spectrum | Spectral Analysis in Python | Video Utils library

What you are seeing is indeed the expected outcome of an FFT (Fourier Transform). The logarithmic f-axis that you're expecting is achieved by the Constant-Q transform.

Now, the implementation of the Constant-Q transform is non-trivial. The Fourier Transform has become popular precisely because there is a fast implementation (the FFT). In practice, the constant-Q transform is often implemented by using an FFT, and combining multiple high-frequency bins. This discards resolution in the higher bins; it doesn't give you more resolution in the lower bins.

To get more frequency resolution in the lower bins of the FFT, just use a longer window. But if you also want to keep the time resolution, you'll have to use a hop size that's smaller than the window size. In other words, your FFT windows will overlap.

what you have there are specific points in a function, but you've defined those points for entire ranges. What you should do is write a function that outputs these values in a smooth fashion.

So let's look at your first number, which I assume is the R (red) value. if(spectrum[i] > 200) red = 255; if(spectrum[i] > 100 && spectrum[i] < 200) red = 127; else red = 0; What if instead of outputting single values, you made a function to map the amplitude directly to a Red value? To start with, make it really simple:

• You need to normalize the fft by 1/N with one of the two following changes (I used the 2nd one):
  S = np.fft.fft(s) --> S = 1/N*np.fft.fft(s)
magnitude = np.abs(S[index[0]]) --> magnitude = 1/N*np.abs(S[index[0]])
• Don't use index, = np.where(np.isclose(frequency, f0, atol=1/(T*N))), the fft is not exact and the highest magnitude may not be at f0, use np.argmax(np.abs(S)) instead which will give you the peak of the signal which will be very close to f0
• np.angle messes up (I think its one of those pi,pi/2 arctan offset things) just do it manually with np.arctan(np.real(x)/np.imag(x))
• use more points (I made N higher) and make T smaller for higher accuracy
• since a DFT (discrete fourier transform) is double sided and has peak signals in both the negative and positive frequencies, the peak in the positive side will only be half the actual magnitude. For an fft you need to multiply every frequency by two except for f=0 to acount for this. I multiplied by 2 in magnitude = np.abs(S[index])*2/N

It's not your fault. It is due to the Bucket Policy on the source bucket. It is not permitting the GetObjectTagging API call.

The awssampledbuswest2 bucket has been setup to permit access from Amazon Redshift as per examples in the AWS documentation. Such access does not attempt to retrieve object tags.

However, when copying between buckets, the AWS CLI aws s3 cp command attempts to make a complete copy of the object including object tags. This causes it to fail.

The copy to your local file system worked successfully because the AWS CLI does not attempt to get tags when copying to a destination outside of S3 because normal operating systems do not have the concept of tags on files.

To avoid this problem, you can use the aws s3api copy-object command to copy the file between buckets, which simply does a copy without attempting to copy the tags:

Constrained_layout was specifically designed with this case in mind. It will work with your gridspec solution above, but more idiomatically:

Numpy codes generally creates many temporary arrays. This is the case for your function find_nearest_line for example. Working on all the items of det_lines simultaneously would results in the creation of many relatively big arrays (1000 * 10_000 * 8 = 76 MiB per array). The thing is big array often do not fit in CPU caches. If so, the array needs to be stored in RAM with a much lower throughput and much higher latency. Moreover, allocating/freeing bigger array takes more time and results often in more page faults (due to the actual implementation of most default standard allocators). It is sometimes faster to use big array because the overhead of the CPython interpreter is huge but both strategies are inefficient in practice.

The thing is that the algorithm is not efficient. Indeed, you can sort the array and use a binary search to find the closest value much more efficiently. np.searchsorted does most of the work but it only returns the index of the closest value greater (or equal) than the target value. Thus, there is some additional operation to do to get the closest value (possibly greater or lesser than the target value). Note that this algorithm do not generate huge array thanks to the binary search.

For the first problem, I just ended up taking the layout file using create_layout(), modifying the locations of the vertices manually (doing some basic trigonometry) before sending the file to ggraph(). For the second problem, I just found all overlapping edges and split the connections dataframe into dataframes of non-overlapping edges, then passed them to geom_conn_bundle() functions separately and with differing tension values. It would be excellent if someone could come up with a better answer to the first problem though! Here's the repository.

First of all: The results here when I tried the code is different then what you displayed in the question.

Let's say we have a gray scaled picture. Each pixel would have a value of integers between [0, 255]. Sometimes these values can be floats between [0, 1].

Here 0 is black and 255 is white. The vales between (0, 255) are grays. Towards 0 it gets more gray, towards 255 its less gray.

(I'm not sure about the term Polychromatic) Colored pixels are not so different then gray scaled ones. The only different is colored pixels storing 3 different values between [0, 255] for each Red, Green and Blue values.

see: https://www.researchgate.net/figure/The-additive-model-of-RGB-Red-green-and-blue-are-the-primary-stimuli-for-human-colour_fig2_328189604

Now let's see what what the image you are creating is like:

You are crating a matrix of zeros with shape of: 256, 256 * 6, 3, which is: 256, 1536, 3.

Then with the first line you are replacing the first column with something else:

There are a lot of things going on there we need to decompose.

First off, you managed to have SHLIB use your enumerated list of object files. Good! I recently had to the same and I used a OBJECTS list. I think you may get lucky if you stick the -fstack-protector into PKG_LIBS because the PKG_* variables are there for your expand on the defaults use (in the hidden Makefile controlled by R). Whereas ... LDFLAGS may just get ignored.

Otherwise, I would recommend to sample among the 4000+ CRAN packages with compiled code. Some will set similar things, the search with the 'CRAN' "org" at GitHub is crude but better than nuttin'. Good luck!

Edit: You could look at my (more complicated still) Makevars.win for RInside. I just grep'ed among all the repos I have here and I don't have a current example of anybody setting -fSOMETHING on Windows.

Edit 2: I do actually have a better example for your. Each and every RcppArmadillo package uses