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fix: explicit Exception for combined fit constant edge case. (#202)
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2 changed files with 23 additions and 0 deletions
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@ -230,6 +230,12 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
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n_parms_ls.append(n_loc)
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n_parms = max(n_parms_ls)
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if len(key_ls) > 1:
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for key in key_ls:
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if np.asarray(yd[key]).shape != funcd[key](np.arange(n_parms), xd[key]).shape:
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raise ValueError(f"Fit function {key} returns the wrong shape ({funcd[key](np.arange(n_parms), xd[key]).shape} instead of {xd[key].shape})\nIf the fit function is just a constant you could try adding x*0 to get the correct shape.")
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if not silent:
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print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
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@ -1143,6 +1143,23 @@ def test_fit_dof():
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assert np.all(np.array(cd[1:]) > 0)
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def test_combined_fit_constant_shape():
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N1 = 16
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N2 = 10
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x = {"a": np.arange(N1),
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"": np.arange(N2)}
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y = {"a": [pe.pseudo_Obs(o + np.random.normal(0.0, 0.1), 0.1, "test") for o in range(N1)],
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"": [pe.pseudo_Obs(o + np.random.normal(0.0, 0.1), 0.1, "test") for o in range(N2)]}
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funcs = {"a": lambda a, x: a[0] + a[1] * x,
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"": lambda a, x: a[1]}
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with pytest.raises(ValueError):
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pe.fits.least_squares(x, y, funcs, method='migrad')
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funcs = {"a": lambda a, x: a[0] + a[1] * x,
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"": lambda a, x: a[1] + x * 0}
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pe.fits.least_squares(x, y, funcs, method='migrad')
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def fit_general(x, y, func, silent=False, **kwargs):
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"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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