A waste of time. Output in my source files pollutes git and prevents me from editing it in Vim. Just let me run the freacking code and render images as standalone PNGs which I can include from Markdown.
Some other basic hits due to how bad Jupyter is:
The people who work on this will go straight to heaven, no questions asked.
The output was also uploaded to: commons.wikimedia.org/wiki/File:DFT_2sin(t)_%2B_sin(4t).svg and added to en.wikipedia.org/w/index.php?title=Discrete_Fourier_transform&oldid=1176616763 only to be later removed of course: Deletionism on Wikipedia.
_%2B_sin(4t).svg){kind=link}
DFT of with 25 points
. Source code at: numpy/fft_plot.py.Output:
With our understanding of the discrete Fourier transform we see clearly that:
sin(t)
fft
real 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
imag 0 -10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10
rfft
real 0 0 0 0 0 0 0 0 0 0 0
imag 0 -10 0 0 0 0 0 0 0 0 0
sin(t) + sin(4t)
fft
real 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
imag 0 -10 0 0 -10 0 0 0 0 0 0 0 0 0 0 0 10 0 0 10
rfft
real 0 0 0 0 0 0 0 0 0 0 0
imag 0 -10 0 0 -10 0 0 0 0 0 0- the signal is being decomposed into sinusoidal components
- because we are doing the Discrete Fourier transform of a real signal, for the
fft, so there is redundancy in the. We also understand thatrfftsimply cuts off and only keeps half of the coefficients
A Python wrapper over a bunch of numeric and computer algebra system packages to try and fully replace MATLAB et. al.
Quickstart tutorial at: www.sagemath.org/tour-quickstart.html From this we see that they are very opinionated, you don't need to import anything, everything has a pre-defined global name, which is convenient, e.g.:
is the 3D vector space over the rationals. This also suggests that they are quite focused on computer algebra as opposed to numerical.
Examples under python/sklearn
. .venv/bin/activate
pip install sklearn matplotlib seabornArticles by others on the same topic
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