'Easy way to cluster entries in a numpy array with a condition
Background: I have a numpy array of float entries. This is basically a set of observations of something, suppose temperature measured during 24 hours. Imagine that one who records the temperature is not available for the entire day, instead he/she takes few (say 5) readings during an hour and again after few hours, takes reading (say 8 times). All the measurements he/she puts in a single np.array and has handed over to me!
Problem: I have no idea when the readings were taken. So I decide to cluster the observations in the following way: maybe, first recognize local peaks in the array and all entries that are close enough (chosen tolerance, say 1 deg) are grouped together, meaning, I want to split the array into a list of sub-arrays. Note, any entry should belong to exactly one group.
One possible approach: First, sort the array, then split it into sub-arrays with two conditions: (1) Difference between the first and last entries is not more than 1 deg, (2) Difference between the last entry of a sub-array and the first entry of the next sub-array is greater than 1 deg. How can I achieve this fast (numpy way)?
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