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License: Other
Notebooks for "Python for Signal Processing" book
License: Other
Hi!
I was reading Maximum Likelihood Estimation (direct link to the github file here) and noticed that there seems to be a tiny error in one of the formula, namely in the "Confidence Intervals" section.
Now here's a link to the exact line I have issue with.
Namely, it seems the denominator in the probability calculations ought be "200", not "100". This is already mended in the nest formula, where we have i.e. the fraction "9999/200".
Hi,
The windowing you are applying to different signals in Windowing.ipynb is in time-domain or frequency domain?
If i have a signal e.g. signal1=numpy.ones((1,50)) and i am applying windowing to it as
wt=numpy.transpose(numpy.kaiser(50,2.5))
signal = signal1*wt.
Is it the correct way of applying windowing?
How to apply windowing in frequency domain?
I am looking forward to read this book, but I have no idea how the notebooks are ordered.
There should be an overview page listing the notebooks in order.
In Cell [4] shouldn't
t = arange(0,2,1/fs)
actually be t = arange(0,1,1/fs)
?
Hello. I was working with this really great notebook, but noticed an error at the very bottom. Line 319 says
z=Lf(xs,mua_step[:,None],mub_step[:,None,None]).sum(axis=2) # numpy broadcasting
So the Lf variable was defined in the scope of the run() function, but not in the global scope. So trying to run the Lf() function is generating the error.
I fixed it by adding this line just above line 319:
Lf=sympy.lambdify((x,mu_a,mu_b), sympy.log(abs(L)),'numpy')
So the total code looks like:
Lf=sympy.lambdify((x,mu_a,mu_b), sympy.log(abs(L)),'numpy')
z=Lf(xs,mua_step[:,None],mub_step[:,None,None]).sum(axis=2) # numpy broadcasting
Hope this helps.
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