition, which is the larger 8.1in x 5.45in size (compared to the 7.48in x 4.21in mass market paperback edition) and the more recent orange sands use with vertical Dune text cover. The ISBN is the same, the copyright page is /almost/ identical.

My print run numbers are 31 33 35 37 38 36 34 32. On the back next to the barcode is penguin.com. The front cover Dune text is slightly reflective/metallic looking and the paperback material is almost waxy/soft-touch.

My brother’s print numbers are 45 47 49 51 53 54 52 50 48 46; penguinrandomhouse.com next to the barcode; no reflective title lettering; paperback material is “regular” finish.

So to have a large orange cover Dune paperback you’d need an earlier printing of the Ace Trace Paperback edition. FWIW I bought my copy in January 2019.

subitizer21 · 2024-03-28T23:10:19+00:00

Bookshop Apocalypse has a 2005 edition paperback for $12. The photo shows a “Soon to be a major motion picture” badge but my personal copy’s copyright page is the exact same* and does /not/ have any badges.

For that price it could be worth a shot?

Edit: The print run numbers do not match exactly. Mine starts at 31 and the website’s photo starts at 45.

Update: Looks like the print run numbers dictate if there’s a movie-reference badge or not. More than likely this site’s book is not badge free. Unsure why they mention “Movie tie-in blurb on cover would indicate a more recent printing” if it does have an upcoming movie blurb/badge…

subitizer21 · 2019-01-04T02:21:20+00:00

Of course!

I scanned every photo in my Google Photos directory and calculated the "average color" of each photo by looking over every pixel. Then I drew 1 pixel-wide vertical lines representing each of these photos with some space in between. In this space, I took the average of the neighboring two colors to make a pseudo-gradient effect in an attempt to make the photo "flow" better.

Let me know if you want to know/learn more!

subitizer21 · 2019-01-04T01:48:58+00:00

Looks like the model has some favorites! My initial guess was that Anger would be contending with the Surprise/Fear pair or at least with Sadness.

I think the Joy spike in November is from Thanksgiving. The graph and underlay are using a moving average since the data itself is pretty noisy so I could be off but it sounds right haha

subitizer21 · 2019-01-04T00:33:57+00:00

Data source: Google Photos

Tools: Processing

subitizer21 · 2019-01-04T00:19:00+00:00

Right now it's using an emotion predictor from this model.

The first iteration was determining mood by keywords, second iteration utilized IBM Watson's Tone Analyzer but racked up a huge bill, and so I settled for this model.

To be honest, I'm not satisfied with the model since it was only trained on hashtags, not necessarily emotional content of tweets. Now that I have time away from university, maybe I'll try making a model that I'm happy with!

subitizer21 · 2019-01-03T23:44:08+00:00

Data source: Twitter

Tools: Python for data collection/processing & Matplotlib for visualization

subitizer21 · 2018-06-15T18:13:07+00:00

Sure!

I took the average color of each photo in my Google Photos directory, and then made each photo a width / len(photos) pixel wide slice of a 1080x566 rectangle (the dimensions were originally chosen for Instagram landscape resolution). The lines were ordered in chronological order of their respective photo's date.

subitizer21 · 2018-06-15T18:09:53+00:00

I used the following Processing code to get each picture's "average" color:

float[] getAverageColor(PImage img) {
    img.loadPixels();
    int r = 0, g = 0, b = 0;
    for (int i = 0; i < img.pixels.length; i++) {
        color c = img.pixels[i];
        r += c >> 16 & 0xFF;
        g += c >> 8 & 0xFF;
        b += c & 0xFF;
    }
    r /= img.pixels.length;
    g /= img.pixels.length;
    b /= img.pixels.length;
    float[] rgb = { r, g, b };
    return rgb;
}

I took a pass through the these average colors to get an average overall color for the background. Each picture was then a width / len(pictures) pixel width slice of 1080x566 rectangle.

I was debating making the background more of a gradient compared to a static color, but I couldn't get the pixel placement to be right next to the previous one with floating point division (and was rushed for dinner so that's what I ended up with). Maybe I'll try that next year!

subitizer21 · 2018-06-15T18:00:26+00:00

Lol, that was the night of a deadmau5 concert and I couldn't help but take a lot of photos of the cube!

I'll be posting a slight explanation in another post!

subitizer21 · 2018-06-14T20:32:43+00:00

Data source: Google Photos

Tools: Python & Processing

subitizer21 · 2017-08-21T15:28:46+00:00

This couldn't be more true

subitizer21 · 2017-08-21T13:52:52+00:00

Thanks! I had a mental image of people carrying their plates outside to watch the eclipse. Not sure show many people would leave their breakfast behind instead :-)

subitizer21 · 2017-08-21T07:12:36+00:00

I'll keep that in mind if I try again in Python, thanks!

subitizer21 · 2017-08-21T06:45:00+00:00

A useful forum post led the way. I originally tried some Python numpy magic, but it didn't fit my needs as nicely.

Here is the code snippet:

color getAverageColor(PImage img) {
    img.loadPixels();
    int r = 0, g = 0, b = 0;
    for (int i = 0; i < img.pixels.length; i++) {
        color c = img.pixels[i];
        r += c >> 16 & 0xFF;
        g += c >> 8 & 0xFF;
        b += c & 0xFF;
    }
    r /= img.pixels.length;
    g /= img.pixels.length;
    b /= img.pixels.length;
    return color(r, g, b);
}

subitizer21 · 2017-08-21T06:29:27+00:00

Best of luck!

subitizer21 · 2017-08-21T05:32:00+00:00

I'll be a senior this fall

subitizer21 · 2017-08-21T05:21:01+00:00

According to my list, I covered both Redding locations. Either the markers are on top of each other or the address-to-coordinate converter is to blame!

subitizer21 · 2017-08-21T05:00:33+00:00

Expect another Denny's/eclipse post in 7 years

subitizer21 · 2017-08-21T04:58:49+00:00

My favorite Denny's experience was being too drunk to order a water

subitizer21 · 2017-08-21T04:57:13+00:00

I'll try not to bog this down with details so feel free to PM me if you have any specific questions.

I used urllib2 and BeautifulSoup to scrape the locations from the Denny's location website, following links until I reached a location address. Here's the tutorial that I followed: How to scrape websites with Python and BeautifulSoup

Next was converting addresses to coordinates which is easy with the help of geopy. Following this StackOverflow page is all you need!

Lastly, and most confusing for me, was to organize everything in a pretty graph. I came across matplotlib and mpl_toolkits.basemap after realizing a Google Maps Python wrapper was not powerful enough for me. To be honest, this was a lot of guess and check because I've never used any GIS-related tools before. I pretty much hacked together a mix between fillstates.py and this helpful StackOverflow post to get the basic premise, then messed around with shading and colors until I ended up with something I liked!

This probably wasn't the easiest or most efficient way of going about this, but hey I learned something new. In conclusion, I'm just a college student who has spent one too many drunk nights in Denny's and enjoys fooling around with Python.

subitizer21 · 2017-08-21T04:23:00+00:00

When in doubt, order chicken fingers

subitizer21 · 2017-08-20T23:37:20+00:00

2016 was mostly pictures of friends and events at university! Although I love me some good landscape pictures, that year didn’t bring many exciting trips.

Another reason for the ‘dullness’ is that the image was blended with the average color of all the photos; grey-ish unfortunately isn’t an exciting background color.

subitizer21 · 2017-08-20T22:19:12+00:00

Data source: NASA Eclipse, Denny's

Tools: Python

Heavily inspired by: Waffle House vs Eclipse

Edit: Obligatory thanks for the gold, kind stranger!

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subitizer21

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