fitting (linear): fix indexing #42
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The linear fitting function used 1-indexed array notation instead of the Python-default 0-indexing. We discovered this by ending up with a divide-by-zero error when our x-values were roughly set to [0, 1], so the two 1 values were subtracted from each other.
drewrisinger
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Jul 1, 2022
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| k = (y[-1] - y[0]) / (x[-1] - x[0]) | ||
| p['a'] = y[0] - x[0] * k |
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I would also accept np.max(y)- np.min(y) / (np.max(x) - np.min(x). The existing code assumes that the x & y arrays are sorted, not necessarily true.
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Good catch. None of the fitting routines should assume sorted data, so the max/min implementation is more appropriate.
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Another thought:
p['b'] = (np.max(y) -np.min(y)) / (np.max(x) - np.min(x))
idx = np.argmin(x)
p['a'] = y[idx] - x[idx] * p['b']IIRC this estimates the offset with the user specified gradient (if supplied). Not that it should matter much for fitting a line...
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The linear fitting function used 1-indexed array notation instead of the Python-default 0-indexing. We discovered this by ending up with a divide-by-zero error when our x-values were roughly set to [0, 1], so the two 1 values were subtracted from each other.