smoothing

I have several data points which seem suitable for fitting a spline through them. When I do this, I get a rather bumpy fit, like overfitting, which is not what I understand as smoothing. Is there a special option / parameter for getting back the function of a really smooth spline like here . The usage of the penalty parameter for smooth.spline didn't have any visible effect. Maybe I did it wrong? Here are data and code: results <- structure( list( beta = c( 0.983790622281964, 0.645152464354322, 0.924104713597375, 0.657703886566088, 0.788138034115623, 0.801080207252363, 1, 0.858337365965949, 0

I am doing research on line generalization, which will be applied to obtain generalized Road Network map from large scale map to small scale map. I am using two operation and two algorithms. It is done in python programming language using shapefile library, it is for vector data in 2d. Operation: Selection and Elimination. For selection I am using condition like, all the roads, width more than 7 meters selected, it is connected with attribute features of the roads. Same with elimination, like all the roads, width less than 5 meter, eliminated. So far it was no much problem. After selection and

I wanted to try to write a simple function to smooth an inputted image. I was trying to do this using the Image and numpy libraries. I was thinking that using a convolution mask would be an approach to this problem and I know numpy has a convolve function build in. How can I use the numpy.convolve to smooth an image? Nice question! tcaswell post here is a great suggestion, but you will not learn much this way because scipy is doing all the work for you! Since your question said you wanted to try and write the function , I will show you a bit more crude and basic kind of way to do it all

I am trying to filter/smooth signal obtained from a pressure transducer of sampling frequency 50 kHz. A sample signal is shown below: I would like to obtain a smooth signal obtained by loess in MATLAB (I am not plotting the same data, values are different). I calculated the power spectral density using matplotlib's psd() function and the power spectral density is also provided below: I have tried using the following code and obtained a filtered signal: import csv import numpy as np import matplotlib.pyplot as plt import scipy as sp from scipy.signal import butter, lfilter, freqz def butter

I am working on creating a contour plot using Matplotlib. I have all of the data in an array that is multidimensional. It is 12 long about 2000 wide. So it is basically a list of 12 lists that are 2000 in length. I have the contour plot working fine, but I need to smooth the data. I have read a lot of examples. Unfortunately, I don't have the math background to understand what is going on with them. So, how can I smooth this data? I have an example of what my graph looks like and what I want it to look more like. This is my graph: What I want it to look more similar too: What means do I have