Octave | Discord bot written in Java and Kotlin using JDA | Bot library

The .NET Core wrapper doesn't support this type of call where you ask for IDispatch at the same time you create the COM object:

C / C++:

In your tuner.dart File the _TurnerState extends the Sate but it needs to extend for example class _TunerState extends State.

Nested functions exist explicitly to share variables with the enclosing function. This is their purpose. If you don’t want a private function to share variables with the calling function, declare it after the calling function in the same M-file. This makes it a “local function”, a function only visible from within this file.

In general nested functions are weird and should only be used in specific circumstances. One place they are useful is to encapsulate variables in a more complex lambda than can be accomplished with an anonymous function:

The distinction between an invertible matrix (i.e. full rank) and a non-invertible one is clear-cut in theory, but not so in practice. A matrix B with large condition number (as in your example) can be inverted, but computing the inverse is numerically unstable. It roughly corresponds to B having a determinant that is "small" (using an appropriate, relative measure of "small"), so the matrix is almost singular. As a result, the inverse matrix will be computed with bad accuracy. In your example B, the condition number (computed with cond) is 2.069e9.

Another way to look at this is: when the condition number is large, it well could be that B is "really" singular, but small numerical errors from previous computations make it look barely non-singular. So you can't be sure.

The rank and rref functions use different algorithms (singular-value decomposition for rank, Gauss-Jordan elimination with partial pivoting for rref). For well-behaved matrices the numerical errors will be small in both cases, and the results will be consistent. But for a bad-conditioned matrix the numerical errors will be large and potentially different in each case, giving inconsistent results.

This is a well known issue with numerical algebra. In general, avoid inverting matrices with large condition number.

Currently, in music21, stream.Voice objects are more of a display concept than a logical concept. Voices and Chords are both simultaneities, and that's all that a MIDI file captures. (In fact, there are pending changes in version 7, to be released this week, that make fewer voices and more chords from MIDI files, in addition to making measures. If there are small overlaps from reverb or from a recorded performance you may get "voices" that an engraver would never print in sheet music.)

In your case, I would probably just take a .flat of the Part object to get rid of Voices (and eventually Measures in v.7), and then run chordify() if you want to ensure there are no overlaps. Otherwise, if you don't want chords at all, you can still take the output of chordify() and find the root of each chord. Several possibilities that all depend on what your sources look like.

I think you need the output_precision function. For example, if you want to have 7 digits in the command window, type this:

Depends on your definition of "builtin function" vs 'workaround'.

A full-window title is simply a title positioned with respect to an empty 'whole window' axis, as opposed to subtitles which are positioned with respect to subplots which take up only a fraction of the window at specific positions.

Therefore the 'builtin' way to add a title on an empty whole-window axis, is to simply add a title on an empty whole-window axis.

After hours of profiling, I finally found out the reason: GaussianBlur.

The pipeline of SIFT algorithm is:

1. Create initial image: convert data type of source image to float, double the resolution, and do GaussianBlur (sigma=1.56)
2. Build gaussian pyramid
3. Find key points: build DoG pyramid and find scale space extrema
4. Calculate descriptors

The num of octaves is calculated according to image resolution (see here). And nOctaveLayers controls num of layers (nOctaveLayers + 3 for GaussianPyramid) in each octave.

Indeed, when nOctaveLayers increases, nums of layers and keypoints both increase. As a result, time cost of step 3 & 4 increases. However, in parallel computation, this time increment is not very remarkable (several milliseconds).

In contrast, the step 2 costs more than half of the total time. It costs 25.27 ms (in 43.49 ms) when nOctaveLayers is 3, and 51.16 ms (in 63.10 ms) when nOctaveLayers is 1. So, why is this happening?

Because the sigma for GaussianBlur() increases faster when layers are fewer, and it's critical for the time consumed by GaussianBlur(). See test below:

Starting here: