savgol | Implements Savitzky-Golay filters | Widget library

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:

I use a script to work on a number of big .csv files one after another. However, since yesterday I get an error which I neither can explain nor resolve, though I haven't changed anything in the python script.

I have a simplified version of the script. It runs when I don't try to plot the data, remove the savgol filter or decrease the number of data points. In the as is version provided below, it stops in the second loop with this error:

** On entry to DLASCLS parameter number 4 had an illegal value
** On entry to DLASCLS parameter number 4 had an illegal value
Traceback (most recent call last):
File "simplified_problem.py", line 29, in
ysmooth = savgol_filter(y,51,5)
File "C:\Programs\Python\Python37\lib\site-packages\scipy\signal_savitzky_golay.py",
line 337, in savgol_filter
coeffs = savgol_coeffs(window_length, polyorder, deriv=deriv, delta=delta)
File "C:\Programs\Python\Python37\lib\site-packages\scipy\signal_savitzky_golay.py", line 139, in savgol_coeffs
coeffs, _, _, _ = lstsq(A, y)
File "C:\Programs\Python\Python37\lib\site-packages\scipy\linalg\basic.py", line 1218, in lstsq
raise LinAlgError("SVD did not converge in Linear Least Squares")
numpy.linalg.LinAlgError: SVD did not converge in Linear Least Squares

I suspect the error has nothing to do with the script itself, since it only appeared after a major window update (currently version 10.0.19041).

Has anybody an idea what exactly causes the problem and how it can be solved? Please let me know if any additional information is needed. Your help would be much appreciated.

I have the following time series data set:

I have a dataframe (an excerpt is shown below):

I'm trying to apply a savgol filter from SciPy to smooth my data. I've successfully applied the filter by selecting each column separately, defining a new y value and plotting it. However I wanted to apply the function in a more efficient way across a dataframe.

I have a Unix time series (x) with an associated signal value (y) which is generated every minute, dropping the first value and appending a new one. I am trying to smooth the resulting curve without loosing time accuracy with a specific emphasis on the final value of the smoothed curve which will be written to a database. I would like to be able to adjust the smoothing to a considerable degree.

I have studied (as mathematical layman, more or less) all options I could find and I could master. I came across Savitzki Golay which looked perfect until I realized it works well on past data but fails to produce a reliable final value if no future data is available for smoothing. I have tried many other methods which produced results but could not be adjusted like Savgol.

I'm new to Python and trying to automate finding optimum operating parameters of a savgol filter that feeds into PLS analysis. First, I had a prediction function calculate results and plot them.

To try and automate optimising, I have a for loop that goes over a range of numbers then feeds this number into t. I want the filter which then goes into the function. The aim is to find the smallest possible result after applying the function and return the arguments that gave rise to it.

I'm not sure how to do this. Also, I don't want the for loop to produce plot every single time. Do I just cut and paste the section of code that does this into the loop function?

Given I have an X and Y array such that:

I have a hypothetical y function of x and trying to find/fit a lognormal distribution curve that would shape over the data best. I am using curve_fit function and was able to fit normal distribution, but the curve does not look optimized.

Below are the give y and x data points where y = f(x).