spectrum | : chart_with_downwards_trend : Spectrum visualizer | Frontend Framework library

I would like to use Python to generate different colors of noise, just like Wikipedia mentions : https://en.wikipedia.org/wiki/Colors_of_noise.

For example, White, Pink, Brownian, Blue and Violet noise. And would like to have similar spectrums just like the website.

It would be a great help if I could just adjust a few parameters to get it done. Any links or tips would be very appreciated! Thanks a lot.

My question is simple. I think I kind of understand FFT and DFT. what I dont understand is why, in Python or matlab, do we use FFT size as the number of samples? why does every sample taken in the time domain corresponds to a frequency bin in the frequency domain.

For example the Scipy's fft pack, in order to plot the spectrum of a .wav file signal we use:

I'm working on some legacy code using R. In the code, frequencies and intensities of a periodogram are extracted and used for some downstream analysis:

I would like to make an animation of TOF-MS spectrometer (time-of-flight mass spectrometry) using the 'for' statement. I am able to make a script which animates the flight of ions inside the tube. This is the script:

Ok so I have been trying for days to deploy on Heroku. I can't even upgrade my account on Heroku to ask the question of how to go about this.

I have a fully functioning emulated Django project on my computer. I initially used PythonAnywhere and loved it. Until I realized Braintree is not working and I can't load php, it is too restricted. I do have a ticked with them.

So I moved to Heroku. The issue with Heroku is I can not even load it onto the site. I have it hooked to my GitHub. Now I can not load it because it refuses to built because of pyodbc will not load. I know this is shown by sql.h not found when installing PyODBC on Heroku. I am starting a new topic, because it is older. That being said I tried everything on here.

I think part of the issue is that I do not have sudo access to either one of these. I tried logging in with su account my account password but apparently we do not have that access. I tried build packs on the Heroku, but they seem to not want to work for the pyodbc library. I also was thinking of an add on on Heroku, but then I would still have change a lot of code.

Is there either a for dummies way to fix this? My sister who has been a programmer for years can't get it to work. Then me who is new can't. So we have a large spectrum experience between us. Sorry for the rant I have literally spent over 40 hours trying to get this to deploy.

_{This is probably an AB mistake on my part.}

While reading my dataset usually some files are NOT a part of it. I want to be robust against that - to just ignore them, simply log the omission.

But I am in love with exceptions thus Spectrum read_csv( const fs:path & dataset ); throws.

I want to keep it that way because the .pdf and other files my supervisor has embedded into the data structure should stay there.

The following implementation seems elegant. But fails at the first wrong file. A try/catch alternative works. But is not as expressive. Can this work somehow while read_csv() throws; those exceptions to be at most logged?

from my knowledge, Power Spectral Density (PSD) should stay relatively constant with the total time sampled (or aka. N-points sampled), however I have having trouble obtaining this result.

As I know from Discrete Fourier Transform (DFT), the amplitude normalization is 1/N. (e.g Amplitude Spectrum = DFT/N). However, from various sources, the PSD is defined as (DFT * DFT-conjugate / N).

How can this be possible? It is true that the Amplitude Spectrum has a 1/N normalization constant, then shouldn't the PSD have a 1/N^2 normalization constant (since DFT is proportional to N and so is its conjugate).

More specifically, I am trying to calcuated the PSD of a continuous electric field wave using the Eq. 9 of this paper. However I can't make sense of it's constants infront of the DFT since the factors of N's cancel out leaving behind only the summation of the window function squared. I tested this result and found that the PSD does not stay relatively constant with sampling size.

In summary, I have having troubles since my PSD varies with the amount of total time of the signal sampled. Any help would be great, thanks!

I have 1024 samples and I want to cut them into 32 chunks of 32 and run FFT on each one of them and plot it via a Frequency-Amplitude spectrum, I have most of the code working just the part of applying FFT to each chunk is not working, however I am able to apply FFT to the whole samples array.

I tried doing something like that:

My code is designed to get data from a serial device and print its contents to a MS Forms Application. The IDE i use is Visual Studio 2019 - Community.

The device does send a variable size packet. First i have to decode the packet "header" to get crucial information for further processing which is the first packet channel as well as the packet length.

Since the packet does neither contain a line ending, nor a fixed character at the end, the functions SerialPort.ReadTo() and SerialPort.ReadLine() are not useful. Therefore only SerialPort.Read(buf,offset,count) can be used

Since sending rather large packets (512bytes) does take time, I've implemented a function for calculation of a desired wait time, which is defined as (1000ms/baud-rate*(8*byte-count))+100ms

While testing, I've experienced delay, much more than the desired wait times, so implemented a measure function for different parts of the function.

In regular cases (with desired wait times) i except a log to console like this: Load Header(+122ms) Load Data (+326ms) Transform (+3ms)

But its only like this for a few variable amount of records, usually 10, after that, the execution times are much worse:

Load Header(+972ms) Load Data (+990ms) Transform (+2ms)

Here you can see the complete function: