savitzky-golay | Savitzky–Golay filter | Widget library

by mljs TypeScript Version: Current License: MIT

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savitzky-golay is a TypeScript library typically used in User Interface, Widget applications. savitzky-golay has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

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savitzky-golay Key Features

Savitzky–Golay filter

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Community Discussions

Trending Discussions on savitzky-golay

Loop for Savitzky-Golay filters
Efficient way of applying a savitzky golay filtering on data.table rows for certain column?
Utilising Savitzky-Golay Filter in R vs Python
Applying Savitzky-Golay filter on a pandas dataframe
How to Savitzky-Golay filtering for more than one sample/column (R)
Optimising parameters for finding peaks in 1D array
Savitzky-Golay derivatives, computed with SciPy's signal.savgol_filter need to be scaled?
How to compare scipy noise filters?

Trending Discussions on savitzky-golay

QUESTION

Loop for Savitzky-Golay filters

Asked 2021-Dec-09 at 14:40

I try to write a loop for my Savitzky-Golay filter code, I have ten columns, and try to repeat them on each column with same process.

Sample dataset:

    PLOT1500=structure(list(system = c("1-Jan-16", "2-Jan-16", "3-Jan-16", 
"4-Jan-16"), evi1500 = c(0.437, 0.3891771, 0.493, 0.261), evi21500 = c(0.467436348, 
0.186, 0.446531, 0.386620096), kndvi1500 = c(0.686, 0.413083514, 
0.452461736, 0.508), ndvi1500 = c(0.482, 0.57, 0.461, 0.198), 
    nirv1500 = c(0.453765489, 0.624, 0.694, 0.457), evi2500 = c(0.439085339, 
    0.415, 0.447462363, 0.683), evi22500 = c(0.391, 0.715, 0.394061226, 
    0.382846651), kndvi2500 = c(0.452913701, 0.414950345, 0.420522988, 
    0.475), ndvi2500 = c(0.32139863, 0.515688255, 0.427, 0.427274026
    ), nirv2500 = c(0.491, 0.173, 0.444, 0.471)), row.names = c(NA, 
4L), class = "data.frame")

code:

library(dplyr) 
library(signal)

sg <- sgolay(p=7, n=11, m=0)
sevi1500 <- filter(sg, PLOT1500$evi1500)
sevi1500
sevi21500 <- filter(sg, PLOT1500$evi21500)
sevi21500
skndvi1500 <- filter(sg, PLOT1500$kndvi1500)
skndvi1500
sndvi1500 <- filter(sg, PLOT1500$ndvi1500)
sndvi1500
snirv1500 <- filter(sg, PLOT1500$nirv1500)
snirv1500
sevi2500 <- filter(sg, PLOT1500$evi2500)
sevi2500
sevi22500 <- filter(sg, PLOT1500$evi22500)
sevi22500
skndvi2500 <- filter(sg, PLOT1500$kndvi2500)
skndvi2500
sndvi2500 <- filter(sg, PLOT1500$ndvi2500)
sndvi2500
snirv2500 <- filter(sg, PLOT1500$nirv2500)
snirv2500
cc=cbind(sevi1500,sevi21500,skndvi1500,sndvi1500,snirv1500,sevi2500,sevi22500,skndvi2500,sndvi2500,snirv2500)
cc

Thanks in advance.

ANSWER

Answered 2021-Dec-09 at 14:40

library(tidyverse)
library(signal)
#> 
#> Attaching package: 'signal'
#> The following object is masked from 'package:dplyr':
#> 
#>     filter
#> The following objects are masked from 'package:stats':
#> 
#>     filter, poly

PLOT1500 <- structure(list(
  system = c(
    "1-Jan-16", "2-Jan-16", "3-Jan-16",
    "4-Jan-16"
  ), evi1500 = c(0.437, 0.3891771, 0.493, 0.261), evi21500 = c(
    0.467436348,
    0.186, 0.446531, 0.386620096
  ), kndvi1500 = c(
    0.686, 0.413083514,
    0.452461736, 0.508
  ), ndvi1500 = c(0.482, 0.57, 0.461, 0.198),
  nirv1500 = c(0.453765489, 0.624, 0.694, 0.457), evi2500 = c(
    0.439085339,
    0.415, 0.447462363, 0.683
  ), evi22500 = c(
    0.391, 0.715, 0.394061226,
    0.382846651
  ), kndvi2500 = c(
    0.452913701, 0.414950345, 0.420522988,
    0.475
  ), ndvi2500 = c(0.32139863, 0.515688255, 0.427, 0.427274026), nirv2500 = c(0.491, 0.173, 0.444, 0.471)
), row.names = c(
  NA,
  4L
), class = "data.frame")

# need more rows for filter
PLOT1500 <- PLOT1500 %>%
  list() %>%
  rep(10) %>%
  bind_rows()

sg <- sgolay(p = 7, n = 11, m = 0)

PLOT1500 %>%
  mutate(across(matches("1500|2500"), ~ signal::filter(sg, .x)))
#>      system   evi1500  evi21500 kndvi1500  ndvi1500  nirv1500   evi2500
#> 1  1-Jan-16 0.4317215 0.4655495 0.6840037 0.4747651 0.4510836 0.4454103
#> 2  2-Jan-16 0.4231690 0.1996236 0.4273279 0.6157175 0.6402125 0.3747353
#> 3  3-Jan-16 0.4056273 0.4036501 0.4091449 0.3485501 0.6573740 0.5481791
#> 4  4-Jan-16 0.3696906 0.4656374 0.5812905 0.3199554 0.4881796 0.5678016
#> 5  1-Jan-16 0.3757619 0.3668323 0.6085295 0.4559650 0.4623747 0.4794025
#> 6  2-Jan-16 0.4141707 0.2904986 0.4728216 0.5251263 0.6016812 0.4315946
#> 7  3-Jan-16 0.4298224 0.3879595 0.4608195 0.4331037 0.6318901 0.4846934
#> 8  4-Jan-16 0.3391415 0.4079327 0.5283815 0.3073739 0.5039268 0.5884699
#> 9  1-Jan-16 0.3970425 0.4001966 0.5975225 0.4453961 0.4912674 0.4797898
#> 10 2-Jan-16 0.4141707 0.2904986 0.4728216 0.5251263 0.6016812 0.4315946
#> 11 3-Jan-16 0.4298224 0.3879595 0.4608195 0.4331037 0.6318901 0.4846934
#> 12 4-Jan-16 0.3391415 0.4079327 0.5283815 0.3073739 0.5039268 0.5884699
#> 13 1-Jan-16 0.3970425 0.4001966 0.5975225 0.4453961 0.4912674 0.4797898
#> 14 2-Jan-16 0.4141707 0.2904986 0.4728216 0.5251263 0.6016812 0.4315946
#> 15 3-Jan-16 0.4298224 0.3879595 0.4608195 0.4331037 0.6318901 0.4846934
#> 16 4-Jan-16 0.3391415 0.4079327 0.5283815 0.3073739 0.5039268 0.5884699
#> 17 1-Jan-16 0.3970425 0.4001966 0.5975225 0.4453961 0.4912674 0.4797898
#> 18 2-Jan-16 0.4141707 0.2904986 0.4728216 0.5251263 0.6016812 0.4315946
#> 19 3-Jan-16 0.4298224 0.3879595 0.4608195 0.4331037 0.6318901 0.4846934
#> 20 4-Jan-16 0.3391415 0.4079327 0.5283815 0.3073739 0.5039268 0.5884699
#> 21 1-Jan-16 0.3970425 0.4001966 0.5975225 0.4453961 0.4912674 0.4797898
#> 22 2-Jan-16 0.4141707 0.2904986 0.4728216 0.5251263 0.6016812 0.4315946
#> 23 3-Jan-16 0.4298224 0.3879595 0.4608195 0.4331037 0.6318901 0.4846934
#> 24 4-Jan-16 0.3391415 0.4079327 0.5283815 0.3073739 0.5039268 0.5884699
#> 25 1-Jan-16 0.3970425 0.4001966 0.5975225 0.4453961 0.4912674 0.4797898
#> 26 2-Jan-16 0.4141707 0.2904986 0.4728216 0.5251263 0.6016812 0.4315946
#> 27 3-Jan-16 0.4298224 0.3879595 0.4608195 0.4331037 0.6318901 0.4846934
#> 28 4-Jan-16 0.3391415 0.4079327 0.5283815 0.3073739 0.5039268 0.5884699
#> 29 1-Jan-16 0.3970425 0.4001966 0.5975225 0.4453961 0.4912674 0.4797898
#> 30 2-Jan-16 0.4141707 0.2904986 0.4728216 0.5251263 0.6016812 0.4315946
#> 31 3-Jan-16 0.4298224 0.3879595 0.4608195 0.4331037 0.6318901 0.4846934
#> 32 4-Jan-16 0.3391415 0.4079327 0.5283815 0.3073739 0.5039268 0.5884699
#> 33 1-Jan-16 0.3970425 0.4001966 0.5975225 0.4453961 0.4912674 0.4797898
#> 34 2-Jan-16 0.4141707 0.2904986 0.4728216 0.5251263 0.6016812 0.4315946
#> 35 3-Jan-16 0.4298224 0.3879595 0.4608195 0.4331037 0.6318901 0.4846934
#> 36 4-Jan-16 0.3451485 0.4596679 0.5446130 0.2676971 0.4990481 0.6103875
#> 37 1-Jan-16 0.3450376 0.3682986 0.5828770 0.3919248 0.4595737 0.5307030
#> 38 2-Jan-16 0.4498211 0.2610299 0.5040675 0.6315108 0.6015259 0.3519612
#> 39 3-Jan-16 0.4715542 0.4178171 0.4158430 0.4391927 0.7055284 0.4700416
#> 40 4-Jan-16 0.2641572 0.3910584 0.5137794 0.2011921 0.4549914 0.6796653
#>     evi22500 kndvi2500  ndvi2500  nirv2500
#> 1  0.3907501 0.4538682 0.3229697 0.4910148
#> 2  0.7147841 0.4090610 0.5045815 0.1746777
#> 3  0.4045248 0.4343358 0.4605200 0.4302151
#> 4  0.3368979 0.4619705 0.3704317 0.5191066
#> 5  0.4902738 0.4516159 0.3837821 0.3961985
#> 6  0.5884952 0.4243567 0.4615026 0.2884093
#> 7  0.4510686 0.4302815 0.4409619 0.4001168
#> 8  0.3940673 0.4595071 0.4097488 0.4628453
#> 9  0.4492767 0.4492416 0.3791475 0.4276285
#> 10 0.5884952 0.4243567 0.4615026 0.2884093
#> 11 0.4510686 0.4302815 0.4409619 0.4001168
#> 12 0.3940673 0.4595071 0.4097488 0.4628453
#> 13 0.4492767 0.4492416 0.3791475 0.4276285
#> 14 0.5884952 0.4243567 0.4615026 0.2884093
#> 15 0.4510686 0.4302815 0.4409619 0.4001168
#> 16 0.3940673 0.4595071 0.4097488 0.4628453
#> 17 0.4492767 0.4492416 0.3791475 0.4276285
#> 18 0.5884952 0.4243567 0.4615026 0.2884093
#> 19 0.4510686 0.4302815 0.4409619 0.4001168
#> 20 0.3940673 0.4595071 0.4097488 0.4628453
#> 21 0.4492767 0.4492416 0.3791475 0.4276285
#> 22 0.5884952 0.4243567 0.4615026 0.2884093
#> 23 0.4510686 0.4302815 0.4409619 0.4001168
#> 24 0.3940673 0.4595071 0.4097488 0.4628453
#> 25 0.4492767 0.4492416 0.3791475 0.4276285
#> 26 0.5884952 0.4243567 0.4615026 0.2884093
#> 27 0.4510686 0.4302815 0.4409619 0.4001168
#> 28 0.3940673 0.4595071 0.4097488 0.4628453
#> 29 0.4492767 0.4492416 0.3791475 0.4276285
#> 30 0.5884952 0.4243567 0.4615026 0.2884093
#> 31 0.4510686 0.4302815 0.4409619 0.4001168
#> 32 0.3940673 0.4595071 0.4097488 0.4628453
#> 33 0.4492767 0.4492416 0.3791475 0.4276285
#> 34 0.5884952 0.4243567 0.4615026 0.2884093
#> 35 0.4510686 0.4302815 0.4409619 0.4001168
#> 36 0.3252712 0.4647988 0.3890248 0.5228858
#> 37 0.4673952 0.4575471 0.3958628 0.4115581
#> 38 0.6558883 0.4145530 0.4540116 0.2368532
#> 39 0.4171119 0.4201153 0.4513231 0.4188209
#> 40 0.3792356 0.4751102 0.4234683 0.4749589

^{Created on 2021-12-09 by the reprex package (v2.0.1)}

Source https://stackoverflow.com/questions/70276439

QUESTION

Efficient way of applying a savitzky golay filtering on data.table rows for certain column?

Asked 2021-Jun-03 at 19:46

I wrote a function to apply a savitzky golay filter to each row in a data.table. The first column having measurement values is given as an argument and all later columns contain measurements values to be filtered too. The processed rows are updated in-place.

My function works, but is slow.

How could the function be changed in order to work more efficient and more data.table like?

MWE:

library(data.table)
library(pracma)
library(datasets)

data(iris)
setDT(iris)

#Reorder columns because function expects columns to apply a filter on 
#starting from a defined column to the last column
setcolorder(iris, "Species")


savitzky_golay <- function(dt, id_of_first_sample_col=2, win_size=5) {
  
  c_names_samples <- colnames(dt)[id_of_first_sample_col:ncol(dt)]
  
  for (i in seq(from=1, to=nrow(dt))) {
    mat <- as.numeric(dt[i,id_of_first_sample_col:ncol(dt)]) # Get sample data as matrix (one row)
    mat <- savgol(mat,fl=win_size,forder=2,dorder=0) # Savitzky-Golay-Filter
    
    dt[i, (c_names_samples) := as.list(mat)] # Update columns of current row by reference
  }
  # Returns nothing as update is done via reference.
}

savitzky_golay(iris)

ANSWER

Answered 2021-Jun-03 at 19:46

Try:

savitzky_golay_new <- function(dt, id_of_first_sample_col=2, win_size=5) {
  c_names_samples <- colnames(dt)[id_of_first_sample_col:ncol(dt)]
  dt[,(c_names_samples):=asplit(apply(.SD,1,function(x) savgol(x,fl=win_size,forder=2,dorder=0)),1)
     ,.SDcols=c_names_samples]
  }

performance comparison:

microbenchmark::microbenchmark(savitzky_golay_new(dt2),savitzky_golay(dt1))
Unit: milliseconds
                    expr     min       lq     mean   median       uq      max neval
 savitzky_golay_new(dt2) 12.7808 13.69695 15.63821 14.31785 15.17705  31.2701   100
     savitzky_golay(dt1) 71.4231 81.96115 87.97737 86.41265 90.42620 239.7945   100

Source https://stackoverflow.com/questions/67822069

QUESTION

Utilising Savitzky-Golay Filter in R vs Python

Asked 2021-May-28 at 12:51

I'm currently trying to render the same results in R as in Python but think I must be misunderstanding the Savitzky-Golay filter. I have the below Python code:

import numpy as np
from scipy.signal import savgol_filter
t = np.linspace(0,1,10)
X = np.vstack((np.sin(t),np.cos(t))).T
sfd = savgol_filter(X, window_length=5, polyorder=3, axis=0)
sfd
array([[-4.78900581e-07,  9.99997881e-01],
       [ 1.10884544e-01,  9.93841986e-01],
       [ 2.20394870e-01,  9.75397369e-01],
       [ 3.27190431e-01,  9.44944627e-01],
       [ 4.29950758e-01,  9.02837899e-01],
       [ 5.27408510e-01,  8.49596486e-01],
       [ 6.18361741e-01,  7.85877015e-01],
       [ 7.01688728e-01,  7.12465336e-01],
       [ 7.76378020e-01,  6.30281243e-01],
       [ 8.41469460e-01,  5.40300758e-01]])

From my understanding, this smooths the matrix and prepares it to develop the derivative term. However, when using pracma in R (the most recently updated version of a Savitzky-Golay function), I get:

library(pracma)
t = seq(0, 1,length = 10)
X = t(rbind(sin(t), cos(t)))
savgol(X[, 1], fl = 5)
[1] 1.229175e-16 1.108826e-01 2.203977e-01 3.271947e-01 4.299564e-01 5.274154e-01 6.183698e-01 7.016979e-01 7.763719e-01 8.414710e-01

Does anyone know why these numbers are so different and how I can produce the same results from Python in R?

Thanks in advance.

ANSWER

Answered 2021-May-28 at 12:14

The SciPy function savgol_filter has several options for handling the ends of the input array; see the mode parameter in the docstring.

It looks like the behavior of the R function savgol corresponds to mode='constant' in SciPy's savgol_filter. Except for the first value (which is effectively 0 in both cases), this output of savgol_filter matches the output of savgol in R:

In [82]: sfd = savgol_filter(X, window_length=5, polyorder=4, axis=0, mode='constant')

In [83]: sfd[:, 0]
Out[83]: 
array([1.95316193e-17, 1.10882629e-01, 2.20397743e-01, 3.27194697e-01,
       4.29956364e-01, 5.27415386e-01, 6.18369803e-01, 7.01697876e-01,
       7.76371921e-01, 8.41470985e-01])

Source https://stackoverflow.com/questions/67737701

QUESTION

Optimising parameters for finding peaks in 1D array

Asked 2020-Jun-07 at 14:04

I need to optimise a method for finding the number of data peaks in a 1D array. The data is a time-series of the amplitude of a wav file.

I have the code implemented already:

from scipy.io.wavfile import read
from scipy.signal import find_peaks


_, amplitudes = read('audio1.wav')


indexes, _ = find_peaks(amplitudes, height=80)
print(f'Number of peaks: {len(indexes)}')

When plotted, the data looks like this:

General scale

The 'peaks' that I am interested in are clear to the human eye - there are 23 in this particular dataset.

However, because the array is so large, the data is extremely variant within the peaks that are clear at a general scale (hence the many hundreds of peaks labelled with blue crosses):

Zoomed in view of one peak

Peak-finding questions have been asked many times before (I've been through a lot of them!) - but I can't find any help or explanation of optimising the parameters for finding only the peaks I want. I know a little about Python, but am blind when it comes to mathematical analysis!

Analysing by width seems useless because, as per the second image, the peaks clear at a large scale are actually interspersed with 'silent' ranges. Distance is not helpful because I do not know how close the peaks will be in other wav files. Prominence has been suggested as the best method but I could not get the results I needed; threshold likewise. I have also tried decimating the signal, smoothing the signal with a Savitzky-Golay filter, and different combinations of parameters and values, all with inaccurate results.

Height alone has been useful because I can see from the charts that peaks always reach above 80.

ANSWER

Answered 2020-Jun-06 at 15:12

Look at 0d persistent homology to find a good strategy, where the parameter you can optimize for is peak persistence. A nice blog post here explains the basics.

But in short the idea is to imagine your graph being filled by water, and then slowly draining the water. Every time a piece of the graph comes above water a new island is born. When two islands are next to each other they merge, which causes the younger island (with the lower peak) to die. Then each data point has a birth time and a death time. The most significant peaks are those with the longest persistence, which is death - birth.

If the water level drops at a continuous rate, then the persistence is defined in terms of peak height. Another possibility is by dropping the water instantaneously from point to point as time goes from step t to step t+1, in wich case the persistence is defined in peak width in terms of signal samples.

For you it seems that using the original definition in terms of peak height > 70 finds all peaks you are interested in, albeit possibly too many, clustered together. You can limit this by choosing the first peak in each cluster or the highest peak in each cluster or by doing both approaches and only choosing peaks that have both great height persistence as well as width persistence.

Source https://stackoverflow.com/questions/62233032

QUESTION

Savitzky-Golay derivatives, computed with SciPy's signal.savgol_filter need to be scaled?

Asked 2020-May-07 at 09:23

I'm computing the first and second derivatives of a signal and then plot. I chose the Savitzky-Golay filter as implemented in SciPy (signal module). I'm wondering if the output needs to be scaled - in the Matlab implementation of the same filter, it is specified that scaling is needed on the output of the filter:

savitzkyGolayFilt(X,N,DN,F) filters the signal X using a Savitzky-Golay (polynomial) filter. The polynomial order, N, must be less than the frame size, F, and F must be odd. DN specifies the differentiation order (DN=0 is smoothing). For a DN higher than zero, you'll have to scale the output by 1/T^DN to acquire the DNth smoothed derivative of input X, where T is the sampling interval.

However, I didn't find anything similar in SciPy's documentation. Has anybody tried and knows if the output in Python is correct and needs no further scaling? The line of code I'm running for the first derivative is this one: first_deriv = signal.savgol_filter(spectra_signal,sigma=7,2, deriv=1, delta=3.1966) The spectra_signal is my "y" variable and delta is the variation of "x" variable.

Also, I tried to compute the first derivative without using the savgol_filter, but using np.diffon the smoothed signal instead (based on the formula derivative = dy/dx).first_deriv_alternative = np.diff(signal.savgol_filter(spectra_signal, sigma=7,2))/3.1966. And the results are not the same.

Working code example:

import numpy as np
from scipy import signal

x =[405.369888, 408.561553, 411.753217, 414.944882, 418.136547, 421.328212, 424.519877, 427.711541, 430.903206]
y =[5.001440644264221191e-01,
    4.990128874778747559e-01,
    4.994551539421081543e-01,
    5.002806782722473145e-01,
    5.027571320533752441e-01,
    5.053851008415222168e-01,
    5.082427263259887695e-01,
    5.122825503349304199e-01,
    5.167465806007385254e-01]

#variation of x variable, constant step 
sampling_step = x[1]-x[0] 
#Method 1: using savgol_filter
deriv1_method1 = signal.savgol_filter(y,5,2,deriv=1, delta=sampling_step)

#Method 2: using np.diff to compute the derivative of the filtered original data
dy=np.diff(signal.savgol_filter(y, 5,2))
dx=np.diff(x)
deriv1_method2=dy/dx

#Method 3: filtering the first derivative of the original data
deriv1_method3=signal.savgol_filter((np.diff(y)/np.diff(x)), 5,2)

ANSWER

Answered 2020-May-06 at 12:18

Under the hood signal.savgol_filter uses signal.savgol_coeffs if you look a the source code it says that "The coefficient assigned to y[deriv] scales the result to take into account the order of the derivative and the sample spacing". The results are hance scaled before performing the fitting and the convolve1d. So by default, it seems that the results are already scaled taking into account the order of derivatives.

I think that performing the derivative after computing Savitzky-Golay filter won't give you the same results because in this case, you are computing the derivative on the spectrum already filtered, while in the first case you are performing the derivative before performing the fitting and the scaling.

Source https://stackoverflow.com/questions/61631978

QUESTION

How to compare scipy noise filters?

Asked 2020-Feb-15 at 09:37

I need to reduce my noise like behavior in my data. I tried one of the method called Savitzky-Golay Filter . On the other hand, I need to find fastest method, because the filtering algorithm will be in the most running script in my code.

I am not familiar with the signal processing methods. Can you suggest faster methods and usage of them briefly?

I do not need complex structure like low-pass, high-pass etc (I know there are thousands of them). As fast as possible smoothening method is what I want to use.

Here my test script:

import numpy as np
import matplotlib.pyplot as plt

noisyData=np.array([
   2.77741650e+43,   1.30016392e+42,   8.05792443e+42,   1.74277713e+43,
   2.33814198e+43,   6.75553976e+42,   2.56642073e+43,   4.71467220e+43,
   4.25047666e+43,   3.07095152e+43,   7.30694187e+43,   7.54411548e+43,
   1.29555422e+43,   8.09272000e+42,   9.18193162e+43,   2.25447063e+44,
   3.43044832e+41,   7.02901256e+43,   2.54438379e+43,   8.72303015e+43,
   7.80333557e+42,   7.55039871e+43,   7.70164773e+43,   4.38740319e+43,
   8.43139041e+43,   6.12168640e+43,   5.64352020e+43,   3.63824769e+42,
   2.35296604e+43,   4.66272666e+43,   5.03660902e+44,   1.65071897e+44,
   2.81055925e+44,   1.46401444e+44,   5.44407940e+43,   4.50672710e+43,
   1.60833084e+44,   1.68038069e+44,   1.08588606e+44,   7.00867980e+43])

xAxis=np.arange(len(noisyData))

# ------------- Savitzky-Golay Filter ---------------------
windowLength = len(xAxis) - 5 
polyOrder = 6

from scipy.signal import savgol_filter

# Function
def set_SavgolFilter(noisyData,windowLength,polyOrder):
    return savgol_filter(noisyData, windowLength, polyOrder)

plt.plot(xAxis,noisyData,alpha=0.5)
plt.plot(xAxis,set_SavgolFilter(noisyData,windowLength,polyOrder))

# ------------- Time Comparison ----------------------
import time
start_time = time.time()
for i in range(50):
    savgolfilter1 = set_SavgolFilter(noisyData,windowLength,polyOrder)
print(" %s seconds " % (time.time() - start_time))

# === OTHER METHODS WILL BE HERE

ANSWER

Answered 2020-Feb-06 at 12:19

Unless you really need polynomial-based smoothing, Savitzky-Golay does not have any particular advantages. It's basically a bad lowpass filter. For more details, see https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5888646

Using a basic Butterworth lowpass filter instead:

from scipy.signal import butter, filtfilt
b, a = butter(5, .2)
datafilt = filtfilt(b, a, noisyData)

The filtfilt call seems to be several times faster than savgol_filter. How much faster do you need? Using lfilter from scipy is at least 10 times faster, but the result will be delayed with respect to the input signal.

Source https://stackoverflow.com/questions/60093456

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