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add some typecasts
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e116e74257
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1 changed files with 9 additions and 6 deletions
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@ -776,7 +776,7 @@ def fit_lin(x: Sequence[Union[Obs, int, float]], y: Sequence[Obs], **kwargs) ->
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Returns
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-------
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fit_parameters : list[Obs]
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LIist of fitted observables.
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List of fitted observables.
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"""
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def f(a, x):
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@ -879,7 +879,10 @@ def error_band(x: list[int], func: Callable, beta: Union[Fit_result, list[Obs]])
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err : np.array(Obs)
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Error band for an array of sample values x
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"""
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cov = covariance(beta)
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if isinstance(beta, Fit_result):
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cov = covariance(np.array(beta.fit_parameters))
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else:
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cov = covariance(beta)
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if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
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warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
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@ -890,9 +893,9 @@ def error_band(x: list[int], func: Callable, beta: Union[Fit_result, list[Obs]])
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err = []
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for i, item in enumerate(x):
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err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
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err = np.array(err)
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err_array = np.array(err)
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return err
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return err_array
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def ks_test(objects: Optional[list[Fit_result]]=None):
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@ -916,7 +919,7 @@ def ks_test(objects: Optional[list[Fit_result]]=None):
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else:
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obs_list = objects
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p_values = [o.p_value for o in obs_list]
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p_values = np.asarray([o.p_value for o in obs_list])
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bins = len(p_values)
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x = np.arange(0, 1.001, 0.001)
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@ -928,7 +931,7 @@ def ks_test(objects: Optional[list[Fit_result]]=None):
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plt.title(str(bins) + ' p-values')
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n = np.arange(1, bins + 1) / np.float64(bins)
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Xs = np.sort(p_values)
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Xs: ndarray[float] = np.sort(p_values)
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plt.step(Xs, n)
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diffs = n - Xs
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loc_max_diff = np.argmax(np.abs(diffs))
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