diff --git a/docs/pyerrors/correlators.html b/docs/pyerrors/correlators.html index b88bb5ac..f592e2b4 100644 --- a/docs/pyerrors/correlators.html +++ b/docs/pyerrors/correlators.html @@ -915,7 +915,7 @@ 702 self.prange = prange 703 return 704 - 705 def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=None, fit_res=None, ylabel=None, save=None, auto_gamma=False): + 705 def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=None, fit_res=None, ylabel=None, save=None, auto_gamma=False, hide_sigma=None): 706 """Plots the correlator using the tag of the correlator as label if available. 707 708 Parameters @@ -937,476 +937,486 @@ 724 path to file in which the figure should be saved 725 auto_gamma : bool 726 Apply the gamma method with standard parameters to all correlators and plateau values before plotting. - 727 """ - 728 if self.N != 1: - 729 raise Exception("Correlator must be projected before plotting") - 730 - 731 if auto_gamma: - 732 self.gamma_method() - 733 - 734 if x_range is None: - 735 x_range = [0, self.T - 1] - 736 - 737 fig = plt.figure() - 738 ax1 = fig.add_subplot(111) - 739 - 740 x, y, y_err = self.plottable() - 741 ax1.errorbar(x, y, y_err, label=self.tag) - 742 if logscale: - 743 ax1.set_yscale('log') - 744 else: - 745 if y_range is None: - 746 try: - 747 y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) - 748 y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) - 749 ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)]) - 750 except Exception: - 751 pass - 752 else: - 753 ax1.set_ylim(y_range) - 754 if comp: - 755 if isinstance(comp, (Corr, list)): - 756 for corr in comp if isinstance(comp, list) else [comp]: - 757 if auto_gamma: - 758 corr.gamma_method() - 759 x, y, y_err = corr.plottable() - 760 plt.errorbar(x, y, y_err, label=corr.tag, mfc=plt.rcParams['axes.facecolor']) - 761 else: - 762 raise Exception("'comp' must be a correlator or a list of correlators.") - 763 - 764 if plateau: - 765 if isinstance(plateau, Obs): - 766 if auto_gamma: - 767 plateau.gamma_method() - 768 ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau)) - 769 ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-') - 770 else: - 771 raise Exception("'plateau' must be an Obs") - 772 if self.prange: - 773 ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',') - 774 ax1.axvline(self.prange[1], 0, 1, ls='-', marker=',') - 775 - 776 if fit_res: - 777 x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05) - 778 ax1.plot(x_samples, - 779 fit_res.fit_function([o.value for o in fit_res.fit_parameters], x_samples), - 780 ls='-', marker=',', lw=2) - 781 - 782 ax1.set_xlabel(r'$x_0 / a$') - 783 if ylabel: - 784 ax1.set_ylabel(ylabel) - 785 ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5]) - 786 - 787 handles, labels = ax1.get_legend_handles_labels() - 788 if labels: - 789 ax1.legend() - 790 plt.draw() + 727 hide_sigma : float + 728 Hides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors. + 729 """ + 730 if self.N != 1: + 731 raise Exception("Correlator must be projected before plotting") + 732 + 733 if auto_gamma: + 734 self.gamma_method() + 735 + 736 if x_range is None: + 737 x_range = [0, self.T - 1] + 738 + 739 fig = plt.figure() + 740 ax1 = fig.add_subplot(111) + 741 + 742 x, y, y_err = self.plottable() + 743 if hide_sigma: + 744 hide_from = np.argmax((hide_sigma * np.array(y_err)) > np.abs(y)) - 1 + 745 else: + 746 hide_from = None + 747 ax1.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=self.tag) + 748 if logscale: + 749 ax1.set_yscale('log') + 750 else: + 751 if y_range is None: + 752 try: + 753 y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) + 754 y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) + 755 ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)]) + 756 except Exception: + 757 pass + 758 else: + 759 ax1.set_ylim(y_range) + 760 if comp: + 761 if isinstance(comp, (Corr, list)): + 762 for corr in comp if isinstance(comp, list) else [comp]: + 763 if auto_gamma: + 764 corr.gamma_method() + 765 x, y, y_err = corr.plottable() + 766 if hide_sigma: + 767 hide_from = np.argmax((hide_sigma * np.array(y_err)) > np.abs(y)) - 1 + 768 else: + 769 hide_from = None + 770 plt.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=corr.tag, mfc=plt.rcParams['axes.facecolor']) + 771 else: + 772 raise Exception("'comp' must be a correlator or a list of correlators.") + 773 + 774 if plateau: + 775 if isinstance(plateau, Obs): + 776 if auto_gamma: + 777 plateau.gamma_method() + 778 ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau)) + 779 ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-') + 780 else: + 781 raise Exception("'plateau' must be an Obs") + 782 if self.prange: + 783 ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',') + 784 ax1.axvline(self.prange[1], 0, 1, ls='-', marker=',') + 785 + 786 if fit_res: + 787 x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05) + 788 ax1.plot(x_samples, + 789 fit_res.fit_function([o.value for o in fit_res.fit_parameters], x_samples), + 790 ls='-', marker=',', lw=2) 791 - 792 if save: - 793 if isinstance(save, str): - 794 fig.savefig(save) - 795 else: - 796 raise Exception("'save' has to be a string.") - 797 - 798 def spaghetti_plot(self, logscale=True): - 799 """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations. - 800 - 801 Parameters - 802 ---------- - 803 logscale : bool - 804 Determines whether the scale of the y-axis is logarithmic or standard. - 805 """ - 806 if self.N != 1: - 807 raise Exception("Correlator needs to be projected first.") - 808 - 809 mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist])) - 810 x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None] - 811 - 812 for name in mc_names: - 813 data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T - 814 - 815 fig = plt.figure() - 816 ax = fig.add_subplot(111) - 817 for dat in data: - 818 ax.plot(x0_vals, dat, ls='-', marker='') - 819 - 820 if logscale is True: - 821 ax.set_yscale('log') - 822 - 823 ax.set_xlabel(r'$x_0 / a$') - 824 plt.title(name) - 825 plt.draw() - 826 - 827 def dump(self, filename, datatype="json.gz", **kwargs): - 828 """Dumps the Corr into a file of chosen type - 829 Parameters - 830 ---------- - 831 filename : str - 832 Name of the file to be saved. - 833 datatype : str - 834 Format of the exported file. Supported formats include - 835 "json.gz" and "pickle" - 836 path : str - 837 specifies a custom path for the file (default '.') - 838 """ - 839 if datatype == "json.gz": - 840 from .input.json import dump_to_json - 841 if 'path' in kwargs: - 842 file_name = kwargs.get('path') + '/' + filename - 843 else: - 844 file_name = filename - 845 dump_to_json(self, file_name) - 846 elif datatype == "pickle": - 847 dump_object(self, filename, **kwargs) - 848 else: - 849 raise Exception("Unknown datatype " + str(datatype)) - 850 - 851 def print(self, range=[0, None]): - 852 print(self.__repr__(range)) - 853 - 854 def __repr__(self, range=[0, None]): - 855 content_string = "" - 856 - 857 content_string += "Corr T=" + str(self.T) + " N=" + str(self.N) + "\n" # +" filled with"+ str(type(self.content[0][0])) there should be a good solution here - 858 - 859 if self.tag is not None: - 860 content_string += "Description: " + self.tag + "\n" - 861 if self.N != 1: - 862 return content_string + 792 ax1.set_xlabel(r'$x_0 / a$') + 793 if ylabel: + 794 ax1.set_ylabel(ylabel) + 795 ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5]) + 796 + 797 handles, labels = ax1.get_legend_handles_labels() + 798 if labels: + 799 ax1.legend() + 800 plt.draw() + 801 + 802 if save: + 803 if isinstance(save, str): + 804 fig.savefig(save) + 805 else: + 806 raise Exception("'save' has to be a string.") + 807 + 808 def spaghetti_plot(self, logscale=True): + 809 """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations. + 810 + 811 Parameters + 812 ---------- + 813 logscale : bool + 814 Determines whether the scale of the y-axis is logarithmic or standard. + 815 """ + 816 if self.N != 1: + 817 raise Exception("Correlator needs to be projected first.") + 818 + 819 mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist])) + 820 x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None] + 821 + 822 for name in mc_names: + 823 data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T + 824 + 825 fig = plt.figure() + 826 ax = fig.add_subplot(111) + 827 for dat in data: + 828 ax.plot(x0_vals, dat, ls='-', marker='') + 829 + 830 if logscale is True: + 831 ax.set_yscale('log') + 832 + 833 ax.set_xlabel(r'$x_0 / a$') + 834 plt.title(name) + 835 plt.draw() + 836 + 837 def dump(self, filename, datatype="json.gz", **kwargs): + 838 """Dumps the Corr into a file of chosen type + 839 Parameters + 840 ---------- + 841 filename : str + 842 Name of the file to be saved. + 843 datatype : str + 844 Format of the exported file. Supported formats include + 845 "json.gz" and "pickle" + 846 path : str + 847 specifies a custom path for the file (default '.') + 848 """ + 849 if datatype == "json.gz": + 850 from .input.json import dump_to_json + 851 if 'path' in kwargs: + 852 file_name = kwargs.get('path') + '/' + filename + 853 else: + 854 file_name = filename + 855 dump_to_json(self, file_name) + 856 elif datatype == "pickle": + 857 dump_object(self, filename, **kwargs) + 858 else: + 859 raise Exception("Unknown datatype " + str(datatype)) + 860 + 861 def print(self, range=[0, None]): + 862 print(self.__repr__(range)) 863 - 864 if range[1]: - 865 range[1] += 1 - 866 content_string += 'x0/a\tCorr(x0/a)\n------------------\n' - 867 for i, sub_corr in enumerate(self.content[range[0]:range[1]]): - 868 if sub_corr is None: - 869 content_string += str(i + range[0]) + '\n' - 870 else: - 871 content_string += str(i + range[0]) - 872 for element in sub_corr: - 873 content_string += '\t' + ' ' * int(element >= 0) + str(element) - 874 content_string += '\n' - 875 return content_string - 876 - 877 def __str__(self): - 878 return self.__repr__() - 879 - 880 # We define the basic operations, that can be performed with correlators. - 881 # While */+- get defined here, they only work for Corr*Obs and not Obs*Corr. - 882 # This is because Obs*Corr checks Obs.__mul__ first and does not catch an exception. - 883 # One could try and tell Obs to check if the y in __mul__ is a Corr and - 884 - 885 def __add__(self, y): - 886 if isinstance(y, Corr): - 887 if ((self.N != y.N) or (self.T != y.T)): - 888 raise Exception("Addition of Corrs with different shape") - 889 newcontent = [] - 890 for t in range(self.T): - 891 if (self.content[t] is None) or (y.content[t] is None): - 892 newcontent.append(None) - 893 else: - 894 newcontent.append(self.content[t] + y.content[t]) - 895 return Corr(newcontent) - 896 - 897 elif isinstance(y, (Obs, int, float, CObs)): - 898 newcontent = [] - 899 for t in range(self.T): - 900 if (self.content[t] is None): - 901 newcontent.append(None) - 902 else: - 903 newcontent.append(self.content[t] + y) - 904 return Corr(newcontent, prange=self.prange) - 905 elif isinstance(y, np.ndarray): - 906 if y.shape == (self.T,): - 907 return Corr(list((np.array(self.content).T + y).T)) - 908 else: - 909 raise ValueError("operands could not be broadcast together") - 910 else: - 911 raise TypeError("Corr + wrong type") - 912 - 913 def __mul__(self, y): - 914 if isinstance(y, Corr): - 915 if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): - 916 raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") - 917 newcontent = [] - 918 for t in range(self.T): - 919 if (self.content[t] is None) or (y.content[t] is None): - 920 newcontent.append(None) - 921 else: - 922 newcontent.append(self.content[t] * y.content[t]) - 923 return Corr(newcontent) - 924 - 925 elif isinstance(y, (Obs, int, float, CObs)): - 926 newcontent = [] - 927 for t in range(self.T): - 928 if (self.content[t] is None): - 929 newcontent.append(None) - 930 else: - 931 newcontent.append(self.content[t] * y) - 932 return Corr(newcontent, prange=self.prange) - 933 elif isinstance(y, np.ndarray): - 934 if y.shape == (self.T,): - 935 return Corr(list((np.array(self.content).T * y).T)) - 936 else: - 937 raise ValueError("operands could not be broadcast together") - 938 else: - 939 raise TypeError("Corr * wrong type") - 940 - 941 def __truediv__(self, y): - 942 if isinstance(y, Corr): - 943 if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): - 944 raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") - 945 newcontent = [] - 946 for t in range(self.T): - 947 if (self.content[t] is None) or (y.content[t] is None): - 948 newcontent.append(None) - 949 else: - 950 newcontent.append(self.content[t] / y.content[t]) - 951 for t in range(self.T): - 952 if newcontent[t] is None: - 953 continue - 954 if np.isnan(np.sum(newcontent[t]).value): - 955 newcontent[t] = None - 956 - 957 if all([item is None for item in newcontent]): - 958 raise Exception("Division returns completely undefined correlator") - 959 return Corr(newcontent) - 960 - 961 elif isinstance(y, (Obs, CObs)): - 962 if isinstance(y, Obs): - 963 if y.value == 0: - 964 raise Exception('Division by zero will return undefined correlator') - 965 if isinstance(y, CObs): - 966 if y.is_zero(): - 967 raise Exception('Division by zero will return undefined correlator') - 968 - 969 newcontent = [] - 970 for t in range(self.T): - 971 if (self.content[t] is None): - 972 newcontent.append(None) - 973 else: - 974 newcontent.append(self.content[t] / y) - 975 return Corr(newcontent, prange=self.prange) - 976 - 977 elif isinstance(y, (int, float)): - 978 if y == 0: - 979 raise Exception('Division by zero will return undefined correlator') - 980 newcontent = [] - 981 for t in range(self.T): - 982 if (self.content[t] is None): - 983 newcontent.append(None) - 984 else: - 985 newcontent.append(self.content[t] / y) - 986 return Corr(newcontent, prange=self.prange) - 987 elif isinstance(y, np.ndarray): - 988 if y.shape == (self.T,): - 989 return Corr(list((np.array(self.content).T / y).T)) - 990 else: - 991 raise ValueError("operands could not be broadcast together") - 992 else: - 993 raise TypeError('Corr / wrong type') - 994 - 995 def __neg__(self): - 996 newcontent = [None if (item is None) else -1. * item for item in self.content] - 997 return Corr(newcontent, prange=self.prange) - 998 - 999 def __sub__(self, y): -1000 return self + (-y) -1001 -1002 def __pow__(self, y): -1003 if isinstance(y, (Obs, int, float, CObs)): -1004 newcontent = [None if (item is None) else item**y for item in self.content] -1005 return Corr(newcontent, prange=self.prange) -1006 else: -1007 raise TypeError('Type of exponent not supported') + 864 def __repr__(self, range=[0, None]): + 865 content_string = "" + 866 + 867 content_string += "Corr T=" + str(self.T) + " N=" + str(self.N) + "\n" # +" filled with"+ str(type(self.content[0][0])) there should be a good solution here + 868 + 869 if self.tag is not None: + 870 content_string += "Description: " + self.tag + "\n" + 871 if self.N != 1: + 872 return content_string + 873 + 874 if range[1]: + 875 range[1] += 1 + 876 content_string += 'x0/a\tCorr(x0/a)\n------------------\n' + 877 for i, sub_corr in enumerate(self.content[range[0]:range[1]]): + 878 if sub_corr is None: + 879 content_string += str(i + range[0]) + '\n' + 880 else: + 881 content_string += str(i + range[0]) + 882 for element in sub_corr: + 883 content_string += '\t' + ' ' * int(element >= 0) + str(element) + 884 content_string += '\n' + 885 return content_string + 886 + 887 def __str__(self): + 888 return self.__repr__() + 889 + 890 # We define the basic operations, that can be performed with correlators. + 891 # While */+- get defined here, they only work for Corr*Obs and not Obs*Corr. + 892 # This is because Obs*Corr checks Obs.__mul__ first and does not catch an exception. + 893 # One could try and tell Obs to check if the y in __mul__ is a Corr and + 894 + 895 def __add__(self, y): + 896 if isinstance(y, Corr): + 897 if ((self.N != y.N) or (self.T != y.T)): + 898 raise Exception("Addition of Corrs with different shape") + 899 newcontent = [] + 900 for t in range(self.T): + 901 if (self.content[t] is None) or (y.content[t] is None): + 902 newcontent.append(None) + 903 else: + 904 newcontent.append(self.content[t] + y.content[t]) + 905 return Corr(newcontent) + 906 + 907 elif isinstance(y, (Obs, int, float, CObs)): + 908 newcontent = [] + 909 for t in range(self.T): + 910 if (self.content[t] is None): + 911 newcontent.append(None) + 912 else: + 913 newcontent.append(self.content[t] + y) + 914 return Corr(newcontent, prange=self.prange) + 915 elif isinstance(y, np.ndarray): + 916 if y.shape == (self.T,): + 917 return Corr(list((np.array(self.content).T + y).T)) + 918 else: + 919 raise ValueError("operands could not be broadcast together") + 920 else: + 921 raise TypeError("Corr + wrong type") + 922 + 923 def __mul__(self, y): + 924 if isinstance(y, Corr): + 925 if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): + 926 raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") + 927 newcontent = [] + 928 for t in range(self.T): + 929 if (self.content[t] is None) or (y.content[t] is None): + 930 newcontent.append(None) + 931 else: + 932 newcontent.append(self.content[t] * y.content[t]) + 933 return Corr(newcontent) + 934 + 935 elif isinstance(y, (Obs, int, float, CObs)): + 936 newcontent = [] + 937 for t in range(self.T): + 938 if (self.content[t] is None): + 939 newcontent.append(None) + 940 else: + 941 newcontent.append(self.content[t] * y) + 942 return Corr(newcontent, prange=self.prange) + 943 elif isinstance(y, np.ndarray): + 944 if y.shape == (self.T,): + 945 return Corr(list((np.array(self.content).T * y).T)) + 946 else: + 947 raise ValueError("operands could not be broadcast together") + 948 else: + 949 raise TypeError("Corr * wrong type") + 950 + 951 def __truediv__(self, y): + 952 if isinstance(y, Corr): + 953 if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): + 954 raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") + 955 newcontent = [] + 956 for t in range(self.T): + 957 if (self.content[t] is None) or (y.content[t] is None): + 958 newcontent.append(None) + 959 else: + 960 newcontent.append(self.content[t] / y.content[t]) + 961 for t in range(self.T): + 962 if newcontent[t] is None: + 963 continue + 964 if np.isnan(np.sum(newcontent[t]).value): + 965 newcontent[t] = None + 966 + 967 if all([item is None for item in newcontent]): + 968 raise Exception("Division returns completely undefined correlator") + 969 return Corr(newcontent) + 970 + 971 elif isinstance(y, (Obs, CObs)): + 972 if isinstance(y, Obs): + 973 if y.value == 0: + 974 raise Exception('Division by zero will return undefined correlator') + 975 if isinstance(y, CObs): + 976 if y.is_zero(): + 977 raise Exception('Division by zero will return undefined correlator') + 978 + 979 newcontent = [] + 980 for t in range(self.T): + 981 if (self.content[t] is None): + 982 newcontent.append(None) + 983 else: + 984 newcontent.append(self.content[t] / y) + 985 return Corr(newcontent, prange=self.prange) + 986 + 987 elif isinstance(y, (int, float)): + 988 if y == 0: + 989 raise Exception('Division by zero will return undefined correlator') + 990 newcontent = [] + 991 for t in range(self.T): + 992 if (self.content[t] is None): + 993 newcontent.append(None) + 994 else: + 995 newcontent.append(self.content[t] / y) + 996 return Corr(newcontent, prange=self.prange) + 997 elif isinstance(y, np.ndarray): + 998 if y.shape == (self.T,): + 999 return Corr(list((np.array(self.content).T / y).T)) +1000 else: +1001 raise ValueError("operands could not be broadcast together") +1002 else: +1003 raise TypeError('Corr / wrong type') +1004 +1005 def __neg__(self): +1006 newcontent = [None if (item is None) else -1. * item for item in self.content] +1007 return Corr(newcontent, prange=self.prange) 1008 -1009 def __abs__(self): -1010 newcontent = [None if (item is None) else np.abs(item) for item in self.content] -1011 return Corr(newcontent, prange=self.prange) -1012 -1013 # The numpy functions: -1014 def sqrt(self): -1015 return self**0.5 -1016 -1017 def log(self): -1018 newcontent = [None if (item is None) else np.log(item) for item in self.content] -1019 return Corr(newcontent, prange=self.prange) -1020 -1021 def exp(self): -1022 newcontent = [None if (item is None) else np.exp(item) for item in self.content] -1023 return Corr(newcontent, prange=self.prange) -1024 -1025 def _apply_func_to_corr(self, func): -1026 newcontent = [None if (item is None) else func(item) for item in self.content] -1027 for t in range(self.T): -1028 if newcontent[t] is None: -1029 continue -1030 if np.isnan(np.sum(newcontent[t]).value): -1031 newcontent[t] = None -1032 if all([item is None for item in newcontent]): -1033 raise Exception('Operation returns undefined correlator') -1034 return Corr(newcontent) -1035 -1036 def sin(self): -1037 return self._apply_func_to_corr(np.sin) -1038 -1039 def cos(self): -1040 return self._apply_func_to_corr(np.cos) -1041 -1042 def tan(self): -1043 return self._apply_func_to_corr(np.tan) -1044 -1045 def sinh(self): -1046 return self._apply_func_to_corr(np.sinh) -1047 -1048 def cosh(self): -1049 return self._apply_func_to_corr(np.cosh) -1050 -1051 def tanh(self): -1052 return self._apply_func_to_corr(np.tanh) -1053 -1054 def arcsin(self): -1055 return self._apply_func_to_corr(np.arcsin) -1056 -1057 def arccos(self): -1058 return self._apply_func_to_corr(np.arccos) -1059 -1060 def arctan(self): -1061 return self._apply_func_to_corr(np.arctan) -1062 -1063 def arcsinh(self): -1064 return self._apply_func_to_corr(np.arcsinh) -1065 -1066 def arccosh(self): -1067 return self._apply_func_to_corr(np.arccosh) -1068 -1069 def arctanh(self): -1070 return self._apply_func_to_corr(np.arctanh) -1071 -1072 # Right hand side operations (require tweak in main module to work) -1073 def __radd__(self, y): -1074 return self + y +1009 def __sub__(self, y): +1010 return self + (-y) +1011 +1012 def __pow__(self, y): +1013 if isinstance(y, (Obs, int, float, CObs)): +1014 newcontent = [None if (item is None) else item**y for item in self.content] +1015 return Corr(newcontent, prange=self.prange) +1016 else: +1017 raise TypeError('Type of exponent not supported') +1018 +1019 def __abs__(self): +1020 newcontent = [None if (item is None) else np.abs(item) for item in self.content] +1021 return Corr(newcontent, prange=self.prange) +1022 +1023 # The numpy functions: +1024 def sqrt(self): +1025 return self**0.5 +1026 +1027 def log(self): +1028 newcontent = [None if (item is None) else np.log(item) for item in self.content] +1029 return Corr(newcontent, prange=self.prange) +1030 +1031 def exp(self): +1032 newcontent = [None if (item is None) else np.exp(item) for item in self.content] +1033 return Corr(newcontent, prange=self.prange) +1034 +1035 def _apply_func_to_corr(self, func): +1036 newcontent = [None if (item is None) else func(item) for item in self.content] +1037 for t in range(self.T): +1038 if newcontent[t] is None: +1039 continue +1040 if np.isnan(np.sum(newcontent[t]).value): +1041 newcontent[t] = None +1042 if all([item is None for item in newcontent]): +1043 raise Exception('Operation returns undefined correlator') +1044 return Corr(newcontent) +1045 +1046 def sin(self): +1047 return self._apply_func_to_corr(np.sin) +1048 +1049 def cos(self): +1050 return self._apply_func_to_corr(np.cos) +1051 +1052 def tan(self): +1053 return self._apply_func_to_corr(np.tan) +1054 +1055 def sinh(self): +1056 return self._apply_func_to_corr(np.sinh) +1057 +1058 def cosh(self): +1059 return self._apply_func_to_corr(np.cosh) +1060 +1061 def tanh(self): +1062 return self._apply_func_to_corr(np.tanh) +1063 +1064 def arcsin(self): +1065 return self._apply_func_to_corr(np.arcsin) +1066 +1067 def arccos(self): +1068 return self._apply_func_to_corr(np.arccos) +1069 +1070 def arctan(self): +1071 return self._apply_func_to_corr(np.arctan) +1072 +1073 def arcsinh(self): +1074 return self._apply_func_to_corr(np.arcsinh) 1075 -1076 def __rsub__(self, y): -1077 return -self + y +1076 def arccosh(self): +1077 return self._apply_func_to_corr(np.arccosh) 1078 -1079 def __rmul__(self, y): -1080 return self * y +1079 def arctanh(self): +1080 return self._apply_func_to_corr(np.arctanh) 1081 -1082 def __rtruediv__(self, y): -1083 return (self / y) ** (-1) -1084 -1085 @property -1086 def real(self): -1087 def return_real(obs_OR_cobs): -1088 if isinstance(obs_OR_cobs, CObs): -1089 return obs_OR_cobs.real -1090 else: -1091 return obs_OR_cobs -1092 -1093 return self._apply_func_to_corr(return_real) +1082 # Right hand side operations (require tweak in main module to work) +1083 def __radd__(self, y): +1084 return self + y +1085 +1086 def __rsub__(self, y): +1087 return -self + y +1088 +1089 def __rmul__(self, y): +1090 return self * y +1091 +1092 def __rtruediv__(self, y): +1093 return (self / y) ** (-1) 1094 1095 @property -1096 def imag(self): -1097 def return_imag(obs_OR_cobs): +1096 def real(self): +1097 def return_real(obs_OR_cobs): 1098 if isinstance(obs_OR_cobs, CObs): -1099 return obs_OR_cobs.imag +1099 return obs_OR_cobs.real 1100 else: -1101 return obs_OR_cobs * 0 # So it stays the right type +1101 return obs_OR_cobs 1102 -1103 return self._apply_func_to_corr(return_imag) +1103 return self._apply_func_to_corr(return_real) 1104 -1105 def prune(self, Ntrunc, tproj=3, t0proj=2, basematrix=None): -1106 r''' Project large correlation matrix to lowest states -1107 -1108 This method can be used to reduce the size of an (N x N) correlation matrix -1109 to (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise -1110 is still small. -1111 -1112 Parameters -1113 ---------- -1114 Ntrunc: int -1115 Rank of the target matrix. -1116 tproj: int -1117 Time where the eigenvectors are evaluated, corresponds to ts in the GEVP method. -1118 The default value is 3. -1119 t0proj: int -1120 Time where the correlation matrix is inverted. Choosing t0proj=1 is strongly -1121 discouraged for O(a) improved theories, since the correctness of the procedure -1122 cannot be granted in this case. The default value is 2. -1123 basematrix : Corr -1124 Correlation matrix that is used to determine the eigenvectors of the -1125 lowest states based on a GEVP. basematrix is taken to be the Corr itself if -1126 is is not specified. -1127 -1128 Notes -1129 ----- -1130 We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving -1131 the GEVP $$C(t) v_n(t, t_0) = \lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \equiv t_\mathrm{proj}$ -1132 and $t_0 \equiv t_{0, \mathrm{proj}}$. The target matrix is projected onto the subspace of the -1133 resulting eigenvectors $v_n, n=1,\dots,N_\mathrm{trunc}$ via -1134 $$G^\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large -1135 correlation matrix and to remove some noise that is added by irrelevant operators. -1136 This may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated -1137 bound $t_0 \leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$. -1138 ''' -1139 -1140 if self.N == 1: -1141 raise Exception('Method cannot be applied to one-dimensional correlators.') -1142 if basematrix is None: -1143 basematrix = self -1144 if Ntrunc >= basematrix.N: -1145 raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N)) -1146 if basematrix.N != self.N: -1147 raise Exception('basematrix and targetmatrix have to be of the same size.') -1148 -1149 evecs = [] -1150 for i in range(Ntrunc): -1151 evecs.append(basematrix.GEVP(t0proj, tproj, state=i)) -1152 -1153 tmpmat = np.empty((Ntrunc, Ntrunc), dtype=object) -1154 rmat = [] -1155 for t in range(basematrix.T): -1156 for i in range(Ntrunc): -1157 for j in range(Ntrunc): -1158 tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j] -1159 rmat.append(np.copy(tmpmat)) -1160 -1161 newcontent = [None if (self.content[t] is None) else rmat[t] for t in range(self.T)] -1162 return Corr(newcontent) -1163 -1164 -1165def _sort_vectors(vec_set, ts): -1166 """Helper function used to find a set of Eigenvectors consistent over all timeslices""" -1167 reference_sorting = np.array(vec_set[ts]) -1168 N = reference_sorting.shape[0] -1169 sorted_vec_set = [] -1170 for t in range(len(vec_set)): -1171 if vec_set[t] is None: -1172 sorted_vec_set.append(None) -1173 elif not t == ts: -1174 perms = [list(o) for o in permutations([i for i in range(N)], N)] -1175 best_score = 0 -1176 for perm in perms: -1177 current_score = 1 -1178 for k in range(N): -1179 new_sorting = reference_sorting.copy() -1180 new_sorting[perm[k], :] = vec_set[t][k] -1181 current_score *= abs(np.linalg.det(new_sorting)) -1182 if current_score > best_score: -1183 best_score = current_score -1184 best_perm = perm -1185 sorted_vec_set.append([vec_set[t][k] for k in best_perm]) -1186 else: -1187 sorted_vec_set.append(vec_set[t]) -1188 -1189 return sorted_vec_set -1190 -1191 -1192def _GEVP_solver(Gt, G0): # Just so normalization an sorting does not need to be repeated. Here we could later put in some checks -1193 sp_val, sp_vecs = scipy.linalg.eigh(Gt, G0) -1194 sp_vecs = [sp_vecs[:, np.argsort(sp_val)[-i]] for i in range(1, sp_vecs.shape[0] + 1)] -1195 sp_vecs = [v / np.sqrt((v.T @ G0 @ v)) for v in sp_vecs] -1196 return sp_vecs +1105 @property +1106 def imag(self): +1107 def return_imag(obs_OR_cobs): +1108 if isinstance(obs_OR_cobs, CObs): +1109 return obs_OR_cobs.imag +1110 else: +1111 return obs_OR_cobs * 0 # So it stays the right type +1112 +1113 return self._apply_func_to_corr(return_imag) +1114 +1115 def prune(self, Ntrunc, tproj=3, t0proj=2, basematrix=None): +1116 r''' Project large correlation matrix to lowest states +1117 +1118 This method can be used to reduce the size of an (N x N) correlation matrix +1119 to (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise +1120 is still small. +1121 +1122 Parameters +1123 ---------- +1124 Ntrunc: int +1125 Rank of the target matrix. +1126 tproj: int +1127 Time where the eigenvectors are evaluated, corresponds to ts in the GEVP method. +1128 The default value is 3. +1129 t0proj: int +1130 Time where the correlation matrix is inverted. Choosing t0proj=1 is strongly +1131 discouraged for O(a) improved theories, since the correctness of the procedure +1132 cannot be granted in this case. The default value is 2. +1133 basematrix : Corr +1134 Correlation matrix that is used to determine the eigenvectors of the +1135 lowest states based on a GEVP. basematrix is taken to be the Corr itself if +1136 is is not specified. +1137 +1138 Notes +1139 ----- +1140 We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving +1141 the GEVP $$C(t) v_n(t, t_0) = \lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \equiv t_\mathrm{proj}$ +1142 and $t_0 \equiv t_{0, \mathrm{proj}}$. The target matrix is projected onto the subspace of the +1143 resulting eigenvectors $v_n, n=1,\dots,N_\mathrm{trunc}$ via +1144 $$G^\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large +1145 correlation matrix and to remove some noise that is added by irrelevant operators. +1146 This may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated +1147 bound $t_0 \leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$. +1148 ''' +1149 +1150 if self.N == 1: +1151 raise Exception('Method cannot be applied to one-dimensional correlators.') +1152 if basematrix is None: +1153 basematrix = self +1154 if Ntrunc >= basematrix.N: +1155 raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N)) +1156 if basematrix.N != self.N: +1157 raise Exception('basematrix and targetmatrix have to be of the same size.') +1158 +1159 evecs = [] +1160 for i in range(Ntrunc): +1161 evecs.append(basematrix.GEVP(t0proj, tproj, state=i)) +1162 +1163 tmpmat = np.empty((Ntrunc, Ntrunc), dtype=object) +1164 rmat = [] +1165 for t in range(basematrix.T): +1166 for i in range(Ntrunc): +1167 for j in range(Ntrunc): +1168 tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j] +1169 rmat.append(np.copy(tmpmat)) +1170 +1171 newcontent = [None if (self.content[t] is None) else rmat[t] for t in range(self.T)] +1172 return Corr(newcontent) +1173 +1174 +1175def _sort_vectors(vec_set, ts): +1176 """Helper function used to find a set of Eigenvectors consistent over all timeslices""" +1177 reference_sorting = np.array(vec_set[ts]) +1178 N = reference_sorting.shape[0] +1179 sorted_vec_set = [] +1180 for t in range(len(vec_set)): +1181 if vec_set[t] is None: +1182 sorted_vec_set.append(None) +1183 elif not t == ts: +1184 perms = [list(o) for o in permutations([i for i in range(N)], N)] +1185 best_score = 0 +1186 for perm in perms: +1187 current_score = 1 +1188 for k in range(N): +1189 new_sorting = reference_sorting.copy() +1190 new_sorting[perm[k], :] = vec_set[t][k] +1191 current_score *= abs(np.linalg.det(new_sorting)) +1192 if current_score > best_score: +1193 best_score = current_score +1194 best_perm = perm +1195 sorted_vec_set.append([vec_set[t][k] for k in best_perm]) +1196 else: +1197 sorted_vec_set.append(vec_set[t]) +1198 +1199 return sorted_vec_set +1200 +1201 +1202def _GEVP_solver(Gt, G0): # Just so normalization an sorting does not need to be repeated. Here we could later put in some checks +1203 sp_val, sp_vecs = scipy.linalg.eigh(Gt, G0) +1204 sp_vecs = [sp_vecs[:, np.argsort(sp_val)[-i]] for i in range(1, sp_vecs.shape[0] + 1)] +1205 sp_vecs = [v / np.sqrt((v.T @ G0 @ v)) for v in sp_vecs] +1206 return sp_vecs @@ -2116,7 +2126,7 @@ 703 self.prange = prange 704 return 705 - 706 def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=None, fit_res=None, ylabel=None, save=None, auto_gamma=False): + 706 def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=None, fit_res=None, ylabel=None, save=None, auto_gamma=False, hide_sigma=None): 707 """Plots the correlator using the tag of the correlator as label if available. 708 709 Parameters @@ -2138,442 +2148,452 @@ 725 path to file in which the figure should be saved 726 auto_gamma : bool 727 Apply the gamma method with standard parameters to all correlators and plateau values before plotting. - 728 """ - 729 if self.N != 1: - 730 raise Exception("Correlator must be projected before plotting") - 731 - 732 if auto_gamma: - 733 self.gamma_method() - 734 - 735 if x_range is None: - 736 x_range = [0, self.T - 1] - 737 - 738 fig = plt.figure() - 739 ax1 = fig.add_subplot(111) - 740 - 741 x, y, y_err = self.plottable() - 742 ax1.errorbar(x, y, y_err, label=self.tag) - 743 if logscale: - 744 ax1.set_yscale('log') - 745 else: - 746 if y_range is None: - 747 try: - 748 y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) - 749 y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) - 750 ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)]) - 751 except Exception: - 752 pass - 753 else: - 754 ax1.set_ylim(y_range) - 755 if comp: - 756 if isinstance(comp, (Corr, list)): - 757 for corr in comp if isinstance(comp, list) else [comp]: - 758 if auto_gamma: - 759 corr.gamma_method() - 760 x, y, y_err = corr.plottable() - 761 plt.errorbar(x, y, y_err, label=corr.tag, mfc=plt.rcParams['axes.facecolor']) - 762 else: - 763 raise Exception("'comp' must be a correlator or a list of correlators.") - 764 - 765 if plateau: - 766 if isinstance(plateau, Obs): - 767 if auto_gamma: - 768 plateau.gamma_method() - 769 ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau)) - 770 ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-') - 771 else: - 772 raise Exception("'plateau' must be an Obs") - 773 if self.prange: - 774 ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',') - 775 ax1.axvline(self.prange[1], 0, 1, ls='-', marker=',') - 776 - 777 if fit_res: - 778 x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05) - 779 ax1.plot(x_samples, - 780 fit_res.fit_function([o.value for o in fit_res.fit_parameters], x_samples), - 781 ls='-', marker=',', lw=2) - 782 - 783 ax1.set_xlabel(r'$x_0 / a$') - 784 if ylabel: - 785 ax1.set_ylabel(ylabel) - 786 ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5]) - 787 - 788 handles, labels = ax1.get_legend_handles_labels() - 789 if labels: - 790 ax1.legend() - 791 plt.draw() + 728 hide_sigma : float + 729 Hides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors. + 730 """ + 731 if self.N != 1: + 732 raise Exception("Correlator must be projected before plotting") + 733 + 734 if auto_gamma: + 735 self.gamma_method() + 736 + 737 if x_range is None: + 738 x_range = [0, self.T - 1] + 739 + 740 fig = plt.figure() + 741 ax1 = fig.add_subplot(111) + 742 + 743 x, y, y_err = self.plottable() + 744 if hide_sigma: + 745 hide_from = np.argmax((hide_sigma * np.array(y_err)) > np.abs(y)) - 1 + 746 else: + 747 hide_from = None + 748 ax1.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=self.tag) + 749 if logscale: + 750 ax1.set_yscale('log') + 751 else: + 752 if y_range is None: + 753 try: + 754 y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) + 755 y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) + 756 ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)]) + 757 except Exception: + 758 pass + 759 else: + 760 ax1.set_ylim(y_range) + 761 if comp: + 762 if isinstance(comp, (Corr, list)): + 763 for corr in comp if isinstance(comp, list) else [comp]: + 764 if auto_gamma: + 765 corr.gamma_method() + 766 x, y, y_err = corr.plottable() + 767 if hide_sigma: + 768 hide_from = np.argmax((hide_sigma * np.array(y_err)) > np.abs(y)) - 1 + 769 else: + 770 hide_from = None + 771 plt.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=corr.tag, mfc=plt.rcParams['axes.facecolor']) + 772 else: + 773 raise Exception("'comp' must be a correlator or a list of correlators.") + 774 + 775 if plateau: + 776 if isinstance(plateau, Obs): + 777 if auto_gamma: + 778 plateau.gamma_method() + 779 ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau)) + 780 ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-') + 781 else: + 782 raise Exception("'plateau' must be an Obs") + 783 if self.prange: + 784 ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',') + 785 ax1.axvline(self.prange[1], 0, 1, ls='-', marker=',') + 786 + 787 if fit_res: + 788 x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05) + 789 ax1.plot(x_samples, + 790 fit_res.fit_function([o.value for o in fit_res.fit_parameters], x_samples), + 791 ls='-', marker=',', lw=2) 792 - 793 if save: - 794 if isinstance(save, str): - 795 fig.savefig(save) - 796 else: - 797 raise Exception("'save' has to be a string.") - 798 - 799 def spaghetti_plot(self, logscale=True): - 800 """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations. - 801 - 802 Parameters - 803 ---------- - 804 logscale : bool - 805 Determines whether the scale of the y-axis is logarithmic or standard. - 806 """ - 807 if self.N != 1: - 808 raise Exception("Correlator needs to be projected first.") - 809 - 810 mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist])) - 811 x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None] - 812 - 813 for name in mc_names: - 814 data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T - 815 - 816 fig = plt.figure() - 817 ax = fig.add_subplot(111) - 818 for dat in data: - 819 ax.plot(x0_vals, dat, ls='-', marker='') - 820 - 821 if logscale is True: - 822 ax.set_yscale('log') - 823 - 824 ax.set_xlabel(r'$x_0 / a$') - 825 plt.title(name) - 826 plt.draw() - 827 - 828 def dump(self, filename, datatype="json.gz", **kwargs): - 829 """Dumps the Corr into a file of chosen type - 830 Parameters - 831 ---------- - 832 filename : str - 833 Name of the file to be saved. - 834 datatype : str - 835 Format of the exported file. Supported formats include - 836 "json.gz" and "pickle" - 837 path : str - 838 specifies a custom path for the file (default '.') - 839 """ - 840 if datatype == "json.gz": - 841 from .input.json import dump_to_json - 842 if 'path' in kwargs: - 843 file_name = kwargs.get('path') + '/' + filename - 844 else: - 845 file_name = filename - 846 dump_to_json(self, file_name) - 847 elif datatype == "pickle": - 848 dump_object(self, filename, **kwargs) - 849 else: - 850 raise Exception("Unknown datatype " + str(datatype)) - 851 - 852 def print(self, range=[0, None]): - 853 print(self.__repr__(range)) - 854 - 855 def __repr__(self, range=[0, None]): - 856 content_string = "" - 857 - 858 content_string += "Corr T=" + str(self.T) + " N=" + str(self.N) + "\n" # +" filled with"+ str(type(self.content[0][0])) there should be a good solution here - 859 - 860 if self.tag is not None: - 861 content_string += "Description: " + self.tag + "\n" - 862 if self.N != 1: - 863 return content_string + 793 ax1.set_xlabel(r'$x_0 / a$') + 794 if ylabel: + 795 ax1.set_ylabel(ylabel) + 796 ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5]) + 797 + 798 handles, labels = ax1.get_legend_handles_labels() + 799 if labels: + 800 ax1.legend() + 801 plt.draw() + 802 + 803 if save: + 804 if isinstance(save, str): + 805 fig.savefig(save) + 806 else: + 807 raise Exception("'save' has to be a string.") + 808 + 809 def spaghetti_plot(self, logscale=True): + 810 """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations. + 811 + 812 Parameters + 813 ---------- + 814 logscale : bool + 815 Determines whether the scale of the y-axis is logarithmic or standard. + 816 """ + 817 if self.N != 1: + 818 raise Exception("Correlator needs to be projected first.") + 819 + 820 mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist])) + 821 x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None] + 822 + 823 for name in mc_names: + 824 data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T + 825 + 826 fig = plt.figure() + 827 ax = fig.add_subplot(111) + 828 for dat in data: + 829 ax.plot(x0_vals, dat, ls='-', marker='') + 830 + 831 if logscale is True: + 832 ax.set_yscale('log') + 833 + 834 ax.set_xlabel(r'$x_0 / a$') + 835 plt.title(name) + 836 plt.draw() + 837 + 838 def dump(self, filename, datatype="json.gz", **kwargs): + 839 """Dumps the Corr into a file of chosen type + 840 Parameters + 841 ---------- + 842 filename : str + 843 Name of the file to be saved. + 844 datatype : str + 845 Format of the exported file. Supported formats include + 846 "json.gz" and "pickle" + 847 path : str + 848 specifies a custom path for the file (default '.') + 849 """ + 850 if datatype == "json.gz": + 851 from .input.json import dump_to_json + 852 if 'path' in kwargs: + 853 file_name = kwargs.get('path') + '/' + filename + 854 else: + 855 file_name = filename + 856 dump_to_json(self, file_name) + 857 elif datatype == "pickle": + 858 dump_object(self, filename, **kwargs) + 859 else: + 860 raise Exception("Unknown datatype " + str(datatype)) + 861 + 862 def print(self, range=[0, None]): + 863 print(self.__repr__(range)) 864 - 865 if range[1]: - 866 range[1] += 1 - 867 content_string += 'x0/a\tCorr(x0/a)\n------------------\n' - 868 for i, sub_corr in enumerate(self.content[range[0]:range[1]]): - 869 if sub_corr is None: - 870 content_string += str(i + range[0]) + '\n' - 871 else: - 872 content_string += str(i + range[0]) - 873 for element in sub_corr: - 874 content_string += '\t' + ' ' * int(element >= 0) + str(element) - 875 content_string += '\n' - 876 return content_string - 877 - 878 def __str__(self): - 879 return self.__repr__() - 880 - 881 # We define the basic operations, that can be performed with correlators. - 882 # While */+- get defined here, they only work for Corr*Obs and not Obs*Corr. - 883 # This is because Obs*Corr checks Obs.__mul__ first and does not catch an exception. - 884 # One could try and tell Obs to check if the y in __mul__ is a Corr and - 885 - 886 def __add__(self, y): - 887 if isinstance(y, Corr): - 888 if ((self.N != y.N) or (self.T != y.T)): - 889 raise Exception("Addition of Corrs with different shape") - 890 newcontent = [] - 891 for t in range(self.T): - 892 if (self.content[t] is None) or (y.content[t] is None): - 893 newcontent.append(None) - 894 else: - 895 newcontent.append(self.content[t] + y.content[t]) - 896 return Corr(newcontent) - 897 - 898 elif isinstance(y, (Obs, int, float, CObs)): - 899 newcontent = [] - 900 for t in range(self.T): - 901 if (self.content[t] is None): - 902 newcontent.append(None) - 903 else: - 904 newcontent.append(self.content[t] + y) - 905 return Corr(newcontent, prange=self.prange) - 906 elif isinstance(y, np.ndarray): - 907 if y.shape == (self.T,): - 908 return Corr(list((np.array(self.content).T + y).T)) - 909 else: - 910 raise ValueError("operands could not be broadcast together") - 911 else: - 912 raise TypeError("Corr + wrong type") - 913 - 914 def __mul__(self, y): - 915 if isinstance(y, Corr): - 916 if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): - 917 raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") - 918 newcontent = [] - 919 for t in range(self.T): - 920 if (self.content[t] is None) or (y.content[t] is None): - 921 newcontent.append(None) - 922 else: - 923 newcontent.append(self.content[t] * y.content[t]) - 924 return Corr(newcontent) - 925 - 926 elif isinstance(y, (Obs, int, float, CObs)): - 927 newcontent = [] - 928 for t in range(self.T): - 929 if (self.content[t] is None): - 930 newcontent.append(None) - 931 else: - 932 newcontent.append(self.content[t] * y) - 933 return Corr(newcontent, prange=self.prange) - 934 elif isinstance(y, np.ndarray): - 935 if y.shape == (self.T,): - 936 return Corr(list((np.array(self.content).T * y).T)) - 937 else: - 938 raise ValueError("operands could not be broadcast together") - 939 else: - 940 raise TypeError("Corr * wrong type") - 941 - 942 def __truediv__(self, y): - 943 if isinstance(y, Corr): - 944 if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): - 945 raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") - 946 newcontent = [] - 947 for t in range(self.T): - 948 if (self.content[t] is None) or (y.content[t] is None): - 949 newcontent.append(None) - 950 else: - 951 newcontent.append(self.content[t] / y.content[t]) - 952 for t in range(self.T): - 953 if newcontent[t] is None: - 954 continue - 955 if np.isnan(np.sum(newcontent[t]).value): - 956 newcontent[t] = None - 957 - 958 if all([item is None for item in newcontent]): - 959 raise Exception("Division returns completely undefined correlator") - 960 return Corr(newcontent) - 961 - 962 elif isinstance(y, (Obs, CObs)): - 963 if isinstance(y, Obs): - 964 if y.value == 0: - 965 raise Exception('Division by zero will return undefined correlator') - 966 if isinstance(y, CObs): - 967 if y.is_zero(): - 968 raise Exception('Division by zero will return undefined correlator') - 969 - 970 newcontent = [] - 971 for t in range(self.T): - 972 if (self.content[t] is None): - 973 newcontent.append(None) - 974 else: - 975 newcontent.append(self.content[t] / y) - 976 return Corr(newcontent, prange=self.prange) - 977 - 978 elif isinstance(y, (int, float)): - 979 if y == 0: - 980 raise Exception('Division by zero will return undefined correlator') - 981 newcontent = [] - 982 for t in range(self.T): - 983 if (self.content[t] is None): - 984 newcontent.append(None) - 985 else: - 986 newcontent.append(self.content[t] / y) - 987 return Corr(newcontent, prange=self.prange) - 988 elif isinstance(y, np.ndarray): - 989 if y.shape == (self.T,): - 990 return Corr(list((np.array(self.content).T / y).T)) - 991 else: - 992 raise ValueError("operands could not be broadcast together") - 993 else: - 994 raise TypeError('Corr / wrong type') - 995 - 996 def __neg__(self): - 997 newcontent = [None if (item is None) else -1. * item for item in self.content] - 998 return Corr(newcontent, prange=self.prange) - 999 -1000 def __sub__(self, y): -1001 return self + (-y) -1002 -1003 def __pow__(self, y): -1004 if isinstance(y, (Obs, int, float, CObs)): -1005 newcontent = [None if (item is None) else item**y for item in self.content] -1006 return Corr(newcontent, prange=self.prange) -1007 else: -1008 raise TypeError('Type of exponent not supported') + 865 def __repr__(self, range=[0, None]): + 866 content_string = "" + 867 + 868 content_string += "Corr T=" + str(self.T) + " N=" + str(self.N) + "\n" # +" filled with"+ str(type(self.content[0][0])) there should be a good solution here + 869 + 870 if self.tag is not None: + 871 content_string += "Description: " + self.tag + "\n" + 872 if self.N != 1: + 873 return content_string + 874 + 875 if range[1]: + 876 range[1] += 1 + 877 content_string += 'x0/a\tCorr(x0/a)\n------------------\n' + 878 for i, sub_corr in enumerate(self.content[range[0]:range[1]]): + 879 if sub_corr is None: + 880 content_string += str(i + range[0]) + '\n' + 881 else: + 882 content_string += str(i + range[0]) + 883 for element in sub_corr: + 884 content_string += '\t' + ' ' * int(element >= 0) + str(element) + 885 content_string += '\n' + 886 return content_string + 887 + 888 def __str__(self): + 889 return self.__repr__() + 890 + 891 # We define the basic operations, that can be performed with correlators. + 892 # While */+- get defined here, they only work for Corr*Obs and not Obs*Corr. + 893 # This is because Obs*Corr checks Obs.__mul__ first and does not catch an exception. + 894 # One could try and tell Obs to check if the y in __mul__ is a Corr and + 895 + 896 def __add__(self, y): + 897 if isinstance(y, Corr): + 898 if ((self.N != y.N) or (self.T != y.T)): + 899 raise Exception("Addition of Corrs with different shape") + 900 newcontent = [] + 901 for t in range(self.T): + 902 if (self.content[t] is None) or (y.content[t] is None): + 903 newcontent.append(None) + 904 else: + 905 newcontent.append(self.content[t] + y.content[t]) + 906 return Corr(newcontent) + 907 + 908 elif isinstance(y, (Obs, int, float, CObs)): + 909 newcontent = [] + 910 for t in range(self.T): + 911 if (self.content[t] is None): + 912 newcontent.append(None) + 913 else: + 914 newcontent.append(self.content[t] + y) + 915 return Corr(newcontent, prange=self.prange) + 916 elif isinstance(y, np.ndarray): + 917 if y.shape == (self.T,): + 918 return Corr(list((np.array(self.content).T + y).T)) + 919 else: + 920 raise ValueError("operands could not be broadcast together") + 921 else: + 922 raise TypeError("Corr + wrong type") + 923 + 924 def __mul__(self, y): + 925 if isinstance(y, Corr): + 926 if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): + 927 raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") + 928 newcontent = [] + 929 for t in range(self.T): + 930 if (self.content[t] is None) or (y.content[t] is None): + 931 newcontent.append(None) + 932 else: + 933 newcontent.append(self.content[t] * y.content[t]) + 934 return Corr(newcontent) + 935 + 936 elif isinstance(y, (Obs, int, float, CObs)): + 937 newcontent = [] + 938 for t in range(self.T): + 939 if (self.content[t] is None): + 940 newcontent.append(None) + 941 else: + 942 newcontent.append(self.content[t] * y) + 943 return Corr(newcontent, prange=self.prange) + 944 elif isinstance(y, np.ndarray): + 945 if y.shape == (self.T,): + 946 return Corr(list((np.array(self.content).T * y).T)) + 947 else: + 948 raise ValueError("operands could not be broadcast together") + 949 else: + 950 raise TypeError("Corr * wrong type") + 951 + 952 def __truediv__(self, y): + 953 if isinstance(y, Corr): + 954 if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): + 955 raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") + 956 newcontent = [] + 957 for t in range(self.T): + 958 if (self.content[t] is None) or (y.content[t] is None): + 959 newcontent.append(None) + 960 else: + 961 newcontent.append(self.content[t] / y.content[t]) + 962 for t in range(self.T): + 963 if newcontent[t] is None: + 964 continue + 965 if np.isnan(np.sum(newcontent[t]).value): + 966 newcontent[t] = None + 967 + 968 if all([item is None for item in newcontent]): + 969 raise Exception("Division returns completely undefined correlator") + 970 return Corr(newcontent) + 971 + 972 elif isinstance(y, (Obs, CObs)): + 973 if isinstance(y, Obs): + 974 if y.value == 0: + 975 raise Exception('Division by zero will return undefined correlator') + 976 if isinstance(y, CObs): + 977 if y.is_zero(): + 978 raise Exception('Division by zero will return undefined correlator') + 979 + 980 newcontent = [] + 981 for t in range(self.T): + 982 if (self.content[t] is None): + 983 newcontent.append(None) + 984 else: + 985 newcontent.append(self.content[t] / y) + 986 return Corr(newcontent, prange=self.prange) + 987 + 988 elif isinstance(y, (int, float)): + 989 if y == 0: + 990 raise Exception('Division by zero will return undefined correlator') + 991 newcontent = [] + 992 for t in range(self.T): + 993 if (self.content[t] is None): + 994 newcontent.append(None) + 995 else: + 996 newcontent.append(self.content[t] / y) + 997 return Corr(newcontent, prange=self.prange) + 998 elif isinstance(y, np.ndarray): + 999 if y.shape == (self.T,): +1000 return Corr(list((np.array(self.content).T / y).T)) +1001 else: +1002 raise ValueError("operands could not be broadcast together") +1003 else: +1004 raise TypeError('Corr / wrong type') +1005 +1006 def __neg__(self): +1007 newcontent = [None if (item is None) else -1. * item for item in self.content] +1008 return Corr(newcontent, prange=self.prange) 1009 -1010 def __abs__(self): -1011 newcontent = [None if (item is None) else np.abs(item) for item in self.content] -1012 return Corr(newcontent, prange=self.prange) -1013 -1014 # The numpy functions: -1015 def sqrt(self): -1016 return self**0.5 -1017 -1018 def log(self): -1019 newcontent = [None if (item is None) else np.log(item) for item in self.content] -1020 return Corr(newcontent, prange=self.prange) -1021 -1022 def exp(self): -1023 newcontent = [None if (item is None) else np.exp(item) for item in self.content] -1024 return Corr(newcontent, prange=self.prange) -1025 -1026 def _apply_func_to_corr(self, func): -1027 newcontent = [None if (item is None) else func(item) for item in self.content] -1028 for t in range(self.T): -1029 if newcontent[t] is None: -1030 continue -1031 if np.isnan(np.sum(newcontent[t]).value): -1032 newcontent[t] = None -1033 if all([item is None for item in newcontent]): -1034 raise Exception('Operation returns undefined correlator') -1035 return Corr(newcontent) -1036 -1037 def sin(self): -1038 return self._apply_func_to_corr(np.sin) -1039 -1040 def cos(self): -1041 return self._apply_func_to_corr(np.cos) -1042 -1043 def tan(self): -1044 return self._apply_func_to_corr(np.tan) -1045 -1046 def sinh(self): -1047 return self._apply_func_to_corr(np.sinh) -1048 -1049 def cosh(self): -1050 return self._apply_func_to_corr(np.cosh) -1051 -1052 def tanh(self): -1053 return self._apply_func_to_corr(np.tanh) -1054 -1055 def arcsin(self): -1056 return self._apply_func_to_corr(np.arcsin) -1057 -1058 def arccos(self): -1059 return self._apply_func_to_corr(np.arccos) -1060 -1061 def arctan(self): -1062 return self._apply_func_to_corr(np.arctan) -1063 -1064 def arcsinh(self): -1065 return self._apply_func_to_corr(np.arcsinh) -1066 -1067 def arccosh(self): -1068 return self._apply_func_to_corr(np.arccosh) -1069 -1070 def arctanh(self): -1071 return self._apply_func_to_corr(np.arctanh) -1072 -1073 # Right hand side operations (require tweak in main module to work) -1074 def __radd__(self, y): -1075 return self + y +1010 def __sub__(self, y): +1011 return self + (-y) +1012 +1013 def __pow__(self, y): +1014 if isinstance(y, (Obs, int, float, CObs)): +1015 newcontent = [None if (item is None) else item**y for item in self.content] +1016 return Corr(newcontent, prange=self.prange) +1017 else: +1018 raise TypeError('Type of exponent not supported') +1019 +1020 def __abs__(self): +1021 newcontent = [None if (item is None) else np.abs(item) for item in self.content] +1022 return Corr(newcontent, prange=self.prange) +1023 +1024 # The numpy functions: +1025 def sqrt(self): +1026 return self**0.5 +1027 +1028 def log(self): +1029 newcontent = [None if (item is None) else np.log(item) for item in self.content] +1030 return Corr(newcontent, prange=self.prange) +1031 +1032 def exp(self): +1033 newcontent = [None if (item is None) else np.exp(item) for item in self.content] +1034 return Corr(newcontent, prange=self.prange) +1035 +1036 def _apply_func_to_corr(self, func): +1037 newcontent = [None if (item is None) else func(item) for item in self.content] +1038 for t in range(self.T): +1039 if newcontent[t] is None: +1040 continue +1041 if np.isnan(np.sum(newcontent[t]).value): +1042 newcontent[t] = None +1043 if all([item is None for item in newcontent]): +1044 raise Exception('Operation returns undefined correlator') +1045 return Corr(newcontent) +1046 +1047 def sin(self): +1048 return self._apply_func_to_corr(np.sin) +1049 +1050 def cos(self): +1051 return self._apply_func_to_corr(np.cos) +1052 +1053 def tan(self): +1054 return self._apply_func_to_corr(np.tan) +1055 +1056 def sinh(self): +1057 return self._apply_func_to_corr(np.sinh) +1058 +1059 def cosh(self): +1060 return self._apply_func_to_corr(np.cosh) +1061 +1062 def tanh(self): +1063 return self._apply_func_to_corr(np.tanh) +1064 +1065 def arcsin(self): +1066 return self._apply_func_to_corr(np.arcsin) +1067 +1068 def arccos(self): +1069 return self._apply_func_to_corr(np.arccos) +1070 +1071 def arctan(self): +1072 return self._apply_func_to_corr(np.arctan) +1073 +1074 def arcsinh(self): +1075 return self._apply_func_to_corr(np.arcsinh) 1076 -1077 def __rsub__(self, y): -1078 return -self + y +1077 def arccosh(self): +1078 return self._apply_func_to_corr(np.arccosh) 1079 -1080 def __rmul__(self, y): -1081 return self * y +1080 def arctanh(self): +1081 return self._apply_func_to_corr(np.arctanh) 1082 -1083 def __rtruediv__(self, y): -1084 return (self / y) ** (-1) -1085 -1086 @property -1087 def real(self): -1088 def return_real(obs_OR_cobs): -1089 if isinstance(obs_OR_cobs, CObs): -1090 return obs_OR_cobs.real -1091 else: -1092 return obs_OR_cobs -1093 -1094 return self._apply_func_to_corr(return_real) +1083 # Right hand side operations (require tweak in main module to work) +1084 def __radd__(self, y): +1085 return self + y +1086 +1087 def __rsub__(self, y): +1088 return -self + y +1089 +1090 def __rmul__(self, y): +1091 return self * y +1092 +1093 def __rtruediv__(self, y): +1094 return (self / y) ** (-1) 1095 1096 @property -1097 def imag(self): -1098 def return_imag(obs_OR_cobs): +1097 def real(self): +1098 def return_real(obs_OR_cobs): 1099 if isinstance(obs_OR_cobs, CObs): -1100 return obs_OR_cobs.imag +1100 return obs_OR_cobs.real 1101 else: -1102 return obs_OR_cobs * 0 # So it stays the right type +1102 return obs_OR_cobs 1103 -1104 return self._apply_func_to_corr(return_imag) +1104 return self._apply_func_to_corr(return_real) 1105 -1106 def prune(self, Ntrunc, tproj=3, t0proj=2, basematrix=None): -1107 r''' Project large correlation matrix to lowest states -1108 -1109 This method can be used to reduce the size of an (N x N) correlation matrix -1110 to (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise -1111 is still small. -1112 -1113 Parameters -1114 ---------- -1115 Ntrunc: int -1116 Rank of the target matrix. -1117 tproj: int -1118 Time where the eigenvectors are evaluated, corresponds to ts in the GEVP method. -1119 The default value is 3. -1120 t0proj: int -1121 Time where the correlation matrix is inverted. Choosing t0proj=1 is strongly -1122 discouraged for O(a) improved theories, since the correctness of the procedure -1123 cannot be granted in this case. The default value is 2. -1124 basematrix : Corr -1125 Correlation matrix that is used to determine the eigenvectors of the -1126 lowest states based on a GEVP. basematrix is taken to be the Corr itself if -1127 is is not specified. -1128 -1129 Notes -1130 ----- -1131 We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving -1132 the GEVP $$C(t) v_n(t, t_0) = \lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \equiv t_\mathrm{proj}$ -1133 and $t_0 \equiv t_{0, \mathrm{proj}}$. The target matrix is projected onto the subspace of the -1134 resulting eigenvectors $v_n, n=1,\dots,N_\mathrm{trunc}$ via -1135 $$G^\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large -1136 correlation matrix and to remove some noise that is added by irrelevant operators. -1137 This may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated -1138 bound $t_0 \leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$. -1139 ''' -1140 -1141 if self.N == 1: -1142 raise Exception('Method cannot be applied to one-dimensional correlators.') -1143 if basematrix is None: -1144 basematrix = self -1145 if Ntrunc >= basematrix.N: -1146 raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N)) -1147 if basematrix.N != self.N: -1148 raise Exception('basematrix and targetmatrix have to be of the same size.') -1149 -1150 evecs = [] -1151 for i in range(Ntrunc): -1152 evecs.append(basematrix.GEVP(t0proj, tproj, state=i)) -1153 -1154 tmpmat = np.empty((Ntrunc, Ntrunc), dtype=object) -1155 rmat = [] -1156 for t in range(basematrix.T): -1157 for i in range(Ntrunc): -1158 for j in range(Ntrunc): -1159 tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j] -1160 rmat.append(np.copy(tmpmat)) -1161 -1162 newcontent = [None if (self.content[t] is None) else rmat[t] for t in range(self.T)] -1163 return Corr(newcontent) +1106 @property +1107 def imag(self): +1108 def return_imag(obs_OR_cobs): +1109 if isinstance(obs_OR_cobs, CObs): +1110 return obs_OR_cobs.imag +1111 else: +1112 return obs_OR_cobs * 0 # So it stays the right type +1113 +1114 return self._apply_func_to_corr(return_imag) +1115 +1116 def prune(self, Ntrunc, tproj=3, t0proj=2, basematrix=None): +1117 r''' Project large correlation matrix to lowest states +1118 +1119 This method can be used to reduce the size of an (N x N) correlation matrix +1120 to (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise +1121 is still small. +1122 +1123 Parameters +1124 ---------- +1125 Ntrunc: int +1126 Rank of the target matrix. +1127 tproj: int +1128 Time where the eigenvectors are evaluated, corresponds to ts in the GEVP method. +1129 The default value is 3. +1130 t0proj: int +1131 Time where the correlation matrix is inverted. Choosing t0proj=1 is strongly +1132 discouraged for O(a) improved theories, since the correctness of the procedure +1133 cannot be granted in this case. The default value is 2. +1134 basematrix : Corr +1135 Correlation matrix that is used to determine the eigenvectors of the +1136 lowest states based on a GEVP. basematrix is taken to be the Corr itself if +1137 is is not specified. +1138 +1139 Notes +1140 ----- +1141 We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving +1142 the GEVP $$C(t) v_n(t, t_0) = \lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \equiv t_\mathrm{proj}$ +1143 and $t_0 \equiv t_{0, \mathrm{proj}}$. The target matrix is projected onto the subspace of the +1144 resulting eigenvectors $v_n, n=1,\dots,N_\mathrm{trunc}$ via +1145 $$G^\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large +1146 correlation matrix and to remove some noise that is added by irrelevant operators. +1147 This may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated +1148 bound $t_0 \leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$. +1149 ''' +1150 +1151 if self.N == 1: +1152 raise Exception('Method cannot be applied to one-dimensional correlators.') +1153 if basematrix is None: +1154 basematrix = self +1155 if Ntrunc >= basematrix.N: +1156 raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N)) +1157 if basematrix.N != self.N: +1158 raise Exception('basematrix and targetmatrix have to be of the same size.') +1159 +1160 evecs = [] +1161 for i in range(Ntrunc): +1162 evecs.append(basematrix.GEVP(t0proj, tproj, state=i)) +1163 +1164 tmpmat = np.empty((Ntrunc, Ntrunc), dtype=object) +1165 rmat = [] +1166 for t in range(basematrix.T): +1167 for i in range(Ntrunc): +1168 for j in range(Ntrunc): +1169 tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j] +1170 rmat.append(np.copy(tmpmat)) +1171 +1172 newcontent = [None if (self.content[t] is None) else rmat[t] for t in range(self.T)] +1173 return Corr(newcontent) @@ -3870,13 +3890,14 @@ apply gamma_method with default parameters to the Corr. Defaults to None fit_res=None, ylabel=None, save=None, - auto_gamma=False + auto_gamma=False, + hide_sigma=None ):
View Source -
706    def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=None, fit_res=None, ylabel=None, save=None, auto_gamma=False):
+            
706    def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=None, fit_res=None, ylabel=None, save=None, auto_gamma=False, hide_sigma=None):
 707        """Plots the correlator using the tag of the correlator as label if available.
 708
 709        Parameters
@@ -3898,76 +3919,86 @@ apply gamma_method with default parameters to the Corr. Defaults to None
 725            path to file in which the figure should be saved
 726        auto_gamma : bool
 727            Apply the gamma method with standard parameters to all correlators and plateau values before plotting.
-728        """
-729        if self.N != 1:
-730            raise Exception("Correlator must be projected before plotting")
-731
-732        if auto_gamma:
-733            self.gamma_method()
-734
-735        if x_range is None:
-736            x_range = [0, self.T - 1]
-737
-738        fig = plt.figure()
-739        ax1 = fig.add_subplot(111)
-740
-741        x, y, y_err = self.plottable()
-742        ax1.errorbar(x, y, y_err, label=self.tag)
-743        if logscale:
-744            ax1.set_yscale('log')
-745        else:
-746            if y_range is None:
-747                try:
-748                    y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
-749                    y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
-750                    ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)])
-751                except Exception:
-752                    pass
-753            else:
-754                ax1.set_ylim(y_range)
-755        if comp:
-756            if isinstance(comp, (Corr, list)):
-757                for corr in comp if isinstance(comp, list) else [comp]:
-758                    if auto_gamma:
-759                        corr.gamma_method()
-760                    x, y, y_err = corr.plottable()
-761                    plt.errorbar(x, y, y_err, label=corr.tag, mfc=plt.rcParams['axes.facecolor'])
-762            else:
-763                raise Exception("'comp' must be a correlator or a list of correlators.")
-764
-765        if plateau:
-766            if isinstance(plateau, Obs):
-767                if auto_gamma:
-768                    plateau.gamma_method()
-769                ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau))
-770                ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-')
-771            else:
-772                raise Exception("'plateau' must be an Obs")
-773        if self.prange:
-774            ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',')
-775            ax1.axvline(self.prange[1], 0, 1, ls='-', marker=',')
-776
-777        if fit_res:
-778            x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05)
-779            ax1.plot(x_samples,
-780                     fit_res.fit_function([o.value for o in fit_res.fit_parameters], x_samples),
-781                     ls='-', marker=',', lw=2)
-782
-783        ax1.set_xlabel(r'$x_0 / a$')
-784        if ylabel:
-785            ax1.set_ylabel(ylabel)
-786        ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5])
-787
-788        handles, labels = ax1.get_legend_handles_labels()
-789        if labels:
-790            ax1.legend()
-791        plt.draw()
+728        hide_sigma : float
+729            Hides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
+730        """
+731        if self.N != 1:
+732            raise Exception("Correlator must be projected before plotting")
+733
+734        if auto_gamma:
+735            self.gamma_method()
+736
+737        if x_range is None:
+738            x_range = [0, self.T - 1]
+739
+740        fig = plt.figure()
+741        ax1 = fig.add_subplot(111)
+742
+743        x, y, y_err = self.plottable()
+744        if hide_sigma:
+745            hide_from = np.argmax((hide_sigma * np.array(y_err)) > np.abs(y)) - 1
+746        else:
+747            hide_from = None
+748        ax1.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=self.tag)
+749        if logscale:
+750            ax1.set_yscale('log')
+751        else:
+752            if y_range is None:
+753                try:
+754                    y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
+755                    y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
+756                    ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)])
+757                except Exception:
+758                    pass
+759            else:
+760                ax1.set_ylim(y_range)
+761        if comp:
+762            if isinstance(comp, (Corr, list)):
+763                for corr in comp if isinstance(comp, list) else [comp]:
+764                    if auto_gamma:
+765                        corr.gamma_method()
+766                    x, y, y_err = corr.plottable()
+767                    if hide_sigma:
+768                        hide_from = np.argmax((hide_sigma * np.array(y_err)) > np.abs(y)) - 1
+769                    else:
+770                        hide_from = None
+771                    plt.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=corr.tag, mfc=plt.rcParams['axes.facecolor'])
+772            else:
+773                raise Exception("'comp' must be a correlator or a list of correlators.")
+774
+775        if plateau:
+776            if isinstance(plateau, Obs):
+777                if auto_gamma:
+778                    plateau.gamma_method()
+779                ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau))
+780                ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-')
+781            else:
+782                raise Exception("'plateau' must be an Obs")
+783        if self.prange:
+784            ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',')
+785            ax1.axvline(self.prange[1], 0, 1, ls='-', marker=',')
+786
+787        if fit_res:
+788            x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05)
+789            ax1.plot(x_samples,
+790                     fit_res.fit_function([o.value for o in fit_res.fit_parameters], x_samples),
+791                     ls='-', marker=',', lw=2)
 792
-793        if save:
-794            if isinstance(save, str):
-795                fig.savefig(save)
-796            else:
-797                raise Exception("'save' has to be a string.")
+793        ax1.set_xlabel(r'$x_0 / a$')
+794        if ylabel:
+795            ax1.set_ylabel(ylabel)
+796        ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5])
+797
+798        handles, labels = ax1.get_legend_handles_labels()
+799        if labels:
+800            ax1.legend()
+801        plt.draw()
+802
+803        if save:
+804            if isinstance(save, str):
+805                fig.savefig(save)
+806            else:
+807                raise Exception("'save' has to be a string.")
 
@@ -3994,6 +4025,8 @@ Label for the y-axis path to file in which the figure should be saved
  • auto_gamma (bool): Apply the gamma method with standard parameters to all correlators and plateau values before plotting.
  • +
  • hide_sigma (float): +Hides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
  • @@ -4009,34 +4042,34 @@ Apply the gamma method with standard parameters to all correlators and plateau v
    View Source -
    799    def spaghetti_plot(self, logscale=True):
    -800        """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
    -801
    -802        Parameters
    -803        ----------
    -804        logscale : bool
    -805            Determines whether the scale of the y-axis is logarithmic or standard.
    -806        """
    -807        if self.N != 1:
    -808            raise Exception("Correlator needs to be projected first.")
    -809
    -810        mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist]))
    -811        x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None]
    -812
    -813        for name in mc_names:
    -814            data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T
    -815
    -816            fig = plt.figure()
    -817            ax = fig.add_subplot(111)
    -818            for dat in data:
    -819                ax.plot(x0_vals, dat, ls='-', marker='')
    -820
    -821            if logscale is True:
    -822                ax.set_yscale('log')
    -823
    -824            ax.set_xlabel(r'$x_0 / a$')
    -825            plt.title(name)
    -826            plt.draw()
    +            
    809    def spaghetti_plot(self, logscale=True):
    +810        """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
    +811
    +812        Parameters
    +813        ----------
    +814        logscale : bool
    +815            Determines whether the scale of the y-axis is logarithmic or standard.
    +816        """
    +817        if self.N != 1:
    +818            raise Exception("Correlator needs to be projected first.")
    +819
    +820        mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist]))
    +821        x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None]
    +822
    +823        for name in mc_names:
    +824            data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T
    +825
    +826            fig = plt.figure()
    +827            ax = fig.add_subplot(111)
    +828            for dat in data:
    +829                ax.plot(x0_vals, dat, ls='-', marker='')
    +830
    +831            if logscale is True:
    +832                ax.set_yscale('log')
    +833
    +834            ax.set_xlabel(r'$x_0 / a$')
    +835            plt.title(name)
    +836            plt.draw()
     
    @@ -4063,29 +4096,29 @@ Determines whether the scale of the y-axis is logarithmic or standard.
    View Source -
    828    def dump(self, filename, datatype="json.gz", **kwargs):
    -829        """Dumps the Corr into a file of chosen type
    -830        Parameters
    -831        ----------
    -832        filename : str
    -833            Name of the file to be saved.
    -834        datatype : str
    -835            Format of the exported file. Supported formats include
    -836            "json.gz" and "pickle"
    -837        path : str
    -838            specifies a custom path for the file (default '.')
    -839        """
    -840        if datatype == "json.gz":
    -841            from .input.json import dump_to_json
    -842            if 'path' in kwargs:
    -843                file_name = kwargs.get('path') + '/' + filename
    -844            else:
    -845                file_name = filename
    -846            dump_to_json(self, file_name)
    -847        elif datatype == "pickle":
    -848            dump_object(self, filename, **kwargs)
    -849        else:
    -850            raise Exception("Unknown datatype " + str(datatype))
    +            
    838    def dump(self, filename, datatype="json.gz", **kwargs):
    +839        """Dumps the Corr into a file of chosen type
    +840        Parameters
    +841        ----------
    +842        filename : str
    +843            Name of the file to be saved.
    +844        datatype : str
    +845            Format of the exported file. Supported formats include
    +846            "json.gz" and "pickle"
    +847        path : str
    +848            specifies a custom path for the file (default '.')
    +849        """
    +850        if datatype == "json.gz":
    +851            from .input.json import dump_to_json
    +852            if 'path' in kwargs:
    +853                file_name = kwargs.get('path') + '/' + filename
    +854            else:
    +855                file_name = filename
    +856            dump_to_json(self, file_name)
    +857        elif datatype == "pickle":
    +858            dump_object(self, filename, **kwargs)
    +859        else:
    +860            raise Exception("Unknown datatype " + str(datatype))
     
    @@ -4117,8 +4150,8 @@ specifies a custom path for the file (default '.')
    View Source -
    852    def print(self, range=[0, None]):
    -853        print(self.__repr__(range))
    +            
    862    def print(self, range=[0, None]):
    +863        print(self.__repr__(range))
     
    @@ -4136,8 +4169,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1015    def sqrt(self):
    -1016        return self**0.5
    +            
    1025    def sqrt(self):
    +1026        return self**0.5
     
    @@ -4155,9 +4188,9 @@ specifies a custom path for the file (default '.')
    View Source -
    1018    def log(self):
    -1019        newcontent = [None if (item is None) else np.log(item) for item in self.content]
    -1020        return Corr(newcontent, prange=self.prange)
    +            
    1028    def log(self):
    +1029        newcontent = [None if (item is None) else np.log(item) for item in self.content]
    +1030        return Corr(newcontent, prange=self.prange)
     
    @@ -4175,9 +4208,9 @@ specifies a custom path for the file (default '.')
    View Source -
    1022    def exp(self):
    -1023        newcontent = [None if (item is None) else np.exp(item) for item in self.content]
    -1024        return Corr(newcontent, prange=self.prange)
    +            
    1032    def exp(self):
    +1033        newcontent = [None if (item is None) else np.exp(item) for item in self.content]
    +1034        return Corr(newcontent, prange=self.prange)
     
    @@ -4195,8 +4228,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1037    def sin(self):
    -1038        return self._apply_func_to_corr(np.sin)
    +            
    1047    def sin(self):
    +1048        return self._apply_func_to_corr(np.sin)
     
    @@ -4214,8 +4247,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1040    def cos(self):
    -1041        return self._apply_func_to_corr(np.cos)
    +            
    1050    def cos(self):
    +1051        return self._apply_func_to_corr(np.cos)
     
    @@ -4233,8 +4266,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1043    def tan(self):
    -1044        return self._apply_func_to_corr(np.tan)
    +            
    1053    def tan(self):
    +1054        return self._apply_func_to_corr(np.tan)
     
    @@ -4252,8 +4285,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1046    def sinh(self):
    -1047        return self._apply_func_to_corr(np.sinh)
    +            
    1056    def sinh(self):
    +1057        return self._apply_func_to_corr(np.sinh)
     
    @@ -4271,8 +4304,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1049    def cosh(self):
    -1050        return self._apply_func_to_corr(np.cosh)
    +            
    1059    def cosh(self):
    +1060        return self._apply_func_to_corr(np.cosh)
     
    @@ -4290,8 +4323,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1052    def tanh(self):
    -1053        return self._apply_func_to_corr(np.tanh)
    +            
    1062    def tanh(self):
    +1063        return self._apply_func_to_corr(np.tanh)
     
    @@ -4309,8 +4342,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1055    def arcsin(self):
    -1056        return self._apply_func_to_corr(np.arcsin)
    +            
    1065    def arcsin(self):
    +1066        return self._apply_func_to_corr(np.arcsin)
     
    @@ -4328,8 +4361,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1058    def arccos(self):
    -1059        return self._apply_func_to_corr(np.arccos)
    +            
    1068    def arccos(self):
    +1069        return self._apply_func_to_corr(np.arccos)
     
    @@ -4347,8 +4380,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1061    def arctan(self):
    -1062        return self._apply_func_to_corr(np.arctan)
    +            
    1071    def arctan(self):
    +1072        return self._apply_func_to_corr(np.arctan)
     
    @@ -4366,8 +4399,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1064    def arcsinh(self):
    -1065        return self._apply_func_to_corr(np.arcsinh)
    +            
    1074    def arcsinh(self):
    +1075        return self._apply_func_to_corr(np.arcsinh)
     
    @@ -4385,8 +4418,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1067    def arccosh(self):
    -1068        return self._apply_func_to_corr(np.arccosh)
    +            
    1077    def arccosh(self):
    +1078        return self._apply_func_to_corr(np.arccosh)
     
    @@ -4404,8 +4437,8 @@ specifies a custom path for the file (default '.')
    View Source -
    1070    def arctanh(self):
    -1071        return self._apply_func_to_corr(np.arctanh)
    +            
    1080    def arctanh(self):
    +1081        return self._apply_func_to_corr(np.arctanh)
     
    @@ -4443,64 +4476,64 @@ specifies a custom path for the file (default '.')
    View Source -
    1106    def prune(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):
    -1107        r''' Project large correlation matrix to lowest states
    -1108
    -1109        This method can be used to reduce the size of an (N x N) correlation matrix
    -1110        to (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise
    -1111        is still small.
    -1112
    -1113        Parameters
    -1114        ----------
    -1115        Ntrunc: int
    -1116            Rank of the target matrix.
    -1117        tproj: int
    -1118            Time where the eigenvectors are evaluated, corresponds to ts in the GEVP method.
    -1119            The default value is 3.
    -1120        t0proj: int
    -1121            Time where the correlation matrix is inverted. Choosing t0proj=1 is strongly
    -1122            discouraged for O(a) improved theories, since the correctness of the procedure
    -1123            cannot be granted in this case. The default value is 2.
    -1124        basematrix : Corr
    -1125            Correlation matrix that is used to determine the eigenvectors of the
    -1126            lowest states based on a GEVP. basematrix is taken to be the Corr itself if
    -1127            is is not specified.
    -1128
    -1129        Notes
    -1130        -----
    -1131        We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving
    -1132        the GEVP $$C(t) v_n(t, t_0) = \lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \equiv t_\mathrm{proj}$
    -1133        and $t_0 \equiv t_{0, \mathrm{proj}}$. The target matrix is projected onto the subspace of the
    -1134        resulting eigenvectors $v_n, n=1,\dots,N_\mathrm{trunc}$ via
    -1135        $$G^\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large
    -1136        correlation matrix and to remove some noise that is added by irrelevant operators.
    -1137        This may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated
    -1138        bound $t_0 \leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.
    -1139        '''
    -1140
    -1141        if self.N == 1:
    -1142            raise Exception('Method cannot be applied to one-dimensional correlators.')
    -1143        if basematrix is None:
    -1144            basematrix = self
    -1145        if Ntrunc >= basematrix.N:
    -1146            raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
    -1147        if basematrix.N != self.N:
    -1148            raise Exception('basematrix and targetmatrix have to be of the same size.')
    -1149
    -1150        evecs = []
    -1151        for i in range(Ntrunc):
    -1152            evecs.append(basematrix.GEVP(t0proj, tproj, state=i))
    -1153
    -1154        tmpmat = np.empty((Ntrunc, Ntrunc), dtype=object)
    -1155        rmat = []
    -1156        for t in range(basematrix.T):
    -1157            for i in range(Ntrunc):
    -1158                for j in range(Ntrunc):
    -1159                    tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j]
    -1160            rmat.append(np.copy(tmpmat))
    -1161
    -1162        newcontent = [None if (self.content[t] is None) else rmat[t] for t in range(self.T)]
    -1163        return Corr(newcontent)
    +            
    1116    def prune(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):
    +1117        r''' Project large correlation matrix to lowest states
    +1118
    +1119        This method can be used to reduce the size of an (N x N) correlation matrix
    +1120        to (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise
    +1121        is still small.
    +1122
    +1123        Parameters
    +1124        ----------
    +1125        Ntrunc: int
    +1126            Rank of the target matrix.
    +1127        tproj: int
    +1128            Time where the eigenvectors are evaluated, corresponds to ts in the GEVP method.
    +1129            The default value is 3.
    +1130        t0proj: int
    +1131            Time where the correlation matrix is inverted. Choosing t0proj=1 is strongly
    +1132            discouraged for O(a) improved theories, since the correctness of the procedure
    +1133            cannot be granted in this case. The default value is 2.
    +1134        basematrix : Corr
    +1135            Correlation matrix that is used to determine the eigenvectors of the
    +1136            lowest states based on a GEVP. basematrix is taken to be the Corr itself if
    +1137            is is not specified.
    +1138
    +1139        Notes
    +1140        -----
    +1141        We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving
    +1142        the GEVP $$C(t) v_n(t, t_0) = \lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \equiv t_\mathrm{proj}$
    +1143        and $t_0 \equiv t_{0, \mathrm{proj}}$. The target matrix is projected onto the subspace of the
    +1144        resulting eigenvectors $v_n, n=1,\dots,N_\mathrm{trunc}$ via
    +1145        $$G^\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large
    +1146        correlation matrix and to remove some noise that is added by irrelevant operators.
    +1147        This may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated
    +1148        bound $t_0 \leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.
    +1149        '''
    +1150
    +1151        if self.N == 1:
    +1152            raise Exception('Method cannot be applied to one-dimensional correlators.')
    +1153        if basematrix is None:
    +1154            basematrix = self
    +1155        if Ntrunc >= basematrix.N:
    +1156            raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
    +1157        if basematrix.N != self.N:
    +1158            raise Exception('basematrix and targetmatrix have to be of the same size.')
    +1159
    +1160        evecs = []
    +1161        for i in range(Ntrunc):
    +1162            evecs.append(basematrix.GEVP(t0proj, tproj, state=i))
    +1163
    +1164        tmpmat = np.empty((Ntrunc, Ntrunc), dtype=object)
    +1165        rmat = []
    +1166        for t in range(basematrix.T):
    +1167            for i in range(Ntrunc):
    +1168                for j in range(Ntrunc):
    +1169                    tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j]
    +1170            rmat.append(np.copy(tmpmat))
    +1171
    +1172        newcontent = [None if (self.content[t] is None) else rmat[t] for t in range(self.T)]
    +1173        return Corr(newcontent)
     
    diff --git a/docs/search.js b/docs/search.js index 41ad033d..4cfdf908 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oWhat is pyerrors?\n\n

    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:

    \n\n
      \n
    • automatic differentiation for exact liner error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
    • \n
    • treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
    • \n
    • coherent error propagation for data from different Markov chains.
    • \n
    • non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
    • \n
    • real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
    • \n
    \n\n

    There exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.

    \n\n

    Basic example

    \n\n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method()                     # Estimate the statistical error\nprint(my_new_obs)                             # Print the result to stdout\n> 0.31498(72)\n
    \n\n

    The Obs class

    \n\n

    pyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.

    \n\n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n\n

    Error propagation

    \n\n

    When performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.

    \n\n

    The Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.

    \n\n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
    \n\n

    Error estimation

    \n\n

    The error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.

    \n\n
    my_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n>  t_int         2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n\n

    We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.

    \n\n
    my_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n>  t_int         3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n\n

    The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_tauint.

    \n\n

    If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the windowsize is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.

    \n\n

    Exponential tails

    \n\n

    Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.

    \n\n
    my_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n>  t_int         3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n\n

    For the full API see pyerrors.obs.Obs.gamma_method.

    \n\n

    Multiple ensembles/replica

    \n\n

    Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.

    \n\n
    obs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 2 ensembles:\n>   \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n>   \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
    \n\n

    pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.

    \n\n
    obs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1'\n>     \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n>     \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
    \n\n

    Error estimation for multiple ensembles

    \n\n

    In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.

    \n\n
    pe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
    \n\n

    In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.

    \n\n

    Irregular Monte Carlo chains

    \n\n

    Obs objects defined on irregular Monte Carlo chains can be initialized with the parameter idl.

    \n\n
    # Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result         9.98319881e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result         9.99100712e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result         1.01718064e+00\n> 5 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
    \n\n

    Obs objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.

    \n\n

    Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.

    \n\n

    For the full API see pyerrors.obs.Obs.

    \n\n

    Correlators

    \n\n

    When one is not interested in single observables but correlation functions, pyerrors offers the Corr class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a Corr objects one needs to arrange the data as a list of Obs

    \n\n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0      0.7957(80)\n> 1      0.5156(51)\n> 2      0.3227(33)\n> 3      0.2041(21)\n
    \n\n

    In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.

    \n\n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0\n> 1      0.7957(80)\n> 2      0.5156(51)\n> 3      0.3227(33)\n> 4      0.2041(21)\n> 5\n
    \n\n

    The individual entries of a correlator can be accessed via slicing

    \n\n
    print(my_corr[3])\n> 0.3227(33)\n
    \n\n

    Error propagation with the Corr class works very similar to Obs objects. Mathematical operations are overloaded and Corr objects can be computed together with other Corr objects, Obs objects or real numbers and integers.

    \n\n
    my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
    \n\n

    pyerrors provides the user with a set of regularly used methods for the manipulation of correlator objects:

    \n\n
      \n
    • Corr.gamma_method applies the gamma method to all entries of the correlator.
    • \n
    • Corr.m_eff to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
    • \n
    • Corr.deriv returns the first derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
    • \n
    • Corr.second_deriv returns the second derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
    • \n
    • Corr.symmetric symmetrizes parity even correlations functions, assuming periodic boundary conditions.
    • \n
    • Corr.anti_symmetric anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
    • \n
    • Corr.T_symmetry averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
    • \n
    • Corr.plateau extracts a plateau value from the correlator in a given range.
    • \n
    • Corr.roll periodically shifts the correlator.
    • \n
    • Corr.reverse reverses the time ordering of the correlator.
    • \n
    • Corr.correlate constructs a disconnected correlation function from the correlator and another Corr or Obs object.
    • \n
    • Corr.reweight reweights the correlator.
    • \n
    \n\n

    pyerrors can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see pyerrors.correlators.Corr.GEVP).

    \n\n

    For the full API see pyerrors.correlators.Corr.

    \n\n

    Complex valued observables

    \n\n

    pyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.

    \n\n
    my_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
    \n\n

    Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.

    \n\n
    my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
    \n\n

    The Covobs class

    \n\n

    In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The Covobs class allows to define such quantities in pyerrors. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.

    \n\n

    This concept is built into the definition of Covobs. In pyerrors, external input is defined by $M$ mean values, a $M\\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters cov_Obs, since the second argument of this function is the covariance matrix of the Covobs.

    \n\n
    import pyerrors.obs as pe\n\nmpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')\nmpi.gamma_method()\nmpi.details()\n> Result         1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)\n>  pi^0 mass     5.00000000e-04\n> 0 samples in 1 ensemble:\n>   \u00b7 Covobs   'pi^0 mass'\n
    \n\n

    The resulting object mpi is an Obs that contains a Covobs. In the following, it may be handled as any other Obs. The contribution of the covariance matrix to the error of an Obs is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of the Obs with respect to the external quantities, which is the $1\\times M$ Jacobian matrix $J$, via\n$$s = \\sqrt{J^T \\Sigma J}\\,,$$\nwhere the Jacobian is computed for each derived quantity via automatic differentiation.

    \n\n

    Correlated auxiliary data is defined similarly to above, e.g., via

    \n\n
    RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')\nprint(RAP)\n> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]\n
    \n\n

    where RAP now is a list of two Obs that contains the two correlated parameters.

    \n\n

    Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the Covobs class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an Obs o with respect to a covariance matrix with the identifying string k may be accessed via

    \n\n
    o.covobs[k].grad\n
    \n\n

    Error propagation in iterative algorithms

    \n\n

    pyerrors supports exact linear error propagation for iterative algorithms like various variants of non-linear least sqaures fits or root finding. The derivatives required for the error propagation are calculated as described in arXiv:1809.01289.

    \n\n

    Least squares fits

    \n\n

    Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with pyerrors.fits.least_squares. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.

    \n\n

    Fit functions have to be of the following form

    \n\n
    import autograd.numpy as anp\n\ndef func(a, x):\n    return a[1] * anp.exp(-a[0] * x)\n
    \n\n

    It is important that numerical functions refer to autograd.numpy instead of numpy for the automatic differentiation in iterative algorithms to work properly.

    \n\n

    Fits can then be performed via

    \n\n
    fit_result = pe.fits.least_squares(x, y, func)\nprint("\\n", fit_result)\n> Fit with 2 parameters\n> Method: Levenberg-Marquardt\n> `ftol` termination condition is satisfied.\n> chisquare/d.o.f.: 0.9593035785160936\n\n>  Goodness of fit:\n> \u03c7\u00b2/d.o.f. = 0.959304\n> p-value   = 0.5673\n> Fit parameters:\n> 0      0.0548(28)\n> 1      1.933(64)\n
    \n\n

    where x is a list or numpy.array of floats and y is a list or numpy.array of Obs.

    \n\n

    Data stored in Corr objects can be fitted directly using the Corr.fit method.

    \n\n
    my_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
    \n\n

    this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.

    \n\n

    For fit functions with multiple independent variables the fit function can be of the form

    \n\n
    def func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n
    \n\n

    pyerrors also supports correlated fits which can be triggered via the parameter correlated_fit=True.\nDetails about how the required covariance matrix is estimated can be found in pyerrors.obs.covariance.

    \n\n

    Direct visualizations of the performed fits can be triggered via resplot=True or qqplot=True. For all available options see pyerrors.fits.least_squares.

    \n\n

    Total least squares fits

    \n\n

    pyerrors can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in scipy, see pyerrors.fits.least_squares. The syntax is identical to the standard least squares case, the only diffrence being that x also has to be a list or numpy.array of Obs.

    \n\n

    For the full API see pyerrors.fits for fits and pyerrors.roots for finding roots of functions.

    \n\n

    Matrix operations

    \n\n

    pyerrors provides wrappers for Obs- and CObs-valued matrix operations based on numpy.linalg. The supported functions include:

    \n\n
      \n
    • inv for the matrix inverse.
    • \n
    • cholseky for the Cholesky decomposition.
    • \n
    • det for the matrix determinant.
    • \n
    • eigh for eigenvalues and eigenvectors of hermitean matrices.
    • \n
    • eig for eigenvalues of general matrices.
    • \n
    • pinv for the Moore-Penrose pseudoinverse.
    • \n
    • svd for the singular-value-decomposition.
    • \n
    \n\n

    For the full API see pyerrors.linalg.

    \n\n

    Export data

    \n\n

    The preferred exported file format within pyerrors is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:

    \n\n
      \n
    • What observables are stored? Possibly: How exactly are they defined.
    • \n
    • How does each single ensemble or external quantity contribute to the error of the observable?
    • \n
    • Who did write the file when and on which machine?
    • \n
    \n\n

    This can be achieved by storing all information in one single file. The export routines of pyerrors are written such that as much information as possible is written automatically as described in the following example

    \n\n
    my_obs = pe.Obs([samples], ["test_ensemble"])\nmy_obs.tag = "My observable"\n\npe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")\n# For a single observable one can equivalently use the class method dump\nmy_obs.dump("test_output_file", description="This file contains a test observable")\n\ncheck = pe.input.json.load_json("test_output_file")\n\nprint(my_obs == check)\n> True\n
    \n\n

    The format also allows to directly write out the content of Corr objects or lists and arrays of Obs objects by passing the desired data to pyerrors.input.json.dump_to_json.

    \n\n

    json.gz format specification

    \n\n

    The first entries of the file provide optional auxiliary information:

    \n\n
      \n
    • program is a string that indicates which program was used to write the file.
    • \n
    • version is a string that specifies the version of the format.
    • \n
    • who is a string that specifies the user name of the creator of the file.
    • \n
    • date is a string and contains the creation date of the file.
    • \n
    • host is a string and contains the hostname of the machine where the file has been written.
    • \n
    • description contains information on the content of the file. This field is not filled automatically in pyerrors. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.
    • \n
    \n\n

    The only necessary entry of the file is the field\n-obsdata, an array that contains the actual data.

    \n\n

    Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of Obs, list, numpy.ndarray, Corr. All Obs inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array obsdata, are treated independently. Each entry of the array obsdata has the following required entries:

    \n\n
      \n
    • type is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
    • \n
    • value is an array that contains the mean values of the Obs inside the structure.\nThe following entries are optional:
    • \n
    • layout is a string that specifies the layout of multi-dimensional structures. Examples are \"2, 2\" for a 2x2 dimensional matrix or \"64, 4, 4\" for a Corr with $T=64$ and 4x4 matrices on each time slices. \"1\" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
    • \n
    • tag is any JSON type. It contains additional information concerning the structure. The tag of an Obs in pyerrors is written here.
    • \n
    • reweighted is a Bool that may be used to specify, whether the Obs in the structure have been reweighted.
    • \n
    • data is an array that contains the data from MC chains. We will define it below.
    • \n
    • cdata is an array that contains the data from external quantities with an error (Covobs in pyerrors). We will define it below.
    • \n
    \n\n

    The array data contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:

    \n\n
      \n
    • id, a string that contains the name of the ensemble
    • \n
    • replica, an array that contains an entry per replica of the ensemble.
    • \n
    \n\n

    Each entry of replica contains\nname, a string that contains the name of the replica\ndeltas, an array that contains the actual data.

    \n\n

    Each entry in deltas corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each Obs inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.

    \n\n

    The array cdata contains information about the contribution of auxiliary observables, represented by Covobs in pyerrors, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:

    \n\n
      \n
    • id, a string that identifies the covariance matrix
    • \n
    • layout, a string that defines the dimensions of the $M\\times M$ covariance matrix (has to be \"M, M\" or \"1\").
    • \n
    • cov, an array that contains the $M\\times M$ many entries of the covariance matrix, stored in row-major format.
    • \n
    • grad, an array that contains N entries, one for each Obs inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.
    • \n
    \n\n

    A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.

    \n\n

    Julia I/O routines for the json.gz format, compatible with ADerrors.jl, can be found here.

    \n\n

    Citing

    \n\n

    If you use pyerrors for research that leads to a publication please consider citing:

    \n\n
      \n
    • Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
    • \n
    • Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.
    • \n
    • Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.
    • \n
    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "type": "module", "doc": "

    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "type": "class", "doc": "

    The class for a correlator (time dependent sequence of pe.Obs).

    \n\n

    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with matrices.

    \n\n

    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nmatrix at every timeslice. Other dependency (eg. spatial) are not supported.

    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "type": "function", "doc": "

    Initialize a Corr object.

    \n\n
    Parameters
    \n\n
      \n
    • data_input (list or array):\nlist of Obs or list of arrays of Obs or array of Corrs
    • \n
    • padding (list, optional):\nList with two entries where the first labels the padding\nat the front of the correlator and the second the padding\nat the back.
    • \n
    • prange (list, optional):\nList containing the first and last timeslice of the plateau\nregion indentified for this correlator.
    • \n
    \n", "signature": "(self, data_input, padding=[0, 0], prange=None)", "funcdef": "def"}, "pyerrors.correlators.Corr.reweighted": {"fullname": "pyerrors.correlators.Corr.reweighted", "modulename": "pyerrors.correlators", "qualname": "Corr.reweighted", "type": "variable", "doc": "

    \n"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "type": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs)", "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "type": "function", "doc": "

    We need to project the Correlator with a Vector to get a single value at each timeslice.

    \n\n

    The method can use one or two vectors.\nIf two are specified it returns v1@G@v2 (the order might be very important.)\nBy default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to

    \n", "signature": "(self, vector_l=None, vector_r=None, normalize=False)", "funcdef": "def"}, "pyerrors.correlators.Corr.item": {"fullname": "pyerrors.correlators.Corr.item", "modulename": "pyerrors.correlators", "qualname": "Corr.item", "type": "function", "doc": "

    Picks the element [i,j] from every matrix and returns a correlator containing one Obs per timeslice.

    \n\n
    Parameters
    \n\n
      \n
    • i (int):\nFirst index to be picked.
    • \n
    • j (int):\nSecond index to be picked.
    • \n
    \n", "signature": "(self, i, j)", "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "type": "function", "doc": "

    Outputs the correlator in a plotable format.

    \n\n

    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.symmetric": {"fullname": "pyerrors.correlators.Corr.symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.symmetric", "type": "function", "doc": "

    Symmetrize the correlator around x0=0.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.anti_symmetric": {"fullname": "pyerrors.correlators.Corr.anti_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.anti_symmetric", "type": "function", "doc": "

    Anti-symmetrize the correlator around x0=0.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.matrix_symmetric", "type": "function", "doc": "

    Symmetrizes the correlator matrices on every timeslice.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.GEVP": {"fullname": "pyerrors.correlators.Corr.GEVP", "modulename": "pyerrors.correlators", "qualname": "Corr.GEVP", "type": "function", "doc": "

    Solve the general eigenvalue problem on the current correlator

    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for G(t)v= lambda G(t_0)v
    • \n
    • ts (int):\nfixed time G(t_s)v= lambda G(t_0)v if return_list=False\nIf return_list=True and sorting=Eigenvector it gives a reference point for the sorting method.
    • \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
    • \n
    • sorted_list (string):\nif this argument is set, a list of vectors (len=self.T) is returned. If it is left as None, only one vector is returned.\n\"Eigenvalue\" - The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.\n\"Eigenvector\" - Use the method described in arXiv:2004.10472 [hep-lat] to find the set of v(t) belonging to the state.\n The reference state is identified by its eigenvalue at t=ts
    • \n
    \n", "signature": "(self, t0, ts=None, state=0, sorted_list=None)", "funcdef": "def"}, "pyerrors.correlators.Corr.Eigenvalue": {"fullname": "pyerrors.correlators.Corr.Eigenvalue", "modulename": "pyerrors.correlators", "qualname": "Corr.Eigenvalue", "type": "function", "doc": "

    Determines the eigenvalue of the GEVP by solving and projecting the correlator

    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for G(t)v= lambda G(t_0)v
    • \n
    • ts (int):\nfixed time G(t_s)v= lambda G(t_0)v if return_list=False\nIf return_list=True and sorting=Eigenvector it gives a reference point for the sorting method.
    • \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
    • \n
    • sorted_list (string):\nif this argument is set, a list of vectors (len=self.T) is returned. If it is left as None, only one vector is returned.\n\"Eigenvalue\" - The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.\n\"Eigenvector\" - Use the method described in arXiv:2004.10472 [hep-lat] to find the set of v(t) belonging to the state.\n The reference state is identified by its eigenvalue at t=ts
    • \n
    \n", "signature": "(self, t0, ts=None, state=0, sorted_list=None)", "funcdef": "def"}, "pyerrors.correlators.Corr.Hankel": {"fullname": "pyerrors.correlators.Corr.Hankel", "modulename": "pyerrors.correlators", "qualname": "Corr.Hankel", "type": "function", "doc": "

    Constructs an NxN Hankel matrix

    \n\n

    C(t) c(t+1) ... c(t+n-1)\nC(t+1) c(t+2) ... c(t+n)\n.................\nC(t+(n-1)) c(t+n) ... c(t+2(n-1))

    \n\n
    Parameters
    \n\n
      \n
    • N (int):\nDimension of the Hankel matrix
    • \n
    • periodic (bool, optional):\ndetermines whether the matrix is extended periodically
    • \n
    \n", "signature": "(self, N, periodic=False)", "funcdef": "def"}, "pyerrors.correlators.Corr.roll": {"fullname": "pyerrors.correlators.Corr.roll", "modulename": "pyerrors.correlators", "qualname": "Corr.roll", "type": "function", "doc": "

    Periodically shift the correlator by dt timeslices

    \n\n
    Parameters
    \n\n
      \n
    • dt (int):\nnumber of timeslices
    • \n
    \n", "signature": "(self, dt)", "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "type": "function", "doc": "

    Reverse the time ordering of the Corr

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.thin": {"fullname": "pyerrors.correlators.Corr.thin", "modulename": "pyerrors.correlators", "qualname": "Corr.thin", "type": "function", "doc": "

    Thin out a correlator to suppress correlations

    \n\n
    Parameters
    \n\n
      \n
    • spacing (int):\nKeep only every 'spacing'th entry of the correlator
    • \n
    • offset (int):\nOffset the equal spacing
    • \n
    \n", "signature": "(self, spacing=2, offset=0)", "funcdef": "def"}, "pyerrors.correlators.Corr.correlate": {"fullname": "pyerrors.correlators.Corr.correlate", "modulename": "pyerrors.correlators", "qualname": "Corr.correlate", "type": "function", "doc": "

    Correlate the correlator with another correlator or Obs

    \n\n
    Parameters
    \n\n
      \n
    • partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
    • \n
    \n", "signature": "(self, partner)", "funcdef": "def"}, "pyerrors.correlators.Corr.reweight": {"fullname": "pyerrors.correlators.Corr.reweight", "modulename": "pyerrors.correlators", "qualname": "Corr.reweight", "type": "function", "doc": "

    Reweight the correlator.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
    • \n
    \n", "signature": "(self, weight, **kwargs)", "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "type": "function", "doc": "

    Return the time symmetry average of the correlator and its partner

    \n\n
    Parameters
    \n\n
      \n
    • partner (Corr):\nTime symmetry partner of the Corr
    • \n
    • partity (int):\nParity quantum number of the correlator, can be +1 or -1
    • \n
    \n", "signature": "(self, partner, parity=1)", "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "type": "function", "doc": "

    Return the first derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, default: symmetric
    • \n
    \n", "signature": "(self, variant='symmetric')", "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "type": "function", "doc": "

    Return the second derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, default: symmetric
    • \n
    \n", "signature": "(self, variant='symmetric')", "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "type": "function", "doc": "

    Returns the effective mass of the correlator as correlator object

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.\nsinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)
    • \n
    • guess (float):\nguess for the root finder, only relevant for the root variant
    • \n
    \n", "signature": "(self, variant='log', guess=1.0)", "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "type": "function", "doc": "

    Fits function to the data

    \n\n
    Parameters
    \n\n
      \n
    • function (obj):\nfunction to fit to the data. See fits.least_squares for details.
    • \n
    • fitrange (list):\nTwo element list containing the timeslices on which the fit is supposed to start and stop.\nCaution: This range is inclusive as opposed to standard python indexing.\nfitrange=[4, 6] corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.
    • \n
    • silent (bool):\nDecides whether output is printed to the standard output.
    • \n
    \n", "signature": "(self, function, fitrange=None, silent=False, **kwargs)", "funcdef": "def"}, "pyerrors.correlators.Corr.plateau": {"fullname": "pyerrors.correlators.Corr.plateau", "modulename": "pyerrors.correlators", "qualname": "Corr.plateau", "type": "function", "doc": "

    Extract a plateau value from a Corr object

    \n\n
    Parameters
    \n\n
      \n
    • plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
    • \n
    • method (str):\nmethod to extract the plateau.\n 'fit' fits a constant to the plateau region\n 'avg', 'average' or 'mean' just average over the given timeslices.
    • \n
    • auto_gamma (bool):\napply gamma_method with default parameters to the Corr. Defaults to None
    • \n
    \n", "signature": "(self, plateau_range=None, method='fit', auto_gamma=False)", "funcdef": "def"}, "pyerrors.correlators.Corr.set_prange": {"fullname": "pyerrors.correlators.Corr.set_prange", "modulename": "pyerrors.correlators", "qualname": "Corr.set_prange", "type": "function", "doc": "

    Sets the attribute prange of the Corr object.

    \n", "signature": "(self, prange)", "funcdef": "def"}, "pyerrors.correlators.Corr.show": {"fullname": "pyerrors.correlators.Corr.show", "modulename": "pyerrors.correlators", "qualname": "Corr.show", "type": "function", "doc": "

    Plots the correlator using the tag of the correlator as label if available.

    \n\n
    Parameters
    \n\n
      \n
    • x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8]
    • \n
    • comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.\nThe tags of these correlators are used as labels if available.
    • \n
    • logscale (bool):\nSets y-axis to logscale
    • \n
    • plateau (Obs):\nPlateau value to be visualized in the figure
    • \n
    • fit_res (Fit_result):\nFit_result object to be visualized
    • \n
    • ylabel (str):\nLabel for the y-axis
    • \n
    • save (str):\npath to file in which the figure should be saved
    • \n
    • auto_gamma (bool):\nApply the gamma method with standard parameters to all correlators and plateau values before plotting.
    • \n
    \n", "signature": "(\n self,\n x_range=None,\n comp=None,\n y_range=None,\n logscale=False,\n plateau=None,\n fit_res=None,\n ylabel=None,\n save=None,\n auto_gamma=False\n)", "funcdef": "def"}, "pyerrors.correlators.Corr.spaghetti_plot": {"fullname": "pyerrors.correlators.Corr.spaghetti_plot", "modulename": "pyerrors.correlators", "qualname": "Corr.spaghetti_plot", "type": "function", "doc": "

    Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.

    \n\n
    Parameters
    \n\n
      \n
    • logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
    • \n
    \n", "signature": "(self, logscale=True)", "funcdef": "def"}, "pyerrors.correlators.Corr.dump": {"fullname": "pyerrors.correlators.Corr.dump", "modulename": "pyerrors.correlators", "qualname": "Corr.dump", "type": "function", "doc": "

    Dumps the Corr into a file of chosen type

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nName of the file to be saved.
    • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(self, filename, datatype='json.gz', **kwargs)", "funcdef": "def"}, "pyerrors.correlators.Corr.print": {"fullname": "pyerrors.correlators.Corr.print", "modulename": "pyerrors.correlators", "qualname": "Corr.print", "type": "function", "doc": "

    \n", "signature": "(self, range=[0, None])", "funcdef": "def"}, "pyerrors.correlators.Corr.sqrt": {"fullname": "pyerrors.correlators.Corr.sqrt", "modulename": "pyerrors.correlators", "qualname": "Corr.sqrt", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.real": {"fullname": "pyerrors.correlators.Corr.real", "modulename": "pyerrors.correlators", "qualname": "Corr.real", "type": "variable", "doc": "

    \n"}, "pyerrors.correlators.Corr.imag": {"fullname": "pyerrors.correlators.Corr.imag", "modulename": "pyerrors.correlators", "qualname": "Corr.imag", "type": "variable", "doc": "

    \n"}, "pyerrors.correlators.Corr.prune": {"fullname": "pyerrors.correlators.Corr.prune", "modulename": "pyerrors.correlators", "qualname": "Corr.prune", "type": "function", "doc": "

    Project large correlation matrix to lowest states

    \n\n

    This method can be used to reduce the size of an (N x N) correlation matrix\nto (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise\nis still small.

    \n\n
    Parameters
    \n\n
      \n
    • Ntrunc (int):\nRank of the target matrix.
    • \n
    • tproj (int):\nTime where the eigenvectors are evaluated, corresponds to ts in the GEVP method.\nThe default value is 3.
    • \n
    • t0proj (int):\nTime where the correlation matrix is inverted. Choosing t0proj=1 is strongly\ndiscouraged for O(a) improved theories, since the correctness of the procedure\ncannot be granted in this case. The default value is 2.
    • \n
    • basematrix (Corr):\nCorrelation matrix that is used to determine the eigenvectors of the\nlowest states based on a GEVP. basematrix is taken to be the Corr itself if\nis is not specified.
    • \n
    \n\n
    Notes
    \n\n

    We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving\nthe GEVP $$C(t) v_n(t, t_0) = \\lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \\equiv t_\\mathrm{proj}$\nand $t_0 \\equiv t_{0, \\mathrm{proj}}$. The target matrix is projected onto the subspace of the\nresulting eigenvectors $v_n, n=1,\\dots,N_\\mathrm{trunc}$ via\n$$G^\\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large\ncorrelation matrix and to remove some noise that is added by irrelevant operators.\nThis may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated\nbound $t_0 \\leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.

    \n", "signature": "(self, Ntrunc, tproj=3, t0proj=2, basematrix=None)", "funcdef": "def"}, "pyerrors.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "type": "module", "doc": "

    \n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "type": "class", "doc": "

    \n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "type": "function", "doc": "

    Initialize Covobs object.

    \n\n
    Parameters
    \n\n
      \n
    • mean (float):\nMean value of the new Obs
    • \n
    • cov (list or array):\n2d Covariance matrix or 1d diagonal entries
    • \n
    • name (str):\nidentifier for the covariance matrix
    • \n
    • pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
    • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
    • \n
    \n", "signature": "(self, mean, cov, name, pos=None, grad=None)", "funcdef": "def"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "type": "function", "doc": "

    Return the variance (= square of the error) of the Covobs

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.covobs.Covobs.cov": {"fullname": "pyerrors.covobs.Covobs.cov", "modulename": "pyerrors.covobs", "qualname": "Covobs.cov", "type": "variable", "doc": "

    \n"}, "pyerrors.covobs.Covobs.grad": {"fullname": "pyerrors.covobs.Covobs.grad", "modulename": "pyerrors.covobs", "qualname": "Covobs.grad", "type": "variable", "doc": "

    \n"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "type": "module", "doc": "

    \n"}, "pyerrors.dirac.epsilon_tensor": {"fullname": "pyerrors.dirac.epsilon_tensor", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor", "type": "function", "doc": "

    Rank-3 epsilon tensor

    \n\n

    Based on https://codegolf.stackexchange.com/a/160375

    \n", "signature": "(i, j, k)", "funcdef": "def"}, "pyerrors.dirac.epsilon_tensor_rank4": {"fullname": "pyerrors.dirac.epsilon_tensor_rank4", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor_rank4", "type": "function", "doc": "

    Rank-4 epsilon tensor

    \n\n

    Extension of https://codegolf.stackexchange.com/a/160375

    \n", "signature": "(i, j, k, o)", "funcdef": "def"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "type": "function", "doc": "

    Returns gamma matrix in Grid labeling.

    \n", "signature": "(gamma_tag)", "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "type": "module", "doc": "

    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "type": "class", "doc": "

    Represents fit results.

    \n\n
    Attributes
    \n\n
      \n
    • fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
    • \n
    \n", "bases": "collections.abc.Sequence"}, "pyerrors.fits.Fit_result.__init__": {"fullname": "pyerrors.fits.Fit_result.__init__", "modulename": "pyerrors.fits", "qualname": "Fit_result.__init__", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "type": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x).

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of floats.
    • \n
    • y (list):\nlist of Obs.
    • \n
    • func (object):\nfit function, has to be of the form

      \n\n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n\n

      For multiple x values func can be of the form

      \n\n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • priors (list, optional):\npriors has to be a list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
    • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
    • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\nnon-linear fits with many parameters.
    • \n
    • method (str, optional):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
    • \n
    • correlated_fit (bool):\nIf True, use the full inverse covariance matrix in the definition of the chisquare cost function.\nFor details about how the covariance matrix is estimated see pyerrors.obs.covariance.\nIn practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).\nThis procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).\nAt the moment this option only works for prior==None and when no method is given.
    • \n
    • expected_chisquare (bool):\nIf True estimates the expected chisquare which is\ncorrected by effects caused by correlated input data (default False).
    • \n
    • resplot (bool):\nIf True, a plot which displays fit, data and residuals is generated (default False).
    • \n
    • qqplot (bool):\nIf True, a quantile-quantile plot of the fit result is generated (default False).
    • \n
    \n", "signature": "(x, y, func, priors=None, silent=False, **kwargs)", "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of Obs, or a tuple of lists of Obs
    • \n
    • y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
    • \n
    • func (object):\nfunc has to be of the form

      \n\n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n\n

      For multiple x values func can be of the form

      \n\n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
    • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
    • \n
    • expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
    • \n
    \n\n
    Notes
    \n\n

    Based on the orthogonal distance regression module of scipy

    \n", "signature": "(x, y, func, silent=False, **kwargs)", "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "type": "function", "doc": "

    Performs a linear fit to y = n + m * x and returns two Obs n, m.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nCan either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.
    • \n
    • y (list):\nList of Obs, the dvalues of the Obs are used as yerror for the fit.
    • \n
    \n", "signature": "(x, y, **kwargs)", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "type": "function", "doc": "

    Generates a quantile-quantile plot of the fit result which can be used to\ncheck if the residuals of the fit are gaussian distributed.

    \n", "signature": "(x, o_y, func, p)", "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "type": "function", "doc": "

    Generates a plot which compares the fit to the data and displays the corresponding residuals

    \n", "signature": "(x, y, func, fit_res)", "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "type": "function", "doc": "

    Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta.

    \n", "signature": "(x, func, beta)", "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "type": "function", "doc": "

    Performs a Kolmogorov\u2013Smirnov test for the p-values of all fit object.

    \n\n
    Parameters
    \n\n
      \n
    • objects (list):\nList of fit results to include in the analysis (optional).
    • \n
    \n", "signature": "(objects=None)", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "type": "module", "doc": "

    pyerrors includes an input submodule in which input routines and parsers for the output of various numerical programs are contained.

    \n\n

    Jackknife samples

    \n\n

    For comparison with other analysis workflows pyerrors can also generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.

    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "type": "module", "doc": "

    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "type": "function", "doc": "

    Extract generic MCMC data from a bdio file

    \n\n

    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
    • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs)", "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "type": "function", "doc": "

    Write Obs to a bdio file according to ADerrors conventions

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
    • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    \n", "signature": "(obs_list, file_path, bdio_path='./libbdio.so', **kwargs)", "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "type": "function", "doc": "

    Extract mesons data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
    • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    • start (int):\nThe first configuration to be read (default 1)
    • \n
    • stop (int):\nThe last configuration to be read (default None)
    • \n
    • step (int):\nFixed step size between two measurements (default 1)
    • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs)", "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "type": "function", "doc": "

    Extract dSdm data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, kappa)

    \n\n

    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
    • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    • start (int):\nThe first configuration to be read (default 1)
    • \n
    • stop (int):\nThe last configuration to be read (default None)
    • \n
    • step (int):\nFixed step size between two measurements (default 1)
    • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs)", "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "type": "module", "doc": "

    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "type": "function", "doc": "

    Read hadrons meson hdf5 file and extract the meson labeled 'meson'

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n", "signature": "(path, filestem, ens_id, meson='meson_0', idl=None)", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "type": "class", "doc": "

    ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)

    \n\n

    An array object represents a multidimensional, homogeneous array\nof fixed-size items. An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)

    \n\n

    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.

    \n\n

    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.

    \n\n
    Parameters
    \n\n
      \n
    • (for the __new__ method; see Notes below)
    • \n
    • shape (tuple of ints):\nShape of created array.
    • \n
    • dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
    • \n
    • buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
    • \n
    • offset (int, optional):\nOffset of array data in buffer.
    • \n
    • strides (tuple of ints, optional):\nStrides of data in memory.
    • \n
    • order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
    • \n
    \n\n
    Attributes
    \n\n
      \n
    • T (ndarray):\nTranspose of the array.
    • \n
    • data (buffer):\nThe array's elements, in memory.
    • \n
    • dtype (dtype object):\nDescribes the format of the elements in the array.
    • \n
    • flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
    • \n
    • flat (numpy.flatiter object):\nFlattened version of the array as an iterator. The iterator\nallows assignments, e.g., x.flat = 3 (See ndarray.flat for\nassignment examples; TODO).
    • \n
    • imag (ndarray):\nImaginary part of the array.
    • \n
    • real (ndarray):\nReal part of the array.
    • \n
    • size (int):\nNumber of elements in the array.
    • \n
    • itemsize (int):\nThe memory use of each array element in bytes.
    • \n
    • nbytes (int):\nThe total number of bytes required to store the array data,\ni.e., itemsize * size.
    • \n
    • ndim (int):\nThe array's number of dimensions.
    • \n
    • shape (tuple of ints):\nShape of the array.
    • \n
    • strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous (3, 4) array of type\nint16 in C-order has strides (8, 2). This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4).
    • \n
    • ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
    • \n
    • base (ndarray):\nIf the array is a view into another array, that array is its base\n(unless that array is also a view). The base array is where the\narray data is actually stored.
    • \n
    \n\n
    See Also
    \n\n

    array: Construct an array.
    \nzeros: Create an array, each element of which is zero.
    \nempty: Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\").
    \ndtype: Create a data-type.
    \nnumpy.typing.NDArray: An ndarray alias :term:generic <generic type>\nw.r.t. its dtype.type <numpy.dtype.type>.

    \n\n
    Notes
    \n\n

    There are two modes of creating an array using __new__:

    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
    2. \n
    3. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
    4. \n
    \n\n

    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.

    \n\n
    Examples
    \n\n

    These examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.

    \n\n

    First mode, buffer is None:

    \n\n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n\n

    Second mode:

    \n\n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n", "bases": "numpy.ndarray"}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"fullname": "pyerrors.input.hadrons.Npr_matrix.__init__", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.__init__", "type": "function", "doc": "

    \n", "signature": "()", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"fullname": "pyerrors.input.hadrons.Npr_matrix.g5H", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.g5H", "type": "variable", "doc": "

    Gamma_5 hermitean conjugate

    \n\n

    Uses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.

    \n"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "type": "function", "doc": "

    Read hadrons ExternalLeg hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None)", "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "type": "function", "doc": "

    Read hadrons Bilinear hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None)", "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "type": "function", "doc": "

    Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    • vertices (list):\nVertex functions to be extracted.
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None, vertices=['VA', 'AV'])", "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "type": "module", "doc": "

    \n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "type": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    \n", "signature": "(ol, description='', indent=1)", "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "type": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
    • \n
    \n", "signature": "(ol, fname, description='', indent=1, gz=True)", "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "type": "function", "doc": "

    Reconstruct a list of Obs or structures containing Obs from a json string.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • json_string (str):\njson string containing the data.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    \n", "signature": "(json_string, verbose=True, full_output=False)", "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "type": "function", "doc": "

    Import a list of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False)", "funcdef": "def"}, "pyerrors.input.json.dump_dict_to_json": {"fullname": "pyerrors.input.json.dump_dict_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_dict_to_json", "type": "function", "doc": "

    Export a dict of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • od (dict):\nDict of JSON valid structures and objects that will be exported.\nAt the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    • reps (str):\nSpecify the structure of the placeholder in exported dict to be reps[0-9]+.
    • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
    • \n
    \n", "signature": "(od, fname, description='', indent=1, reps='DICTOBS', gz=True)", "funcdef": "def"}, "pyerrors.input.json.load_json_dict": {"fullname": "pyerrors.input.json.load_json_dict", "modulename": "pyerrors.input.json", "qualname": "load_json_dict", "type": "function", "doc": "

    Import a dict of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    • reps (str):\nSpecify the structure of the placeholder in imported dict to be reps[0-9]+.
    • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS')", "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "type": "module", "doc": "

    \n"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "type": "function", "doc": "

    Read pbp format from given folder structure. Returns a list of length nrw

    \n\n
    Parameters
    \n\n
      \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
    • \n
    \n", "signature": "(path, prefix, **kwargs)", "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "type": "module", "doc": "

    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "type": "function", "doc": "

    Read rwms format from given folder structure. Returns a list of length nrw

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath that contains the data files
    • \n
    • prefix (str):\nall files in path that start with prefix are considered as input files.\nMay be used together postfix to consider only special file endings.\nPrefix is ignored, if the keyword 'files' is used.
    • \n
    • version (str):\nversion of openQCD, default 2.0
    • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
    • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
    • \n
    • postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
    • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
    • \n
    • print_err (bool):\nPrint additional information that is useful for debugging.
    • \n
    \n", "signature": "(path, prefix, version='2.0', names=None, **kwargs)", "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "type": "function", "doc": "

    Extract t0 from given .ms.dat files. Returns t0 as Obs.

    \n\n

    It is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3\nis fitted with a linear function\nfrom which the exact root is extracted.

    \n\n

    It is assumed that one measurement is performed for each config.\nIf this is not the case, the resulting idl, as well as the handling\nof r_start, r_stop and r_step is wrong and the user has to correct\nthis in the resulting observable.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to .ms.dat files
    • \n
    • prefix (str):\nEnsemble prefix
    • \n
    • dtr_read (int):\nDetermines how many trajectories should be skipped\nwhen reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
    • \n
    • xmin (int):\nFirst timeslice where the boundary\neffects have sufficiently decayed.
    • \n
    • spatial_extent (int):\nspatial extent of the lattice, required for normalization.
    • \n
    • fit_range (int):\nNumber of data points left and right of the zero\ncrossing to be included in the linear fit. (Default: 5)
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
    • \n
    • plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
    • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
    • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
    • \n
    • plot_fit (bool):\nIf true, the fit for the extraction of t0 is shown together with the data.
    • \n
    • assume_thermalization (bool):\nIf True: If the first record divided by the distance between two measurements is larger than\n1, it is assumed that this is due to thermalization and the first measurement belongs\nto the first config (default).\nIf False: The config numbers are assumed to be traj_number // difference
    • \n
    \n", "signature": "(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs)", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop": {"fullname": "pyerrors.input.openQCD.read_qtop", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop", "type": "function", "doc": "

    Read the topologial charge based on openQCD gradient flow measurements.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\ncycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • version (str):\nEither openQCD or sfqcd, depending on the data.
    • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
    • \n
    • integer_charge (bool):\nIf True, the charge is rounded towards the nearest integer on each config.
    • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, version='openQCD', **kwargs)", "funcdef": "def"}, "pyerrors.input.openQCD.qtop_projection": {"fullname": "pyerrors.input.openQCD.qtop_projection", "modulename": "pyerrors.input.openQCD", "qualname": "qtop_projection", "type": "function", "doc": "

    Returns the projection to the topological charge sector defined by target.

    \n\n
    Parameters
    \n\n
      \n
    • path (Obs):\nTopological charge.
    • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
    • \n
    \n", "signature": "(qtop, target=0)", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop_sector": {"fullname": "pyerrors.input.openQCD.read_qtop_sector", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop_sector", "type": "function", "doc": "

    Constructs reweighting factors to a specified topological sector.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L
    • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of trajectories\nbetween two configs.\nif it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\ncycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • version (str):\nversion string of the openQCD (sfqcd) version used to create\nthe ensemble. Default is 2.0. May also be set to sfqcd.
    • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
    • \n
    • r_start (list):\noffset of the first ensemble, making it easier to match\nlater on with other Obs
    • \n
    • r_stop (list):\nlast configurations that need to be read (per replicum)
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
    • \n
    \n", "signature": "(path, prefix, c, target=0, **kwargs)", "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "type": "module", "doc": "

    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "type": "function", "doc": "

    Read sfcf c format from given folder structure.

    \n\n
    Parameters
    \n\n
      \n
    • quarks (str):\nLabel of the quarks used in the sfcf input file. e.g. \"quark quark\"\nfor version 0.0 this does NOT need to be given with the typical \" - \"\nthat is present in the output file,\nthis is done automatically for this version
    • \n
    • noffset (int):\nOffset of the source (only relevant when wavefunctions are used)
    • \n
    • wf (int):\nID of wave function
    • \n
    • wf2 (int):\nID of the second wavefunction\n(only relevant for boundary-to-boundary correlation functions)
    • \n
    • im (bool):\nif True, read imaginary instead of real part\nof the correlation function.
    • \n
    • corr_type (str):\nchange between bi (boundary - inner) (default) bib (boundary - inner - boundary) and bb (boundary - boundary)\ncorrelator types
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
    • \n
    • ens_name (str):\nreplaces the name of the ensemble
    • \n
    • version (str):\nversion of SFCF, with which the measurement was done.\nif the compact output option (-c) was specified,\nappend a \"c\" to the version (e.g. \"1.0c\")\nif the append output option (-a) was specified,\nappend an \"a\" to the version
    • \n
    • cfg_separator (str):\nString that separates the ensemble identifier from the configuration number (default 'n').
    • \n
    • replica (list):\nlist of replica to be read, default is all
    • \n
    • files (list):\nlist of files to be read per replica, default is all.\nfor non-compact output format, hand the folders to be read here.
    • \n
    • check_configs:: list of list of supposed configs, eg. [range(1,1000)]\nfor one replicum with 1000 configs
    • \n
    \n", "signature": "(\n path,\n prefix,\n name,\n quarks='.*',\n corr_type='bi',\n noffset=0,\n wf=0,\n wf2=0,\n version='1.0c',\n cfg_separator='n',\n **kwargs\n)", "funcdef": "def"}, "pyerrors.input.utils": {"fullname": "pyerrors.input.utils", "modulename": "pyerrors.input.utils", "type": "module", "doc": "

    Utilities for the input

    \n"}, "pyerrors.input.utils.check_idl": {"fullname": "pyerrors.input.utils.check_idl", "modulename": "pyerrors.input.utils", "qualname": "check_idl", "type": "function", "doc": "

    Checks if list of configurations is contained in an idl

    \n\n
    Parameters
    \n\n
      \n
    • idl (range or list):\nidl of the current replicum
    • \n
    • che (list):\nlist of configurations to be checked against
    • \n
    \n", "signature": "(idl, che)", "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "type": "module", "doc": "

    \n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "type": "function", "doc": "

    Matrix multiply all operands.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    • This implementation is faster compared to standard multiplication via the @ operator.
    • \n
    \n", "signature": "(*operands)", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "type": "function", "doc": "

    Matrix multiply both operands making use of the jackknife approximation.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    • For large matrices this is considerably faster compared to matmul.
    • \n
    \n", "signature": "(*operands)", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "type": "function", "doc": "

    Wrapper for numpy.einsum

    \n\n
    Parameters
    \n\n
      \n
    • subscripts (str):\nSubscripts for summation (see numpy documentation for details)
    • \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    \n", "signature": "(subscripts, *operands)", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "type": "function", "doc": "

    Inverse of Obs or CObs valued matrices.

    \n", "signature": "(x)", "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "type": "function", "doc": "

    Cholesky decomposition of Obs or CObs valued matrices.

    \n", "signature": "(x)", "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "type": "function", "doc": "

    Determinant of Obs valued matrices.

    \n", "signature": "(x)", "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "type": "function", "doc": "

    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.

    \n", "signature": "(obs, **kwargs)", "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "type": "function", "doc": "

    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.

    \n", "signature": "(obs, **kwargs)", "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "type": "function", "doc": "

    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.

    \n", "signature": "(obs, **kwargs)", "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "type": "function", "doc": "

    Computes the singular value decomposition of a matrix of Obs.

    \n", "signature": "(obs, **kwargs)", "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "type": "module", "doc": "

    \n"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "type": "function", "doc": "

    Dump object into pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • obj (object):\nobject to be saved in the pickle file
    • \n
    • name (str):\nname of the file
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(obj, name, **kwargs)", "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "type": "function", "doc": "

    Load object from pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the file
    • \n
    \n", "signature": "(path)", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "type": "function", "doc": "

    Generate an Obs object with given value, dvalue and name for test purposes

    \n\n
    Parameters
    \n\n
      \n
    • value (float):\ncentral value of the Obs to be generated.
    • \n
    • dvalue (float):\nerror of the Obs to be generated.
    • \n
    • name (str):\nname of the ensemble for which the Obs is to be generated.
    • \n
    • samples (int):\nnumber of samples for the Obs (default 1000).
    • \n
    \n", "signature": "(value, dvalue, name, samples=1000)", "funcdef": "def"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "type": "function", "doc": "

    Generate observables with given covariance and autocorrelation times.

    \n\n
    Parameters
    \n\n
      \n
    • means (list):\nlist containing the mean value of each observable.
    • \n
    • cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
    • \n
    • name (str):\nensemble name for the data to be geneated.
    • \n
    • tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
    • \n
    • samples (int):\nnumber of samples to be generated for each observable.
    • \n
    \n", "signature": "(means, cov, name, tau=0.5, samples=1000)", "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "type": "module", "doc": "

    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "type": "function", "doc": "

    Matrix pencil method to extract k energy levels from data

    \n\n

    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    \n\n
    Parameters
    \n\n
      \n
    • data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
    • \n
    • k (int):\nNumber of states to extract (default 1).
    • \n
    • p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
    • \n
    \n", "signature": "(corrs, k=1, p=None, **kwargs)", "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "type": "module", "doc": "

    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "type": "class", "doc": "

    Class for a general observable.

    \n\n

    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.

    \n\n
    Attributes
    \n\n
      \n
    • S_global (float):\nStandard value for S (default 2.0)
    • \n
    • S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
    • \n
    • tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
    • \n
    • tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
    • \n
    • N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
    • \n
    • N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
    • \n
    \n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "type": "function", "doc": "

    Initialize Obs object.

    \n\n
    Parameters
    \n\n
      \n
    • samples (list):\nlist of numpy arrays containing the Monte Carlo samples
    • \n
    • names (list):\nlist of strings labeling the individual samples
    • \n
    • idl (list, optional):\nlist of ranges or lists on which the samples are defined
    • \n
    \n", "signature": "(self, samples, names, idl=None, **kwargs)", "funcdef": "def"}, "pyerrors.obs.Obs.S_global": {"fullname": "pyerrors.obs.Obs.S_global", "modulename": "pyerrors.obs", "qualname": "Obs.S_global", "type": "variable", "doc": "

    \n", "default_value": " = 2.0"}, "pyerrors.obs.Obs.S_dict": {"fullname": "pyerrors.obs.Obs.S_dict", "modulename": "pyerrors.obs", "qualname": "Obs.S_dict", "type": "variable", "doc": "

    \n", "default_value": " = {}"}, "pyerrors.obs.Obs.tau_exp_global": {"fullname": "pyerrors.obs.Obs.tau_exp_global", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp_global", "type": "variable", "doc": "

    \n", "default_value": " = 0.0"}, "pyerrors.obs.Obs.tau_exp_dict": {"fullname": "pyerrors.obs.Obs.tau_exp_dict", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp_dict", "type": "variable", "doc": "

    \n", "default_value": " = {}"}, "pyerrors.obs.Obs.N_sigma_global": {"fullname": "pyerrors.obs.Obs.N_sigma_global", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma_global", "type": "variable", "doc": "

    \n", "default_value": " = 1.0"}, "pyerrors.obs.Obs.N_sigma_dict": {"fullname": "pyerrors.obs.Obs.N_sigma_dict", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma_dict", "type": "variable", "doc": "

    \n", "default_value": " = {}"}, "pyerrors.obs.Obs.filter_eps": {"fullname": "pyerrors.obs.Obs.filter_eps", "modulename": "pyerrors.obs", "qualname": "Obs.filter_eps", "type": "variable", "doc": "

    \n", "default_value": " = 1e-10"}, "pyerrors.obs.Obs.names": {"fullname": "pyerrors.obs.Obs.names", "modulename": "pyerrors.obs", "qualname": "Obs.names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.shape": {"fullname": "pyerrors.obs.Obs.shape", "modulename": "pyerrors.obs", "qualname": "Obs.shape", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.r_values": {"fullname": "pyerrors.obs.Obs.r_values", "modulename": "pyerrors.obs", "qualname": "Obs.r_values", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.deltas": {"fullname": "pyerrors.obs.Obs.deltas", "modulename": "pyerrors.obs", "qualname": "Obs.deltas", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.N": {"fullname": "pyerrors.obs.Obs.N", "modulename": "pyerrors.obs", "qualname": "Obs.N", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.is_merged": {"fullname": "pyerrors.obs.Obs.is_merged", "modulename": "pyerrors.obs", "qualname": "Obs.is_merged", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.idl": {"fullname": "pyerrors.obs.Obs.idl", "modulename": "pyerrors.obs", "qualname": "Obs.idl", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.ddvalue": {"fullname": "pyerrors.obs.Obs.ddvalue", "modulename": "pyerrors.obs", "qualname": "Obs.ddvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.reweighted": {"fullname": "pyerrors.obs.Obs.reweighted", "modulename": "pyerrors.obs", "qualname": "Obs.reweighted", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tag": {"fullname": "pyerrors.obs.Obs.tag", "modulename": "pyerrors.obs", "qualname": "Obs.tag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.value": {"fullname": "pyerrors.obs.Obs.value", "modulename": "pyerrors.obs", "qualname": "Obs.value", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.dvalue": {"fullname": "pyerrors.obs.Obs.dvalue", "modulename": "pyerrors.obs", "qualname": "Obs.dvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_names": {"fullname": "pyerrors.obs.Obs.e_names", "modulename": "pyerrors.obs", "qualname": "Obs.e_names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.cov_names": {"fullname": "pyerrors.obs.Obs.cov_names", "modulename": "pyerrors.obs", "qualname": "Obs.cov_names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.mc_names": {"fullname": "pyerrors.obs.Obs.mc_names", "modulename": "pyerrors.obs", "qualname": "Obs.mc_names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_content": {"fullname": "pyerrors.obs.Obs.e_content", "modulename": "pyerrors.obs", "qualname": "Obs.e_content", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.covobs": {"fullname": "pyerrors.obs.Obs.covobs", "modulename": "pyerrors.obs", "qualname": "Obs.covobs", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "type": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
    • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
    • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
    • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
    • \n
    \n", "signature": "(self, **kwargs)", "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "type": "function", "doc": "

    Output detailed properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • ens_content (bool):\nprint details about the ensembles and replica if true.
    • \n
    \n", "signature": "(self, ens_content=True)", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "type": "function", "doc": "

    Checks whether the observable is zero within 'sigma' standard errors.

    \n\n
    Parameters
    \n\n
      \n
    • sigma (int):\nNumber of standard errors used for the check.
    • \n
    • Works only properly when the gamma method was run.
    • \n
    \n", "signature": "(self, sigma=1)", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "type": "function", "doc": "

    Checks whether the observable is zero within a given tolerance.

    \n\n
    Parameters
    \n\n
      \n
    • atol (float):\nAbsolute tolerance (for details see numpy documentation).
    • \n
    \n", "signature": "(self, atol=1e-10)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "type": "function", "doc": "

    Plot integrated autocorrelation time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "type": "function", "doc": "

    Plot normalized autocorrelation function time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "type": "function", "doc": "

    Plot replica distribution for each ensemble with more than one replicum.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "type": "function", "doc": "

    Plot derived Monte Carlo history for each ensemble

    \n\n
    Parameters
    \n\n
      \n
    • expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
    • \n
    \n", "signature": "(self, expand=True)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "type": "function", "doc": "

    Plot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "type": "function", "doc": "

    Dump the Obs to a file 'name' of chosen format.

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nname of the file to be saved.
    • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
    • \n
    • description (str):\nDescription for output file, only relevant for json.gz format.
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(self, filename, datatype='json.gz', description='', **kwargs)", "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "type": "function", "doc": "

    Export jackknife samples from the Obs

    \n\n
    Returns
    \n\n
      \n
    • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
    • \n
    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.N_sigma": {"fullname": "pyerrors.obs.Obs.N_sigma", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.S": {"fullname": "pyerrors.obs.Obs.S", "modulename": "pyerrors.obs", "qualname": "Obs.S", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_ddvalue": {"fullname": "pyerrors.obs.Obs.e_ddvalue", "modulename": "pyerrors.obs", "qualname": "Obs.e_ddvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_drho": {"fullname": "pyerrors.obs.Obs.e_drho", "modulename": "pyerrors.obs", "qualname": "Obs.e_drho", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_dtauint": {"fullname": "pyerrors.obs.Obs.e_dtauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_dtauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_dvalue": {"fullname": "pyerrors.obs.Obs.e_dvalue", "modulename": "pyerrors.obs", "qualname": "Obs.e_dvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_n_dtauint": {"fullname": "pyerrors.obs.Obs.e_n_dtauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_n_dtauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_n_tauint": {"fullname": "pyerrors.obs.Obs.e_n_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_n_tauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_rho": {"fullname": "pyerrors.obs.Obs.e_rho", "modulename": "pyerrors.obs", "qualname": "Obs.e_rho", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_tauint": {"fullname": "pyerrors.obs.Obs.e_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_tauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_windowsize": {"fullname": "pyerrors.obs.Obs.e_windowsize", "modulename": "pyerrors.obs", "qualname": "Obs.e_windowsize", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tau_exp": {"fullname": "pyerrors.obs.Obs.tau_exp", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "type": "class", "doc": "

    Class for a complex valued observable.

    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "type": "function", "doc": "

    \n", "signature": "(self, real, imag=0.0)", "funcdef": "def"}, "pyerrors.obs.CObs.tag": {"fullname": "pyerrors.obs.CObs.tag", "modulename": "pyerrors.obs", "qualname": "CObs.tag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.real": {"fullname": "pyerrors.obs.CObs.real", "modulename": "pyerrors.obs", "qualname": "CObs.real", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.imag": {"fullname": "pyerrors.obs.CObs.imag", "modulename": "pyerrors.obs", "qualname": "CObs.imag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "type": "function", "doc": "

    Executes the gamma_method for the real and the imaginary part.

    \n", "signature": "(self, **kwargs)", "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "type": "function", "doc": "

    Checks whether both real and imaginary part are zero within machine precision.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "type": "function", "doc": "

    Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.

    \n\n
    Parameters
    \n\n
      \n
    • func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
    • \n
    • data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
    • \n
    • num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
    • \n
    • man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
    • \n
    \n\n
    Notes
    \n\n

    For simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use

    \n\n

    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])

    \n", "signature": "(func, data, array_mode=False, **kwargs)", "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "type": "function", "doc": "

    Reweight a list of observables.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
    • \n
    \n", "signature": "(weight, obs, **kwargs)", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "type": "function", "doc": "

    Correlate two observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs_a (Obs):\nFirst observable
    • \n
    • obs_b (Obs):\nSecond observable
    • \n
    \n\n
    Notes
    \n\n

    Keep in mind to only correlate primary observables which have not been reweighted\nyet. The reweighting has to be applied after correlating the observables.\nCurrently only works if ensembles are identical (this is not strictly necessary).

    \n", "signature": "(obs_a, obs_b)", "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "type": "function", "doc": "

    Calculates the covariance matrix of a set of observables.

    \n\n

    The gamma method has to be applied first to all observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs (list or numpy.ndarray):\nList or one dimensional array of Obs
    • \n
    • visualize (bool):\nIf True plots the corresponding normalized correlation matrix (default False).
    • \n
    • correlation (bool):\nIf True the correlation instead of the covariance is returned (default False).
    • \n
    • smooth (None or int):\nIf smooth is an integer 'E' between 2 and the dimension of the matrix minus 1 the eigenvalue\nsmoothing procedure of hep-lat/9412087 is applied to the correlation matrix which leaves the\nlargest E eigenvalues essentially unchanged and smoothes the smaller eigenvalues to avoid extremely\nsmall ones.
    • \n
    \n\n
    Notes
    \n\n

    The covariance is estimated by calculating the correlation matrix assuming no autocorrelation and then rescaling the correlation matrix by the full errors including the previous gamma method estimate for the autocorrelation of the observables. The covariance at windowsize 0 is guaranteed to be positive semi-definite\n$$v_i\\Gamma_{ij}(0)v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i,j}v_i\\delta_i^s\\delta_j^s v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i}|v_i\\delta_i^s|^2\\geq 0\\,,$$ for every $v\\in\\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.\nFor observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.\n$$\\tau_{\\mathrm{int}, ij}=\\sqrt{\\tau_{\\mathrm{int}, i}\\times \\tau_{\\mathrm{int}, j}}$$\nThis construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).

    \n", "signature": "(obs, visualize=False, correlation=False, smooth=None, **kwargs)", "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "type": "function", "doc": "

    Imports jackknife samples and returns an Obs

    \n\n
    Parameters
    \n\n
      \n
    • jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
    • \n
    • name (str):\nname of the ensemble the samples are defined on.
    • \n
    \n", "signature": "(jacks, name, idl=None)", "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "type": "function", "doc": "

    Combine all observables in list_of_obs into one new observable

    \n\n
    Parameters
    \n\n
      \n
    • list_of_obs (list):\nlist of the Obs object to be combined
    • \n
    \n\n
    Notes
    \n\n

    It is not possible to combine obs which are based on the same replicum

    \n", "signature": "(list_of_obs)", "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "type": "function", "doc": "

    Create an Obs based on mean(s) and a covariance matrix

    \n\n
    Parameters
    \n\n
      \n
    • mean (list of floats or float):\nN mean value(s) of the new Obs
    • \n
    • cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
    • \n
    • name (str):\nidentifier for the covariance matrix
    • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
    • \n
    \n", "signature": "(means, cov, name, grad=None)", "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "type": "module", "doc": "

    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "type": "function", "doc": "

    Finds the root of the function func(x, d) where d is an Obs.

    \n\n
    Parameters
    \n\n
      \n
    • d (Obs):\nObs passed to the function.
    • \n
    • func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:\npython\nimport autograd.numpy as anp\ndef root_func(x, d):\n return anp.exp(-x ** 2) - d\n
    • \n
    • guess (float):\nInitial guess for the minimization.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • Obs: Obs valued root of the function.
    • \n
    \n", "signature": "(d, func, guess=1.0, **kwargs)", "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "type": "module", "doc": "

    \n"}}, "docInfo": {"pyerrors": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 7902}, "pyerrors.correlators": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 108}, "pyerrors.correlators.Corr.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 94}, "pyerrors.correlators.Corr.reweighted": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.gamma_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 13}, "pyerrors.correlators.Corr.projected": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 64}, "pyerrors.correlators.Corr.item": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 53}, "pyerrors.correlators.Corr.plottable": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 31}, "pyerrors.correlators.Corr.symmetric": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 9}, "pyerrors.correlators.Corr.anti_symmetric": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 10}, "pyerrors.correlators.Corr.matrix_symmetric": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 10}, "pyerrors.correlators.Corr.GEVP": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 178}, "pyerrors.correlators.Corr.Eigenvalue": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 181}, "pyerrors.correlators.Corr.Hankel": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 67}, "pyerrors.correlators.Corr.roll": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 26}, "pyerrors.correlators.Corr.reverse": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 9}, "pyerrors.correlators.Corr.thin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 43}, "pyerrors.correlators.Corr.correlate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 53}, "pyerrors.correlators.Corr.reweight": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 79}, "pyerrors.correlators.Corr.T_symmetry": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 51}, "pyerrors.correlators.Corr.deriv": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 46}, "pyerrors.correlators.Corr.second_deriv": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 44}, "pyerrors.correlators.Corr.m_eff": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 135}, "pyerrors.correlators.Corr.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 110}, "pyerrors.correlators.Corr.plateau": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 92}, "pyerrors.correlators.Corr.set_prange": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 11}, "pyerrors.correlators.Corr.show": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 174}, "pyerrors.correlators.Corr.spaghetti_plot": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 42}, "pyerrors.correlators.Corr.dump": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 69}, "pyerrors.correlators.Corr.print": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.sqrt": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.log": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.exp": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.sin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.cos": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.tan": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.sinh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.cosh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.tanh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arcsin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arccos": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arctan": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arcsinh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arccosh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arctanh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.real": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.imag": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.prune": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 325}, "pyerrors.covobs": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.covobs.Covobs": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.covobs.Covobs.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 100}, "pyerrors.covobs.Covobs.errsq": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 12}, "pyerrors.covobs.Covobs.cov": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.covobs.Covobs.grad": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.dirac": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.dirac.epsilon_tensor": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 13}, "pyerrors.dirac.epsilon_tensor_rank4": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 13}, "pyerrors.dirac.Grid_gamma": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 9}, "pyerrors.fits": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.fits.Fit_result": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 32}, "pyerrors.fits.Fit_result.__init__": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.fits.Fit_result.gamma_method": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 10}, "pyerrors.fits.least_squares": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 609}, "pyerrors.fits.total_least_squares": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 417}, "pyerrors.fits.fit_lin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 90}, "pyerrors.fits.qqplot": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 27}, "pyerrors.fits.residual_plot": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 17}, "pyerrors.fits.error_band": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 23}, "pyerrors.fits.ks_test": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 40}, "pyerrors.input": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 81}, "pyerrors.input.bdio": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.bdio.read_ADerrors": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 106}, "pyerrors.input.bdio.write_ADerrors": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 108}, "pyerrors.input.bdio.read_mesons": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 194}, "pyerrors.input.bdio.read_dSdm": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 191}, "pyerrors.input.hadrons": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.hadrons.read_meson_hd5": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 110}, "pyerrors.input.hadrons.Npr_matrix": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 2, "doc": 1065}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 2, "bases": 0, "doc": 3}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 30}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 81}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 81}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 13, "bases": 0, "doc": 94}, "pyerrors.input.json": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.json.create_json_string": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 116}, "pyerrors.input.json.dump_to_json": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 147}, "pyerrors.input.json.import_json_string": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 108}, "pyerrors.input.json.load_json": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 128}, "pyerrors.input.json.dump_dict_to_json": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 172}, "pyerrors.input.json.load_json_dict": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 12, "bases": 0, "doc": 135}, "pyerrors.input.misc": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.misc.read_pbp": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 62}, "pyerrors.input.openQCD": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.openQCD.read_rwms": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 254}, "pyerrors.input.openQCD.extract_t0": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 13, "bases": 0, "doc": 457}, "pyerrors.input.openQCD.read_qtop": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 346}, "pyerrors.input.openQCD.qtop_projection": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 49}, "pyerrors.input.openQCD.read_qtop_sector": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 340}, "pyerrors.input.sfcf": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.sfcf.read_sfcf": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 331}, "pyerrors.input.utils": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 6}, "pyerrors.input.utils.check_idl": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 47}, "pyerrors.linalg": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.linalg.matmul": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 54}, "pyerrors.linalg.jack_matmul": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 58}, "pyerrors.linalg.einsum": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 52}, "pyerrors.linalg.inv": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 10}, "pyerrors.linalg.cholesky": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 11}, "pyerrors.linalg.det": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 8}, "pyerrors.linalg.eigh": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 20}, "pyerrors.linalg.eig": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 17}, "pyerrors.linalg.pinv": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 13}, "pyerrors.linalg.svd": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 13}, "pyerrors.misc": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.misc.dump_object": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 57}, "pyerrors.misc.load_object": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 26}, "pyerrors.misc.pseudo_Obs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 89}, "pyerrors.misc.gen_correlated_data": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 109}, "pyerrors.mpm": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.mpm.matrix_pencil_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 147}, "pyerrors.obs": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 238}, "pyerrors.obs.Obs.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 62}, "pyerrors.obs.Obs.S_global": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 3, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.S_dict": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tau_exp_global": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 3, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tau_exp_dict": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.N_sigma_global": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 3, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.N_sigma_dict": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.filter_eps": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 3, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.names": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.shape": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.r_values": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.deltas": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.N": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.is_merged": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.idl": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.ddvalue": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.reweighted": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tag": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.value": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.dvalue": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_names": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.cov_names": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.mc_names": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_content": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.covobs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.gamma_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 133}, "pyerrors.obs.Obs.details": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 34}, "pyerrors.obs.Obs.is_zero_within_error": {"qualname": 5, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 50}, "pyerrors.obs.Obs.is_zero": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 35}, "pyerrors.obs.Obs.plot_tauint": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 34}, "pyerrors.obs.Obs.plot_rho": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 35}, "pyerrors.obs.Obs.plot_rep_dist": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 14}, "pyerrors.obs.Obs.plot_history": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 35}, "pyerrors.obs.Obs.plot_piechart": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 23}, "pyerrors.obs.Obs.dump": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 89}, "pyerrors.obs.Obs.export_jackknife": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 101}, "pyerrors.obs.Obs.sqrt": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.log": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.exp": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.sin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.cos": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tan": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arcsin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arccos": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arctan": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.sinh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.cosh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tanh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arcsinh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arccosh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arctanh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.N_sigma": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.S": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_ddvalue": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_drho": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_dtauint": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_dvalue": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_n_dtauint": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_n_tauint": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_rho": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_tauint": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_windowsize": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tau_exp": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.CObs": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "pyerrors.obs.CObs.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 3}, "pyerrors.obs.CObs.tag": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.CObs.real": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.CObs.imag": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.CObs.gamma_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 14}, "pyerrors.obs.CObs.is_zero": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 15}, "pyerrors.obs.CObs.conjugate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.derived_observable": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 184}, "pyerrors.obs.reweight": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 97}, "pyerrors.obs.correlate": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 75}, "pyerrors.obs.covariance": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 299}, "pyerrors.obs.import_jackknife": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 61}, "pyerrors.obs.merge_obs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 56}, "pyerrors.obs.cov_Obs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 90}, "pyerrors.roots": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.roots.find_root": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 117}, "pyerrors.version": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}}, "length": 204, "save": true}, "index": {"qualname": {"root": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 47, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2, "d": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}}, "df": 2}}, "v": {"docs": {"pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.covobs.Covobs": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.covobs.Covobs.grad": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_content": {"tf": 1}}, "df": 1}}}}, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 8}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.linalg.cholesky": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6}}, "v": {"docs": {"pyerrors.linalg.inv": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}}, "df": 2}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}}, "df": 2}}, "s": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 4}}, "r": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}}, "df": 2}}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}}, "df": 2}, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 12}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 3}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"4": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}, "w": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}}, "df": 2}}}, "g": {"5": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 5}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1}}, "n": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.grad": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 3}}}}}}, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 5}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 5}}}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {"pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 1}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}}, "df": 1}}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.print": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}}, "df": 3, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}}, "df": 3}}, "y": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.shape": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}, "q": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}}, "df": 2}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}}, "df": 3}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 2}}}}}, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.svd": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}}, "df": 2}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 2}}}}}}}}, "e": {"docs": {"pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 11, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 5, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}}, "df": 1}}}}}, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "docs": {}, "df": 0}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}}, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}}, "df": 2}}, "u": {"docs": {"pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}}, "df": 3}}}}, "g": {"docs": {"pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}}, "df": 2}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}}, "o": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {"pyerrors.linalg.det": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.deltas": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 5}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}}, "df": 5}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}}, "df": 2}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}}, "df": 2}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_drho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}}, "df": 5}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}}, "df": 2}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 3}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1}}, "df": 1}}}, "q": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}}}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 1}}}}}}}}}}, "n": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}}, "df": 6, "p": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 4}}}}}, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}, "pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.shape": {"tf": 1}, "pyerrors.obs.Obs.r_values": {"tf": 1}, "pyerrors.obs.Obs.deltas": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}, "pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.Obs.value": {"tf": 1}, "pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 67, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.value": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1}}}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 3}}}}}}, "fullname": {"root": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6, "p": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs": {"tf": 1}, "pyerrors.covobs.Covobs": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.covobs.Covobs.grad": {"tf": 1}, "pyerrors.dirac": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}, "pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.shape": {"tf": 1}, "pyerrors.obs.Obs.r_values": {"tf": 1}, "pyerrors.obs.Obs.deltas": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}, "pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.Obs.value": {"tf": 1}, "pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}, "pyerrors.version": {"tf": 1}}, "df": 204}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}}, "df": 1}}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.print": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 47, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 48}}}, "e": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2, "d": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}}, "df": 2}}, "v": {"docs": {"pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.covobs": {"tf": 1}, "pyerrors.covobs.Covobs": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.cov": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.grad": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.covobs": {"tf": 1}}, "df": 7}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_content": {"tf": 1}}, "df": 1}}}}, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 8}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.linalg.cholesky": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input": {"tf": 1}, "pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 33}}}, "v": {"docs": {"pyerrors.linalg.inv": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}}, "df": 2}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}}, "df": 2}}, "s": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 4}}, "r": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}}, "df": 2}}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}}, "df": 2}, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 12}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 3}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.roots": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"4": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}, "w": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}}, "df": 2}}}, "g": {"5": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 5}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1}}, "n": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.grad": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 3}}}}}}, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 5}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 5}}}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.misc": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 7}}}, "p": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.mpm": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}, "c": {"docs": {"pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}}, "df": 3, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}}, "df": 3}}, "y": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.shape": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}, "q": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}}, "df": 2}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}}, "df": 3}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 2}}}}}, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 2}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.svd": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}}, "df": 2}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 2}}}}}}}}, "e": {"docs": {"pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 11, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 5, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 8}}}}}}, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "docs": {}, "df": 0}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}}, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}}, "df": 2}}, "u": {"docs": {"pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}}, "df": 3}}}}, "g": {"docs": {"pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}}, "df": 2}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}}, "o": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {"pyerrors.linalg.det": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.deltas": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 5}}}, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.dirac": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 4}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}}, "df": 5}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}}, "df": 2}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}}, "df": 2}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_drho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}}, "df": 5, "s": {"docs": {"pyerrors.fits": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}}, "df": 11}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}}, "df": 2}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 3}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 11}}}}}}, "q": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}}}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 5}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 1}}}}}}}}}}, "n": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}}, "df": 6, "p": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 4}}}}}, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}}, "df": 7}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}}}}}, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 6}}}}}}, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.filter_eps": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.shape": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.r_values": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.deltas": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_merged": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.idl": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.ddvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.reweighted": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tag": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.value": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cov_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.mc_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_content": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.covobs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sqrt": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.log": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.exp": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_drho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 81, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.utils": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 2}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.value": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.version": {"tf": 1}}, "df": 1}}}}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 3}}}}}}, "annotation": {"root": {"docs": {}, "df": 0}}, "default_value": {"root": {"0": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 3}, "1": {"0": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 1, "e": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}, "2": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 7}}, "signature": {"root": {"0": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 14, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "1": {"0": {"0": {"0": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 10, "e": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}, "2": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 3}, "3": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}, "5": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 2}, "docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.symmetric": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reverse": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.set_prange": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.print": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.sqrt": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.log": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.exp": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.sin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.cos": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.tan": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.sinh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.cosh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.tanh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arcsin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arccos": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arctan": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arccosh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arctanh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.dirac.epsilon_tensor": {"tf": 1.4142135623730951}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}, "pyerrors.fits.residual_plot": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1.4142135623730951}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.utils.check_idl": {"tf": 1.4142135623730951}, "pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 1.4142135623730951}, "pyerrors.linalg.inv": {"tf": 1.4142135623730951}, "pyerrors.linalg.cholesky": {"tf": 1.4142135623730951}, "pyerrors.linalg.det": {"tf": 1.4142135623730951}, "pyerrors.linalg.eigh": {"tf": 1.4142135623730951}, "pyerrors.linalg.eig": {"tf": 1.4142135623730951}, "pyerrors.linalg.pinv": {"tf": 1.4142135623730951}, "pyerrors.linalg.svd": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sqrt": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.log": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.exp": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctanh": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.conjugate": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 134, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 78}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2}}}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 3}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 3}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2}}}}}}}, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1, "r": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 2}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}}}}}}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 3, "n": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}}}}, "d": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4, "l": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 7}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 15}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 6}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}}}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1}}}, "n": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 24}}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 7, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 2}}}}}, "k": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 31}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}}}}}, "a": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 3}}}}}, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 4}}, "b": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}}}}}}, "r": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 2}, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2}}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "l": {"docs": {"pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 1}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 12}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}}, "j": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 3, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}}, "t": {"0": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}, "docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 8}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}, "u": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}}, "df": 2, "f": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 9}}, "l": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}}}}}, "d": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 1}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}}, "df": 3}}}}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2}}}}}, "f": {"2": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 3}}}}, "z": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 6}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}}, "v": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "x": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}}, "df": 10, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "v": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3}, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "h": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}, "y": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 6, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "i": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 5}}, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}, "q": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}}}, "bases": {"root": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}}}}, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "doc": {"root": {"0": {"0": {"0": {"0": {"0": {"0": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "6": {"9": {"7": {"9": {"5": {"8": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "1": {"2": {"8": {"9": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"1": {"8": {"0": {"6": {"4": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 3}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "3": {"4": {"4": {"5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "4": {"5": {"8": {"5": {"6": {"5": {"0": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "5": {"4": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"5": {"6": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 6.164414002968976}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 2.6457513110645907}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 2}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 23, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"0": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}, "1": {"0": {"0": {"0": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 3}, "3": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "4": {"7": {"2": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "1": {"9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "2": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "3": {"4": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "4": {"3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "5": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "7": {"6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "9": {"0": {"6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "8": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"0": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {"pyerrors": {"tf": 6.164414002968976}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 19, "}": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "\\": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "+": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}, "*": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "/": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}, "2": {"0": {"0": {"4": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "1": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "4": {"1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2}}, "df": 1}, "1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "3": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"2": {"1": {"8": {"6": {"6": {"7": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"0": {"9": {"7": {"7": {"6": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2}}, "df": 1}, "9": {"9": {"0": {"9": {"7": {"0": {"3": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {"pyerrors": {"tf": 5}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 12, "x": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 5}, "*": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "3": {"0": {"6": {"7": {"5": {"2": {"0": {"1": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "1": {"4": {"9": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"2": {"7": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "3": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "3": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "4": {"9": {"7": {"6": {"8": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "8": {"3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 7.745966692414834}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}, "docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 6, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "4": {"0": {"3": {"2": {"0": {"9": {"8": {"3": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "9": {"5": {"9": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 6, "x": {"4": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "5": {"0": {"0": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}, "1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "1": {"5": {"6": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "9": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "2": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"8": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "3": {"8": {"0": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "4": {"8": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"7": {"3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "4": {"6": {"5": {"9": {"8": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "8": {"3": {"4": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 5, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "6": {"4": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "5": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}}, "df": 2}, "7": {"0": {"0": {"0": {"0": {"0": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "1": {"4": {"2": {"2": {"9": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"4": {"6": {"6": {"5": {"8": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "4": {"5": {"7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"3": {"1": {"0": {"1": {"0": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"0": {"7": {"7": {"5": {"2": {"4": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"7": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "8": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "1": {"4": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "2": {"4": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "4": {"5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 5}, "9": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "3": {"3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "4": {"7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "5": {"9": {"3": {"0": {"3": {"5": {"7": {"8": {"5": {"1": {"6": {"0": {"9": {"3": {"6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "4": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"6": {"8": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"3": {"1": {"9": {"8": {"8": {"1": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"1": {"0": {"0": {"7": {"1": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"8": {"3": {"6": {"5": {"4": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}, "docs": {"pyerrors": {"tf": 62.37788069500277}, "pyerrors.correlators": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 3}, "pyerrors.correlators.Corr.__init__": {"tf": 5.0990195135927845}, "pyerrors.correlators.Corr.reweighted": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.item": {"tf": 4.58257569495584}, "pyerrors.correlators.Corr.plottable": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.symmetric": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 5.477225575051661}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 5.477225575051661}, "pyerrors.correlators.Corr.Hankel": {"tf": 4.58257569495584}, "pyerrors.correlators.Corr.roll": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.reverse": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 4.242640687119285}, "pyerrors.correlators.Corr.correlate": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.reweight": {"tf": 4.58257569495584}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 4.242640687119285}, "pyerrors.correlators.Corr.deriv": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.second_deriv": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.m_eff": {"tf": 5.830951894845301}, "pyerrors.correlators.Corr.fit": {"tf": 5.291502622129181}, "pyerrors.correlators.Corr.plateau": {"tf": 5}, "pyerrors.correlators.Corr.set_prange": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 7.14142842854285}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 3.872983346207417}, "pyerrors.correlators.Corr.dump": {"tf": 5.0990195135927845}, "pyerrors.correlators.Corr.print": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.sqrt": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.log": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.exp": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.sin": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.cos": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.tan": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.sinh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.cosh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.tanh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arcsin": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arccos": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arctan": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arccosh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arctanh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.real": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.imag": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 6.855654600401044}, "pyerrors.covobs": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 5.916079783099616}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.cov": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.grad": {"tf": 1.7320508075688772}, "pyerrors.dirac": {"tf": 1.7320508075688772}, "pyerrors.dirac.epsilon_tensor": {"tf": 2}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 2}, "pyerrors.dirac.Grid_gamma": {"tf": 1.7320508075688772}, "pyerrors.fits": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result": {"tf": 3.872983346207417}, "pyerrors.fits.Fit_result.__init__": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 15.748015748023622}, "pyerrors.fits.total_least_squares": {"tf": 14.66287829861518}, "pyerrors.fits.fit_lin": {"tf": 4.795831523312719}, "pyerrors.fits.qqplot": {"tf": 1.7320508075688772}, "pyerrors.fits.residual_plot": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1.7320508075688772}, "pyerrors.fits.ks_test": {"tf": 3.872983346207417}, "pyerrors.input": {"tf": 4.69041575982343}, "pyerrors.input.bdio": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_ADerrors": {"tf": 5.196152422706632}, "pyerrors.input.bdio.write_ADerrors": {"tf": 5.196152422706632}, "pyerrors.input.bdio.read_mesons": {"tf": 7.416198487095663}, "pyerrors.input.bdio.read_dSdm": {"tf": 7.416198487095663}, "pyerrors.input.hadrons": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 6}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 20.808652046684813}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 5.385164807134504}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 5.385164807134504}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 5.916079783099616}, "pyerrors.input.json": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 5.0990195135927845}, "pyerrors.input.json.dump_to_json": {"tf": 6.164414002968976}, "pyerrors.input.json.import_json_string": {"tf": 5.477225575051661}, "pyerrors.input.json.load_json": {"tf": 6}, "pyerrors.input.json.dump_dict_to_json": {"tf": 6.6332495807108}, "pyerrors.input.json.load_json_dict": {"tf": 6.4031242374328485}, "pyerrors.input.misc": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 4.242640687119285}, "pyerrors.input.openQCD": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 7.874007874011811}, "pyerrors.input.openQCD.extract_t0": {"tf": 9.899494936611665}, "pyerrors.input.openQCD.read_qtop": {"tf": 9.433981132056603}, "pyerrors.input.openQCD.qtop_projection": {"tf": 4.58257569495584}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 9.219544457292887}, "pyerrors.input.sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 8.888194417315589}, "pyerrors.input.utils": {"tf": 1.4142135623730951}, "pyerrors.input.utils.check_idl": {"tf": 4.242640687119285}, "pyerrors.linalg": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 4.58257569495584}, "pyerrors.linalg.jack_matmul": {"tf": 4.47213595499958}, "pyerrors.linalg.einsum": {"tf": 4.47213595499958}, "pyerrors.linalg.inv": {"tf": 1.7320508075688772}, "pyerrors.linalg.cholesky": {"tf": 1.7320508075688772}, "pyerrors.linalg.det": {"tf": 1.7320508075688772}, "pyerrors.linalg.eigh": {"tf": 1.7320508075688772}, "pyerrors.linalg.eig": {"tf": 1.7320508075688772}, "pyerrors.linalg.pinv": {"tf": 1.7320508075688772}, "pyerrors.linalg.svd": {"tf": 1.7320508075688772}, "pyerrors.misc": {"tf": 1.7320508075688772}, "pyerrors.misc.dump_object": {"tf": 5}, "pyerrors.misc.load_object": {"tf": 3.7416573867739413}, "pyerrors.misc.pseudo_Obs": {"tf": 5.656854249492381}, "pyerrors.misc.gen_correlated_data": {"tf": 6.244997998398398}, "pyerrors.mpm": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 5.385164807134504}, "pyerrors.obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 6.928203230275509}, "pyerrors.obs.Obs.__init__": {"tf": 4.898979485566356}, "pyerrors.obs.Obs.S_global": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.S_dict": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.filter_eps": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.names": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.shape": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.r_values": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.deltas": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.N": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_merged": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.idl": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.ddvalue": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.reweighted": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tag": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.value": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.dvalue": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_names": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.cov_names": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.mc_names": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_content": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.covobs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 5.744562646538029}, "pyerrors.obs.Obs.details": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 4.47213595499958}, "pyerrors.obs.Obs.is_zero": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.plot_tauint": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.plot_rho": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_history": {"tf": 3.7416573867739413}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.dump": {"tf": 5.744562646538029}, "pyerrors.obs.Obs.export_jackknife": {"tf": 3.7416573867739413}, "pyerrors.obs.Obs.sqrt": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.log": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.exp": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.sin": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.cos": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tan": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arcsin": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arccos": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arctan": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.sinh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.cosh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tanh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arcsinh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arccosh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arctanh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.N_sigma": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.S": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_drho": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_rho": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_tauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tau_exp": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.__init__": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.tag": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.real": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.imag": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.is_zero": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.conjugate": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 6.4031242374328485}, "pyerrors.obs.reweight": {"tf": 5.196152422706632}, "pyerrors.obs.correlate": {"tf": 4.898979485566356}, "pyerrors.obs.covariance": {"tf": 6.4031242374328485}, "pyerrors.obs.import_jackknife": {"tf": 4.47213595499958}, "pyerrors.obs.merge_obs": {"tf": 4.123105625617661}, "pyerrors.obs.cov_Obs": {"tf": 5.385164807134504}, "pyerrors.roots": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 6.782329983125268}, "pyerrors.version": {"tf": 1.7320508075688772}}, "df": 204, "w": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 34}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 6}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 8}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 9}}}}}, "o": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 5.830951894845301}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 26, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 4}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}, "/": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 8}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 3, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 6}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 5}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 2, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "f": {"2": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "i": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 7, "s": {"docs": {"pyerrors": {"tf": 8}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 3.4641016151377544}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 3.3166247903554}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.4641016151377544}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 3.605551275463989}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.449489742783178}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 2.8284271247461903}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 42}, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 18, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 5, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}, "n": {"docs": {"pyerrors": {"tf": 8.12403840463596}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.3166247903554}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.23606797749979}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 32, "t": {"1": {"6": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 27, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 3}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 10}, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 1}}, "v": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}}, "df": 3}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 4, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3}}}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}}}}}}, "x": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 3, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils": {"tf": 1}}, "df": 11}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 6}}}}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}}}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1}}}}, "m": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 8, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 4}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 3}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 5}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}, "d": {"0": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}, "docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 6, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3}, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "r": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}}, "df": 10}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "f": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 2}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 2.23606797749979}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 3}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.8284271247461903}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.449489742783178}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 38}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}}, "/": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 2}}}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "\\": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "j": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}, "^": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "|": {"docs": {}, "df": 0, "^": {"2": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "docs": {}, "df": 0}}}}, "}": {"docs": {}, "df": 0, "|": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}, "\\": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 3, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 6.557438524302}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input": {"tf": 2}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 4}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 5, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.7320508075688772}}, "df": 3}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 2}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 76}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 2}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 2}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2}, "pyerrors.input.bdio.read_mesons": {"tf": 2}, "pyerrors.input.bdio.read_dSdm": {"tf": 2}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 18}}, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1.7320508075688772}}, "df": 2}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 3.872983346207417}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 3}}}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 7, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}}}}}, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 2}}}}, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 6, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 4}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 3}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {"pyerrors": {"tf": 5.477225575051661}, "pyerrors.correlators.Corr": {"tf": 1}}, "df": 2, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 4}}, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}}, "df": 4}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 3}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4}}}}}}}}}, "n": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 11, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}}, "df": 4}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2}}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "^": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}}, "df": 1}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}, "h": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 5, "s": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 5}, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 2}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}}, "df": 2}}}}}}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}}, "a": {"docs": {"pyerrors": {"tf": 8.306623862918075}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 3.605551275463989}, "pyerrors.fits.total_least_squares": {"tf": 3.3166247903554}, "pyerrors.fits.fit_lin": {"tf": 1.7320508075688772}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 2}, "pyerrors.input.bdio.read_dSdm": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 58, "n": {"docs": {"pyerrors": {"tf": 5.0990195135927845}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.605551275463989}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 25, "d": {"docs": {"pyerrors": {"tf": 6.928203230275509}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 53}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 7}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 3}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 4}}}}, "n": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 5}}, "r": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, ":": {"1": {"0": {"0": {"9": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"5": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"0": {"9": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"0": {"4": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}}}, "e": {"docs": {"pyerrors": {"tf": 5.477225575051661}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 34}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 4.47213595499958}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 6.082762530298219}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 13, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 9}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4}}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 3, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 4, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2}}}}}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 5}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 2}}, "df": 6}}}}}}}}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}}}}}, "s": {"docs": {"pyerrors": {"tf": 6}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 17, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 7}, "s": {"docs": {"pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}}, "df": 2}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 4}}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "g": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1, "^": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "l": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 26, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 3}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 6}}, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 3}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 15, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3}}}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 8}}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 6}}}}}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "i": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 2}}}, "y": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 4}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}}}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 2}}}}}}, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 3}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 6}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 3}}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}}, "f": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 6.782329983125268}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.8284271247461903}, "pyerrors.fits.total_least_squares": {"tf": 2}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1.4142135623730951}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.449489742783178}, "pyerrors.input.utils": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1.7320508075688772}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 2}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 55, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 5, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}}, "df": 8, "s": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 4.358898943540674}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.7320508075688772}}, "df": 22}, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "{": {"1": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "{": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "}": {"docs": {}, "df": 0, "+": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "docs": {}, "df": 0}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.6457513110645907}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1.7320508075688772}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}}, "df": 14, "s": {"docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 5}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 16}}}, "x": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 8}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 4.358898943540674}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 2}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.load_json_dict": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 2}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 2.23606797749979}}, "df": 27, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 10, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}}, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 3}}}}}, "^": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 2}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 6, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.fit": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 2}}, "df": 11, "s": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 7}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 8, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 10, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "w": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}}, "df": 2}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 14}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 12, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 5.830951894845301}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 9, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 3}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "x": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 2, "/": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 1}}}}, "p": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 4, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 6, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 6}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 8, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2.8284271247461903}}, "df": 1, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 2}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.linalg.eigh": {"tf": 1}}, "df": 3}}}}}}}}}, "h": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 9}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 6, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"1": {"docs": {"pyerrors": {"tf": 3.4641016151377544}}, "df": 1, "|": {"docs": {}, "df": 0, "r": {"0": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "2": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 5.5677643628300215}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 21, "s": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 3, "/": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 9}}}, "y": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}}, "df": 7}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 4}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 3}, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 2}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 2}}}, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 3}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 14}}, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "q": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 1, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}, "g": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2}, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "c": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 3}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}}, "df": 7, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 3}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}, "s": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 4}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}}, "df": 5}, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 3}, "s": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input": {"tf": 1}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 2}}}}}, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "/": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "/": {"1": {"6": {"0": {"3": {"7": {"5": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": null}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 17}}}, "s": {"docs": {"pyerrors": {"tf": 5}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 11}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 3}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 2}}}, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}}, "df": 4, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.utils.check_idl": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}}, "df": 14}}}}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 7}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 2}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 6.6332495807108}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 18, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}}, "df": 3, "d": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 4}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 2}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 23, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 4}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.prune": {"tf": 2.23606797749979}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 2.449489742783178}}, "df": 6, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 2}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 4}}}, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 5}}}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}}, "df": 6}}, "v": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 4, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 4}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 2.23606797749979}, "pyerrors.obs.cov_Obs": {"tf": 2}}, "df": 6}}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.7320508075688772}}, "df": 1}, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 2}, "/": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.4142135623730951}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 5}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 4}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 3}}, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "n": {"docs": {"pyerrors": {"tf": 5.656854249492381}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 26, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 5}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}}, "df": 5}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 3, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "p": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 3}}}, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 7, "f": {"docs": {"pyerrors": {"tf": 10.295630140987}, "pyerrors.correlators.Corr": {"tf": 2}, "pyerrors.correlators.Corr.__init__": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 2.6457513110645907}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 3}, "pyerrors.fits.total_least_squares": {"tf": 3}, "pyerrors.fits.fit_lin": {"tf": 2.23606797749979}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 5.0990195135927845}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 2.6457513110645907}, "pyerrors.input.json.dump_to_json": {"tf": 2.6457513110645907}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2.8284271247461903}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.extract_t0": {"tf": 3.1622776601683795}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.8284271247461903}, "pyerrors.input.sfcf.read_sfcf": {"tf": 3.4641016151377544}, "pyerrors.input.utils.check_idl": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1.4142135623730951}, "pyerrors.linalg.eig": {"tf": 1.4142135623730951}, "pyerrors.linalg.pinv": {"tf": 1.4142135623730951}, "pyerrors.linalg.svd": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2.6457513110645907}, "pyerrors.obs.Obs": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs.__init__": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.derived_observable": {"tf": 2.449489742783178}, "pyerrors.obs.reweight": {"tf": 2}, "pyerrors.obs.covariance": {"tf": 3.1622776601683795}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1.7320508075688772}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 82, "f": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {"pyerrors": {"tf": 5.291502622129181}, "pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 24, "e": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 18, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}}, "df": 22}}, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 1}}, "df": 3}}}}}, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 13}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {}, "df": 0, "s": {"1": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3}, "2": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3}, "3": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3}, "docs": {"pyerrors": {"tf": 9.591663046625438}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.fit_lin": {"tf": 2.23606797749979}, "pyerrors.input": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 2}, "pyerrors.input.json.dump_to_json": {"tf": 2}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 2.23606797749979}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 2}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 2}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 48, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 12, "s": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 2}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 9}}}}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "[": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2}}}, "j": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}, "pyerrors.misc.dump_object": {"tf": 1.7320508075688772}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 20, "s": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 6}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {"pyerrors": {"tf": 4.123105625617661}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.cov_Obs": {"tf": 2}}, "df": 35, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 5, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 3, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}}, "d": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 17, "s": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "r": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "w": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}, "m": {"docs": {"pyerrors": {"tf": 3.872983346207417}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.449489742783178}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 3, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 4.795831523312719}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 2.8284271247461903}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 2.6457513110645907}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 19}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}}, "df": 7}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}}}}}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}, "n": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "y": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 5}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 2, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "y": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}}}, "x": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 3}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 5}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4, "a": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 4.69041575982343}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 16, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 4}}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.449489742783178}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}}, "df": 3}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 8, "s": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 7}}}}}}}}}, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}}, "df": 1}}}}}, "y": {"docs": {"pyerrors": {"tf": 7.681145747868608}}, "df": 1}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}, "y": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 3}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}}, "df": 1}}}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 2}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}}}, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}, "\\": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}, "c": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "m": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "s": {"1": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 2, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3, "a": {"docs": {"pyerrors": {"tf": 4.795831523312719}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 20, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 4}}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}}, "df": 7, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "y": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 5}}}}}}, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}}, "df": 5}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}}}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 2}}}}}, "k": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 3}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 3}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 4}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}}}}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3}, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4}}}, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 5}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 2}}, "s": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 7}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "s": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 5}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 4, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 4}}}, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 11}, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 2}, "pyerrors.input.bdio.read_dSdm": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 2}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 27, "s": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr": {"tf": 1}}, "df": 2}, "c": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}}}}}}, "o": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 7}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "w": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 2}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}}, "df": 1, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}}, "df": 1}}}, "r": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 3}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 1}}}}, "b": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 8}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "{": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 6.082762530298219}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.6457513110645907}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.6457513110645907}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 49, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.23606797749979}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}, "a": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 18, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 5}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}}, "df": 4}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.6457513110645907}}, "df": 3}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 3}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 25}, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_mesons": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.6457513110645907}}, "df": 4}}}, "i": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "b": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "t": {"0": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 5, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.GEVP": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.prune": {"tf": 4.47213595499958}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 8, "h": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1, "e": {"docs": {"pyerrors": {"tf": 16.1245154965971}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 3}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 2}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 2}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 3.4641016151377544}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.reweight": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 2}, "pyerrors.correlators.Corr.deriv": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.m_eff": {"tf": 3}, "pyerrors.correlators.Corr.fit": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.plateau": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.set_prange": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.dump": {"tf": 2}, "pyerrors.correlators.Corr.prune": {"tf": 4.795831523312719}, "pyerrors.covobs.Covobs.__init__": {"tf": 2.23606797749979}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 4.47213595499958}, "pyerrors.fits.total_least_squares": {"tf": 3.4641016151377544}, "pyerrors.fits.fit_lin": {"tf": 2.449489742783178}, "pyerrors.fits.qqplot": {"tf": 1.7320508075688772}, "pyerrors.fits.residual_plot": {"tf": 1.7320508075688772}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 2}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2}, "pyerrors.input.bdio.read_mesons": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.6457513110645907}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.6457513110645907}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 5.830951894845301}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 2}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 2.8284271247461903}, "pyerrors.input.json.dump_to_json": {"tf": 3}, "pyerrors.input.json.import_json_string": {"tf": 2.6457513110645907}, "pyerrors.input.json.load_json": {"tf": 2.6457513110645907}, "pyerrors.input.json.dump_dict_to_json": {"tf": 3.3166247903554}, "pyerrors.input.json.load_json_dict": {"tf": 2.6457513110645907}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 3}, "pyerrors.input.openQCD.extract_t0": {"tf": 5.385164807134504}, "pyerrors.input.openQCD.read_qtop": {"tf": 4.47213595499958}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 4.242640687119285}, "pyerrors.input.sfcf.read_sfcf": {"tf": 4.242640687119285}, "pyerrors.input.utils": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.7320508075688772}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 2.23606797749979}, "pyerrors.misc.gen_correlated_data": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2.23606797749979}, "pyerrors.obs.Obs": {"tf": 3.1622776601683795}, "pyerrors.obs.Obs.__init__": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 3.4641016151377544}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.dump": {"tf": 2}, "pyerrors.obs.Obs.export_jackknife": {"tf": 3.3166247903554}, "pyerrors.obs.CObs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 2.8284271247461903}, "pyerrors.obs.reweight": {"tf": 2.23606797749979}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 4.898979485566356}, "pyerrors.obs.import_jackknife": {"tf": 2}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 2}, "pyerrors.roots.find_root": {"tf": 2.449489742783178}}, "df": 97, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 6}}, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 6.244997998398398}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 22}, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 4}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 24}, "n": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 2}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 22}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "j": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "s": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {"pyerrors": {"tf": 8.660254037844387}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.plateau": {"tf": 2}, "pyerrors.correlators.Corr.show": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 3.1622776601683795}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_mesons": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.6457513110645907}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.7416573867739413}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.extract_t0": {"tf": 3.4641016151377544}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 3.4641016151377544}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.8284271247461903}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 2.23606797749979}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 2.8284271247461903}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 72, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "w": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.23606797749979}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 18}}, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 3}, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "y": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 5, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 7}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 11, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 10, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 5}}}}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 3}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 3}, "+": {"1": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.7320508075688772}}, "df": 2}, "2": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1.7320508075688772}}, "df": 1}}, "/": {"2": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 4}}}}, "^": {"2": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 2}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 14, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"5": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 4.242640687119285}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 11}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {"pyerrors": {"tf": 8.306623862918075}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}}, "df": 2}, "e": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 4, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}}, "df": 3}, "s": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 2.23606797749979}}, "df": 4}}, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 22}, "s": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "z": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 8, "i": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}}}, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 4}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}, "^": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "l": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 2, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2, "/": {"0": {"3": {"0": {"6": {"0": {"1": {"7": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"4": {"1": {"2": {"0": {"8": {"7": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1, "x": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "r": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "y": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 5}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 9}}, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4}}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2, "r": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 5}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.__init__": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.GEVP": {"tf": 2}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.6457513110645907}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.fit_lin": {"tf": 2.23606797749979}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 2}, "pyerrors.input.json.dump_to_json": {"tf": 2}, "pyerrors.input.json.import_json_string": {"tf": 2}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_rwms": {"tf": 3.1622776601683795}, "pyerrors.input.openQCD.extract_t0": {"tf": 3}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.6457513110645907}, "pyerrors.input.utils.check_idl": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 2.449489742783178}, "pyerrors.obs.derived_observable": {"tf": 2}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 2}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 36, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 6}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}, "b": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_mesons": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.23606797749979}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 2}}, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 2}}, "d": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 3, "g": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 9}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}, "q": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "/": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 8, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_mesons": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 5, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 3}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {"pyerrors": {"tf": 3.605551275463989}}, "df": 1, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 7}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2}}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.einsum": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "y": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}, "e": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4}}, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}}, "df": 4}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 3, "s": {"1": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 3.872983346207417}, "pyerrors.input": {"tf": 1.7320508075688772}, "pyerrors.misc.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 2}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2.23606797749979}, "pyerrors.obs.import_jackknife": {"tf": 1.7320508075688772}}, "df": 8}}}}, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 8}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1.4142135623730951}}, "df": 3, "d": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}, "s": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 5}}}, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2.23606797749979}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}}, "df": 10}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 9}}, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}}}}, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.23606797749979}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 35, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 9, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 3}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 8, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}}, "df": 7}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 8}}, "y": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 7}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 2}}}}, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10, "s": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}, "e": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 8}, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 4}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 11}, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 8}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "y": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 7}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1.7320508075688772}}, "df": 1}}}, "e": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.4142135623730951}}, "df": 3}, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}}, "df": 2}, "d": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 5}}}, "w": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "s": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}}, "df": 4}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 2}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2, "{": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "\\": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "k": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "f": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}}, "df": 2}}}, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}}, "df": 1}}}}, "n": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 2.8284271247461903}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 10, "o": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 7, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 11}}, "t": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 19, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 7}}}, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 21}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 18, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "p": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 4}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3.872983346207417}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 16, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 8, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}, "d": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 2}}}, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 6}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "x": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.605551275463989}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 15}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "x": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 2}}}, "x": {"0": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 6}, "1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 3}, "2": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 3}, "docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 2.8284271247461903}, "pyerrors.fits.total_least_squares": {"tf": 3}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 10, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}, "[": {"0": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}, "1": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "y": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 7, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}}, "r": {"0": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 7, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 9}, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.misc.read_pbp": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.23606797749979}}, "df": 14, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 4}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 5, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 3}}}, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}}, "df": 4}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 3}}}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 7}, "s": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 5}}}}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 4, "s": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 11}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}}}}, "s": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2, "[": {"0": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 13, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 16}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 6}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 4}}}}}}}}}, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 4, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 5}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 11, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 2}}}, "k": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 3}, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}}, "df": 2}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 2}}, "w": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}, "v": {"1": {"docs": {}, "df": 0, "@": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "@": {"docs": {}, "df": 0, "v": {"2": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}, "docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.prune": {"tf": 2.23606797749979}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 4, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 18, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 7}, "d": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 9}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 2}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}}, "df": 2}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 2}}}, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}}, "df": 5}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 3}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.23606797749979}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.449489742783178}}, "df": 6}}}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3}}}}}}, "\\": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}, "j": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 4, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}}}}, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input": {"tf": 2.23606797749979}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "s": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2.449489742783178}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 9}}}, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "}": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}, "^": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 4}}}, "s": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 5}}}, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 2}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 13, "d": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 18}, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 2}, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.utils": {"tf": 1}}, "df": 1}}}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2.449489742783178}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}}, "df": 2}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 4}}, "df": 1, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 9, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 14}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}}, "df": 1}}}}, "s": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 12}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 2}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1.4142135623730951}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, ":": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "f": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "docs": {}, "df": 0}}, "u": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "k": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 2, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 3}}, "y": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "\u2013": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"1": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; + /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"pyerrors": {"fullname": "pyerrors", "modulename": "pyerrors", "type": "module", "doc": "

    What is pyerrors?

    \n\n

    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:

    \n\n
      \n
    • automatic differentiation for exact liner error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
    • \n
    • treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
    • \n
    • coherent error propagation for data from different Markov chains.
    • \n
    • non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
    • \n
    • real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
    • \n
    \n\n

    There exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.

    \n\n

    Basic example

    \n\n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method()                     # Estimate the statistical error\nprint(my_new_obs)                             # Print the result to stdout\n> 0.31498(72)\n
    \n\n

    The Obs class

    \n\n

    pyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.

    \n\n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n\n

    Error propagation

    \n\n

    When performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.

    \n\n

    The Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.

    \n\n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
    \n\n

    Error estimation

    \n\n

    The error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.

    \n\n
    my_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n>  t_int         2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n\n

    We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.

    \n\n
    my_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n>  t_int         3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n\n

    The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_tauint.

    \n\n

    If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the windowsize is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.

    \n\n

    Exponential tails

    \n\n

    Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.

    \n\n
    my_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n>  t_int         3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n\n

    For the full API see pyerrors.obs.Obs.gamma_method.

    \n\n

    Multiple ensembles/replica

    \n\n

    Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.

    \n\n
    obs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 2 ensembles:\n>   \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n>   \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
    \n\n

    pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.

    \n\n
    obs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1'\n>     \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n>     \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
    \n\n

    Error estimation for multiple ensembles

    \n\n

    In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.

    \n\n
    pe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
    \n\n

    In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.

    \n\n

    Irregular Monte Carlo chains

    \n\n

    Obs objects defined on irregular Monte Carlo chains can be initialized with the parameter idl.

    \n\n
    # Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result         9.98319881e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result         9.99100712e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result         1.01718064e+00\n> 5 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
    \n\n

    Obs objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.

    \n\n

    Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.

    \n\n

    For the full API see pyerrors.obs.Obs.

    \n\n

    Correlators

    \n\n

    When one is not interested in single observables but correlation functions, pyerrors offers the Corr class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a Corr objects one needs to arrange the data as a list of Obs

    \n\n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0      0.7957(80)\n> 1      0.5156(51)\n> 2      0.3227(33)\n> 3      0.2041(21)\n
    \n\n

    In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.

    \n\n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0\n> 1      0.7957(80)\n> 2      0.5156(51)\n> 3      0.3227(33)\n> 4      0.2041(21)\n> 5\n
    \n\n

    The individual entries of a correlator can be accessed via slicing

    \n\n
    print(my_corr[3])\n> 0.3227(33)\n
    \n\n

    Error propagation with the Corr class works very similar to Obs objects. Mathematical operations are overloaded and Corr objects can be computed together with other Corr objects, Obs objects or real numbers and integers.

    \n\n
    my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
    \n\n

    pyerrors provides the user with a set of regularly used methods for the manipulation of correlator objects:

    \n\n
      \n
    • Corr.gamma_method applies the gamma method to all entries of the correlator.
    • \n
    • Corr.m_eff to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
    • \n
    • Corr.deriv returns the first derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
    • \n
    • Corr.second_deriv returns the second derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
    • \n
    • Corr.symmetric symmetrizes parity even correlations functions, assuming periodic boundary conditions.
    • \n
    • Corr.anti_symmetric anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
    • \n
    • Corr.T_symmetry averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
    • \n
    • Corr.plateau extracts a plateau value from the correlator in a given range.
    • \n
    • Corr.roll periodically shifts the correlator.
    • \n
    • Corr.reverse reverses the time ordering of the correlator.
    • \n
    • Corr.correlate constructs a disconnected correlation function from the correlator and another Corr or Obs object.
    • \n
    • Corr.reweight reweights the correlator.
    • \n
    \n\n

    pyerrors can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see pyerrors.correlators.Corr.GEVP).

    \n\n

    For the full API see pyerrors.correlators.Corr.

    \n\n

    Complex valued observables

    \n\n

    pyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.

    \n\n
    my_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
    \n\n

    Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.

    \n\n
    my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
    \n\n

    The Covobs class

    \n\n

    In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The Covobs class allows to define such quantities in pyerrors. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.

    \n\n

    This concept is built into the definition of Covobs. In pyerrors, external input is defined by $M$ mean values, a $M\\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters cov_Obs, since the second argument of this function is the covariance matrix of the Covobs.

    \n\n
    import pyerrors.obs as pe\n\nmpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')\nmpi.gamma_method()\nmpi.details()\n> Result         1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)\n>  pi^0 mass     5.00000000e-04\n> 0 samples in 1 ensemble:\n>   \u00b7 Covobs   'pi^0 mass'\n
    \n\n

    The resulting object mpi is an Obs that contains a Covobs. In the following, it may be handled as any other Obs. The contribution of the covariance matrix to the error of an Obs is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of the Obs with respect to the external quantities, which is the $1\\times M$ Jacobian matrix $J$, via\n$$s = \\sqrt{J^T \\Sigma J}\\,,$$\nwhere the Jacobian is computed for each derived quantity via automatic differentiation.

    \n\n

    Correlated auxiliary data is defined similarly to above, e.g., via

    \n\n
    RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')\nprint(RAP)\n> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]\n
    \n\n

    where RAP now is a list of two Obs that contains the two correlated parameters.

    \n\n

    Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the Covobs class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an Obs o with respect to a covariance matrix with the identifying string k may be accessed via

    \n\n
    o.covobs[k].grad\n
    \n\n

    Error propagation in iterative algorithms

    \n\n

    pyerrors supports exact linear error propagation for iterative algorithms like various variants of non-linear least sqaures fits or root finding. The derivatives required for the error propagation are calculated as described in arXiv:1809.01289.

    \n\n

    Least squares fits

    \n\n

    Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with pyerrors.fits.least_squares. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.

    \n\n

    Fit functions have to be of the following form

    \n\n
    import autograd.numpy as anp\n\ndef func(a, x):\n    return a[1] * anp.exp(-a[0] * x)\n
    \n\n

    It is important that numerical functions refer to autograd.numpy instead of numpy for the automatic differentiation in iterative algorithms to work properly.

    \n\n

    Fits can then be performed via

    \n\n
    fit_result = pe.fits.least_squares(x, y, func)\nprint("\\n", fit_result)\n> Fit with 2 parameters\n> Method: Levenberg-Marquardt\n> `ftol` termination condition is satisfied.\n> chisquare/d.o.f.: 0.9593035785160936\n\n>  Goodness of fit:\n> \u03c7\u00b2/d.o.f. = 0.959304\n> p-value   = 0.5673\n> Fit parameters:\n> 0      0.0548(28)\n> 1      1.933(64)\n
    \n\n

    where x is a list or numpy.array of floats and y is a list or numpy.array of Obs.

    \n\n

    Data stored in Corr objects can be fitted directly using the Corr.fit method.

    \n\n
    my_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
    \n\n

    this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.

    \n\n

    For fit functions with multiple independent variables the fit function can be of the form

    \n\n
    def func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n
    \n\n

    pyerrors also supports correlated fits which can be triggered via the parameter correlated_fit=True.\nDetails about how the required covariance matrix is estimated can be found in pyerrors.obs.covariance.

    \n\n

    Direct visualizations of the performed fits can be triggered via resplot=True or qqplot=True. For all available options see pyerrors.fits.least_squares.

    \n\n

    Total least squares fits

    \n\n

    pyerrors can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in scipy, see pyerrors.fits.least_squares. The syntax is identical to the standard least squares case, the only diffrence being that x also has to be a list or numpy.array of Obs.

    \n\n

    For the full API see pyerrors.fits for fits and pyerrors.roots for finding roots of functions.

    \n\n

    Matrix operations

    \n\n

    pyerrors provides wrappers for Obs- and CObs-valued matrix operations based on numpy.linalg. The supported functions include:

    \n\n
      \n
    • inv for the matrix inverse.
    • \n
    • cholseky for the Cholesky decomposition.
    • \n
    • det for the matrix determinant.
    • \n
    • eigh for eigenvalues and eigenvectors of hermitean matrices.
    • \n
    • eig for eigenvalues of general matrices.
    • \n
    • pinv for the Moore-Penrose pseudoinverse.
    • \n
    • svd for the singular-value-decomposition.
    • \n
    \n\n

    For the full API see pyerrors.linalg.

    \n\n

    Export data

    \n\n

    The preferred exported file format within pyerrors is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:

    \n\n
      \n
    • What observables are stored? Possibly: How exactly are they defined.
    • \n
    • How does each single ensemble or external quantity contribute to the error of the observable?
    • \n
    • Who did write the file when and on which machine?
    • \n
    \n\n

    This can be achieved by storing all information in one single file. The export routines of pyerrors are written such that as much information as possible is written automatically as described in the following example

    \n\n
    my_obs = pe.Obs([samples], ["test_ensemble"])\nmy_obs.tag = "My observable"\n\npe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")\n# For a single observable one can equivalently use the class method dump\nmy_obs.dump("test_output_file", description="This file contains a test observable")\n\ncheck = pe.input.json.load_json("test_output_file")\n\nprint(my_obs == check)\n> True\n
    \n\n

    The format also allows to directly write out the content of Corr objects or lists and arrays of Obs objects by passing the desired data to pyerrors.input.json.dump_to_json.

    \n\n

    json.gz format specification

    \n\n

    The first entries of the file provide optional auxiliary information:

    \n\n
      \n
    • program is a string that indicates which program was used to write the file.
    • \n
    • version is a string that specifies the version of the format.
    • \n
    • who is a string that specifies the user name of the creator of the file.
    • \n
    • date is a string and contains the creation date of the file.
    • \n
    • host is a string and contains the hostname of the machine where the file has been written.
    • \n
    • description contains information on the content of the file. This field is not filled automatically in pyerrors. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.
    • \n
    \n\n

    The only necessary entry of the file is the field\n-obsdata, an array that contains the actual data.

    \n\n

    Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of Obs, list, numpy.ndarray, Corr. All Obs inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array obsdata, are treated independently. Each entry of the array obsdata has the following required entries:

    \n\n
      \n
    • type is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
    • \n
    • value is an array that contains the mean values of the Obs inside the structure.\nThe following entries are optional:
    • \n
    • layout is a string that specifies the layout of multi-dimensional structures. Examples are \"2, 2\" for a 2x2 dimensional matrix or \"64, 4, 4\" for a Corr with $T=64$ and 4x4 matrices on each time slices. \"1\" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
    • \n
    • tag is any JSON type. It contains additional information concerning the structure. The tag of an Obs in pyerrors is written here.
    • \n
    • reweighted is a Bool that may be used to specify, whether the Obs in the structure have been reweighted.
    • \n
    • data is an array that contains the data from MC chains. We will define it below.
    • \n
    • cdata is an array that contains the data from external quantities with an error (Covobs in pyerrors). We will define it below.
    • \n
    \n\n

    The array data contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:

    \n\n
      \n
    • id, a string that contains the name of the ensemble
    • \n
    • replica, an array that contains an entry per replica of the ensemble.
    • \n
    \n\n

    Each entry of replica contains\nname, a string that contains the name of the replica\ndeltas, an array that contains the actual data.

    \n\n

    Each entry in deltas corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each Obs inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.

    \n\n

    The array cdata contains information about the contribution of auxiliary observables, represented by Covobs in pyerrors, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:

    \n\n
      \n
    • id, a string that identifies the covariance matrix
    • \n
    • layout, a string that defines the dimensions of the $M\\times M$ covariance matrix (has to be \"M, M\" or \"1\").
    • \n
    • cov, an array that contains the $M\\times M$ many entries of the covariance matrix, stored in row-major format.
    • \n
    • grad, an array that contains N entries, one for each Obs inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.
    • \n
    \n\n

    A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.

    \n\n

    Julia I/O routines for the json.gz format, compatible with ADerrors.jl, can be found here.

    \n\n

    Citing

    \n\n

    If you use pyerrors for research that leads to a publication please consider citing:

    \n\n
      \n
    • Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
    • \n
    • Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.
    • \n
    • Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.
    • \n
    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "type": "module", "doc": "

    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "type": "class", "doc": "

    The class for a correlator (time dependent sequence of pe.Obs).

    \n\n

    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with matrices.

    \n\n

    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nmatrix at every timeslice. Other dependency (eg. spatial) are not supported.

    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "type": "function", "doc": "

    Initialize a Corr object.

    \n\n
    Parameters
    \n\n
      \n
    • data_input (list or array):\nlist of Obs or list of arrays of Obs or array of Corrs
    • \n
    • padding (list, optional):\nList with two entries where the first labels the padding\nat the front of the correlator and the second the padding\nat the back.
    • \n
    • prange (list, optional):\nList containing the first and last timeslice of the plateau\nregion indentified for this correlator.
    • \n
    \n", "signature": "(self, data_input, padding=[0, 0], prange=None)", "funcdef": "def"}, "pyerrors.correlators.Corr.reweighted": {"fullname": "pyerrors.correlators.Corr.reweighted", "modulename": "pyerrors.correlators", "qualname": "Corr.reweighted", "type": "variable", "doc": "

    \n"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "type": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs)", "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "type": "function", "doc": "

    We need to project the Correlator with a Vector to get a single value at each timeslice.

    \n\n

    The method can use one or two vectors.\nIf two are specified it returns v1@G@v2 (the order might be very important.)\nBy default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to

    \n", "signature": "(self, vector_l=None, vector_r=None, normalize=False)", "funcdef": "def"}, "pyerrors.correlators.Corr.item": {"fullname": "pyerrors.correlators.Corr.item", "modulename": "pyerrors.correlators", "qualname": "Corr.item", "type": "function", "doc": "

    Picks the element [i,j] from every matrix and returns a correlator containing one Obs per timeslice.

    \n\n
    Parameters
    \n\n
      \n
    • i (int):\nFirst index to be picked.
    • \n
    • j (int):\nSecond index to be picked.
    • \n
    \n", "signature": "(self, i, j)", "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "type": "function", "doc": "

    Outputs the correlator in a plotable format.

    \n\n

    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.symmetric": {"fullname": "pyerrors.correlators.Corr.symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.symmetric", "type": "function", "doc": "

    Symmetrize the correlator around x0=0.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.anti_symmetric": {"fullname": "pyerrors.correlators.Corr.anti_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.anti_symmetric", "type": "function", "doc": "

    Anti-symmetrize the correlator around x0=0.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.matrix_symmetric", "type": "function", "doc": "

    Symmetrizes the correlator matrices on every timeslice.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.GEVP": {"fullname": "pyerrors.correlators.Corr.GEVP", "modulename": "pyerrors.correlators", "qualname": "Corr.GEVP", "type": "function", "doc": "

    Solve the general eigenvalue problem on the current correlator

    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for G(t)v= lambda G(t_0)v
    • \n
    • ts (int):\nfixed time G(t_s)v= lambda G(t_0)v if return_list=False\nIf return_list=True and sorting=Eigenvector it gives a reference point for the sorting method.
    • \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
    • \n
    • sorted_list (string):\nif this argument is set, a list of vectors (len=self.T) is returned. If it is left as None, only one vector is returned.\n\"Eigenvalue\" - The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.\n\"Eigenvector\" - Use the method described in arXiv:2004.10472 [hep-lat] to find the set of v(t) belonging to the state.\n The reference state is identified by its eigenvalue at t=ts
    • \n
    \n", "signature": "(self, t0, ts=None, state=0, sorted_list=None)", "funcdef": "def"}, "pyerrors.correlators.Corr.Eigenvalue": {"fullname": "pyerrors.correlators.Corr.Eigenvalue", "modulename": "pyerrors.correlators", "qualname": "Corr.Eigenvalue", "type": "function", "doc": "

    Determines the eigenvalue of the GEVP by solving and projecting the correlator

    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for G(t)v= lambda G(t_0)v
    • \n
    • ts (int):\nfixed time G(t_s)v= lambda G(t_0)v if return_list=False\nIf return_list=True and sorting=Eigenvector it gives a reference point for the sorting method.
    • \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
    • \n
    • sorted_list (string):\nif this argument is set, a list of vectors (len=self.T) is returned. If it is left as None, only one vector is returned.\n\"Eigenvalue\" - The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.\n\"Eigenvector\" - Use the method described in arXiv:2004.10472 [hep-lat] to find the set of v(t) belonging to the state.\n The reference state is identified by its eigenvalue at t=ts
    • \n
    \n", "signature": "(self, t0, ts=None, state=0, sorted_list=None)", "funcdef": "def"}, "pyerrors.correlators.Corr.Hankel": {"fullname": "pyerrors.correlators.Corr.Hankel", "modulename": "pyerrors.correlators", "qualname": "Corr.Hankel", "type": "function", "doc": "

    Constructs an NxN Hankel matrix

    \n\n

    C(t) c(t+1) ... c(t+n-1)\nC(t+1) c(t+2) ... c(t+n)\n.................\nC(t+(n-1)) c(t+n) ... c(t+2(n-1))

    \n\n
    Parameters
    \n\n
      \n
    • N (int):\nDimension of the Hankel matrix
    • \n
    • periodic (bool, optional):\ndetermines whether the matrix is extended periodically
    • \n
    \n", "signature": "(self, N, periodic=False)", "funcdef": "def"}, "pyerrors.correlators.Corr.roll": {"fullname": "pyerrors.correlators.Corr.roll", "modulename": "pyerrors.correlators", "qualname": "Corr.roll", "type": "function", "doc": "

    Periodically shift the correlator by dt timeslices

    \n\n
    Parameters
    \n\n
      \n
    • dt (int):\nnumber of timeslices
    • \n
    \n", "signature": "(self, dt)", "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "type": "function", "doc": "

    Reverse the time ordering of the Corr

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.thin": {"fullname": "pyerrors.correlators.Corr.thin", "modulename": "pyerrors.correlators", "qualname": "Corr.thin", "type": "function", "doc": "

    Thin out a correlator to suppress correlations

    \n\n
    Parameters
    \n\n
      \n
    • spacing (int):\nKeep only every 'spacing'th entry of the correlator
    • \n
    • offset (int):\nOffset the equal spacing
    • \n
    \n", "signature": "(self, spacing=2, offset=0)", "funcdef": "def"}, "pyerrors.correlators.Corr.correlate": {"fullname": "pyerrors.correlators.Corr.correlate", "modulename": "pyerrors.correlators", "qualname": "Corr.correlate", "type": "function", "doc": "

    Correlate the correlator with another correlator or Obs

    \n\n
    Parameters
    \n\n
      \n
    • partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
    • \n
    \n", "signature": "(self, partner)", "funcdef": "def"}, "pyerrors.correlators.Corr.reweight": {"fullname": "pyerrors.correlators.Corr.reweight", "modulename": "pyerrors.correlators", "qualname": "Corr.reweight", "type": "function", "doc": "

    Reweight the correlator.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
    • \n
    \n", "signature": "(self, weight, **kwargs)", "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "type": "function", "doc": "

    Return the time symmetry average of the correlator and its partner

    \n\n
    Parameters
    \n\n
      \n
    • partner (Corr):\nTime symmetry partner of the Corr
    • \n
    • partity (int):\nParity quantum number of the correlator, can be +1 or -1
    • \n
    \n", "signature": "(self, partner, parity=1)", "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "type": "function", "doc": "

    Return the first derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, default: symmetric
    • \n
    \n", "signature": "(self, variant='symmetric')", "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "type": "function", "doc": "

    Return the second derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, default: symmetric
    • \n
    \n", "signature": "(self, variant='symmetric')", "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "type": "function", "doc": "

    Returns the effective mass of the correlator as correlator object

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.\nsinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)
    • \n
    • guess (float):\nguess for the root finder, only relevant for the root variant
    • \n
    \n", "signature": "(self, variant='log', guess=1.0)", "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "type": "function", "doc": "

    Fits function to the data

    \n\n
    Parameters
    \n\n
      \n
    • function (obj):\nfunction to fit to the data. See fits.least_squares for details.
    • \n
    • fitrange (list):\nTwo element list containing the timeslices on which the fit is supposed to start and stop.\nCaution: This range is inclusive as opposed to standard python indexing.\nfitrange=[4, 6] corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.
    • \n
    • silent (bool):\nDecides whether output is printed to the standard output.
    • \n
    \n", "signature": "(self, function, fitrange=None, silent=False, **kwargs)", "funcdef": "def"}, "pyerrors.correlators.Corr.plateau": {"fullname": "pyerrors.correlators.Corr.plateau", "modulename": "pyerrors.correlators", "qualname": "Corr.plateau", "type": "function", "doc": "

    Extract a plateau value from a Corr object

    \n\n
    Parameters
    \n\n
      \n
    • plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
    • \n
    • method (str):\nmethod to extract the plateau.\n 'fit' fits a constant to the plateau region\n 'avg', 'average' or 'mean' just average over the given timeslices.
    • \n
    • auto_gamma (bool):\napply gamma_method with default parameters to the Corr. Defaults to None
    • \n
    \n", "signature": "(self, plateau_range=None, method='fit', auto_gamma=False)", "funcdef": "def"}, "pyerrors.correlators.Corr.set_prange": {"fullname": "pyerrors.correlators.Corr.set_prange", "modulename": "pyerrors.correlators", "qualname": "Corr.set_prange", "type": "function", "doc": "

    Sets the attribute prange of the Corr object.

    \n", "signature": "(self, prange)", "funcdef": "def"}, "pyerrors.correlators.Corr.show": {"fullname": "pyerrors.correlators.Corr.show", "modulename": "pyerrors.correlators", "qualname": "Corr.show", "type": "function", "doc": "

    Plots the correlator using the tag of the correlator as label if available.

    \n\n
    Parameters
    \n\n
      \n
    • x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8]
    • \n
    • comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.\nThe tags of these correlators are used as labels if available.
    • \n
    • logscale (bool):\nSets y-axis to logscale
    • \n
    • plateau (Obs):\nPlateau value to be visualized in the figure
    • \n
    • fit_res (Fit_result):\nFit_result object to be visualized
    • \n
    • ylabel (str):\nLabel for the y-axis
    • \n
    • save (str):\npath to file in which the figure should be saved
    • \n
    • auto_gamma (bool):\nApply the gamma method with standard parameters to all correlators and plateau values before plotting.
    • \n
    • hide_sigma (float):\nHides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
    • \n
    \n", "signature": "(\n self,\n x_range=None,\n comp=None,\n y_range=None,\n logscale=False,\n plateau=None,\n fit_res=None,\n ylabel=None,\n save=None,\n auto_gamma=False,\n hide_sigma=None\n)", "funcdef": "def"}, "pyerrors.correlators.Corr.spaghetti_plot": {"fullname": "pyerrors.correlators.Corr.spaghetti_plot", "modulename": "pyerrors.correlators", "qualname": "Corr.spaghetti_plot", "type": "function", "doc": "

    Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.

    \n\n
    Parameters
    \n\n
      \n
    • logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
    • \n
    \n", "signature": "(self, logscale=True)", "funcdef": "def"}, "pyerrors.correlators.Corr.dump": {"fullname": "pyerrors.correlators.Corr.dump", "modulename": "pyerrors.correlators", "qualname": "Corr.dump", "type": "function", "doc": "

    Dumps the Corr into a file of chosen type

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nName of the file to be saved.
    • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(self, filename, datatype='json.gz', **kwargs)", "funcdef": "def"}, "pyerrors.correlators.Corr.print": {"fullname": "pyerrors.correlators.Corr.print", "modulename": "pyerrors.correlators", "qualname": "Corr.print", "type": "function", "doc": "

    \n", "signature": "(self, range=[0, None])", "funcdef": "def"}, "pyerrors.correlators.Corr.sqrt": {"fullname": "pyerrors.correlators.Corr.sqrt", "modulename": "pyerrors.correlators", "qualname": "Corr.sqrt", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.correlators.Corr.real": {"fullname": "pyerrors.correlators.Corr.real", "modulename": "pyerrors.correlators", "qualname": "Corr.real", "type": "variable", "doc": "

    \n"}, "pyerrors.correlators.Corr.imag": {"fullname": "pyerrors.correlators.Corr.imag", "modulename": "pyerrors.correlators", "qualname": "Corr.imag", "type": "variable", "doc": "

    \n"}, "pyerrors.correlators.Corr.prune": {"fullname": "pyerrors.correlators.Corr.prune", "modulename": "pyerrors.correlators", "qualname": "Corr.prune", "type": "function", "doc": "

    Project large correlation matrix to lowest states

    \n\n

    This method can be used to reduce the size of an (N x N) correlation matrix\nto (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise\nis still small.

    \n\n
    Parameters
    \n\n
      \n
    • Ntrunc (int):\nRank of the target matrix.
    • \n
    • tproj (int):\nTime where the eigenvectors are evaluated, corresponds to ts in the GEVP method.\nThe default value is 3.
    • \n
    • t0proj (int):\nTime where the correlation matrix is inverted. Choosing t0proj=1 is strongly\ndiscouraged for O(a) improved theories, since the correctness of the procedure\ncannot be granted in this case. The default value is 2.
    • \n
    • basematrix (Corr):\nCorrelation matrix that is used to determine the eigenvectors of the\nlowest states based on a GEVP. basematrix is taken to be the Corr itself if\nis is not specified.
    • \n
    \n\n
    Notes
    \n\n

    We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving\nthe GEVP $$C(t) v_n(t, t_0) = \\lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \\equiv t_\\mathrm{proj}$\nand $t_0 \\equiv t_{0, \\mathrm{proj}}$. The target matrix is projected onto the subspace of the\nresulting eigenvectors $v_n, n=1,\\dots,N_\\mathrm{trunc}$ via\n$$G^\\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large\ncorrelation matrix and to remove some noise that is added by irrelevant operators.\nThis may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated\nbound $t_0 \\leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.

    \n", "signature": "(self, Ntrunc, tproj=3, t0proj=2, basematrix=None)", "funcdef": "def"}, "pyerrors.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "type": "module", "doc": "

    \n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "type": "class", "doc": "

    \n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "type": "function", "doc": "

    Initialize Covobs object.

    \n\n
    Parameters
    \n\n
      \n
    • mean (float):\nMean value of the new Obs
    • \n
    • cov (list or array):\n2d Covariance matrix or 1d diagonal entries
    • \n
    • name (str):\nidentifier for the covariance matrix
    • \n
    • pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
    • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
    • \n
    \n", "signature": "(self, mean, cov, name, pos=None, grad=None)", "funcdef": "def"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "type": "function", "doc": "

    Return the variance (= square of the error) of the Covobs

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.covobs.Covobs.cov": {"fullname": "pyerrors.covobs.Covobs.cov", "modulename": "pyerrors.covobs", "qualname": "Covobs.cov", "type": "variable", "doc": "

    \n"}, "pyerrors.covobs.Covobs.grad": {"fullname": "pyerrors.covobs.Covobs.grad", "modulename": "pyerrors.covobs", "qualname": "Covobs.grad", "type": "variable", "doc": "

    \n"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "type": "module", "doc": "

    \n"}, "pyerrors.dirac.epsilon_tensor": {"fullname": "pyerrors.dirac.epsilon_tensor", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor", "type": "function", "doc": "

    Rank-3 epsilon tensor

    \n\n

    Based on https://codegolf.stackexchange.com/a/160375

    \n", "signature": "(i, j, k)", "funcdef": "def"}, "pyerrors.dirac.epsilon_tensor_rank4": {"fullname": "pyerrors.dirac.epsilon_tensor_rank4", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor_rank4", "type": "function", "doc": "

    Rank-4 epsilon tensor

    \n\n

    Extension of https://codegolf.stackexchange.com/a/160375

    \n", "signature": "(i, j, k, o)", "funcdef": "def"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "type": "function", "doc": "

    Returns gamma matrix in Grid labeling.

    \n", "signature": "(gamma_tag)", "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "type": "module", "doc": "

    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "type": "class", "doc": "

    Represents fit results.

    \n\n
    Attributes
    \n\n
      \n
    • fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
    • \n
    \n", "bases": "collections.abc.Sequence"}, "pyerrors.fits.Fit_result.__init__": {"fullname": "pyerrors.fits.Fit_result.__init__", "modulename": "pyerrors.fits", "qualname": "Fit_result.__init__", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "type": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x).

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of floats.
    • \n
    • y (list):\nlist of Obs.
    • \n
    • func (object):\nfit function, has to be of the form

      \n\n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n\n

      For multiple x values func can be of the form

      \n\n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • priors (list, optional):\npriors has to be a list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
    • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
    • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\nnon-linear fits with many parameters.
    • \n
    • method (str, optional):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
    • \n
    • correlated_fit (bool):\nIf True, use the full inverse covariance matrix in the definition of the chisquare cost function.\nFor details about how the covariance matrix is estimated see pyerrors.obs.covariance.\nIn practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).\nThis procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).\nAt the moment this option only works for prior==None and when no method is given.
    • \n
    • expected_chisquare (bool):\nIf True estimates the expected chisquare which is\ncorrected by effects caused by correlated input data (default False).
    • \n
    • resplot (bool):\nIf True, a plot which displays fit, data and residuals is generated (default False).
    • \n
    • qqplot (bool):\nIf True, a quantile-quantile plot of the fit result is generated (default False).
    • \n
    \n", "signature": "(x, y, func, priors=None, silent=False, **kwargs)", "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of Obs, or a tuple of lists of Obs
    • \n
    • y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
    • \n
    • func (object):\nfunc has to be of the form

      \n\n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n\n

      For multiple x values func can be of the form

      \n\n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
    • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
    • \n
    • expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
    • \n
    \n\n
    Notes
    \n\n

    Based on the orthogonal distance regression module of scipy

    \n", "signature": "(x, y, func, silent=False, **kwargs)", "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "type": "function", "doc": "

    Performs a linear fit to y = n + m * x and returns two Obs n, m.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nCan either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.
    • \n
    • y (list):\nList of Obs, the dvalues of the Obs are used as yerror for the fit.
    • \n
    \n", "signature": "(x, y, **kwargs)", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "type": "function", "doc": "

    Generates a quantile-quantile plot of the fit result which can be used to\ncheck if the residuals of the fit are gaussian distributed.

    \n", "signature": "(x, o_y, func, p)", "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "type": "function", "doc": "

    Generates a plot which compares the fit to the data and displays the corresponding residuals

    \n", "signature": "(x, y, func, fit_res)", "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "type": "function", "doc": "

    Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta.

    \n", "signature": "(x, func, beta)", "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "type": "function", "doc": "

    Performs a Kolmogorov\u2013Smirnov test for the p-values of all fit object.

    \n\n
    Parameters
    \n\n
      \n
    • objects (list):\nList of fit results to include in the analysis (optional).
    • \n
    \n", "signature": "(objects=None)", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "type": "module", "doc": "

    pyerrors includes an input submodule in which input routines and parsers for the output of various numerical programs are contained.

    \n\n

    Jackknife samples

    \n\n

    For comparison with other analysis workflows pyerrors can also generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.

    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "type": "module", "doc": "

    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "type": "function", "doc": "

    Extract generic MCMC data from a bdio file

    \n\n

    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
    • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs)", "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "type": "function", "doc": "

    Write Obs to a bdio file according to ADerrors conventions

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
    • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    \n", "signature": "(obs_list, file_path, bdio_path='./libbdio.so', **kwargs)", "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "type": "function", "doc": "

    Extract mesons data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
    • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    • start (int):\nThe first configuration to be read (default 1)
    • \n
    • stop (int):\nThe last configuration to be read (default None)
    • \n
    • step (int):\nFixed step size between two measurements (default 1)
    • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs)", "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "type": "function", "doc": "

    Extract dSdm data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, kappa)

    \n\n

    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
    • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    • start (int):\nThe first configuration to be read (default 1)
    • \n
    • stop (int):\nThe last configuration to be read (default None)
    • \n
    • step (int):\nFixed step size between two measurements (default 1)
    • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs)", "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "type": "module", "doc": "

    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "type": "function", "doc": "

    Read hadrons meson hdf5 file and extract the meson labeled 'meson'

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n", "signature": "(path, filestem, ens_id, meson='meson_0', idl=None)", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "type": "class", "doc": "

    ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)

    \n\n

    An array object represents a multidimensional, homogeneous array\nof fixed-size items. An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)

    \n\n

    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.

    \n\n

    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.

    \n\n
    Parameters
    \n\n
      \n
    • (for the __new__ method; see Notes below)
    • \n
    • shape (tuple of ints):\nShape of created array.
    • \n
    • dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
    • \n
    • buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
    • \n
    • offset (int, optional):\nOffset of array data in buffer.
    • \n
    • strides (tuple of ints, optional):\nStrides of data in memory.
    • \n
    • order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
    • \n
    \n\n
    Attributes
    \n\n
      \n
    • T (ndarray):\nTranspose of the array.
    • \n
    • data (buffer):\nThe array's elements, in memory.
    • \n
    • dtype (dtype object):\nDescribes the format of the elements in the array.
    • \n
    • flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
    • \n
    • flat (numpy.flatiter object):\nFlattened version of the array as an iterator. The iterator\nallows assignments, e.g., x.flat = 3 (See ndarray.flat for\nassignment examples; TODO).
    • \n
    • imag (ndarray):\nImaginary part of the array.
    • \n
    • real (ndarray):\nReal part of the array.
    • \n
    • size (int):\nNumber of elements in the array.
    • \n
    • itemsize (int):\nThe memory use of each array element in bytes.
    • \n
    • nbytes (int):\nThe total number of bytes required to store the array data,\ni.e., itemsize * size.
    • \n
    • ndim (int):\nThe array's number of dimensions.
    • \n
    • shape (tuple of ints):\nShape of the array.
    • \n
    • strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous (3, 4) array of type\nint16 in C-order has strides (8, 2). This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4).
    • \n
    • ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
    • \n
    • base (ndarray):\nIf the array is a view into another array, that array is its base\n(unless that array is also a view). The base array is where the\narray data is actually stored.
    • \n
    \n\n
    See Also
    \n\n

    array: Construct an array.
    \nzeros: Create an array, each element of which is zero.
    \nempty: Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\").
    \ndtype: Create a data-type.
    \nnumpy.typing.NDArray: An ndarray alias :term:generic <generic type>\nw.r.t. its dtype.type <numpy.dtype.type>.

    \n\n
    Notes
    \n\n

    There are two modes of creating an array using __new__:

    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
    2. \n
    3. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
    4. \n
    \n\n

    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.

    \n\n
    Examples
    \n\n

    These examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.

    \n\n

    First mode, buffer is None:

    \n\n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n\n

    Second mode:

    \n\n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n", "bases": "numpy.ndarray"}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"fullname": "pyerrors.input.hadrons.Npr_matrix.__init__", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.__init__", "type": "function", "doc": "

    \n", "signature": "()", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"fullname": "pyerrors.input.hadrons.Npr_matrix.g5H", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.g5H", "type": "variable", "doc": "

    Gamma_5 hermitean conjugate

    \n\n

    Uses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.

    \n"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "type": "function", "doc": "

    Read hadrons ExternalLeg hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None)", "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "type": "function", "doc": "

    Read hadrons Bilinear hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None)", "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "type": "function", "doc": "

    Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    • vertices (list):\nVertex functions to be extracted.
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None, vertices=['VA', 'AV'])", "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "type": "module", "doc": "

    \n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "type": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    \n", "signature": "(ol, description='', indent=1)", "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "type": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
    • \n
    \n", "signature": "(ol, fname, description='', indent=1, gz=True)", "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "type": "function", "doc": "

    Reconstruct a list of Obs or structures containing Obs from a json string.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • json_string (str):\njson string containing the data.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    \n", "signature": "(json_string, verbose=True, full_output=False)", "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "type": "function", "doc": "

    Import a list of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False)", "funcdef": "def"}, "pyerrors.input.json.dump_dict_to_json": {"fullname": "pyerrors.input.json.dump_dict_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_dict_to_json", "type": "function", "doc": "

    Export a dict of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • od (dict):\nDict of JSON valid structures and objects that will be exported.\nAt the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    • reps (str):\nSpecify the structure of the placeholder in exported dict to be reps[0-9]+.
    • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
    • \n
    \n", "signature": "(od, fname, description='', indent=1, reps='DICTOBS', gz=True)", "funcdef": "def"}, "pyerrors.input.json.load_json_dict": {"fullname": "pyerrors.input.json.load_json_dict", "modulename": "pyerrors.input.json", "qualname": "load_json_dict", "type": "function", "doc": "

    Import a dict of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    • reps (str):\nSpecify the structure of the placeholder in imported dict to be reps[0-9]+.
    • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS')", "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "type": "module", "doc": "

    \n"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "type": "function", "doc": "

    Read pbp format from given folder structure. Returns a list of length nrw

    \n\n
    Parameters
    \n\n
      \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
    • \n
    \n", "signature": "(path, prefix, **kwargs)", "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "type": "module", "doc": "

    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "type": "function", "doc": "

    Read rwms format from given folder structure. Returns a list of length nrw

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath that contains the data files
    • \n
    • prefix (str):\nall files in path that start with prefix are considered as input files.\nMay be used together postfix to consider only special file endings.\nPrefix is ignored, if the keyword 'files' is used.
    • \n
    • version (str):\nversion of openQCD, default 2.0
    • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
    • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
    • \n
    • postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
    • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
    • \n
    • print_err (bool):\nPrint additional information that is useful for debugging.
    • \n
    \n", "signature": "(path, prefix, version='2.0', names=None, **kwargs)", "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "type": "function", "doc": "

    Extract t0 from given .ms.dat files. Returns t0 as Obs.

    \n\n

    It is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3\nis fitted with a linear function\nfrom which the exact root is extracted.

    \n\n

    It is assumed that one measurement is performed for each config.\nIf this is not the case, the resulting idl, as well as the handling\nof r_start, r_stop and r_step is wrong and the user has to correct\nthis in the resulting observable.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to .ms.dat files
    • \n
    • prefix (str):\nEnsemble prefix
    • \n
    • dtr_read (int):\nDetermines how many trajectories should be skipped\nwhen reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
    • \n
    • xmin (int):\nFirst timeslice where the boundary\neffects have sufficiently decayed.
    • \n
    • spatial_extent (int):\nspatial extent of the lattice, required for normalization.
    • \n
    • fit_range (int):\nNumber of data points left and right of the zero\ncrossing to be included in the linear fit. (Default: 5)
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
    • \n
    • plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
    • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
    • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
    • \n
    • plot_fit (bool):\nIf true, the fit for the extraction of t0 is shown together with the data.
    • \n
    • assume_thermalization (bool):\nIf True: If the first record divided by the distance between two measurements is larger than\n1, it is assumed that this is due to thermalization and the first measurement belongs\nto the first config (default).\nIf False: The config numbers are assumed to be traj_number // difference
    • \n
    \n", "signature": "(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs)", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop": {"fullname": "pyerrors.input.openQCD.read_qtop", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop", "type": "function", "doc": "

    Read the topologial charge based on openQCD gradient flow measurements.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\ncycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • version (str):\nEither openQCD or sfqcd, depending on the data.
    • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
    • \n
    • integer_charge (bool):\nIf True, the charge is rounded towards the nearest integer on each config.
    • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, version='openQCD', **kwargs)", "funcdef": "def"}, "pyerrors.input.openQCD.qtop_projection": {"fullname": "pyerrors.input.openQCD.qtop_projection", "modulename": "pyerrors.input.openQCD", "qualname": "qtop_projection", "type": "function", "doc": "

    Returns the projection to the topological charge sector defined by target.

    \n\n
    Parameters
    \n\n
      \n
    • path (Obs):\nTopological charge.
    • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
    • \n
    \n", "signature": "(qtop, target=0)", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop_sector": {"fullname": "pyerrors.input.openQCD.read_qtop_sector", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop_sector", "type": "function", "doc": "

    Constructs reweighting factors to a specified topological sector.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L
    • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of trajectories\nbetween two configs.\nif it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\ncycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • version (str):\nversion string of the openQCD (sfqcd) version used to create\nthe ensemble. Default is 2.0. May also be set to sfqcd.
    • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
    • \n
    • r_start (list):\noffset of the first ensemble, making it easier to match\nlater on with other Obs
    • \n
    • r_stop (list):\nlast configurations that need to be read (per replicum)
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
    • \n
    \n", "signature": "(path, prefix, c, target=0, **kwargs)", "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "type": "module", "doc": "

    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "type": "function", "doc": "

    Read sfcf c format from given folder structure.

    \n\n
    Parameters
    \n\n
      \n
    • quarks (str):\nLabel of the quarks used in the sfcf input file. e.g. \"quark quark\"\nfor version 0.0 this does NOT need to be given with the typical \" - \"\nthat is present in the output file,\nthis is done automatically for this version
    • \n
    • noffset (int):\nOffset of the source (only relevant when wavefunctions are used)
    • \n
    • wf (int):\nID of wave function
    • \n
    • wf2 (int):\nID of the second wavefunction\n(only relevant for boundary-to-boundary correlation functions)
    • \n
    • im (bool):\nif True, read imaginary instead of real part\nof the correlation function.
    • \n
    • corr_type (str):\nchange between bi (boundary - inner) (default) bib (boundary - inner - boundary) and bb (boundary - boundary)\ncorrelator types
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
    • \n
    • ens_name (str):\nreplaces the name of the ensemble
    • \n
    • version (str):\nversion of SFCF, with which the measurement was done.\nif the compact output option (-c) was specified,\nappend a \"c\" to the version (e.g. \"1.0c\")\nif the append output option (-a) was specified,\nappend an \"a\" to the version
    • \n
    • cfg_separator (str):\nString that separates the ensemble identifier from the configuration number (default 'n').
    • \n
    • replica (list):\nlist of replica to be read, default is all
    • \n
    • files (list):\nlist of files to be read per replica, default is all.\nfor non-compact output format, hand the folders to be read here.
    • \n
    • check_configs:: list of list of supposed configs, eg. [range(1,1000)]\nfor one replicum with 1000 configs
    • \n
    \n", "signature": "(\n path,\n prefix,\n name,\n quarks='.*',\n corr_type='bi',\n noffset=0,\n wf=0,\n wf2=0,\n version='1.0c',\n cfg_separator='n',\n **kwargs\n)", "funcdef": "def"}, "pyerrors.input.utils": {"fullname": "pyerrors.input.utils", "modulename": "pyerrors.input.utils", "type": "module", "doc": "

    Utilities for the input

    \n"}, "pyerrors.input.utils.check_idl": {"fullname": "pyerrors.input.utils.check_idl", "modulename": "pyerrors.input.utils", "qualname": "check_idl", "type": "function", "doc": "

    Checks if list of configurations is contained in an idl

    \n\n
    Parameters
    \n\n
      \n
    • idl (range or list):\nidl of the current replicum
    • \n
    • che (list):\nlist of configurations to be checked against
    • \n
    \n", "signature": "(idl, che)", "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "type": "module", "doc": "

    \n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "type": "function", "doc": "

    Matrix multiply all operands.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    • This implementation is faster compared to standard multiplication via the @ operator.
    • \n
    \n", "signature": "(*operands)", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "type": "function", "doc": "

    Matrix multiply both operands making use of the jackknife approximation.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    • For large matrices this is considerably faster compared to matmul.
    • \n
    \n", "signature": "(*operands)", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "type": "function", "doc": "

    Wrapper for numpy.einsum

    \n\n
    Parameters
    \n\n
      \n
    • subscripts (str):\nSubscripts for summation (see numpy documentation for details)
    • \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    \n", "signature": "(subscripts, *operands)", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "type": "function", "doc": "

    Inverse of Obs or CObs valued matrices.

    \n", "signature": "(x)", "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "type": "function", "doc": "

    Cholesky decomposition of Obs or CObs valued matrices.

    \n", "signature": "(x)", "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "type": "function", "doc": "

    Determinant of Obs valued matrices.

    \n", "signature": "(x)", "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "type": "function", "doc": "

    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.

    \n", "signature": "(obs, **kwargs)", "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "type": "function", "doc": "

    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.

    \n", "signature": "(obs, **kwargs)", "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "type": "function", "doc": "

    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.

    \n", "signature": "(obs, **kwargs)", "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "type": "function", "doc": "

    Computes the singular value decomposition of a matrix of Obs.

    \n", "signature": "(obs, **kwargs)", "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "type": "module", "doc": "

    \n"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "type": "function", "doc": "

    Dump object into pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • obj (object):\nobject to be saved in the pickle file
    • \n
    • name (str):\nname of the file
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(obj, name, **kwargs)", "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "type": "function", "doc": "

    Load object from pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the file
    • \n
    \n", "signature": "(path)", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "type": "function", "doc": "

    Generate an Obs object with given value, dvalue and name for test purposes

    \n\n
    Parameters
    \n\n
      \n
    • value (float):\ncentral value of the Obs to be generated.
    • \n
    • dvalue (float):\nerror of the Obs to be generated.
    • \n
    • name (str):\nname of the ensemble for which the Obs is to be generated.
    • \n
    • samples (int):\nnumber of samples for the Obs (default 1000).
    • \n
    \n", "signature": "(value, dvalue, name, samples=1000)", "funcdef": "def"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "type": "function", "doc": "

    Generate observables with given covariance and autocorrelation times.

    \n\n
    Parameters
    \n\n
      \n
    • means (list):\nlist containing the mean value of each observable.
    • \n
    • cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
    • \n
    • name (str):\nensemble name for the data to be geneated.
    • \n
    • tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
    • \n
    • samples (int):\nnumber of samples to be generated for each observable.
    • \n
    \n", "signature": "(means, cov, name, tau=0.5, samples=1000)", "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "type": "module", "doc": "

    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "type": "function", "doc": "

    Matrix pencil method to extract k energy levels from data

    \n\n

    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    \n\n
    Parameters
    \n\n
      \n
    • data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
    • \n
    • k (int):\nNumber of states to extract (default 1).
    • \n
    • p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
    • \n
    \n", "signature": "(corrs, k=1, p=None, **kwargs)", "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "type": "module", "doc": "

    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "type": "class", "doc": "

    Class for a general observable.

    \n\n

    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.

    \n\n
    Attributes
    \n\n
      \n
    • S_global (float):\nStandard value for S (default 2.0)
    • \n
    • S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
    • \n
    • tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
    • \n
    • tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
    • \n
    • N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
    • \n
    • N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
    • \n
    \n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "type": "function", "doc": "

    Initialize Obs object.

    \n\n
    Parameters
    \n\n
      \n
    • samples (list):\nlist of numpy arrays containing the Monte Carlo samples
    • \n
    • names (list):\nlist of strings labeling the individual samples
    • \n
    • idl (list, optional):\nlist of ranges or lists on which the samples are defined
    • \n
    \n", "signature": "(self, samples, names, idl=None, **kwargs)", "funcdef": "def"}, "pyerrors.obs.Obs.S_global": {"fullname": "pyerrors.obs.Obs.S_global", "modulename": "pyerrors.obs", "qualname": "Obs.S_global", "type": "variable", "doc": "

    \n", "default_value": " = 2.0"}, "pyerrors.obs.Obs.S_dict": {"fullname": "pyerrors.obs.Obs.S_dict", "modulename": "pyerrors.obs", "qualname": "Obs.S_dict", "type": "variable", "doc": "

    \n", "default_value": " = {}"}, "pyerrors.obs.Obs.tau_exp_global": {"fullname": "pyerrors.obs.Obs.tau_exp_global", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp_global", "type": "variable", "doc": "

    \n", "default_value": " = 0.0"}, "pyerrors.obs.Obs.tau_exp_dict": {"fullname": "pyerrors.obs.Obs.tau_exp_dict", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp_dict", "type": "variable", "doc": "

    \n", "default_value": " = {}"}, "pyerrors.obs.Obs.N_sigma_global": {"fullname": "pyerrors.obs.Obs.N_sigma_global", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma_global", "type": "variable", "doc": "

    \n", "default_value": " = 1.0"}, "pyerrors.obs.Obs.N_sigma_dict": {"fullname": "pyerrors.obs.Obs.N_sigma_dict", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma_dict", "type": "variable", "doc": "

    \n", "default_value": " = {}"}, "pyerrors.obs.Obs.filter_eps": {"fullname": "pyerrors.obs.Obs.filter_eps", "modulename": "pyerrors.obs", "qualname": "Obs.filter_eps", "type": "variable", "doc": "

    \n", "default_value": " = 1e-10"}, "pyerrors.obs.Obs.names": {"fullname": "pyerrors.obs.Obs.names", "modulename": "pyerrors.obs", "qualname": "Obs.names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.shape": {"fullname": "pyerrors.obs.Obs.shape", "modulename": "pyerrors.obs", "qualname": "Obs.shape", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.r_values": {"fullname": "pyerrors.obs.Obs.r_values", "modulename": "pyerrors.obs", "qualname": "Obs.r_values", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.deltas": {"fullname": "pyerrors.obs.Obs.deltas", "modulename": "pyerrors.obs", "qualname": "Obs.deltas", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.N": {"fullname": "pyerrors.obs.Obs.N", "modulename": "pyerrors.obs", "qualname": "Obs.N", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.is_merged": {"fullname": "pyerrors.obs.Obs.is_merged", "modulename": "pyerrors.obs", "qualname": "Obs.is_merged", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.idl": {"fullname": "pyerrors.obs.Obs.idl", "modulename": "pyerrors.obs", "qualname": "Obs.idl", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.ddvalue": {"fullname": "pyerrors.obs.Obs.ddvalue", "modulename": "pyerrors.obs", "qualname": "Obs.ddvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.reweighted": {"fullname": "pyerrors.obs.Obs.reweighted", "modulename": "pyerrors.obs", "qualname": "Obs.reweighted", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tag": {"fullname": "pyerrors.obs.Obs.tag", "modulename": "pyerrors.obs", "qualname": "Obs.tag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.value": {"fullname": "pyerrors.obs.Obs.value", "modulename": "pyerrors.obs", "qualname": "Obs.value", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.dvalue": {"fullname": "pyerrors.obs.Obs.dvalue", "modulename": "pyerrors.obs", "qualname": "Obs.dvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_names": {"fullname": "pyerrors.obs.Obs.e_names", "modulename": "pyerrors.obs", "qualname": "Obs.e_names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.cov_names": {"fullname": "pyerrors.obs.Obs.cov_names", "modulename": "pyerrors.obs", "qualname": "Obs.cov_names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.mc_names": {"fullname": "pyerrors.obs.Obs.mc_names", "modulename": "pyerrors.obs", "qualname": "Obs.mc_names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_content": {"fullname": "pyerrors.obs.Obs.e_content", "modulename": "pyerrors.obs", "qualname": "Obs.e_content", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.covobs": {"fullname": "pyerrors.obs.Obs.covobs", "modulename": "pyerrors.obs", "qualname": "Obs.covobs", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "type": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
    • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
    • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
    • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
    • \n
    \n", "signature": "(self, **kwargs)", "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "type": "function", "doc": "

    Output detailed properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • ens_content (bool):\nprint details about the ensembles and replica if true.
    • \n
    \n", "signature": "(self, ens_content=True)", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "type": "function", "doc": "

    Checks whether the observable is zero within 'sigma' standard errors.

    \n\n
    Parameters
    \n\n
      \n
    • sigma (int):\nNumber of standard errors used for the check.
    • \n
    • Works only properly when the gamma method was run.
    • \n
    \n", "signature": "(self, sigma=1)", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "type": "function", "doc": "

    Checks whether the observable is zero within a given tolerance.

    \n\n
    Parameters
    \n\n
      \n
    • atol (float):\nAbsolute tolerance (for details see numpy documentation).
    • \n
    \n", "signature": "(self, atol=1e-10)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "type": "function", "doc": "

    Plot integrated autocorrelation time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "type": "function", "doc": "

    Plot normalized autocorrelation function time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "type": "function", "doc": "

    Plot replica distribution for each ensemble with more than one replicum.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "type": "function", "doc": "

    Plot derived Monte Carlo history for each ensemble

    \n\n
    Parameters
    \n\n
      \n
    • expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
    • \n
    \n", "signature": "(self, expand=True)", "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "type": "function", "doc": "

    Plot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "type": "function", "doc": "

    Dump the Obs to a file 'name' of chosen format.

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nname of the file to be saved.
    • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
    • \n
    • description (str):\nDescription for output file, only relevant for json.gz format.
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(self, filename, datatype='json.gz', description='', **kwargs)", "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "type": "function", "doc": "

    Export jackknife samples from the Obs

    \n\n
    Returns
    \n\n
      \n
    • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
    • \n
    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.Obs.N_sigma": {"fullname": "pyerrors.obs.Obs.N_sigma", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.S": {"fullname": "pyerrors.obs.Obs.S", "modulename": "pyerrors.obs", "qualname": "Obs.S", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_ddvalue": {"fullname": "pyerrors.obs.Obs.e_ddvalue", "modulename": "pyerrors.obs", "qualname": "Obs.e_ddvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_drho": {"fullname": "pyerrors.obs.Obs.e_drho", "modulename": "pyerrors.obs", "qualname": "Obs.e_drho", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_dtauint": {"fullname": "pyerrors.obs.Obs.e_dtauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_dtauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_dvalue": {"fullname": "pyerrors.obs.Obs.e_dvalue", "modulename": "pyerrors.obs", "qualname": "Obs.e_dvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_n_dtauint": {"fullname": "pyerrors.obs.Obs.e_n_dtauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_n_dtauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_n_tauint": {"fullname": "pyerrors.obs.Obs.e_n_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_n_tauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_rho": {"fullname": "pyerrors.obs.Obs.e_rho", "modulename": "pyerrors.obs", "qualname": "Obs.e_rho", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_tauint": {"fullname": "pyerrors.obs.Obs.e_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_tauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_windowsize": {"fullname": "pyerrors.obs.Obs.e_windowsize", "modulename": "pyerrors.obs", "qualname": "Obs.e_windowsize", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tau_exp": {"fullname": "pyerrors.obs.Obs.tau_exp", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "type": "class", "doc": "

    Class for a complex valued observable.

    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "type": "function", "doc": "

    \n", "signature": "(self, real, imag=0.0)", "funcdef": "def"}, "pyerrors.obs.CObs.tag": {"fullname": "pyerrors.obs.CObs.tag", "modulename": "pyerrors.obs", "qualname": "CObs.tag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.real": {"fullname": "pyerrors.obs.CObs.real", "modulename": "pyerrors.obs", "qualname": "CObs.real", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.imag": {"fullname": "pyerrors.obs.CObs.imag", "modulename": "pyerrors.obs", "qualname": "CObs.imag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "type": "function", "doc": "

    Executes the gamma_method for the real and the imaginary part.

    \n", "signature": "(self, **kwargs)", "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "type": "function", "doc": "

    Checks whether both real and imaginary part are zero within machine precision.

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "type": "function", "doc": "

    \n", "signature": "(self)", "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "type": "function", "doc": "

    Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.

    \n\n
    Parameters
    \n\n
      \n
    • func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
    • \n
    • data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
    • \n
    • num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
    • \n
    • man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
    • \n
    \n\n
    Notes
    \n\n

    For simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use

    \n\n

    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])

    \n", "signature": "(func, data, array_mode=False, **kwargs)", "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "type": "function", "doc": "

    Reweight a list of observables.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
    • \n
    \n", "signature": "(weight, obs, **kwargs)", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "type": "function", "doc": "

    Correlate two observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs_a (Obs):\nFirst observable
    • \n
    • obs_b (Obs):\nSecond observable
    • \n
    \n\n
    Notes
    \n\n

    Keep in mind to only correlate primary observables which have not been reweighted\nyet. The reweighting has to be applied after correlating the observables.\nCurrently only works if ensembles are identical (this is not strictly necessary).

    \n", "signature": "(obs_a, obs_b)", "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "type": "function", "doc": "

    Calculates the covariance matrix of a set of observables.

    \n\n

    The gamma method has to be applied first to all observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs (list or numpy.ndarray):\nList or one dimensional array of Obs
    • \n
    • visualize (bool):\nIf True plots the corresponding normalized correlation matrix (default False).
    • \n
    • correlation (bool):\nIf True the correlation instead of the covariance is returned (default False).
    • \n
    • smooth (None or int):\nIf smooth is an integer 'E' between 2 and the dimension of the matrix minus 1 the eigenvalue\nsmoothing procedure of hep-lat/9412087 is applied to the correlation matrix which leaves the\nlargest E eigenvalues essentially unchanged and smoothes the smaller eigenvalues to avoid extremely\nsmall ones.
    • \n
    \n\n
    Notes
    \n\n

    The covariance is estimated by calculating the correlation matrix assuming no autocorrelation and then rescaling the correlation matrix by the full errors including the previous gamma method estimate for the autocorrelation of the observables. The covariance at windowsize 0 is guaranteed to be positive semi-definite\n$$v_i\\Gamma_{ij}(0)v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i,j}v_i\\delta_i^s\\delta_j^s v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i}|v_i\\delta_i^s|^2\\geq 0\\,,$$ for every $v\\in\\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.\nFor observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.\n$$\\tau_{\\mathrm{int}, ij}=\\sqrt{\\tau_{\\mathrm{int}, i}\\times \\tau_{\\mathrm{int}, j}}$$\nThis construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).

    \n", "signature": "(obs, visualize=False, correlation=False, smooth=None, **kwargs)", "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "type": "function", "doc": "

    Imports jackknife samples and returns an Obs

    \n\n
    Parameters
    \n\n
      \n
    • jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
    • \n
    • name (str):\nname of the ensemble the samples are defined on.
    • \n
    \n", "signature": "(jacks, name, idl=None)", "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "type": "function", "doc": "

    Combine all observables in list_of_obs into one new observable

    \n\n
    Parameters
    \n\n
      \n
    • list_of_obs (list):\nlist of the Obs object to be combined
    • \n
    \n\n
    Notes
    \n\n

    It is not possible to combine obs which are based on the same replicum

    \n", "signature": "(list_of_obs)", "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "type": "function", "doc": "

    Create an Obs based on mean(s) and a covariance matrix

    \n\n
    Parameters
    \n\n
      \n
    • mean (list of floats or float):\nN mean value(s) of the new Obs
    • \n
    • cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
    • \n
    • name (str):\nidentifier for the covariance matrix
    • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
    • \n
    \n", "signature": "(means, cov, name, grad=None)", "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "type": "module", "doc": "

    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "type": "function", "doc": "

    Finds the root of the function func(x, d) where d is an Obs.

    \n\n
    Parameters
    \n\n
      \n
    • d (Obs):\nObs passed to the function.
    • \n
    • func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:\npython\nimport autograd.numpy as anp\ndef root_func(x, d):\n return anp.exp(-x ** 2) - d\n
    • \n
    • guess (float):\nInitial guess for the minimization.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • Obs: Obs valued root of the function.
    • \n
    \n", "signature": "(d, func, guess=1.0, **kwargs)", "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "type": "module", "doc": "

    \n"}}, "docInfo": {"pyerrors": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 7902}, "pyerrors.correlators": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 108}, "pyerrors.correlators.Corr.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 94}, "pyerrors.correlators.Corr.reweighted": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.gamma_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 13}, "pyerrors.correlators.Corr.projected": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 64}, "pyerrors.correlators.Corr.item": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 53}, "pyerrors.correlators.Corr.plottable": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 31}, "pyerrors.correlators.Corr.symmetric": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 9}, "pyerrors.correlators.Corr.anti_symmetric": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 10}, "pyerrors.correlators.Corr.matrix_symmetric": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 10}, "pyerrors.correlators.Corr.GEVP": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 178}, "pyerrors.correlators.Corr.Eigenvalue": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 181}, "pyerrors.correlators.Corr.Hankel": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 67}, "pyerrors.correlators.Corr.roll": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 26}, "pyerrors.correlators.Corr.reverse": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 9}, "pyerrors.correlators.Corr.thin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 43}, "pyerrors.correlators.Corr.correlate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 53}, "pyerrors.correlators.Corr.reweight": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 79}, "pyerrors.correlators.Corr.T_symmetry": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 51}, "pyerrors.correlators.Corr.deriv": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 46}, "pyerrors.correlators.Corr.second_deriv": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 44}, "pyerrors.correlators.Corr.m_eff": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 135}, "pyerrors.correlators.Corr.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 110}, "pyerrors.correlators.Corr.plateau": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 92}, "pyerrors.correlators.Corr.set_prange": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 11}, "pyerrors.correlators.Corr.show": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 201}, "pyerrors.correlators.Corr.spaghetti_plot": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 42}, "pyerrors.correlators.Corr.dump": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 69}, "pyerrors.correlators.Corr.print": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.sqrt": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.log": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.exp": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.sin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.cos": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.tan": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.sinh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.cosh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.tanh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arcsin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arccos": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arctan": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arcsinh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arccosh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.arctanh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.real": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.imag": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.prune": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 325}, "pyerrors.covobs": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.covobs.Covobs": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.covobs.Covobs.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 100}, "pyerrors.covobs.Covobs.errsq": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 12}, "pyerrors.covobs.Covobs.cov": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.covobs.Covobs.grad": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.dirac": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.dirac.epsilon_tensor": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 13}, "pyerrors.dirac.epsilon_tensor_rank4": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 13}, "pyerrors.dirac.Grid_gamma": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 9}, "pyerrors.fits": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.fits.Fit_result": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 32}, "pyerrors.fits.Fit_result.__init__": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.fits.Fit_result.gamma_method": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 10}, "pyerrors.fits.least_squares": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 609}, "pyerrors.fits.total_least_squares": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 417}, "pyerrors.fits.fit_lin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 90}, "pyerrors.fits.qqplot": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 27}, "pyerrors.fits.residual_plot": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 17}, "pyerrors.fits.error_band": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 23}, "pyerrors.fits.ks_test": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 40}, "pyerrors.input": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 81}, "pyerrors.input.bdio": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.bdio.read_ADerrors": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 106}, "pyerrors.input.bdio.write_ADerrors": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 108}, "pyerrors.input.bdio.read_mesons": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 194}, "pyerrors.input.bdio.read_dSdm": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 191}, "pyerrors.input.hadrons": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.hadrons.read_meson_hd5": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 110}, "pyerrors.input.hadrons.Npr_matrix": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 2, "doc": 1065}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 2, "bases": 0, "doc": 3}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 30}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 81}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 81}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 13, "bases": 0, "doc": 94}, "pyerrors.input.json": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.json.create_json_string": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 116}, "pyerrors.input.json.dump_to_json": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 147}, "pyerrors.input.json.import_json_string": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 108}, "pyerrors.input.json.load_json": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 128}, "pyerrors.input.json.dump_dict_to_json": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 172}, "pyerrors.input.json.load_json_dict": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 12, "bases": 0, "doc": 135}, "pyerrors.input.misc": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.misc.read_pbp": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 62}, "pyerrors.input.openQCD": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.openQCD.read_rwms": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 254}, "pyerrors.input.openQCD.extract_t0": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 13, "bases": 0, "doc": 457}, "pyerrors.input.openQCD.read_qtop": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 346}, "pyerrors.input.openQCD.qtop_projection": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 49}, "pyerrors.input.openQCD.read_qtop_sector": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 340}, "pyerrors.input.sfcf": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.input.sfcf.read_sfcf": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 331}, "pyerrors.input.utils": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 6}, "pyerrors.input.utils.check_idl": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 47}, "pyerrors.linalg": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.linalg.matmul": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 54}, "pyerrors.linalg.jack_matmul": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 58}, "pyerrors.linalg.einsum": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 52}, "pyerrors.linalg.inv": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 10}, "pyerrors.linalg.cholesky": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 11}, "pyerrors.linalg.det": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 8}, "pyerrors.linalg.eigh": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 20}, "pyerrors.linalg.eig": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 17}, "pyerrors.linalg.pinv": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 13}, "pyerrors.linalg.svd": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 13}, "pyerrors.misc": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.misc.dump_object": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 57}, "pyerrors.misc.load_object": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 26}, "pyerrors.misc.pseudo_Obs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 89}, "pyerrors.misc.gen_correlated_data": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 109}, "pyerrors.mpm": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.mpm.matrix_pencil_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 147}, "pyerrors.obs": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 238}, "pyerrors.obs.Obs.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 62}, "pyerrors.obs.Obs.S_global": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 3, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.S_dict": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tau_exp_global": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 3, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tau_exp_dict": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.N_sigma_global": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 3, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.N_sigma_dict": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.filter_eps": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 3, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.names": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.shape": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.r_values": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.deltas": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.N": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.is_merged": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.idl": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.ddvalue": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.reweighted": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tag": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.value": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.dvalue": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_names": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.cov_names": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.mc_names": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_content": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.covobs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.gamma_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 133}, "pyerrors.obs.Obs.details": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 34}, "pyerrors.obs.Obs.is_zero_within_error": {"qualname": 5, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 50}, "pyerrors.obs.Obs.is_zero": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 35}, "pyerrors.obs.Obs.plot_tauint": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 34}, "pyerrors.obs.Obs.plot_rho": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 35}, "pyerrors.obs.Obs.plot_rep_dist": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 14}, "pyerrors.obs.Obs.plot_history": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 35}, "pyerrors.obs.Obs.plot_piechart": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 23}, "pyerrors.obs.Obs.dump": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 89}, "pyerrors.obs.Obs.export_jackknife": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 101}, "pyerrors.obs.Obs.sqrt": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.log": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.exp": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.sin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.cos": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tan": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arcsin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arccos": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arctan": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.sinh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.cosh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tanh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arcsinh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arccosh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.arctanh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.N_sigma": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.S": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_ddvalue": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_drho": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_dtauint": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_dvalue": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_n_dtauint": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_n_tauint": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_rho": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_tauint": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.e_windowsize": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.Obs.tau_exp": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.CObs": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9}, "pyerrors.obs.CObs.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 3}, "pyerrors.obs.CObs.tag": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.CObs.real": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.CObs.imag": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.obs.CObs.gamma_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 4, "bases": 0, "doc": 14}, "pyerrors.obs.CObs.is_zero": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 15}, "pyerrors.obs.CObs.conjugate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 3, "bases": 0, "doc": 3}, "pyerrors.obs.derived_observable": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 184}, "pyerrors.obs.reweight": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 97}, "pyerrors.obs.correlate": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 75}, "pyerrors.obs.covariance": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 299}, "pyerrors.obs.import_jackknife": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 6, "bases": 0, "doc": 61}, "pyerrors.obs.merge_obs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 5, "bases": 0, "doc": 56}, "pyerrors.obs.cov_Obs": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 90}, "pyerrors.roots": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.roots.find_root": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 8, "bases": 0, "doc": 117}, "pyerrors.version": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}}, "length": 204, "save": true}, "index": {"qualname": {"root": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 47, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2, "d": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}}, "df": 2}}, "v": {"docs": {"pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.covobs.Covobs": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.covobs.Covobs.grad": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_content": {"tf": 1}}, "df": 1}}}}, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 8}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.linalg.cholesky": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6}}, "v": {"docs": {"pyerrors.linalg.inv": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}}, "df": 2}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}}, "df": 2}}, "s": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 4}}, "r": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}}, "df": 2}}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}}, "df": 2}, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 12}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 3}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"4": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}, "w": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}}, "df": 2}}}, "g": {"5": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 5}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1}}, "n": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.grad": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 3}}}}}}, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 5}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 5}}}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {"pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 1}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}}, "df": 1}}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.print": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}}, "df": 3, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}}, "df": 3}}, "y": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.shape": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}, "q": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}}, "df": 2}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}}, "df": 3}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 2}}}}}, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.svd": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}}, "df": 2}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 2}}}}}}}}, "e": {"docs": {"pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 11, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 5, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}}, "df": 1}}}}}, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "docs": {}, "df": 0}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}}, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}}, "df": 2}}, "u": {"docs": {"pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}}, "df": 3}}}}, "g": {"docs": {"pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}}, "df": 2}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}}, "o": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {"pyerrors.linalg.det": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.deltas": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 5}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}}, "df": 5}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}}, "df": 2}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}}, "df": 2}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_drho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}}, "df": 5}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}}, "df": 2}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 3}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1}}, "df": 1}}}, "q": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}}}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 1}}}}}}}}}}, "n": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}}, "df": 6, "p": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 4}}}}}, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}, "pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.shape": {"tf": 1}, "pyerrors.obs.Obs.r_values": {"tf": 1}, "pyerrors.obs.Obs.deltas": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}, "pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.Obs.value": {"tf": 1}, "pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 67, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.value": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1}}}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 3}}}}}}, "fullname": {"root": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6, "p": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs": {"tf": 1}, "pyerrors.covobs.Covobs": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.covobs.Covobs.grad": {"tf": 1}, "pyerrors.dirac": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}, "pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.shape": {"tf": 1}, "pyerrors.obs.Obs.r_values": {"tf": 1}, "pyerrors.obs.Obs.deltas": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}, "pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.Obs.value": {"tf": 1}, "pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}, "pyerrors.version": {"tf": 1}}, "df": 204}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}}, "df": 1}}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.print": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 47, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 48}}}, "e": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2, "d": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}}, "df": 2}}, "v": {"docs": {"pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.covobs": {"tf": 1}, "pyerrors.covobs.Covobs": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.cov": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.grad": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.covobs": {"tf": 1}}, "df": 7}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_content": {"tf": 1}}, "df": 1}}}}, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 8}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.linalg.cholesky": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input": {"tf": 1}, "pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 33}}}, "v": {"docs": {"pyerrors.linalg.inv": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.imag": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}}, "df": 2}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}}, "df": 2}}, "s": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 4}}, "r": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}}, "df": 2}}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.real": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}}, "df": 2}, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 12}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 3}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.roots": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"4": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}, "w": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}}, "df": 2}}}, "g": {"5": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 5}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1}}, "n": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.grad": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 3}}}}}}, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 5}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 5}}}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.misc": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 7}}}, "p": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.mpm": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}, "c": {"docs": {"pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}}, "df": 3, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}}, "df": 3}}, "y": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.shape": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}, "q": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}}, "df": 2}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}}, "df": 3}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 2}}}}}, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 2}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.svd": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}}, "df": 2}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 2}}}}}}}}, "e": {"docs": {"pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 11, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 5, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 8}}}}}}, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "docs": {}, "df": 0}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}}, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}}, "df": 2}}, "u": {"docs": {"pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}}, "df": 3}}}}, "g": {"docs": {"pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}}, "df": 2}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}}, "o": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {"pyerrors.linalg.det": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.deltas": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 5}}}, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.dirac": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 4}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}}, "df": 5}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}}, "df": 2}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}}, "df": 2}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_drho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}}, "df": 5, "s": {"docs": {"pyerrors.fits": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}}, "df": 11}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}}, "df": 2}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 3}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 11}}}}}}, "q": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}}}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 5}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 1}}}}}}}}}}, "n": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}}, "df": 6, "p": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 4}}}}}, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}}, "df": 7}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}}}}}, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 6}}}}}}, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.filter_eps": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.shape": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.r_values": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.deltas": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_merged": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.idl": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.ddvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.reweighted": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tag": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.value": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cov_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.mc_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_content": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.covobs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sqrt": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.log": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.exp": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_drho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 81, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.utils": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 2}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.value": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.version": {"tf": 1}}, "df": 1}}}}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 3}}}}}}, "annotation": {"root": {"docs": {}, "df": 0}}, "default_value": {"root": {"0": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 3}, "1": {"0": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 1, "e": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}, "2": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 7}}, "signature": {"root": {"0": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 14, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "1": {"0": {"0": {"0": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 10, "e": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}, "2": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 3}, "3": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}, "5": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 2}, "docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.symmetric": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reverse": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.set_prange": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.print": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.sqrt": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.log": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.exp": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.sin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.cos": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.tan": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.sinh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.cosh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.tanh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arcsin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arccos": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arctan": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arccosh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.arctanh": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.dirac.epsilon_tensor": {"tf": 1.4142135623730951}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}, "pyerrors.fits.residual_plot": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1.4142135623730951}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.utils.check_idl": {"tf": 1.4142135623730951}, "pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 1.4142135623730951}, "pyerrors.linalg.inv": {"tf": 1.4142135623730951}, "pyerrors.linalg.cholesky": {"tf": 1.4142135623730951}, "pyerrors.linalg.det": {"tf": 1.4142135623730951}, "pyerrors.linalg.eigh": {"tf": 1.4142135623730951}, "pyerrors.linalg.eig": {"tf": 1.4142135623730951}, "pyerrors.linalg.pinv": {"tf": 1.4142135623730951}, "pyerrors.linalg.svd": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sqrt": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.log": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.exp": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctanh": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.conjugate": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 134, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 78}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2}}}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 3}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 3}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2}}}}}}}, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1, "r": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 2}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}}}}}}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 3, "n": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}}}}, "d": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4, "l": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 7}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 15}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 6}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}}}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1}}}, "n": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 24}}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 7, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 2}}}}}, "k": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 31}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}}}}}, "a": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 3}}}}}, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 4}}, "b": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}}}}}}, "r": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 2}, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2}}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "l": {"docs": {"pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 1}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 12}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}}, "j": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 3, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}}, "t": {"0": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}, "docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 8}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}, "u": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}}, "df": 2, "f": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 9}}, "l": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}}}}}, "d": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 1}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}}, "df": 3}}}}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2}}}}}, "f": {"2": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 3}}}}, "z": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 6}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}}, "v": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "x": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}}, "df": 10, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "v": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3}, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "h": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}, "y": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 6, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}, "b": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "i": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 5}}, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}, "q": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}}}, "bases": {"root": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}}}}, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "doc": {"root": {"0": {"0": {"0": {"0": {"0": {"0": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "6": {"9": {"7": {"9": {"5": {"8": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "1": {"2": {"8": {"9": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"1": {"8": {"0": {"6": {"4": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 3}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "3": {"4": {"4": {"5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "4": {"5": {"8": {"5": {"6": {"5": {"0": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "5": {"4": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"5": {"6": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 6.164414002968976}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 2.6457513110645907}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 2}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 23, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"0": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}, "1": {"0": {"0": {"0": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 3}, "3": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "4": {"7": {"2": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "1": {"9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "2": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "3": {"4": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "4": {"3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "5": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "7": {"6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "9": {"0": {"6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "8": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"0": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {"pyerrors": {"tf": 6.164414002968976}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 19, "}": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "\\": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "+": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}, "*": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "/": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}, "2": {"0": {"0": {"4": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "1": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "4": {"1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2}}, "df": 1}, "1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "3": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"2": {"1": {"8": {"6": {"6": {"7": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"0": {"9": {"7": {"7": {"6": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2}}, "df": 1}, "9": {"9": {"0": {"9": {"7": {"0": {"3": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {"pyerrors": {"tf": 5}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 12, "x": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 5}, "*": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "3": {"0": {"6": {"7": {"5": {"2": {"0": {"1": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "1": {"4": {"9": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"2": {"7": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "3": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "3": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "4": {"9": {"7": {"6": {"8": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "8": {"3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 7.745966692414834}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}, "docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 6, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "4": {"0": {"3": {"2": {"0": {"9": {"8": {"3": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "9": {"5": {"9": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 6, "x": {"4": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "5": {"0": {"0": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}, "1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "1": {"5": {"6": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "9": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "2": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"8": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "3": {"8": {"0": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "4": {"8": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"7": {"3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "4": {"6": {"5": {"9": {"8": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "8": {"3": {"4": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 5, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "6": {"4": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "5": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}}, "df": 2}, "7": {"0": {"0": {"0": {"0": {"0": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "1": {"4": {"2": {"2": {"9": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"4": {"6": {"6": {"5": {"8": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "4": {"5": {"7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"3": {"1": {"0": {"1": {"0": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"0": {"7": {"7": {"5": {"2": {"4": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"7": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "8": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "1": {"4": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "2": {"4": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "4": {"5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 5}, "9": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "3": {"3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "4": {"7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "5": {"9": {"3": {"0": {"3": {"5": {"7": {"8": {"5": {"1": {"6": {"0": {"9": {"3": {"6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "4": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"6": {"8": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"3": {"1": {"9": {"8": {"8": {"1": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"1": {"0": {"0": {"7": {"1": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"8": {"3": {"6": {"5": {"4": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}, "docs": {"pyerrors": {"tf": 62.37788069500277}, "pyerrors.correlators": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 3}, "pyerrors.correlators.Corr.__init__": {"tf": 5.0990195135927845}, "pyerrors.correlators.Corr.reweighted": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.item": {"tf": 4.58257569495584}, "pyerrors.correlators.Corr.plottable": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.symmetric": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 5.477225575051661}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 5.477225575051661}, "pyerrors.correlators.Corr.Hankel": {"tf": 4.58257569495584}, "pyerrors.correlators.Corr.roll": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.reverse": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 4.242640687119285}, "pyerrors.correlators.Corr.correlate": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.reweight": {"tf": 4.58257569495584}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 4.242640687119285}, "pyerrors.correlators.Corr.deriv": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.second_deriv": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.m_eff": {"tf": 5.830951894845301}, "pyerrors.correlators.Corr.fit": {"tf": 5.291502622129181}, "pyerrors.correlators.Corr.plateau": {"tf": 5}, "pyerrors.correlators.Corr.set_prange": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 7.54983443527075}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 3.872983346207417}, "pyerrors.correlators.Corr.dump": {"tf": 5.0990195135927845}, "pyerrors.correlators.Corr.print": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.sqrt": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.log": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.exp": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.sin": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.cos": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.tan": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.sinh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.cosh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.tanh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arcsin": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arccos": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arctan": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arccosh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.arctanh": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.real": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.imag": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 6.855654600401044}, "pyerrors.covobs": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 5.916079783099616}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.cov": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.grad": {"tf": 1.7320508075688772}, "pyerrors.dirac": {"tf": 1.7320508075688772}, "pyerrors.dirac.epsilon_tensor": {"tf": 2}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 2}, "pyerrors.dirac.Grid_gamma": {"tf": 1.7320508075688772}, "pyerrors.fits": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result": {"tf": 3.872983346207417}, "pyerrors.fits.Fit_result.__init__": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 15.748015748023622}, "pyerrors.fits.total_least_squares": {"tf": 14.66287829861518}, "pyerrors.fits.fit_lin": {"tf": 4.795831523312719}, "pyerrors.fits.qqplot": {"tf": 1.7320508075688772}, "pyerrors.fits.residual_plot": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1.7320508075688772}, "pyerrors.fits.ks_test": {"tf": 3.872983346207417}, "pyerrors.input": {"tf": 4.69041575982343}, "pyerrors.input.bdio": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_ADerrors": {"tf": 5.196152422706632}, "pyerrors.input.bdio.write_ADerrors": {"tf": 5.196152422706632}, "pyerrors.input.bdio.read_mesons": {"tf": 7.416198487095663}, "pyerrors.input.bdio.read_dSdm": {"tf": 7.416198487095663}, "pyerrors.input.hadrons": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 6}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 20.808652046684813}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 5.385164807134504}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 5.385164807134504}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 5.916079783099616}, "pyerrors.input.json": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 5.0990195135927845}, "pyerrors.input.json.dump_to_json": {"tf": 6.164414002968976}, "pyerrors.input.json.import_json_string": {"tf": 5.477225575051661}, "pyerrors.input.json.load_json": {"tf": 6}, "pyerrors.input.json.dump_dict_to_json": {"tf": 6.6332495807108}, "pyerrors.input.json.load_json_dict": {"tf": 6.4031242374328485}, "pyerrors.input.misc": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 4.242640687119285}, "pyerrors.input.openQCD": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 7.874007874011811}, "pyerrors.input.openQCD.extract_t0": {"tf": 9.899494936611665}, "pyerrors.input.openQCD.read_qtop": {"tf": 9.433981132056603}, "pyerrors.input.openQCD.qtop_projection": {"tf": 4.58257569495584}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 9.219544457292887}, "pyerrors.input.sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 8.888194417315589}, "pyerrors.input.utils": {"tf": 1.4142135623730951}, "pyerrors.input.utils.check_idl": {"tf": 4.242640687119285}, "pyerrors.linalg": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 4.58257569495584}, "pyerrors.linalg.jack_matmul": {"tf": 4.47213595499958}, "pyerrors.linalg.einsum": {"tf": 4.47213595499958}, "pyerrors.linalg.inv": {"tf": 1.7320508075688772}, "pyerrors.linalg.cholesky": {"tf": 1.7320508075688772}, "pyerrors.linalg.det": {"tf": 1.7320508075688772}, "pyerrors.linalg.eigh": {"tf": 1.7320508075688772}, "pyerrors.linalg.eig": {"tf": 1.7320508075688772}, "pyerrors.linalg.pinv": {"tf": 1.7320508075688772}, "pyerrors.linalg.svd": {"tf": 1.7320508075688772}, "pyerrors.misc": {"tf": 1.7320508075688772}, "pyerrors.misc.dump_object": {"tf": 5}, "pyerrors.misc.load_object": {"tf": 3.7416573867739413}, "pyerrors.misc.pseudo_Obs": {"tf": 5.656854249492381}, "pyerrors.misc.gen_correlated_data": {"tf": 6.244997998398398}, "pyerrors.mpm": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 5.385164807134504}, "pyerrors.obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 6.928203230275509}, "pyerrors.obs.Obs.__init__": {"tf": 4.898979485566356}, "pyerrors.obs.Obs.S_global": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.S_dict": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.filter_eps": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.names": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.shape": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.r_values": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.deltas": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.N": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_merged": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.idl": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.ddvalue": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.reweighted": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tag": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.value": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.dvalue": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_names": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.cov_names": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.mc_names": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_content": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.covobs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 5.744562646538029}, "pyerrors.obs.Obs.details": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 4.47213595499958}, "pyerrors.obs.Obs.is_zero": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.plot_tauint": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.plot_rho": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_history": {"tf": 3.7416573867739413}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.dump": {"tf": 5.744562646538029}, "pyerrors.obs.Obs.export_jackknife": {"tf": 3.7416573867739413}, "pyerrors.obs.Obs.sqrt": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.log": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.exp": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.sin": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.cos": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tan": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arcsin": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arccos": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arctan": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.sinh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.cosh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tanh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arcsinh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arccosh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.arctanh": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.N_sigma": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.S": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_drho": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_rho": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_tauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.tau_exp": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.__init__": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.tag": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.real": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.imag": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.is_zero": {"tf": 1.7320508075688772}, "pyerrors.obs.CObs.conjugate": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 6.4031242374328485}, "pyerrors.obs.reweight": {"tf": 5.196152422706632}, "pyerrors.obs.correlate": {"tf": 4.898979485566356}, "pyerrors.obs.covariance": {"tf": 6.4031242374328485}, "pyerrors.obs.import_jackknife": {"tf": 4.47213595499958}, "pyerrors.obs.merge_obs": {"tf": 4.123105625617661}, "pyerrors.obs.cov_Obs": {"tf": 5.385164807134504}, "pyerrors.roots": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 6.782329983125268}, "pyerrors.version": {"tf": 1.7320508075688772}}, "df": 204, "w": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 34}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 6}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 8}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 9}}}}}, "o": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 5.830951894845301}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 26, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 5}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}, "/": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 8}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 3, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 6}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 5}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 2, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "f": {"2": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "i": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 7, "s": {"docs": {"pyerrors": {"tf": 8}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 3.4641016151377544}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 3.3166247903554}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.4641016151377544}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 3.605551275463989}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.449489742783178}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 2.8284271247461903}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 43}, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 18, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 5, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}, "n": {"docs": {"pyerrors": {"tf": 8.12403840463596}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.3166247903554}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.23606797749979}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 32, "t": {"1": {"6": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 27, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 3}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 10}, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 1}}, "v": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}}, "df": 3}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 4, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3}}}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}}}}}}, "x": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 3, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils": {"tf": 1}}, "df": 11}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 6}}}}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}}}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1}}}}, "m": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 8, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 4}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 3}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 5}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}, "d": {"0": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}, "docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 6, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3}, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "r": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}}, "df": 10}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "f": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 2}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 2.23606797749979}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 3}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.8284271247461903}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.449489742783178}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 38}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}}, "/": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 2}}}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "\\": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "j": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}, "^": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "|": {"docs": {}, "df": 0, "^": {"2": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "docs": {}, "df": 0}}}}, "}": {"docs": {}, "df": 0, "|": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}, "\\": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 3, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 6.557438524302}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input": {"tf": 2}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 4}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 5, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.7320508075688772}}, "df": 3}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 2}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 76}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 2}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 2}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2}, "pyerrors.input.bdio.read_mesons": {"tf": 2}, "pyerrors.input.bdio.read_dSdm": {"tf": 2}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 18}}, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1.7320508075688772}}, "df": 2}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 3.872983346207417}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 3}}}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 7, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}}}}}, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 2}}}}, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 6, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 4}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 3}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {"pyerrors": {"tf": 5.477225575051661}, "pyerrors.correlators.Corr": {"tf": 1}}, "df": 2, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 4}}, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}}, "df": 4}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 3}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4}}}}}}}}}, "n": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 11, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}}, "df": 4}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2}}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "^": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}}, "df": 1}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}, "h": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 5, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 5}, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 2}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}}, "df": 2}}}}}}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}}, "a": {"docs": {"pyerrors": {"tf": 8.306623862918075}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 3.605551275463989}, "pyerrors.fits.total_least_squares": {"tf": 3.3166247903554}, "pyerrors.fits.fit_lin": {"tf": 1.7320508075688772}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 2}, "pyerrors.input.bdio.read_dSdm": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 58, "n": {"docs": {"pyerrors": {"tf": 5.0990195135927845}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.605551275463989}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 25, "d": {"docs": {"pyerrors": {"tf": 6.928203230275509}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 53}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 7}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 3}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 4}}}}, "n": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 5}}, "r": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, ":": {"1": {"0": {"0": {"9": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"5": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"0": {"9": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"0": {"4": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}}}, "e": {"docs": {"pyerrors": {"tf": 5.477225575051661}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 34}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 4.47213595499958}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 6.082762530298219}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 13, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 9}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4}}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 3, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 4, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2}}}}}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 5}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 2}}, "df": 6}}}}}}}}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}}}}}, "s": {"docs": {"pyerrors": {"tf": 6}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 17, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 7}, "s": {"docs": {"pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}}, "df": 2}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 4}}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "g": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1, "^": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "l": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 26, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 3}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 6}}, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 3}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 15, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3}}}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 8}}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 6}}}}}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "i": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 2}}}, "y": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 4}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}}}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 2}}}}}}, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 3}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 6}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 3}}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}}, "f": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 6.782329983125268}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.8284271247461903}, "pyerrors.fits.total_least_squares": {"tf": 2}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1.4142135623730951}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.449489742783178}, "pyerrors.input.utils": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1.7320508075688772}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 3.872983346207417}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 2}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 55, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 5, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}}, "df": 8, "s": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 4.358898943540674}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.7320508075688772}}, "df": 23}, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "{": {"1": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "{": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "}": {"docs": {}, "df": 0, "+": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "docs": {}, "df": 0}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.6457513110645907}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1.7320508075688772}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}}, "df": 14, "s": {"docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 5}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 17}}}, "x": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 8}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}, "s": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 4.358898943540674}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 2}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.load_json_dict": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 2}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 2.23606797749979}}, "df": 27, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 10, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}}, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 3}}}}}, "^": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 2}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 6, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.fit": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 2}}, "df": 11, "s": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 7}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 8, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 11, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "w": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}}, "df": 2}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 14}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 12, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 5.830951894845301}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 9, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 4}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "x": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 2, "/": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 1}}}}, "p": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 4, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 6, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 6}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 8, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2.8284271247461903}}, "df": 1, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 2}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.linalg.eigh": {"tf": 1}}, "df": 3}}}}}}}}}, "h": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 9}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 6, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"1": {"docs": {"pyerrors": {"tf": 3.4641016151377544}}, "df": 1, "|": {"docs": {}, "df": 0, "r": {"0": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "2": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 5.5677643628300215}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 21, "s": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 3, "/": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 9}}}, "y": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}}, "df": 7}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 4}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 3}, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 2}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 2}}}, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 3}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 14}}, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "q": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 1, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}, "g": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2}, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "c": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 3}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}}, "df": 7, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 3}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}, "s": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 4}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}}, "df": 5}, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 3}, "s": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input": {"tf": 1}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 2}}}}}, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "/": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "/": {"1": {"6": {"0": {"3": {"7": {"5": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": null}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 17}}}, "s": {"docs": {"pyerrors": {"tf": 5}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 11}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 3}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 2}}}, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}}, "df": 4, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.utils.check_idl": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}}, "df": 14}}}}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 7}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 2}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 6.6332495807108}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 18, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}}, "df": 3, "d": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 4}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 2}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 23, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 4}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.prune": {"tf": 2.23606797749979}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 2.449489742783178}}, "df": 6, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 2}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 4}}}, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 5}}}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}}, "df": 6}}, "v": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 4, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 4}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 2.23606797749979}, "pyerrors.obs.cov_Obs": {"tf": 2}}, "df": 6}}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.7320508075688772}}, "df": 1}, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 2}, "/": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.4142135623730951}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 5}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 4}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 3}}, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "n": {"docs": {"pyerrors": {"tf": 5.656854249492381}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 26, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 5}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}}, "df": 5}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 3, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "p": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 3}}}, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 7, "f": {"docs": {"pyerrors": {"tf": 10.295630140987}, "pyerrors.correlators.Corr": {"tf": 2}, "pyerrors.correlators.Corr.__init__": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 2.6457513110645907}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 3}, "pyerrors.fits.total_least_squares": {"tf": 3}, "pyerrors.fits.fit_lin": {"tf": 2.23606797749979}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 5.0990195135927845}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 2.6457513110645907}, "pyerrors.input.json.dump_to_json": {"tf": 2.6457513110645907}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2.8284271247461903}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.extract_t0": {"tf": 3.1622776601683795}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.8284271247461903}, "pyerrors.input.sfcf.read_sfcf": {"tf": 3.4641016151377544}, "pyerrors.input.utils.check_idl": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1.4142135623730951}, "pyerrors.linalg.eig": {"tf": 1.4142135623730951}, "pyerrors.linalg.pinv": {"tf": 1.4142135623730951}, "pyerrors.linalg.svd": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2.6457513110645907}, "pyerrors.obs.Obs": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs.__init__": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.derived_observable": {"tf": 2.449489742783178}, "pyerrors.obs.reweight": {"tf": 2}, "pyerrors.obs.covariance": {"tf": 3.1622776601683795}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1.7320508075688772}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 82, "f": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {"pyerrors": {"tf": 5.291502622129181}, "pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 25, "e": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 18, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}}, "df": 22}}, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 1}}, "df": 3}}}}}, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 13}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {}, "df": 0, "s": {"1": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3}, "2": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3}, "3": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3}, "docs": {"pyerrors": {"tf": 9.591663046625438}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.fit_lin": {"tf": 2.23606797749979}, "pyerrors.input": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 2}, "pyerrors.input.json.dump_to_json": {"tf": 2}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 2.23606797749979}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 2}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 2}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 48, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 12, "s": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 2}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 9}}}}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "[": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2}}}, "j": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}, "pyerrors.misc.dump_object": {"tf": 1.7320508075688772}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 20, "s": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 6}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "r": {"docs": {"pyerrors": {"tf": 4.123105625617661}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.cov_Obs": {"tf": 2}}, "df": 35, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 5, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 3, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}}, "d": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 17, "s": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "r": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "w": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}, "m": {"docs": {"pyerrors": {"tf": 3.872983346207417}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.449489742783178}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 3, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 4.795831523312719}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 2.8284271247461903}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 2.6457513110645907}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 19}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}}, "df": 7}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}}}}}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}, "n": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "y": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 5}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 2, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "y": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}}}, "x": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 3}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 5}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4, "a": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 4.69041575982343}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 16, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 4}}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.449489742783178}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}}, "df": 3}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 8, "s": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 7}}}}}}}}}, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}}, "df": 1}}}}}, "y": {"docs": {"pyerrors": {"tf": 7.681145747868608}}, "df": 1}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}, "y": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 3}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}}, "df": 1}}}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 2}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}}}, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}, "\\": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}, "c": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "m": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "s": {"1": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 2, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3, "a": {"docs": {"pyerrors": {"tf": 4.795831523312719}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 21, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 4}}}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}}, "df": 7, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "y": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 5}}}}}}, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}}, "df": 5}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}}}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 2}}}}}, "k": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 3}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 3}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 4}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}}}}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3}, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4}}}, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 5}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 2}}, "s": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 7}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "s": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 5}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 4, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 4}}}, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 11}, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 2}, "pyerrors.input.bdio.read_dSdm": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 2}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 27, "s": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr": {"tf": 1}}, "df": 2}, "c": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}}}}}}, "o": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 7}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "w": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 2}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}}, "df": 1, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}}, "df": 1}}}, "r": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 3}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.misc.pseudo_Obs": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 1}}}}, "b": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 8}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "{": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}}, "df": 1, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 6.082762530298219}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.6457513110645907}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.6457513110645907}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 49, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.23606797749979}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}, "a": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 18, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 5}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}}, "df": 4}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.6457513110645907}}, "df": 3}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 3}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 25}, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_mesons": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.6457513110645907}}, "df": 4}}}, "i": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "b": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "t": {"0": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 5, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.GEVP": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.prune": {"tf": 4.47213595499958}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 8, "h": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1, "e": {"docs": {"pyerrors": {"tf": 16.1245154965971}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 3}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.projected": {"tf": 2}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 2}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 3.4641016151377544}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.thin": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.reweight": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 2}, "pyerrors.correlators.Corr.deriv": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.m_eff": {"tf": 3}, "pyerrors.correlators.Corr.fit": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.plateau": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.set_prange": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.dump": {"tf": 2}, "pyerrors.correlators.Corr.prune": {"tf": 4.795831523312719}, "pyerrors.covobs.Covobs.__init__": {"tf": 2.23606797749979}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 4.47213595499958}, "pyerrors.fits.total_least_squares": {"tf": 3.4641016151377544}, "pyerrors.fits.fit_lin": {"tf": 2.449489742783178}, "pyerrors.fits.qqplot": {"tf": 1.7320508075688772}, "pyerrors.fits.residual_plot": {"tf": 1.7320508075688772}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 2}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2}, "pyerrors.input.bdio.read_mesons": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.6457513110645907}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.6457513110645907}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 5.830951894845301}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 2}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 2.8284271247461903}, "pyerrors.input.json.dump_to_json": {"tf": 3}, "pyerrors.input.json.import_json_string": {"tf": 2.6457513110645907}, "pyerrors.input.json.load_json": {"tf": 2.6457513110645907}, "pyerrors.input.json.dump_dict_to_json": {"tf": 3.3166247903554}, "pyerrors.input.json.load_json_dict": {"tf": 2.6457513110645907}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 3}, "pyerrors.input.openQCD.extract_t0": {"tf": 5.385164807134504}, "pyerrors.input.openQCD.read_qtop": {"tf": 4.47213595499958}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 4.242640687119285}, "pyerrors.input.sfcf.read_sfcf": {"tf": 4.242640687119285}, "pyerrors.input.utils": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.7320508075688772}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 2.23606797749979}, "pyerrors.misc.gen_correlated_data": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2.23606797749979}, "pyerrors.obs.Obs": {"tf": 3.1622776601683795}, "pyerrors.obs.Obs.__init__": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 3.4641016151377544}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.dump": {"tf": 2}, "pyerrors.obs.Obs.export_jackknife": {"tf": 3.3166247903554}, "pyerrors.obs.CObs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 2.8284271247461903}, "pyerrors.obs.reweight": {"tf": 2.23606797749979}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 4.898979485566356}, "pyerrors.obs.import_jackknife": {"tf": 2}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 2}, "pyerrors.roots.find_root": {"tf": 2.449489742783178}}, "df": 97, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 6}}, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 6.244997998398398}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 22}, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 4}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 24}, "n": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 2}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 22}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "j": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "s": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {"pyerrors": {"tf": 8.660254037844387}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.plateau": {"tf": 2}, "pyerrors.correlators.Corr.show": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 3.1622776601683795}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.7320508075688772}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_mesons": {"tf": 2.6457513110645907}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.6457513110645907}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.7416573867739413}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.extract_t0": {"tf": 3.4641016151377544}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 3.4641016151377544}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.8284271247461903}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 2.23606797749979}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 2.8284271247461903}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 72, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "w": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.23606797749979}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 18}}, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 3}, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "y": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 5, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 7}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 11, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 10, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 5}}}}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}}, "df": 3}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 3}, "+": {"1": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.7320508075688772}}, "df": 2}, "2": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1.7320508075688772}}, "df": 1}}, "/": {"2": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 4}}}}, "^": {"2": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 2}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 14, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"5": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 4.242640687119285}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 11}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {"pyerrors": {"tf": 8.306623862918075}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}}, "df": 2}, "e": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 4, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}}, "df": 3}, "s": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 2.23606797749979}}, "df": 4}}, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 22}, "s": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "z": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 8, "i": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 4}}}}}}, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 4}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}, "^": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "l": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 2, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2, "/": {"0": {"3": {"0": {"6": {"0": {"1": {"7": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"4": {"1": {"2": {"0": {"8": {"7": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1, "x": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "r": {"docs": {"pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "y": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 5}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 9}}, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4}}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2, "r": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 5}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.__init__": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.GEVP": {"tf": 2}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.6457513110645907}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.fit_lin": {"tf": 2.23606797749979}, "pyerrors.fits.ks_test": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 2}, "pyerrors.input.json.dump_to_json": {"tf": 2}, "pyerrors.input.json.import_json_string": {"tf": 2}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_rwms": {"tf": 3.1622776601683795}, "pyerrors.input.openQCD.extract_t0": {"tf": 3}, "pyerrors.input.openQCD.read_qtop": {"tf": 2.449489742783178}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.6457513110645907}, "pyerrors.input.utils.check_idl": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 2.449489742783178}, "pyerrors.obs.derived_observable": {"tf": 2}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 2}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 36, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 6}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}, "b": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_mesons": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.23606797749979}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}}, "df": 2}}, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 2}}, "d": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 3, "g": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 9}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}, "q": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "/": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}, "s": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 8, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_mesons": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 5, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 3}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 2}}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {"pyerrors": {"tf": 3.605551275463989}}, "df": 1, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 7}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 2}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2}}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.einsum": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input": {"tf": 1}}, "df": 1}}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "y": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}, "e": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4}}, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}}, "df": 5}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 3, "s": {"1": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 3.872983346207417}, "pyerrors.input": {"tf": 1.7320508075688772}, "pyerrors.misc.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 2}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2.23606797749979}, "pyerrors.obs.import_jackknife": {"tf": 1.7320508075688772}}, "df": 8}}}}, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 8}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1.4142135623730951}}, "df": 3, "d": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}, "s": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 5}}}, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2.23606797749979}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}}, "df": 10}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 9}}, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}}}}, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.23606797749979}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 35, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 9, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 3}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 8, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}}, "df": 7}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 6, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 8}}, "y": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 7}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.qtop_projection": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 2}}}}, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10, "s": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}, "e": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 8}, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 4}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 11}, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 8}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "y": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 7}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 4}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.thin": {"tf": 1.7320508075688772}}, "df": 1}}}, "e": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 3}}, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 1}}}}}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1.4142135623730951}}, "df": 3}, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.matrix_symmetric": {"tf": 1}}, "df": 2}, "d": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 5}}}, "w": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "s": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}}, "df": 4}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 2}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2, "{": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "\\": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "k": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "f": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2}}, "df": 2}}}, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}}, "df": 1}}}}, "n": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.Hankel": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.prune": {"tf": 2.8284271247461903}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 10, "o": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 7, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 11}}, "t": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 19, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 7}}}, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 21}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 18, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "p": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 4}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3.872983346207417}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 16, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 8, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}, "d": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 2}}}, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 6}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 3}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "x": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.605551275463989}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 15}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "x": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 2}}}, "x": {"0": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 6}, "1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 3}, "2": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 3}, "docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 2.8284271247461903}, "pyerrors.fits.total_least_squares": {"tf": 3}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 10, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}, "[": {"0": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}, "1": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "y": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.spaghetti_plot": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 7, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}}, "r": {"0": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 7, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 9}, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.misc.read_pbp": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.openQCD.read_qtop": {"tf": 2}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.23606797749979}}, "df": 14, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}}}, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 4}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 5, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 3}}}, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}}, "df": 4}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 3}}}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 7}, "s": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 5}}}}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 4, "s": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 11}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}}}}, "s": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2, "[": {"0": {"docs": {"pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 13, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.item": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 16}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.openQCD.qtop_projection": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 6}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 4}}}}}}}}}, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 4, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 5}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.utils.check_idl": {"tf": 1}}, "df": 11, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 2}}}, "k": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 3}, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 4, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}}, "df": 2}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 2}}, "w": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}, "v": {"1": {"docs": {}, "df": 0, "@": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "@": {"docs": {}, "df": 0, "v": {"2": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}, "docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.prune": {"tf": 2.23606797749979}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 4, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 18, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.fits.ks_test": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 7}, "d": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 9}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}}, "df": 2}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input": {"tf": 1}}, "df": 2}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 3, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 2}}}, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}}, "df": 5}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "d": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 3}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 2.23606797749979}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2.449489742783178}}, "df": 6}}}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.json.load_json_dict": {"tf": 1}}, "df": 3}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3, "s": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 3}}}}}}, "\\": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}, "j": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.item": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}}, "df": 4, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}}}}, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input": {"tf": 2.23606797749979}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "s": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_dict_to_json": {"tf": 2.449489742783178}, "pyerrors.input.json.load_json_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 9}}}, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "}": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}, "^": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 4}}}, "s": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 2}}}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 5}}}, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 2}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 13, "d": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 18}, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 2}, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.utils": {"tf": 1}}, "df": 1}}}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2.449489742783178}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}}, "df": 2}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 4}}, "df": 1, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 10, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.prune": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.dump_dict_to_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 14}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.Hankel": {"tf": 1.4142135623730951}}, "df": 1}}}}, "s": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 12}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 2}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1.4142135623730951}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 3}}, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.openQCD.read_qtop": {"tf": 1}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.correlators.Corr.prune": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, ":": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "f": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "docs": {}, "df": 0}}, "u": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "k": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 2, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.thin": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 3}}, "y": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1}}, "df": 2, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "\u2013": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.fits.ks_test": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"1": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; // mirrored in build-search-index.js (part 1) // Also split on html tags. this is a cheap heuristic, but good enough.