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Does Python/Scipy have a firls( ) replacement (i.e. a weighted, least squares, FIR filter design)?


Does Python/Scipy have a firls( ) replacement (i.e. a weighted, least squares, FIR filter design)?

By : berniez
Date : November 20 2020, 07:00 PM
hope this fix your issue The firls equivalent in python now appears to be implemented as part of the signal package: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.firls.html#scipy.signal.firls
Also I agree with everything that @pev hall stated above especially how firls is optimum in many situations (such as when overall signal to noise is being optimized for a given number of taps), and to not use the boxcar window as he stated, they are not equivalent at all! firls generally outperforms all window and frequency sampling approaches to filter design when designing traditional FIR filters.
code :


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Python least squares with scipy.integrate.quad

Python least squares with scipy.integrate.quad


By : subhajit ghosh
Date : March 29 2020, 07:55 AM
To fix this issue With funeval(90.,0.001, 0.0002), Temp is a singular value; however, when you are calling scipy.optimize you are passing the entire T array to funeval causing scipy.integrate to crash.
A quick fix would be to do something like:
code :
def funeval(Temp,eps,sig):
    out=[]
    for T in Temp:
        val = scipy.integrate.quad( lambda x: np.expm1( ((4.*eps)/T)* ((sig/x)**12.-(sig/x)**6.)* (x**2.) ), 0.0, np.inf )[0]
        out.append(val)
    return np.array(out)

def residuals(p,y,Temp):
    eps,sig = p
    err = y-(funeval(Temp,eps,sig) )
    return err

print funeval([90],0.001, 0.0002)

plsq = scipy.optimize.leastsq(residuals, [0.00001, 0.0002], args=(B, T))
(array([  3.52991175e-06,   9.04143361e-02]), 1)
Python scipy.signal.remez high pass filter design yields strange transfer function

Python scipy.signal.remez high pass filter design yields strange transfer function


By : Camilo Andrés Rosero
Date : March 29 2020, 07:55 AM
like below fixes the issue For a highpass filter with the default remez argument type='bandpass', use an odd number of taps. With an even number of taps, remez creates a Type II filter, which has a zero at the Nyquist frequency. The algorithm has a hard time creating a highpass filter with such a constraint.
Here's a plot of the gain when L = 41:
Weighted random sample without replacement in python

Weighted random sample without replacement in python


By : Rass Tarro
Date : March 29 2020, 07:55 AM
To fix this issue I need to obtain a k-sized sample without replacement from a population, where each member of the population has a associated weight (W). , You can use np.random.choice with replace=False as follows:
code :
np.random.choice(vec,size,replace=False, p=P)
import numpy as np
vec=[1,2,3]
P=[0.5,0.2,0.3]
np.random.choice(vec,size=2,replace=False, p=P)
Scipy.curve_fit() vs. Matlab fit() weighted nonlinear least squares

Scipy.curve_fit() vs. Matlab fit() weighted nonlinear least squares


By : user3423484
Date : March 29 2020, 07:55 AM
seems to work fine Ok, so after further investigation I can offer the answer, at least for this simple example.
code :
import numpy as np
import scipy as sp
import scipy.optimize

def modelFun(x, m, b):
    return m * x + b

def testFit():
    w = np.diag([1.0, 1/0.7290, 1/0.5120, 1/0.3430, 1/0.2160, 1/0.1250, 1/0.0640, 1/0.0270, 1/0.0080, 1/0.0010])
    x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    y = np.array([0.1075, 1.3668, 1.5482, 3.1724, 4.0638, 4.7385, 5.9133, 7.0685, 8.7157, 9.5539])

    popt = sp.optimize.curve_fit(modelFun, x, y, sigma=w)

    print(popt[0])
    print(popt[1])
Unexpected standard errors with weighted least squares in Python Pandas

Unexpected standard errors with weighted least squares in Python Pandas


By : Joshua Smith
Date : March 29 2020, 07:55 AM
it helps some times Not directly answering your question here, but, in general, you should prefer the statsmodels code to pandas for modeling. There were some recently discovered problems with WLS in statsmodels that are now fixed. AFAIK, they were also fixed in pandas, but for the most part the pandas modeling code is not maintained and the medium term goal is to make sure everything available in pandas is deprecated and has been moved to statsmodels (next release 0.6.0 for statsmodels should do it).
To be a little clearer, pandas is now a dependency of statsmodels. You can pass DataFrames to statsmodels or use formulas in statsmodels. This is the intended relationship going forward.
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