savitzky-golay | Savitzky–Golay filter | Widget library

by mljs TypeScript Version: Current License: MIT

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kandi X-RAY | savitzky-golay Summary

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.

Savitzky–Golay filter

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savitzky-golay has a low active ecosystem.

It has 26 star(s) with 4 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 11 have been closed. On average issues are closed in 56 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of savitzky-golay is current.

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savitzky-golay has 0 bugs and 0 code smells.

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savitzky-golay has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

savitzky-golay code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

savitzky-golay is licensed under the MIT License. This license is Permissive.

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Reuse

savitzky-golay releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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savitzky-golay Examples and Code Snippets

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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?

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:

...

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

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:

...

ANSWER

Answered 2021-Jun-03 at 19:46

Try:

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:

...

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:

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

QUESTION

Applying Savitzky-Golay filter on a pandas dataframe

Asked 2020-Oct-07 at 13:23

I have the following time series data set:

...

ANSWER

Answered 2020-Oct-07 at 13:16

This actually worked:

Source https://stackoverflow.com/questions/64224483

QUESTION

How to Savitzky-Golay filtering for more than one sample/column (R)

Asked 2020-Aug-10 at 22:50

I would like to apply a Savitzky-Golay filter (prospectr package) to a dataset. However, all the examples available run the filter for only one sample (curve with a determined number of wavelengths). Is there anyway I can filter the data of more samples (70 samples) at once?

...

ANSWER

Answered 2020-Aug-10 at 22:50

Usually spectral data matrices are organized with observations (samples) in rows and (spectral) variables in columns. The savitzkyGolay function does work for multiple observations. A proper way to call the function for your data is:

Source https://stackoverflow.com/questions/63341467

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:

...

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:

...

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:

...

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:

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

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