How to use the pykrige.core.calc_cR function in PyKrige

print("Nugget:", self.variogram_model_parameters[2], "\n")
        if self.enable_plotting:
            self.display_variogram_model()

        if self.verbose:
            print("Calculating statistics on variogram model fit...")
        self.delta, self.sigma, self.epsilon = _find_statistics(
            np.vstack((self.X_ADJUSTED, self.Y_ADJUSTED)).T,
            self.Z,
            self.variogram_function,
            self.variogram_model_parameters,
            "euclidean",
        )
        self.Q1 = core.calcQ1(self.epsilon)
        self.Q2 = core.calcQ2(self.epsilon)
        self.cR = core.calc_cR(self.Q2, self.sigma)
        if self.verbose:
            print("Q1 =", self.Q1)
            print("Q2 =", self.Q2)
            print("cR =", self.cR, "\n")

        if self.verbose:
            print("Initializing drift terms...")

        # Note that the regional linear drift values will be based
        # on the adjusted coordinate system, Really, it doesn't actually
        # matter which coordinate system is used here.
        if "regional_linear" in drift_terms:
            self.regional_linear_drift = True
            if self.verbose:
                print("Implementing regional linear drift.")
        else:

print("Nugget:", self.variogram_model_parameters[2], "\n")
        if self.enable_plotting:
            self.display_variogram_model()

        if self.verbose:
            print("Calculating statistics on variogram model fit...")
        self.delta, self.sigma, self.epsilon = _find_statistics(
            np.vstack((self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED)).T,
            self.VALUES,
            self.variogram_function,
            self.variogram_model_parameters,
            "euclidean",
        )
        self.Q1 = core.calcQ1(self.epsilon)
        self.Q2 = core.calcQ2(self.epsilon)
        self.cR = core.calc_cR(self.Q2, self.sigma)
        if self.verbose:
            print("Q1 =", self.Q1)
            print("Q2 =", self.Q2)
            print("cR =", self.cR, "\n")

print("Nugget:", self.variogram_model_parameters[2], "\n")
        if self.enable_plotting:
            self.display_variogram_model()

        if self.verbose:
            print("Calculating statistics on variogram model fit...")
        self.delta, self.sigma, self.epsilon = _find_statistics(
            np.vstack((self.X_ADJUSTED, self.Y_ADJUSTED)).T,
            self.Z,
            self.variogram_function,
            self.variogram_model_parameters,
            self.coordinates_type,
        )
        self.Q1 = core.calcQ1(self.epsilon)
        self.Q2 = core.calcQ2(self.epsilon)
        self.cR = core.calc_cR(self.Q2, self.sigma)
        if self.verbose:
            print("Q1 =", self.Q1)
            print("Q2 =", self.Q2)
            print("cR =", self.cR, "\n")

print("Nugget:", self.variogram_model_parameters[2], "\n")
        if self.enable_plotting:
            self.display_variogram_model()

        if self.verbose:
            print("Calculating statistics on variogram model fit...")
        self.delta, self.sigma, self.epsilon = _find_statistics(
            np.vstack((self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED)).T,
            self.VALUES,
            self.variogram_function,
            self.variogram_model_parameters,
            "euclidean",
        )
        self.Q1 = core.calcQ1(self.epsilon)
        self.Q2 = core.calcQ2(self.epsilon)
        self.cR = core.calc_cR(self.Q2, self.sigma)
        if self.verbose:
            print("Q1 =", self.Q1)
            print("Q2 =", self.Q2)
            print("cR =", self.cR, "\n")

        if self.verbose:
            print("Initializing drift terms...")

        # Note that the regional linear drift values will be based on the
        # adjusted coordinate system. Really, it doesn't actually matter
        # which coordinate system is used here.
        if "regional_linear" in drift_terms:
            self.regional_linear_drift = True
            if self.verbose:
                print("Implementing regional linear drift.")
        else:

if self.enable_plotting:
            self.display_variogram_model()

        if self.verbose:
            print("Calculating statistics on variogram model fit...")
        if enable_statistics:
            self.delta, self.sigma, self.epsilon = _find_statistics(
                np.vstack((self.X_ADJUSTED, self.Y_ADJUSTED)).T,
                self.Z,
                self.variogram_function,
                self.variogram_model_parameters,
                self.coordinates_type,
            )
            self.Q1 = core.calcQ1(self.epsilon)
            self.Q2 = core.calcQ2(self.epsilon)
            self.cR = core.calc_cR(self.Q2, self.sigma)
            if self.verbose:
                print("Q1 =", self.Q1)
                print("Q2 =", self.Q2)
                print("cR =", self.cR, "\n")
        else:
            self.delta, self.sigma, self.epsilon, self.Q1, self.Q2, self.cR = [None] * 6

print("Nugget:", self.variogram_model_parameters[2], "\n")
        if self.enable_plotting:
            self.display_variogram_model()

        if self.verbose:
            print("Calculating statistics on variogram model fit...")
        self.delta, self.sigma, self.epsilon = _find_statistics(
            np.vstack((self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED)).T,
            self.VALUES,
            self.variogram_function,
            self.variogram_model_parameters,
            "euclidean",
        )
        self.Q1 = core.calcQ1(self.epsilon)
        self.Q2 = core.calcQ2(self.epsilon)
        self.cR = core.calc_cR(self.Q2, self.sigma)
        if self.verbose:
            print("Q1 =", self.Q1)
            print("Q2 =", self.Q2)
            print("cR =", self.cR, "\n")