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Alex Rivera
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I am trying to make a gaussian fit over many data points. E.g. I have a 256 x 262144 array of data. Where the 256 points need to be fitted to a gaussian distribution, and I need 262144 of them. Sometimes the peak of the gaussian distribution is outside the data-range, so to get an accurate mean result curve-fitting is the best approach. Even if the peak is inside the range, curve-fitting gives a better sigma because other data is not in the range. I have this working for one data point, using code from http://www.scipy.org/Cookbook/FittingData . I have tried to just repeat this algorithm, but it looks like it is going to take something in the order of 43 minutes to solve this. Is there an already-written fast way of doing this in parallel or more efficiently? from scipy import optimize from numpy import * import numpy # Fitting code taken from: http://www.scipy.org/Cookbook/FittingData class Parameter: def __init__(self, value): self.value = value
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