1,497 questions
2
votes
1
answer
150
views
Automatic smoothing parameters with make_smoothing_spline go wrong
Related to this question:
I would like to determine optimal smoothing parameters for any kind of time series, with mixed units, varying lengths, varying framerates, such as pixel or meter trajectories,...
- 113
3
votes
1
answer
111
views
How to avoid unintended "jaggedness" in 3D Plotly plots?
I’m using Plotly to plot a 3D dataset and the best-fit line to that dataset (Plotly version 6.2.0, Mac OS Monterey). When I plot the data in its original coordinates, the dataset and best-fit line ...
- 31
1
vote
1
answer
161
views
Calculate Exponential decay rate constant from graph
I would like to calculate the lifetime of the particles from the extrapolated curve below, I know that lifetime is 1/decay constant but I get values for the decay constant that dont make sense.
import ...
- 29
-1
votes
1
answer
61
views
Smoothing scroll animations in js
So my question is extremely simple, i see it on lots of websites
https://www.jordangilroy.com/#work
this one for example is my reference, when you scroll it feels alot smoother and the animations ...
- 1
1
vote
1
answer
55
views
Error running GAM with soap smoother - data outside soap boundary
I am running a gam model with a soap smoother
mod_aut_X10 <- bam(occurrenceStatus ~
s(x, y, bs = "so", xt = list(bnd = shap_bnd_ls, nmax = 1000)) +
s(...
- 541
0
votes
0
answers
33
views
How to convolve a 3D array with Lorentzian kernel along axis=2?
I have an array img_data of shape (x, x, n_channels) and I want to convolve / smooth along the axis=2.
Specifically, I would like the output shape to be (x,x,n_channels//3), after convolving the ...
- 21
0
votes
1
answer
104
views
How to optimize the weight for TV filter?
I have 2d data which has background noise and assembled high values. I'm trying to apply the TV filter to denoise the data. Is there a suitable method to avoid over-denoising the data?
I have tried to ...
- 1,039
3
votes
1
answer
77
views
How to use skimage to denoise 2d array with nan values?
I'm trying to apply the TV filter to 2D array which includes many nan values:
from skimage.restoration import denoise_tv_chambolle
import numpy as np
data_random = np.random.random ([100,100])*100
...
- 1,039
1
vote
0
answers
80
views
Pandas shaping with adjustment
problem
I have a smooth shaping/scaling factor that I need to apply to a stepped timeseries which has a label and flat value for that time period.
the result must
match the value of the step within ...
0
votes
1
answer
102
views
Derivative of smoothed data in python
I have the problem to take the derivative of some experimental data. I tried to smooth the data I got because the original derivative made no sense to me. I attached a picture with my experimental ...
- 23
0
votes
0
answers
45
views
Local Quadratic Regression with locfit(): Choose the optimal nearest neighbor
I am running a local quadratic regression with locfit in R. By default, it seems to select adaptive bandwidth with nn=0.7, meaning that 70% of the nearest neighbor variables must be included around ...
- 306
0
votes
1
answer
59
views
Smoothing values with known monthly plans [closed]
I have some planned monthly values, and the java model runs by the hour. Within a month (except for smoothing periods), the flow is constant I need to make a smooth one. (Smoothed) transition ...
- 75
0
votes
1
answer
82
views
Laplacian Smooth disconnecting faces
I have implemented Laplacian smooth in JavaScript and using Three.js, but it seems to be not working as expected. Whenever I use hover and smooth over meshes, the faces are disconnecting and getting ...
1
vote
2
answers
196
views
Smoothing out the sharp corners and jumps of a piecewise regression load-displacement curve in python
I am having a stubborn problem with smoothing out some sharp corners that the simulation software does not really like.
I have the following displacement/ load/ damage vs step/time:
The source data ...
- 2,784
0
votes
1
answer
85
views
error in stat_smooth because object is not found
I would like to use stat_smooth and the number of points at which to evaluate smoother should be the number of rows in the dataset.
data <- tibble(x=runif(20), y=runif(20))
data %>%
mutate(m=...
- 303