As a next step, I find it interesting to see what our reviews are telling us when I show each category of a movie. I can imagine that the titles of the films are on the left. Suppose we start from the first 100 movies we have watched from the beginning of 2015? What does it look like? And what conclusions can we commit to? I’ll try programming this version slightly smarter than the earlier version.
I start by creating a grid of numbers. There are 13 categories (13 columns) with decreasing numbers from top to bottom and from 10 to 0. The size of the display window is a bit of guesswork. I now work on a size of 800 by 800 pixels. On the left side of the display window film titles have yet to be placed. And all 13 category labels should still come on top. I expect that I need much more space than 800 pixels in width and height. In the program I have added an empty draw block. Otherwise functions as keyReleased and timeStamp do not work.
Placing the film titles is a matter of creating a text file with 100 titles of films that we have seen since the beginning of 2015. Then read this text file into Processing and displaying it in the display window. The order (from top to bottom) corresponds to the viewing order. The list starts with the film ‘Boyhood’. Which is the first film that we saw in 2015. The list ends with the film ‘Restless’. And that’s the hundredth film we’ve seen. However, there is only one-third of the list visible. This is up to the film ‘Calvary’. And that is film number 38. Putting another 62 films in this display height makes no sense because the point size would become too small to read.
To get all the movies titles on the left in the picture, I have a few options. Reduce the line spacing. Reduce the point size of the font. Or I can increase the size of the display window. In this case I have used all three possibilities. I end up with 1500 x 1300 pixels. I also added the names of categories.
Another stage where I further optimize the distances. The category names (the labels of the columns) are still too far from the category columns. I’m going to put them closer and place them on an angle of 45º. The category numbers are now placed on an imaginary square. The display window is now 1460 x 1228 pixels. And the grid is built with squares of 90 x 90 pixels. Testing a first line which is drawn through the numbers who rated the film ‘Boyhood’. That does not look good. The lines are too stiff. It should be more fluid. VMD_04_04
In order to make more fluid lines I did one attempt with the curveVertex function. The problem here is that the curveVertex function uses Catmull-Rom splines. It does not make beautiful curves. In the end I opted for bezier curves. For the quality of the curve that is the best solution, but it requires more passes of data to describe the curve. Four anchor points and four control points per line. That means 13 x 8 points per bezier curve. That is 104 numbers for the first movie. Thus, in total there must be 10.400 points calculated to make the final visualization.
The first six films drawn using bezier curves.
I have now drawn 26 films with bezier curves. And it shows directly the weakness of this visualization method. Since all lines have the same color and thickness it is difficult to see which movie has scored which number in which category. At a later stage I will do something about that. But the problem is not completely solvable.
About half way with the positioning of bezier curves. I place the curves in a very straightforward way. I know that this can be done with more intelligence but I will not have time enough to solve this problem now. I think it requires an additional study which I might do in a later stage.
And about to place a fourth number of bezier curves.
All bezier curves are now positioned. On the left, it has become a pretty organized chaos. Looking at the line patterns you can conclude that most movies have brought us a 6, 7 or 8. What might also be said of our rating. Is our rating mediocre?
With all the lines in their place, it is now the time to bring in the Futura font. I have changed the background color to black. Font color is white. The color of the lines is gray with 50% transparency.
Time for a number of tests with line widths. Some are absolutely exaggerated. Others are functional. These variations also show that the number columns have to be written as a last item. Otherwise they will be overwritten by the bezier lines. And I shifted the column with movie titles slightly to create some space between the start of the bezier lines and the end of the movie titles.
Trying to solve a problem that popped up in VMD_04_07. To what extent is it possible to get more distinction between the bezier curves themselves. I start with two colors. Red and green. There seems to be a strange effect to occur. When a certain amount of red and green lines overlap it creates an additional color. It looks like orange. At least that seems to be orange but if you make the lines thicker it seems to be some light version of something brown-ish.
Added a blue color. Now it seems that there are many more shades of additional color variations possible.
What happens if I make an ascending color scale from 0 to 360? I switch to color mode HSB. HSB is easier to work with (as a human).
Which movies have been honored with at least once the highest possible value of 10 points?
Which movies have been awarded with at least once the highest value of 9 points or higher?
And finally: which movies have been rewarded with at least once the highest value of 8 points or more?
A quick conclusion. I am tempting to say that if a film did not score one 8, 9 or 10 in the assessment it would be not a good movie. That means it is of a lower level than films who scored at least one 8. Or one 9. Or one 10. This visualization is showing the worst films of all 100 films we have seen since the beginning of 2015. In total these are only 27 movies. So a little over a quarter. That means that three-quarters of the 100 films that we have seen always had something of good quality in them. And that’s very reassuring. For the filmmakers, the film industry and for us.