diff --git a/docs/pyerrors/fits.html b/docs/pyerrors/fits.html
index 4ce5a268..5d49a01e 100644
--- a/docs/pyerrors/fits.html
+++ b/docs/pyerrors/fits.html
@@ -662,187 +662,190 @@
559
560 fitp = fit_result.x
561 try:
-562 hess = jacobian(jacobian(chisqfunc))(fitp)
-563 except TypeError:
-564 raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
-565
-566 if kwargs.get('correlated_fit') is True:
-567 def chisqfunc_compact(d):
-568 model = func(d[:n_parms], x)
-569 chisq = anp.sum(anp.dot(chol_inv, (d[n_parms:] - model)) ** 2)
-570 return chisq
-571
-572 else:
-573 def chisqfunc_compact(d):
-574 model = func(d[:n_parms], x)
-575 chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
-576 return chisq
-577
-578 jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f)))
-579
-580 # Compute hess^{-1} @ jac_jac[:n_parms, n_parms:] using LAPACK dgesv
-581 try:
-582 deriv = -scipy.linalg.solve(hess, jac_jac[:n_parms, n_parms:])
-583 except np.linalg.LinAlgError:
-584 raise Exception("Cannot invert hessian matrix.")
-585
-586 result = []
-587 for i in range(n_parms):
-588 result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
-589
-590 output.fit_parameters = result
-591
-592 output.chisquare = chisquare
-593 output.dof = x.shape[-1] - n_parms
-594 output.p_value = 1 - chi2.cdf(output.chisquare, output.dof)
-595
-596 if kwargs.get('resplot') is True:
-597 residual_plot(x, y, func, result)
+562 if kwargs.get('correlated_fit') is True:
+563 hess = jacobian(jacobian(chisqfunc_corr))(fitp)
+564 else:
+565 hess = jacobian(jacobian(chisqfunc))(fitp)
+566 except TypeError:
+567 raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
+568
+569 if kwargs.get('correlated_fit') is True:
+570 def chisqfunc_compact(d):
+571 model = func(d[:n_parms], x)
+572 chisq = anp.sum(anp.dot(chol_inv, (d[n_parms:] - model)) ** 2)
+573 return chisq
+574
+575 else:
+576 def chisqfunc_compact(d):
+577 model = func(d[:n_parms], x)
+578 chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
+579 return chisq
+580
+581 jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f)))
+582
+583 # Compute hess^{-1} @ jac_jac[:n_parms, n_parms:] using LAPACK dgesv
+584 try:
+585 deriv = -scipy.linalg.solve(hess, jac_jac[:n_parms, n_parms:])
+586 except np.linalg.LinAlgError:
+587 raise Exception("Cannot invert hessian matrix.")
+588
+589 result = []
+590 for i in range(n_parms):
+591 result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
+592
+593 output.fit_parameters = result
+594
+595 output.chisquare = chisquare
+596 output.dof = x.shape[-1] - n_parms
+597 output.p_value = 1 - chi2.cdf(output.chisquare, output.dof)
598
-599 if kwargs.get('qqplot') is True:
-600 qqplot(x, y, func, result)
+599 if kwargs.get('resplot') is True:
+600 residual_plot(x, y, func, result)
601
-602 return output
-603
+602 if kwargs.get('qqplot') is True:
+603 qqplot(x, y, func, result)
604
-605def fit_lin(x, y, **kwargs):
-606 """Performs a linear fit to y = n + m * x and returns two Obs n, m.
+605 return output
+606
607
-608 Parameters
-609 ----------
-610 x : list
-611 Can either be a list of floats in which case no xerror is assumed, or
-612 a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
-613 y : list
-614 List of Obs, the dvalues of the Obs are used as yerror for the fit.
-615 """
-616
-617 def f(a, x):
-618 y = a[0] + a[1] * x
-619 return y
-620
-621 if all(isinstance(n, Obs) for n in x):
-622 out = total_least_squares(x, y, f, **kwargs)
-623 return out.fit_parameters
-624 elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
-625 out = least_squares(x, y, f, **kwargs)
+608def fit_lin(x, y, **kwargs):
+609 """Performs a linear fit to y = n + m * x and returns two Obs n, m.
+610
+611 Parameters
+612 ----------
+613 x : list
+614 Can either be a list of floats in which case no xerror is assumed, or
+615 a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
+616 y : list
+617 List of Obs, the dvalues of the Obs are used as yerror for the fit.
+618 """
+619
+620 def f(a, x):
+621 y = a[0] + a[1] * x
+622 return y
+623
+624 if all(isinstance(n, Obs) for n in x):
+625 out = total_least_squares(x, y, f, **kwargs)
626 return out.fit_parameters
-627 else:
-628 raise Exception('Unsupported types for x')
-629
-630
-631def qqplot(x, o_y, func, p):
-632 """Generates a quantile-quantile plot of the fit result which can be used to
-633 check if the residuals of the fit are gaussian distributed.
-634 """
-635
-636 residuals = []
-637 for i_x, i_y in zip(x, o_y):
-638 residuals.append((i_y - func(p, i_x)) / i_y.dvalue)
-639 residuals = sorted(residuals)
-640 my_y = [o.value for o in residuals]
-641 probplot = scipy.stats.probplot(my_y)
-642 my_x = probplot[0][0]
-643 plt.figure(figsize=(8, 8 / 1.618))
-644 plt.errorbar(my_x, my_y, fmt='o')
-645 fit_start = my_x[0]
-646 fit_stop = my_x[-1]
-647 samples = np.arange(fit_start, fit_stop, 0.01)
-648 plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
-649 plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
-650
-651 plt.xlabel('Theoretical quantiles')
-652 plt.ylabel('Ordered Values')
-653 plt.legend()
-654 plt.draw()
-655
-656
-657def residual_plot(x, y, func, fit_res):
-658 """ Generates a plot which compares the fit to the data and displays the corresponding residuals"""
-659 sorted_x = sorted(x)
-660 xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
-661 xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
-662 x_samples = np.arange(xstart, xstop + 0.01, 0.01)
-663
-664 plt.figure(figsize=(8, 8 / 1.618))
-665 gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
-666 ax0 = plt.subplot(gs[0])
-667 ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
-668 ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
-669 ax0.set_xticklabels([])
-670 ax0.set_xlim([xstart, xstop])
-671 ax0.set_xticklabels([])
-672 ax0.legend()
-673
-674 residuals = (np.asarray([o.value for o in y]) - func([o.value for o in fit_res], x)) / np.asarray([o.dvalue for o in y])
-675 ax1 = plt.subplot(gs[1])
-676 ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
-677 ax1.tick_params(direction='out')
-678 ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
-679 ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
-680 ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
-681 ax1.set_xlim([xstart, xstop])
-682 ax1.set_ylabel('Residuals')
-683 plt.subplots_adjust(wspace=None, hspace=None)
-684 plt.draw()
-685
-686
-687def error_band(x, func, beta):
-688 """Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
-689 cov = covariance(beta)
-690 if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
-691 warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
-692
-693 deriv = []
-694 for i, item in enumerate(x):
-695 deriv.append(np.array(egrad(func)([o.value for o in beta], item)))
-696
-697 err = []
-698 for i, item in enumerate(x):
-699 err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
-700 err = np.array(err)
-701
-702 return err
-703
+627 elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
+628 out = least_squares(x, y, f, **kwargs)
+629 return out.fit_parameters
+630 else:
+631 raise Exception('Unsupported types for x')
+632
+633
+634def qqplot(x, o_y, func, p):
+635 """Generates a quantile-quantile plot of the fit result which can be used to
+636 check if the residuals of the fit are gaussian distributed.
+637 """
+638
+639 residuals = []
+640 for i_x, i_y in zip(x, o_y):
+641 residuals.append((i_y - func(p, i_x)) / i_y.dvalue)
+642 residuals = sorted(residuals)
+643 my_y = [o.value for o in residuals]
+644 probplot = scipy.stats.probplot(my_y)
+645 my_x = probplot[0][0]
+646 plt.figure(figsize=(8, 8 / 1.618))
+647 plt.errorbar(my_x, my_y, fmt='o')
+648 fit_start = my_x[0]
+649 fit_stop = my_x[-1]
+650 samples = np.arange(fit_start, fit_stop, 0.01)
+651 plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
+652 plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
+653
+654 plt.xlabel('Theoretical quantiles')
+655 plt.ylabel('Ordered Values')
+656 plt.legend()
+657 plt.draw()
+658
+659
+660def residual_plot(x, y, func, fit_res):
+661 """ Generates a plot which compares the fit to the data and displays the corresponding residuals"""
+662 sorted_x = sorted(x)
+663 xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
+664 xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
+665 x_samples = np.arange(xstart, xstop + 0.01, 0.01)
+666
+667 plt.figure(figsize=(8, 8 / 1.618))
+668 gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
+669 ax0 = plt.subplot(gs[0])
+670 ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
+671 ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
+672 ax0.set_xticklabels([])
+673 ax0.set_xlim([xstart, xstop])
+674 ax0.set_xticklabels([])
+675 ax0.legend()
+676
+677 residuals = (np.asarray([o.value for o in y]) - func([o.value for o in fit_res], x)) / np.asarray([o.dvalue for o in y])
+678 ax1 = plt.subplot(gs[1])
+679 ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
+680 ax1.tick_params(direction='out')
+681 ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
+682 ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
+683 ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
+684 ax1.set_xlim([xstart, xstop])
+685 ax1.set_ylabel('Residuals')
+686 plt.subplots_adjust(wspace=None, hspace=None)
+687 plt.draw()
+688
+689
+690def error_band(x, func, beta):
+691 """Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
+692 cov = covariance(beta)
+693 if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
+694 warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
+695
+696 deriv = []
+697 for i, item in enumerate(x):
+698 deriv.append(np.array(egrad(func)([o.value for o in beta], item)))
+699
+700 err = []
+701 for i, item in enumerate(x):
+702 err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
+703 err = np.array(err)
704
-705def ks_test(objects=None):
-706 """Performs a Kolmogorov–Smirnov test for the p-values of all fit object.
+705 return err
+706
707
-708 Parameters
-709 ----------
-710 objects : list
-711 List of fit results to include in the analysis (optional).
-712 """
-713
-714 if objects is None:
-715 obs_list = []
-716 for obj in gc.get_objects():
-717 if isinstance(obj, Fit_result):
-718 obs_list.append(obj)
-719 else:
-720 obs_list = objects
-721
-722 p_values = [o.p_value for o in obs_list]
-723
-724 bins = len(p_values)
-725 x = np.arange(0, 1.001, 0.001)
-726 plt.plot(x, x, 'k', zorder=1)
-727 plt.xlim(0, 1)
-728 plt.ylim(0, 1)
-729 plt.xlabel('p-value')
-730 plt.ylabel('Cumulative probability')
-731 plt.title(str(bins) + ' p-values')
-732
-733 n = np.arange(1, bins + 1) / np.float64(bins)
-734 Xs = np.sort(p_values)
-735 plt.step(Xs, n)
-736 diffs = n - Xs
-737 loc_max_diff = np.argmax(np.abs(diffs))
-738 loc = Xs[loc_max_diff]
-739 plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
-740 plt.draw()
-741
-742 print(scipy.stats.kstest(p_values, 'uniform'))
+708def ks_test(objects=None):
+709 """Performs a Kolmogorov–Smirnov test for the p-values of all fit object.
+710
+711 Parameters
+712 ----------
+713 objects : list
+714 List of fit results to include in the analysis (optional).
+715 """
+716
+717 if objects is None:
+718 obs_list = []
+719 for obj in gc.get_objects():
+720 if isinstance(obj, Fit_result):
+721 obs_list.append(obj)
+722 else:
+723 obs_list = objects
+724
+725 p_values = [o.p_value for o in obs_list]
+726
+727 bins = len(p_values)
+728 x = np.arange(0, 1.001, 0.001)
+729 plt.plot(x, x, 'k', zorder=1)
+730 plt.xlim(0, 1)
+731 plt.ylim(0, 1)
+732 plt.xlabel('p-value')
+733 plt.ylabel('Cumulative probability')
+734 plt.title(str(bins) + ' p-values')
+735
+736 n = np.arange(1, bins + 1) / np.float64(bins)
+737 Xs = np.sort(p_values)
+738 plt.step(Xs, n)
+739 diffs = n - Xs
+740 loc_max_diff = np.argmax(np.abs(diffs))
+741 loc = Xs[loc_max_diff]
+742 plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
+743 plt.draw()
+744
+745 print(scipy.stats.kstest(p_values, 'uniform'))
@@ -1356,30 +1359,30 @@ has to be calculated (default False).
- 606def fit_lin(x, y, **kwargs):
-607 """Performs a linear fit to y = n + m * x and returns two Obs n, m.
-608
-609 Parameters
-610 ----------
-611 x : list
-612 Can either be a list of floats in which case no xerror is assumed, or
-613 a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
-614 y : list
-615 List of Obs, the dvalues of the Obs are used as yerror for the fit.
-616 """
-617
-618 def f(a, x):
-619 y = a[0] + a[1] * x
-620 return y
-621
-622 if all(isinstance(n, Obs) for n in x):
-623 out = total_least_squares(x, y, f, **kwargs)
-624 return out.fit_parameters
-625 elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
-626 out = least_squares(x, y, f, **kwargs)
+ 609def fit_lin(x, y, **kwargs):
+610 """Performs a linear fit to y = n + m * x and returns two Obs n, m.
+611
+612 Parameters
+613 ----------
+614 x : list
+615 Can either be a list of floats in which case no xerror is assumed, or
+616 a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
+617 y : list
+618 List of Obs, the dvalues of the Obs are used as yerror for the fit.
+619 """
+620
+621 def f(a, x):
+622 y = a[0] + a[1] * x
+623 return y
+624
+625 if all(isinstance(n, Obs) for n in x):
+626 out = total_least_squares(x, y, f, **kwargs)
627 return out.fit_parameters
-628 else:
-629 raise Exception('Unsupported types for x')
+628 elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
+629 out = least_squares(x, y, f, **kwargs)
+630 return out.fit_parameters
+631 else:
+632 raise Exception('Unsupported types for x')
632def qqplot(x, o_y, func, p):
-633 """Generates a quantile-quantile plot of the fit result which can be used to
-634 check if the residuals of the fit are gaussian distributed.
-635 """
-636
-637 residuals = []
-638 for i_x, i_y in zip(x, o_y):
-639 residuals.append((i_y - func(p, i_x)) / i_y.dvalue)
-640 residuals = sorted(residuals)
-641 my_y = [o.value for o in residuals]
-642 probplot = scipy.stats.probplot(my_y)
-643 my_x = probplot[0][0]
-644 plt.figure(figsize=(8, 8 / 1.618))
-645 plt.errorbar(my_x, my_y, fmt='o')
-646 fit_start = my_x[0]
-647 fit_stop = my_x[-1]
-648 samples = np.arange(fit_start, fit_stop, 0.01)
-649 plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
-650 plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
-651
-652 plt.xlabel('Theoretical quantiles')
-653 plt.ylabel('Ordered Values')
-654 plt.legend()
-655 plt.draw()
+ 635def qqplot(x, o_y, func, p):
+636 """Generates a quantile-quantile plot of the fit result which can be used to
+637 check if the residuals of the fit are gaussian distributed.
+638 """
+639
+640 residuals = []
+641 for i_x, i_y in zip(x, o_y):
+642 residuals.append((i_y - func(p, i_x)) / i_y.dvalue)
+643 residuals = sorted(residuals)
+644 my_y = [o.value for o in residuals]
+645 probplot = scipy.stats.probplot(my_y)
+646 my_x = probplot[0][0]
+647 plt.figure(figsize=(8, 8 / 1.618))
+648 plt.errorbar(my_x, my_y, fmt='o')
+649 fit_start = my_x[0]
+650 fit_stop = my_x[-1]
+651 samples = np.arange(fit_start, fit_stop, 0.01)
+652 plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
+653 plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
+654
+655 plt.xlabel('Theoretical quantiles')
+656 plt.ylabel('Ordered Values')
+657 plt.legend()
+658 plt.draw()
658def residual_plot(x, y, func, fit_res):
-659 """ Generates a plot which compares the fit to the data and displays the corresponding residuals"""
-660 sorted_x = sorted(x)
-661 xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
-662 xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
-663 x_samples = np.arange(xstart, xstop + 0.01, 0.01)
-664
-665 plt.figure(figsize=(8, 8 / 1.618))
-666 gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
-667 ax0 = plt.subplot(gs[0])
-668 ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
-669 ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
-670 ax0.set_xticklabels([])
-671 ax0.set_xlim([xstart, xstop])
-672 ax0.set_xticklabels([])
-673 ax0.legend()
-674
-675 residuals = (np.asarray([o.value for o in y]) - func([o.value for o in fit_res], x)) / np.asarray([o.dvalue for o in y])
-676 ax1 = plt.subplot(gs[1])
-677 ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
-678 ax1.tick_params(direction='out')
-679 ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
-680 ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
-681 ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
-682 ax1.set_xlim([xstart, xstop])
-683 ax1.set_ylabel('Residuals')
-684 plt.subplots_adjust(wspace=None, hspace=None)
-685 plt.draw()
+ 661def residual_plot(x, y, func, fit_res):
+662 """ Generates a plot which compares the fit to the data and displays the corresponding residuals"""
+663 sorted_x = sorted(x)
+664 xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
+665 xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
+666 x_samples = np.arange(xstart, xstop + 0.01, 0.01)
+667
+668 plt.figure(figsize=(8, 8 / 1.618))
+669 gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
+670 ax0 = plt.subplot(gs[0])
+671 ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
+672 ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
+673 ax0.set_xticklabels([])
+674 ax0.set_xlim([xstart, xstop])
+675 ax0.set_xticklabels([])
+676 ax0.legend()
+677
+678 residuals = (np.asarray([o.value for o in y]) - func([o.value for o in fit_res], x)) / np.asarray([o.dvalue for o in y])
+679 ax1 = plt.subplot(gs[1])
+680 ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
+681 ax1.tick_params(direction='out')
+682 ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
+683 ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
+684 ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
+685 ax1.set_xlim([xstart, xstop])
+686 ax1.set_ylabel('Residuals')
+687 plt.subplots_adjust(wspace=None, hspace=None)
+688 plt.draw()
688def error_band(x, func, beta):
-689 """Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
-690 cov = covariance(beta)
-691 if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
-692 warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
-693
-694 deriv = []
-695 for i, item in enumerate(x):
-696 deriv.append(np.array(egrad(func)([o.value for o in beta], item)))
-697
-698 err = []
-699 for i, item in enumerate(x):
-700 err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
-701 err = np.array(err)
-702
-703 return err
+ 691def error_band(x, func, beta):
+692 """Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
+693 cov = covariance(beta)
+694 if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
+695 warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
+696
+697 deriv = []
+698 for i, item in enumerate(x):
+699 deriv.append(np.array(egrad(func)([o.value for o in beta], item)))
+700
+701 err = []
+702 for i, item in enumerate(x):
+703 err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
+704 err = np.array(err)
+705
+706 return err
706def ks_test(objects=None):
-707 """Performs a Kolmogorov–Smirnov test for the p-values of all fit object.
-708
-709 Parameters
-710 ----------
-711 objects : list
-712 List of fit results to include in the analysis (optional).
-713 """
-714
-715 if objects is None:
-716 obs_list = []
-717 for obj in gc.get_objects():
-718 if isinstance(obj, Fit_result):
-719 obs_list.append(obj)
-720 else:
-721 obs_list = objects
-722
-723 p_values = [o.p_value for o in obs_list]
-724
-725 bins = len(p_values)
-726 x = np.arange(0, 1.001, 0.001)
-727 plt.plot(x, x, 'k', zorder=1)
-728 plt.xlim(0, 1)
-729 plt.ylim(0, 1)
-730 plt.xlabel('p-value')
-731 plt.ylabel('Cumulative probability')
-732 plt.title(str(bins) + ' p-values')
-733
-734 n = np.arange(1, bins + 1) / np.float64(bins)
-735 Xs = np.sort(p_values)
-736 plt.step(Xs, n)
-737 diffs = n - Xs
-738 loc_max_diff = np.argmax(np.abs(diffs))
-739 loc = Xs[loc_max_diff]
-740 plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
-741 plt.draw()
-742
-743 print(scipy.stats.kstest(p_values, 'uniform'))
+ 709def ks_test(objects=None):
+710 """Performs a Kolmogorov–Smirnov test for the p-values of all fit object.
+711
+712 Parameters
+713 ----------
+714 objects : list
+715 List of fit results to include in the analysis (optional).
+716 """
+717
+718 if objects is None:
+719 obs_list = []
+720 for obj in gc.get_objects():
+721 if isinstance(obj, Fit_result):
+722 obs_list.append(obj)
+723 else:
+724 obs_list = objects
+725
+726 p_values = [o.p_value for o in obs_list]
+727
+728 bins = len(p_values)
+729 x = np.arange(0, 1.001, 0.001)
+730 plt.plot(x, x, 'k', zorder=1)
+731 plt.xlim(0, 1)
+732 plt.ylim(0, 1)
+733 plt.xlabel('p-value')
+734 plt.ylabel('Cumulative probability')
+735 plt.title(str(bins) + ' p-values')
+736
+737 n = np.arange(1, bins + 1) / np.float64(bins)
+738 Xs = np.sort(p_values)
+739 plt.step(Xs, n)
+740 diffs = n - Xs
+741 loc_max_diff = np.argmax(np.abs(diffs))
+742 loc = Xs[loc_max_diff]
+743 plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
+744 plt.draw()
+745
+746 print(scipy.stats.kstest(p_values, 'uniform'))