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feat: stdout of fit routines improved
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1 changed files with 5 additions and 5 deletions
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@ -198,9 +198,9 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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n_parms = i
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if not silent:
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print('Fit with', n_parms, 'parameters')
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print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
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if(len(const_par) > 0):
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print('\t and %d constrained parameter%s' % (len(const_par), 's' if len(const_par) > 1 else ''), const_par)
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print('and %d constrained parameter%s' % (len(const_par), 's' if len(const_par) > 1 else ''), const_par)
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x_f = np.vectorize(lambda o: o.value)(x)
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dx_f = np.vectorize(lambda o: o.dvalue)(x)
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@ -362,7 +362,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
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output.priors = loc_priors
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if not silent:
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print('Fit with', n_parms, 'parameters')
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print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
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y_f = [o.value for o in y]
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dy_f = [o.dvalue for o in y]
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@ -482,9 +482,9 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
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n_parms = i
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if not silent:
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print('Fit with', n_parms, 'parameters')
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print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
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if(len(const_par) > 0):
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print('\t and %d constrained parameter%s' % (len(const_par), 's' if len(const_par) > 1 else ''), const_par)
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print('and %d constrained parameter%s' % (len(const_par), 's' if len(const_par) > 1 else ''), const_par)
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y_f = [o.value for o in y]
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dy_f = [o.dvalue for o in y]
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