Fir lowpass filter python

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Fir lowpass filter python

SciPy Cookbook Date:2011-06-06 (last modified), 2010-08-27 (created) This sample cookbook shows how to design and use a low-pass FIR filter using the functions of scipy.signal. Pylab module from matplotlib is used to make plots. #!python from numpy import cos, son, pi, absolute, arange from scipy.signal import kaiserord, lfilter, firwin, freqz from pylab

import figures, clf, plot, xlabel, ylabel, xlim, ylim, title, grid, axes, show #------------------------------------------------ # Create a signal for demonstration. #------------------------------------------------ sample_rate = 100.0 nsamples = 400 t = arange(nsamples) / sample_rate x = cos(2*pi*0.5*t) + 0.2*sin(2*pi*2.5*t+0.1) + \ 0.2*sin(2*pi sin(2*pi *015.3*t) + 0.1*sin(2*pi*16.7*t

+ 0.1) + \ 0.1*sin(2*pi*23.45*t+.8) #------------------------------------------------ # Create an FIR filter and apply it to x. #------------------------------------------------ # Nyquist signal rate. nyq_rate = sample_rate / 2.0 # Desired width of transition from pass to stop, # relative to nyquist rate. We will design a filter # with a transition width of 5 Hz. width = 5.0/nyq_rate #

Desired loosening in stop band, in dB. ripple_db = 60.0 # Calculate row and Kaiser parameter for FIR filter. N, beta = kaiserord (ripple_db, width) # Filter frequency cut off. cutoff_hz = 10.0 # Use firwin with Kaiser window to create a lowpass FIR filter. taps = firwin(N, cutoff_hz/nyq_rate, window=('kaiser', beta)) # Use lfilter to filter x with FIR filter. filtered_x =

lfilter(taps, 1.0, x) #------------------------------------------------ # Plot FIR filter odds. #------------------------------------------------ figures(1) plot(taps, 'bo-', linewidth=2) title('Filter Odds (%d taps)' % N) grid(True) #------------------------------------------------ # Plot the magnitude response of the filter. #------------------------------------------------(2) clf() w, h = freqz(taps,

worN=8000) plot(((w/pi)*nyq_rate, absolute(h), linewidth=2) xlabel('Frequency (Hz)') ylabel('Gain') title('Frequency Response') ylim (-0.05, 1.05) grid(True) #Upper inset. ax1 = axes([0.42) 0.6, .45, .25]) land((w/pi)*nyq_rate, absolute(h), linewidth=2) xlim (0.8.0) ylim (0.9985, 1,001) grid (Truth) # Bottom insert of pitch ax2 = axes([0.42, 0.25) .45, .25])

plot((w/pi)*nyq_rate, absolute(h), linewidth=2) xlim(12.0, 20.0) ylim(0.0, 0.0025) grid(True) #------------------------------------------------ # Plot original and filtered signals. #------------------------------------------------ # Phased delay to filtered signal. delay = 0.5 * (N-1) / sample_rate number (3) # Plot the original signal. plot(t, x) # Plot the filtered signal, shifted to

compensate for the delay in the phase. plot(t-delay, filtered_x, 'r-') # Plot only a good part of the filtered signal. The first N-1 # samples are corrupted by the initial conditions. plot(t[N-1:]-delay, filtered_x[N-1:], 'g', linewidth=4) xlabel('t') show() The final plots show the original signal (thin blue line), filtered signal (shifted by the corresponding phase delay to

match the original signal; thin red line) and a good portion of the filtered signal (heavy green line). A good part is part of the signal that is not affected by the initial conditions. Section author: WarrenWeckesser Attachments fir_demo_freq_resp.png fir_demo_signals.png fir_demo_taps.png ? Copyright 2015, Various authors Audits 5e2833af. Built with sphinx

using a theme provided by Read the Docs. This is an example of a document that can be published using Pweave. Text is written using restructuredText and <> code between > and @ is executed and the results are included in the resulting document. You can define different options for code parts to control code execution and formatting (see FIR

design with SciPy). We will implement lowpass, highpass and ' bandpass FIR filters. If you want to read more about DSP, I recommend the Digital Signal Processing Guide, which is available online for free. First, let's define functions for ploting filter properties. from import pylab * import scipy.signal as signal def mfreqz(b,a=1): w,h = signal.freqz(b,a) h_dB =

20 * log10 (abs(h)) subplot(211) plot(w/max(w),h_dB) ylim(-150, 5) ylabel(Magnitude (db)) xlabel (r'Normalized frequency (x$\pi$work/pattern)) title (r'Frequency Response') subplot(212) h_Phase = unwry (arktan2(imag(h), actual(h))) parcel(w/max(w),h_Phase <4>) ylabel(Stage (radians)) xlabel(r'Normalized frequency (x$\pi$work/pattern)) title(r'Phase

response') subplots_adjust(hspace=0.5) def impz(b,a=1): l = len(b) impulse = repeat(0.,l); impulse[0] =1. x = arange(0.l) response = signal.lfilter(b,a,impulse) subplot(211) stem(x, response) ylabel(Amplitude) xlabel(r'n (samples)) title (r'Impulse response) subplot(212) step = cumsum(response) stem(x, step) ylabel('Amplitude') xlabel(r'n (samples)') title(r'Step

response') subplots_adjust(hspace=0.5) Designing a lowpass FIR filter is very easy to work with SciPy, all you have to do is define the length of the window, cut off the frequency and window. The hamming window is defined as: w(n) = ¦Á ? ¦Âcos(2¦Ðn)/(N ? 1), where ¦Á = 0.54 and ¦Â = 0.46 The next part of the code is executed in term mode, see the source

document for syntax. Notice also that Pweave can now capture more figures/code. n = 61 a = signal.firwin(n, cutoff = 0.3, window = hamming) mfreqz(s) show(2) figures(2) <matplotlib.figures.Figure at= 0x7f83f3f52b00=> <matplotlib.figures.Figure at= 0x7f83f3f52b00=>impz(a) show() Let's define highpass FIR filter: n = 101 a = signal.firwin(n, cutoff

= 0.3, window = hanning, pass_zero=False) mfreqz(s) show() Notice that the action has a title defined in the code part options. n = 1001 a = signal.firwin(n, cutoff = [0.2, 0.5], window = 'blackmanharris', pass_zero = False) mfreqz(s) show() Bandpass FIR filter. Share code, notes, and clips now. You can't do that action right now. Signed

</matplotlib.figure.Figure> </matplotlib.figure.Figure> card or window. Reload to refresh the session. You signed in on another card or window. Reload to refresh the session. scipy.signal.firwin(numtaps, cutoff, width=None, window='hamming', pass_zero=True, scale=True, nyq=None, fs=None)[source]? FIR filter design using the window method.

This function calculates the coefficients of the final impulse response filter. The filter will have a linear phase; It'll be type I if the numtaps are weird and type II if they're numtaps even. Type II filters always have zero responses to nyquist frequency, so the ValueError exception is raised if firwin is even called numtaps and has a pass bar whose right end is on

the nyquist frequency. Parameters of the numtapsintLength filter (number of coefficients, i.e. filter order + 1). numtaps must be strange if the tape includes nyquist frequency. cutofffloat or 1-D array_likeCutoff filter frequency (expressed in the same units as fs) OR a series of severed frequencies (that is, band edges). In the latter case, the interruption

frequencies should be positive and increase monotonously between 0 and fs/2. Values 0 and fs/2 must not be included in the interruption. widthfloat or None, optionalAs the width is none, then let's assume that this is the approximate width of the transition region (expressed in the same units as fs) for use in kaiser FIR filter design. In this case, the window

argument is ignored. Windowstring or tuple of strings and value parameters, optionDesired window to use. See scipy.signal.get_window list of windows and required parameters. pass_zero{True, False, 'bandpass', 'lowpass', 'highpass', 'bandstop'}, optionalIf True, winnings at frequency 0 (also, DC gain) is 1. If it's fake, D.C.'s winnings are 0. It can also be a

string argument for the filter type you want (corresponding to the btype in IIR design functions). New in version 1.3.0: Support for string arguments. scalebool, optionalSet to True for the odds scale so that the frequency response is exactly unity at a certain frequency. This frequency is either: 0 (DC) if the first pass bar starts pass_zero at 0 (i.e. true) fs/2

(Nyquist frequency) if the first pass bar ends in fs/2 (i.e. the filter is a single-band highpass filter); center of the first passband otherwise nyqfloat, optionalDeprecated. Use fs instead. This is Nyquist's frequency. Each frequency in the break must be between 0 and nyq. The default setting is 1. fsfloat, optionalAddition of signal sampling. Each frequency in the

break must be between 0 and fs/2. The default setting is 2. Returns h(numtaps,) ndarrayCoefficients length numtaps FIR filter. Raises ValueError If any break value is less than or equal to 0 or greater than or equal to fs/2, if the break values are not strictly monotonously increasing, or if the numtaps are equal, but the pass bar includes nyquist frequency. See

also firwin2 firls minimum_phase remez Examples Low-pass from 0 to f: >>> from scipy import signal numtaps = 3 >>> f = 0.1 >>> signal.firwin(numtaps, f) f) 0.06799017, 0.86401967, 0.06799017]) Koristite odre?enu funkciju prozora: >>> signal.firwin(numtaps, f, window='nuttall') array([ 3.56607041e-04, 9.99286786e-01,

3.56607041e-04]) High-pass ('stop' od 0 do f): >>> signal.firwin(numtaps, f, pass_zero=False) array ([-0.00859313, 0.98281375, -0.00859313]) Band-pass: >>> f1, f2 = 0,1, 0,2 >>> signal.firwin(numtaps, [f1, f2], pass_zero=La?no) polje([ 0,06301614, 0,88770441, 0,06301614]] ) Band-stop : >>> signal.firwin(numtaps, [f1, f2])

array([-0.00801395, 1.0160279 , -0.00801395]) Multi-band (passbands su [0 , f1], [f2, f3] i [f4, 1]): >>> f3, f4 = 0,3, 0,4 >>> signal.firwin(numtaps, [f1, f2, f3, f4]) polje([-0.01376344, 1.02752689, -0.01376344]) Multi-band (passbands su [f1, f2] i [f3,f4]): >>> signal.firwin(numtaps, [f1, f2, f3, f4], pass_zero=False) polje([ 0,04890915 ,

0,91284326, 0,04890915]) 0,04890915])

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