Find local extremes and flat intermediate values with average values

How do I find the location of the local extreme values of a large data set, and change the values between each two extreme values by their average value?

Answer 1

[Image Analyst]

Try this

data = randi(100, 1, 40)
minValue = min(data(:))
maxValue = max(data(:))
meanValue = mean(data(:))
linearIndex1 = find(data(:) == minValue) % Find all occurences of the min value.
linearIndex2 = find(data(:) == maxValue) % Find all occurences of the max value.
allIndexes = sort([linearIndex1; linearIndex2], 'ascend')
data(allIndexes(1) : allIndexes(end)) = meanValue

[Humberto Munoz]

Thank you for the code. I need to give more details of my goals in this project: The data set has two columns, the Resistivity (OHMM) as a function of Depth (FT). The resistivity has many local extremes followed by small deviations over no equally-spaced intervals. I need to determine the location of the peaks in the data set, and transform the function in a step function using the average value over each subinterval (see an example below).

EX: Data = {(1,2),(2,2.1),(3,1.8),(4,8), (5, 8.01), (6,8.2), (7,5), (8,5.2), (9,15), (10,14.8), (11,15.01), (12, 14.9)}

New Data = {(1,1.97),(2,1.97),(3,1.97),(4,8.07), (5, 8.07), (6,8.07), (7,5.15), (8,5.15), (9,14.95), (10,14.95), (11,14.95), (12, 14.95)}

For the local extremes and their location I used successfully the function findpeaks.

For the transformation of the resistivity values, I need to use the meanValue of the values close to each local extreme. I need to find the number of values close to each local extreme. I will thank any help you can give me.

Humberto