A Function for multiple 2D Gaussians

In summary, the conversation revolves around creating a function that sums multiple 2D Gaussian functions in one graph. The function definition is formed based on research, but there is concern about the notation and placement of the variable i. It is suggested to use a symbol g for a single Gaussian and to put the index i next to the function name. The conversation also clarifies that f is a function of x and y, not a function of Gaussian. The final conclusion is to use the symbol g and index i for the function.
  • #1
ThunderLight
27
2
I'm trying to put together a function that sums multiple 2D Gaussian functions in one graph (i.e.; multiple Gaussian pyramids).

So far from my research, I formed the following function definition:
Screen Shot 2017-03-09 at 9.42.13 PM.png


I'm not sure if my research led me to the correct way of forming the function. Is this how Gaussians can be represented to give me a graph of multiple Gaussians in one surface?
 
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  • #2
Yes. Kind of a landscape pocked with lots of hills.
 
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  • #3
BvU said:
Yes. Kind of a landscape pocked with lots of hills.

Yes, that's what I'm looking for. But I'm worried about the notation, and placement of the i
Is it also correct for me to say ΣGaussians to represent how many hills, n? - Or there's a more formal notation for that?
 
  • #4
Yes, I would say ##n## is the count of the hills. You confuse the issue somewhat by using the symbol ##f## twice.
 
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  • #5
BvU said:
Yes, I would say ##n## is the count of the hills. You confuse the issue somewhat by using the symbol ##f## twice.

Would it be more proper/clearer to use only the first ##f## and go on directly to saying ##=... ## instead of redefining what ##f## function is?Thank you for your answer and affirmation.
 
Last edited:
  • #6
Either that, or you use a symbol g for a single gaussian:
upload_2017-3-9_13-57-54.png


I take it you want to pass ##f## to a surface plot program or something.
 
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  • #7
I would put the index i next to the function name: ##g_i(x,y)##

"f(gaussians)" doesn't make sense. f is a function of x and y: f(x,y).
 
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  • #8
BvU said:
Either that, or you use a symbol g for a single gaussian:
View attachment 114288

I take it you want to pass ##f## to a surface plot program or something.

Great, thank you for your tip.
 
  • #9
mfb said:
I would put the index i next to the function name: ##g_i(x,y)##

"f(gaussians)" doesn't make sense. f is a function of x and y: f(x,y).

I just realized that, thank you for pointing this out. You're right.
Thank you very much
 

1. What is a Function for multiple 2D Gaussians?

A Function for multiple 2D Gaussians is a mathematical function used to describe a 2D distribution of data points. It is commonly used in various fields of science, such as physics, chemistry, and data analysis.

2. How does a Function for multiple 2D Gaussians work?

A Function for multiple 2D Gaussians works by using a combination of multiple Gaussian functions to accurately describe the distribution of data points in a 2D plane. Each Gaussian function represents a peak or cluster of data points, and the sum of all the Gaussian functions creates a smooth, continuous distribution.

3. What are the applications of a Function for multiple 2D Gaussians?

A Function for multiple 2D Gaussians has various applications, such as image processing, pattern recognition, data fitting, and data smoothing. It is also commonly used in scientific research to analyze and interpret data from experiments.

4. How is a Function for multiple 2D Gaussians different from a single 2D Gaussian?

The main difference between a Function for multiple 2D Gaussians and a single 2D Gaussian is that the former uses a combination of multiple Gaussian functions, while the latter only uses one Gaussian function. This allows for a more accurate and precise description of the data distribution.

5. Can a Function for multiple 2D Gaussians be used for non-linear data?

Yes, a Function for multiple 2D Gaussians can be used for non-linear data. The multiple Gaussian functions can be adjusted to fit the data points in a non-linear manner, allowing for a better representation of the data. However, the accuracy of the function may depend on the complexity of the data distribution.

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