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fitting (linear): fix indexing #42

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fitting (linear): fix indexing #42

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4 changes: 2 additions & 2 deletions oitg/fitting/line.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,8 @@
from . import FitBase

def parameter_initialiser(x, y, p):
k = (y[-1] - y[1]) / (x[-1] - x[1])
p['a'] = y[1] - x[1] * k
k = (y[-1] - y[0]) / (x[-1] - x[0])
p['a'] = y[0] - x[0] * k
Comment on lines +7 to +8
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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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@pathfinder49 pathfinder49 Jul 2, 2022 •
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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...

p['b'] = k

def fitting_function(x, p):
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