rad | general purpose build tool. statically typed | Build Tool library

The table is not indexed with keys directly, but with hashes of keys. Different keys may have the same hash. This is called hash collisions.

1. This implementation stores all values that correspond to keys with the same hash as a linked list, thus a self referential structure. The table stores linked lists of key-value pairs. All keys in the same list have the same hash. (This is not the only method of handling collisions).
2. In case of a collision, the loop does work more than once. It you don't see this loop executing more than once, keep adding entries until you hit a collision.
3. No, it does not assign a node to itself. It inserts a newly allocated node at the head of the linked list. The former head of the list becomes the second node in the list.

Taking inspiration from @HoratioNullbuilt's comment I was able to make an extensible modification of the example they had linked:

http://blog.fofwebdesign.co.uk/16-circular-segment-pie-chart-menu-experimental

The reason why it needed to be extended was that the many of the values were hardcoded, for example you couldn't have different layers with different numbers of elements in them.

After the modification here is the result:

https://codepen.io/cuppajoeman/pen/oNGwxzQ

radial.js

For a regular grid with empty plots at specific positions, patchwork::plot_layout takes a design argument where empty fields can be place with #

You're almost there, but there are a couple of gotchas:

1. you declare variables at the top (e.g. float x, y, x2, y2, rad, rad2, dist, dist2;, etc.), however you don't initialize them with values. Because JavaScript is untyped (unlike Java), the interpreter can't guess what type you meant and the variables will be initialised to undefined (while in Java, because they're float type they'll default to 0). Doing math operations on undefined results in NaN (not a number).
2. you're accidentally incrementing some values by strings instead of floats: rotateBy += '.003'; yIn += '.005';
3. optional: fill(#02021A, 10); won't work in p5.js, however you can use fill(r,g,b,a) and pass your values in hex notation: fill(0x02, 0x02, 0x1A, 10);

This is your code with these two fixes applied:

Now I checked again and I noticed that I put the function to wrong place. I will answer it below.

I got a successful solution by decreasing the final time (max=0.04 for successful solution) and rearranging the equations to avoid a possible divide-by-zero:

• Access the secondary_y axes with ax2.set_ylabel('right-Y', fontsize=30), or access it from ax1 with the .right_ax attribute. dir(ax1) will show all of the available methods for ax1.
  • .right_ax does not work if ax2 is implemented with .twinx():
    • ax2 = ax1.twinx() and df.plot(y='freq: 2x', ax=ax2)
    • ax2.set_ylabel('right-Y', fontsize=30) works with .twinx()
• See pandas User Guide: Plotting on a secondary y-axis
• Tested in python 3.8.12, pandas 1.3.4, matplotlib 3.4.3

The correct header to use is asm/current.h, do not use asm-generic. This applies to anything under asm really. Headers in the asm-generic folder are provided (as the name suggests) as a "generic" default implementation of macros/functions, then each architecture /arch/xxx has its own asm include folder, where if needed it can define the same macros/functions in an architecture-specific way.

This is done both because it could be actually needed (some archs might have an implementation that is not compatible with the generic one) and for performance since there might be a better and more optimized way of achieving the same result under a specific arch.

Indeed, if we look at how each arch defines get_current() or get_current_thread_info() we can see that some of them (e.g. alpha, spark) keep a reference to the current task in the thread_info struct and keep a pointer to the current thread_info in a register for performance. Others directly keep a pointer to current in a register (e.g. powerpc 32bit), and others define a global per-cpu variable (e.g. x86). On x86 in particular, the thread_info struct doesn't even have a pointer to the current task, it's a very simple 16-byte structure made to fit in a cache line for performance.

I was able to get about 25 % faster by combining the three processes.