From 3517b3eec9a7cc4c1bfac84908ea66285f4694f9 Mon Sep 17 00:00:00 2001 From: dgiovanis Date: Tue, 16 Mar 2021 15:43:55 -0400 Subject: [PATCH] Fixes AKMCS & Inference examples --- .../Bayesian model selection.ipynb | 91 +- ...ection - compare different materials.ipynb | 253 - ...estimation - material homogenization.ipynb | 360 - .../compare_elastic_materials.py | 267 - .../material_homogenization.py | 218 - .../mcmc_results_last.pkl | Bin 520288 -> 0 bytes .../output/coefs_le.h5 | Bin 9872 -> 0 bytes .../output/coefs_le.txt | 38 - .../output/corrs_le.h5 | Bin 393292 -> 0 bytes .../output/corrs_le.vtk | 16146 ---------------- .../output/homogenization_opt.py | 171 - .../output/matrix_fiber_rand.vtk | 5730 ------ .../output/specimen.vtk | 1170 -- .../sfepy_log.txt | 1152 -- .../SubsetSimulation_Example1.ipynb | 139 - .../SubsetSimulation_Example1/pfn.py | 13 - src/UQpy/SampleMethods/AKMCS.py | 72 +- 17 files changed, 112 insertions(+), 25708 deletions(-) delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/Model selection - compare different materials.ipynb delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/Parameter estimation - material homogenization.ipynb delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/compare_elastic_materials.py delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/material_homogenization.py delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/mcmc_results_last.pkl delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/output/coefs_le.h5 delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/output/coefs_le.txt delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/output/corrs_le.h5 delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/output/corrs_le.vtk delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/output/homogenization_opt.py delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/output/matrix_fiber_rand.vtk delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/output/specimen.vtk delete mode 100644 example/Inference/More advanced examples with FE models - Sfepy/sfepy_log.txt delete mode 100644 example/Reliability/SubsetSimulation/SubsetSimulation_Example1/SubsetSimulation_Example1.ipynb delete mode 100644 example/Reliability/SubsetSimulation/SubsetSimulation_Example1/pfn.py diff --git a/example/Inference/BayesModelSelection/Bayesian model selection.ipynb b/example/Inference/BayesModelSelection/Bayesian model selection.ipynb index 849bf8088..f3f4ce16d 100644 --- a/example/Inference/BayesModelSelection/Bayesian model selection.ipynb +++ b/example/Inference/BayesModelSelection/Bayesian model selection.ipynb @@ -69,7 +69,8 @@ "from UQpy.RunModel import RunModel # required to run the quadratic model\n", "from sklearn.neighbors import KernelDensity # for the plots\n", "from statsmodels.nonparametric.kde import KDEUnivariate\n", - "from UQpy.Distributions import Normal" + "from UQpy.Distributions import Normal\n", + "from scipy.stats import multivariate_normal, norm" ] }, { @@ -143,18 +144,22 @@ "output_type": "stream", "text": [ "posterior mean and covariance for model_linear\n", - "[16.16834799] [[0.00059394]]\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'multivariate_normal' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;31m# compute evidence, evaluate the formula at the posterior mean\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mlike_theta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmultivariate_normal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mm_posterior\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcov\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merror_covariance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[0mprior_theta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmultivariate_normal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_posterior\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mm_prior\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcov\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mS_prior\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[0mposterior_theta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmultivariate_normal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_posterior\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mm_posterior\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcov\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mS_posterior\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'multivariate_normal' is not defined" + "[16.16834799] [[0.00059394]]\n", + "evidence for model_linear= 0.0\n", + "\n", + "posterior mean and covariance for model_quadratic\n", + "[1.03170078 1.99796922] [[ 0.00941698 -0.00116538]\n", + " [-0.00116538 0.00015392]]\n", + "evidence for model_quadratic= 5.653592388036191e-34\n", + "\n", + "posterior mean and covariance for model_cubic\n", + "[1.19598744 1.94306887 0.00410884] [[ 5.60383740e-02 -1.66457308e-02 1.15371992e-03]\n", + " [-1.66457308e-02 5.29411877e-03 -3.83090725e-04]\n", + " [ 1.15371992e-03 -3.83090725e-04 2.85512405e-05]]\n", + "evidence for model_cubic= 3.6290043818823425e-35\n", + "\n", + "posterior probabilities of all three models\n", + "[0.0, 0.9396823949317383, 0.06031760506826164]\n" ] } ], @@ -208,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -233,9 +238,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "UQpy: Initialization of MH algorithm complete.\n", + "UQpy: Running MCMC...\n", + "UQpy: MCMC run successfully !\n", + "UQpy: Parameter estimation with MH completed successfully!\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Quadratic model\n", "from UQpy.SampleMethods import MH\n", @@ -266,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +312,31 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "UQpy: Initialization of MH algorithm complete.\n", + "\n", + "UQpy: Initialization of MH algorithm complete.\n", + "\n", + "UQpy: Initialization of MH algorithm complete.\n", + "UQpy: Running Bayesian Model Selection.\n", + "UQpy: Running MCMC for model model_linear\n", + "UQpy: Running MCMC...\n", + "UQpy: MCMC run successfully !\n", + "UQpy: Parameter estimation with MH completed successfully!\n", + "UQpy: Running MCMC for model model_quadratic\n", + "UQpy: Running MCMC...\n", + "UQpy: MCMC run successfully !\n", + "UQpy: Parameter estimation with MH completed successfully!\n", + "UQpy: Running MCMC for model model_cubic\n", + "UQpy: Running MCMC...\n" + ] + } + ], "source": [ "selection = BayesModelSelection(\n", " data=data, candidate_models=candidate_models, prior_probabilities=[1./3., 1./3., 1./3.],\n", @@ -362,7 +415,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/example/Inference/More advanced examples with FE models - Sfepy/Model selection - compare different materials.ipynb b/example/Inference/More advanced examples with FE models - Sfepy/Model selection - compare different materials.ipynb deleted file mode 100644 index c888d9f19..000000000 --- a/example/Inference/More advanced examples with FE models - Sfepy/Model selection - compare different materials.ipynb +++ /dev/null @@ -1,253 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model selection - compare different material laws\n", - "\n", - "Author: Audrey Olivier, 12/13/2018" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook, we revisit a small finite element problem from Sfepy, see http://sfepy.org/doc/examples/large_deformation/compare_elastic_materials.html, where three material laws are compared: linear elastic, hyperelastic neo-Hookean, hyperelastic Mooney-Rivlin (the python package Sfepy should be downloaded prior to running this example).\n", - "\n", - "We illustrate the use of Model Selection for choosing the materials law that best explain some data. Noisy data is generated synthetically.\n", - "\n", - "First let's look at one run of each of these models." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from compare_elastic_materials import *" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "disp={}\n", - "load={}\n", - "colors={'linear':'red', 'neo_hookean':'green', 'mooney_rivlin':'blue'}\n", - "# Linear elastic\n", - "x0 = [5.769, 3.846]\n", - "disp['linear'], load['linear'] = one_simulation_linear(x0, plot_mesh_bool=True, return_load=True)\n", - "# neo-Hookean\n", - "x0 = [3.846]\n", - "disp['neo_hookean'], load['neo_hookean'] = one_simulation_neo_hookean(x0, plot_mesh_bool=False, return_load=True)\n", - "# Mooney-Rivlin\n", - "x0 = [1.923, 1.923]\n", - "disp['mooney_rivlin'], load['mooney_rivlin'] = one_simulation_mooney_rivlin(x0, plot_mesh_bool=False, return_load=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot displacements\n", - "fig, ax = plt.subplots()\n", - "for key, val in disp.items():\n", - " ax.plot(load[key], val, label=key, color=colors[key])\n", - "ax.legend()\n", - "ax.grid(True)\n", - "ax.set_xlabel('tension [kPa]')\n", - "ax.set_ylabel('displacement at the top [mm]')\n", - "ax.set_title('Different material laws')\n", - "plt.show(fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate synthetic noisy data from Mooney-Rivlin material" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "var_noise = 0.2**2\n", - "noisy_data = disp['mooney_rivlin']+np.random.normal(scale=np.sqrt(var_noise), size=disp['mooney_rivlin'].shape)\n", - "fig, ax = plt.subplots()\n", - "ax.plot(load['mooney_rivlin'],disp['mooney_rivlin'],color=colors['mooney_rivlin'],label='mooney_rivlin')\n", - "ax.plot(load['mooney_rivlin'], noisy_data, marker='.', linestyle='none', label='noisy data')\n", - "ax.legend()\n", - "ax.grid(True)\n", - "ax.set_xlabel('tension [kPa]')\n", - "ax.set_ylabel('displacement at the top [mm]')\n", - "ax.set_title('Synthetic data')\n", - "plt.show(fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Learn which model best fits the data using information criteria" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'Model' from 'UQpy.Inference' (C:\\Users\\dimtsap\\Anaconda3\\lib\\site-packages\\UQpy\\Inference.py)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Import necessary packages from UQpy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mUQpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInference\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mInfoModelSelection\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mImportError\u001b[0m: cannot import name 'Model' from 'UQpy.Inference' (C:\\Users\\dimtsap\\Anaconda3\\lib\\site-packages\\UQpy\\Inference.py)" - ] - } - ], - "source": [ - "# Import necessary packages from UQpy\n", - "from UQpy.Inference import Model, InfoModelSelection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create instances of the Model class for three models: linear, quadratic and cubic\n", - "names = ['linear', 'neo_hookean', 'mooney_rivlin']\n", - "n_param_list = [2, 1, 2]\n", - "candidate_models = []\n", - "for i, model_name in enumerate(names):\n", - " M = Model(model_type='python', n_params=n_param_list[i], model_name=model_name,\n", - " model_script='compare_elastic_materials.py', model_object_name = 'one_simulation_'+model_name,\n", - " error_covariance=var_noise)\n", - " candidate_models.append(M)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Perform model selection using BIC criterion\n", - "selector = InfoModelSelection(candidate_models = candidate_models, data = noisy_data, method = 'BIC', \n", - " sorted_outputs = False, \n", - " iter_optim = [3,3,3], bounds=[((1,7), (1,7)), ((1,7),), ((1,7), (1,7))])\n", - "for name, proba in zip(selector.model_names, selector.probabilities):\n", - " print('probability of model {}: {}'.format(name, proba))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Re-run the forward model with the fitted parameters\n", - "disp={}\n", - "load={}\n", - "colors={'linear':'red', 'neo_hookean':'green', 'mooney_rivlin':'blue'}\n", - "# Linear elastic\n", - "x0 = selector.fitted_params[0]\n", - "disp['linear'], load['linear'] = one_simulation_linear(x0, return_load=True)\n", - "# neo-Hookean\n", - "x0 = selector.fitted_params[1]\n", - "disp['neo_hookean'], load['neo_hookean'] = one_simulation_neo_hookean(x0, return_load=True)\n", - "# Mooney-Rivlin\n", - "x0 = selector.fitted_params[2]\n", - "disp['mooney_rivlin'], load['mooney_rivlin'] = one_simulation_mooney_rivlin(x0, return_load=True)\n", - "\n", - "# plot displacements\n", - "fig, ax = plt.subplots()\n", - "for key, val in disp.items():\n", - " ax.plot(load[key], val, label=key, color=colors[key])\n", - "ax.plot(load['mooney_rivlin'], noisy_data, marker='.', linestyle='none', label='noisy data')\n", - "ax.legend()\n", - "ax.grid(True)\n", - "ax.set_xlabel('tension [kPa]')\n", - "ax.set_ylabel('displacement at the top [mm]')\n", - "ax.set_title('Material laws fitted to noisy data')\n", - "plt.show(fig)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/example/Inference/More advanced examples with FE models - Sfepy/Parameter estimation - material homogenization.ipynb b/example/Inference/More advanced examples with FE models - Sfepy/Parameter estimation - material homogenization.ipynb deleted file mode 100644 index fb8ddf52c..000000000 --- a/example/Inference/More advanced examples with FE models - Sfepy/Parameter estimation - material homogenization.ipynb +++ /dev/null @@ -1,360 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameter estimation in material homogenization\n", - "\n", - "This notebook illustrates performing parameter estimation tasks in UQpy for a finite element model. The problem is adapted from http://sfepy.org/doc-devel/mat_optim.html and uses the python package Sfepy to solve the finite element equations (Sfepy should be downloaded prior to running this example).\n", - "\n", - "This inference task consists in the identification of material parameters of a composite structure using data (force-displacement curves) obtained by a standard tensile test. The composite microstructure is shown below (see mesh plot for microstructure problem), and consists of linear elastic fibers randomly dispersed in a linear elastic matrix. The four parameters to be learnt from data are the young's moduli and poisson ratio of both the matrix and the fibers. The data consists in the slope of the force-displacement curves from four experiments (tensile tests of four specimen with different fiber orientations). Briefly, the homogenization equations are solved as follows:\n", - "- equations for a representative volume of the microstructure are solved under periodic boundary conditions, yielding the stiffness matrix of the representative volume (first row of mesh plots below),\n", - "- knowing the stiffness of a representative volume, one can solve the equations for the macro-problem, i.e., the specimen subjected to the tensile test (second row of mesh plots below).\n", - "\n", - "### Illustration of one simulation run\n", - "\n", - "Codes are adapted from http://sfepy.org/doc-devel/mat_optim.html, the main file material_homogenization.py calls functions and data files from the package Sfepy. The following cells show how to run one simulation, for a given parameter value. Alternatively, one could also use RunModel, which will be used later when creating a model and running Inference." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'Model' from 'UQpy.Inference' (C:\\Users\\dimtsap\\Anaconda3\\lib\\site-packages\\UQpy\\Inference.py)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmaterial_homogenization\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mUQpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInference\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMLEstimation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBayesParameterEstimation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mUQpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDistributions\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDistribution\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mImportError\u001b[0m: cannot import name 'Model' from 'UQpy.Inference' (C:\\Users\\dimtsap\\Anaconda3\\lib\\site-packages\\UQpy\\Inference.py)" - ] - } - ], - "source": [ - "import numpy as np\n", - "from material_homogenization import *\n", - "from UQpy.Inference import Model, MLEstimation, BayesParameterEstimation\n", - "from UQpy.Distributions import Distribution\n", - "\n", - "# Define data for maximization algorithms as in http://sfepy.org/doc-devel/mat_optim.html.\n", - "data = np.array([1051140., 197330., 101226., 95474.])\n", - "var_names = ['E_fiber', 'v_fiber', 'E_matrix', 'v_matrix']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x0 = np.array([160.e9, 0.25, 5.e9, 0.45])\n", - "print('E_fiber = {} GPa, v_fiber = {}, E_matrix = {} GPa, v_matrix = {}'.format(x0[0]/1e9, x0[1], x0[2]/1e9, x0[3]))\n", - "x0 = x_real2norm(x0)\n", - "qoi = one_simulation(x0, plot_meshes_bool=True)\n", - "print('Computed slopes of the force-elongation tangent lines for fiber orientations 0, 30, 60 and 90 degrees')\n", - "print(qoi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Maximization as performed in original code presented in Sfepy\n", - "\n", - "See http://sfepy.org/doc-devel/mat_optim.html. The function to be minimized is \n", - "$$ \\Phi = \\sum_{\\phi=0, 30, 60, 90} \\left( 1-\\frac{k_{computed, \\phi}}{k_{experiment, \\phi}} \\right)^{2} $$\n", - "The identified parameters in this reference are: E_f=171 GPa, v_f=0.32, E_m=2.33 GPa, v_m = 0.20, but the authors note that the results may vary across SciPy versions and related libraries.\n", - "\n", - "The parameters are defined over well-defined bounds, see x_L and x_U in material_homogenization.py. It is often best practice to scale the parameters so that they have comparable orders of magnitude, thus in the following the parameters are scaled so that they evolve in the $[0, 1]$ bounds. The functions x_real2norm and x_norm2real perform this scaling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the maximization function, as in http://sfepy.org/doc-devel/mat_optim.html.\n", - "def func(x0, exp_k):\n", - " comp_k = one_simulation(x0)\n", - " \n", - " val = 0.0\n", - " for e_k, c_k in zip(exp_k, comp_k):\n", - " val += (1.0 - c_k / e_k)**2\n", - " #val = np.sqrt(val)\n", - " return val" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from scipy.optimize import minimize\n", - "x0 = x_real2norm([160.e9, 0.25, 5.e9, 0.45])\n", - "xopt = minimize(func, x0, args=(data,), bounds=[(0, 1) for _ in range(4)], method = 'SLSQP')\n", - "print('number of function evaluations required for optimization: '.format(xopt.nit))\n", - "xfinal = x_norm2real(xopt.x)\n", - "print('Results of optimization procedure:')\n", - "print('E_fiber = {} GPa, v_fiber = {}, E_matrix = {} GPa, v_matrix = {}'.\n", - " format(xfinal[0]/1e9, xfinal[1], xfinal[2]/1e9, xfinal[3]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Maximum likelihood with UQpy\n", - "\n", - "Recall that maximizing the likelihood of a model $y_{\\phi}=f_{\\phi}(\\theta)+\\varepsilon$, where $\\varepsilon \\sim N(\\cdot; 0, \\sigma_{\\phi}^2)$ is equivalent to minimizing the weighted sum of squares $\\frac{(y_{\\phi}-f_{\\phi}(\\theta))^2}{2\\sigma_{\\phi}^2}$. Thus the above maximization can be performed using UQpy maximum likelihood estimator, setting $y_{\\phi}=k_{experiment, \\phi}, f_{\\phi}(\\theta)=k_{computed, \\phi}, \\sigma_{\\phi}^2=\\frac{1}{2} k_{experiment, \\phi}^2$. This is done in the following by setting the error_covariance input accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a model\n", - "model = Model(n_params=4, model_type='python', \n", - " model_script='material_homogenization.py', model_object_name = 'one_simulation',\n", - " error_covariance=1/2*data**2, var_names = var_names)\n", - "\n", - "# Maximum likelihood with weighted variance\n", - "ml_estimator = MLEstimation(model=model, data=data, bounds=[(0, 1) for _ in range(4)],\n", - " x0=x_real2norm([160.e9, 0.25, 5.e9, 0.45]))\n", - "xfinal = x_norm2real(ml_estimator.param)\n", - "print('Results of ML procedure:')\n", - "print('E_fiber = {} GPa, v_fiber = {}, E_matrix = {} GPa, v_matrix = {}'.\n", - " format(xfinal[0]/1e9, xfinal[1], xfinal[2]/1e9, xfinal[3]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When defining a model in UQPy, it is possible to plot the log-likelihood function as a function of the parameters. In the following cell we fix three parameters $\\theta_{i1, i2, i3}$ out of the four unknown parameters and plot the conditional log-likelihood as we vary the fourth parameter over its range. In the following cell, we plot the variation of the log-likelihood as we vary two parameters.\n", - "\n", - "Such analysis helps in understanding which parameters most affect the likelihood." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = Model(n_params=4, model_type='python', \n", - " model_script='material_homogenization.py', model_object_name = 'one_simulation',\n", - " error_covariance=1/2*data**2, var_names = ['E_f', 'v_f', 'E_m', 'v_m'])\n", - "\n", - "# Look at the likelihood surfaces in 2D, fix the remaining two parameters\n", - "fixed_p = x_real2norm([161.79e9, 0.3490, 2.3207e9, 0.20])\n", - "\n", - "print('Conditional log likelihood, when three parameters are fixed to their max likelihood value.')\n", - "npoints = 20\n", - "fig, ax = plt.subplots(ncols=4, figsize=(16,3.5))\n", - "for j in range(4):\n", - " xx = np.linspace(0, 1, npoints)\n", - " samples = fixed_p.reshape((1,4))*np.ones((npoints,4))\n", - " samples[:,j] = xx\n", - " tmp = np.array([x_norm2real(s) for s in samples])\n", - " zz = model.log_like(data, samples)\n", - " ax[j].plot(tmp[:,j], zz)\n", - " ax[j].set_xlabel(var_names[j])\n", - "plt.show(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print('Conditional log likelihood, when two parameters are fixed to their max likelihood value.')\n", - "npoints = 8\n", - "vars_d2 = [[0, 1], [2, 3], [0, 2], [1, 3]]\n", - "axs = [[0,0], [0,1], [1,0], [1,1]]\n", - "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(14,8))\n", - "for j, var_d2 in enumerate(vars_d2):\n", - " x = np.linspace(0, 1, npoints)\n", - " y = np.linspace(0, 1, npoints)\n", - " xx, yy = np.meshgrid(x, y)\n", - " xx, yy = xx.reshape((-1,)), yy.reshape((-1,))\n", - " samples = fixed_p.reshape((1,4))*np.ones((npoints**2,4))\n", - " samples[:,var_d2[0]] = xx\n", - " samples[:,var_d2[1]] = yy\n", - " zz = model.log_like(data, samples)\n", - " tmp = np.array([x_norm2real(s) for s in samples])\n", - " x, y = tmp[:,var_d2[0]], tmp[:,var_d2[1]]\n", - " ax_j = ax[axs[j][0],axs[j][1]]\n", - " t = ax_j.contourf(x.reshape((npoints,npoints)),y.reshape((npoints,npoints)),zz.reshape((npoints,npoints)), 20)\n", - " ax_j.set_title('Conditional log likelihood')\n", - " ax_j.set_xlabel(var_names[var_d2[0]])\n", - " ax_j.set_ylabel(var_names[var_d2[1]])\n", - " cbar = plt.colorbar(t, ax=ax_j)\n", - "plt.show(fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pdf estimation with UQpy\n", - "\n", - "Run MCMC on the problem defined above. When performing a Bayesian analysis, it is important to carefully define both the prior of the parameters and the error_covariance of the model. Here the prior is chosen uniform over [0, 1], the error_covariance as $1/100*data**2$, meaning that the error in the measurements is proportional to the measured value, with a coefficient of variation of 10%." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a model\n", - "model = Model(n_params=4, model_type='python', \n", - " model_script='material_homogenization.py', model_object_name = 'one_simulation',\n", - " error_covariance=1/100*data**2, var_names = ['E_f', 'v_f', 'E_m', 'v_m'],\n", - " ntasks=4, prior_name=['uniform']*4, prior_params=[[0,1]]*4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Several small MCMC runs were performed to try and find the best scale parameters for this problem. Note that it may take about 30 minutes to run MCMC with 300 samples. The code to run to perform parameter estimation with MCMC is shown in the following cell. Results of several runs were saved in a file called 'mcmc_results_last.pkl'. The following scale parameters were tried:\n", - "- mcmc_0: [0.1, 0.1, 0.02, 0.05]\n", - "- mcmc_1: [0.15, 0.2, 0.02, 0.1]\n", - "- mcmc_2: [0.2, 0.25, 0.04, 0.1]\n", - "- mcmc_3: [0.2, 0.25, 0.2, 0.2]\n", - "- mcmc_4: [0.2, 0.25, 0.04, 0.1], 5000 samples\n", - "- mcmc_4: [0.2, 0.25, 0.04, 0.1], 10000 samples
\n", - "In the following we show the effect of modifying the scale parameter on MCMC results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## This would be the code to run to perform parameter estimation using MCMC\n", - "#be = BayesParameterEstimation(data=data, model=model, sampling_method = 'MCMC', nsamples=300,\n", - "# algorithm = 'MH', jump=1, nburn=0, pdf_proposal_type = 'Normal',\n", - "# pdf_proposal_scale = [0.1, 0.1, 0.02, 0.05], \n", - "# seed = x_real2norm([162e9, 0.35, 2.32e9, 0.25]))\n", - "## Save the data into a pickle file\n", - "#import pickle\n", - "#with open('mcmc_results_test.pkl', 'wb') as f:\n", - "# pickle.dump({'mcmc_0': be}, f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import pickle\n", - "with open('mcmc_results_last.pkl', 'rb') as f:\n", - " mcmc_results = pickle.load(f)\n", - "l = ['mcmc_3', 'mcmc_0', 'mcmc_2', 'mcmc_4']\n", - "to_print = ['scale parameter is too large, acceptance ratio too small:', 'scale_parameter is too small:', \n", - " 'good scale - 300 samples:', 'good scale - 5000 samples:']\n", - "for j, l_j in enumerate(l):\n", - " be = mcmc_results[l_j]\n", - " print('\\n'+to_print[j])\n", - " print('acceptance ratio: {}'.format(be.accept_ratio))\n", - " print('last_sample: {}'.format(be.samples[-1,:]))\n", - " print('sample std: {}'.format(np.std(be.samples, axis=0)))\n", - " fig, ax = plt.subplots(ncols=4, figsize=(20,3))\n", - " tmp = np.array([x_norm2real(s) for s in be.samples])\n", - " for i, param_name in enumerate(var_names):\n", - " ax[i].plot(tmp[:,i])\n", - " ax[i].set_title(param_name)\n", - " ax[i].set_ylabel('Markov Chain')\n", - " plt.show(fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we use the previous 5000 samples to plot the posterior pdfs. We take out a few samples at the beginning, since the seed was given as the maximum likelihood estimate we can assume that the burn-in period would be small in this example. We also keep only 1 out of 5 samples to avoid for them to be correlated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nburn = 200\n", - "jump = 4\n", - "\n", - "be = mcmc_results['mcmc_5']\n", - "samples = be.samples[nburn::jump]\n", - "print(samples.shape)\n", - "samples = np.array([x_norm2real(x) for x in samples])\n", - "x_L = x_norm2real([0, 0, 0, 0])\n", - "x_U = x_norm2real([1, 1, 1, 1])\n", - "\n", - "fig, ax = plt.subplots(ncols=4, figsize=(22,3.5))\n", - "for i, param_name in enumerate(var_names):\n", - " ax[i].hist(samples[:,i], density=True, bins=30)\n", - " ax[i].set_title(param_name)\n", - " ax[i].set_ylabel('posterior pdf')\n", - " ax[i].set_xlim([x_L[i], x_U[i]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from UQpy.Utilities import diagnostics\n", - "diagnostics(sampling_method='MCMC', samples=samples)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/example/Inference/More advanced examples with FE models - Sfepy/compare_elastic_materials.py b/example/Inference/More advanced examples with FE models - Sfepy/compare_elastic_materials.py deleted file mode 100644 index 352b5ba5d..000000000 --- a/example/Inference/More advanced examples with FE models - Sfepy/compare_elastic_materials.py +++ /dev/null @@ -1,267 +0,0 @@ -#!/usr/bin/env python - -# This code was adapted from http://sfepy.org/doc-devel/mat_optim.html. -""" -Compare various elastic materials frequency.r.time. uniaxial tension/compression test. - -Requires Matplotlib. -""" -from __future__ import absolute_import -from argparse import ArgumentParser, RawDescriptionHelpFormatter -import sys -import six -sys.path.append('.') -from sfepy.base.base import output -from sfepy.base.conf import ProblemConf, get_standard_keywords -from sfepy.discrete import Problem -from sfepy.base.plotutils import plt -from matplotlib.collections import PolyCollection -from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection - -import numpy as np -from functools import partial - -def define( K=8.333, mu_nh=3.846, mu_mr=1.923, kappa=1.923, lam=5.769, mu=3.846 ): - """Define the problem to solve.""" - from sfepy.discrete.fem.meshio import UserMeshIO - from sfepy.mesh.mesh_generators import gen_block_mesh - from sfepy.mechanics.matcoefs import stiffness_from_lame - - def mesh_hook(mesh, mode): - """ - Generate the block mesh. - """ - if mode == 'read': - mesh = gen_block_mesh([2, 2, 3], [2, 2, 4], [0, 0, 1.5], name='el3', - verbose=False) - return mesh - - elif mode == 'write': - pass - - filename_mesh = UserMeshIO(mesh_hook) - - options = { - 'nls' : 'newton', - 'ls' : 'ls', - 'ts' : 'ts', - 'save_times' : 'all', - } - - functions = { - 'linear_pressure' : (linear_pressure,), - 'empty' : (lambda ts, coor, mode, region, ig: None,), - } - - fields = { - 'displacement' : ('real', 3, 'Omega', 1), - } - - # Coefficients are chosen so that the tangent stiffness is the same for all - # material for zero strains. - materials = { - 'solid' : ({ - 'K' : K, # bulk modulus - 'mu_nh' : mu_nh, # shear modulus of neoHookean term - 'mu_mr' : mu_mr, # shear modulus of Mooney-Rivlin term - 'kappa' : kappa, # second modulus of Mooney-Rivlin term - # elasticity for LE term - 'D' : stiffness_from_lame(dim=3, lam=lam, mu=mu), - },), - 'load' : 'empty', - } - - variables = { - 'u' : ('unknown field', 'displacement', 0), - 'v' : ('test field', 'displacement', 'u'), - } - - regions = { - 'Omega' : 'all', - 'Bottom' : ('vertices in (z < 0.1)', 'facet'), - 'Top' : ('vertices in (z > 2.9)', 'facet'), - } - - ebcs = { - 'fixb' : ('Bottom', {'u.all' : 0.0}), - 'fixt' : ('Top', {'u.[0,1]' : 0.0}), - } - - integrals = { - 'i' : 1, - 'isurf' : 2, - } - equations = { - 'linear' : """dw_lin_elastic.i.Omega(solid.D, v, u) - = dw_surface_ltr.isurf.Top(load.val, v)""", - 'neo-Hookean' : """dw_tl_he_neohook.i.Omega(solid.mu_nh, v, u) - + dw_tl_bulk_penalty.i.Omega(solid.K, v, u) - = dw_surface_ltr.isurf.Top(load.val, v)""", - 'Mooney-Rivlin' : """dw_tl_he_neohook.i.Omega(solid.mu_mr, v, u) - + dw_tl_he_mooney_rivlin.i.Omega(solid.kappa, v, u) - + dw_tl_bulk_penalty.i.Omega(solid.K, v, u) - = dw_surface_ltr.isurf.Top(load.val, v)""", - } - - solvers = { - 'ls' : ('ls.scipy_direct', {}), - 'newton' : ('nls.newton', { - 'i_max' : 5, - 'eps_a' : 1e-10, - 'eps_r' : 1.0, - }), - 'ts' : ('ts.simple', { - 't0' : 0, - 't1' : 1, - 'time_interval' : None, - 'n_step' : 26, # has precedence over time_interval! - 'verbose' : 1, - }), - } - - return locals() - -## -# Pressure tractions. -def linear_pressure(ts, coor, mode=None, coef=1, **kwargs): - if mode == 'qp': - val = np.tile(coef * ts.step, (coor.shape[0], 1, 1)) - return {'val' : val} - -def store_top_u(displacements): - """Function _store() will be called at the end of each loading step. Top - displacements will be stored into `displacements`.""" - def _store(problem, ts, state): - - top = problem.domain.regions['Top'] - top_u = problem.get_variables()['u'].get_state_in_region(top) - displacements.append(np.mean(top_u[:,-1])) - - return _store - -def solve_branch(problem, branch_function, material_type): - - eq = problem.conf.equations[material_type] - problem.set_equations({material_type : eq}) - - load = problem.get_materials()['load'] - load.set_function(branch_function) - - out = [] - problem.solve(save_results=False, step_hook=store_top_u(out)) - displacements = np.array(out, dtype=np.float64) - - return displacements - -helps = { - 'no_plot' : 'do not show plot window', -} - -def plot_mesh(pb): - # plot mesh for macro problem - coors = pb.domain.mesh.coors - graph = pb.domain.mesh.get_conn(pb.domain.mesh.descs[0]) - fig2 = plt.figure(figsize=(5,6)) - ax = fig2.add_subplot(111, projection='3d') - for e in range(graph.shape[0]): - tupleList = coors[graph[e,:],:] - vertices = [[0, 1, 2, 3], [4, 5, 6, 7], - [0, 1, 5, 4], [1, 2, 6, 5], [2, 3, 7, 6], [3, 0, 4, 7]] - verts = [[tupleList[vertices[ix][iy]] for iy in range(len(vertices[0]))] - for ix in range(len(vertices))] - pc3d = Poly3DCollection(verts=verts, facecolors='white', - edgecolors='black', linewidths=1, alpha=0.5) - ax.add_collection3d(pc3d) - ax.set_xlim3d(-1.2, 1.2) - ax.set_ylim3d(-1.2, 1.2) - ax.set_zlim3d(-0.01, 3.2) - ax.set_title('3D plot of macro system') - plt.show(fig2) - return None - -def one_simulation(material_type, define_args, coef_tension=0.25, coef_compression=-0.25, - plot_mesh_bool=False, return_load=False): - - #parser = ArgumentParser(description=__doc__, - # formatter_class=RawDescriptionHelpFormatter) - #parser.add_argument('--version', action='version', version='%(prog)s') - #options = parser.parse_args() - output.set_output(filename='sfepy_log.txt', quiet=True) - - required, other = get_standard_keywords() - # Use this file as the input file. - conf = ProblemConf.from_file(__file__, required, other, - define_args=define_args) - - # Create problem instance, but do not set equations. - problem = Problem.from_conf(conf, init_equations=False) - if plot_mesh_bool: - plot_mesh(problem) - - # Solve the problem. Output is ignored, results stored by using the - # step_hook. - linear_tension = partial(linear_pressure, coef=coef_tension) - u_t = solve_branch(problem, linear_tension, material_type) - linear_compression = partial(linear_pressure, coef=coef_compression) - u_c = solve_branch(problem, linear_compression, material_type) - - # Get pressure load by calling linear_*() for each time step. - ts = problem.get_timestepper() - load_t = np.array([linear_tension(ts, np.array([[0.0]]), 'qp')['val'] - for aux in ts.iter_from(0)], - dtype=np.float64).squeeze() - load_c = np.array([linear_compression(ts, np.array([[0.0]]), 'qp')['val'] - for aux in ts.iter_from(0)], - dtype=np.float64).squeeze() - - # Join the branches. - displacements = np.r_[u_c[::-1], u_t] - load = np.r_[load_c[::-1], load_t] - - if return_load: - return displacements, load - else: - return displacements - -def one_simulation_linear(theta, plot_mesh_bool=False, return_load=False): - material_type = 'linear' - theta = np.array(theta).reshape((-1, )) - define_args = {'lam':theta[0], 'mu':theta[1]} # bulk modulus - return one_simulation(material_type=material_type, plot_mesh_bool=plot_mesh_bool, - define_args=define_args, return_load=return_load) - -def one_simulation_neo_hookean(theta, plot_mesh_bool=False, return_load=False): - material_type = 'neo-Hookean' - theta = np.array(theta).reshape((-1, )) - define_args = {'mu_nh':theta[0]} # bulk modulus - return one_simulation(material_type=material_type, plot_mesh_bool=plot_mesh_bool, - define_args=define_args, return_load=return_load) - -def one_simulation_mooney_rivlin(theta, plot_mesh_bool=False, return_load=False): - material_type = 'Mooney-Rivlin' - theta = np.array(theta).reshape((-1, )) - define_args = {'mu_mr':theta[0], 'kappa':theta[1]} # bulk modulus - return one_simulation(material_type=material_type, plot_mesh_bool=plot_mesh_bool, - define_args=define_args, return_load=return_load) - -def one_simulation_linear_v2(theta, plot_mesh_bool=False, return_load=False): - material_type = 'linear' - theta = np.array(theta).reshape((-1, )) - define_args = {'lam':theta[0], 'mu':theta[1]} # bulk modulus - return one_simulation(material_type=material_type, plot_mesh_bool=plot_mesh_bool, - define_args=define_args, return_load=return_load, coef_tension=0.15/5) - -def one_simulation_neo_hookean_v2(theta, plot_mesh_bool=False, return_load=False): - material_type = 'neo-Hookean' - theta = np.array(theta).reshape((-1, )) - define_args = {'mu_nh':theta[0]} # bulk modulus - return one_simulation(material_type=material_type, plot_mesh_bool=plot_mesh_bool, - define_args=define_args, return_load=return_load, coef_tension=0.15/5) - -def one_simulation_mooney_rivlin_v2(theta, plot_mesh_bool=False, return_load=False): - material_type = 'Mooney-Rivlin' - theta = np.array(theta).reshape((-1, )) - define_args = {'mu_mr':theta[0], 'kappa':theta[1]} # bulk modulus - return one_simulation(material_type=material_type, plot_mesh_bool=plot_mesh_bool, - define_args=define_args, return_load=return_load, coef_tension=0.15/5) - diff --git a/example/Inference/More advanced examples with FE models - Sfepy/material_homogenization.py b/example/Inference/More advanced examples with FE models - Sfepy/material_homogenization.py deleted file mode 100644 index f7426ea00..000000000 --- a/example/Inference/More advanced examples with FE models - Sfepy/material_homogenization.py +++ /dev/null @@ -1,218 +0,0 @@ -#!/usr/bin/env python - -# This code was adapted from http://sfepy.org/doc-devel/mat_optim.html. - -from __future__ import print_function -from __future__ import absolute_import -import sys -sys.path.append('.') - -import matplotlib as mlp -import matplotlib.pyplot as plt -from matplotlib.collections import PolyCollection -from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection - -import numpy as np - -from sfepy.base.base import Struct, output -from sfepy.base.log import Log -from sfepy import data_dir - -class MaterialSimulator(object): - - @staticmethod - def create_app(filename, is_homog=False, **kwargs): - from sfepy.base.conf import ProblemConf, get_standard_keywords - from sfepy.homogenization.homogen_app import HomogenizationApp - from sfepy.applications import PDESolverApp - - required, other = get_standard_keywords() - if is_homog: - required.remove('equations') - - conf = ProblemConf.from_file(filename, required, other, - define_args=kwargs) - options = Struct(output_filename_trunk=None, - save_ebc=False, - save_ebc_nodes=False, - save_regions=False, - save_regions_as_groups=False, - save_field_meshes=False, - solve_not=False, - ) - output.set_output(filename='sfepy_log.txt', quiet=True) - - if is_homog: - app = HomogenizationApp(conf, options, 'material_opt_micro:') - - else: - app = PDESolverApp(conf, options, 'material_opt_macro:') - - app.conf.opt_data = {} - opts = conf.options - if hasattr(opts, 'parametric_hook'): # Parametric study. - parametric_hook = conf.get_function(opts.parametric_hook) - app.parametrize(parametric_hook) - - return app - - def __init__(self, macro_fn, micro_fn, phis, plot_meshes_bool=False): - self.macro_app = self.create_app(macro_fn, is_homog=False, is_opt=True) - self.micro_app = self.create_app(micro_fn, is_homog=True, is_opt=True) - self.phis = phis - self.plot_meshes_bool = plot_meshes_bool - - @staticmethod - def rotate_mat(D, angle): - s = np.sin(angle) - c = np.cos(angle) - s2 = s**2 - c2 = c**2 - sc = s * c - T = np.array([[c2, 0, s2, 0, 2*sc,0], - [0, 1, 0, 0, 0, 0], - [s2, 0, c2, 0, -2*sc, 0], - [0, 0, 0, c, 0, -s], - [-sc, 0, sc, 0, c2 - s2, 0], - [0, 0, 0, s, 0, c]]) - - return np.dot(np.dot(T, D), T.T) - - def plot_meshes(self): - # plot mesh for micro problem - pb = self.micro_app.problem - coors = pb.domain.mesh.coors - #print(set(coors[:,2])) - graph = pb.domain.mesh.get_conn(pb.domain.mesh.descs[0]) - graph_slice = np.zeros((graph.shape[0], 4)) - for j in range(graph.shape[0]): - graph_slice[j,:] = graph[j,coors[graph[j,:],2] == 0] - cells_matrix = pb.domain.regions['Ym'].get_cells() - cells_fibers = pb.domain.regions['Yf'].get_cells() - fig = plt.figure(figsize = (12, 5)) - ax = fig.add_subplot(121) - pc = PolyCollection(verts=coors[graph[cells_matrix,0:4],:2], facecolors='white', - edgecolors='black') - ax.add_collection(pc) - pc = PolyCollection(verts=coors[graph[cells_fibers,0:4],:2], facecolors='gray', - edgecolors='black') - ax.add_collection(pc) - ax.axis('equal') - ax.set_title('2D plot of microstructure') - ax = fig.add_subplot(122, projection='3d') - for e in range(graph.shape[0]): - if e in cells_fibers: - color = 'gray' - else: - color = 'white' - tupleList = coors[graph[e,:],:] - vertices = [[0, 1, 2, 3], [4, 5, 6, 7], - [0, 1, 5, 4], [1, 2, 6, 5], [2, 3, 7, 6], [3, 0, 4, 7]] - verts = [[tupleList[vertices[ix][iy]] for iy in range(len(vertices[0]))] - for ix in range(len(vertices))] - pc3d = Poly3DCollection(verts=verts, facecolors=color, - edgecolors='black', linewidths=1, alpha=0.5) - ax.add_collection3d(pc3d) - ax.set_title('3D plot of microstructure') - plt.show(fig) - - # plot mesh for macro problem - pb = self.macro_app.problem - coors = pb.domain.mesh.coors - graph = pb.domain.mesh.get_conn(pb.domain.mesh.descs[0]) - fig2 = plt.figure(figsize=(5,6)) - ax = fig2.add_subplot(111, projection='3d') - for e in range(graph.shape[0]): - tupleList = coors[graph[e,:],:] - vertices = [[0, 1, 2, 3], [4, 5, 6, 7], - [0, 1, 5, 4], [1, 2, 6, 5], [2, 3, 7, 6], [3, 0, 4, 7]] - verts = [[tupleList[vertices[ix][iy]] for iy in range(len(vertices[0]))] - for ix in range(len(vertices))] - pc3d = Poly3DCollection(verts=verts, facecolors='white', - edgecolors='black', linewidths=1, alpha=0.5) - ax.add_collection3d(pc3d) - ax.set_xlim3d(-0.03, 0.03) - ax.set_ylim3d(-0.01, 0.01) - ax.set_zlim3d(-0.01, 0.1) - ax.set_title('3D plot of macro system') - plt.show(fig2) - return None - - def mat_eval(self, x): - mic_od = self.micro_app.conf.opt_data - mac_od = self.macro_app.conf.opt_data - - mic_od['coefs'] = {} - mic_od['mat_params'] = x_norm2real(x) - self.micro_app() - - D = mic_od['D_homog'] - comp_k = [] - for phi in self.phis: - #print('phi = %d' % phi) - - mac_od['D_homog'] = self.rotate_mat(D, np.deg2rad(phi)) - self.macro_app() - - comp_k.append(mac_od['k']) - - # added by Audrey: get a plot of a slice of the mesh - if self.plot_meshes_bool: - self.plot_meshes() - - return comp_k - -def bounds(): - x_L = [120e9, 0.2, 2e9, 0.2] - x_U = [200e9, 0.45, 8e9, 0.45] - return x_L, x_U - -def x_norm2real(x): - x_L, x_U = np.array(bounds()) - return x * (x_U - x_L) + x_L - -def x_real2norm(x): - x_L, x_U = np.array(bounds()) - return (x - x_L) / (x_U - x_L) - -micro_filename = data_dir + '/examples/homogenization/' + 'homogenization_opt.py' -macro_filename = data_dir + '/examples/homogenization/' + 'linear_elasticity_opt.py' - -def one_simulation(x0, plot_meshes_bool=False): - """ - This function is the main callable here: it takes in as input the parameter vector, - here x0=[E_fiber, nu_fiber, E_matrix, nu_matrix], and returns the simulated output - (here slope of the force-elongation curve obtained during a tensile test), to be compared - with the measured data. - """ - x0 = x0.reshape((-1, )) - phis = [0, 30, 60, 90] - #exp_data = zip([0, 30, 60, 90], [1051140., 197330., 101226., 95474.]) - ms = MaterialSimulator(macro_filename, micro_filename, - phis, - plot_meshes_bool=plot_meshes_bool) - qoi = ms.mat_eval(x0) - return qoi - -def one_simulation_2params(x0, plot_meshes_bool=False): - x0 = x0.reshape((-1, )) - x0 = np.array([x0[0], 0.45, x0[1], 0.]) - phis = [0, 30, 60, 90] - #exp_data = zip([0, 30, 60, 90], [1051140., 197330., 101226., 95474.]) - ms = MaterialSimulator(macro_filename, micro_filename, - phis, plot_meshes_bool=plot_meshes_bool) - - qoi = ms.mat_eval(x0) - return qoi - -def one_simulation_2params_rvs(x0, plot_meshes_bool=False): - x0 = x0.reshape((-1, )) - x0 = np.array([x0[0], 0.45, x0[1], 0.]) - phis = [0, 30, 60, 90] - ms = MaterialSimulator(macro_filename, micro_filename, - phis, - plot_meshes_bool=plot_meshes_bool) - - qoi = ms.mat_eval(x0) - qoi = np.tile(np.array(qoi), 100) - return qoi diff --git a/example/Inference/More advanced examples with FE models - Sfepy/mcmc_results_last.pkl b/example/Inference/More advanced examples with FE models - Sfepy/mcmc_results_last.pkl deleted file mode 100644 index caf26b8bc1ff07266e63c5b5c41c81c5ce27d521..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 520288 zcmeF)cTiMKqBd}nWDpb;MFk8X2C`y6ACMph3@D0%AW1+09fFDyL{I@m0hOeZbIuth z=bUpGaz;=w0pGm4d+%=5y>(}+D9iWV&-p`SeoWKToO9-!?&oyhr?w(cY*V47prA0) zHqzD*CQxcCpSfuvc+~i+u8FR(wl3pgO$%Mq)0!rlM!IIYCUT}`21c4@1~-fu2~+~M z1k&qiwyDsNUazBRrb$?)ZES8tdb#!u6J0?gb3-!&O%oGM3q}nSUF{pjre-GQ+GdOd z+Q0u4qp{B4l|Xl#;vs=vhEj{b@R-1;L}1!>obou$Ljv% zn*MKJ{ZhV2{qa4|sW~aaN;3k7mI^)TTVqpAqnn1hri4|BJ1JF`lb-q869&e58l*R` 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6.328604e-02 -2.944777e-15 -2.091053e-02 2.682390e-02 -2.936813e-15 -8.411883e-02 2.709257e-02 -2.933688e-15 --3.793463e-02 5.737117e-02 -2.913009e-15 -5.422381e-03 7.688413e-02 -2.922617e-15 --7.146905e-02 3.079381e-02 -2.860783e-15 -5.121034e-02 6.322800e-02 -2.941709e-15 -8.136398e-02 4.609607e-02 -2.938746e-15 -3.887009e-02 1.661716e-02 -2.930433e-15 -2.254201e-02 4.417930e-02 -2.937144e-15 --6.706860e-02 4.849467e-02 -2.890570e-15 --2.656238e-02 4.203772e-02 -2.896714e-15 --3.985493e-03 1.712265e-02 -2.919612e-15 -2.791849e-02 7.743547e-02 -2.942462e-15 -1.050068e-01 1.803149e-02 -2.906893e-15 -6.177195e-02 1.667617e-02 -2.936357e-15 --1.806310e-02 7.433276e-02 -2.925793e-15 -4.939919e-02 7.653113e-02 -2.949145e-15 -7.012333e-03 3.172753e-02 -2.924157e-15 -1.004184e-01 3.350258e-02 -2.936150e-15 -5.803069e-03 5.634771e-02 -2.929916e-15 -6.722022e-02 3.122044e-02 -2.946946e-15 --1.461810e-02 5.943120e-02 -2.927306e-15 --3.933143e-02 2.140719e-02 -2.912145e-15 -1.807971e-02 1.549974e-02 -2.933492e-15 -3.461612e-02 5.264429e-02 -2.943104e-15 -4.052403e-02 3.340547e-02 -2.936577e-15 -7.457317e-02 6.365235e-02 -2.932267e-15 --4.891360e-02 6.653710e-02 -2.911973e-15 --8.845853e-02 1.365239e-02 -2.853261e-15 --4.101902e-02 3.258430e-02 -2.905585e-15 --7.052147e-02 1.031189e-02 -2.861877e-15 --4.618195e-02 4.650245e-02 -2.893556e-15 -8.629323e-02 1.658895e-02 -2.920663e-15 -6.876408e-02 7.335426e-02 -2.937144e-15 --8.710265e-02 1.462947e-02 -3.495133e-15 --3.650344e-02 -2.649877e-02 -3.473002e-15 --4.448704e-02 4.959130e-02 -3.472580e-15 --9.401366e-02 -2.009317e-02 -3.494654e-15 --9.830095e-02 6.678734e-02 -3.488022e-15 --1.293669e-01 1.816174e-02 -3.504234e-15 --2.443479e-02 1.028401e-02 -3.464777e-15 --7.324037e-02 -3.779873e-02 -3.483058e-15 --5.540781e-02 7.494698e-02 -3.470408e-15 --1.320116e-01 -6.281627e-03 -3.504193e-15 --1.413867e-02 3.529476e-02 -3.463919e-15 --1.690715e-02 -3.203644e-02 -3.463510e-15 --1.302911e-01 4.146957e-02 -3.521219e-15 --1.117207e-02 6.004812e-02 -3.455936e-15 --6.975920e-02 -6.035874e-03 -3.489346e-15 --6.897120e-02 4.036838e-02 -3.483415e-15 --1.088053e-01 3.976683e-04 -3.500763e-15 --4.491166e-02 -4.882681e-02 -3.473011e-15 --7.333239e-03 -5.709430e-03 -3.455747e-15 --3.988113e-02 3.462435e-02 -3.475619e-15 --9.766672e-02 -5.330544e-02 -3.488194e-15 --1.082217e-01 3.154826e-02 -3.488854e-15 --1.007964e-01 8.168631e-02 -3.473721e-15 --1.299516e-01 -3.122375e-02 -3.491653e-15 --1.501442e-01 7.741774e-03 -3.520331e-15 --1.215956e-01 6.590376e-02 -3.499671e-15 --4.687167e-02 -1.026615e-02 -3.474716e-15 --1.499397e-01 3.282809e-02 -3.534771e-15 -8.979532e-04 1.937254e-02 -3.456989e-15 --4.508205e-02 1.232816e-02 -3.466443e-15 --1.818508e-02 -5.172248e-02 -3.454786e-15 --7.310957e-02 -2.315984e-02 -3.494894e-15 --6.667580e-02 -5.734212e-02 -3.479680e-15 --2.805113e-02 7.959079e-02 -3.460400e-15 --7.433065e-02 5.630488e-02 -3.474979e-15 --7.846800e-02 8.632279e-02 -3.462654e-15 --5.610852e-02 8.928482e-02 -3.462809e-15 --1.433548e-01 -2.120086e-02 -3.495733e-15 --3.477633e-03 4.895427e-02 -3.458343e-15 --1.948867e-02 -2.078341e-02 -3.459633e-15 --1.539240e-01 -1.276070e-02 -3.511733e-15 --1.131219e-01 4.712058e-02 -3.498857e-15 -3.459158e-04 4.669371e-03 -3.453465e-15 --2.799078e-02 -3.923960e-02 -3.465307e-15 --1.164765e-01 -5.025764e-02 -3.486561e-15 --5.341117e-02 -3.385521e-02 -3.485828e-15 --2.126360e-02 4.459910e-02 -3.463241e-15 --1.231461e-01 3.604458e-03 -3.505331e-15 --1.151199e-01 -1.456912e-02 -3.484744e-15 --5.579706e-02 6.291474e-02 -3.478614e-15 --3.160300e-02 6.108203e-02 -3.473167e-15 --8.985638e-02 -3.002690e-03 -3.494768e-15 --3.498587e-03 6.973011e-02 -3.455050e-15 --1.649696e-03 -1.636813e-02 -3.456121e-15 --1.325401e-03 -3.634692e-02 -3.451365e-15 --1.073074e-01 1.615597e-02 -3.499924e-15 --2.231490e-02 2.548321e-02 -3.468274e-15 --1.529512e-01 4.737967e-02 -3.531584e-15 --9.380160e-02 5.118086e-02 -3.499861e-15 --2.272587e-02 -5.233347e-03 -3.462799e-15 --5.398531e-02 5.100781e-02 -3.478077e-15 --6.656737e-02 1.199263e-02 -3.490650e-15 --1.590073e-01 1.768554e-02 -3.509497e-15 -1.554863e-03 3.463241e-02 -3.460564e-15 --5.866468e-02 -2.396981e-02 -3.489168e-15 --1.147119e-01 8.033811e-02 -3.488451e-15 --9.671038e-02 -3.606871e-02 -3.488777e-15 --8.958373e-02 3.384222e-02 -3.497135e-15 --3.123158e-02 -5.870266e-02 -3.458478e-15 --1.223000e-01 3.125829e-02 -3.510619e-15 --3.867306e-02 9.156248e-02 -3.453869e-15 --1.412468e-01 5.499610e-02 -3.528311e-15 --7.793331e-02 7.082472e-02 -3.468935e-15 -3.861936e-02 1.809046e-02 -3.459231e-15 -3.590749e-02 1.190195e-02 -3.449243e-15 -3.457007e-02 -1.983738e-02 -3.438505e-15 -3.407453e-02 4.837399e-02 -3.449411e-15 --6.336392e-02 4.256339e-02 -2.990141e-15 --5.946102e-02 9.260595e-02 -2.981095e-15 --7.125897e-02 -8.706359e-03 -2.969333e-15 --1.224948e-01 2.920497e-02 -3.025508e-15 --4.576054e-03 5.512138e-02 -2.968494e-15 --1.022019e-01 7.404329e-02 -3.011342e-15 --2.464394e-02 1.031454e-02 -2.970449e-15 --1.001028e-01 -3.357376e-03 -2.976350e-15 --2.878347e-02 8.772143e-02 -2.957112e-15 --9.409498e-02 9.391516e-02 -2.998488e-15 --3.478898e-02 -1.021516e-02 -2.967114e-15 --1.381657e-01 5.730863e-02 -3.010224e-15 -1.179910e-02 2.604309e-02 -2.971223e-15 --4.677458e-02 6.348076e-02 -2.982656e-15 --8.168883e-02 2.106509e-02 -2.982318e-15 --7.243981e-02 6.978414e-02 -2.996631e-15 --5.575150e-02 1.463258e-02 -2.978476e-15 --9.804725e-02 4.318575e-02 -3.008695e-15 --2.814852e-02 4.193346e-02 -2.973287e-15 --3.788269e-02 1.076970e-01 -2.961022e-15 --9.139182e-02 -2.369541e-02 -2.955319e-15 --1.326878e-01 6.820486e-03 -3.000846e-15 -5.544867e-03 7.800787e-02 -2.956327e-15 --1.269615e-01 8.628148e-02 -3.021392e-15 --1.138594e-03 -4.865774e-03 -2.971322e-15 --6.814189e-02 1.100976e-01 -2.984492e-15 --6.200608e-02 -2.568407e-02 -2.946755e-15 --1.467265e-01 3.032133e-02 -3.029598e-15 -2.070150e-02 5.275255e-02 -2.968815e-15 --1.477190e-01 7.071403e-02 -3.014506e-15 -2.207003e-02 1.291745e-02 -2.969783e-15 --1.137717e-01 4.857171e-02 -3.020722e-15 --1.330232e-02 3.540614e-02 -2.970131e-15 --1.102847e-01 -2.586812e-02 -2.970541e-15 --1.897760e-02 1.101149e-01 -2.956164e-15 --8.120635e-02 8.281104e-02 -2.994446e-15 --4.763264e-02 1.130481e-03 -2.976768e-15 --1.245475e-01 -1.274527e-02 -2.972250e-15 --4.104224e-03 9.724810e-02 -2.951099e-15 --1.130866e-01 1.024973e-01 -3.014839e-15 --1.515859e-02 -1.889507e-02 -2.970364e-15 --5.172516e-02 7.859742e-02 -2.975746e-15 --7.875818e-02 5.455809e-03 -2.975423e-15 -3.008165e-02 3.532929e-02 -2.964955e-15 --1.553077e-01 4.862201e-02 -3.031840e-15 --1.012130e-01 1.126165e-01 -3.005508e-15 --2.781233e-02 -2.835868e-02 -2.968307e-15 --2.498766e-02 7.053968e-02 -2.963348e-15 --1.033771e-01 1.410327e-02 -2.993978e-15 --1.068503e-01 8.446469e-02 -3.012265e-15 --2.119619e-02 -1.186769e-03 -2.967175e-15 --5.902761e-02 1.175539e-01 -2.970683e-15 --7.132710e-02 -3.326612e-02 -2.961137e-15 --1.488566e-01 1.331244e-02 -3.012675e-15 -2.280651e-02 7.120916e-02 -2.955942e-15 --1.328502e-01 4.619065e-02 -3.023429e-15 -6.368137e-03 3.745364e-02 -2.970809e-15 --9.180213e-02 6.121662e-02 -3.001516e-15 --3.468020e-02 2.342739e-02 -2.975243e-15 --6.604647e-02 5.591127e-02 -2.994389e-15 --1.000810e-01 3.101600e-02 -3.009421e-15 --2.824455e-02 5.380208e-02 -2.969350e-15 --6.072850e-02 2.851443e-02 -2.980072e-15 --7.903266e-02 5.162185e-02 -2.995353e-15 --4.179554e-02 3.598747e-02 -2.977970e-15 --1.591750e-01 6.046307e-02 -3.017334e-15 -3.399197e-02 2.378700e-02 -2.967632e-15 --1.201664e-01 6.621070e-02 -3.015466e-15 --6.859157e-03 1.721793e-02 -2.972337e-15 --8.156447e-02 3.617742e-02 -3.000799e-15 --4.503506e-02 4.903053e-02 -2.976470e-15 -3.818905e-03 3.717141e-02 -3.232191e-15 --7.150650e-03 -1.386851e-02 -3.255995e-15 -1.526958e-02 8.827125e-02 -3.220533e-15 --5.630507e-02 3.101135e-02 -3.263046e-15 -6.557193e-02 4.238832e-02 -3.218141e-15 --4.580561e-02 7.094940e-02 -3.256641e-15 -5.577154e-02 2.186234e-03 -3.255134e-15 -4.851583e-02 7.822809e-02 -3.198621e-15 --3.893487e-02 -3.304209e-03 -3.250748e-15 -3.316689e-02 -2.004336e-02 -3.258082e-15 --2.544578e-02 9.382395e-02 -3.244505e-15 --6.834167e-02 5.858863e-02 -3.235383e-15 --1.904409e-02 1.212305e-02 -3.251539e-15 -2.738155e-02 6.259667e-02 -3.221748e-15 -1.528185e-02 1.755235e-02 -3.238532e-15 --7.280331e-03 5.686278e-02 -3.231387e-15 -4.172681e-02 9.718430e-02 -3.208669e-15 --3.286688e-02 -2.222332e-02 -3.273839e-15 --6.919780e-02 1.219473e-02 -3.236740e-15 -7.953755e-02 6.235778e-02 -3.189116e-15 -2.770927e-02 3.168219e-02 -3.247955e-15 --2.127421e-02 4.237982e-02 -3.243064e-15 --1.525217e-03 -3.472486e-02 -3.268616e-15 -9.074254e-03 1.079915e-01 -3.205166e-15 --8.274978e-02 3.388045e-02 -3.265800e-15 -9.174317e-02 3.981409e-02 -3.202731e-15 --5.124676e-02 9.372611e-02 -3.250593e-15 -5.905006e-02 -2.090521e-02 -3.263526e-15 --6.838638e-02 7.680285e-02 -3.249456e-15 -7.516123e-02 -7.640407e-03 -3.263987e-15 --4.585550e-02 4.934734e-02 -3.237286e-15 -5.045831e-02 2.568247e-02 -3.237520e-15 -2.446720e-02 -3.735344e-03 -3.257484e-15 --1.621875e-02 7.774393e-02 -3.229547e-15 -6.513573e-02 9.571379e-02 -3.204899e-15 --5.433500e-02 -1.976847e-02 -3.264196e-15 -7.167880e-02 8.322057e-02 -3.201694e-15 --6.162427e-02 -7.668916e-03 -3.258605e-15 --4.057320e-02 1.359164e-02 -3.233519e-15 -5.012393e-02 6.115531e-02 -3.206502e-15 --2.316645e-02 1.193199e-04 -3.244199e-15 -3.167796e-02 7.486309e-02 -3.206968e-15 -2.038655e-02 -3.445898e-02 -3.274477e-15 --1.334568e-02 1.077154e-01 -3.224462e-15 --8.694892e-02 5.582326e-02 -3.235419e-15 -1.001187e-01 2.003377e-02 -3.223838e-15 -4.030218e-03 3.393099e-03 -3.256807e-15 -4.209378e-03 7.103268e-02 -3.231555e-15 --2.790801e-02 6.403469e-02 -3.250887e-15 -3.624187e-02 1.005810e-02 -3.247707e-15 --8.678383e-02 6.895821e-02 -3.241649e-15 -3.957483e-02 -3.163740e-02 -3.279392e-15 -8.953028e-02 7.768193e-03 -3.219003e-15 --3.206718e-02 1.054422e-01 -3.219711e-15 -1.612694e-02 5.087799e-02 -3.239593e-15 --8.008880e-03 2.379612e-02 -3.240937e-15 --1.853779e-02 -3.573424e-02 -3.267211e-15 -2.642158e-02 1.101807e-01 -3.208420e-15 --8.771756e-02 2.210458e-02 -3.249997e-15 -9.689087e-02 5.213881e-02 -3.212251e-15 -1.668274e-02 -1.825372e-02 -3.254184e-15 --8.790840e-03 9.247475e-02 -3.243644e-15 --6.657156e-02 4.771247e-02 -3.239263e-15 --3.190860e-02 2.774780e-02 -3.256958e-15 -4.011673e-02 4.664720e-02 -3.225702e-15 --5.367385e-02 6.076181e-02 -3.230936e-15 -7.114648e-02 1.792329e-02 -3.234943e-15 --3.444126e-02 8.434238e-02 -3.244116e-15 -4.218457e-02 -1.092428e-02 -3.263374e-15 -3.327102e-02 2.551618e-02 -3.244434e-15 --8.445409e-02 2.503315e-02 -3.235004e-15 --5.332857e-02 3.280269e-02 -3.332767e-15 --5.977751e-02 3.615958e-03 -3.189116e-15 --1.244208e-02 2.203023e-02 -3.208795e-15 -7.239350e-02 4.761321e-02 -3.078779e-15 --8.401565e-02 3.931658e-02 -2.988679e-15 -8.581681e-02 1.894380e-02 -3.135527e-15 --6.488011e-02 1.766049e-02 -3.141151e-15 -1.476877e-02 4.165835e-02 -3.016756e-15 --1.360415e-01 4.111499e-02 -3.236314e-15 --6.823613e-02 2.296190e-02 -3.215818e-15 -5.470296e-02 -2.166709e-04 -3.229254e-15 --6.776263e-02 1.332695e-02 -2.909030e-15 --2.304647e-02 1.333179e-02 -3.270611e-15 -6.762716e-03 5.259235e-02 -3.119244e-15 -1.720888e-02 5.327363e-02 -2.589722e-15 --1.318256e-01 -2.832398e-02 -3.319826e-15 -1.281851e-02 2.240155e-02 -3.242251e-15 --1.758955e-02 2.889186e-02 -3.332205e-15 --9.459818e-02 -8.067721e-03 -3.323185e-15 --7.773049e-02 6.069561e-02 -3.076514e-15 -6.875696e-02 3.965276e-02 -3.131991e-15 --1.135749e-01 4.841212e-02 -3.325154e-15 -8.334008e-02 3.328865e-02 -3.043432e-15 --6.803209e-02 1.865245e-02 -3.024114e-15 --2.622517e-02 3.618769e-02 -3.107702e-15 -4.018326e-02 3.574774e-02 -3.031024e-15 --1.302231e-01 6.010632e-03 -3.098482e-15 --1.236721e-01 3.301606e-02 -3.067862e-15 --1.034775e-01 4.628672e-02 -3.419612e-15 --6.785087e-03 1.628447e-02 -3.188963e-15 --4.831325e-02 1.478399e-02 -2.800779e-15 --8.400788e-02 1.867427e-02 -3.386336e-15 -2.659259e-03 2.035455e-02 -3.068891e-15 --5.352860e-02 2.562847e-02 -3.212501e-15 -7.733804e-02 1.996538e-02 -3.083315e-15 -8.848684e-02 -7.445910e-03 -3.211173e-15 -7.174469e-03 6.122182e-02 -2.882343e-15 --1.359370e-01 -3.108702e-02 -3.347648e-15 --1.084009e-01 -1.422069e-02 -3.282130e-15 --8.436645e-02 -1.141477e-02 -2.906396e-15 --3.616532e-02 3.231354e-02 -3.334195e-15 -6.172315e-02 4.709062e-02 -3.070879e-15 -8.625630e-02 8.321367e-03 -3.018633e-15 --1.149707e-02 2.551357e-02 -3.291484e-15 -7.399412e-02 7.249445e-02 -3.107035e-15 --5.997005e-02 9.413211e-03 -3.318938e-15 -8.656849e-02 7.608765e-02 -3.127637e-15 --9.695546e-02 -4.704900e-03 -3.163521e-15 --3.019436e-02 5.473245e-02 -3.068017e-15 --1.367320e-01 6.378193e-02 -3.360546e-15 --5.310598e-02 2.574236e-02 -3.115743e-15 --4.136935e-02 4.434383e-02 -3.100093e-15 -1.198183e-02 1.155751e-02 -3.126338e-15 --8.040623e-02 7.172445e-02 -3.094203e-15 --1.276382e-01 4.220930e-02 -3.214590e-15 --7.173958e-02 6.474763e-02 -3.085546e-15 -1.562798e-02 7.058948e-02 -2.759713e-15 --3.259735e-03 1.833352e-02 -3.209578e-15 --1.324058e-01 3.640396e-02 -3.106605e-15 --7.136381e-02 3.077220e-02 -3.299097e-15 -7.014271e-03 4.918775e-02 -3.366250e-15 --2.323601e-02 1.091061e-03 -2.904137e-15 --5.126815e-02 -6.223944e-04 -2.684632e-15 --6.130915e-02 4.734801e-02 -2.924029e-15 -2.230924e-02 -1.478305e-04 -2.912365e-15 --3.746385e-02 2.027811e-02 -3.143960e-15 -7.303627e-03 3.426874e-02 -3.190951e-15 -5.836463e-02 -6.417442e-03 -3.259747e-15 -8.907541e-02 5.020948e-02 -3.045685e-15 -1.158781e-02 -3.026357e-02 -2.548639e-15 -8.121746e-03 -2.295181e-03 -2.961788e-15 --2.379495e-02 5.989426e-02 -3.068934e-15 -7.662144e-02 2.591022e-02 -3.163099e-15 -5.004523e-02 6.230168e-03 -2.945014e-15 -5.598562e-03 3.356010e-02 -3.077097e-15 --2.891580e-04 1.747557e-02 -3.096308e-15 -8.054461e-02 3.390013e-04 -2.989664e-15 -4.078957e-02 1.742095e-02 -3.114519e-15 -6.238229e-02 6.365198e-03 -3.219915e-15 --1.046773e-01 4.912143e-03 -3.141175e-15 --8.428291e-02 4.072677e-03 -3.360467e-15 --7.372211e-02 -8.074994e-03 -2.843495e-15 --1.014549e-01 3.117986e-02 -3.182740e-15 --4.790560e-02 -6.572901e-03 -2.318524e-15 -6.564044e-02 7.187095e-02 -3.137970e-15 --1.119565e-01 5.138953e-02 -3.418641e-15 --4.723695e-02 3.233816e-02 -2.875024e-15 --1.194282e-01 3.419524e-02 -3.227360e-15 -4.052104e-02 4.583336e-02 -3.072192e-15 --9.757836e-02 7.416809e-02 -3.102207e-15 --7.843626e-02 1.891765e-02 -3.027786e-15 -1.530856e-02 2.437501e-02 -2.128998e-15 -1.494205e-02 4.823060e-02 -3.122981e-15 --1.361115e-01 -1.648309e-02 -3.268425e-15 --8.854953e-02 1.677179e-02 -3.175205e-15 -1.627483e-02 5.442098e-02 -2.969011e-15 -8.535115e-02 4.227932e-02 -3.088049e-15 --1.138380e-01 -3.296382e-02 -3.351978e-15 --6.273350e-02 5.990213e-02 -2.984525e-15 --2.345600e-02 7.452024e-03 -3.188189e-15 --1.174721e-02 2.334012e-02 -3.114623e-15 -1.223364e-02 7.371031e-02 -2.874345e-15 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1.375037e-02 -3.062090e-15 --8.135200e-02 1.845383e-02 -3.157200e-15 --1.414006e-01 -3.942449e-02 -3.398781e-15 --1.258607e-01 6.813421e-02 -3.082952e-15 --4.573792e-02 4.350364e-03 -3.077636e-15 -8.208929e-02 5.618828e-02 -3.089403e-15 --1.225288e-01 8.617819e-03 -3.111932e-15 -9.413525e-03 1.988340e-02 -3.117235e-15 --7.703138e-02 -3.988852e-02 -3.382272e-15 -1.662190e-02 6.434918e-02 -2.951835e-15 -1.288953e-01 2.953096e-03 -2.904060e-15 --1.126373e-01 1.516214e-03 -2.879119e-15 -1.288953e-01 2.953096e-03 -2.904060e-15 --1.126373e-01 1.516214e-03 -2.879119e-15 -9.214124e-03 9.544346e-02 -2.962488e-15 -2.277013e-02 7.344195e-02 -3.460704e-15 -2.364491e-02 -4.553390e-02 -3.424213e-15 --1.765003e-01 1.495524e-02 -3.492633e-15 -2.277013e-02 7.344195e-02 -3.460704e-15 -2.364491e-02 -4.553390e-02 -3.424213e-15 --6.581534e-02 -4.889309e-02 -2.955164e-15 --6.505040e-02 1.336616e-01 -2.985242e-15 -3.003023e-03 -5.316407e-02 -3.268818e-15 -3.942497e-03 1.270843e-01 -3.210062e-15 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -1.051517e-01 2.947959e-03 -2.902467e-15 -8.144546e-02 2.828313e-03 -2.910590e-15 -5.761873e-02 2.642699e-03 -2.922435e-15 -3.363108e-02 2.407522e-03 -2.920440e-15 -9.484056e-03 2.152086e-03 -2.917133e-15 --1.477530e-02 1.899500e-03 -2.917497e-15 --3.910784e-02 1.685454e-03 -2.897260e-15 --6.345901e-02 1.516587e-03 -2.876965e-15 --8.789432e-02 1.461375e-03 -2.874929e-15 -1.236208e-01 -1.384562e-02 -2.917795e-15 -1.135723e-01 -2.983930e-02 -2.934576e-15 -9.923919e-02 -4.443589e-02 -2.916565e-15 -8.118503e-02 -5.681875e-02 -2.937871e-15 -5.979063e-02 -6.602590e-02 -2.942080e-15 -3.577372e-02 -7.140419e-02 -2.931470e-15 -1.000183e-02 -7.323242e-02 -2.913671e-15 --1.599189e-02 -7.133473e-02 -2.918642e-15 --4.091161e-02 -6.590884e-02 -2.904017e-15 --6.345170e-02 -5.709856e-02 -2.897018e-15 --8.250652e-02 -4.507379e-02 -2.869977e-15 --9.751696e-02 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1.815032e-02 -2.814443e-15 -2.267392e-02 5.299059e-02 -3.452188e-15 -2.268434e-02 3.364693e-02 -3.445898e-15 -2.279755e-02 1.455990e-02 -3.451420e-15 -2.299905e-02 -4.785604e-03 -3.440103e-15 -2.331314e-02 -2.469873e-02 -3.444246e-15 -6.789677e-03 8.652720e-02 -3.438848e-15 --1.184125e-02 9.663816e-02 -3.441432e-15 --3.273487e-02 1.028215e-01 -3.452175e-15 --5.513781e-02 1.050542e-01 -3.462184e-15 --7.837035e-02 1.036154e-01 -3.438846e-15 --1.013843e-01 9.896919e-02 -3.452220e-15 --1.229334e-01 9.108034e-02 -3.469033e-15 --1.416470e-01 8.015256e-02 -3.495330e-15 --1.567193e-01 6.641258e-02 -3.513491e-15 --1.676199e-01 5.037847e-02 -3.530074e-15 --1.742956e-01 3.299066e-02 -3.523712e-15 --1.742324e-01 -3.231118e-03 -3.509792e-15 --1.674830e-01 -2.087414e-02 -3.504030e-15 --1.566563e-01 -3.722272e-02 -3.484835e-15 --1.418569e-01 -5.140387e-02 -3.476481e-15 --1.234722e-01 -6.282737e-02 -3.483913e-15 --1.022972e-01 -7.104416e-02 -3.475706e-15 --7.944739e-02 -7.581687e-02 -3.475488e-15 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-3.020083e-15 --1.652348e-01 8.097379e-02 -3.011735e-15 --1.521418e-01 9.825083e-02 -3.023688e-15 --1.349370e-01 1.131615e-01 -3.015210e-15 --1.141462e-01 1.244868e-01 -3.025124e-15 --9.045240e-02 1.314279e-01 -3.002957e-15 --3.911863e-02 1.312048e-01 -2.978629e-15 --1.411179e-02 1.244017e-01 -2.966006e-15 -8.358331e-03 1.134165e-01 -2.930071e-15 -2.684220e-02 9.880558e-02 -2.922921e-15 -4.063199e-02 8.150672e-02 -2.942367e-15 -4.919044e-02 6.250078e-02 -2.952473e-15 -5.204426e-02 4.264306e-02 -2.969354e-15 -4.910012e-02 2.280346e-02 -2.957940e-15 -4.057912e-02 3.483524e-03 -2.965563e-15 -2.686495e-02 -1.417787e-02 -2.973944e-15 -8.312575e-03 -2.897267e-02 -2.962523e-15 --1.443679e-02 -3.977949e-02 -2.967104e-15 --3.971766e-02 -4.640135e-02 -2.962049e-15 --2.309615e-02 -5.090091e-02 -3.251104e-15 --4.724111e-02 -4.389959e-02 -3.249084e-15 --6.817432e-02 -3.222510e-02 -3.259340e-15 --8.566616e-02 -1.684976e-02 -3.269078e-15 --9.940522e-02 4.604439e-04 -3.289732e-15 --1.088023e-01 1.843690e-02 -3.233567e-15 --1.122306e-01 3.717147e-02 -3.220033e-15 --1.096489e-01 5.630049e-02 -3.259666e-15 --1.012734e-01 7.510380e-02 -3.206723e-15 --8.769885e-02 9.261291e-02 -3.249291e-15 --6.962096e-02 1.073748e-01 -3.192173e-15 --4.763510e-02 1.182849e-01 -3.204928e-15 --2.263753e-02 1.248639e-01 -3.196565e-15 -3.066251e-02 1.249471e-01 -3.207120e-15 -5.582863e-02 1.185179e-01 -3.191260e-15 -7.789326e-02 1.077595e-01 -3.174062e-15 -9.576165e-02 9.299955e-02 -3.200420e-15 -1.089407e-01 7.521039e-02 -3.218987e-15 -1.171539e-01 5.582751e-02 -3.225773e-15 -1.201787e-01 3.593309e-02 -3.225441e-15 -1.175865e-01 1.613671e-02 -3.213240e-15 -1.093540e-01 -2.782430e-03 -3.249153e-15 -9.576375e-02 -1.992910e-02 -3.255955e-15 -7.739683e-02 -3.418255e-02 -3.277669e-15 -5.497586e-02 -4.466401e-02 -3.276480e-15 -2.966226e-02 -5.107236e-02 -3.268909e-15 --5.948123e-03 1.127272e-02 -2.184959e-15 --1.127532e-02 2.757458e-02 -2.589157e-15 --1.479770e-02 4.526889e-02 -2.769996e-15 --1.537008e-02 5.941834e-02 -2.875148e-15 --1.168678e-02 6.095089e-02 -2.951777e-15 --6.982765e-03 5.328319e-02 -2.983646e-15 --1.027623e-02 4.273104e-02 -3.023928e-15 --1.905257e-02 3.638383e-02 -3.053624e-15 --2.805311e-02 3.827526e-02 -3.125179e-15 --3.714973e-02 3.673545e-02 -3.167989e-15 --3.692305e-02 3.208079e-02 -3.215083e-15 --2.687291e-02 2.800075e-02 -3.265243e-15 --6.767835e-03 3.920137e-02 -3.388188e-15 -6.148195e-03 -1.772605e-02 -2.804706e-15 -1.873182e-02 1.273085e-03 -2.294360e-15 -1.807091e-03 -4.637711e-03 -2.937859e-15 --2.117207e-02 -8.847041e-03 -3.192959e-15 --4.820510e-02 -9.609396e-03 -3.286825e-15 --7.262003e-02 -2.405947e-03 -3.283959e-15 --9.843280e-02 5.048750e-03 -3.246882e-15 --1.202709e-01 9.189891e-03 -3.231593e-15 --1.355923e-01 8.693292e-03 -3.184851e-15 --1.254621e-01 7.877740e-03 -3.151640e-15 --8.596882e-02 6.557315e-03 -3.104413e-15 --8.543619e-03 4.054908e-03 -2.996256e-15 --3.848293e-02 -1.866611e-03 -2.037074e-15 -6.148195e-03 -1.772605e-02 -2.804706e-15 --6.767835e-03 3.920137e-02 -3.388188e-15 --2.687291e-02 2.800075e-02 -3.265243e-15 --3.692305e-02 3.208079e-02 -3.215083e-15 --3.714973e-02 3.673545e-02 -3.167989e-15 --2.805311e-02 3.827526e-02 -3.125179e-15 --1.905257e-02 3.638383e-02 -3.053624e-15 --1.027623e-02 4.273104e-02 -3.023928e-15 --6.982765e-03 5.328319e-02 -2.983646e-15 --1.168678e-02 6.095089e-02 -2.951777e-15 --1.537008e-02 5.941834e-02 -2.875148e-15 --1.479770e-02 4.526889e-02 -2.769996e-15 --1.127532e-02 2.757458e-02 -2.589157e-15 --5.948123e-03 1.127272e-02 -2.184959e-15 --3.848293e-02 -1.866611e-03 -2.037074e-15 --8.543619e-03 4.054908e-03 -2.996256e-15 --8.596882e-02 6.557315e-03 -3.104413e-15 --1.254621e-01 7.877740e-03 -3.151640e-15 --1.355923e-01 8.693292e-03 -3.184851e-15 --1.202709e-01 9.189891e-03 -3.231593e-15 --9.843280e-02 5.048750e-03 -3.246882e-15 --7.262003e-02 -2.405947e-03 -3.283959e-15 --4.820510e-02 -9.609396e-03 -3.286825e-15 --2.117207e-02 -8.847041e-03 -3.192959e-15 -1.807091e-03 -4.637711e-03 -2.937859e-15 -1.873182e-02 1.273085e-03 -2.294360e-15 -2.868863e-02 -3.721316e-02 -2.918365e-15 --2.474759e-02 -2.680003e-02 -2.901209e-15 -6.514313e-02 -3.094462e-02 -2.925002e-15 --6.163511e-02 -2.066343e-02 -2.890957e-15 --3.432188e-03 -4.131648e-02 -2.906684e-15 --2.259145e-03 -2.480411e-02 -2.913542e-15 -5.262566e-02 -4.219581e-02 -2.933726e-15 -8.946688e-02 -1.696343e-02 -2.919663e-15 -2.711737e-02 -1.338337e-02 -2.918865e-15 -1.926930e-02 -5.715898e-02 -2.922541e-15 --5.981936e-02 -3.521194e-02 -2.885638e-15 --2.006124e-02 -1.180128e-02 -2.906834e-15 --3.525710e-02 -4.393807e-02 -2.908286e-15 --8.358692e-02 -1.269752e-02 -2.885181e-15 -5.918654e-02 -1.760874e-02 -2.913767e-15 -6.821569e-02 -4.767905e-02 -2.941499e-15 --4.211210e-02 -1.137095e-02 -2.907111e-15 -8.349429e-02 -3.286791e-02 -2.934063e-15 --7.509226e-02 -2.691910e-02 -2.895020e-15 -4.226596e-02 -5.621279e-02 -2.926806e-15 --5.500178e-03 -5.816350e-02 -2.903675e-15 -1.066713e-01 -8.808060e-03 -2.916435e-15 --4.587369e-02 -2.463961e-02 -2.896904e-15 -5.689483e-02 -5.664635e-02 -2.936943e-15 -3.328330e-03 -1.189637e-02 -2.909147e-15 --6.375679e-02 -1.143081e-02 -2.899527e-15 --2.723488e-02 -5.634186e-02 -2.895314e-15 -1.848468e-02 -2.464718e-02 -2.907912e-15 --1.142179e-02 3.825053e-02 -2.899306e-15 -5.137322e-02 4.968859e-02 -2.929332e-15 --5.202658e-02 3.625011e-02 -2.885403e-15 -2.951447e-02 6.328604e-02 -2.944777e-15 -2.091053e-02 2.682390e-02 -2.936813e-15 -8.411883e-02 2.709257e-02 -2.933688e-15 --3.793463e-02 5.737117e-02 -2.913009e-15 -5.422381e-03 7.688413e-02 -2.922617e-15 --7.146905e-02 3.079381e-02 -2.860783e-15 -5.121034e-02 6.322800e-02 -2.941709e-15 -8.136398e-02 4.609607e-02 -2.938746e-15 -3.887009e-02 1.661716e-02 -2.930433e-15 -2.254201e-02 4.417930e-02 -2.937144e-15 --6.706860e-02 4.849467e-02 -2.890570e-15 --2.656238e-02 4.203772e-02 -2.896714e-15 --3.985493e-03 1.712265e-02 -2.919612e-15 -2.791849e-02 7.743547e-02 -2.942462e-15 -1.050068e-01 1.803149e-02 -2.906893e-15 -6.177195e-02 1.667617e-02 -2.936357e-15 --1.806310e-02 7.433276e-02 -2.925793e-15 -4.939919e-02 7.653113e-02 -2.949145e-15 -7.012333e-03 3.172753e-02 -2.924157e-15 -1.004184e-01 3.350258e-02 -2.936150e-15 -5.803069e-03 5.634771e-02 -2.929916e-15 -6.722022e-02 3.122044e-02 -2.946946e-15 --1.461810e-02 5.943120e-02 -2.927306e-15 --3.933143e-02 2.140719e-02 -2.912145e-15 -1.807971e-02 1.549974e-02 -2.933492e-15 -3.461612e-02 5.264429e-02 -2.943104e-15 -4.052403e-02 3.340547e-02 -2.936577e-15 -7.457317e-02 6.365235e-02 -2.932267e-15 --4.891360e-02 6.653710e-02 -2.911973e-15 --8.845853e-02 1.365239e-02 -2.853261e-15 --4.101902e-02 3.258430e-02 -2.905585e-15 --7.052147e-02 1.031189e-02 -2.861877e-15 --4.618195e-02 4.650245e-02 -2.893556e-15 -8.629323e-02 1.658895e-02 -2.920663e-15 -6.876408e-02 7.335426e-02 -2.937144e-15 --8.710265e-02 1.462947e-02 -3.495133e-15 --3.650344e-02 -2.649877e-02 -3.473002e-15 --4.448704e-02 4.959130e-02 -3.472580e-15 --9.401366e-02 -2.009317e-02 -3.494654e-15 --9.830095e-02 6.678734e-02 -3.488022e-15 --1.293669e-01 1.816174e-02 -3.504234e-15 --2.443479e-02 1.028401e-02 -3.464777e-15 --7.324037e-02 -3.779873e-02 -3.483058e-15 --5.540781e-02 7.494698e-02 -3.470408e-15 --1.320116e-01 -6.281627e-03 -3.504193e-15 --1.413867e-02 3.529476e-02 -3.463919e-15 --1.690715e-02 -3.203644e-02 -3.463510e-15 --1.302911e-01 4.146957e-02 -3.521219e-15 --1.117207e-02 6.004812e-02 -3.455936e-15 --6.975920e-02 -6.035874e-03 -3.489346e-15 --6.897120e-02 4.036838e-02 -3.483415e-15 --1.088053e-01 3.976683e-04 -3.500763e-15 --4.491166e-02 -4.882681e-02 -3.473011e-15 --7.333239e-03 -5.709430e-03 -3.455747e-15 --3.988113e-02 3.462435e-02 -3.475619e-15 --9.766672e-02 -5.330544e-02 -3.488194e-15 --1.082217e-01 3.154826e-02 -3.488854e-15 --1.007964e-01 8.168631e-02 -3.473721e-15 --1.299516e-01 -3.122375e-02 -3.491653e-15 --1.501442e-01 7.741774e-03 -3.520331e-15 --1.215956e-01 6.590376e-02 -3.499671e-15 --4.687167e-02 -1.026615e-02 -3.474716e-15 --1.499397e-01 3.282809e-02 -3.534771e-15 -8.979532e-04 1.937254e-02 -3.456989e-15 --4.508205e-02 1.232816e-02 -3.466443e-15 --1.818508e-02 -5.172248e-02 -3.454786e-15 --7.310957e-02 -2.315984e-02 -3.494894e-15 --6.667580e-02 -5.734212e-02 -3.479680e-15 --2.805113e-02 7.959079e-02 -3.460400e-15 --7.433065e-02 5.630488e-02 -3.474979e-15 --7.846800e-02 8.632279e-02 -3.462654e-15 --5.610852e-02 8.928482e-02 -3.462809e-15 --1.433548e-01 -2.120086e-02 -3.495733e-15 --3.477633e-03 4.895427e-02 -3.458343e-15 --1.948867e-02 -2.078341e-02 -3.459633e-15 --1.539240e-01 -1.276070e-02 -3.511733e-15 --1.131219e-01 4.712058e-02 -3.498857e-15 -3.459158e-04 4.669371e-03 -3.453465e-15 --2.799078e-02 -3.923960e-02 -3.465307e-15 --1.164765e-01 -5.025764e-02 -3.486561e-15 --5.341117e-02 -3.385521e-02 -3.485828e-15 --2.126360e-02 4.459910e-02 -3.463241e-15 --1.231461e-01 3.604458e-03 -3.505331e-15 --1.151199e-01 -1.456912e-02 -3.484744e-15 --5.579706e-02 6.291474e-02 -3.478614e-15 --3.160300e-02 6.108203e-02 -3.473167e-15 --8.985638e-02 -3.002690e-03 -3.494768e-15 --3.498587e-03 6.973011e-02 -3.455050e-15 --1.649696e-03 -1.636813e-02 -3.456121e-15 --1.325401e-03 -3.634692e-02 -3.451365e-15 --1.073074e-01 1.615597e-02 -3.499924e-15 --2.231490e-02 2.548321e-02 -3.468274e-15 --1.529512e-01 4.737967e-02 -3.531584e-15 --9.380160e-02 5.118086e-02 -3.499861e-15 --2.272587e-02 -5.233347e-03 -3.462799e-15 --5.398531e-02 5.100781e-02 -3.478077e-15 --6.656737e-02 1.199263e-02 -3.490650e-15 --1.590073e-01 1.768554e-02 -3.509497e-15 -1.554863e-03 3.463241e-02 -3.460564e-15 --5.866468e-02 -2.396981e-02 -3.489168e-15 --1.147119e-01 8.033811e-02 -3.488451e-15 --9.671038e-02 -3.606871e-02 -3.488777e-15 --8.958373e-02 3.384222e-02 -3.497135e-15 --3.123158e-02 -5.870266e-02 -3.458478e-15 --1.223000e-01 3.125829e-02 -3.510619e-15 --3.867306e-02 9.156248e-02 -3.453869e-15 --1.412468e-01 5.499610e-02 -3.528311e-15 --7.793331e-02 7.082472e-02 -3.468935e-15 -3.861936e-02 1.809046e-02 -3.459231e-15 -3.590749e-02 1.190195e-02 -3.449243e-15 -3.457007e-02 -1.983738e-02 -3.438505e-15 -3.407453e-02 4.837399e-02 -3.449411e-15 --6.336392e-02 4.256339e-02 -2.990141e-15 --5.946102e-02 9.260595e-02 -2.981095e-15 --7.125897e-02 -8.706359e-03 -2.969333e-15 --1.224948e-01 2.920497e-02 -3.025508e-15 --4.576054e-03 5.512138e-02 -2.968494e-15 --1.022019e-01 7.404329e-02 -3.011342e-15 --2.464394e-02 1.031454e-02 -2.970449e-15 --1.001028e-01 -3.357376e-03 -2.976350e-15 --2.878347e-02 8.772143e-02 -2.957112e-15 --9.409498e-02 9.391516e-02 -2.998488e-15 --3.478898e-02 -1.021516e-02 -2.967114e-15 --1.381657e-01 5.730863e-02 -3.010224e-15 -1.179910e-02 2.604309e-02 -2.971223e-15 --4.677458e-02 6.348076e-02 -2.982656e-15 --8.168883e-02 2.106509e-02 -2.982318e-15 --7.243981e-02 6.978414e-02 -2.996631e-15 --5.575150e-02 1.463258e-02 -2.978476e-15 --9.804725e-02 4.318575e-02 -3.008695e-15 --2.814852e-02 4.193346e-02 -2.973287e-15 --3.788269e-02 1.076970e-01 -2.961022e-15 --9.139182e-02 -2.369541e-02 -2.955319e-15 --1.326878e-01 6.820486e-03 -3.000846e-15 -5.544867e-03 7.800787e-02 -2.956327e-15 --1.269615e-01 8.628148e-02 -3.021392e-15 --1.138594e-03 -4.865774e-03 -2.971322e-15 --6.814189e-02 1.100976e-01 -2.984492e-15 --6.200608e-02 -2.568407e-02 -2.946755e-15 --1.467265e-01 3.032133e-02 -3.029598e-15 -2.070150e-02 5.275255e-02 -2.968815e-15 --1.477190e-01 7.071403e-02 -3.014506e-15 -2.207003e-02 1.291745e-02 -2.969783e-15 --1.137717e-01 4.857171e-02 -3.020722e-15 --1.330232e-02 3.540614e-02 -2.970131e-15 --1.102847e-01 -2.586812e-02 -2.970541e-15 --1.897760e-02 1.101149e-01 -2.956164e-15 --8.120635e-02 8.281104e-02 -2.994446e-15 --4.763264e-02 1.130481e-03 -2.976768e-15 --1.245475e-01 -1.274527e-02 -2.972250e-15 --4.104224e-03 9.724810e-02 -2.951099e-15 --1.130866e-01 1.024973e-01 -3.014839e-15 --1.515859e-02 -1.889507e-02 -2.970364e-15 --5.172516e-02 7.859742e-02 -2.975746e-15 --7.875818e-02 5.455809e-03 -2.975423e-15 -3.008165e-02 3.532929e-02 -2.964955e-15 --1.553077e-01 4.862201e-02 -3.031840e-15 --1.012130e-01 1.126165e-01 -3.005508e-15 --2.781233e-02 -2.835868e-02 -2.968307e-15 --2.498766e-02 7.053968e-02 -2.963348e-15 --1.033771e-01 1.410327e-02 -2.993978e-15 --1.068503e-01 8.446469e-02 -3.012265e-15 --2.119619e-02 -1.186769e-03 -2.967175e-15 --5.902761e-02 1.175539e-01 -2.970683e-15 --7.132710e-02 -3.326612e-02 -2.961137e-15 --1.488566e-01 1.331244e-02 -3.012675e-15 -2.280651e-02 7.120916e-02 -2.955942e-15 --1.328502e-01 4.619065e-02 -3.023429e-15 -6.368137e-03 3.745364e-02 -2.970809e-15 --9.180213e-02 6.121662e-02 -3.001516e-15 --3.468020e-02 2.342739e-02 -2.975243e-15 --6.604647e-02 5.591127e-02 -2.994389e-15 --1.000810e-01 3.101600e-02 -3.009421e-15 --2.824455e-02 5.380208e-02 -2.969350e-15 --6.072850e-02 2.851443e-02 -2.980072e-15 --7.903266e-02 5.162185e-02 -2.995353e-15 --4.179554e-02 3.598747e-02 -2.977970e-15 --1.591750e-01 6.046307e-02 -3.017334e-15 -3.399197e-02 2.378700e-02 -2.967632e-15 --1.201664e-01 6.621070e-02 -3.015466e-15 --6.859157e-03 1.721793e-02 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9.355138e-02 -2.937809e-15 --4.111199e-02 8.797160e-02 -2.950567e-15 --6.285803e-02 7.863703e-02 -2.936039e-15 --8.137472e-02 6.593669e-02 -2.914329e-15 --9.593410e-02 5.068830e-02 -2.888040e-15 --1.063027e-01 3.468467e-02 -2.907252e-15 --1.121206e-01 1.815032e-02 -2.847526e-15 -2.267392e-02 5.299059e-02 -3.438710e-15 -2.268434e-02 3.364693e-02 -3.444116e-15 -2.279755e-02 1.455990e-02 -3.437604e-15 -2.299905e-02 -4.785604e-03 -3.445696e-15 -2.331314e-02 -2.469873e-02 -3.451473e-15 -6.789677e-03 8.652720e-02 -3.438039e-15 --1.184125e-02 9.663816e-02 -3.423235e-15 --3.273487e-02 1.028215e-01 -3.439824e-15 --5.513781e-02 1.050542e-01 -3.437734e-15 --7.837035e-02 1.036154e-01 -3.417424e-15 --1.013843e-01 9.896919e-02 -3.451592e-15 --1.229334e-01 9.108034e-02 -3.474228e-15 --1.416470e-01 8.015256e-02 -3.492093e-15 --1.567193e-01 6.641258e-02 -3.510910e-15 --1.676199e-01 5.037847e-02 -3.556457e-15 --1.742956e-01 3.299066e-02 -3.552030e-15 --1.742324e-01 -3.231118e-03 -3.520514e-15 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-2.974716e-15 --1.356939e-01 -2.927439e-02 -2.982984e-15 --1.529119e-01 -1.427912e-02 -2.991483e-15 --1.657305e-01 3.580798e-03 -2.999974e-15 --1.736975e-01 2.287286e-02 -3.055158e-15 --1.764539e-01 4.275159e-02 -3.051098e-15 --1.735990e-01 6.227507e-02 -3.009493e-15 --1.652348e-01 8.097379e-02 -2.989285e-15 --1.521418e-01 9.825083e-02 -3.031631e-15 --1.349370e-01 1.131615e-01 -3.002499e-15 --1.141462e-01 1.244868e-01 -3.016103e-15 --9.045240e-02 1.314279e-01 -2.982710e-15 --3.911863e-02 1.312048e-01 -2.964314e-15 --1.411179e-02 1.244017e-01 -2.957242e-15 -8.358331e-03 1.134165e-01 -2.921860e-15 -2.684220e-02 9.880558e-02 -2.917202e-15 -4.063199e-02 8.150672e-02 -2.936070e-15 -4.919044e-02 6.250078e-02 -2.936982e-15 -5.204426e-02 4.264306e-02 -2.953856e-15 -4.910012e-02 2.280346e-02 -2.948850e-15 -4.057912e-02 3.483524e-03 -2.961794e-15 -2.686495e-02 -1.417787e-02 -2.960395e-15 -8.312575e-03 -2.897267e-02 -2.971905e-15 --1.443679e-02 -3.977949e-02 -2.954256e-15 --3.971766e-02 -4.640135e-02 -2.973015e-15 --2.309615e-02 -5.090091e-02 -3.249712e-15 --4.724111e-02 -4.389959e-02 -3.240121e-15 --6.817432e-02 -3.222510e-02 -3.261798e-15 --8.566616e-02 -1.684976e-02 -3.263021e-15 --9.940522e-02 4.604439e-04 -3.257201e-15 --1.088023e-01 1.843690e-02 -3.244812e-15 --1.122306e-01 3.717147e-02 -3.233087e-15 --1.096489e-01 5.630049e-02 -3.200972e-15 --1.012734e-01 7.510380e-02 -3.229720e-15 --8.769885e-02 9.261291e-02 -3.224088e-15 --6.962096e-02 1.073748e-01 -3.206643e-15 --4.763510e-02 1.182849e-01 -3.217157e-15 --2.263753e-02 1.248639e-01 -3.194241e-15 -3.066251e-02 1.249471e-01 -3.169789e-15 -5.582863e-02 1.185179e-01 -3.154545e-15 -7.789326e-02 1.077595e-01 -3.167692e-15 -9.576165e-02 9.299955e-02 -3.177690e-15 -1.089407e-01 7.521039e-02 -3.201923e-15 -1.171539e-01 5.582751e-02 -3.201701e-15 -1.201787e-01 3.593309e-02 -3.210905e-15 -1.175865e-01 1.613671e-02 -3.206616e-15 -1.093540e-01 -2.782430e-03 -3.218126e-15 -9.576375e-02 -1.992910e-02 -3.251100e-15 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-3.222515e-15 --1.355923e-01 8.693292e-03 -3.165648e-15 --1.254621e-01 7.877740e-03 -3.120164e-15 --8.596882e-02 6.557315e-03 -3.090926e-15 --8.543619e-03 4.054908e-03 -3.001846e-15 --3.848293e-02 -1.866611e-03 -2.034500e-15 -6.148195e-03 -1.772605e-02 -2.805994e-15 --6.767835e-03 3.920137e-02 -3.393919e-15 --2.687291e-02 2.800075e-02 -3.261323e-15 --3.692305e-02 3.208079e-02 -3.197696e-15 --3.714973e-02 3.673545e-02 -3.158229e-15 --2.805311e-02 3.827526e-02 -3.107119e-15 --1.905257e-02 3.638383e-02 -3.056505e-15 --1.027623e-02 4.273104e-02 -3.007425e-15 --6.982765e-03 5.328319e-02 -2.973511e-15 --1.168678e-02 6.095089e-02 -2.936810e-15 --1.537008e-02 5.941834e-02 -2.869727e-15 --1.479770e-02 4.526889e-02 -2.768411e-15 --1.127532e-02 2.757458e-02 -2.581458e-15 --5.948123e-03 1.127272e-02 -2.201347e-15 --3.848293e-02 -1.866611e-03 -2.034500e-15 --8.543619e-03 4.054908e-03 -3.001846e-15 --8.596882e-02 6.557315e-03 -3.090926e-15 --1.254621e-01 7.877740e-03 -3.120164e-15 --1.355923e-01 8.693292e-03 -3.165648e-15 --1.202709e-01 9.189891e-03 -3.222515e-15 --9.843280e-02 5.048750e-03 -3.225439e-15 --7.262003e-02 -2.405947e-03 -3.277821e-15 --4.820510e-02 -9.609396e-03 -3.286557e-15 --2.117207e-02 -8.847041e-03 -3.194279e-15 -1.807091e-03 -4.637711e-03 -2.941970e-15 -1.873182e-02 1.273085e-03 -2.314493e-15 -2.868863e-02 -3.721316e-02 -2.904475e-15 --2.474759e-02 -2.680003e-02 -2.899577e-15 -6.514313e-02 -3.094462e-02 -2.905348e-15 --6.163511e-02 -2.066343e-02 -2.891758e-15 --3.432188e-03 -4.131648e-02 -2.899156e-15 --2.259145e-03 -2.480411e-02 -2.909630e-15 -5.262566e-02 -4.219581e-02 -2.913975e-15 -8.946688e-02 -1.696343e-02 -2.905270e-15 -2.711737e-02 -1.338337e-02 -2.909763e-15 -1.926930e-02 -5.715898e-02 -2.896713e-15 --5.981936e-02 -3.521194e-02 -2.893209e-15 --2.006124e-02 -1.180128e-02 -2.903095e-15 --3.525710e-02 -4.393807e-02 -2.897677e-15 --8.358692e-02 -1.269752e-02 -2.881084e-15 -5.918654e-02 -1.760874e-02 -2.903618e-15 -6.821569e-02 -4.767905e-02 -2.924028e-15 --4.211210e-02 -1.137095e-02 -2.897584e-15 -8.349429e-02 -3.286791e-02 -2.905607e-15 --7.509226e-02 -2.691910e-02 -2.892863e-15 -4.226596e-02 -5.621279e-02 -2.915754e-15 --5.500178e-03 -5.816350e-02 -2.901668e-15 -1.066713e-01 -8.808060e-03 -2.905814e-15 --4.587369e-02 -2.463961e-02 -2.900621e-15 -5.689483e-02 -5.664635e-02 -2.925122e-15 -3.328330e-03 -1.189637e-02 -2.900950e-15 --6.375679e-02 -1.143081e-02 -2.889877e-15 --2.723488e-02 -5.634186e-02 -2.895801e-15 -1.848468e-02 -2.464718e-02 -2.904749e-15 --1.142179e-02 3.825053e-02 -2.923417e-15 -5.137322e-02 4.968859e-02 -2.965677e-15 --5.202658e-02 3.625011e-02 -2.898698e-15 -2.951447e-02 6.328604e-02 -2.965289e-15 -2.091053e-02 2.682390e-02 -2.936957e-15 -8.411883e-02 2.709257e-02 -2.897065e-15 --3.793463e-02 5.737117e-02 -2.910103e-15 -5.422381e-03 7.688413e-02 -2.983679e-15 --7.146905e-02 3.079381e-02 -2.886397e-15 -5.121034e-02 6.322800e-02 -2.957644e-15 -8.136398e-02 4.609607e-02 -2.945119e-15 -3.887009e-02 1.661716e-02 -2.932487e-15 -2.254201e-02 4.417930e-02 -2.939557e-15 --6.706860e-02 4.849467e-02 -2.897857e-15 --2.656238e-02 4.203772e-02 -2.912513e-15 --3.985493e-03 1.712265e-02 -2.915979e-15 -2.791849e-02 7.743547e-02 -2.979927e-15 -1.050068e-01 1.803149e-02 -2.899382e-15 -6.177195e-02 1.667617e-02 -2.895207e-15 --1.806310e-02 7.433276e-02 -2.935731e-15 -4.939919e-02 7.653113e-02 -2.961209e-15 -7.012333e-03 3.172753e-02 -2.942607e-15 -1.004184e-01 3.350258e-02 -2.910545e-15 -5.803069e-03 5.634771e-02 -2.968363e-15 -6.722022e-02 3.122044e-02 -2.920584e-15 --1.461810e-02 5.943120e-02 -2.938192e-15 --3.933143e-02 2.140719e-02 -2.894312e-15 -1.807971e-02 1.549974e-02 -2.914492e-15 -3.461612e-02 5.264429e-02 -2.941224e-15 -4.052403e-02 3.340547e-02 -2.942005e-15 -7.457317e-02 6.365235e-02 -2.941048e-15 --4.891360e-02 6.653710e-02 -2.922895e-15 --8.845853e-02 1.365239e-02 -2.858085e-15 --4.101902e-02 3.258430e-02 -2.886165e-15 --7.052147e-02 1.031189e-02 -2.872176e-15 --4.618195e-02 4.650245e-02 -2.904466e-15 -8.629323e-02 1.658895e-02 -2.903759e-15 -6.876408e-02 7.335426e-02 -2.950481e-15 --8.710265e-02 1.462947e-02 -3.475922e-15 --3.650344e-02 -2.649877e-02 -3.475376e-15 --4.448704e-02 4.959130e-02 -3.448097e-15 --9.401366e-02 -2.009317e-02 -3.493042e-15 --9.830095e-02 6.678734e-02 -3.477999e-15 --1.293669e-01 1.816174e-02 -3.498608e-15 --2.443479e-02 1.028401e-02 -3.455861e-15 --7.324037e-02 -3.779873e-02 -3.482157e-15 --5.540781e-02 7.494698e-02 -3.452179e-15 --1.320116e-01 -6.281627e-03 -3.493411e-15 --1.413867e-02 3.529476e-02 -3.445022e-15 --1.690715e-02 -3.203644e-02 -3.456401e-15 --1.302911e-01 4.146957e-02 -3.509848e-15 --1.117207e-02 6.004812e-02 -3.439638e-15 --6.975920e-02 -6.035874e-03 -3.467086e-15 --6.897120e-02 4.036838e-02 -3.465705e-15 --1.088053e-01 3.976683e-04 -3.506909e-15 --4.491166e-02 -4.882681e-02 -3.470704e-15 --7.333239e-03 -5.709430e-03 -3.455654e-15 --3.988113e-02 3.462435e-02 -3.462555e-15 --9.766672e-02 -5.330544e-02 -3.464599e-15 --1.082217e-01 3.154826e-02 -3.492225e-15 --1.007964e-01 8.168631e-02 -3.473661e-15 --1.299516e-01 -3.122375e-02 -3.483562e-15 --1.501442e-01 7.741774e-03 -3.511133e-15 --1.215956e-01 6.590376e-02 -3.506431e-15 --4.687167e-02 -1.026615e-02 -3.471518e-15 --1.499397e-01 3.282809e-02 -3.529761e-15 -8.979532e-04 1.937254e-02 -3.435859e-15 --4.508205e-02 1.232816e-02 -3.469696e-15 --1.818508e-02 -5.172248e-02 -3.457855e-15 --7.310957e-02 -2.315984e-02 -3.486548e-15 --6.667580e-02 -5.734212e-02 -3.469751e-15 --2.805113e-02 7.959079e-02 -3.435989e-15 --7.433065e-02 5.630488e-02 -3.468183e-15 --7.846800e-02 8.632279e-02 -3.451437e-15 --5.610852e-02 8.928482e-02 -3.439301e-15 --1.433548e-01 -2.120086e-02 -3.487947e-15 --3.477633e-03 4.895427e-02 -3.450163e-15 --1.948867e-02 -2.078341e-02 -3.451750e-15 --1.539240e-01 -1.276070e-02 -3.498821e-15 --1.131219e-01 4.712058e-02 -3.495485e-15 -3.459158e-04 4.669371e-03 -3.440635e-15 --2.799078e-02 -3.923960e-02 -3.459232e-15 --1.164765e-01 -5.025764e-02 -3.464871e-15 --5.341117e-02 -3.385521e-02 -3.472204e-15 --2.126360e-02 4.459910e-02 -3.441105e-15 --1.231461e-01 3.604458e-03 -3.501487e-15 --1.151199e-01 -1.456912e-02 -3.479628e-15 --5.579706e-02 6.291474e-02 -3.449992e-15 --3.160300e-02 6.108203e-02 -3.447349e-15 --8.985638e-02 -3.002690e-03 -3.483322e-15 --3.498587e-03 6.973011e-02 -3.444168e-15 --1.649696e-03 -1.636813e-02 -3.451269e-15 --1.325401e-03 -3.634692e-02 -3.437021e-15 --1.073074e-01 1.615597e-02 -3.496626e-15 --2.231490e-02 2.548321e-02 -3.451275e-15 --1.529512e-01 4.737967e-02 -3.533943e-15 --9.380160e-02 5.118086e-02 -3.490622e-15 --2.272587e-02 -5.233347e-03 -3.460244e-15 --5.398531e-02 5.100781e-02 -3.460522e-15 --6.656737e-02 1.199263e-02 -3.471354e-15 --1.590073e-01 1.768554e-02 -3.509937e-15 -1.554863e-03 3.463241e-02 -3.430108e-15 --5.866468e-02 -2.396981e-02 -3.484379e-15 --1.147119e-01 8.033811e-02 -3.493736e-15 --9.671038e-02 -3.606871e-02 -3.471425e-15 --8.958373e-02 3.384222e-02 -3.491184e-15 --3.123158e-02 -5.870266e-02 -3.465960e-15 --1.223000e-01 3.125829e-02 -3.499729e-15 --3.867306e-02 9.156248e-02 -3.432503e-15 --1.412468e-01 5.499610e-02 -3.534605e-15 --7.793331e-02 7.082472e-02 -3.469339e-15 -3.861936e-02 1.809046e-02 -3.437159e-15 -3.590749e-02 1.190195e-02 -3.437958e-15 -3.457007e-02 -1.983738e-02 -3.443461e-15 -3.407453e-02 4.837399e-02 -3.426516e-15 --6.336392e-02 4.256339e-02 -2.979672e-15 --5.946102e-02 9.260595e-02 -2.964727e-15 --7.125897e-02 -8.706359e-03 -2.949037e-15 --1.224948e-01 2.920497e-02 -3.024580e-15 --4.576054e-03 5.512138e-02 -2.958949e-15 --1.022019e-01 7.404329e-02 -3.007421e-15 --2.464394e-02 1.031454e-02 -2.966296e-15 --1.001028e-01 -3.357376e-03 -2.975502e-15 --2.878347e-02 8.772143e-02 -2.947503e-15 --9.409498e-02 9.391516e-02 -2.984426e-15 --3.478898e-02 -1.021516e-02 -2.967473e-15 --1.381657e-01 5.730863e-02 -3.007330e-15 -1.179910e-02 2.604309e-02 -2.962728e-15 --4.677458e-02 6.348076e-02 -2.971690e-15 --8.168883e-02 2.106509e-02 -2.985695e-15 --7.243981e-02 6.978414e-02 -2.983582e-15 --5.575150e-02 1.463258e-02 -2.969620e-15 --9.804725e-02 4.318575e-02 -2.998338e-15 --2.814852e-02 4.193346e-02 -2.965975e-15 --3.788269e-02 1.076970e-01 -2.951612e-15 --9.139182e-02 -2.369541e-02 -2.954724e-15 --1.326878e-01 6.820486e-03 -3.003092e-15 -5.544867e-03 7.800787e-02 -2.950037e-15 --1.269615e-01 8.628148e-02 -3.020534e-15 --1.138594e-03 -4.865774e-03 -2.966039e-15 --6.814189e-02 1.100976e-01 -2.980866e-15 --6.200608e-02 -2.568407e-02 -2.941898e-15 --1.467265e-01 3.032133e-02 -3.037242e-15 -2.070150e-02 5.275255e-02 -2.963321e-15 --1.477190e-01 7.071403e-02 -3.007755e-15 -2.207003e-02 1.291745e-02 -2.952795e-15 --1.137717e-01 4.857171e-02 -3.010463e-15 --1.330232e-02 3.540614e-02 -2.963560e-15 --1.102847e-01 -2.586812e-02 -2.973882e-15 --1.897760e-02 1.101149e-01 -2.950531e-15 --8.120635e-02 8.281104e-02 -2.986593e-15 --4.763264e-02 1.130481e-03 -2.960574e-15 --1.245475e-01 -1.274527e-02 -2.987428e-15 --4.104224e-03 9.724810e-02 -2.941820e-15 --1.130866e-01 1.024973e-01 -3.001576e-15 --1.515859e-02 -1.889507e-02 -2.963787e-15 --5.172516e-02 7.859742e-02 -2.962404e-15 --7.875818e-02 5.455809e-03 -2.971629e-15 -3.008165e-02 3.532929e-02 -2.956651e-15 --1.553077e-01 4.862201e-02 -3.034320e-15 --1.012130e-01 1.126165e-01 -2.999292e-15 --2.781233e-02 -2.835868e-02 -2.958727e-15 --2.498766e-02 7.053968e-02 -2.953742e-15 --1.033771e-01 1.410327e-02 -2.988210e-15 --1.068503e-01 8.446469e-02 -3.002829e-15 --2.119619e-02 -1.186769e-03 -2.966537e-15 --5.902761e-02 1.175539e-01 -2.965129e-15 --7.132710e-02 -3.326612e-02 -2.949588e-15 --1.488566e-01 1.331244e-02 -3.006702e-15 -2.280651e-02 7.120916e-02 -2.946961e-15 --1.328502e-01 4.619065e-02 -3.023660e-15 -6.368137e-03 3.745364e-02 -2.961053e-15 --9.180213e-02 6.121662e-02 -2.988138e-15 --3.468020e-02 2.342739e-02 -2.964536e-15 --6.604647e-02 5.591127e-02 -2.985701e-15 --1.000810e-01 3.101600e-02 -3.002142e-15 --2.824455e-02 5.380208e-02 -2.959339e-15 --6.072850e-02 2.851443e-02 -2.973969e-15 --7.903266e-02 5.162185e-02 -2.984435e-15 --4.179554e-02 3.598747e-02 -2.970063e-15 --1.591750e-01 6.046307e-02 -3.006716e-15 -3.399197e-02 2.378700e-02 -2.951768e-15 --1.201664e-01 6.621070e-02 -3.014198e-15 --6.859157e-03 1.721793e-02 -2.966143e-15 --8.156447e-02 3.617742e-02 -2.985834e-15 --4.503506e-02 4.903053e-02 -2.966000e-15 -3.818905e-03 3.717141e-02 -3.221573e-15 --7.150650e-03 -1.386851e-02 -3.228486e-15 -1.526958e-02 8.827125e-02 -3.208903e-15 --5.630507e-02 3.101135e-02 -3.238857e-15 -6.557193e-02 4.238832e-02 -3.189273e-15 --4.580561e-02 7.094940e-02 -3.215344e-15 -5.577154e-02 2.186234e-03 -3.242074e-15 -4.851583e-02 7.822809e-02 -3.181677e-15 --3.893487e-02 -3.304209e-03 -3.231309e-15 -3.316689e-02 -2.004336e-02 -3.240158e-15 --2.544578e-02 9.382395e-02 -3.210411e-15 --6.834167e-02 5.858863e-02 -3.226340e-15 --1.904409e-02 1.212305e-02 -3.222897e-15 -2.738155e-02 6.259667e-02 -3.213618e-15 -1.528185e-02 1.755235e-02 -3.233553e-15 --7.280331e-03 5.686278e-02 -3.215219e-15 -4.172681e-02 9.718430e-02 -3.186637e-15 --3.286688e-02 -2.222332e-02 -3.244839e-15 --6.919780e-02 1.219473e-02 -3.244437e-15 -7.953755e-02 6.235778e-02 -3.177782e-15 -2.770927e-02 3.168219e-02 -3.221274e-15 --2.127421e-02 4.237982e-02 -3.216155e-15 --1.525217e-03 -3.472486e-02 -3.235473e-15 -9.074254e-03 1.079915e-01 -3.201094e-15 --8.274978e-02 3.388045e-02 -3.214552e-15 -9.174317e-02 3.981409e-02 -3.181281e-15 --5.124676e-02 9.372611e-02 -3.218190e-15 -5.905006e-02 -2.090521e-02 -3.267330e-15 --6.838638e-02 7.680285e-02 -3.234179e-15 -7.516123e-02 -7.640407e-03 -3.255353e-15 --4.585550e-02 4.934734e-02 -3.213401e-15 -5.045831e-02 2.568247e-02 -3.225238e-15 -2.446720e-02 -3.735344e-03 -3.232080e-15 --1.621875e-02 7.774393e-02 -3.217039e-15 -6.513573e-02 9.571379e-02 -3.174573e-15 --5.433500e-02 -1.976847e-02 -3.237944e-15 -7.167880e-02 8.322057e-02 -3.173648e-15 --6.162427e-02 -7.668916e-03 -3.244512e-15 --4.057320e-02 1.359164e-02 -3.225931e-15 -5.012393e-02 6.115531e-02 -3.202801e-15 --2.316645e-02 1.193199e-04 -3.227968e-15 -3.167796e-02 7.486309e-02 -3.208055e-15 -2.038655e-02 -3.445898e-02 -3.230407e-15 --1.334568e-02 1.077154e-01 -3.205198e-15 --8.694892e-02 5.582326e-02 -3.227369e-15 -1.001187e-01 2.003377e-02 -3.210797e-15 -4.030218e-03 3.393099e-03 -3.217976e-15 -4.209378e-03 7.103268e-02 -3.223638e-15 --2.790801e-02 6.403469e-02 -3.219215e-15 -3.624187e-02 1.005810e-02 -3.233485e-15 --8.678383e-02 6.895821e-02 -3.220816e-15 -3.957483e-02 -3.163740e-02 -3.253799e-15 -8.953028e-02 7.768193e-03 -3.234815e-15 --3.206718e-02 1.054422e-01 -3.221926e-15 -1.612694e-02 5.087799e-02 -3.213666e-15 --8.008880e-03 2.379612e-02 -3.229367e-15 --1.853779e-02 -3.573424e-02 -3.236765e-15 -2.642158e-02 1.101807e-01 -3.195318e-15 --8.771756e-02 2.210458e-02 -3.246898e-15 -9.689087e-02 5.213881e-02 -3.186262e-15 -1.668274e-02 -1.825372e-02 -3.236803e-15 --8.790840e-03 9.247475e-02 -3.212169e-15 --6.657156e-02 4.771247e-02 -3.223056e-15 --3.190860e-02 2.774780e-02 -3.220991e-15 -4.011673e-02 4.664720e-02 -3.220509e-15 --5.367385e-02 6.076181e-02 -3.220801e-15 -7.114648e-02 1.792329e-02 -3.222790e-15 --3.444126e-02 8.434238e-02 -3.230031e-15 -4.218457e-02 -1.092428e-02 -3.260036e-15 -3.327102e-02 2.551618e-02 -3.224958e-15 --8.445409e-02 2.503315e-02 -3.238764e-15 --5.332857e-02 3.280269e-02 -3.333240e-15 --5.977751e-02 3.615958e-03 -3.182481e-15 --1.244208e-02 2.203023e-02 -3.232345e-15 -7.239350e-02 4.761321e-02 -3.065792e-15 --8.401565e-02 3.931658e-02 -2.996116e-15 -8.581681e-02 1.894380e-02 -3.140412e-15 --6.488011e-02 1.766049e-02 -3.126325e-15 -1.476877e-02 4.165835e-02 -3.003166e-15 --1.360415e-01 4.111499e-02 -3.241882e-15 --6.823613e-02 2.296190e-02 -3.209569e-15 -5.470296e-02 -2.166709e-04 -3.236551e-15 --6.776263e-02 1.332695e-02 -2.892634e-15 --2.304647e-02 1.333179e-02 -3.271280e-15 -6.762716e-03 5.259235e-02 -3.121738e-15 -1.720888e-02 5.327363e-02 -2.591545e-15 --1.318256e-01 -2.832398e-02 -3.289387e-15 -1.281851e-02 2.240155e-02 -3.248212e-15 --1.758955e-02 2.889186e-02 -3.324735e-15 --9.459818e-02 -8.067721e-03 -3.314579e-15 --7.773049e-02 6.069561e-02 -3.068734e-15 -6.875696e-02 3.965276e-02 -3.137719e-15 --1.135749e-01 4.841212e-02 -3.313435e-15 -8.334008e-02 3.328865e-02 -3.033436e-15 --6.803209e-02 1.865245e-02 -3.027279e-15 --2.622517e-02 3.618769e-02 -3.109165e-15 -4.018326e-02 3.574774e-02 -3.025457e-15 --1.302231e-01 6.010632e-03 -3.100941e-15 --1.236721e-01 3.301606e-02 -3.049644e-15 --1.034775e-01 4.628672e-02 -3.431947e-15 --6.785087e-03 1.628447e-02 -3.187439e-15 --4.831325e-02 1.478399e-02 -2.803606e-15 --8.400788e-02 1.867427e-02 -3.405833e-15 -2.659259e-03 2.035455e-02 -3.052983e-15 --5.352860e-02 2.562847e-02 -3.212650e-15 -7.733804e-02 1.996538e-02 -3.097624e-15 -8.848684e-02 -7.445910e-03 -3.183999e-15 -7.174469e-03 6.122182e-02 -2.881412e-15 --1.359370e-01 -3.108702e-02 -3.333753e-15 --1.084009e-01 -1.422069e-02 -3.285445e-15 --8.436645e-02 -1.141477e-02 -2.912141e-15 --3.616532e-02 3.231354e-02 -3.333854e-15 -6.172315e-02 4.709062e-02 -3.058667e-15 -8.625630e-02 8.321367e-03 -2.996350e-15 --1.149707e-02 2.551357e-02 -3.286203e-15 -7.399412e-02 7.249445e-02 -3.085810e-15 --5.997005e-02 9.413211e-03 -3.321473e-15 -8.656849e-02 7.608765e-02 -3.131903e-15 --9.695546e-02 -4.704900e-03 -3.154736e-15 --3.019436e-02 5.473245e-02 -3.054857e-15 --1.367320e-01 6.378193e-02 -3.373284e-15 --5.310598e-02 2.574236e-02 -3.102975e-15 --4.136935e-02 4.434383e-02 -3.097428e-15 -1.198183e-02 1.155751e-02 -3.115315e-15 --8.040623e-02 7.172445e-02 -3.111219e-15 --1.276382e-01 4.220930e-02 -3.201043e-15 --7.173958e-02 6.474763e-02 -3.101283e-15 -1.562798e-02 7.058948e-02 -2.757540e-15 --3.259735e-03 1.833352e-02 -3.196780e-15 --1.324058e-01 3.640396e-02 -3.090641e-15 --7.136381e-02 3.077220e-02 -3.286580e-15 -7.014271e-03 4.918775e-02 -3.366045e-15 --2.323601e-02 1.091061e-03 -2.926207e-15 --5.126815e-02 -6.223944e-04 -2.677660e-15 --6.130915e-02 4.734801e-02 -2.941073e-15 -2.230924e-02 -1.478305e-04 -2.912990e-15 --3.746385e-02 2.027811e-02 -3.161447e-15 -7.303627e-03 3.426874e-02 -3.180724e-15 -5.836463e-02 -6.417442e-03 -3.249433e-15 -8.907541e-02 5.020948e-02 -3.048057e-15 -1.158781e-02 -3.026357e-02 -2.557584e-15 -8.121746e-03 -2.295181e-03 -2.948523e-15 --2.379495e-02 5.989426e-02 -3.065901e-15 -7.662144e-02 2.591022e-02 -3.144556e-15 -5.004523e-02 6.230168e-03 -2.942006e-15 -5.598562e-03 3.356010e-02 -3.049590e-15 --2.891580e-04 1.747557e-02 -3.073037e-15 -8.054461e-02 3.390013e-04 -2.960906e-15 -4.078957e-02 1.742095e-02 -3.132938e-15 -6.238229e-02 6.365198e-03 -3.218345e-15 --1.046773e-01 4.912143e-03 -3.116590e-15 --8.428291e-02 4.072677e-03 -3.359457e-15 --7.372211e-02 -8.074994e-03 -2.853855e-15 --1.014549e-01 3.117986e-02 -3.177853e-15 --4.790560e-02 -6.572901e-03 -2.324036e-15 -6.564044e-02 7.187095e-02 -3.129548e-15 --1.119565e-01 5.138953e-02 -3.400009e-15 --4.723695e-02 3.233816e-02 -2.856142e-15 --1.194282e-01 3.419524e-02 -3.231880e-15 -4.052104e-02 4.583336e-02 -3.059693e-15 --9.757836e-02 7.416809e-02 -3.085442e-15 --7.843626e-02 1.891765e-02 -3.018573e-15 -1.530856e-02 2.437501e-02 -2.135031e-15 -1.494205e-02 4.823060e-02 -3.117881e-15 --1.361115e-01 -1.648309e-02 -3.241785e-15 --8.854953e-02 1.677179e-02 -3.163018e-15 -1.627483e-02 5.442098e-02 -2.956666e-15 -8.535115e-02 4.227932e-02 -3.104977e-15 --1.138380e-01 -3.296382e-02 -3.333593e-15 --6.273350e-02 5.990213e-02 -2.979338e-15 --2.345600e-02 7.452024e-03 -3.183624e-15 --1.174721e-02 2.334012e-02 -3.114718e-15 -1.223364e-02 7.371031e-02 -2.870703e-15 --3.077842e-02 2.644042e-03 -2.485598e-15 --3.755696e-02 2.218053e-02 -3.369056e-15 --3.149327e-02 1.455498e-02 -3.194348e-15 -3.880665e-03 1.189549e-02 -3.278671e-15 --4.475010e-02 2.336761e-02 -3.314767e-15 --7.375543e-02 -2.328648e-02 -2.930111e-15 -6.073753e-03 4.693562e-02 -3.334314e-15 -1.430182e-02 6.968397e-02 -2.914584e-15 -4.382132e-03 2.501812e-02 -3.241983e-15 --6.077014e-02 5.436190e-02 -3.155508e-15 --1.392394e-01 6.966504e-02 -3.377491e-15 --6.581011e-02 6.250520e-02 -3.159934e-15 --1.335473e-01 7.607279e-03 -3.123159e-15 --8.275828e-02 7.830182e-02 -3.122321e-15 --4.233765e-02 6.181946e-02 -3.108308e-15 -1.108362e-02 4.289529e-02 -2.525365e-15 --7.331758e-02 4.126780e-02 -3.124783e-15 -4.899922e-02 4.077161e-02 -3.033557e-15 --5.821782e-02 4.370796e-02 -3.078213e-15 --1.327738e-01 7.154886e-02 -3.004285e-15 --4.558019e-02 1.646994e-02 -3.232772e-15 --6.540701e-03 -9.404205e-03 -3.301852e-15 --5.475948e-02 5.665062e-02 -3.043811e-15 --5.257723e-02 6.062606e-02 -3.016458e-15 -4.696046e-02 5.820269e-02 -3.076333e-15 --9.220144e-02 3.368800e-02 -3.443547e-15 -3.508087e-02 3.510692e-03 -3.233349e-15 --7.939612e-02 2.346917e-02 -2.941033e-15 --6.944654e-02 6.728184e-02 -3.095910e-15 -8.697384e-02 5.424593e-02 -3.033803e-15 -1.607391e-02 3.307706e-02 -2.627940e-15 --5.989796e-02 -3.857638e-02 -3.370921e-15 --2.197665e-02 3.519790e-02 -3.258748e-15 -1.624752e-02 -1.076744e-03 -3.124856e-15 -7.697951e-02 -1.128685e-02 -3.234527e-15 -2.031812e-02 1.860486e-02 -3.265853e-15 --6.755842e-02 2.367444e-03 -3.245364e-15 --4.304569e-03 3.589288e-02 -3.330801e-15 --7.957901e-02 3.912911e-02 -3.048336e-15 --9.282330e-02 5.288058e-02 -2.996443e-15 --1.155377e-01 -1.777084e-02 -3.262571e-15 --9.226556e-02 -3.687398e-02 -3.348498e-15 -4.140029e-02 3.400843e-02 -3.172404e-15 -1.789157e-02 7.812697e-02 -2.758939e-15 --8.359370e-02 5.039750e-02 -3.031314e-15 -1.504745e-02 8.642507e-03 -3.012178e-15 --3.460937e-02 3.617702e-02 -3.187223e-15 --1.052015e-01 1.375037e-02 -3.067774e-15 --8.135200e-02 1.845383e-02 -3.165973e-15 --1.414006e-01 -3.942449e-02 -3.375904e-15 --1.258607e-01 6.813421e-02 -3.071007e-15 --4.573792e-02 4.350364e-03 -3.078489e-15 -8.208929e-02 5.618828e-02 -3.077754e-15 --1.225288e-01 8.617819e-03 -3.098951e-15 -9.413525e-03 1.988340e-02 -3.107847e-15 --7.703138e-02 -3.988852e-02 -3.381271e-15 -1.662190e-02 6.434918e-02 -2.937167e-15 -1.288953e-01 2.953096e-03 -2.888124e-15 --1.126373e-01 1.516214e-03 -2.850657e-15 -1.288953e-01 2.953096e-03 -2.888124e-15 --1.126373e-01 1.516214e-03 -2.850657e-15 -9.214124e-03 9.544346e-02 -2.949192e-15 -2.277013e-02 7.344195e-02 -3.433128e-15 -2.364491e-02 -4.553390e-02 -3.458914e-15 --1.765003e-01 1.495524e-02 -3.504803e-15 -2.277013e-02 7.344195e-02 -3.433128e-15 -2.364491e-02 -4.553390e-02 -3.458914e-15 --6.581534e-02 -4.889309e-02 -2.947116e-15 --6.505040e-02 1.336616e-01 -2.969695e-15 -3.003023e-03 -5.316407e-02 -3.243302e-15 -3.942497e-03 1.270843e-01 -3.212338e-15 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -1.051517e-01 2.947959e-03 -2.905868e-15 -8.144546e-02 2.828313e-03 -2.899774e-15 -5.761873e-02 2.642699e-03 -2.902377e-15 -3.363108e-02 2.407522e-03 -2.917744e-15 -9.484056e-03 2.152086e-03 -2.904817e-15 --1.477530e-02 1.899500e-03 -2.897441e-15 --3.910784e-02 1.685454e-03 -2.899820e-15 --6.345901e-02 1.516587e-03 -2.874854e-15 --8.789432e-02 1.461375e-03 -2.870474e-15 -1.236208e-01 -1.384562e-02 -2.899762e-15 -1.135723e-01 -2.983930e-02 -2.910269e-15 -9.923919e-02 -4.443589e-02 -2.900675e-15 -8.118503e-02 -5.681875e-02 -2.922103e-15 -5.979063e-02 -6.602590e-02 -2.923835e-15 -3.577372e-02 -7.140419e-02 -2.915130e-15 -1.000183e-02 -7.323242e-02 -2.901631e-15 --1.599189e-02 -7.133473e-02 -2.901947e-15 --4.091161e-02 -6.590884e-02 -2.900885e-15 --6.345170e-02 -5.709856e-02 -2.876405e-15 --8.250652e-02 -4.507379e-02 -2.895017e-15 --9.751696e-02 -3.086038e-02 -2.871856e-15 --1.076708e-01 -1.500759e-02 -2.865879e-15 -1.051517e-01 2.947959e-03 -2.905868e-15 -8.144546e-02 2.828313e-03 -2.899774e-15 -5.761873e-02 2.642699e-03 -2.902377e-15 -3.363108e-02 2.407522e-03 -2.917744e-15 -9.484056e-03 2.152086e-03 -2.904817e-15 --1.477530e-02 1.899500e-03 -2.897441e-15 --3.910784e-02 1.685454e-03 -2.899820e-15 --6.345901e-02 1.516587e-03 -2.874854e-15 --8.789432e-02 1.461375e-03 -2.870474e-15 -1.290001e-01 2.010204e-02 -2.884418e-15 -1.239661e-01 3.707874e-02 -2.882614e-15 -1.139748e-01 5.326000e-02 -2.897271e-15 -9.941913e-02 6.778526e-02 -2.925206e-15 -8.084893e-02 7.980457e-02 -2.960389e-15 -5.911275e-02 8.866365e-02 -2.959918e-15 -3.484638e-02 9.382704e-02 -2.972298e-15 --1.654223e-02 9.355138e-02 -2.937809e-15 --4.111199e-02 8.797160e-02 -2.950567e-15 --6.285803e-02 7.863703e-02 -2.936039e-15 --8.137472e-02 6.593669e-02 -2.914329e-15 --9.593410e-02 5.068830e-02 -2.888040e-15 --1.063027e-01 3.468467e-02 -2.907252e-15 --1.121206e-01 1.815032e-02 -2.847526e-15 -2.267392e-02 5.299059e-02 -3.438710e-15 -2.268434e-02 3.364693e-02 -3.444116e-15 -2.279755e-02 1.455990e-02 -3.437604e-15 -2.299905e-02 -4.785604e-03 -3.445696e-15 -2.331314e-02 -2.469873e-02 -3.451473e-15 -6.789677e-03 8.652720e-02 -3.438039e-15 --1.184125e-02 9.663816e-02 -3.423235e-15 --3.273487e-02 1.028215e-01 -3.439824e-15 --5.513781e-02 1.050542e-01 -3.437734e-15 --7.837035e-02 1.036154e-01 -3.417424e-15 --1.013843e-01 9.896919e-02 -3.451592e-15 --1.229334e-01 9.108034e-02 -3.474228e-15 --1.416470e-01 8.015256e-02 -3.492093e-15 --1.567193e-01 6.641258e-02 -3.510910e-15 --1.676199e-01 5.037847e-02 -3.556457e-15 --1.742956e-01 3.299066e-02 -3.552030e-15 --1.742324e-01 -3.231118e-03 -3.520514e-15 --1.674830e-01 -2.087414e-02 -3.501893e-15 --1.566563e-01 -3.722272e-02 -3.479393e-15 --1.418569e-01 -5.140387e-02 -3.462792e-15 --1.234722e-01 -6.282737e-02 -3.442804e-15 --1.022972e-01 -7.104416e-02 -3.456061e-15 --7.944739e-02 -7.581687e-02 -3.474043e-15 --5.609389e-02 -7.703630e-02 -3.478450e-15 --3.307545e-02 -7.465731e-02 -3.486175e-15 --1.175991e-02 -6.849481e-02 -3.438838e-15 -7.334282e-03 -5.863838e-02 -3.428749e-15 -2.267392e-02 5.299059e-02 -3.438710e-15 -2.268434e-02 3.364693e-02 -3.444116e-15 -2.279755e-02 1.455990e-02 -3.437604e-15 -2.299905e-02 -4.785604e-03 -3.445696e-15 -2.331314e-02 -2.469873e-02 -3.451473e-15 -3.509405e-02 5.961921e-02 -3.417100e-15 -4.489746e-02 4.516753e-02 -3.425548e-15 -5.082015e-02 3.001572e-02 -3.423960e-15 -5.304058e-02 1.468219e-02 -3.445895e-15 -5.094889e-02 -6.273246e-04 -3.416140e-15 -4.518932e-02 -1.641806e-02 -3.436968e-15 -3.578921e-02 -3.143879e-02 -3.459311e-15 --9.124906e-02 -4.707614e-02 -2.988833e-15 --1.148595e-01 -4.051008e-02 -2.974716e-15 --1.356939e-01 -2.927439e-02 -2.982984e-15 --1.529119e-01 -1.427912e-02 -2.991483e-15 --1.657305e-01 3.580798e-03 -2.999974e-15 --1.736975e-01 2.287286e-02 -3.055158e-15 --1.764539e-01 4.275159e-02 -3.051098e-15 --1.735990e-01 6.227507e-02 -3.009493e-15 --1.652348e-01 8.097379e-02 -2.989285e-15 --1.521418e-01 9.825083e-02 -3.031631e-15 --1.349370e-01 1.131615e-01 -3.002499e-15 --1.141462e-01 1.244868e-01 -3.016103e-15 --9.045240e-02 1.314279e-01 -2.982710e-15 --3.911863e-02 1.312048e-01 -2.964314e-15 --1.411179e-02 1.244017e-01 -2.957242e-15 -8.358331e-03 1.134165e-01 -2.921860e-15 -2.684220e-02 9.880558e-02 -2.917202e-15 -4.063199e-02 8.150672e-02 -2.936070e-15 -4.919044e-02 6.250078e-02 -2.936982e-15 -5.204426e-02 4.264306e-02 -2.953856e-15 -4.910012e-02 2.280346e-02 -2.948850e-15 -4.057912e-02 3.483524e-03 -2.961794e-15 -2.686495e-02 -1.417787e-02 -2.960395e-15 -8.312575e-03 -2.897267e-02 -2.971905e-15 --1.443679e-02 -3.977949e-02 -2.954256e-15 --3.971766e-02 -4.640135e-02 -2.973015e-15 --2.309615e-02 -5.090091e-02 -3.249712e-15 --4.724111e-02 -4.389959e-02 -3.240121e-15 --6.817432e-02 -3.222510e-02 -3.261798e-15 --8.566616e-02 -1.684976e-02 -3.263021e-15 --9.940522e-02 4.604439e-04 -3.257201e-15 --1.088023e-01 1.843690e-02 -3.244812e-15 --1.122306e-01 3.717147e-02 -3.233087e-15 --1.096489e-01 5.630049e-02 -3.200972e-15 --1.012734e-01 7.510380e-02 -3.229720e-15 --8.769885e-02 9.261291e-02 -3.224088e-15 --6.962096e-02 1.073748e-01 -3.206643e-15 --4.763510e-02 1.182849e-01 -3.217157e-15 --2.263753e-02 1.248639e-01 -3.194241e-15 -3.066251e-02 1.249471e-01 -3.169789e-15 -5.582863e-02 1.185179e-01 -3.154545e-15 -7.789326e-02 1.077595e-01 -3.167692e-15 -9.576165e-02 9.299955e-02 -3.177690e-15 -1.089407e-01 7.521039e-02 -3.201923e-15 -1.171539e-01 5.582751e-02 -3.201701e-15 -1.201787e-01 3.593309e-02 -3.210905e-15 -1.175865e-01 1.613671e-02 -3.206616e-15 -1.093540e-01 -2.782430e-03 -3.218126e-15 -9.576375e-02 -1.992910e-02 -3.251100e-15 -7.739683e-02 -3.418255e-02 -3.275982e-15 -5.497586e-02 -4.466401e-02 -3.254395e-15 -2.966226e-02 -5.107236e-02 -3.239777e-15 --5.948123e-03 1.127272e-02 -2.201347e-15 --1.127532e-02 2.757458e-02 -2.581458e-15 --1.479770e-02 4.526889e-02 -2.768411e-15 --1.537008e-02 5.941834e-02 -2.869727e-15 --1.168678e-02 6.095089e-02 -2.936810e-15 --6.982765e-03 5.328319e-02 -2.973511e-15 --1.027623e-02 4.273104e-02 -3.007425e-15 --1.905257e-02 3.638383e-02 -3.056505e-15 --2.805311e-02 3.827526e-02 -3.107119e-15 --3.714973e-02 3.673545e-02 -3.158229e-15 --3.692305e-02 3.208079e-02 -3.197696e-15 --2.687291e-02 2.800075e-02 -3.261323e-15 --6.767835e-03 3.920137e-02 -3.393919e-15 -6.148195e-03 -1.772605e-02 -2.805994e-15 -1.873182e-02 1.273085e-03 -2.314493e-15 -1.807091e-03 -4.637711e-03 -2.941970e-15 --2.117207e-02 -8.847041e-03 -3.194279e-15 --4.820510e-02 -9.609396e-03 -3.286557e-15 --7.262003e-02 -2.405947e-03 -3.277821e-15 --9.843280e-02 5.048750e-03 -3.225439e-15 --1.202709e-01 9.189891e-03 -3.222515e-15 --1.355923e-01 8.693292e-03 -3.165648e-15 --1.254621e-01 7.877740e-03 -3.120164e-15 --8.596882e-02 6.557315e-03 -3.090926e-15 --8.543619e-03 4.054908e-03 -3.001846e-15 --3.848293e-02 -1.866611e-03 -2.034500e-15 -6.148195e-03 -1.772605e-02 -2.805994e-15 --6.767835e-03 3.920137e-02 -3.393919e-15 --2.687291e-02 2.800075e-02 -3.261323e-15 --3.692305e-02 3.208079e-02 -3.197696e-15 --3.714973e-02 3.673545e-02 -3.158229e-15 --2.805311e-02 3.827526e-02 -3.107119e-15 --1.905257e-02 3.638383e-02 -3.056505e-15 --1.027623e-02 4.273104e-02 -3.007425e-15 --6.982765e-03 5.328319e-02 -2.973511e-15 --1.168678e-02 6.095089e-02 -2.936810e-15 --1.537008e-02 5.941834e-02 -2.869727e-15 --1.479770e-02 4.526889e-02 -2.768411e-15 --1.127532e-02 2.757458e-02 -2.581458e-15 --5.948123e-03 1.127272e-02 -2.201347e-15 --3.848293e-02 -1.866611e-03 -2.034500e-15 --8.543619e-03 4.054908e-03 -3.001846e-15 --8.596882e-02 6.557315e-03 -3.090926e-15 --1.254621e-01 7.877740e-03 -3.120164e-15 --1.355923e-01 8.693292e-03 -3.165648e-15 --1.202709e-01 9.189891e-03 -3.222515e-15 --9.843280e-02 5.048750e-03 -3.225439e-15 --7.262003e-02 -2.405947e-03 -3.277821e-15 --4.820510e-02 -9.609396e-03 -3.286557e-15 --2.117207e-02 -8.847041e-03 -3.194279e-15 -1.807091e-03 -4.637711e-03 -2.941970e-15 -1.873182e-02 1.273085e-03 -2.314493e-15 -2.868863e-02 -3.721316e-02 -2.904475e-15 --2.474759e-02 -2.680003e-02 -2.899577e-15 -6.514313e-02 -3.094462e-02 -2.905348e-15 --6.163511e-02 -2.066343e-02 -2.891758e-15 --3.432188e-03 -4.131648e-02 -2.899156e-15 --2.259145e-03 -2.480411e-02 -2.909630e-15 -5.262566e-02 -4.219581e-02 -2.913975e-15 -8.946688e-02 -1.696343e-02 -2.905270e-15 -2.711737e-02 -1.338337e-02 -2.909763e-15 -1.926930e-02 -5.715898e-02 -2.896713e-15 --5.981936e-02 -3.521194e-02 -2.893209e-15 --2.006124e-02 -1.180128e-02 -2.903095e-15 --3.525710e-02 -4.393807e-02 -2.897677e-15 --8.358692e-02 -1.269752e-02 -2.881084e-15 -5.918654e-02 -1.760874e-02 -2.903618e-15 -6.821569e-02 -4.767905e-02 -2.924028e-15 --4.211210e-02 -1.137095e-02 -2.897584e-15 -8.349429e-02 -3.286791e-02 -2.905607e-15 --7.509226e-02 -2.691910e-02 -2.892863e-15 -4.226596e-02 -5.621279e-02 -2.915754e-15 --5.500178e-03 -5.816350e-02 -2.901668e-15 -1.066713e-01 -8.808060e-03 -2.905814e-15 --4.587369e-02 -2.463961e-02 -2.900621e-15 -5.689483e-02 -5.664635e-02 -2.925122e-15 -3.328330e-03 -1.189637e-02 -2.900950e-15 --6.375679e-02 -1.143081e-02 -2.889877e-15 --2.723488e-02 -5.634186e-02 -2.895801e-15 -1.848468e-02 -2.464718e-02 -2.904749e-15 --1.142179e-02 3.825053e-02 -2.923417e-15 -5.137322e-02 4.968859e-02 -2.965677e-15 --5.202658e-02 3.625011e-02 -2.898698e-15 -2.951447e-02 6.328604e-02 -2.965289e-15 -2.091053e-02 2.682390e-02 -2.936957e-15 -8.411883e-02 2.709257e-02 -2.897065e-15 --3.793463e-02 5.737117e-02 -2.910103e-15 -5.422381e-03 7.688413e-02 -2.983679e-15 --7.146905e-02 3.079381e-02 -2.886397e-15 -5.121034e-02 6.322800e-02 -2.957644e-15 -8.136398e-02 4.609607e-02 -2.945119e-15 -3.887009e-02 1.661716e-02 -2.932487e-15 -2.254201e-02 4.417930e-02 -2.939557e-15 --6.706860e-02 4.849467e-02 -2.897857e-15 --2.656238e-02 4.203772e-02 -2.912513e-15 --3.985493e-03 1.712265e-02 -2.915979e-15 -2.791849e-02 7.743547e-02 -2.979927e-15 -1.050068e-01 1.803149e-02 -2.899382e-15 -6.177195e-02 1.667617e-02 -2.895207e-15 --1.806310e-02 7.433276e-02 -2.935731e-15 -4.939919e-02 7.653113e-02 -2.961209e-15 -7.012333e-03 3.172753e-02 -2.942607e-15 -1.004184e-01 3.350258e-02 -2.910545e-15 -5.803069e-03 5.634771e-02 -2.968363e-15 -6.722022e-02 3.122044e-02 -2.920584e-15 --1.461810e-02 5.943120e-02 -2.938192e-15 --3.933143e-02 2.140719e-02 -2.894312e-15 -1.807971e-02 1.549974e-02 -2.914492e-15 -3.461612e-02 5.264429e-02 -2.941224e-15 -4.052403e-02 3.340547e-02 -2.942005e-15 -7.457317e-02 6.365235e-02 -2.941048e-15 --4.891360e-02 6.653710e-02 -2.922895e-15 --8.845853e-02 1.365239e-02 -2.858085e-15 --4.101902e-02 3.258430e-02 -2.886165e-15 --7.052147e-02 1.031189e-02 -2.872176e-15 --4.618195e-02 4.650245e-02 -2.904466e-15 -8.629323e-02 1.658895e-02 -2.903759e-15 -6.876408e-02 7.335426e-02 -2.950481e-15 --8.710265e-02 1.462947e-02 -3.475922e-15 --3.650344e-02 -2.649877e-02 -3.475376e-15 --4.448704e-02 4.959130e-02 -3.448097e-15 --9.401366e-02 -2.009317e-02 -3.493042e-15 --9.830095e-02 6.678734e-02 -3.477999e-15 --1.293669e-01 1.816174e-02 -3.498608e-15 --2.443479e-02 1.028401e-02 -3.455861e-15 --7.324037e-02 -3.779873e-02 -3.482157e-15 --5.540781e-02 7.494698e-02 -3.452179e-15 --1.320116e-01 -6.281627e-03 -3.493411e-15 --1.413867e-02 3.529476e-02 -3.445022e-15 --1.690715e-02 -3.203644e-02 -3.456401e-15 --1.302911e-01 4.146957e-02 -3.509848e-15 --1.117207e-02 6.004812e-02 -3.439638e-15 --6.975920e-02 -6.035874e-03 -3.467086e-15 --6.897120e-02 4.036838e-02 -3.465705e-15 --1.088053e-01 3.976683e-04 -3.506909e-15 --4.491166e-02 -4.882681e-02 -3.470704e-15 --7.333239e-03 -5.709430e-03 -3.455654e-15 --3.988113e-02 3.462435e-02 -3.462555e-15 --9.766672e-02 -5.330544e-02 -3.464599e-15 --1.082217e-01 3.154826e-02 -3.492225e-15 --1.007964e-01 8.168631e-02 -3.473661e-15 --1.299516e-01 -3.122375e-02 -3.483562e-15 --1.501442e-01 7.741774e-03 -3.511133e-15 --1.215956e-01 6.590376e-02 -3.506431e-15 --4.687167e-02 -1.026615e-02 -3.471518e-15 --1.499397e-01 3.282809e-02 -3.529761e-15 -8.979532e-04 1.937254e-02 -3.435859e-15 --4.508205e-02 1.232816e-02 -3.469696e-15 --1.818508e-02 -5.172248e-02 -3.457855e-15 --7.310957e-02 -2.315984e-02 -3.486548e-15 --6.667580e-02 -5.734212e-02 -3.469751e-15 --2.805113e-02 7.959079e-02 -3.435989e-15 --7.433065e-02 5.630488e-02 -3.468183e-15 --7.846800e-02 8.632279e-02 -3.451437e-15 --5.610852e-02 8.928482e-02 -3.439301e-15 --1.433548e-01 -2.120086e-02 -3.487947e-15 --3.477633e-03 4.895427e-02 -3.450163e-15 --1.948867e-02 -2.078341e-02 -3.451750e-15 --1.539240e-01 -1.276070e-02 -3.498821e-15 --1.131219e-01 4.712058e-02 -3.495485e-15 -3.459158e-04 4.669371e-03 -3.440635e-15 --2.799078e-02 -3.923960e-02 -3.459232e-15 --1.164765e-01 -5.025764e-02 -3.464871e-15 --5.341117e-02 -3.385521e-02 -3.472204e-15 --2.126360e-02 4.459910e-02 -3.441105e-15 --1.231461e-01 3.604458e-03 -3.501487e-15 --1.151199e-01 -1.456912e-02 -3.479628e-15 --5.579706e-02 6.291474e-02 -3.449992e-15 --3.160300e-02 6.108203e-02 -3.447349e-15 --8.985638e-02 -3.002690e-03 -3.483322e-15 --3.498587e-03 6.973011e-02 -3.444168e-15 --1.649696e-03 -1.636813e-02 -3.451269e-15 --1.325401e-03 -3.634692e-02 -3.437021e-15 --1.073074e-01 1.615597e-02 -3.496626e-15 --2.231490e-02 2.548321e-02 -3.451275e-15 --1.529512e-01 4.737967e-02 -3.533943e-15 --9.380160e-02 5.118086e-02 -3.490622e-15 --2.272587e-02 -5.233347e-03 -3.460244e-15 --5.398531e-02 5.100781e-02 -3.460522e-15 --6.656737e-02 1.199263e-02 -3.471354e-15 --1.590073e-01 1.768554e-02 -3.509937e-15 -1.554863e-03 3.463241e-02 -3.430108e-15 --5.866468e-02 -2.396981e-02 -3.484379e-15 --1.147119e-01 8.033811e-02 -3.493736e-15 --9.671038e-02 -3.606871e-02 -3.471425e-15 --8.958373e-02 3.384222e-02 -3.491184e-15 --3.123158e-02 -5.870266e-02 -3.465960e-15 --1.223000e-01 3.125829e-02 -3.499729e-15 --3.867306e-02 9.156248e-02 -3.432503e-15 --1.412468e-01 5.499610e-02 -3.534605e-15 --7.793331e-02 7.082472e-02 -3.469339e-15 -3.861936e-02 1.809046e-02 -3.437159e-15 -3.590749e-02 1.190195e-02 -3.437958e-15 -3.457007e-02 -1.983738e-02 -3.443461e-15 -3.407453e-02 4.837399e-02 -3.426516e-15 --6.336392e-02 4.256339e-02 -2.979672e-15 --5.946102e-02 9.260595e-02 -2.964727e-15 --7.125897e-02 -8.706359e-03 -2.949037e-15 --1.224948e-01 2.920497e-02 -3.024580e-15 --4.576054e-03 5.512138e-02 -2.958949e-15 --1.022019e-01 7.404329e-02 -3.007421e-15 --2.464394e-02 1.031454e-02 -2.966296e-15 --1.001028e-01 -3.357376e-03 -2.975502e-15 --2.878347e-02 8.772143e-02 -2.947503e-15 --9.409498e-02 9.391516e-02 -2.984426e-15 --3.478898e-02 -1.021516e-02 -2.967473e-15 --1.381657e-01 5.730863e-02 -3.007330e-15 -1.179910e-02 2.604309e-02 -2.962728e-15 --4.677458e-02 6.348076e-02 -2.971690e-15 --8.168883e-02 2.106509e-02 -2.985695e-15 --7.243981e-02 6.978414e-02 -2.983582e-15 --5.575150e-02 1.463258e-02 -2.969620e-15 --9.804725e-02 4.318575e-02 -2.998338e-15 --2.814852e-02 4.193346e-02 -2.965975e-15 --3.788269e-02 1.076970e-01 -2.951612e-15 --9.139182e-02 -2.369541e-02 -2.954724e-15 --1.326878e-01 6.820486e-03 -3.003092e-15 -5.544867e-03 7.800787e-02 -2.950037e-15 --1.269615e-01 8.628148e-02 -3.020534e-15 --1.138594e-03 -4.865774e-03 -2.966039e-15 --6.814189e-02 1.100976e-01 -2.980866e-15 --6.200608e-02 -2.568407e-02 -2.941898e-15 --1.467265e-01 3.032133e-02 -3.037242e-15 -2.070150e-02 5.275255e-02 -2.963321e-15 --1.477190e-01 7.071403e-02 -3.007755e-15 -2.207003e-02 1.291745e-02 -2.952795e-15 --1.137717e-01 4.857171e-02 -3.010463e-15 --1.330232e-02 3.540614e-02 -2.963560e-15 --1.102847e-01 -2.586812e-02 -2.973882e-15 --1.897760e-02 1.101149e-01 -2.950531e-15 --8.120635e-02 8.281104e-02 -2.986593e-15 --4.763264e-02 1.130481e-03 -2.960574e-15 --1.245475e-01 -1.274527e-02 -2.987428e-15 --4.104224e-03 9.724810e-02 -2.941820e-15 --1.130866e-01 1.024973e-01 -3.001576e-15 --1.515859e-02 -1.889507e-02 -2.963787e-15 --5.172516e-02 7.859742e-02 -2.962404e-15 --7.875818e-02 5.455809e-03 -2.971629e-15 -3.008165e-02 3.532929e-02 -2.956651e-15 --1.553077e-01 4.862201e-02 -3.034320e-15 --1.012130e-01 1.126165e-01 -2.999292e-15 --2.781233e-02 -2.835868e-02 -2.958727e-15 --2.498766e-02 7.053968e-02 -2.953742e-15 --1.033771e-01 1.410327e-02 -2.988210e-15 --1.068503e-01 8.446469e-02 -3.002829e-15 --2.119619e-02 -1.186769e-03 -2.966537e-15 --5.902761e-02 1.175539e-01 -2.965129e-15 --7.132710e-02 -3.326612e-02 -2.949588e-15 --1.488566e-01 1.331244e-02 -3.006702e-15 -2.280651e-02 7.120916e-02 -2.946961e-15 --1.328502e-01 4.619065e-02 -3.023660e-15 -6.368137e-03 3.745364e-02 -2.961053e-15 --9.180213e-02 6.121662e-02 -2.988138e-15 --3.468020e-02 2.342739e-02 -2.964536e-15 --6.604647e-02 5.591127e-02 -2.985701e-15 --1.000810e-01 3.101600e-02 -3.002142e-15 --2.824455e-02 5.380208e-02 -2.959339e-15 --6.072850e-02 2.851443e-02 -2.973969e-15 --7.903266e-02 5.162185e-02 -2.984435e-15 --4.179554e-02 3.598747e-02 -2.970063e-15 --1.591750e-01 6.046307e-02 -3.006716e-15 -3.399197e-02 2.378700e-02 -2.951768e-15 --1.201664e-01 6.621070e-02 -3.014198e-15 --6.859157e-03 1.721793e-02 -2.966143e-15 --8.156447e-02 3.617742e-02 -2.985834e-15 --4.503506e-02 4.903053e-02 -2.966000e-15 -3.818905e-03 3.717141e-02 -3.221573e-15 --7.150650e-03 -1.386851e-02 -3.228486e-15 -1.526958e-02 8.827125e-02 -3.208903e-15 --5.630507e-02 3.101135e-02 -3.238857e-15 -6.557193e-02 4.238832e-02 -3.189273e-15 --4.580561e-02 7.094940e-02 -3.215344e-15 -5.577154e-02 2.186234e-03 -3.242074e-15 -4.851583e-02 7.822809e-02 -3.181677e-15 --3.893487e-02 -3.304209e-03 -3.231309e-15 -3.316689e-02 -2.004336e-02 -3.240158e-15 --2.544578e-02 9.382395e-02 -3.210411e-15 --6.834167e-02 5.858863e-02 -3.226340e-15 --1.904409e-02 1.212305e-02 -3.222897e-15 -2.738155e-02 6.259667e-02 -3.213618e-15 -1.528185e-02 1.755235e-02 -3.233553e-15 --7.280331e-03 5.686278e-02 -3.215219e-15 -4.172681e-02 9.718430e-02 -3.186637e-15 --3.286688e-02 -2.222332e-02 -3.244839e-15 --6.919780e-02 1.219473e-02 -3.244437e-15 -7.953755e-02 6.235778e-02 -3.177782e-15 -2.770927e-02 3.168219e-02 -3.221274e-15 --2.127421e-02 4.237982e-02 -3.216155e-15 --1.525217e-03 -3.472486e-02 -3.235473e-15 -9.074254e-03 1.079915e-01 -3.201094e-15 --8.274978e-02 3.388045e-02 -3.214552e-15 -9.174317e-02 3.981409e-02 -3.181281e-15 --5.124676e-02 9.372611e-02 -3.218190e-15 -5.905006e-02 -2.090521e-02 -3.267330e-15 --6.838638e-02 7.680285e-02 -3.234179e-15 -7.516123e-02 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7.371031e-02 -2.870703e-15 --3.077842e-02 2.644042e-03 -2.485598e-15 --3.755696e-02 2.218053e-02 -3.369056e-15 --3.149327e-02 1.455498e-02 -3.194348e-15 -3.880665e-03 1.189549e-02 -3.278671e-15 --4.475010e-02 2.336761e-02 -3.314767e-15 --7.375543e-02 -2.328648e-02 -2.930111e-15 -6.073753e-03 4.693562e-02 -3.334314e-15 -1.430182e-02 6.968397e-02 -2.914584e-15 -4.382132e-03 2.501812e-02 -3.241983e-15 --6.077014e-02 5.436190e-02 -3.155508e-15 --1.392394e-01 6.966504e-02 -3.377491e-15 --6.581011e-02 6.250520e-02 -3.159934e-15 --1.335473e-01 7.607279e-03 -3.123159e-15 --8.275828e-02 7.830182e-02 -3.122321e-15 --4.233765e-02 6.181946e-02 -3.108308e-15 -1.108362e-02 4.289529e-02 -2.525365e-15 --7.331758e-02 4.126780e-02 -3.124783e-15 -4.899922e-02 4.077161e-02 -3.033557e-15 --5.821782e-02 4.370796e-02 -3.078213e-15 --1.327738e-01 7.154886e-02 -3.004285e-15 --4.558019e-02 1.646994e-02 -3.232772e-15 --6.540701e-03 -9.404205e-03 -3.301852e-15 --5.475948e-02 5.665062e-02 -3.043811e-15 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-3.187223e-15 --1.052015e-01 1.375037e-02 -3.067774e-15 --8.135200e-02 1.845383e-02 -3.165973e-15 --1.414006e-01 -3.942449e-02 -3.375904e-15 --1.258607e-01 6.813421e-02 -3.071007e-15 --4.573792e-02 4.350364e-03 -3.078489e-15 -8.208929e-02 5.618828e-02 -3.077754e-15 --1.225288e-01 8.617819e-03 -3.098951e-15 -9.413525e-03 1.988340e-02 -3.107847e-15 --7.703138e-02 -3.988852e-02 -3.381271e-15 -1.662190e-02 6.434918e-02 -2.937167e-15 - -VECTORS u_11 float --1.538706e-02 3.315249e-01 1.608168e-14 -8.128578e-03 -8.465188e-02 1.582321e-14 --1.538706e-02 3.315249e-01 1.608168e-14 -8.128578e-03 -8.465188e-02 1.582321e-14 -6.766987e-04 1.391181e-01 1.600605e-14 -1.184028e-03 1.771560e-01 1.757191e-14 -1.243584e-03 1.704231e-01 1.740705e-14 -1.052156e-02 -1.873673e-01 1.757202e-14 -1.184028e-03 1.771560e-01 1.757191e-14 -1.243584e-03 1.704231e-01 1.740705e-14 -7.510855e-03 9.129974e-02 1.891583e-14 -1.432535e-02 1.192644e-01 1.893412e-14 --4.631947e-03 1.563586e-01 1.875724e-14 -4.120707e-03 1.332968e-01 1.869447e-14 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 --1.300274e-02 2.896086e-01 1.608188e-14 --1.071560e-02 2.478900e-01 1.607992e-14 --8.487997e-03 2.063235e-01 1.604285e-14 --6.315841e-03 1.648986e-01 1.600910e-14 --4.148132e-03 1.235728e-01 1.599339e-14 --1.964481e-03 8.224774e-02 1.598805e-14 -3.045147e-04 4.081129e-02 1.597966e-14 -2.677644e-03 -7.238513e-04 1.593636e-14 -5.223753e-03 -4.254607e-02 1.590739e-14 --1.474616e-02 3.198229e-01 1.610812e-14 --1.375593e-02 2.994128e-01 1.613058e-14 --1.259960e-02 2.715137e-01 1.609659e-14 --1.144734e-02 2.375636e-01 1.622550e-14 --1.027124e-02 1.988380e-01 1.619179e-14 --8.873388e-03 1.564532e-01 1.616864e-14 --7.166245e-03 1.124806e-01 1.612531e-14 --4.837608e-03 6.872045e-02 1.610927e-14 --1.913652e-03 2.745724e-02 1.608125e-14 -1.160829e-03 -9.253749e-03 1.605181e-14 -3.904709e-03 -3.983544e-02 1.599219e-14 -6.016064e-03 -6.357427e-02 1.594730e-14 -7.470869e-03 -7.883251e-02 1.587205e-14 --1.300274e-02 2.896086e-01 1.608188e-14 --1.071560e-02 2.478900e-01 1.607992e-14 --8.487997e-03 2.063235e-01 1.604285e-14 --6.315841e-03 1.648986e-01 1.600910e-14 --4.148132e-03 1.235728e-01 1.599339e-14 --1.964481e-03 8.224774e-02 1.598805e-14 -3.045147e-04 4.081129e-02 1.597966e-14 -2.677644e-03 -7.238513e-04 1.593636e-14 -5.223753e-03 -4.254607e-02 1.590739e-14 --1.549634e-02 3.338659e-01 1.608959e-14 --1.482119e-02 3.268794e-01 1.601532e-14 --1.341205e-02 3.114891e-01 1.597525e-14 --1.134977e-02 2.884249e-01 1.597341e-14 --8.662235e-03 2.584585e-01 1.595768e-14 --5.490791e-03 2.227273e-01 1.594673e-14 --2.169698e-03 1.823137e-01 1.596702e-14 -2.895901e-03 9.524383e-02 1.598872e-14 -4.356975e-03 5.307344e-02 1.598833e-14 -5.323750e-03 1.452233e-02 1.598513e-14 -6.226156e-03 -1.856712e-02 1.599515e-14 -6.872062e-03 -4.572768e-02 1.598105e-14 -7.472467e-03 -6.711055e-02 1.591761e-14 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2.438242e-02 1.746864e-14 -1.356789e-03 6.546094e-02 1.743590e-14 -1.220929e-03 1.045164e-01 1.736711e-14 -1.327871e-03 1.399209e-01 1.735619e-14 -9.712325e-04 1.761911e-01 1.756641e-14 -1.280153e-03 1.756365e-01 1.755468e-14 -1.687279e-03 1.745022e-01 1.753303e-14 -1.891572e-03 1.729032e-01 1.746690e-14 -1.780549e-03 1.713489e-01 1.743003e-14 -2.638195e-04 1.995165e-01 1.758424e-14 -8.614337e-05 2.165885e-01 1.759889e-14 -5.039794e-05 2.270876e-01 1.757736e-14 -4.061478e-04 2.297768e-01 1.746874e-14 -1.007993e-03 2.242677e-01 1.745236e-14 -1.131790e-03 2.121354e-01 1.741490e-14 -1.373298e-03 1.937928e-01 1.740575e-14 -1.138542e-02 4.550735e-02 1.890668e-14 -1.510943e-02 3.281448e-03 1.890360e-14 -1.844249e-02 -3.323312e-02 1.889493e-14 -2.121721e-02 -6.309962e-02 1.891182e-14 -2.325859e-02 -8.480589e-02 1.890294e-14 -2.427955e-02 -9.784083e-02 1.891638e-14 -2.433144e-02 -1.008254e-01 1.889098e-14 -2.367390e-02 -9.298942e-02 1.888198e-14 -2.253348e-02 -7.459325e-02 1.885360e-14 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3.322345e-02 1.881856e-14 --1.059050e-02 1.113495e-01 1.888746e-14 -6.777451e-03 1.957478e-01 1.892089e-14 -1.213234e-02 2.484155e-01 1.890124e-14 -1.199838e-02 2.682383e-01 1.875446e-14 -1.075759e-02 2.537297e-01 1.847004e-14 -1.210776e-02 2.105936e-01 1.788707e-14 -1.308405e-02 1.438221e-01 1.687672e-14 -1.415211e-02 7.361676e-02 1.523021e-14 -1.006545e-02 2.000272e-02 1.262330e-14 --1.664641e-03 -3.735972e-02 1.123317e-14 --1.585974e-02 2.469946e-01 1.678618e-14 --1.668020e-02 1.784454e-01 1.727891e-14 --1.693587e-02 1.233604e-01 1.770344e-14 --1.493999e-02 8.192695e-02 1.798874e-14 --1.078644e-02 6.121907e-02 1.804797e-14 --4.131197e-03 4.912930e-02 1.809732e-14 -4.130297e-03 4.764804e-02 1.796805e-14 -8.860949e-03 5.525154e-02 1.772789e-14 -1.690959e-02 6.340567e-02 1.717390e-14 -1.834157e-02 6.432260e-02 1.592641e-14 -1.281951e-02 3.691488e-02 1.243077e-14 --7.287363e-03 1.494094e-01 1.610358e-14 --2.145056e-03 6.038850e-02 1.601619e-14 --9.912072e-03 2.135680e-01 1.614251e-14 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1.341992e-01 1.605343e-14 --5.652511e-04 9.485159e-02 1.595925e-14 --6.171713e-03 2.030548e-01 1.595987e-14 -2.717105e-03 2.580299e-02 1.593541e-14 --3.069974e-03 1.680992e-01 1.593406e-14 --4.097478e-03 1.474652e-01 1.597425e-14 --1.054789e-02 2.561891e-01 1.603884e-14 -2.675555e-03 5.381530e-02 1.595430e-14 -1.475743e-04 1.297858e-01 1.595602e-14 -4.144677e-03 -8.438314e-03 1.593802e-14 --5.538530e-03 2.048576e-01 1.593870e-14 --9.755609e-03 2.540029e-01 1.599064e-14 --6.259616e-03 1.763737e-01 1.598473e-14 --3.393924e-03 1.531976e-01 1.596199e-14 -4.422739e-03 2.873412e-03 1.595897e-14 -9.396560e-04 7.004763e-02 1.594367e-14 --2.251500e-03 1.034284e-01 1.598374e-14 --2.147980e-03 1.676678e-01 1.594957e-14 --1.290984e-02 2.914551e-01 1.604324e-14 --8.495789e-03 2.158306e-01 1.604362e-14 -2.089337e-03 9.006619e-02 1.597994e-14 --4.724052e-03 2.039226e-01 1.594437e-14 --2.546111e-03 1.247637e-01 1.597463e-14 --1.223164e-02 2.854326e-01 1.602817e-14 --1.065770e-03 1.270826e-01 1.594123e-14 --8.628458e-03 2.274893e-01 1.602134e-14 -9.250437e-04 9.335704e-02 1.596078e-14 -1.025549e-03 4.428775e-02 1.593847e-14 --4.353613e-03 1.406269e-01 1.598233e-14 --4.194912e-03 1.750420e-01 1.595115e-14 --5.723304e-03 1.819708e-01 1.596918e-14 --8.355980e-03 2.449015e-01 1.595451e-14 -3.958183e-03 3.699242e-02 1.596127e-14 -5.406506e-03 -4.118429e-02 1.590457e-14 -1.663458e-03 4.370309e-02 1.594512e-14 -3.563818e-03 -1.111557e-02 1.592137e-14 -2.746415e-03 3.785245e-02 1.595091e-14 --1.097738e-02 2.584667e-01 1.605644e-14 --7.290451e-03 2.365295e-01 1.595412e-14 -6.808394e-03 -2.718102e-02 1.755283e-14 -3.131202e-03 6.165006e-02 1.748558e-14 -5.782486e-03 5.095198e-02 1.757541e-14 -5.494688e-03 -4.091104e-02 1.752671e-14 -9.829921e-03 -4.522149e-02 1.761124e-14 -8.858204e-03 -1.023815e-01 1.757807e-14 -3.667061e-03 8.637011e-02 1.750279e-14 -3.810890e-03 -4.901694e-03 1.750481e-14 -7.552698e-03 3.162535e-02 1.759978e-14 -7.942475e-03 -1.079196e-01 1.754108e-14 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1.759236e-14 -9.674108e-03 -9.185769e-03 1.762806e-14 -8.183156e-03 3.113525e-02 1.761238e-14 -7.869345e-03 -1.285517e-01 1.754414e-14 -2.861485e-03 1.269351e-01 1.755835e-14 -2.841943e-03 9.303263e-02 1.747222e-14 -8.732464e-03 -1.473371e-01 1.753491e-14 -9.496156e-03 -7.225523e-02 1.759803e-14 -2.580006e-03 1.318835e-01 1.749790e-14 -2.466727e-03 7.614136e-02 1.744928e-14 -5.140463e-03 -8.180075e-02 1.750946e-14 -3.331340e-03 3.066684e-02 1.748911e-14 -4.053009e-03 9.364756e-02 1.755229e-14 -7.954662e-03 -9.182825e-02 1.755365e-14 -6.748837e-03 -7.814055e-02 1.753830e-14 -7.080406e-03 3.054163e-02 1.758889e-14 -5.183748e-03 7.474419e-02 1.757464e-14 -6.124261e-03 -3.278841e-02 1.754018e-14 -3.069122e-03 1.274202e-01 1.757172e-14 -2.478757e-03 1.261726e-01 1.746955e-14 -2.030435e-03 1.249190e-01 1.743315e-14 -7.814569e-03 -6.316145e-02 1.756644e-14 -3.790987e-03 9.111611e-02 1.752587e-14 -1.073390e-02 -1.429844e-01 1.762231e-14 -8.840517e-03 -3.786385e-02 1.759063e-14 -3.317983e-03 8.838831e-02 1.749502e-14 -6.477937e-03 3.365171e-02 1.757723e-14 -5.708234e-03 9.604319e-03 1.754377e-14 -9.977973e-03 -1.555597e-01 1.757834e-14 -2.474848e-03 1.360504e-01 1.754822e-14 -3.925620e-03 2.190804e-02 1.749905e-14 -1.111019e-02 -7.335299e-02 1.763966e-14 -4.826459e-03 -4.636801e-02 1.751509e-14 -7.806829e-03 -3.093916e-02 1.757369e-14 -1.868505e-03 6.931256e-02 1.743535e-14 -9.128233e-03 -8.924631e-02 1.759392e-14 -6.763217e-03 6.328507e-02 1.761489e-14 -1.072815e-02 -1.215837e-01 1.762925e-14 -8.917376e-03 -8.976008e-03 1.760732e-14 -9.654505e-04 2.039058e-01 1.750777e-14 -1.246900e-03 1.983678e-01 1.748036e-14 -1.512516e-03 1.925026e-01 1.741771e-14 -4.615757e-04 1.970777e-01 1.757038e-14 -1.126769e-02 1.101933e-01 1.894860e-14 -1.252822e-02 1.245151e-01 1.895222e-14 -1.005869e-02 8.862876e-02 1.893285e-14 -1.792057e-02 -5.306493e-04 1.890008e-14 -5.290146e-03 2.175432e-01 1.900498e-14 -1.622042e-02 4.334824e-02 1.890567e-14 -5.510573e-03 1.744518e-01 1.901489e-14 -1.415679e-02 3.689246e-02 1.891340e-14 -9.210110e-03 1.789915e-01 1.900121e-14 -1.576714e-02 6.125957e-02 1.890972e-14 -5.557514e-03 1.532148e-01 1.897235e-14 -1.990991e-02 -2.657201e-02 1.888969e-14 -2.451580e-03 2.416665e-01 1.905698e-14 -1.023401e-02 1.432474e-01 1.895965e-14 -1.262498e-02 7.388469e-02 1.892712e-14 -1.312302e-02 9.760838e-02 1.893938e-14 -9.229230e-03 1.198442e-01 1.896680e-14 -1.524367e-02 4.673466e-02 1.892128e-14 -7.359027e-03 1.733861e-01 1.899523e-14 -1.092398e-02 1.653557e-01 1.896626e-14 -1.219843e-02 4.988042e-02 1.892330e-14 -1.886519e-02 -2.223047e-02 1.890830e-14 -4.972261e-03 2.389557e-01 1.900428e-14 -1.872935e-02 -1.232323e-03 1.888633e-14 -2.315404e-03 2.131234e-01 1.901741e-14 -1.388361e-02 1.112020e-01 1.893927e-14 -7.992192e-03 1.026390e-01 1.894381e-14 -2.094953e-02 -4.595725e-02 1.890648e-14 -2.409720e-03 2.622911e-01 1.902117e-14 -2.084873e-02 -4.288354e-02 1.887677e-14 -8.979342e-04 2.572906e-01 1.908779e-14 -1.712804e-02 1.821343e-02 1.890513e-14 -5.484707e-03 1.987919e-01 1.902812e-14 -1.487733e-02 1.458374e-02 1.890259e-14 -8.960092e-03 1.992523e-01 1.897122e-14 -1.434175e-02 8.344326e-02 1.892412e-14 -7.615493e-03 1.323050e-01 1.897496e-14 -1.730766e-02 -9.732490e-03 1.890955e-14 -6.785469e-03 2.241503e-01 1.900044e-14 -1.757859e-02 2.721083e-02 1.889565e-14 -2.949853e-03 1.862540e-01 1.898958e-14 -1.129295e-02 1.365202e-01 1.896680e-14 -1.164976e-02 7.709841e-02 1.894021e-14 -8.550545e-04 2.759299e-01 1.906652e-14 -2.189406e-02 -6.015247e-02 1.889478e-14 -1.671200e-02 5.056420e-02 1.890667e-14 -3.858832e-03 1.625545e-01 1.894724e-14 -8.154348e-03 1.833837e-01 1.899355e-14 -1.514495e-02 3.317003e-02 1.891148e-14 -1.681914e-02 3.619016e-02 1.890546e-14 -4.550026e-03 1.786066e-01 1.900300e-14 -1.328410e-02 1.285020e-01 1.894264e-14 -8.953167e-03 8.451388e-02 1.892762e-14 -2.110896e-02 -5.192340e-02 1.890702e-14 -2.661520e-03 2.687881e-01 1.901816e-14 -1.931160e-02 -1.794218e-02 1.889507e-14 -3.447963e-03 2.340935e-01 1.904415e-14 -1.492590e-02 6.077597e-02 1.892467e-14 -7.251989e-03 1.587930e-01 1.900646e-14 -1.203152e-02 1.072513e-01 1.893603e-14 -1.519181e-02 4.141124e-02 1.891680e-14 -7.860321e-03 1.750729e-01 1.900968e-14 -1.042092e-02 1.129254e-01 1.896132e-14 -1.330898e-02 8.293302e-02 1.893526e-14 -8.601471e-03 1.480644e-01 1.899461e-14 -2.218161e-02 -6.599089e-02 1.888345e-14 -5.578871e-05 2.805890e-01 1.908929e-14 -1.798547e-02 8.597278e-03 1.889630e-14 -3.984432e-03 2.070522e-01 1.903370e-14 -1.313210e-02 7.615111e-02 1.893348e-14 -9.494861e-03 1.442261e-01 1.898086e-14 --3.765206e-04 1.446529e-01 1.877956e-14 --3.622549e-03 1.333574e-01 1.880999e-14 -3.210097e-03 1.577765e-01 1.873217e-14 --5.541593e-03 3.809097e-02 1.873842e-14 -5.055688e-03 2.530893e-01 1.883491e-14 --3.433674e-03 5.257843e-02 1.871959e-14 -2.510475e-03 2.417121e-01 1.882533e-14 -5.308011e-03 2.172837e-01 1.881289e-14 --5.205202e-03 7.488430e-02 1.878133e-14 --6.902123e-04 2.050798e-01 1.880812e-14 --3.391007e-04 8.607076e-02 1.872585e-14 --6.167942e-03 1.354953e-02 1.870938e-14 --3.283611e-03 1.079827e-01 1.877580e-14 -2.852194e-03 1.824338e-01 1.878525e-14 --3.890152e-04 1.679445e-01 1.879440e-14 --3.845156e-04 1.222844e-01 1.876943e-14 -5.757313e-03 2.027390e-01 1.877122e-14 --5.569303e-03 8.892255e-02 1.875393e-14 --6.834192e-03 1.736538e-02 1.874319e-14 -6.994649e-03 2.747552e-01 1.887987e-14 -1.335083e-03 1.876736e-01 1.880421e-14 --2.237427e-03 9.942670e-02 1.876819e-14 --4.208693e-03 1.463002e-01 1.876643e-14 -3.706106e-03 1.446058e-01 1.872790e-14 --7.901055e-03 -1.011117e-02 1.871885e-14 -7.327249e-03 3.004468e-01 1.886310e-14 --3.127648e-03 4.076154e-02 1.870319e-14 -1.874232e-03 2.506180e-01 1.881110e-14 --5.686147e-03 1.200857e-02 1.871055e-14 -4.188175e-03 2.773931e-01 1.882008e-14 --4.205150e-03 5.472422e-02 1.874221e-14 -3.024042e-03 2.288245e-01 1.881812e-14 --6.704472e-04 1.874411e-01 1.878321e-14 --1.985400e-04 1.040272e-01 1.873752e-14 -7.263452e-03 2.441862e-01 1.882076e-14 --6.610462e-03 4.945905e-02 1.874662e-14 -7.218209e-03 2.574873e-01 1.885222e-14 --6.683234e-03 3.441173e-02 1.873727e-14 --4.769531e-03 6.914865e-02 1.877634e-14 -4.609590e-03 2.226844e-01 1.880964e-14 --4.057039e-03 1.025847e-01 1.877888e-14 -3.824410e-03 1.882278e-01 1.879924e-14 --2.534055e-03 1.846990e-01 1.877173e-14 -1.571373e-03 1.056108e-01 1.869769e-14 --8.188735e-03 -1.953093e-02 1.870214e-14 -7.749384e-03 3.185576e-01 1.885665e-14 --1.968403e-03 1.503231e-01 1.879799e-14 -1.331899e-03 1.406487e-01 1.874452e-14 --1.955448e-03 8.499121e-02 1.872185e-14 -1.029868e-03 2.060805e-01 1.882370e-14 --7.953440e-03 -2.015566e-02 1.870106e-14 --6.324553e-04 2.178957e-01 1.881232e-14 -6.277792e-03 3.010970e-01 1.883603e-14 --5.003693e-04 7.316086e-02 1.870213e-14 -1.321559e-03 1.643456e-01 1.878652e-14 --1.949599e-03 1.257476e-01 1.877926e-14 --5.279406e-03 1.161938e-01 1.877034e-14 -5.226395e-03 1.745297e-01 1.873024e-14 --8.351716e-03 -1.767145e-02 1.873982e-14 -8.131992e-03 3.078723e-01 1.884277e-14 --2.050708e-03 1.759551e-01 1.878187e-14 -1.258562e-03 1.153368e-01 1.870813e-14 --6.208172e-03 1.774797e-02 1.871861e-14 --3.667321e-03 8.252574e-02 1.877510e-14 -3.098684e-03 2.072578e-01 1.878819e-14 --4.617146e-03 3.957174e-02 1.873036e-14 -4.666522e-03 2.667886e-01 1.883101e-14 --1.700442e-03 7.131333e-02 1.871163e-14 -5.834044e-04 2.196212e-01 1.879889e-14 -8.242640e-03 4.547339e-02 1.743124e-14 -9.959675e-03 1.333886e-01 1.838377e-14 -1.551718e-02 4.191762e-02 1.799853e-14 --2.023296e-02 1.157297e-01 1.694591e-14 -6.929733e-03 3.248377e-02 1.704047e-14 -2.066434e-02 7.605949e-02 1.790920e-14 -4.233572e-03 1.415920e-01 1.779855e-14 --5.872207e-03 1.479813e-01 1.738098e-14 --4.481859e-03 1.732095e-01 1.876226e-14 -9.060945e-03 5.203756e-02 1.775278e-14 -3.163122e-02 3.847947e-02 1.831307e-14 -7.177895e-03 1.473224e-01 1.753071e-14 --5.372486e-03 1.564207e-01 1.812371e-14 -3.051372e-03 1.076195e-01 1.685627e-14 -3.035901e-03 1.677393e-01 1.836191e-14 -3.888389e-02 5.556034e-02 1.865918e-14 -2.133512e-02 1.093531e-01 1.549879e-14 --2.161429e-02 1.456809e-02 1.747844e-14 --2.018517e-02 6.842384e-02 1.831879e-14 -1.423873e-02 -1.924313e-02 1.753585e-14 --6.690835e-03 -3.591096e-02 1.729531e-14 -1.628298e-02 1.605804e-01 1.833961e-14 -8.407881e-03 1.113158e-01 1.688823e-14 -2.654733e-02 3.004553e-02 1.805098e-14 -1.058477e-02 1.323815e-01 1.751653e-14 --2.942556e-03 9.672153e-02 1.732317e-14 -2.927263e-02 6.691662e-02 1.872771e-14 --6.441579e-03 2.049966e-01 1.654688e-14 -8.835081e-03 8.875015e-02 1.868035e-14 --2.684574e-03 1.282450e-01 1.790139e-14 -2.002451e-02 -8.345749e-02 1.770590e-14 --3.070597e-03 2.489896e-01 1.871018e-14 -6.905268e-03 8.134064e-02 1.634285e-14 -1.421104e-02 -1.014130e-01 1.740632e-14 --1.916918e-02 2.171849e-01 1.665804e-14 -2.334777e-02 1.495902e-01 1.699027e-14 --9.281308e-03 1.813699e-01 1.726881e-14 --9.772037e-03 1.831596e-01 1.803448e-14 -2.055320e-02 1.218329e-01 1.706290e-14 --1.447627e-02 7.851258e-03 1.769019e-14 --1.949336e-02 9.167752e-03 1.729483e-14 --4.381355e-03 7.431382e-02 1.678388e-14 -1.474501e-02 -2.636641e-03 1.770831e-14 -9.543495e-03 1.596685e-01 1.658084e-14 -4.589478e-03 1.282873e-01 1.712653e-14 -1.417497e-02 -1.274878e-02 1.720932e-14 -1.004009e-02 1.605059e-01 1.807343e-14 --1.038825e-02 1.329488e-01 1.812343e-14 -2.809209e-02 1.444545e-01 1.843270e-14 --2.543072e-02 1.376284e-01 1.720904e-14 -3.110343e-02 -1.757028e-02 1.841941e-14 -3.071950e-02 -1.840129e-02 1.800636e-14 -5.583026e-03 1.177195e-01 1.736257e-14 --4.219152e-03 9.713089e-02 1.764101e-14 --2.911056e-02 8.946085e-02 1.868740e-14 -1.628210e-03 1.032260e-01 1.821203e-14 -2.967954e-02 1.248624e-02 1.826192e-14 -1.939700e-02 1.354890e-01 1.865261e-14 -1.921498e-02 1.743464e-01 1.710827e-14 --1.504126e-02 9.865360e-02 1.828625e-14 --7.265859e-03 1.004046e-01 1.812164e-14 -1.462420e-02 9.313285e-02 1.826361e-14 --2.071759e-03 6.139555e-02 1.795842e-14 --1.326855e-02 1.151965e-02 1.558125e-14 -6.689810e-03 1.760474e-02 1.536081e-14 -1.211632e-02 1.925630e-01 1.793090e-14 --5.359122e-03 2.015172e-01 1.692197e-14 -2.203842e-02 1.085388e-01 1.667367e-14 --1.574017e-03 2.513178e-01 1.874952e-14 --4.033629e-03 2.411038e-01 1.847304e-14 --8.396369e-05 1.712359e-01 1.659840e-14 --9.572927e-04 6.431148e-02 1.308386e-14 --7.393482e-03 2.434807e-01 1.679227e-14 -1.311813e-02 1.154904e-01 1.800203e-14 -1.559770e-03 1.069409e-01 1.724457e-14 --6.477094e-03 1.515949e-01 1.705451e-14 --2.593417e-04 1.191067e-01 1.748177e-14 --2.106757e-02 1.246891e-01 1.884064e-14 --3.714312e-03 1.390157e-01 1.696150e-14 --2.632970e-03 1.853263e-01 1.741457e-14 --4.154494e-03 1.235094e-01 1.787987e-14 --2.718515e-03 7.862305e-02 1.873501e-14 -6.987906e-03 -7.656883e-02 1.732906e-14 -6.628510e-04 5.050091e-02 1.639884e-14 -2.022047e-02 2.168937e-02 1.830513e-14 -6.157061e-03 -2.652866e-02 1.314066e-14 -3.311870e-02 1.751951e-01 1.877603e-14 -2.564499e-02 -3.622692e-02 1.786822e-14 -8.487058e-03 1.439032e-01 1.716079e-14 -2.221759e-02 8.678068e-02 1.835320e-14 -3.331679e-03 1.410272e-01 1.765019e-14 -8.476843e-04 8.303536e-02 1.856317e-14 --1.766993e-03 1.750750e-01 1.750983e-14 -1.664540e-02 5.290912e-02 1.229402e-14 -1.776817e-02 1.867787e-01 1.886384e-14 --2.133917e-02 5.843192e-02 1.766738e-14 -3.391702e-03 1.569737e-01 1.855919e-14 -7.276766e-03 2.883575e-01 1.887815e-14 --7.272025e-03 1.729486e-01 1.678243e-14 --3.695026e-03 -3.400392e-03 1.778762e-14 -1.325040e-02 2.552501e-02 1.807671e-14 -3.903260e-03 8.872459e-02 1.673101e-14 --5.766717e-04 1.276813e-01 1.735425e-14 -1.057289e-02 2.320757e-01 1.810696e-14 --1.248739e-02 -2.375597e-02 1.333510e-14 --1.656249e-02 1.419717e-01 1.796844e-14 --6.384811e-03 8.038086e-02 1.688803e-14 -2.001610e-03 1.170631e-01 1.812109e-14 -1.322783e-03 1.768226e-01 1.764176e-14 --4.097569e-03 5.484071e-02 1.663809e-14 --1.198557e-02 7.130833e-02 1.802019e-14 -6.905102e-03 2.675203e-01 1.856629e-14 --1.695076e-02 3.335227e-02 1.837046e-14 --9.364706e-03 3.864070e-02 1.880241e-14 -2.529713e-02 -4.649525e-02 1.788158e-14 -4.367191e-03 6.535452e-02 1.878269e-14 -2.607093e-02 4.531570e-02 1.862062e-14 -1.177907e-02 9.832551e-02 1.851829e-14 -8.544284e-03 1.075700e-01 1.831262e-14 -1.956516e-02 9.164572e-02 1.504722e-14 -1.364022e-02 1.163099e-01 1.689800e-14 -1.330563e-02 6.051382e-02 1.774116e-14 -3.442993e-02 -1.568457e-02 1.868273e-14 -1.349530e-02 6.918889e-02 1.834396e-14 -4.208838e-03 1.743251e-01 1.844879e-14 --3.570334e-03 9.882307e-02 1.691338e-14 -1.759874e-02 2.084742e-01 1.851398e-14 -1.740982e-02 -1.383986e-02 1.828014e-14 -3.318627e-02 6.072624e-02 1.834607e-14 -1.682861e-02 -1.033982e-01 1.760520e-14 --5.151794e-04 2.586482e-01 1.857652e-14 -4.880043e-03 1.321084e-01 1.727910e-14 -3.854123e-03 9.518027e-02 1.849751e-14 -6.726351e-03 1.770400e-01 1.650901e-14 -2.149817e-02 7.688254e-02 1.503510e-14 -7.098774e-03 2.958331e-02 1.762518e-14 --4.494099e-03 1.069415e-01 1.821060e-14 --2.153668e-02 1.206116e-01 1.859025e-14 --6.743002e-03 2.225868e-01 1.844148e-14 -4.896570e-03 6.447800e-02 1.781392e-14 -8.804228e-04 1.260851e-01 1.798181e-14 --9.327015e-03 1.600003e-01 1.798650e-14 --6.323754e-04 1.188040e-01 1.770206e-14 -1.380584e-02 8.770507e-02 1.798673e-14 --2.411357e-02 4.436369e-02 1.727020e-14 -1.763747e-03 5.449149e-03 1.776966e-14 -1.264070e-02 6.186031e-02 1.710706e-14 -1.963584e-02 1.608550e-01 1.707472e-14 -1.054925e-02 1.557326e-01 1.806657e-14 --1.929878e-02 2.561406e-01 1.658575e-14 --1.607932e-03 8.687915e-02 1.816761e-14 -1.553328e-02 1.381540e-02 1.886432e-14 -1.436915e-02 4.200900e-02 1.840003e-14 --1.036452e-02 -4.144435e-02 1.753926e-14 --1.032652e-03 8.348095e-02 1.847381e-14 --2.234238e-02 2.026169e-01 1.690801e-14 -2.106794e-02 1.078964e-01 1.802831e-14 -2.713554e-02 3.004899e-02 1.855007e-14 --1.313352e-03 1.514015e-01 1.739835e-14 -5.099592e-03 1.492551e-02 1.770621e-14 -6.091943e-03 2.853634e-01 1.878446e-14 --1.538706e-02 3.315249e-01 1.608168e-14 -8.128578e-03 -8.465188e-02 1.582321e-14 --1.538706e-02 3.315249e-01 1.608168e-14 -8.128578e-03 -8.465188e-02 1.582321e-14 -6.766987e-04 1.391181e-01 1.600605e-14 -1.184028e-03 1.771560e-01 1.757191e-14 -1.243584e-03 1.704231e-01 1.740705e-14 -1.052156e-02 -1.873673e-01 1.757202e-14 -1.184028e-03 1.771560e-01 1.757191e-14 -1.243584e-03 1.704231e-01 1.740705e-14 -7.510855e-03 9.129974e-02 1.891583e-14 -1.432535e-02 1.192644e-01 1.893412e-14 --4.631947e-03 1.563586e-01 1.875724e-14 -4.120707e-03 1.332968e-01 1.869447e-14 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 --1.300274e-02 2.896086e-01 1.608188e-14 --1.071560e-02 2.478900e-01 1.607992e-14 --8.487997e-03 2.063235e-01 1.604285e-14 --6.315841e-03 1.648986e-01 1.600910e-14 --4.148132e-03 1.235728e-01 1.599339e-14 --1.964481e-03 8.224774e-02 1.598805e-14 -3.045147e-04 4.081129e-02 1.597966e-14 -2.677644e-03 -7.238513e-04 1.593636e-14 -5.223753e-03 -4.254607e-02 1.590739e-14 --1.474616e-02 3.198229e-01 1.610812e-14 --1.375593e-02 2.994128e-01 1.613058e-14 --1.259960e-02 2.715137e-01 1.609659e-14 --1.144734e-02 2.375636e-01 1.622550e-14 --1.027124e-02 1.988380e-01 1.619179e-14 --8.873388e-03 1.564532e-01 1.616864e-14 --7.166245e-03 1.124806e-01 1.612531e-14 --4.837608e-03 6.872045e-02 1.610927e-14 --1.913652e-03 2.745724e-02 1.608125e-14 -1.160829e-03 -9.253749e-03 1.605181e-14 -3.904709e-03 -3.983544e-02 1.599219e-14 -6.016064e-03 -6.357427e-02 1.594730e-14 -7.470869e-03 -7.883251e-02 1.587205e-14 --1.300274e-02 2.896086e-01 1.608188e-14 --1.071560e-02 2.478900e-01 1.607992e-14 --8.487997e-03 2.063235e-01 1.604285e-14 --6.315841e-03 1.648986e-01 1.600910e-14 --4.148132e-03 1.235728e-01 1.599339e-14 --1.964481e-03 8.224774e-02 1.598805e-14 -3.045147e-04 4.081129e-02 1.597966e-14 -2.677644e-03 -7.238513e-04 1.593636e-14 -5.223753e-03 -4.254607e-02 1.590739e-14 --1.549634e-02 3.338659e-01 1.608959e-14 --1.482119e-02 3.268794e-01 1.601532e-14 --1.341205e-02 3.114891e-01 1.597525e-14 --1.134977e-02 2.884249e-01 1.597341e-14 --8.662235e-03 2.584585e-01 1.595768e-14 --5.490791e-03 2.227273e-01 1.594673e-14 --2.169698e-03 1.823137e-01 1.596702e-14 -2.895901e-03 9.524383e-02 1.598872e-14 -4.356975e-03 5.307344e-02 1.598833e-14 -5.323750e-03 1.452233e-02 1.598513e-14 -6.226156e-03 -1.856712e-02 1.599515e-14 -6.872062e-03 -4.572768e-02 1.598105e-14 -7.472467e-03 -6.711055e-02 1.591761e-14 -8.060500e-03 -8.058086e-02 1.585206e-14 -9.712325e-04 1.761911e-01 1.756641e-14 -1.280153e-03 1.756365e-01 1.755468e-14 -1.687279e-03 1.745022e-01 1.753303e-14 -1.891572e-03 1.729032e-01 1.746690e-14 -1.780549e-03 1.713489e-01 1.743003e-14 -2.556735e-03 1.477541e-01 1.760586e-14 -4.445268e-03 1.135895e-01 1.761868e-14 -6.598237e-03 7.550568e-02 1.761836e-14 -8.681578e-03 3.463978e-02 1.763162e-14 -1.038993e-02 -7.472382e-03 1.764284e-14 -1.139718e-02 -4.872299e-02 1.764959e-14 -1.181988e-02 -8.701301e-02 1.764223e-14 -1.167074e-02 -1.206206e-01 1.764082e-14 -1.144206e-02 -1.482367e-01 1.762673e-14 -1.109599e-02 -1.691847e-01 1.762427e-14 -1.085737e-02 -1.824031e-01 1.759195e-14 -9.985113e-03 -1.838310e-01 1.754535e-14 -9.183777e-03 -1.720081e-01 1.753909e-14 -7.958716e-03 -1.526795e-01 1.752392e-14 -6.596000e-03 -1.266774e-01 1.749592e-14 -4.973695e-03 -9.457225e-02 1.752215e-14 -3.494045e-03 -5.763287e-02 1.750901e-14 -2.317311e-03 -1.741621e-02 1.749055e-14 -1.778110e-03 2.438242e-02 1.746864e-14 -1.356789e-03 6.546094e-02 1.743590e-14 -1.220929e-03 1.045164e-01 1.736711e-14 -1.327871e-03 1.399209e-01 1.735619e-14 -9.712325e-04 1.761911e-01 1.756641e-14 -1.280153e-03 1.756365e-01 1.755468e-14 -1.687279e-03 1.745022e-01 1.753303e-14 -1.891572e-03 1.729032e-01 1.746690e-14 -1.780549e-03 1.713489e-01 1.743003e-14 -2.638195e-04 1.995165e-01 1.758424e-14 -8.614337e-05 2.165885e-01 1.759889e-14 -5.039794e-05 2.270876e-01 1.757736e-14 -4.061478e-04 2.297768e-01 1.746874e-14 -1.007993e-03 2.242677e-01 1.745236e-14 -1.131790e-03 2.121354e-01 1.741490e-14 -1.373298e-03 1.937928e-01 1.740575e-14 -1.138542e-02 4.550735e-02 1.890668e-14 -1.510943e-02 3.281448e-03 1.890360e-14 -1.844249e-02 -3.323312e-02 1.889493e-14 -2.121721e-02 -6.309962e-02 1.891182e-14 -2.325859e-02 -8.480589e-02 1.890294e-14 -2.427955e-02 -9.784083e-02 1.891638e-14 -2.433144e-02 -1.008254e-01 1.889098e-14 -2.367390e-02 -9.298942e-02 1.888198e-14 -2.253348e-02 -7.459325e-02 1.885360e-14 -2.114592e-02 -4.719494e-02 1.884522e-14 -1.959025e-02 -1.249814e-02 1.887224e-14 -1.792830e-02 2.802197e-02 1.889173e-14 -1.624130e-02 7.256706e-02 1.891422e-14 -1.185362e-02 1.657998e-01 1.893248e-14 -8.953806e-03 2.094748e-01 1.894756e-14 -5.794723e-03 2.481007e-01 1.901614e-14 -2.944416e-03 2.793796e-01 1.904381e-14 -6.869664e-04 3.020383e-01 1.903344e-14 --7.381465e-04 3.147137e-01 1.907840e-14 --1.439251e-03 3.165484e-01 1.910208e-14 --1.620073e-03 3.071951e-01 1.909958e-14 --1.324885e-03 2.880960e-01 1.906156e-14 --6.991434e-04 2.606173e-01 1.901825e-14 -3.434923e-04 2.256797e-01 1.896370e-14 -1.885756e-03 1.842515e-01 1.895244e-14 -4.408650e-03 1.384540e-01 1.891035e-14 --6.064479e-03 1.095233e-01 1.873548e-14 --6.960842e-03 6.517743e-02 1.873007e-14 --7.664501e-03 2.532248e-02 1.873023e-14 --8.330388e-03 -8.472744e-03 1.873452e-14 --9.229643e-03 -3.587964e-02 1.871415e-14 --1.030949e-02 -5.499278e-02 1.871351e-14 --1.080533e-02 -6.301504e-02 1.872714e-14 --1.060059e-02 -5.997998e-02 1.870924e-14 --9.405746e-03 -4.625945e-02 1.870028e-14 --7.385666e-03 -2.387518e-02 1.868907e-14 --4.791127e-03 6.585727e-03 1.868413e-14 --1.763754e-03 4.410131e-02 1.868304e-14 -1.341879e-03 8.711268e-02 1.867656e-14 -6.197936e-03 1.802896e-01 1.871726e-14 -7.576819e-03 2.250381e-01 1.878141e-14 -8.540413e-03 2.653599e-01 1.885205e-14 -9.230045e-03 2.994444e-01 1.884687e-14 -9.726917e-03 3.261484e-01 1.881475e-14 -1.003624e-02 3.442962e-01 1.880318e-14 -1.008523e-02 3.528782e-01 1.882245e-14 -9.641456e-03 3.507468e-01 1.883736e-14 -8.401499e-03 3.381167e-01 1.880240e-14 -6.332729e-03 3.156743e-01 1.880400e-14 -3.563620e-03 2.847286e-01 1.878713e-14 -4.334604e-04 2.466452e-01 1.877528e-14 --2.419475e-03 2.031294e-01 1.878087e-14 -1.006545e-02 2.000272e-02 1.262330e-14 -1.415211e-02 7.361676e-02 1.523021e-14 -1.308405e-02 1.438221e-01 1.687672e-14 -1.210776e-02 2.105936e-01 1.788707e-14 -1.075759e-02 2.537297e-01 1.847004e-14 -1.199838e-02 2.682383e-01 1.875446e-14 -1.213234e-02 2.484155e-01 1.890124e-14 -6.777451e-03 1.957478e-01 1.892089e-14 --1.059050e-02 1.113495e-01 1.888746e-14 --2.223667e-02 3.322345e-02 1.881856e-14 --1.938082e-02 -5.170603e-03 1.860950e-14 --6.119012e-03 -5.693989e-03 1.841198e-14 -6.880468e-03 4.859307e-02 1.800557e-14 --8.353711e-03 3.613437e-02 1.468426e-14 -1.281951e-02 3.691488e-02 1.243077e-14 -1.834157e-02 6.432260e-02 1.592641e-14 -1.690959e-02 6.340567e-02 1.717390e-14 -8.860949e-03 5.525154e-02 1.772789e-14 -4.130297e-03 4.764804e-02 1.796805e-14 --4.131197e-03 4.912930e-02 1.809732e-14 --1.078644e-02 6.121907e-02 1.804797e-14 --1.493999e-02 8.192695e-02 1.798874e-14 --1.693587e-02 1.233604e-01 1.770344e-14 --1.668020e-02 1.784454e-01 1.727891e-14 --1.585974e-02 2.469946e-01 1.678618e-14 --1.664641e-03 -3.735972e-02 1.123317e-14 --8.353711e-03 3.613437e-02 1.468426e-14 -6.880468e-03 4.859307e-02 1.800557e-14 --6.119012e-03 -5.693989e-03 1.841198e-14 --1.938082e-02 -5.170603e-03 1.860950e-14 --2.223667e-02 3.322345e-02 1.881856e-14 --1.059050e-02 1.113495e-01 1.888746e-14 -6.777451e-03 1.957478e-01 1.892089e-14 -1.213234e-02 2.484155e-01 1.890124e-14 -1.199838e-02 2.682383e-01 1.875446e-14 -1.075759e-02 2.537297e-01 1.847004e-14 -1.210776e-02 2.105936e-01 1.788707e-14 -1.308405e-02 1.438221e-01 1.687672e-14 -1.415211e-02 7.361676e-02 1.523021e-14 -1.006545e-02 2.000272e-02 1.262330e-14 --1.664641e-03 -3.735972e-02 1.123317e-14 --1.585974e-02 2.469946e-01 1.678618e-14 --1.668020e-02 1.784454e-01 1.727891e-14 --1.693587e-02 1.233604e-01 1.770344e-14 --1.493999e-02 8.192695e-02 1.798874e-14 --1.078644e-02 6.121907e-02 1.804797e-14 --4.131197e-03 4.912930e-02 1.809732e-14 -4.130297e-03 4.764804e-02 1.796805e-14 -8.860949e-03 5.525154e-02 1.772789e-14 -1.690959e-02 6.340567e-02 1.717390e-14 -1.834157e-02 6.432260e-02 1.592641e-14 -1.281951e-02 3.691488e-02 1.243077e-14 --7.287363e-03 1.494094e-01 1.610358e-14 --2.145056e-03 6.038850e-02 1.601619e-14 --9.912072e-03 2.135680e-01 1.614251e-14 -1.980247e-03 -1.242141e-03 1.597232e-14 --4.789889e-03 9.418511e-02 1.605547e-14 --4.195945e-03 9.884010e-02 1.603782e-14 --9.252268e-03 1.898117e-01 1.616155e-14 --1.165850e-02 2.586977e-01 1.609921e-14 --6.340339e-03 1.509188e-01 1.604801e-14 --7.278874e-03 1.302450e-01 1.611907e-14 -1.398452e-03 -1.962694e-04 1.599013e-14 --2.012212e-03 7.081693e-02 1.600711e-14 --1.761839e-03 4.011109e-02 1.604524e-14 -4.594149e-03 -3.748265e-02 1.591916e-14 --9.125927e-03 2.054623e-01 1.608700e-14 --1.044893e-02 2.160996e-01 1.620527e-14 -1.603145e-04 3.341791e-02 1.598073e-14 --1.132250e-02 2.454484e-01 1.612743e-14 -3.386137e-03 -2.501234e-02 1.596241e-14 --8.892958e-03 1.696878e-01 1.615512e-14 --5.300894e-03 8.819973e-02 1.608814e-14 --1.316685e-02 2.905233e-01 1.609023e-14 -1.250111e-04 2.492533e-02 1.599173e-14 --9.891843e-03 1.949895e-01 1.619450e-14 --4.177667e-03 1.105715e-01 1.602284e-14 -2.412314e-03 -3.457073e-03 1.595661e-14 --3.067400e-03 5.185684e-02 1.607124e-14 --5.996389e-03 1.341992e-01 1.605343e-14 --5.652511e-04 9.485159e-02 1.595925e-14 --6.171713e-03 2.030548e-01 1.595987e-14 -2.717105e-03 2.580299e-02 1.593541e-14 --3.069974e-03 1.680992e-01 1.593406e-14 --4.097478e-03 1.474652e-01 1.597425e-14 --1.054789e-02 2.561891e-01 1.603884e-14 -2.675555e-03 5.381530e-02 1.595430e-14 -1.475743e-04 1.297858e-01 1.595602e-14 -4.144677e-03 -8.438314e-03 1.593802e-14 --5.538530e-03 2.048576e-01 1.593870e-14 --9.755609e-03 2.540029e-01 1.599064e-14 --6.259616e-03 1.763737e-01 1.598473e-14 --3.393924e-03 1.531976e-01 1.596199e-14 -4.422739e-03 2.873412e-03 1.595897e-14 -9.396560e-04 7.004763e-02 1.594367e-14 --2.251500e-03 1.034284e-01 1.598374e-14 --2.147980e-03 1.676678e-01 1.594957e-14 --1.290984e-02 2.914551e-01 1.604324e-14 --8.495789e-03 2.158306e-01 1.604362e-14 -2.089337e-03 9.006619e-02 1.597994e-14 --4.724052e-03 2.039226e-01 1.594437e-14 --2.546111e-03 1.247637e-01 1.597463e-14 --1.223164e-02 2.854326e-01 1.602817e-14 --1.065770e-03 1.270826e-01 1.594123e-14 --8.628458e-03 2.274893e-01 1.602134e-14 -9.250437e-04 9.335704e-02 1.596078e-14 -1.025549e-03 4.428775e-02 1.593847e-14 --4.353613e-03 1.406269e-01 1.598233e-14 --4.194912e-03 1.750420e-01 1.595115e-14 --5.723304e-03 1.819708e-01 1.596918e-14 --8.355980e-03 2.449015e-01 1.595451e-14 -3.958183e-03 3.699242e-02 1.596127e-14 -5.406506e-03 -4.118429e-02 1.590457e-14 -1.663458e-03 4.370309e-02 1.594512e-14 -3.563818e-03 -1.111557e-02 1.592137e-14 -2.746415e-03 3.785245e-02 1.595091e-14 --1.097738e-02 2.584667e-01 1.605644e-14 --7.290451e-03 2.365295e-01 1.595412e-14 -6.808394e-03 -2.718102e-02 1.755283e-14 -3.131202e-03 6.165006e-02 1.748558e-14 -5.782486e-03 5.095198e-02 1.757541e-14 -5.494688e-03 -4.091104e-02 1.752671e-14 -9.829921e-03 -4.522149e-02 1.761124e-14 -8.858204e-03 -1.023815e-01 1.757807e-14 -3.667061e-03 8.637011e-02 1.750279e-14 -3.810890e-03 -4.901694e-03 1.750481e-14 -7.552698e-03 3.162535e-02 1.759978e-14 -7.942475e-03 -1.079196e-01 1.754108e-14 -3.458299e-03 1.066810e-01 1.754603e-14 -2.463249e-03 9.685986e-02 1.745906e-14 -9.862477e-03 -1.029360e-01 1.760963e-14 -3.564630e-03 1.127724e-01 1.757191e-14 -5.100463e-03 3.016294e-03 1.751622e-14 -6.990527e-03 6.189445e-03 1.757031e-14 -7.152462e-03 -6.640171e-02 1.754785e-14 -2.511577e-03 4.504620e-02 1.746468e-14 -2.786717e-03 1.167308e-01 1.748205e-14 -5.045173e-03 5.894264e-02 1.754754e-14 -4.034860e-03 -4.877554e-02 1.751333e-14 -8.565107e-03 -6.420792e-02 1.757555e-14 -1.066629e-02 -4.888412e-02 1.763195e-14 -6.704648e-03 -1.049362e-01 1.751895e-14 -9.296496e-03 -1.399459e-01 1.756330e-14 -1.070533e-02 -8.625481e-02 1.762238e-14 -4.029041e-03 4.400315e-02 1.749891e-14 -1.017420e-02 -1.384702e-01 1.759644e-14 -2.556502e-03 1.340639e-01 1.752110e-14 -4.686447e-03 4.860453e-02 1.752034e-14 -1.848175e-03 9.322647e-02 1.742108e-14 -4.485397e-03 -3.881207e-03 1.751317e-14 -2.682238e-03 5.774299e-03 1.748176e-14 -5.455670e-03 8.185940e-02 1.759707e-14 -8.013626e-03 -2.994606e-03 1.759236e-14 -9.674108e-03 -9.185769e-03 1.762806e-14 -8.183156e-03 3.113525e-02 1.761238e-14 -7.869345e-03 -1.285517e-01 1.754414e-14 -2.861485e-03 1.269351e-01 1.755835e-14 -2.841943e-03 9.303263e-02 1.747222e-14 -8.732464e-03 -1.473371e-01 1.753491e-14 -9.496156e-03 -7.225523e-02 1.759803e-14 -2.580006e-03 1.318835e-01 1.749790e-14 -2.466727e-03 7.614136e-02 1.744928e-14 -5.140463e-03 -8.180075e-02 1.750946e-14 -3.331340e-03 3.066684e-02 1.748911e-14 -4.053009e-03 9.364756e-02 1.755229e-14 -7.954662e-03 -9.182825e-02 1.755365e-14 -6.748837e-03 -7.814055e-02 1.753830e-14 -7.080406e-03 3.054163e-02 1.758889e-14 -5.183748e-03 7.474419e-02 1.757464e-14 -6.124261e-03 -3.278841e-02 1.754018e-14 -3.069122e-03 1.274202e-01 1.757172e-14 -2.478757e-03 1.261726e-01 1.746955e-14 -2.030435e-03 1.249190e-01 1.743315e-14 -7.814569e-03 -6.316145e-02 1.756644e-14 -3.790987e-03 9.111611e-02 1.752587e-14 -1.073390e-02 -1.429844e-01 1.762231e-14 -8.840517e-03 -3.786385e-02 1.759063e-14 -3.317983e-03 8.838831e-02 1.749502e-14 -6.477937e-03 3.365171e-02 1.757723e-14 -5.708234e-03 9.604319e-03 1.754377e-14 -9.977973e-03 -1.555597e-01 1.757834e-14 -2.474848e-03 1.360504e-01 1.754822e-14 -3.925620e-03 2.190804e-02 1.749905e-14 -1.111019e-02 -7.335299e-02 1.763966e-14 -4.826459e-03 -4.636801e-02 1.751509e-14 -7.806829e-03 -3.093916e-02 1.757369e-14 -1.868505e-03 6.931256e-02 1.743535e-14 -9.128233e-03 -8.924631e-02 1.759392e-14 -6.763217e-03 6.328507e-02 1.761489e-14 -1.072815e-02 -1.215837e-01 1.762925e-14 -8.917376e-03 -8.976008e-03 1.760732e-14 -9.654505e-04 2.039058e-01 1.750777e-14 -1.246900e-03 1.983678e-01 1.748036e-14 -1.512516e-03 1.925026e-01 1.741771e-14 -4.615757e-04 1.970777e-01 1.757038e-14 -1.126769e-02 1.101933e-01 1.894860e-14 -1.252822e-02 1.245151e-01 1.895222e-14 -1.005869e-02 8.862876e-02 1.893285e-14 -1.792057e-02 -5.306493e-04 1.890008e-14 -5.290146e-03 2.175432e-01 1.900498e-14 -1.622042e-02 4.334824e-02 1.890567e-14 -5.510573e-03 1.744518e-01 1.901489e-14 -1.415679e-02 3.689246e-02 1.891340e-14 -9.210110e-03 1.789915e-01 1.900121e-14 -1.576714e-02 6.125957e-02 1.890972e-14 -5.557514e-03 1.532148e-01 1.897235e-14 -1.990991e-02 -2.657201e-02 1.888969e-14 -2.451580e-03 2.416665e-01 1.905698e-14 -1.023401e-02 1.432474e-01 1.895965e-14 -1.262498e-02 7.388469e-02 1.892712e-14 -1.312302e-02 9.760838e-02 1.893938e-14 -9.229230e-03 1.198442e-01 1.896680e-14 -1.524367e-02 4.673466e-02 1.892128e-14 -7.359027e-03 1.733861e-01 1.899523e-14 -1.092398e-02 1.653557e-01 1.896626e-14 -1.219843e-02 4.988042e-02 1.892330e-14 -1.886519e-02 -2.223047e-02 1.890830e-14 -4.972261e-03 2.389557e-01 1.900428e-14 -1.872935e-02 -1.232323e-03 1.888633e-14 -2.315404e-03 2.131234e-01 1.901741e-14 -1.388361e-02 1.112020e-01 1.893927e-14 -7.992192e-03 1.026390e-01 1.894381e-14 -2.094953e-02 -4.595725e-02 1.890648e-14 -2.409720e-03 2.622911e-01 1.902117e-14 -2.084873e-02 -4.288354e-02 1.887677e-14 -8.979342e-04 2.572906e-01 1.908779e-14 -1.712804e-02 1.821343e-02 1.890513e-14 -5.484707e-03 1.987919e-01 1.902812e-14 -1.487733e-02 1.458374e-02 1.890259e-14 -8.960092e-03 1.992523e-01 1.897122e-14 -1.434175e-02 8.344326e-02 1.892412e-14 -7.615493e-03 1.323050e-01 1.897496e-14 -1.730766e-02 -9.732490e-03 1.890955e-14 -6.785469e-03 2.241503e-01 1.900044e-14 -1.757859e-02 2.721083e-02 1.889565e-14 -2.949853e-03 1.862540e-01 1.898958e-14 -1.129295e-02 1.365202e-01 1.896680e-14 -1.164976e-02 7.709841e-02 1.894021e-14 -8.550545e-04 2.759299e-01 1.906652e-14 -2.189406e-02 -6.015247e-02 1.889478e-14 -1.671200e-02 5.056420e-02 1.890667e-14 -3.858832e-03 1.625545e-01 1.894724e-14 -8.154348e-03 1.833837e-01 1.899355e-14 -1.514495e-02 3.317003e-02 1.891148e-14 -1.681914e-02 3.619016e-02 1.890546e-14 -4.550026e-03 1.786066e-01 1.900300e-14 -1.328410e-02 1.285020e-01 1.894264e-14 -8.953167e-03 8.451388e-02 1.892762e-14 -2.110896e-02 -5.192340e-02 1.890702e-14 -2.661520e-03 2.687881e-01 1.901816e-14 -1.931160e-02 -1.794218e-02 1.889507e-14 -3.447963e-03 2.340935e-01 1.904415e-14 -1.492590e-02 6.077597e-02 1.892467e-14 -7.251989e-03 1.587930e-01 1.900646e-14 -1.203152e-02 1.072513e-01 1.893603e-14 -1.519181e-02 4.141124e-02 1.891680e-14 -7.860321e-03 1.750729e-01 1.900968e-14 -1.042092e-02 1.129254e-01 1.896132e-14 -1.330898e-02 8.293302e-02 1.893526e-14 -8.601471e-03 1.480644e-01 1.899461e-14 -2.218161e-02 -6.599089e-02 1.888345e-14 -5.578871e-05 2.805890e-01 1.908929e-14 -1.798547e-02 8.597278e-03 1.889630e-14 -3.984432e-03 2.070522e-01 1.903370e-14 -1.313210e-02 7.615111e-02 1.893348e-14 -9.494861e-03 1.442261e-01 1.898086e-14 --3.765206e-04 1.446529e-01 1.877956e-14 --3.622549e-03 1.333574e-01 1.880999e-14 -3.210097e-03 1.577765e-01 1.873217e-14 --5.541593e-03 3.809097e-02 1.873842e-14 -5.055688e-03 2.530893e-01 1.883491e-14 --3.433674e-03 5.257843e-02 1.871959e-14 -2.510475e-03 2.417121e-01 1.882533e-14 -5.308011e-03 2.172837e-01 1.881289e-14 --5.205202e-03 7.488430e-02 1.878133e-14 --6.902123e-04 2.050798e-01 1.880812e-14 --3.391007e-04 8.607076e-02 1.872585e-14 --6.167942e-03 1.354953e-02 1.870938e-14 --3.283611e-03 1.079827e-01 1.877580e-14 -2.852194e-03 1.824338e-01 1.878525e-14 --3.890152e-04 1.679445e-01 1.879440e-14 --3.845156e-04 1.222844e-01 1.876943e-14 -5.757313e-03 2.027390e-01 1.877122e-14 --5.569303e-03 8.892255e-02 1.875393e-14 --6.834192e-03 1.736538e-02 1.874319e-14 -6.994649e-03 2.747552e-01 1.887987e-14 -1.335083e-03 1.876736e-01 1.880421e-14 --2.237427e-03 9.942670e-02 1.876819e-14 --4.208693e-03 1.463002e-01 1.876643e-14 -3.706106e-03 1.446058e-01 1.872790e-14 --7.901055e-03 -1.011117e-02 1.871885e-14 -7.327249e-03 3.004468e-01 1.886310e-14 --3.127648e-03 4.076154e-02 1.870319e-14 -1.874232e-03 2.506180e-01 1.881110e-14 --5.686147e-03 1.200857e-02 1.871055e-14 -4.188175e-03 2.773931e-01 1.882008e-14 --4.205150e-03 5.472422e-02 1.874221e-14 -3.024042e-03 2.288245e-01 1.881812e-14 --6.704472e-04 1.874411e-01 1.878321e-14 --1.985400e-04 1.040272e-01 1.873752e-14 -7.263452e-03 2.441862e-01 1.882076e-14 --6.610462e-03 4.945905e-02 1.874662e-14 -7.218209e-03 2.574873e-01 1.885222e-14 --6.683234e-03 3.441173e-02 1.873727e-14 --4.769531e-03 6.914865e-02 1.877634e-14 -4.609590e-03 2.226844e-01 1.880964e-14 --4.057039e-03 1.025847e-01 1.877888e-14 -3.824410e-03 1.882278e-01 1.879924e-14 --2.534055e-03 1.846990e-01 1.877173e-14 -1.571373e-03 1.056108e-01 1.869769e-14 --8.188735e-03 -1.953093e-02 1.870214e-14 -7.749384e-03 3.185576e-01 1.885665e-14 --1.968403e-03 1.503231e-01 1.879799e-14 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--8.019344e-16 1.522554e-14 -1.191828e-01 --6.587630e-16 1.735518e-14 1.353355e-01 --6.589260e-16 1.715425e-14 1.357237e-01 --6.715156e-16 1.692340e-14 1.358676e-01 --6.917517e-16 1.670568e-14 1.356854e-01 --7.169057e-16 1.650125e-14 1.354201e-01 --5.381502e-16 1.763961e-14 1.058585e-01 --4.088452e-16 1.773129e-14 7.211860e-02 --2.701192e-16 1.778098e-14 3.473381e-02 --1.230959e-16 1.780313e-14 -5.183813e-03 -3.280760e-17 1.779661e-14 -4.613312e-02 -1.840184e-16 1.775175e-14 -8.630972e-02 -3.318190e-16 1.768196e-14 -1.235696e-01 -4.624157e-16 1.756609e-14 -1.560864e-01 -5.451204e-16 1.745403e-14 -1.823853e-01 -6.200143e-16 1.733155e-14 -2.019560e-01 -6.773037e-16 1.716798e-14 -2.138974e-01 -7.015247e-16 1.678240e-14 -2.136569e-01 -6.637440e-16 1.660252e-14 -2.014085e-01 -5.664421e-16 1.643221e-14 -1.818187e-01 -4.599913e-16 1.628298e-14 -1.557922e-01 -3.227998e-16 1.617441e-14 -1.237279e-01 -1.773503e-16 1.610383e-14 -8.703991e-02 -3.213488e-17 1.603800e-14 -4.723070e-02 --1.175989e-16 1.600227e-14 -6.248892e-03 --2.603194e-16 1.598458e-14 3.401487e-02 --4.017306e-16 1.601658e-14 7.194375e-02 --5.703414e-16 1.613418e-14 1.061518e-01 --6.587630e-16 1.735518e-14 1.353355e-01 --6.589260e-16 1.715425e-14 1.357237e-01 --6.715156e-16 1.692340e-14 1.358676e-01 --6.917517e-16 1.670568e-14 1.356854e-01 --7.169057e-16 1.650125e-14 1.354201e-01 --7.281330e-16 1.741955e-14 1.572847e-01 --8.109894e-16 1.728346e-14 1.745282e-01 --8.672126e-16 1.711990e-14 1.853690e-01 --8.965463e-16 1.689386e-14 1.890156e-01 --8.864199e-16 1.672146e-14 1.851015e-01 --8.685141e-16 1.656067e-14 1.743750e-01 --8.207879e-16 1.642218e-14 1.574664e-01 --9.053648e-16 1.814857e-14 -2.547015e-02 --1.020142e-15 1.811276e-14 -6.769223e-02 --1.139443e-15 1.804195e-14 -1.049769e-01 --1.245584e-15 1.796216e-14 -1.360916e-01 --1.340748e-15 1.786693e-14 -1.595253e-01 --1.378857e-15 1.774420e-14 -1.744330e-01 --1.406037e-15 1.759287e-14 -1.796866e-01 --1.366027e-15 1.747262e-14 -1.745855e-01 --1.316708e-15 1.734108e-14 -1.593221e-01 --1.275663e-15 1.721817e-14 -1.351600e-01 --1.187815e-15 1.712165e-14 -1.034590e-01 --1.084261e-15 1.705801e-14 -6.560358e-02 --9.699713e-16 1.701363e-14 -2.305189e-02 --7.433650e-16 1.698974e-14 6.825059e-02 --6.466906e-16 1.702190e-14 1.120994e-01 --5.348360e-16 1.711745e-14 1.513816e-01 --4.228495e-16 1.726583e-14 1.839271e-01 --3.532929e-16 1.738546e-14 2.083314e-01 --3.077242e-16 1.752872e-14 2.233542e-01 --2.955842e-16 1.766925e-14 2.280852e-01 --3.175536e-16 1.782808e-14 2.222115e-01 --3.637105e-16 1.793014e-14 2.064419e-01 --4.217643e-16 1.800215e-14 1.819253e-01 --5.082355e-16 1.806752e-14 1.493657e-01 --5.947896e-16 1.812393e-14 1.098581e-01 --6.840986e-16 1.815587e-14 6.567007e-02 --1.521057e-16 1.750709e-14 4.307941e-02 --1.255783e-16 1.753855e-14 5.891136e-04 --1.205330e-16 1.755536e-14 -3.698459e-02 --8.533742e-17 1.759647e-14 -6.852613e-02 --9.246757e-17 1.763224e-14 -9.330830e-02 --7.671849e-17 1.767494e-14 -1.096859e-01 --8.052187e-17 1.771579e-14 -1.154140e-01 --5.673855e-17 1.775283e-14 -1.105153e-01 --1.098281e-16 1.778308e-14 -9.545434e-02 --1.024477e-16 1.780361e-14 -7.155940e-02 --1.306281e-16 1.781104e-14 -3.998438e-02 --1.531380e-16 1.782335e-14 -1.851557e-03 --1.749904e-16 1.783825e-14 4.123243e-02 --1.895857e-16 1.785550e-14 1.330131e-01 --2.334545e-16 1.787838e-14 1.763059e-01 --2.811777e-16 1.787120e-14 2.146440e-01 --3.182115e-16 1.786192e-14 2.464015e-01 --3.443280e-16 1.782376e-14 2.704549e-01 --3.837183e-16 1.777775e-14 2.857709e-01 --4.267393e-16 1.774416e-14 2.916062e-01 --4.068192e-16 1.768526e-14 2.872087e-01 --3.782084e-16 1.761967e-14 2.727487e-01 --3.789487e-16 1.758982e-14 2.489466e-01 --3.405935e-16 1.753835e-14 2.170467e-01 --2.798029e-16 1.749499e-14 1.783461e-01 --2.167175e-16 1.749971e-14 1.347590e-01 --1.117634e-16 1.376677e-14 7.399632e-03 -4.199741e-17 1.564437e-14 3.699488e-02 --1.396180e-16 1.661523e-14 7.862874e-02 --2.685202e-16 1.706792e-14 1.208592e-01 --3.300156e-16 1.733895e-14 1.522798e-01 --3.292753e-16 1.754440e-14 1.670328e-01 --3.175253e-16 1.771923e-14 1.515116e-01 --2.837013e-16 1.781843e-14 1.107870e-01 --2.417965e-16 1.785230e-14 4.937657e-02 --2.308462e-16 1.785546e-14 -7.086700e-03 --2.503563e-16 1.778209e-14 -3.687902e-02 --3.318424e-16 1.775060e-14 -3.201354e-02 --4.818396e-16 1.764305e-14 2.297170e-02 --9.251492e-16 1.525088e-14 2.960891e-02 --7.070802e-16 9.055821e-15 4.243520e-02 --8.651538e-16 1.306929e-14 5.553790e-02 --3.903148e-16 1.469432e-14 3.908246e-02 --2.211440e-16 1.560821e-14 1.268552e-02 --2.031732e-16 1.603183e-14 -1.458897e-02 --2.929288e-16 1.627651e-14 -3.675529e-02 --3.857151e-16 1.638256e-14 -4.563458e-02 --4.229390e-16 1.641908e-14 -3.939534e-02 --3.801561e-16 1.633280e-14 6.463857e-04 --2.395098e-16 1.615383e-14 6.695346e-02 -7.806008e-17 1.591796e-14 1.602953e-01 --4.517070e-16 9.509998e-15 -5.234120e-02 --9.251492e-16 1.525088e-14 2.960891e-02 --4.818396e-16 1.764305e-14 2.297170e-02 --3.318424e-16 1.775060e-14 -3.201354e-02 --2.503563e-16 1.778209e-14 -3.687902e-02 --2.308462e-16 1.785546e-14 -7.086700e-03 --2.417965e-16 1.785230e-14 4.937657e-02 --2.837013e-16 1.781843e-14 1.107870e-01 --3.175253e-16 1.771923e-14 1.515116e-01 --3.292753e-16 1.754440e-14 1.670328e-01 --3.300156e-16 1.733895e-14 1.522798e-01 --2.685202e-16 1.706792e-14 1.208592e-01 --1.396180e-16 1.661523e-14 7.862874e-02 -4.199741e-17 1.564437e-14 3.699488e-02 --1.117634e-16 1.376677e-14 7.399632e-03 --4.517070e-16 9.509998e-15 -5.234120e-02 -7.806008e-17 1.591796e-14 1.602953e-01 --2.395098e-16 1.615383e-14 6.695346e-02 --3.801561e-16 1.633280e-14 6.463857e-04 --4.229390e-16 1.641908e-14 -3.939534e-02 --3.857151e-16 1.638256e-14 -4.563458e-02 --2.929288e-16 1.627651e-14 -3.675529e-02 --2.031732e-16 1.603183e-14 -1.458897e-02 --2.211440e-16 1.560821e-14 1.268552e-02 --3.903148e-16 1.469432e-14 3.908246e-02 --8.651538e-16 1.306929e-14 5.553790e-02 --7.070802e-16 9.055821e-15 4.243520e-02 --2.145950e-18 1.588365e-14 1.149331e-01 --3.187552e-16 1.572645e-14 2.580925e-02 -2.244782e-16 1.586056e-14 1.767883e-01 --5.106621e-16 1.562697e-14 -3.563563e-02 --1.859865e-16 1.589220e-14 6.116840e-02 --1.992485e-16 1.572357e-14 6.337813e-02 -1.425627e-16 1.596640e-14 1.551934e-01 -3.726880e-16 1.573538e-14 2.187462e-01 --3.498061e-17 1.566567e-14 1.129449e-01 --3.599431e-17 1.607456e-14 9.887843e-02 --4.937081e-16 1.578285e-14 -3.260891e-02 --3.017368e-16 1.557494e-14 3.386140e-02 --3.601822e-16 1.589368e-14 8.213276e-03 --6.247499e-16 1.550350e-14 -7.219841e-02 -1.731817e-16 1.572418e-14 1.670199e-01 -2.386146e-16 1.603093e-14 1.815394e-01 --4.201936e-16 1.555107e-14 -2.987265e-03 -3.401288e-16 1.588906e-14 2.080645e-01 --5.820548e-16 1.568102e-14 -5.801250e-02 -9.217689e-17 1.607356e-14 1.374017e-01 --1.866774e-16 1.607037e-14 5.760308e-02 -4.853002e-16 1.566468e-14 2.484209e-01 --4.272875e-16 1.568425e-14 -9.395764e-03 -1.744488e-16 1.609819e-14 1.621357e-01 --1.697601e-16 1.561049e-14 7.301783e-02 --5.295446e-16 1.553150e-14 -3.914002e-02 --3.124852e-16 1.602435e-14 2.150937e-02 --7.105971e-17 1.575265e-14 9.813556e-02 --2.933344e-16 1.519634e-14 4.985727e-02 -4.354247e-17 1.513374e-14 1.551525e-01 --5.008720e-16 1.517991e-14 -1.805213e-02 --7.225845e-17 1.503884e-14 1.187820e-01 --1.015324e-16 1.528274e-14 1.036002e-01 -2.897635e-16 1.533859e-14 2.102652e-01 --4.220109e-16 1.507257e-14 6.428283e-03 --2.011174e-16 1.494487e-14 7.896278e-02 --6.076906e-16 1.520278e-14 -5.088756e-02 -4.606758e-17 1.503583e-14 1.550519e-01 -2.146205e-16 1.515720e-14 2.056501e-01 -1.004550e-17 1.537999e-14 1.335520e-01 --1.010089e-16 1.515533e-14 1.067623e-01 --5.635960e-16 1.512154e-14 -4.261854e-02 --3.679564e-16 1.516062e-14 2.472915e-02 --2.424334e-16 1.534266e-14 6.156105e-02 --8.473484e-17 1.494852e-14 1.163515e-01 -4.507890e-16 1.543855e-14 2.457419e-01 -1.549121e-16 1.539672e-14 1.722525e-01 --3.282113e-16 1.496861e-14 3.998136e-02 -3.298783e-17 1.496174e-14 1.522137e-01 --1.944924e-16 1.523584e-14 8.044475e-02 -3.939289e-16 1.528396e-14 2.379120e-01 --1.985288e-16 1.507395e-14 7.915430e-02 -1.604358e-16 1.527754e-14 1.816648e-01 --3.131323e-16 1.505606e-14 4.527600e-02 --4.278731e-16 1.528796e-14 2.519802e-03 --1.138755e-16 1.537535e-14 9.852865e-02 --3.753471e-17 1.510571e-14 1.271257e-01 --3.276554e-18 1.524603e-14 1.366804e-01 -1.969510e-16 1.502969e-14 1.943161e-01 --4.774276e-16 1.502358e-14 -1.146834e-02 --6.759194e-16 1.527265e-14 -7.997642e-02 --4.378989e-16 1.521692e-14 1.968017e-04 --5.879864e-16 1.533202e-14 -5.005832e-02 --4.719635e-16 1.512739e-14 -7.803242e-03 -3.326286e-16 1.543785e-14 2.138852e-01 -1.555398e-16 1.497524e-14 1.846847e-01 -9.827009e-17 1.696457e-14 -6.037574e-02 --2.411040e-16 1.651640e-14 2.873760e-02 --1.928342e-16 1.730171e-14 1.452546e-02 -1.376147e-16 1.660316e-14 -7.241167e-02 -1.673477e-16 1.745285e-14 -8.078356e-02 -3.748609e-16 1.702126e-14 -1.346298e-01 --3.364193e-16 1.690624e-14 5.098129e-02 -5.930469e-18 1.641122e-14 -3.619187e-02 --1.187359e-16 1.753765e-14 -5.709211e-03 -3.927679e-16 1.676382e-14 -1.390405e-01 --4.031087e-16 1.716765e-14 6.935301e-02 --3.796632e-16 1.644997e-14 6.340202e-02 -3.882984e-16 1.723856e-14 -1.363444e-01 --4.191842e-16 1.741096e-14 7.396095e-02 --2.072411e-17 1.674502e-14 -2.973616e-02 --2.699057e-17 1.721370e-14 -2.877395e-02 -2.322631e-16 1.683229e-14 -9.839498e-02 --1.763707e-16 1.627590e-14 1.336472e-02 --4.592709e-16 1.672856e-14 8.134441e-02 --2.239606e-16 1.715958e-14 2.308972e-02 -1.569110e-16 1.626018e-14 -7.889703e-02 -2.402135e-16 1.713529e-14 -9.764543e-02 -1.832901e-16 1.759221e-14 -8.529297e-02 -3.694244e-16 1.649935e-14 -1.351544e-01 -5.129397e-16 1.690277e-14 -1.711096e-01 -3.350414e-16 1.744446e-14 -1.212714e-01 --1.746551e-16 1.669298e-14 1.069637e-02 -5.026900e-16 1.716968e-14 -1.708543e-01 --5.130632e-16 1.699421e-14 9.662001e-02 --1.911658e-16 1.693507e-14 1.407712e-02 --3.592182e-16 1.622529e-14 6.055551e-02 -1.276941e-18 1.656345e-14 -3.578318e-02 --3.682900e-17 1.620630e-14 -2.493525e-02 --3.063994e-16 1.758564e-14 4.291500e-02 -1.251341e-17 1.736053e-14 -3.861039e-02 -3.578395e-17 1.763955e-14 -4.647829e-02 --1.151546e-16 1.766730e-14 -7.183542e-03 -4.768890e-16 1.659469e-14 -1.587684e-01 --4.747244e-16 1.730380e-14 8.823946e-02 --3.666369e-16 1.657419e-14 5.912393e-02 -5.523477e-16 1.668170e-14 -1.775257e-01 -2.734051e-16 1.728464e-14 -1.063739e-01 --5.119164e-16 1.683238e-14 9.542420e-02 --2.951947e-16 1.637322e-14 4.346116e-02 -2.778070e-16 1.629925e-14 -1.116126e-01 --1.255314e-16 1.644131e-14 -1.326639e-03 --3.509468e-16 1.725881e-14 5.629061e-02 -3.291003e-16 1.686192e-14 -1.235856e-01 -2.746006e-16 1.667049e-14 -1.093555e-01 --1.151355e-16 1.742778e-14 -6.070451e-03 --2.825144e-16 1.741670e-14 3.711898e-02 -1.124794e-16 1.678732e-14 -6.511756e-02 --4.740773e-16 1.750325e-14 8.750969e-02 --5.050950e-16 1.661323e-14 9.112195e-02 --5.066899e-16 1.639913e-14 9.096589e-02 -2.295830e-16 1.698198e-14 -9.589070e-02 --3.472894e-16 1.706275e-14 5.479750e-02 -5.301362e-16 1.730389e-14 -1.760766e-01 -1.422596e-16 1.731462e-14 -7.266921e-02 --3.487095e-16 1.674025e-14 5.377555e-02 --1.276724e-16 1.731286e-14 -2.427077e-03 --4.501964e-17 1.693840e-14 -2.409785e-02 -5.764552e-16 1.700911e-14 -1.868530e-01 --5.128872e-16 1.715953e-14 9.769841e-02 --9.210032e-17 1.655126e-14 -1.037380e-02 -2.766826e-16 1.757935e-14 -1.093824e-01 -1.556042e-16 1.643943e-14 -7.716809e-02 -1.097100e-16 1.716003e-14 -6.496944e-02 --2.693915e-16 1.615440e-14 3.737150e-02 -3.303096e-16 1.714221e-14 -1.223143e-01 --2.299935e-16 1.769086e-14 2.389241e-02 -4.542726e-16 1.736555e-14 -1.554856e-01 -3.272136e-17 1.749701e-14 -4.529458e-02 --7.767324e-16 1.695390e-14 1.638822e-01 --7.644587e-16 1.687778e-14 1.590260e-01 --7.969776e-16 1.653903e-14 1.555869e-01 --7.267523e-16 1.731260e-14 1.555972e-01 --8.303189e-16 1.763533e-14 2.487111e-02 --8.384423e-16 1.728120e-14 3.226134e-02 --8.350774e-16 1.794911e-14 1.079915e-02 --1.109480e-15 1.770212e-14 -8.137308e-02 --5.696999e-16 1.757029e-14 1.288511e-01 --1.033279e-15 1.740339e-14 -4.462786e-02 --6.428449e-16 1.786582e-14 9.286946e-02 --9.729494e-16 1.790681e-14 -4.064769e-02 --6.971654e-16 1.732662e-14 8.646975e-02 --9.910137e-16 1.726423e-14 -2.975207e-02 --6.777402e-16 1.797737e-14 7.481916e-02 --1.197162e-15 1.749891e-14 -1.101100e-01 --4.954632e-16 1.779192e-14 1.570309e-01 --7.666397e-16 1.749612e-14 5.450459e-02 --9.125196e-16 1.776723e-14 -7.815783e-03 --8.878044e-16 1.744170e-14 8.864630e-03 --7.781967e-16 1.781971e-14 3.824934e-02 --1.000212e-15 1.761662e-14 -3.727986e-02 --6.643639e-16 1.765711e-14 8.720673e-02 --7.461574e-16 1.716255e-14 7.051501e-02 --9.202404e-16 1.802517e-14 -2.503068e-02 --1.137923e-15 1.783800e-14 -9.955037e-02 --5.257932e-16 1.741776e-14 1.467955e-01 --1.154083e-15 1.731437e-14 -8.938466e-02 --5.371889e-16 1.796493e-14 1.335190e-01 --8.748710e-16 1.715097e-14 1.699030e-02 --7.810062e-16 1.805049e-14 2.706958e-02 --1.233086e-15 1.768035e-14 -1.254442e-01 --4.487228e-16 1.759516e-14 1.732701e-01 --1.245746e-15 1.741796e-14 -1.275156e-01 --4.412300e-16 1.788222e-14 1.745523e-01 --1.075919e-15 1.757708e-14 -6.570708e-02 --6.066253e-16 1.770580e-14 1.132380e-01 --1.003445e-15 1.803247e-14 -5.907400e-02 --6.703913e-16 1.714346e-14 1.036490e-01 --9.355844e-16 1.734317e-14 -6.708459e-03 --7.354390e-16 1.790806e-14 5.246811e-02 --1.088221e-15 1.795051e-14 -8.468043e-02 --5.845898e-16 1.725816e-14 1.297716e-01 --1.081517e-15 1.720555e-14 -6.388202e-02 --5.991492e-16 1.802474e-14 1.088527e-01 --7.980370e-16 1.738685e-14 4.586346e-02 --8.817174e-16 1.786360e-14 -2.528706e-03 --4.072037e-16 1.772179e-14 1.893507e-01 --1.282637e-15 1.755507e-14 -1.413328e-01 --1.026281e-15 1.713745e-14 -4.238737e-02 --6.424442e-16 1.806622e-14 8.670739e-02 --6.642508e-16 1.745640e-14 9.305587e-02 --1.007707e-15 1.779938e-14 -4.667220e-02 --1.056482e-15 1.732856e-14 -5.290554e-02 --6.252726e-16 1.793114e-14 9.869039e-02 --8.356877e-16 1.709913e-14 3.314321e-02 --8.203521e-16 1.808398e-14 1.044109e-02 --1.232413e-15 1.779941e-14 -1.290613e-01 --4.467955e-16 1.745999e-14 1.770604e-01 --1.166230e-15 1.758271e-14 -1.003696e-01 --5.181893e-16 1.770271e-14 1.477902e-01 --9.805086e-16 1.748892e-14 -2.598691e-02 --6.924997e-16 1.777516e-14 7.545361e-02 --8.540304e-16 1.754027e-14 2.017000e-02 --1.005755e-15 1.769484e-14 -4.087927e-02 --6.674366e-16 1.757519e-14 8.717908e-02 --8.101874e-16 1.773001e-14 2.949491e-02 --9.081132e-16 1.756496e-14 -3.072530e-03 --7.282033e-16 1.769365e-14 6.306278e-02 --1.293954e-15 1.748300e-14 -1.484125e-01 --3.817649e-16 1.782394e-14 1.958083e-01 --1.118174e-15 1.744765e-14 -7.727875e-02 --5.672211e-16 1.783862e-14 1.241265e-01 --9.151057e-16 1.767284e-14 -7.631925e-03 --7.503925e-16 1.760166e-14 5.745393e-02 --1.718359e-16 1.773875e-14 8.740655e-02 --1.655804e-16 1.760393e-14 7.010377e-02 --1.914688e-16 1.782573e-14 1.065309e-01 --1.181299e-16 1.771767e-14 -1.789415e-02 --2.717015e-16 1.774346e-14 1.949861e-01 --1.317696e-16 1.778061e-14 7.707372e-04 --2.607907e-16 1.763819e-14 1.789377e-01 --2.377458e-16 1.782221e-14 1.641235e-01 --1.307367e-16 1.762969e-14 1.388734e-02 --2.294887e-16 1.759548e-14 1.402117e-01 --1.565690e-16 1.780801e-14 3.637640e-02 --1.224988e-16 1.775789e-14 -3.874003e-02 --1.450559e-16 1.767383e-14 4.821810e-02 --2.188512e-16 1.778458e-14 1.278212e-01 --1.986177e-16 1.769122e-14 1.079944e-01 --1.532853e-16 1.776931e-14 6.772777e-02 --2.117570e-16 1.783598e-14 1.520102e-01 --1.327409e-16 1.758732e-14 2.552356e-02 --1.045904e-16 1.766543e-14 -4.038169e-02 --2.963230e-16 1.779584e-14 2.187758e-01 --2.098535e-16 1.771842e-14 1.292023e-01 --1.353666e-16 1.773880e-14 4.348002e-02 --1.746548e-16 1.754820e-14 8.060482e-02 --1.874122e-16 1.783248e-14 9.583335e-02 --8.568705e-17 1.770567e-14 -6.441422e-02 --3.211412e-16 1.774395e-14 2.411021e-01 --1.293362e-16 1.780593e-14 -8.212387e-03 --3.020688e-16 1.757633e-14 1.850368e-01 --1.227018e-16 1.777658e-14 -3.833971e-02 --3.236687e-16 1.760374e-14 2.128861e-01 --1.225382e-16 1.774587e-14 4.423765e-04 --2.586154e-16 1.770222e-14 1.690663e-01 --2.183492e-16 1.763410e-14 1.246861e-01 --1.645709e-16 1.779491e-14 5.218478e-02 --2.594256e-16 1.784886e-14 1.926184e-01 --1.173904e-16 1.758763e-14 -1.267451e-02 --2.781686e-16 1.783159e-14 2.042926e-01 --1.114376e-16 1.761315e-14 -2.613979e-02 --1.227477e-16 1.767682e-14 1.031250e-02 --2.584838e-16 1.779130e-14 1.673809e-01 --1.496480e-16 1.764193e-14 4.147916e-02 --2.247893e-16 1.779712e-14 1.350299e-01 --1.867530e-16 1.754727e-14 1.184666e-01 --1.750337e-16 1.782364e-14 5.730113e-02 --9.906089e-17 1.774457e-14 -7.122348e-02 --3.603794e-16 1.768598e-14 2.563257e-01 --1.716530e-16 1.764105e-14 8.889140e-02 --1.837315e-16 1.778881e-14 8.754107e-02 --1.400351e-16 1.777235e-14 3.183085e-02 --2.362208e-16 1.766173e-14 1.447364e-01 --1.021385e-16 1.777753e-14 -7.055360e-02 --2.381710e-16 1.756208e-14 1.515348e-01 --3.570673e-16 1.766040e-14 2.378602e-01 --1.629934e-16 1.781546e-14 2.505250e-02 --1.969925e-16 1.776768e-14 1.085220e-01 --1.591320e-16 1.770427e-14 6.712804e-02 --1.500619e-16 1.755863e-14 5.098667e-02 --1.993743e-16 1.783537e-14 1.256407e-01 --9.463594e-17 1.768616e-14 -7.308439e-02 --3.360211e-16 1.777464e-14 2.498659e-01 --1.998402e-16 1.759748e-14 1.116494e-01 --1.679861e-16 1.780950e-14 6.508711e-02 --1.159526e-16 1.773939e-14 -3.583408e-02 --1.323370e-16 1.771130e-14 2.512272e-02 --2.414767e-16 1.774256e-14 1.503785e-01 --1.309965e-16 1.775527e-14 -1.308575e-02 --2.976577e-16 1.768804e-14 2.054638e-01 --1.544214e-16 1.779665e-14 2.071833e-02 --2.525757e-16 1.762262e-14 1.556205e-01 --2.358930e-16 1.665334e-14 5.537117e-02 --4.084108e-16 1.745597e-14 3.097192e-02 --2.002423e-16 1.723042e-14 -3.056171e-03 -1.692465e-16 1.603439e-14 4.573610e-02 -1.396466e-16 1.625769e-14 1.794650e-02 --1.333485e-17 1.723603e-14 8.407251e-02 --3.856090e-16 1.710672e-14 3.391319e-02 --3.312397e-16 1.644198e-14 1.523730e-01 --6.663227e-16 1.769205e-14 6.994570e-02 --2.193032e-16 1.700485e-14 2.687556e-02 --3.312014e-16 1.754601e-14 -6.591761e-02 --1.172232e-16 1.664137e-14 5.104251e-02 --3.736610e-16 1.721238e-14 1.320326e-01 --2.454200e-16 1.644410e-14 2.841650e-02 --3.895165e-16 1.743107e-14 9.042348e-02 --5.511034e-16 1.764592e-14 2.482841e-02 --1.278429e-15 1.455598e-14 7.851609e-02 -1.239376e-16 1.623911e-14 -6.441004e-02 --4.122405e-16 1.793094e-14 5.488240e-02 --9.294579e-17 1.682846e-14 -1.732366e-02 -3.282435e-16 1.638279e-14 -6.816676e-02 --3.207546e-16 1.749383e-14 5.121852e-02 --1.411416e-16 1.605617e-14 1.157752e-01 --1.539980e-16 1.735066e-14 -5.085674e-02 -1.284324e-16 1.708828e-14 1.418744e-01 --4.248961e-16 1.690607e-14 1.397815e-02 --7.230644e-16 1.769164e-14 1.723187e-02 -3.113536e-16 1.569771e-14 1.531141e-01 --7.132807e-16 1.772112e-14 -2.838495e-02 --6.327219e-16 1.649523e-14 3.989750e-03 -1.715615e-16 1.713220e-14 -1.088111e-01 --4.765872e-16 1.762726e-14 1.536876e-01 -4.728639e-17 1.617052e-14 2.305690e-02 -2.726399e-16 1.675334e-14 -1.091878e-01 -2.448684e-16 1.579813e-14 1.449319e-01 --3.484738e-16 1.623188e-14 7.297368e-02 --3.177489e-16 1.622994e-14 1.655337e-01 --4.106891e-16 1.706669e-14 1.728587e-01 --9.106733e-16 1.477648e-14 8.291014e-02 --1.339977e-17 1.625822e-14 -8.100817e-02 -2.623325e-16 1.628523e-14 -4.899673e-02 --4.688909e-16 1.636276e-14 -2.683874e-03 --1.377592e-16 1.702428e-14 -1.874991e-02 -1.301573e-16 1.572847e-14 1.361245e-01 -2.035414e-16 1.678725e-14 1.360484e-01 -6.794693e-18 1.649986e-14 -9.536805e-03 --2.457933e-17 1.747150e-14 1.559332e-01 --1.795156e-16 1.719801e-14 4.885094e-02 --1.205816e-16 1.759705e-14 1.425741e-01 --5.889171e-17 1.610054e-14 3.554258e-02 --6.094859e-16 1.740996e-14 -4.973582e-02 -9.387487e-18 1.742450e-14 -9.530992e-02 --9.469617e-17 1.628381e-14 2.465313e-02 --3.266471e-16 1.717539e-14 3.117808e-02 --3.487014e-16 1.792312e-14 6.117867e-02 --3.234368e-16 1.747044e-14 8.719768e-03 --3.067503e-16 1.765748e-14 -7.555047e-02 --2.536764e-16 1.771830e-14 4.257034e-02 --5.824381e-16 1.660030e-14 1.124220e-01 --3.918612e-16 1.805078e-14 7.172359e-02 --6.543548e-16 1.655378e-14 -2.498105e-02 --3.046773e-16 1.739153e-14 1.544556e-02 --4.712209e-16 1.774905e-14 4.342166e-02 --1.612449e-15 1.534100e-14 -7.488592e-03 -4.790690e-16 1.558320e-14 -1.774726e-02 --3.391443e-16 1.714926e-14 8.424664e-02 -2.809855e-17 1.624702e-14 1.381353e-01 --6.461625e-16 1.590462e-14 5.468143e-02 --5.680758e-16 1.770299e-14 1.619441e-01 --3.944488e-16 1.745193e-14 1.827144e-01 --1.181636e-16 1.567018e-14 1.657897e-01 -3.366584e-16 1.296747e-14 5.441435e-02 -2.769328e-17 1.601491e-14 1.646412e-01 --5.307666e-16 1.569862e-14 5.786063e-02 --3.101910e-16 1.639783e-14 1.212559e-01 -9.696840e-17 1.652010e-14 1.155696e-01 --1.593972e-16 1.711786e-14 8.233788e-02 --3.124647e-16 1.790469e-14 7.374554e-02 -2.081317e-16 1.661649e-14 1.311554e-01 --2.451852e-16 1.624320e-14 1.356399e-01 --3.845174e-16 1.701909e-14 1.201485e-01 --9.375163e-16 1.776318e-14 -1.343628e-02 -3.323483e-16 1.656969e-14 -9.085241e-02 --2.321785e-16 1.605854e-14 -9.289403e-03 --3.185853e-16 1.782517e-14 -4.901788e-02 -1.498196e-15 1.351106e-14 -4.744290e-02 --3.559361e-16 1.775816e-14 1.411738e-01 -1.531295e-17 1.728068e-14 -8.557795e-02 --1.887853e-16 1.665881e-14 6.242456e-02 --3.801921e-16 1.749062e-14 -2.276573e-02 --7.651815e-17 1.722421e-14 1.246806e-01 --7.102474e-16 1.668613e-14 -2.218199e-02 --4.072365e-16 1.633653e-14 6.754854e-02 --1.770301e-15 1.192814e-14 4.356379e-02 --5.916187e-16 1.780662e-14 1.198429e-01 --1.197301e-16 1.628510e-14 -4.692820e-02 --5.459802e-16 1.759327e-14 4.276306e-02 --3.224732e-16 1.754825e-14 1.916356e-01 --2.054338e-16 1.582710e-14 1.666665e-01 --2.843483e-17 1.621139e-14 -7.282397e-02 --6.265324e-16 1.702551e-14 -3.252615e-02 --9.306861e-16 1.581468e-14 4.583064e-02 --2.062439e-16 1.613652e-14 5.992850e-02 --3.620238e-16 1.710443e-14 1.473038e-01 --2.579952e-15 1.370711e-14 -3.645216e-02 --3.600530e-16 1.726999e-14 7.126333e-02 -2.109586e-16 1.606658e-14 3.777757e-02 --3.334339e-16 1.725719e-14 7.683173e-02 --2.778617e-16 1.686994e-14 9.425306e-02 --4.895658e-16 1.637806e-14 -1.027504e-02 --4.142139e-16 1.787429e-14 5.499121e-02 --3.325856e-16 1.731914e-14 1.734004e-01 --3.917590e-16 1.786074e-14 1.953776e-02 --1.738524e-16 1.781458e-14 -1.478071e-02 -9.737696e-17 1.751779e-14 -1.139179e-01 --1.901280e-16 1.781065e-14 2.273916e-03 --6.578607e-16 1.776884e-14 -6.039536e-02 --2.452646e-16 1.766783e-14 7.091454e-03 --4.900970e-16 1.605809e-14 3.792931e-02 --8.812866e-16 1.513378e-14 6.174465e-02 --1.991467e-16 1.602494e-14 2.303803e-02 --6.974344e-17 1.707144e-14 5.420362e-02 --8.285139e-16 1.759776e-14 -5.866261e-02 --9.278641e-16 1.680060e-14 -4.252538e-02 --4.203688e-16 1.747486e-14 8.112864e-02 --1.065370e-15 1.594119e-14 6.251022e-02 --3.440795e-16 1.753361e-14 9.890629e-02 --6.492573e-16 1.725618e-14 -5.923838e-02 --2.502292e-16 1.748068e-14 5.463996e-02 -2.155227e-16 1.700340e-14 -1.157386e-01 --4.077789e-16 1.753994e-14 1.824703e-01 --3.520378e-16 1.675178e-14 3.489513e-02 --5.650703e-16 1.646419e-14 8.025481e-03 -2.342886e-17 1.559898e-14 1.648340e-01 --1.347850e-15 1.299206e-14 6.327421e-02 --9.443098e-17 1.575641e-14 -8.212086e-03 --4.214419e-16 1.758102e-14 5.482776e-02 --4.163501e-16 1.801139e-14 9.412480e-02 --3.947601e-16 1.740729e-14 1.829307e-01 --2.960744e-16 1.700909e-14 5.739114e-02 --3.579030e-17 1.699163e-14 3.036758e-02 --5.228607e-16 1.738459e-14 1.022536e-01 --3.904501e-16 1.712705e-14 1.820930e-02 --6.963616e-16 1.676067e-14 -1.081598e-03 -1.175394e-16 1.618527e-14 -3.497787e-02 --3.525871e-17 1.609687e-14 -4.967612e-02 --1.186192e-16 1.625945e-14 6.871646e-02 --9.387670e-16 1.579402e-14 1.091902e-01 --3.658479e-16 1.731533e-14 4.344252e-02 -2.168844e-16 1.579066e-14 1.779878e-01 --3.384582e-16 1.801248e-14 4.053764e-02 --1.048991e-15 1.782323e-14 -5.683955e-02 --3.885114e-16 1.796105e-14 -1.896831e-02 -2.119638e-16 1.626082e-14 -1.067787e-01 --8.268241e-16 1.673505e-14 -3.771143e-02 -4.811624e-17 1.594121e-14 1.100065e-01 --9.808880e-18 1.735772e-14 1.170658e-01 --5.908484e-16 1.781283e-14 -5.913989e-02 --2.087116e-16 1.617546e-14 9.182605e-02 --3.752419e-17 1.596892e-14 -3.131891e-02 --3.284215e-16 1.744485e-14 1.883527e-01 -6.058812e-16 1.558858e-14 2.864801e-01 --7.748792e-16 1.529823e-14 -1.203358e-01 -6.058812e-16 1.558858e-14 2.864801e-01 --7.748792e-16 1.529823e-14 -1.203358e-01 --1.618098e-16 1.484781e-14 8.559456e-02 --6.564500e-16 1.753598e-14 1.349262e-01 --7.308161e-16 1.628304e-14 1.353369e-01 -7.127859e-16 1.697065e-14 -2.178491e-01 --6.564500e-16 1.753598e-14 1.349262e-01 --7.308161e-16 1.628304e-14 1.353369e-01 --7.898867e-16 1.816683e-14 1.967637e-02 --8.512645e-16 1.698359e-14 2.229429e-02 --1.834114e-16 1.749442e-14 8.864261e-02 --1.902862e-16 1.782692e-14 8.699035e-02 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -4.679293e-16 1.556832e-14 2.459445e-01 -3.117278e-16 1.556402e-14 2.054586e-01 -1.542288e-16 1.552341e-14 1.649326e-01 --1.340339e-17 1.551427e-14 1.243582e-01 --1.540592e-16 1.549350e-14 8.371888e-02 --2.776554e-16 1.545838e-14 4.302866e-02 --4.179901e-16 1.543445e-14 2.294139e-03 --5.386281e-16 1.540700e-14 -3.846628e-02 --6.623916e-16 1.535630e-14 -7.930221e-02 -5.902861e-16 1.570442e-14 2.774031e-01 -5.279749e-16 1.588066e-14 2.598554e-01 -4.178505e-16 1.600426e-14 2.347883e-01 -3.202153e-16 1.610923e-14 2.034880e-01 -1.936383e-16 1.616329e-14 1.670174e-01 -5.387657e-17 1.621141e-14 1.264543e-01 --8.504795e-17 1.621805e-14 8.343281e-02 --2.394367e-16 1.619009e-14 4.014865e-02 --3.881756e-16 1.611765e-14 -1.216045e-03 --5.048004e-16 1.601815e-14 -3.852675e-02 --6.248992e-16 1.589250e-14 -7.013922e-02 --6.931323e-16 1.570444e-14 -9.512329e-02 --7.401986e-16 1.550517e-14 -1.122011e-01 -4.679293e-16 1.556832e-14 2.459445e-01 -3.117278e-16 1.556402e-14 2.054586e-01 -1.542288e-16 1.552341e-14 1.649326e-01 --1.340339e-17 1.551427e-14 1.243582e-01 --1.540592e-16 1.549350e-14 8.371888e-02 --2.776554e-16 1.545838e-14 4.302866e-02 --4.179901e-16 1.543445e-14 2.294139e-03 --5.386281e-16 1.540700e-14 -3.846628e-02 --6.623916e-16 1.535630e-14 -7.930221e-02 -6.064492e-16 1.542221e-14 2.865557e-01 -5.983099e-16 1.524749e-14 2.777342e-01 -5.065288e-16 1.511141e-14 2.607351e-01 -3.485983e-16 1.498820e-14 2.362260e-01 -2.172340e-16 1.492165e-14 2.051506e-01 -1.015143e-16 1.488322e-14 1.688123e-01 --4.485648e-17 1.485742e-14 1.283100e-01 --2.957238e-16 1.484908e-14 4.265432e-02 --4.405710e-16 1.490418e-14 1.868537e-03 --5.343528e-16 1.497164e-14 -3.471429e-02 --6.202268e-16 1.504970e-14 -6.583485e-02 --6.967720e-16 1.511009e-14 -9.070071e-02 --7.803211e-16 1.517219e-14 -1.088576e-01 --8.019344e-16 1.522554e-14 -1.191828e-01 --6.587630e-16 1.735518e-14 1.353355e-01 --6.589260e-16 1.715425e-14 1.357237e-01 --6.715156e-16 1.692340e-14 1.358676e-01 --6.917517e-16 1.670568e-14 1.356854e-01 --7.169057e-16 1.650125e-14 1.354201e-01 --5.381502e-16 1.763961e-14 1.058585e-01 --4.088452e-16 1.773129e-14 7.211860e-02 --2.701192e-16 1.778098e-14 3.473381e-02 --1.230959e-16 1.780313e-14 -5.183813e-03 -3.280760e-17 1.779661e-14 -4.613312e-02 -1.840184e-16 1.775175e-14 -8.630972e-02 -3.318190e-16 1.768196e-14 -1.235696e-01 -4.624157e-16 1.756609e-14 -1.560864e-01 -5.451204e-16 1.745403e-14 -1.823853e-01 -6.200143e-16 1.733155e-14 -2.019560e-01 -6.773037e-16 1.716798e-14 -2.138974e-01 -7.015247e-16 1.678240e-14 -2.136569e-01 -6.637440e-16 1.660252e-14 -2.014085e-01 -5.664421e-16 1.643221e-14 -1.818187e-01 -4.599913e-16 1.628298e-14 -1.557922e-01 -3.227998e-16 1.617441e-14 -1.237279e-01 -1.773503e-16 1.610383e-14 -8.703991e-02 -3.213488e-17 1.603800e-14 -4.723070e-02 --1.175989e-16 1.600227e-14 -6.248892e-03 --2.603194e-16 1.598458e-14 3.401487e-02 --4.017306e-16 1.601658e-14 7.194375e-02 --5.703414e-16 1.613418e-14 1.061518e-01 --6.587630e-16 1.735518e-14 1.353355e-01 --6.589260e-16 1.715425e-14 1.357237e-01 --6.715156e-16 1.692340e-14 1.358676e-01 --6.917517e-16 1.670568e-14 1.356854e-01 --7.169057e-16 1.650125e-14 1.354201e-01 --7.281330e-16 1.741955e-14 1.572847e-01 --8.109894e-16 1.728346e-14 1.745282e-01 --8.672126e-16 1.711990e-14 1.853690e-01 --8.965463e-16 1.689386e-14 1.890156e-01 --8.864199e-16 1.672146e-14 1.851015e-01 --8.685141e-16 1.656067e-14 1.743750e-01 --8.207879e-16 1.642218e-14 1.574664e-01 --9.053648e-16 1.814857e-14 -2.547015e-02 --1.020142e-15 1.811276e-14 -6.769223e-02 --1.139443e-15 1.804195e-14 -1.049769e-01 --1.245584e-15 1.796216e-14 -1.360916e-01 --1.340748e-15 1.786693e-14 -1.595253e-01 --1.378857e-15 1.774420e-14 -1.744330e-01 --1.406037e-15 1.759287e-14 -1.796866e-01 --1.366027e-15 1.747262e-14 -1.745855e-01 --1.316708e-15 1.734108e-14 -1.593221e-01 --1.275663e-15 1.721817e-14 -1.351600e-01 --1.187815e-15 1.712165e-14 -1.034590e-01 --1.084261e-15 1.705801e-14 -6.560358e-02 --9.699713e-16 1.701363e-14 -2.305189e-02 --7.433650e-16 1.698974e-14 6.825059e-02 --6.466906e-16 1.702190e-14 1.120994e-01 --5.348360e-16 1.711745e-14 1.513816e-01 --4.228495e-16 1.726583e-14 1.839271e-01 --3.532929e-16 1.738546e-14 2.083314e-01 --3.077242e-16 1.752872e-14 2.233542e-01 --2.955842e-16 1.766925e-14 2.280852e-01 --3.175536e-16 1.782808e-14 2.222115e-01 --3.637105e-16 1.793014e-14 2.064419e-01 --4.217643e-16 1.800215e-14 1.819253e-01 --5.082355e-16 1.806752e-14 1.493657e-01 --5.947896e-16 1.812393e-14 1.098581e-01 --6.840986e-16 1.815587e-14 6.567007e-02 --1.521057e-16 1.750709e-14 4.307941e-02 --1.255783e-16 1.753855e-14 5.891136e-04 --1.205330e-16 1.755536e-14 -3.698459e-02 --8.533742e-17 1.759647e-14 -6.852613e-02 --9.246757e-17 1.763224e-14 -9.330830e-02 --7.671849e-17 1.767494e-14 -1.096859e-01 --8.052187e-17 1.771579e-14 -1.154140e-01 --5.673855e-17 1.775283e-14 -1.105153e-01 --1.098281e-16 1.778308e-14 -9.545434e-02 --1.024477e-16 1.780361e-14 -7.155940e-02 --1.306281e-16 1.781104e-14 -3.998438e-02 --1.531380e-16 1.782335e-14 -1.851557e-03 --1.749904e-16 1.783825e-14 4.123243e-02 --1.895857e-16 1.785550e-14 1.330131e-01 --2.334545e-16 1.787838e-14 1.763059e-01 --2.811777e-16 1.787120e-14 2.146440e-01 --3.182115e-16 1.786192e-14 2.464015e-01 --3.443280e-16 1.782376e-14 2.704549e-01 --3.837183e-16 1.777775e-14 2.857709e-01 --4.267393e-16 1.774416e-14 2.916062e-01 --4.068192e-16 1.768526e-14 2.872087e-01 --3.782084e-16 1.761967e-14 2.727487e-01 --3.789487e-16 1.758982e-14 2.489466e-01 --3.405935e-16 1.753835e-14 2.170467e-01 --2.798029e-16 1.749499e-14 1.783461e-01 --2.167175e-16 1.749971e-14 1.347590e-01 --1.117634e-16 1.376677e-14 7.399632e-03 -4.199741e-17 1.564437e-14 3.699488e-02 --1.396180e-16 1.661523e-14 7.862874e-02 --2.685202e-16 1.706792e-14 1.208592e-01 --3.300156e-16 1.733895e-14 1.522798e-01 --3.292753e-16 1.754440e-14 1.670328e-01 --3.175253e-16 1.771923e-14 1.515116e-01 --2.837013e-16 1.781843e-14 1.107870e-01 --2.417965e-16 1.785230e-14 4.937657e-02 --2.308462e-16 1.785546e-14 -7.086700e-03 --2.503563e-16 1.778209e-14 -3.687902e-02 --3.318424e-16 1.775060e-14 -3.201354e-02 --4.818396e-16 1.764305e-14 2.297170e-02 --9.251492e-16 1.525088e-14 2.960891e-02 --7.070802e-16 9.055821e-15 4.243520e-02 --8.651538e-16 1.306929e-14 5.553790e-02 --3.903148e-16 1.469432e-14 3.908246e-02 --2.211440e-16 1.560821e-14 1.268552e-02 --2.031732e-16 1.603183e-14 -1.458897e-02 --2.929288e-16 1.627651e-14 -3.675529e-02 --3.857151e-16 1.638256e-14 -4.563458e-02 --4.229390e-16 1.641908e-14 -3.939534e-02 --3.801561e-16 1.633280e-14 6.463857e-04 --2.395098e-16 1.615383e-14 6.695346e-02 -7.806008e-17 1.591796e-14 1.602953e-01 --4.517070e-16 9.509998e-15 -5.234120e-02 --9.251492e-16 1.525088e-14 2.960891e-02 --4.818396e-16 1.764305e-14 2.297170e-02 --3.318424e-16 1.775060e-14 -3.201354e-02 --2.503563e-16 1.778209e-14 -3.687902e-02 --2.308462e-16 1.785546e-14 -7.086700e-03 --2.417965e-16 1.785230e-14 4.937657e-02 --2.837013e-16 1.781843e-14 1.107870e-01 --3.175253e-16 1.771923e-14 1.515116e-01 --3.292753e-16 1.754440e-14 1.670328e-01 --3.300156e-16 1.733895e-14 1.522798e-01 --2.685202e-16 1.706792e-14 1.208592e-01 --1.396180e-16 1.661523e-14 7.862874e-02 -4.199741e-17 1.564437e-14 3.699488e-02 --1.117634e-16 1.376677e-14 7.399632e-03 --4.517070e-16 9.509998e-15 -5.234120e-02 -7.806008e-17 1.591796e-14 1.602953e-01 --2.395098e-16 1.615383e-14 6.695346e-02 --3.801561e-16 1.633280e-14 6.463857e-04 --4.229390e-16 1.641908e-14 -3.939534e-02 --3.857151e-16 1.638256e-14 -4.563458e-02 --2.929288e-16 1.627651e-14 -3.675529e-02 --2.031732e-16 1.603183e-14 -1.458897e-02 --2.211440e-16 1.560821e-14 1.268552e-02 --3.903148e-16 1.469432e-14 3.908246e-02 --8.651538e-16 1.306929e-14 5.553790e-02 --7.070802e-16 9.055821e-15 4.243520e-02 --2.145950e-18 1.588365e-14 1.149331e-01 --3.187552e-16 1.572645e-14 2.580925e-02 -2.244782e-16 1.586056e-14 1.767883e-01 --5.106621e-16 1.562697e-14 -3.563563e-02 --1.859865e-16 1.589220e-14 6.116840e-02 --1.992485e-16 1.572357e-14 6.337813e-02 -1.425627e-16 1.596640e-14 1.551934e-01 -3.726880e-16 1.573538e-14 2.187462e-01 --3.498061e-17 1.566567e-14 1.129449e-01 --3.599431e-17 1.607456e-14 9.887843e-02 --4.937081e-16 1.578285e-14 -3.260891e-02 --3.017368e-16 1.557494e-14 3.386140e-02 --3.601822e-16 1.589368e-14 8.213276e-03 --6.247499e-16 1.550350e-14 -7.219841e-02 -1.731817e-16 1.572418e-14 1.670199e-01 -2.386146e-16 1.603093e-14 1.815394e-01 --4.201936e-16 1.555107e-14 -2.987265e-03 -3.401288e-16 1.588906e-14 2.080645e-01 --5.820548e-16 1.568102e-14 -5.801250e-02 -9.217689e-17 1.607356e-14 1.374017e-01 --1.866774e-16 1.607037e-14 5.760308e-02 -4.853002e-16 1.566468e-14 2.484209e-01 --4.272875e-16 1.568425e-14 -9.395764e-03 -1.744488e-16 1.609819e-14 1.621357e-01 --1.697601e-16 1.561049e-14 7.301783e-02 --5.295446e-16 1.553150e-14 -3.914002e-02 --3.124852e-16 1.602435e-14 2.150937e-02 --7.105971e-17 1.575265e-14 9.813556e-02 --2.933344e-16 1.519634e-14 4.985727e-02 -4.354247e-17 1.513374e-14 1.551525e-01 --5.008720e-16 1.517991e-14 -1.805213e-02 --7.225845e-17 1.503884e-14 1.187820e-01 --1.015324e-16 1.528274e-14 1.036002e-01 -2.897635e-16 1.533859e-14 2.102652e-01 --4.220109e-16 1.507257e-14 6.428283e-03 --2.011174e-16 1.494487e-14 7.896278e-02 --6.076906e-16 1.520278e-14 -5.088756e-02 -4.606758e-17 1.503583e-14 1.550519e-01 -2.146205e-16 1.515720e-14 2.056501e-01 -1.004550e-17 1.537999e-14 1.335520e-01 --1.010089e-16 1.515533e-14 1.067623e-01 --5.635960e-16 1.512154e-14 -4.261854e-02 --3.679564e-16 1.516062e-14 2.472915e-02 --2.424334e-16 1.534266e-14 6.156105e-02 --8.473484e-17 1.494852e-14 1.163515e-01 -4.507890e-16 1.543855e-14 2.457419e-01 -1.549121e-16 1.539672e-14 1.722525e-01 --3.282113e-16 1.496861e-14 3.998136e-02 -3.298783e-17 1.496174e-14 1.522137e-01 --1.944924e-16 1.523584e-14 8.044475e-02 -3.939289e-16 1.528396e-14 2.379120e-01 --1.985288e-16 1.507395e-14 7.915430e-02 -1.604358e-16 1.527754e-14 1.816648e-01 --3.131323e-16 1.505606e-14 4.527600e-02 --4.278731e-16 1.528796e-14 2.519802e-03 --1.138755e-16 1.537535e-14 9.852865e-02 --3.753471e-17 1.510571e-14 1.271257e-01 --3.276554e-18 1.524603e-14 1.366804e-01 -1.969510e-16 1.502969e-14 1.943161e-01 --4.774276e-16 1.502358e-14 -1.146834e-02 --6.759194e-16 1.527265e-14 -7.997642e-02 --4.378989e-16 1.521692e-14 1.968017e-04 --5.879864e-16 1.533202e-14 -5.005832e-02 --4.719635e-16 1.512739e-14 -7.803242e-03 -3.326286e-16 1.543785e-14 2.138852e-01 -1.555398e-16 1.497524e-14 1.846847e-01 -9.827009e-17 1.696457e-14 -6.037574e-02 --2.411040e-16 1.651640e-14 2.873760e-02 --1.928342e-16 1.730171e-14 1.452546e-02 -1.376147e-16 1.660316e-14 -7.241167e-02 -1.673477e-16 1.745285e-14 -8.078356e-02 -3.748609e-16 1.702126e-14 -1.346298e-01 --3.364193e-16 1.690624e-14 5.098129e-02 -5.930469e-18 1.641122e-14 -3.619187e-02 --1.187359e-16 1.753765e-14 -5.709211e-03 -3.927679e-16 1.676382e-14 -1.390405e-01 --4.031087e-16 1.716765e-14 6.935301e-02 --3.796632e-16 1.644997e-14 6.340202e-02 -3.882984e-16 1.723856e-14 -1.363444e-01 --4.191842e-16 1.741096e-14 7.396095e-02 --2.072411e-17 1.674502e-14 -2.973616e-02 --2.699057e-17 1.721370e-14 -2.877395e-02 -2.322631e-16 1.683229e-14 -9.839498e-02 --1.763707e-16 1.627590e-14 1.336472e-02 --4.592709e-16 1.672856e-14 8.134441e-02 --2.239606e-16 1.715958e-14 2.308972e-02 -1.569110e-16 1.626018e-14 -7.889703e-02 -2.402135e-16 1.713529e-14 -9.764543e-02 -1.832901e-16 1.759221e-14 -8.529297e-02 -3.694244e-16 1.649935e-14 -1.351544e-01 -5.129397e-16 1.690277e-14 -1.711096e-01 -3.350414e-16 1.744446e-14 -1.212714e-01 --1.746551e-16 1.669298e-14 1.069637e-02 -5.026900e-16 1.716968e-14 -1.708543e-01 --5.130632e-16 1.699421e-14 9.662001e-02 --1.911658e-16 1.693507e-14 1.407712e-02 --3.592182e-16 1.622529e-14 6.055551e-02 -1.276941e-18 1.656345e-14 -3.578318e-02 --3.682900e-17 1.620630e-14 -2.493525e-02 --3.063994e-16 1.758564e-14 4.291500e-02 -1.251341e-17 1.736053e-14 -3.861039e-02 -3.578395e-17 1.763955e-14 -4.647829e-02 --1.151546e-16 1.766730e-14 -7.183542e-03 -4.768890e-16 1.659469e-14 -1.587684e-01 --4.747244e-16 1.730380e-14 8.823946e-02 --3.666369e-16 1.657419e-14 5.912393e-02 -5.523477e-16 1.668170e-14 -1.775257e-01 -2.734051e-16 1.728464e-14 -1.063739e-01 --5.119164e-16 1.683238e-14 9.542420e-02 --2.951947e-16 1.637322e-14 4.346116e-02 -2.778070e-16 1.629925e-14 -1.116126e-01 --1.255314e-16 1.644131e-14 -1.326639e-03 --3.509468e-16 1.725881e-14 5.629061e-02 -3.291003e-16 1.686192e-14 -1.235856e-01 -2.746006e-16 1.667049e-14 -1.093555e-01 --1.151355e-16 1.742778e-14 -6.070451e-03 --2.825144e-16 1.741670e-14 3.711898e-02 -1.124794e-16 1.678732e-14 -6.511756e-02 --4.740773e-16 1.750325e-14 8.750969e-02 --5.050950e-16 1.661323e-14 9.112195e-02 --5.066899e-16 1.639913e-14 9.096589e-02 -2.295830e-16 1.698198e-14 -9.589070e-02 --3.472894e-16 1.706275e-14 5.479750e-02 -5.301362e-16 1.730389e-14 -1.760766e-01 -1.422596e-16 1.731462e-14 -7.266921e-02 --3.487095e-16 1.674025e-14 5.377555e-02 --1.276724e-16 1.731286e-14 -2.427077e-03 --4.501964e-17 1.693840e-14 -2.409785e-02 -5.764552e-16 1.700911e-14 -1.868530e-01 --5.128872e-16 1.715953e-14 9.769841e-02 --9.210032e-17 1.655126e-14 -1.037380e-02 -2.766826e-16 1.757935e-14 -1.093824e-01 -1.556042e-16 1.643943e-14 -7.716809e-02 -1.097100e-16 1.716003e-14 -6.496944e-02 --2.693915e-16 1.615440e-14 3.737150e-02 -3.303096e-16 1.714221e-14 -1.223143e-01 --2.299935e-16 1.769086e-14 2.389241e-02 -4.542726e-16 1.736555e-14 -1.554856e-01 -3.272136e-17 1.749701e-14 -4.529458e-02 --7.767324e-16 1.695390e-14 1.638822e-01 --7.644587e-16 1.687778e-14 1.590260e-01 --7.969776e-16 1.653903e-14 1.555869e-01 --7.267523e-16 1.731260e-14 1.555972e-01 --8.303189e-16 1.763533e-14 2.487111e-02 --8.384423e-16 1.728120e-14 3.226134e-02 --8.350774e-16 1.794911e-14 1.079915e-02 --1.109480e-15 1.770212e-14 -8.137308e-02 --5.696999e-16 1.757029e-14 1.288511e-01 --1.033279e-15 1.740339e-14 -4.462786e-02 --6.428449e-16 1.786582e-14 9.286946e-02 --9.729494e-16 1.790681e-14 -4.064769e-02 --6.971654e-16 1.732662e-14 8.646975e-02 --9.910137e-16 1.726423e-14 -2.975207e-02 --6.777402e-16 1.797737e-14 7.481916e-02 --1.197162e-15 1.749891e-14 -1.101100e-01 --4.954632e-16 1.779192e-14 1.570309e-01 --7.666397e-16 1.749612e-14 5.450459e-02 --9.125196e-16 1.776723e-14 -7.815783e-03 --8.878044e-16 1.744170e-14 8.864630e-03 --7.781967e-16 1.781971e-14 3.824934e-02 --1.000212e-15 1.761662e-14 -3.727986e-02 --6.643639e-16 1.765711e-14 8.720673e-02 --7.461574e-16 1.716255e-14 7.051501e-02 --9.202404e-16 1.802517e-14 -2.503068e-02 --1.137923e-15 1.783800e-14 -9.955037e-02 --5.257932e-16 1.741776e-14 1.467955e-01 --1.154083e-15 1.731437e-14 -8.938466e-02 --5.371889e-16 1.796493e-14 1.335190e-01 --8.748710e-16 1.715097e-14 1.699030e-02 --7.810062e-16 1.805049e-14 2.706958e-02 --1.233086e-15 1.768035e-14 -1.254442e-01 --4.487228e-16 1.759516e-14 1.732701e-01 --1.245746e-15 1.741796e-14 -1.275156e-01 --4.412300e-16 1.788222e-14 1.745523e-01 --1.075919e-15 1.757708e-14 -6.570708e-02 --6.066253e-16 1.770580e-14 1.132380e-01 --1.003445e-15 1.803247e-14 -5.907400e-02 --6.703913e-16 1.714346e-14 1.036490e-01 --9.355844e-16 1.734317e-14 -6.708459e-03 --7.354390e-16 1.790806e-14 5.246811e-02 --1.088221e-15 1.795051e-14 -8.468043e-02 --5.845898e-16 1.725816e-14 1.297716e-01 --1.081517e-15 1.720555e-14 -6.388202e-02 --5.991492e-16 1.802474e-14 1.088527e-01 --7.980370e-16 1.738685e-14 4.586346e-02 --8.817174e-16 1.786360e-14 -2.528706e-03 --4.072037e-16 1.772179e-14 1.893507e-01 --1.282637e-15 1.755507e-14 -1.413328e-01 --1.026281e-15 1.713745e-14 -4.238737e-02 --6.424442e-16 1.806622e-14 8.670739e-02 --6.642508e-16 1.745640e-14 9.305587e-02 --1.007707e-15 1.779938e-14 -4.667220e-02 --1.056482e-15 1.732856e-14 -5.290554e-02 --6.252726e-16 1.793114e-14 9.869039e-02 --8.356877e-16 1.709913e-14 3.314321e-02 --8.203521e-16 1.808398e-14 1.044109e-02 --1.232413e-15 1.779941e-14 -1.290613e-01 --4.467955e-16 1.745999e-14 1.770604e-01 --1.166230e-15 1.758271e-14 -1.003696e-01 --5.181893e-16 1.770271e-14 1.477902e-01 --9.805086e-16 1.748892e-14 -2.598691e-02 --6.924997e-16 1.777516e-14 7.545361e-02 --8.540304e-16 1.754027e-14 2.017000e-02 --1.005755e-15 1.769484e-14 -4.087927e-02 --6.674366e-16 1.757519e-14 8.717908e-02 --8.101874e-16 1.773001e-14 2.949491e-02 --9.081132e-16 1.756496e-14 -3.072530e-03 --7.282033e-16 1.769365e-14 6.306278e-02 --1.293954e-15 1.748300e-14 -1.484125e-01 --3.817649e-16 1.782394e-14 1.958083e-01 --1.118174e-15 1.744765e-14 -7.727875e-02 --5.672211e-16 1.783862e-14 1.241265e-01 --9.151057e-16 1.767284e-14 -7.631925e-03 --7.503925e-16 1.760166e-14 5.745393e-02 --1.718359e-16 1.773875e-14 8.740655e-02 --1.655804e-16 1.760393e-14 7.010377e-02 --1.914688e-16 1.782573e-14 1.065309e-01 --1.181299e-16 1.771767e-14 -1.789415e-02 --2.717015e-16 1.774346e-14 1.949861e-01 --1.317696e-16 1.778061e-14 7.707372e-04 --2.607907e-16 1.763819e-14 1.789377e-01 --2.377458e-16 1.782221e-14 1.641235e-01 --1.307367e-16 1.762969e-14 1.388734e-02 --2.294887e-16 1.759548e-14 1.402117e-01 --1.565690e-16 1.780801e-14 3.637640e-02 --1.224988e-16 1.775789e-14 -3.874003e-02 --1.450559e-16 1.767383e-14 4.821810e-02 --2.188512e-16 1.778458e-14 1.278212e-01 --1.986177e-16 1.769122e-14 1.079944e-01 --1.532853e-16 1.776931e-14 6.772777e-02 --2.117570e-16 1.783598e-14 1.520102e-01 --1.327409e-16 1.758732e-14 2.552356e-02 --1.045904e-16 1.766543e-14 -4.038169e-02 --2.963230e-16 1.779584e-14 2.187758e-01 --2.098535e-16 1.771842e-14 1.292023e-01 --1.353666e-16 1.773880e-14 4.348002e-02 --1.746548e-16 1.754820e-14 8.060482e-02 --1.874122e-16 1.783248e-14 9.583335e-02 --8.568705e-17 1.770567e-14 -6.441422e-02 --3.211412e-16 1.774395e-14 2.411021e-01 --1.293362e-16 1.780593e-14 -8.212387e-03 --3.020688e-16 1.757633e-14 1.850368e-01 --1.227018e-16 1.777658e-14 -3.833971e-02 --3.236687e-16 1.760374e-14 2.128861e-01 --1.225382e-16 1.774587e-14 4.423765e-04 --2.586154e-16 1.770222e-14 1.690663e-01 --2.183492e-16 1.763410e-14 1.246861e-01 --1.645709e-16 1.779491e-14 5.218478e-02 --2.594256e-16 1.784886e-14 1.926184e-01 --1.173904e-16 1.758763e-14 -1.267451e-02 --2.781686e-16 1.783159e-14 2.042926e-01 --1.114376e-16 1.761315e-14 -2.613979e-02 --1.227477e-16 1.767682e-14 1.031250e-02 --2.584838e-16 1.779130e-14 1.673809e-01 --1.496480e-16 1.764193e-14 4.147916e-02 --2.247893e-16 1.779712e-14 1.350299e-01 --1.867530e-16 1.754727e-14 1.184666e-01 --1.750337e-16 1.782364e-14 5.730113e-02 --9.906089e-17 1.774457e-14 -7.122348e-02 --3.603794e-16 1.768598e-14 2.563257e-01 --1.716530e-16 1.764105e-14 8.889140e-02 --1.837315e-16 1.778881e-14 8.754107e-02 --1.400351e-16 1.777235e-14 3.183085e-02 --2.362208e-16 1.766173e-14 1.447364e-01 --1.021385e-16 1.777753e-14 -7.055360e-02 --2.381710e-16 1.756208e-14 1.515348e-01 --3.570673e-16 1.766040e-14 2.378602e-01 --1.629934e-16 1.781546e-14 2.505250e-02 --1.969925e-16 1.776768e-14 1.085220e-01 --1.591320e-16 1.770427e-14 6.712804e-02 --1.500619e-16 1.755863e-14 5.098667e-02 --1.993743e-16 1.783537e-14 1.256407e-01 --9.463594e-17 1.768616e-14 -7.308439e-02 --3.360211e-16 1.777464e-14 2.498659e-01 --1.998402e-16 1.759748e-14 1.116494e-01 --1.679861e-16 1.780950e-14 6.508711e-02 --1.159526e-16 1.773939e-14 -3.583408e-02 --1.323370e-16 1.771130e-14 2.512272e-02 --2.414767e-16 1.774256e-14 1.503785e-01 --1.309965e-16 1.775527e-14 -1.308575e-02 --2.976577e-16 1.768804e-14 2.054638e-01 --1.544214e-16 1.779665e-14 2.071833e-02 --2.525757e-16 1.762262e-14 1.556205e-01 --2.358930e-16 1.665334e-14 5.537117e-02 --4.084108e-16 1.745597e-14 3.097192e-02 --2.002423e-16 1.723042e-14 -3.056171e-03 -1.692465e-16 1.603439e-14 4.573610e-02 -1.396466e-16 1.625769e-14 1.794650e-02 --1.333485e-17 1.723603e-14 8.407251e-02 --3.856090e-16 1.710672e-14 3.391319e-02 --3.312397e-16 1.644198e-14 1.523730e-01 --6.663227e-16 1.769205e-14 6.994570e-02 --2.193032e-16 1.700485e-14 2.687556e-02 --3.312014e-16 1.754601e-14 -6.591761e-02 --1.172232e-16 1.664137e-14 5.104251e-02 --3.736610e-16 1.721238e-14 1.320326e-01 --2.454200e-16 1.644410e-14 2.841650e-02 --3.895165e-16 1.743107e-14 9.042348e-02 --5.511034e-16 1.764592e-14 2.482841e-02 --1.278429e-15 1.455598e-14 7.851609e-02 -1.239376e-16 1.623911e-14 -6.441004e-02 --4.122405e-16 1.793094e-14 5.488240e-02 --9.294579e-17 1.682846e-14 -1.732366e-02 -3.282435e-16 1.638279e-14 -6.816676e-02 --3.207546e-16 1.749383e-14 5.121852e-02 --1.411416e-16 1.605617e-14 1.157752e-01 --1.539980e-16 1.735066e-14 -5.085674e-02 -1.284324e-16 1.708828e-14 1.418744e-01 --4.248961e-16 1.690607e-14 1.397815e-02 --7.230644e-16 1.769164e-14 1.723187e-02 -3.113536e-16 1.569771e-14 1.531141e-01 --7.132807e-16 1.772112e-14 -2.838495e-02 --6.327219e-16 1.649523e-14 3.989750e-03 -1.715615e-16 1.713220e-14 -1.088111e-01 --4.765872e-16 1.762726e-14 1.536876e-01 -4.728639e-17 1.617052e-14 2.305690e-02 -2.726399e-16 1.675334e-14 -1.091878e-01 -2.448684e-16 1.579813e-14 1.449319e-01 --3.484738e-16 1.623188e-14 7.297368e-02 --3.177489e-16 1.622994e-14 1.655337e-01 --4.106891e-16 1.706669e-14 1.728587e-01 --9.106733e-16 1.477648e-14 8.291014e-02 --1.339977e-17 1.625822e-14 -8.100817e-02 -2.623325e-16 1.628523e-14 -4.899673e-02 --4.688909e-16 1.636276e-14 -2.683874e-03 --1.377592e-16 1.702428e-14 -1.874991e-02 -1.301573e-16 1.572847e-14 1.361245e-01 -2.035414e-16 1.678725e-14 1.360484e-01 -6.794693e-18 1.649986e-14 -9.536805e-03 --2.457933e-17 1.747150e-14 1.559332e-01 --1.795156e-16 1.719801e-14 4.885094e-02 --1.205816e-16 1.759705e-14 1.425741e-01 --5.889171e-17 1.610054e-14 3.554258e-02 --6.094859e-16 1.740996e-14 -4.973582e-02 -9.387487e-18 1.742450e-14 -9.530992e-02 --9.469617e-17 1.628381e-14 2.465313e-02 --3.266471e-16 1.717539e-14 3.117808e-02 --3.487014e-16 1.792312e-14 6.117867e-02 --3.234368e-16 1.747044e-14 8.719768e-03 --3.067503e-16 1.765748e-14 -7.555047e-02 --2.536764e-16 1.771830e-14 4.257034e-02 --5.824381e-16 1.660030e-14 1.124220e-01 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1.428100e-14 1.763059e-01 --2.899298e-16 1.427865e-14 2.146440e-01 --3.326329e-16 1.425544e-14 2.464015e-01 --3.643302e-16 1.422868e-14 2.704549e-01 --3.979695e-16 1.417445e-14 2.857709e-01 --4.360000e-16 1.413795e-14 2.916062e-01 --3.952252e-16 1.407185e-14 2.872087e-01 --3.785107e-16 1.404169e-14 2.727487e-01 --3.806650e-16 1.399325e-14 2.489466e-01 --3.568351e-16 1.394876e-14 2.170467e-01 --2.832167e-16 1.391370e-14 1.783461e-01 --2.249928e-16 1.391756e-14 1.347590e-01 --2.080436e-16 1.099093e-14 7.399632e-03 --1.360393e-16 1.249580e-14 3.699488e-02 --2.965401e-16 1.328147e-14 7.862874e-02 --4.139516e-16 1.364823e-14 1.208592e-01 --4.778468e-16 1.387803e-14 1.522798e-01 --4.710387e-16 1.403806e-14 1.670328e-01 --4.559427e-16 1.418243e-14 1.515116e-01 --4.031619e-16 1.424673e-14 1.107870e-01 --3.535279e-16 1.426535e-14 4.937657e-02 --3.124700e-16 1.425615e-14 -7.086700e-03 --3.063309e-16 1.418351e-14 -3.687902e-02 --3.489072e-16 1.415537e-14 -3.201354e-02 --4.528290e-16 1.404319e-14 2.297170e-02 --8.360800e-16 1.223537e-14 2.960891e-02 --7.130816e-16 7.280473e-15 4.243520e-02 --8.585782e-16 1.053595e-14 5.553790e-02 --4.994898e-16 1.186495e-14 3.908246e-02 --3.830248e-16 1.261234e-14 1.268552e-02 --3.849778e-16 1.294897e-14 -1.458897e-02 --4.684059e-16 1.312962e-14 -3.675529e-02 --5.447074e-16 1.318242e-14 -4.563458e-02 --5.600509e-16 1.319142e-14 -3.939534e-02 --4.863425e-16 1.308829e-14 6.463857e-04 --3.145075e-16 1.290802e-14 6.695346e-02 -4.255795e-17 1.269604e-14 1.602953e-01 --5.000045e-16 7.548917e-15 -5.234120e-02 --8.360800e-16 1.223537e-14 2.960891e-02 --4.528290e-16 1.404319e-14 2.297170e-02 --3.489072e-16 1.415537e-14 -3.201354e-02 --3.063309e-16 1.418351e-14 -3.687902e-02 --3.124700e-16 1.425615e-14 -7.086700e-03 --3.535279e-16 1.426535e-14 4.937657e-02 --4.031619e-16 1.424673e-14 1.107870e-01 --4.559427e-16 1.418243e-14 1.515116e-01 --4.710387e-16 1.403806e-14 1.670328e-01 --4.778468e-16 1.387803e-14 1.522798e-01 --4.139516e-16 1.364823e-14 1.208592e-01 --2.965401e-16 1.328147e-14 7.862874e-02 --1.360393e-16 1.249580e-14 3.699488e-02 --2.080436e-16 1.099093e-14 7.399632e-03 --5.000045e-16 7.548917e-15 -5.234120e-02 -4.255795e-17 1.269604e-14 1.602953e-01 --3.145075e-16 1.290802e-14 6.695346e-02 --4.863425e-16 1.308829e-14 6.463857e-04 --5.600509e-16 1.319142e-14 -3.939534e-02 --5.447074e-16 1.318242e-14 -4.563458e-02 --4.684059e-16 1.312962e-14 -3.675529e-02 --3.849778e-16 1.294897e-14 -1.458897e-02 --3.830248e-16 1.261234e-14 1.268552e-02 --4.994898e-16 1.186495e-14 3.908246e-02 --8.585782e-16 1.053595e-14 5.553790e-02 --7.130816e-16 7.280473e-15 4.243520e-02 --1.653334e-17 1.269834e-14 1.149331e-01 --3.246209e-16 1.253282e-14 2.580925e-02 -2.120891e-16 1.266282e-14 1.767883e-01 --5.136874e-16 1.244147e-14 -3.563563e-02 --1.938713e-16 1.269571e-14 6.116840e-02 --2.109560e-16 1.253304e-14 6.337813e-02 -1.275102e-16 1.277155e-14 1.551934e-01 -3.674003e-16 1.254511e-14 2.187462e-01 --4.436946e-17 1.246392e-14 1.129449e-01 --5.936124e-17 1.287707e-14 9.887843e-02 --4.983224e-16 1.259309e-14 -3.260891e-02 --3.125873e-16 1.239336e-14 3.386140e-02 --3.703910e-16 1.269459e-14 8.213276e-03 --6.313231e-16 1.233434e-14 -7.219841e-02 -1.688461e-16 1.253095e-14 1.670199e-01 -2.277723e-16 1.283204e-14 1.815394e-01 --4.271749e-16 1.236754e-14 -2.987265e-03 -3.249802e-16 1.269989e-14 2.080645e-01 --5.905887e-16 1.249917e-14 -5.801250e-02 -7.539902e-17 1.288228e-14 1.374017e-01 --1.878380e-16 1.286074e-14 5.760308e-02 -4.845972e-16 1.247153e-14 2.484209e-01 --4.296167e-16 1.249491e-14 -9.395764e-03 -1.607156e-16 1.289880e-14 1.621357e-01 --1.811665e-16 1.242164e-14 7.301783e-02 --5.363411e-16 1.234628e-14 -3.914002e-02 --3.117870e-16 1.282824e-14 2.150937e-02 --8.031429e-17 1.255451e-14 9.813556e-02 --2.795186e-16 1.200403e-14 4.985727e-02 -6.481755e-17 1.193846e-14 1.551525e-01 --4.928371e-16 1.199595e-14 -1.805213e-02 --6.410373e-17 1.183371e-14 1.187820e-01 --1.113117e-16 1.209872e-14 1.036002e-01 -2.840551e-16 1.215320e-14 2.102652e-01 --4.170801e-16 1.187062e-14 6.428283e-03 --1.807724e-16 1.174665e-14 7.896278e-02 --5.917968e-16 1.202630e-14 -5.088756e-02 -6.257638e-17 1.183937e-14 1.550519e-01 -2.323551e-16 1.197341e-14 2.056501e-01 -2.085162e-18 1.218027e-14 1.335520e-01 --1.049108e-16 1.197251e-14 1.067623e-01 --5.692907e-16 1.193056e-14 -4.261854e-02 --3.610162e-16 1.196521e-14 2.472915e-02 --2.486704e-16 1.216584e-14 6.156105e-02 --7.233182e-17 1.174901e-14 1.163515e-01 -4.422219e-16 1.224321e-14 2.457419e-01 -1.517257e-16 1.221204e-14 1.722525e-01 --3.056322e-16 1.177796e-14 3.998136e-02 -4.823366e-17 1.174935e-14 1.522137e-01 --1.924591e-16 1.205316e-14 8.044475e-02 -3.879610e-16 1.209984e-14 2.379120e-01 --1.924871e-16 1.188197e-14 7.915430e-02 -1.608626e-16 1.209157e-14 1.816648e-01 --3.020166e-16 1.186027e-14 4.527600e-02 --4.364341e-16 1.209664e-14 2.519802e-03 --1.261356e-16 1.218535e-14 9.852865e-02 --3.311402e-17 1.190850e-14 1.271257e-01 --4.513922e-18 1.204773e-14 1.366804e-01 -2.087625e-16 1.183457e-14 1.943161e-01 --4.702609e-16 1.182179e-14 -1.146834e-02 --6.820391e-16 1.211072e-14 -7.997642e-02 --4.472608e-16 1.202387e-14 1.968017e-04 --5.804617e-16 1.214785e-14 -5.005832e-02 --4.627787e-16 1.194132e-14 -7.803242e-03 -3.280888e-16 1.224457e-14 2.138852e-01 -1.690253e-16 1.176671e-14 1.846847e-01 --1.246235e-16 1.361735e-14 -6.037574e-02 --3.366112e-16 1.332204e-14 2.873760e-02 --2.979990e-16 1.382966e-14 1.452546e-02 --1.018972e-16 1.338834e-14 -7.241167e-02 --7.404707e-17 1.393175e-14 -8.078356e-02 -4.773164e-17 1.366212e-14 -1.346298e-01 --3.900509e-16 1.356926e-14 5.098129e-02 --1.857044e-16 1.326034e-14 -3.619187e-02 --2.520550e-16 1.398208e-14 -5.709211e-03 -5.746556e-17 1.349774e-14 -1.390405e-01 --4.305540e-16 1.374100e-14 6.935301e-02 --4.230945e-16 1.327762e-14 6.340202e-02 -6.299406e-17 1.380355e-14 -1.363444e-01 --4.386890e-16 1.390054e-14 7.396095e-02 --1.976123e-16 1.347441e-14 -2.973616e-02 --1.955818e-16 1.377513e-14 -2.877395e-02 --4.249837e-17 1.353835e-14 -9.839498e-02 --2.992276e-16 1.316634e-14 1.336472e-02 --4.694112e-16 1.345402e-14 8.134441e-02 --3.179903e-16 1.373574e-14 2.308972e-02 --9.410913e-17 1.316270e-14 -7.889703e-02 --3.003224e-17 1.373395e-14 -9.764543e-02 --6.404255e-17 1.402263e-14 -8.529297e-02 -4.276040e-17 1.331945e-14 -1.351544e-01 -1.330324e-16 1.359036e-14 -1.711096e-01 -3.365948e-17 1.392629e-14 -1.212714e-01 --2.945607e-16 1.344039e-14 1.069637e-02 -1.258951e-16 1.376299e-14 -1.708543e-01 --5.000755e-16 1.362405e-14 9.662001e-02 --2.990987e-16 1.359233e-14 1.407712e-02 --4.089687e-16 1.312648e-14 6.055551e-02 --1.861470e-16 1.335927e-14 -3.578318e-02 --2.141653e-16 1.312093e-14 -2.493525e-02 --3.721241e-16 1.401046e-14 4.291500e-02 --1.712640e-16 1.387085e-14 -3.861039e-02 --1.569701e-16 1.404622e-14 -4.647829e-02 --2.531743e-16 1.406402e-14 -7.183542e-03 -1.146378e-16 1.338822e-14 -1.587684e-01 --4.735791e-16 1.382966e-14 8.823946e-02 --4.134474e-16 1.335906e-14 5.912393e-02 -1.620214e-16 1.343969e-14 -1.775257e-01 --8.028772e-18 1.382991e-14 -1.063739e-01 --5.005586e-16 1.351836e-14 9.542420e-02 --3.697963e-16 1.322787e-14 4.346116e-02 --1.563504e-17 1.319042e-14 -1.116126e-01 --2.644731e-16 1.327895e-14 -1.326639e-03 --3.959180e-16 1.380028e-14 5.629061e-02 -1.564006e-17 1.355955e-14 -1.235856e-01 --1.169774e-17 1.343328e-14 -1.093555e-01 --2.497245e-16 1.391207e-14 -6.070451e-03 --3.548506e-16 1.390428e-14 3.711898e-02 --1.122915e-16 1.350701e-14 -6.511756e-02 --4.749062e-16 1.396167e-14 8.750969e-02 --5.005199e-16 1.338054e-14 9.112195e-02 --5.029469e-16 1.324318e-14 9.096589e-02 --4.085045e-17 1.363416e-14 -9.589070e-02 --3.953151e-16 1.367062e-14 5.479750e-02 -1.450569e-16 1.384731e-14 -1.760766e-01 --8.979357e-17 1.384150e-14 -7.266921e-02 --4.007541e-16 1.346504e-14 5.377555e-02 --2.588232e-16 1.383906e-14 -2.427077e-03 --2.084654e-16 1.360015e-14 -2.409785e-02 -1.719884e-16 1.366111e-14 -1.868530e-01 --4.991622e-16 1.373290e-14 9.769841e-02 --2.446497e-16 1.334838e-14 -1.037380e-02 --3.336549e-18 1.401094e-14 -1.093824e-01 --9.195556e-17 1.328299e-14 -7.716809e-02 --1.103360e-16 1.374667e-14 -6.496944e-02 --3.547412e-16 1.308024e-14 3.737150e-02 -2.437156e-17 1.373964e-14 -1.223143e-01 --3.237509e-16 1.407853e-14 2.389241e-02 -1.025934e-16 1.388174e-14 -1.554856e-01 --1.561717e-16 1.395948e-14 -4.529458e-02 --6.760873e-16 1.357429e-14 1.638822e-01 --6.613645e-16 1.352337e-14 1.590260e-01 --6.860927e-16 1.331564e-14 1.555869e-01 --6.347936e-16 1.383227e-14 1.555972e-01 --9.242974e-16 1.411834e-14 2.487111e-02 --9.328453e-16 1.384877e-14 3.226134e-02 --9.191183e-16 1.436111e-14 1.079915e-02 --1.130829e-15 1.416800e-14 -8.137308e-02 --7.236545e-16 1.407201e-14 1.288511e-01 --1.080889e-15 1.393745e-14 -4.462786e-02 --7.735630e-16 1.430510e-14 9.286946e-02 --1.024271e-15 1.432476e-14 -4.064769e-02 --8.231390e-16 1.388641e-14 8.646975e-02 --1.053135e-15 1.383365e-14 -2.975207e-02 --7.967314e-16 1.438414e-14 7.481916e-02 --1.201238e-15 1.400974e-14 -1.101100e-01 --6.664049e-16 1.425040e-14 1.570309e-01 --8.766991e-16 1.401384e-14 5.450459e-02 --9.757846e-16 1.422011e-14 -7.815783e-03 --9.716538e-16 1.397110e-14 8.864630e-03 --8.789313e-16 1.426257e-14 3.824934e-02 --1.049782e-15 1.410444e-14 -3.727986e-02 --7.938810e-16 1.413653e-14 8.720673e-02 --8.633062e-16 1.375644e-14 7.051501e-02 --9.796900e-16 1.441324e-14 -2.503068e-02 --1.151970e-15 1.426716e-14 -9.955037e-02 --6.901407e-16 1.396026e-14 1.467955e-01 --1.169884e-15 1.387174e-14 -8.938466e-02 --6.940284e-16 1.437704e-14 1.335190e-01 --9.565965e-16 1.374808e-14 1.699030e-02 --8.738055e-16 1.443773e-14 2.706958e-02 --1.222655e-15 1.414558e-14 -1.254442e-01 --6.287707e-16 1.409358e-14 1.732701e-01 --1.237460e-15 1.394979e-14 -1.275156e-01 --6.230275e-16 1.432265e-14 1.745523e-01 --1.112667e-15 1.406851e-14 -6.570708e-02 --7.501773e-16 1.417841e-14 1.132380e-01 --1.050738e-15 1.441223e-14 -5.907400e-02 --8.061182e-16 1.374473e-14 1.036490e-01 --1.005756e-15 1.389568e-14 -6.708459e-03 --8.439704e-16 1.433129e-14 5.246811e-02 --1.110684e-15 1.435631e-14 -8.468043e-02 --7.374932e-16 1.383801e-14 1.297716e-01 --1.122827e-15 1.378566e-14 -6.388202e-02 --7.394726e-16 1.442439e-14 1.088527e-01 --9.021913e-16 1.392881e-14 4.586346e-02 --9.541769e-16 1.429308e-14 -2.528706e-03 --5.970135e-16 1.419501e-14 1.893507e-01 --1.267667e-15 1.405165e-14 -1.413328e-01 --1.075430e-15 1.373372e-14 -4.238737e-02 --7.693515e-16 1.444671e-14 8.670739e-02 --7.950016e-16 1.398286e-14 9.305587e-02 --1.053606e-15 1.423908e-14 -4.667220e-02 --1.100533e-15 1.388522e-14 -5.290554e-02 --7.589784e-16 1.434918e-14 9.869039e-02 --9.285064e-16 1.370895e-14 3.314321e-02 --9.037191e-16 1.445902e-14 1.044109e-02 --1.233139e-15 1.424090e-14 -1.290613e-01 --6.298603e-16 1.399214e-14 1.770604e-01 --1.176425e-15 1.407399e-14 -1.003696e-01 --6.838402e-16 1.417937e-14 1.477902e-01 --1.039796e-15 1.401006e-14 -2.598691e-02 --8.137835e-16 1.423157e-14 7.545361e-02 --9.430363e-16 1.404729e-14 2.017000e-02 --1.055264e-15 1.416579e-14 -4.087927e-02 --7.971223e-16 1.407695e-14 8.717908e-02 --9.064680e-16 1.418983e-14 2.949491e-02 --9.862089e-16 1.406472e-14 -3.072530e-03 --8.446211e-16 1.416436e-14 6.306278e-02 --1.278892e-15 1.399926e-14 -1.484125e-01 --5.750427e-16 1.427920e-14 1.958083e-01 --1.141825e-15 1.397400e-14 -7.727875e-02 --7.193799e-16 1.428421e-14 1.241265e-01 --9.882222e-16 1.414513e-14 -7.631925e-03 --8.636778e-16 1.409389e-14 5.745393e-02 --1.792770e-16 1.412160e-14 8.740655e-02 --1.776806e-16 1.401293e-14 7.010377e-02 --2.043811e-16 1.421675e-14 1.065309e-01 --1.356532e-16 1.410502e-14 -1.789415e-02 --2.828702e-16 1.414240e-14 1.949861e-01 --1.513650e-16 1.416917e-14 7.707372e-04 --2.726548e-16 1.404770e-14 1.789377e-01 --2.474323e-16 1.421212e-14 1.641235e-01 --1.401640e-16 1.403973e-14 1.388734e-02 --2.352549e-16 1.400351e-14 1.402117e-01 --1.702838e-16 1.422020e-14 3.637640e-02 --1.225487e-16 1.413907e-14 -3.874003e-02 --1.607606e-16 1.406834e-14 4.821810e-02 --2.199897e-16 1.417552e-14 1.278212e-01 --2.036703e-16 1.408315e-14 1.079944e-01 --1.642505e-16 1.416066e-14 6.772777e-02 --2.243463e-16 1.423795e-14 1.520102e-01 --1.525596e-16 1.399089e-14 2.552356e-02 --1.133420e-16 1.405940e-14 -4.038169e-02 --3.015180e-16 1.420116e-14 2.187758e-01 --2.245549e-16 1.411355e-14 1.292023e-01 --1.529103e-16 1.413444e-14 4.348002e-02 --1.908162e-16 1.395554e-14 8.060482e-02 --1.952576e-16 1.423403e-14 9.583335e-02 --1.203588e-16 1.411244e-14 -6.441422e-02 --3.285439e-16 1.414012e-14 2.411021e-01 --1.498607e-16 1.419529e-14 -8.212387e-03 --3.044372e-16 1.399486e-14 1.850368e-01 --1.330534e-16 1.417150e-14 -3.833971e-02 --3.321382e-16 1.402305e-14 2.128861e-01 --1.349531e-16 1.414128e-14 4.423765e-04 --2.677208e-16 1.409941e-14 1.690663e-01 --2.247875e-16 1.403297e-14 1.246861e-01 --1.666187e-16 1.419270e-14 5.218478e-02 --2.793924e-16 1.424698e-14 1.926184e-01 --1.307772e-16 1.400070e-14 -1.267451e-02 --2.959258e-16 1.423490e-14 2.042926e-01 --1.252995e-16 1.401834e-14 -2.613979e-02 --1.307746e-16 1.407533e-14 1.031250e-02 --2.612655e-16 1.418409e-14 1.673809e-01 --1.545043e-16 1.404397e-14 4.147916e-02 --2.220909e-16 1.421259e-14 1.350299e-01 --2.054998e-16 1.395129e-14 1.184666e-01 --1.814427e-16 1.422741e-14 5.730113e-02 --1.115828e-16 1.413511e-14 -7.122348e-02 --3.586240e-16 1.408114e-14 2.563257e-01 --1.887821e-16 1.404564e-14 8.889140e-02 --1.912664e-16 1.418454e-14 8.754107e-02 --1.528202e-16 1.415899e-14 3.183085e-02 --2.430281e-16 1.406458e-14 1.447364e-01 --1.230267e-16 1.415089e-14 -7.055360e-02 --2.479133e-16 1.397098e-14 1.515348e-01 --3.542980e-16 1.406719e-14 2.378602e-01 --1.627049e-16 1.422294e-14 2.505250e-02 --2.095435e-16 1.415648e-14 1.085220e-01 --1.701903e-16 1.409001e-14 6.712804e-02 --1.643744e-16 1.395886e-14 5.098667e-02 --2.094557e-16 1.424561e-14 1.256407e-01 --1.112955e-16 1.409512e-14 -7.308439e-02 --3.539498e-16 1.418113e-14 2.498659e-01 --2.023184e-16 1.399282e-14 1.116494e-01 --1.831932e-16 1.420857e-14 6.508711e-02 --1.198989e-16 1.412933e-14 -3.583408e-02 --1.538141e-16 1.410112e-14 2.512272e-02 --2.442101e-16 1.413904e-14 1.503785e-01 --1.332832e-16 1.415340e-14 -1.308575e-02 --3.020321e-16 1.409875e-14 2.054638e-01 --1.535116e-16 1.419223e-14 2.071833e-02 --2.611810e-16 1.402867e-14 1.556205e-01 --3.156386e-16 1.327861e-14 5.537117e-02 --5.268141e-16 1.391220e-14 3.097192e-02 --3.649326e-16 1.375425e-14 -3.056171e-03 -1.643483e-17 1.282083e-14 4.573610e-02 --1.264190e-17 1.299691e-14 1.794650e-02 --6.729208e-17 1.380103e-14 8.407251e-02 --3.981121e-16 1.368180e-14 3.391319e-02 --3.100135e-16 1.305147e-14 1.523730e-01 --7.113703e-16 1.411247e-14 6.994570e-02 --2.473649e-16 1.361549e-14 2.687556e-02 --5.055498e-16 1.399058e-14 -6.591761e-02 --1.335135e-16 1.325905e-14 5.104251e-02 --4.068094e-16 1.371120e-14 1.320326e-01 --3.489744e-16 1.313563e-14 2.841650e-02 --4.830458e-16 1.389354e-14 9.042348e-02 --5.751090e-16 1.410691e-14 2.482841e-02 --1.219756e-15 1.166321e-14 7.851609e-02 --1.199082e-16 1.306360e-14 -6.441004e-02 --4.962449e-16 1.431137e-14 5.488240e-02 --2.585262e-16 1.345428e-14 -1.732366e-02 -6.195758e-17 1.315216e-14 -6.816676e-02 --3.569195e-16 1.397732e-14 5.121852e-02 --1.670021e-16 1.275273e-14 1.157752e-01 --3.592152e-16 1.383792e-14 -5.085674e-02 -8.183953e-17 1.368965e-14 1.418744e-01 --4.034897e-16 1.353324e-14 1.397815e-02 --7.367439e-16 1.412801e-14 1.723187e-02 -2.413934e-16 1.248941e-14 1.531141e-01 --7.883089e-16 1.414644e-14 -2.838495e-02 --6.863329e-16 1.320033e-14 3.989750e-03 --1.138302e-16 1.369902e-14 -1.088111e-01 --5.280900e-16 1.405060e-14 1.536876e-01 --1.235737e-16 1.290824e-14 2.305690e-02 --1.138542e-17 1.342437e-14 -1.091878e-01 -1.667267e-16 1.259014e-14 1.449319e-01 --3.237643e-16 1.293897e-14 7.297368e-02 --2.791673e-16 1.283778e-14 1.655337e-01 --3.993277e-16 1.358080e-14 1.728587e-01 --9.529959e-16 1.186161e-14 8.291014e-02 --2.450783e-16 1.311148e-14 -8.100817e-02 -2.379160e-17 1.307951e-14 -4.899673e-02 --4.860251e-16 1.309570e-14 -2.683874e-03 --3.081220e-16 1.359142e-14 -1.874991e-02 -7.670685e-17 1.249251e-14 1.361245e-01 -1.542876e-16 1.345062e-14 1.360484e-01 --1.562619e-16 1.317973e-14 -9.536805e-03 --6.744636e-17 1.397131e-14 1.559332e-01 --1.781876e-16 1.371570e-14 4.885094e-02 --1.545211e-16 1.407176e-14 1.425741e-01 --1.883714e-16 1.288061e-14 3.554258e-02 --6.329921e-16 1.392608e-14 -4.973582e-02 --2.447473e-16 1.389102e-14 -9.530992e-02 --1.254734e-16 1.290072e-14 2.465313e-02 --3.151983e-16 1.374837e-14 3.117808e-02 --4.620093e-16 1.431885e-14 6.117867e-02 --3.015722e-16 1.396145e-14 8.719768e-03 --4.894386e-16 1.407294e-14 -7.555047e-02 --2.960373e-16 1.414285e-14 4.257034e-02 --6.809042e-16 1.328355e-14 1.124220e-01 --5.136460e-16 1.440499e-14 7.172359e-02 --7.334554e-16 1.326775e-14 -2.498105e-02 --4.526629e-16 1.388171e-14 1.544556e-02 --4.861080e-16 1.413871e-14 4.342166e-02 --1.395960e-15 1.224905e-14 -7.488592e-03 -2.241483e-16 1.243484e-14 -1.774726e-02 --4.057614e-16 1.370579e-14 8.424664e-02 -2.426947e-17 1.297764e-14 1.381353e-01 --5.753637e-16 1.267265e-14 5.468143e-02 --5.990834e-16 1.411428e-14 1.619441e-01 --4.282323e-16 1.389469e-14 1.827144e-01 --9.280439e-17 1.239284e-14 1.657897e-01 -1.803323e-16 1.044715e-14 5.441435e-02 -1.663904e-17 1.277294e-14 1.646412e-01 --6.596187e-16 1.262555e-14 5.786063e-02 --3.173773e-16 1.303170e-14 1.212559e-01 -7.966916e-17 1.322217e-14 1.155696e-01 --1.745722e-16 1.371213e-14 8.233788e-02 --4.392428e-16 1.430987e-14 7.374554e-02 -1.654591e-16 1.330700e-14 1.311554e-01 --2.234416e-16 1.283587e-14 1.356399e-01 --4.082121e-16 1.355995e-14 1.201485e-01 --9.640711e-16 1.417317e-14 -1.343628e-02 -5.490007e-17 1.329383e-14 -9.085241e-02 --3.278696e-16 1.281929e-14 -9.289403e-03 --4.889044e-16 1.420598e-14 -4.901788e-02 -1.052744e-15 1.074956e-14 -4.744290e-02 --3.835029e-16 1.419256e-14 1.411738e-01 --2.454987e-16 1.379924e-14 -8.557795e-02 --3.195804e-16 1.331412e-14 6.242456e-02 --5.235980e-16 1.394386e-14 -2.276573e-02 --1.005200e-16 1.379131e-14 1.246806e-01 --8.180508e-16 1.340355e-14 -2.218199e-02 --4.349913e-16 1.306179e-14 6.754854e-02 --1.565667e-15 9.562924e-15 4.356379e-02 --6.079816e-16 1.422602e-14 1.198429e-01 --3.102156e-16 1.309490e-14 -4.692820e-02 --6.297460e-16 1.402840e-14 4.276306e-02 --4.964795e-16 1.404998e-14 1.916356e-01 --1.800418e-16 1.249831e-14 1.666665e-01 --2.542947e-16 1.310211e-14 -7.282397e-02 --6.246017e-16 1.363680e-14 -3.252615e-02 --8.335656e-16 1.264286e-14 4.583064e-02 --2.113238e-16 1.274963e-14 5.992850e-02 --5.214074e-16 1.369119e-14 1.473038e-01 --2.179553e-15 1.092361e-14 -3.645216e-02 --3.262877e-16 1.376317e-14 7.126333e-02 -5.672350e-17 1.283665e-14 3.777757e-02 --4.222233e-16 1.375722e-14 7.683173e-02 --2.541603e-16 1.347431e-14 9.425306e-02 --4.851037e-16 1.311076e-14 -1.027504e-02 --4.789428e-16 1.424726e-14 5.499121e-02 --5.065132e-16 1.386755e-14 1.734004e-01 --4.497105e-16 1.425190e-14 1.953776e-02 --2.473395e-16 1.421681e-14 -1.478071e-02 --1.711904e-16 1.395840e-14 -1.139179e-01 --2.576605e-16 1.420566e-14 2.273916e-03 --7.624059e-16 1.418216e-14 -6.039536e-02 --2.709132e-16 1.410149e-14 7.091454e-03 --6.349797e-16 1.292080e-14 3.792931e-02 --8.811639e-16 1.210707e-14 6.174465e-02 --2.155129e-16 1.271387e-14 2.303803e-02 --1.167889e-16 1.366899e-14 5.420362e-02 --8.326447e-16 1.407432e-14 -5.866261e-02 --9.502476e-16 1.345259e-14 -4.252538e-02 --5.155365e-16 1.392707e-14 8.112864e-02 --9.400959e-16 1.278851e-14 6.251022e-02 --4.273264e-16 1.401626e-14 9.890629e-02 --6.562198e-16 1.381926e-14 -5.923838e-02 --2.883581e-16 1.398478e-14 5.463996e-02 --7.905560e-17 1.362242e-14 -1.157386e-01 --4.538831e-16 1.396855e-14 1.824703e-01 --3.969255e-16 1.337770e-14 3.489513e-02 --6.989867e-16 1.324443e-14 8.025481e-03 -1.981297e-17 1.235273e-14 1.648340e-01 --1.266141e-15 1.042833e-14 6.327421e-02 --2.733228e-16 1.275694e-14 -8.212086e-03 --3.806204e-16 1.402983e-14 5.482776e-02 --5.423758e-16 1.439215e-14 9.412480e-02 --4.048762e-16 1.386590e-14 1.829307e-01 --3.757037e-16 1.357131e-14 5.739114e-02 --6.078962e-17 1.352161e-14 3.036758e-02 --4.563082e-16 1.387957e-14 1.022536e-01 --3.773643e-16 1.369624e-14 1.820930e-02 --6.966968e-16 1.342547e-14 -1.081598e-03 --9.687514e-17 1.298608e-14 -3.497787e-02 --2.473644e-16 1.302491e-14 -4.967612e-02 --1.981341e-16 1.296096e-14 6.871646e-02 --9.805802e-16 1.265380e-14 1.091902e-01 --3.936554e-16 1.383957e-14 4.344252e-02 -1.696013e-16 1.257390e-14 1.779878e-01 --4.751107e-16 1.435008e-14 4.053764e-02 --1.046031e-15 1.424878e-14 -5.683955e-02 --5.371524e-16 1.431722e-14 -1.896831e-02 --7.511477e-17 1.312110e-14 -1.067787e-01 --8.976796e-16 1.341574e-14 -3.771143e-02 --3.224531e-17 1.272124e-14 1.100065e-01 --5.696693e-17 1.389213e-14 1.170658e-01 --7.160598e-16 1.420920e-14 -5.913989e-02 --2.116573e-16 1.278064e-14 9.182605e-02 --2.398179e-16 1.292876e-14 -3.131891e-02 --5.055896e-16 1.396890e-14 1.883527e-01 -6.110349e-16 1.240184e-14 2.864801e-01 --7.897067e-16 1.213137e-14 -1.203358e-01 -6.110349e-16 1.240184e-14 2.864801e-01 --7.897067e-16 1.213137e-14 -1.203358e-01 --1.704444e-16 1.165163e-14 8.559456e-02 --5.889755e-16 1.398053e-14 1.349262e-01 --6.468148e-16 1.316263e-14 1.353369e-01 -2.651815e-16 1.363483e-14 -2.178491e-01 --5.889755e-16 1.398053e-14 1.349262e-01 --6.468148e-16 1.316263e-14 1.353369e-01 --8.740546e-16 1.452464e-14 1.967637e-02 --9.440842e-16 1.362081e-14 2.229429e-02 --1.924734e-16 1.391130e-14 8.864261e-02 --1.899241e-16 1.425763e-14 8.699035e-02 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -4.646512e-16 1.237213e-14 2.459445e-01 -3.112783e-16 1.235848e-14 2.054586e-01 -1.488899e-16 1.234038e-14 1.649326e-01 --1.952210e-17 1.231232e-14 1.243582e-01 --1.610613e-16 1.229911e-14 8.371888e-02 --2.990857e-16 1.227142e-14 4.302866e-02 --4.195139e-16 1.226091e-14 2.294139e-03 --5.349840e-16 1.222500e-14 -3.846628e-02 --6.733568e-16 1.219544e-14 -7.930221e-02 -5.831183e-16 1.251587e-14 2.774031e-01 -5.175018e-16 1.268416e-14 2.598554e-01 -4.121290e-16 1.280763e-14 2.347883e-01 -3.121436e-16 1.291135e-14 2.034880e-01 -1.800532e-16 1.297739e-14 1.670174e-01 -3.282082e-17 1.300618e-14 1.264543e-01 --9.566184e-17 1.301138e-14 8.343281e-02 --2.490026e-16 1.298431e-14 4.014865e-02 --3.949357e-16 1.292469e-14 -1.216045e-03 --5.264022e-16 1.283010e-14 -3.852675e-02 --6.231791e-16 1.269317e-14 -7.013922e-02 --7.118012e-16 1.253753e-14 -9.512329e-02 --7.557146e-16 1.234507e-14 -1.122011e-01 -4.646512e-16 1.237213e-14 2.459445e-01 -3.112783e-16 1.235848e-14 2.054586e-01 -1.488899e-16 1.234038e-14 1.649326e-01 --1.952210e-17 1.231232e-14 1.243582e-01 --1.610613e-16 1.229911e-14 8.371888e-02 --2.990857e-16 1.227142e-14 4.302866e-02 --4.195139e-16 1.226091e-14 2.294139e-03 --5.349840e-16 1.222500e-14 -3.846628e-02 --6.733568e-16 1.219544e-14 -7.930221e-02 -6.003064e-16 1.223007e-14 2.865557e-01 -5.932139e-16 1.206328e-14 2.777342e-01 -5.070281e-16 1.191330e-14 2.607351e-01 -3.676091e-16 1.179367e-14 2.362260e-01 -2.311208e-16 1.172010e-14 2.051506e-01 -1.100314e-16 1.166724e-14 1.688123e-01 --3.499957e-17 1.163653e-14 1.283100e-01 --2.885184e-16 1.165705e-14 4.265432e-02 --4.164604e-16 1.170387e-14 1.868537e-03 --5.378130e-16 1.175865e-14 -3.471429e-02 --6.287893e-16 1.184959e-14 -6.583485e-02 --6.978731e-16 1.193832e-14 -9.070071e-02 --7.663052e-16 1.199469e-14 -1.088576e-01 --7.837213e-16 1.205745e-14 -1.191828e-01 --5.941673e-16 1.386663e-14 1.353355e-01 --5.905681e-16 1.372827e-14 1.357237e-01 --6.023701e-16 1.357223e-14 1.358676e-01 --6.160888e-16 1.342835e-14 1.356854e-01 --6.387524e-16 1.330476e-14 1.354201e-01 --5.146228e-16 1.404519e-14 1.058585e-01 --4.339600e-16 1.410242e-14 7.211860e-02 --3.505055e-16 1.413424e-14 3.473381e-02 --2.591613e-16 1.414622e-14 -5.183813e-03 --1.635071e-16 1.414395e-14 -4.613312e-02 --6.649662e-17 1.412267e-14 -8.630972e-02 -2.856912e-17 1.407825e-14 -1.235696e-01 -1.136597e-16 1.399567e-14 -1.560864e-01 -1.571342e-16 1.393601e-14 -1.823853e-01 -2.002528e-16 1.386217e-14 -2.019560e-01 -2.362265e-16 1.376310e-14 -2.138974e-01 -2.588902e-16 1.350239e-14 -2.136569e-01 -2.347844e-16 1.339164e-14 -2.014085e-01 -1.719271e-16 1.327922e-14 -1.818187e-01 -1.030077e-16 1.317260e-14 -1.557922e-01 -1.461555e-17 1.311269e-14 -1.237279e-01 --7.839692e-17 1.305689e-14 -8.703991e-02 --1.743198e-16 1.301334e-14 -4.723070e-02 --2.660102e-16 1.298911e-14 -6.248892e-03 --3.510339e-16 1.297190e-14 3.401487e-02 --4.359747e-16 1.298459e-14 7.194375e-02 --5.456817e-16 1.306181e-14 1.061518e-01 --5.941673e-16 1.386663e-14 1.353355e-01 --5.905681e-16 1.372827e-14 1.357237e-01 --6.023701e-16 1.357223e-14 1.358676e-01 --6.160888e-16 1.342835e-14 1.356854e-01 --6.387524e-16 1.330476e-14 1.354201e-01 --6.315734e-16 1.390400e-14 1.572847e-01 --6.931071e-16 1.381531e-14 1.745282e-01 --7.331140e-16 1.370662e-14 1.853690e-01 --7.506789e-16 1.352943e-14 1.890156e-01 --7.316062e-16 1.342131e-14 1.851015e-01 --7.253731e-16 1.332580e-14 1.743750e-01 --7.014419e-16 1.324318e-14 1.574664e-01 --9.722507e-16 1.450378e-14 -2.547015e-02 --1.058210e-15 1.447478e-14 -6.769223e-02 --1.147841e-15 1.442347e-14 -1.049769e-01 --1.239261e-15 1.436248e-14 -1.360916e-01 --1.308528e-15 1.429637e-14 -1.595253e-01 --1.356733e-15 1.419669e-14 -1.744330e-01 --1.355824e-15 1.407584e-14 -1.796866e-01 --1.326661e-15 1.399752e-14 -1.745855e-01 --1.296977e-15 1.388598e-14 -1.593221e-01 --1.252440e-15 1.379869e-14 -1.351600e-01 --1.200490e-15 1.372529e-14 -1.034590e-01 --1.120185e-15 1.367306e-14 -6.560358e-02 --1.040550e-15 1.363916e-14 -2.305189e-02 --8.633618e-16 1.362527e-14 6.825059e-02 --7.882509e-16 1.364570e-14 1.120994e-01 --7.005773e-16 1.372808e-14 1.513816e-01 --6.116945e-16 1.384753e-14 1.839271e-01 --5.588546e-16 1.393510e-14 2.083314e-01 --5.219101e-16 1.404788e-14 2.233542e-01 --5.104216e-16 1.415503e-14 2.280852e-01 --5.264602e-16 1.428165e-14 2.222115e-01 --5.628802e-16 1.435712e-14 2.064419e-01 --6.077510e-16 1.440375e-14 1.819253e-01 --6.729865e-16 1.444919e-14 1.493657e-01 --7.342178e-16 1.449103e-14 1.098581e-01 --7.984136e-16 1.450545e-14 6.567007e-02 --1.625145e-16 1.392269e-14 4.307941e-02 --1.323481e-16 1.394840e-14 5.891136e-04 --1.220477e-16 1.396973e-14 -3.698459e-02 --1.141963e-16 1.401128e-14 -6.852613e-02 --1.199664e-16 1.403018e-14 -9.330830e-02 --8.424821e-17 1.407768e-14 -1.096859e-01 --8.869253e-17 1.412849e-14 -1.154140e-01 --1.190742e-16 1.413697e-14 -1.105153e-01 --1.090933e-16 1.416472e-14 -9.545434e-02 --1.258184e-16 1.418001e-14 -7.155940e-02 --1.204512e-16 1.420721e-14 -3.998438e-02 --1.524208e-16 1.422844e-14 -1.851557e-03 --1.770849e-16 1.423583e-14 4.123243e-02 --2.135676e-16 1.426327e-14 1.330131e-01 --2.601794e-16 1.428100e-14 1.763059e-01 --2.899298e-16 1.427865e-14 2.146440e-01 --3.326329e-16 1.425544e-14 2.464015e-01 --3.643302e-16 1.422868e-14 2.704549e-01 --3.979695e-16 1.417445e-14 2.857709e-01 --4.360000e-16 1.413795e-14 2.916062e-01 --3.952252e-16 1.407185e-14 2.872087e-01 --3.785107e-16 1.404169e-14 2.727487e-01 --3.806650e-16 1.399325e-14 2.489466e-01 --3.568351e-16 1.394876e-14 2.170467e-01 --2.832167e-16 1.391370e-14 1.783461e-01 --2.249928e-16 1.391756e-14 1.347590e-01 --2.080436e-16 1.099093e-14 7.399632e-03 --1.360393e-16 1.249580e-14 3.699488e-02 --2.965401e-16 1.328147e-14 7.862874e-02 --4.139516e-16 1.364823e-14 1.208592e-01 --4.778468e-16 1.387803e-14 1.522798e-01 --4.710387e-16 1.403806e-14 1.670328e-01 --4.559427e-16 1.418243e-14 1.515116e-01 --4.031619e-16 1.424673e-14 1.107870e-01 --3.535279e-16 1.426535e-14 4.937657e-02 --3.124700e-16 1.425615e-14 -7.086700e-03 --3.063309e-16 1.418351e-14 -3.687902e-02 --3.489072e-16 1.415537e-14 -3.201354e-02 --4.528290e-16 1.404319e-14 2.297170e-02 --8.360800e-16 1.223537e-14 2.960891e-02 --7.130816e-16 7.280473e-15 4.243520e-02 --8.585782e-16 1.053595e-14 5.553790e-02 --4.994898e-16 1.186495e-14 3.908246e-02 --3.830248e-16 1.261234e-14 1.268552e-02 --3.849778e-16 1.294897e-14 -1.458897e-02 --4.684059e-16 1.312962e-14 -3.675529e-02 --5.447074e-16 1.318242e-14 -4.563458e-02 --5.600509e-16 1.319142e-14 -3.939534e-02 --4.863425e-16 1.308829e-14 6.463857e-04 --3.145075e-16 1.290802e-14 6.695346e-02 -4.255795e-17 1.269604e-14 1.602953e-01 --5.000045e-16 7.548917e-15 -5.234120e-02 --8.360800e-16 1.223537e-14 2.960891e-02 --4.528290e-16 1.404319e-14 2.297170e-02 --3.489072e-16 1.415537e-14 -3.201354e-02 --3.063309e-16 1.418351e-14 -3.687902e-02 --3.124700e-16 1.425615e-14 -7.086700e-03 --3.535279e-16 1.426535e-14 4.937657e-02 --4.031619e-16 1.424673e-14 1.107870e-01 --4.559427e-16 1.418243e-14 1.515116e-01 --4.710387e-16 1.403806e-14 1.670328e-01 --4.778468e-16 1.387803e-14 1.522798e-01 --4.139516e-16 1.364823e-14 1.208592e-01 --2.965401e-16 1.328147e-14 7.862874e-02 --1.360393e-16 1.249580e-14 3.699488e-02 --2.080436e-16 1.099093e-14 7.399632e-03 --5.000045e-16 7.548917e-15 -5.234120e-02 -4.255795e-17 1.269604e-14 1.602953e-01 --3.145075e-16 1.290802e-14 6.695346e-02 --4.863425e-16 1.308829e-14 6.463857e-04 --5.600509e-16 1.319142e-14 -3.939534e-02 --5.447074e-16 1.318242e-14 -4.563458e-02 --4.684059e-16 1.312962e-14 -3.675529e-02 --3.849778e-16 1.294897e-14 -1.458897e-02 --3.830248e-16 1.261234e-14 1.268552e-02 --4.994898e-16 1.186495e-14 3.908246e-02 --8.585782e-16 1.053595e-14 5.553790e-02 --7.130816e-16 7.280473e-15 4.243520e-02 --1.653334e-17 1.269834e-14 1.149331e-01 --3.246209e-16 1.253282e-14 2.580925e-02 -2.120891e-16 1.266282e-14 1.767883e-01 --5.136874e-16 1.244147e-14 -3.563563e-02 --1.938713e-16 1.269571e-14 6.116840e-02 --2.109560e-16 1.253304e-14 6.337813e-02 -1.275102e-16 1.277155e-14 1.551934e-01 -3.674003e-16 1.254511e-14 2.187462e-01 --4.436946e-17 1.246392e-14 1.129449e-01 --5.936124e-17 1.287707e-14 9.887843e-02 --4.983224e-16 1.259309e-14 -3.260891e-02 --3.125873e-16 1.239336e-14 3.386140e-02 --3.703910e-16 1.269459e-14 8.213276e-03 --6.313231e-16 1.233434e-14 -7.219841e-02 -1.688461e-16 1.253095e-14 1.670199e-01 -2.277723e-16 1.283204e-14 1.815394e-01 --4.271749e-16 1.236754e-14 -2.987265e-03 -3.249802e-16 1.269989e-14 2.080645e-01 --5.905887e-16 1.249917e-14 -5.801250e-02 -7.539902e-17 1.288228e-14 1.374017e-01 --1.878380e-16 1.286074e-14 5.760308e-02 -4.845972e-16 1.247153e-14 2.484209e-01 --4.296167e-16 1.249491e-14 -9.395764e-03 -1.607156e-16 1.289880e-14 1.621357e-01 --1.811665e-16 1.242164e-14 7.301783e-02 --5.363411e-16 1.234628e-14 -3.914002e-02 --3.117870e-16 1.282824e-14 2.150937e-02 --8.031429e-17 1.255451e-14 9.813556e-02 --2.795186e-16 1.200403e-14 4.985727e-02 -6.481755e-17 1.193846e-14 1.551525e-01 --4.928371e-16 1.199595e-14 -1.805213e-02 --6.410373e-17 1.183371e-14 1.187820e-01 --1.113117e-16 1.209872e-14 1.036002e-01 -2.840551e-16 1.215320e-14 2.102652e-01 --4.170801e-16 1.187062e-14 6.428283e-03 --1.807724e-16 1.174665e-14 7.896278e-02 --5.917968e-16 1.202630e-14 -5.088756e-02 -6.257638e-17 1.183937e-14 1.550519e-01 -2.323551e-16 1.197341e-14 2.056501e-01 -2.085162e-18 1.218027e-14 1.335520e-01 --1.049108e-16 1.197251e-14 1.067623e-01 --5.692907e-16 1.193056e-14 -4.261854e-02 --3.610162e-16 1.196521e-14 2.472915e-02 --2.486704e-16 1.216584e-14 6.156105e-02 --7.233182e-17 1.174901e-14 1.163515e-01 -4.422219e-16 1.224321e-14 2.457419e-01 -1.517257e-16 1.221204e-14 1.722525e-01 --3.056322e-16 1.177796e-14 3.998136e-02 -4.823366e-17 1.174935e-14 1.522137e-01 --1.924591e-16 1.205316e-14 8.044475e-02 -3.879610e-16 1.209984e-14 2.379120e-01 --1.924871e-16 1.188197e-14 7.915430e-02 -1.608626e-16 1.209157e-14 1.816648e-01 --3.020166e-16 1.186027e-14 4.527600e-02 --4.364341e-16 1.209664e-14 2.519802e-03 --1.261356e-16 1.218535e-14 9.852865e-02 --3.311402e-17 1.190850e-14 1.271257e-01 --4.513922e-18 1.204773e-14 1.366804e-01 -2.087625e-16 1.183457e-14 1.943161e-01 --4.702609e-16 1.182179e-14 -1.146834e-02 --6.820391e-16 1.211072e-14 -7.997642e-02 --4.472608e-16 1.202387e-14 1.968017e-04 --5.804617e-16 1.214785e-14 -5.005832e-02 --4.627787e-16 1.194132e-14 -7.803242e-03 -3.280888e-16 1.224457e-14 2.138852e-01 -1.690253e-16 1.176671e-14 1.846847e-01 --1.246235e-16 1.361735e-14 -6.037574e-02 --3.366112e-16 1.332204e-14 2.873760e-02 --2.979990e-16 1.382966e-14 1.452546e-02 --1.018972e-16 1.338834e-14 -7.241167e-02 --7.404707e-17 1.393175e-14 -8.078356e-02 -4.773164e-17 1.366212e-14 -1.346298e-01 --3.900509e-16 1.356926e-14 5.098129e-02 --1.857044e-16 1.326034e-14 -3.619187e-02 --2.520550e-16 1.398208e-14 -5.709211e-03 -5.746556e-17 1.349774e-14 -1.390405e-01 --4.305540e-16 1.374100e-14 6.935301e-02 --4.230945e-16 1.327762e-14 6.340202e-02 -6.299406e-17 1.380355e-14 -1.363444e-01 --4.386890e-16 1.390054e-14 7.396095e-02 --1.976123e-16 1.347441e-14 -2.973616e-02 --1.955818e-16 1.377513e-14 -2.877395e-02 --4.249837e-17 1.353835e-14 -9.839498e-02 --2.992276e-16 1.316634e-14 1.336472e-02 --4.694112e-16 1.345402e-14 8.134441e-02 --3.179903e-16 1.373574e-14 2.308972e-02 --9.410913e-17 1.316270e-14 -7.889703e-02 --3.003224e-17 1.373395e-14 -9.764543e-02 --6.404255e-17 1.402263e-14 -8.529297e-02 -4.276040e-17 1.331945e-14 -1.351544e-01 -1.330324e-16 1.359036e-14 -1.711096e-01 -3.365948e-17 1.392629e-14 -1.212714e-01 --2.945607e-16 1.344039e-14 1.069637e-02 -1.258951e-16 1.376299e-14 -1.708543e-01 --5.000755e-16 1.362405e-14 9.662001e-02 --2.990987e-16 1.359233e-14 1.407712e-02 --4.089687e-16 1.312648e-14 6.055551e-02 --1.861470e-16 1.335927e-14 -3.578318e-02 --2.141653e-16 1.312093e-14 -2.493525e-02 --3.721241e-16 1.401046e-14 4.291500e-02 --1.712640e-16 1.387085e-14 -3.861039e-02 --1.569701e-16 1.404622e-14 -4.647829e-02 --2.531743e-16 1.406402e-14 -7.183542e-03 -1.146378e-16 1.338822e-14 -1.587684e-01 --4.735791e-16 1.382966e-14 8.823946e-02 --4.134474e-16 1.335906e-14 5.912393e-02 -1.620214e-16 1.343969e-14 -1.775257e-01 --8.028772e-18 1.382991e-14 -1.063739e-01 --5.005586e-16 1.351836e-14 9.542420e-02 --3.697963e-16 1.322787e-14 4.346116e-02 --1.563504e-17 1.319042e-14 -1.116126e-01 --2.644731e-16 1.327895e-14 -1.326639e-03 --3.959180e-16 1.380028e-14 5.629061e-02 -1.564006e-17 1.355955e-14 -1.235856e-01 --1.169774e-17 1.343328e-14 -1.093555e-01 --2.497245e-16 1.391207e-14 -6.070451e-03 --3.548506e-16 1.390428e-14 3.711898e-02 --1.122915e-16 1.350701e-14 -6.511756e-02 --4.749062e-16 1.396167e-14 8.750969e-02 --5.005199e-16 1.338054e-14 9.112195e-02 --5.029469e-16 1.324318e-14 9.096589e-02 --4.085045e-17 1.363416e-14 -9.589070e-02 --3.953151e-16 1.367062e-14 5.479750e-02 -1.450569e-16 1.384731e-14 -1.760766e-01 --8.979357e-17 1.384150e-14 -7.266921e-02 --4.007541e-16 1.346504e-14 5.377555e-02 --2.588232e-16 1.383906e-14 -2.427077e-03 --2.084654e-16 1.360015e-14 -2.409785e-02 -1.719884e-16 1.366111e-14 -1.868530e-01 --4.991622e-16 1.373290e-14 9.769841e-02 --2.446497e-16 1.334838e-14 -1.037380e-02 --3.336549e-18 1.401094e-14 -1.093824e-01 --9.195556e-17 1.328299e-14 -7.716809e-02 --1.103360e-16 1.374667e-14 -6.496944e-02 --3.547412e-16 1.308024e-14 3.737150e-02 -2.437156e-17 1.373964e-14 -1.223143e-01 --3.237509e-16 1.407853e-14 2.389241e-02 -1.025934e-16 1.388174e-14 -1.554856e-01 --1.561717e-16 1.395948e-14 -4.529458e-02 --6.760873e-16 1.357429e-14 1.638822e-01 --6.613645e-16 1.352337e-14 1.590260e-01 --6.860927e-16 1.331564e-14 1.555869e-01 --6.347936e-16 1.383227e-14 1.555972e-01 --9.242974e-16 1.411834e-14 2.487111e-02 --9.328453e-16 1.384877e-14 3.226134e-02 --9.191183e-16 1.436111e-14 1.079915e-02 --1.130829e-15 1.416800e-14 -8.137308e-02 --7.236545e-16 1.407201e-14 1.288511e-01 --1.080889e-15 1.393745e-14 -4.462786e-02 --7.735630e-16 1.430510e-14 9.286946e-02 --1.024271e-15 1.432476e-14 -4.064769e-02 --8.231390e-16 1.388641e-14 8.646975e-02 --1.053135e-15 1.383365e-14 -2.975207e-02 --7.967314e-16 1.438414e-14 7.481916e-02 --1.201238e-15 1.400974e-14 -1.101100e-01 --6.664049e-16 1.425040e-14 1.570309e-01 --8.766991e-16 1.401384e-14 5.450459e-02 --9.757846e-16 1.422011e-14 -7.815783e-03 --9.716538e-16 1.397110e-14 8.864630e-03 --8.789313e-16 1.426257e-14 3.824934e-02 --1.049782e-15 1.410444e-14 -3.727986e-02 --7.938810e-16 1.413653e-14 8.720673e-02 --8.633062e-16 1.375644e-14 7.051501e-02 --9.796900e-16 1.441324e-14 -2.503068e-02 --1.151970e-15 1.426716e-14 -9.955037e-02 --6.901407e-16 1.396026e-14 1.467955e-01 --1.169884e-15 1.387174e-14 -8.938466e-02 --6.940284e-16 1.437704e-14 1.335190e-01 --9.565965e-16 1.374808e-14 1.699030e-02 --8.738055e-16 1.443773e-14 2.706958e-02 --1.222655e-15 1.414558e-14 -1.254442e-01 --6.287707e-16 1.409358e-14 1.732701e-01 --1.237460e-15 1.394979e-14 -1.275156e-01 --6.230275e-16 1.432265e-14 1.745523e-01 --1.112667e-15 1.406851e-14 -6.570708e-02 --7.501773e-16 1.417841e-14 1.132380e-01 --1.050738e-15 1.441223e-14 -5.907400e-02 --8.061182e-16 1.374473e-14 1.036490e-01 --1.005756e-15 1.389568e-14 -6.708459e-03 --8.439704e-16 1.433129e-14 5.246811e-02 --1.110684e-15 1.435631e-14 -8.468043e-02 --7.374932e-16 1.383801e-14 1.297716e-01 --1.122827e-15 1.378566e-14 -6.388202e-02 --7.394726e-16 1.442439e-14 1.088527e-01 --9.021913e-16 1.392881e-14 4.586346e-02 --9.541769e-16 1.429308e-14 -2.528706e-03 --5.970135e-16 1.419501e-14 1.893507e-01 --1.267667e-15 1.405165e-14 -1.413328e-01 --1.075430e-15 1.373372e-14 -4.238737e-02 --7.693515e-16 1.444671e-14 8.670739e-02 --7.950016e-16 1.398286e-14 9.305587e-02 --1.053606e-15 1.423908e-14 -4.667220e-02 --1.100533e-15 1.388522e-14 -5.290554e-02 --7.589784e-16 1.434918e-14 9.869039e-02 --9.285064e-16 1.370895e-14 3.314321e-02 --9.037191e-16 1.445902e-14 1.044109e-02 --1.233139e-15 1.424090e-14 -1.290613e-01 --6.298603e-16 1.399214e-14 1.770604e-01 --1.176425e-15 1.407399e-14 -1.003696e-01 --6.838402e-16 1.417937e-14 1.477902e-01 --1.039796e-15 1.401006e-14 -2.598691e-02 --8.137835e-16 1.423157e-14 7.545361e-02 --9.430363e-16 1.404729e-14 2.017000e-02 --1.055264e-15 1.416579e-14 -4.087927e-02 --7.971223e-16 1.407695e-14 8.717908e-02 --9.064680e-16 1.418983e-14 2.949491e-02 --9.862089e-16 1.406472e-14 -3.072530e-03 --8.446211e-16 1.416436e-14 6.306278e-02 --1.278892e-15 1.399926e-14 -1.484125e-01 --5.750427e-16 1.427920e-14 1.958083e-01 --1.141825e-15 1.397400e-14 -7.727875e-02 --7.193799e-16 1.428421e-14 1.241265e-01 --9.882222e-16 1.414513e-14 -7.631925e-03 --8.636778e-16 1.409389e-14 5.745393e-02 --1.792770e-16 1.412160e-14 8.740655e-02 --1.776806e-16 1.401293e-14 7.010377e-02 --2.043811e-16 1.421675e-14 1.065309e-01 --1.356532e-16 1.410502e-14 -1.789415e-02 --2.828702e-16 1.414240e-14 1.949861e-01 --1.513650e-16 1.416917e-14 7.707372e-04 --2.726548e-16 1.404770e-14 1.789377e-01 --2.474323e-16 1.421212e-14 1.641235e-01 --1.401640e-16 1.403973e-14 1.388734e-02 --2.352549e-16 1.400351e-14 1.402117e-01 --1.702838e-16 1.422020e-14 3.637640e-02 --1.225487e-16 1.413907e-14 -3.874003e-02 --1.607606e-16 1.406834e-14 4.821810e-02 --2.199897e-16 1.417552e-14 1.278212e-01 --2.036703e-16 1.408315e-14 1.079944e-01 --1.642505e-16 1.416066e-14 6.772777e-02 --2.243463e-16 1.423795e-14 1.520102e-01 --1.525596e-16 1.399089e-14 2.552356e-02 --1.133420e-16 1.405940e-14 -4.038169e-02 --3.015180e-16 1.420116e-14 2.187758e-01 --2.245549e-16 1.411355e-14 1.292023e-01 --1.529103e-16 1.413444e-14 4.348002e-02 --1.908162e-16 1.395554e-14 8.060482e-02 --1.952576e-16 1.423403e-14 9.583335e-02 --1.203588e-16 1.411244e-14 -6.441422e-02 --3.285439e-16 1.414012e-14 2.411021e-01 --1.498607e-16 1.419529e-14 -8.212387e-03 --3.044372e-16 1.399486e-14 1.850368e-01 --1.330534e-16 1.417150e-14 -3.833971e-02 --3.321382e-16 1.402305e-14 2.128861e-01 --1.349531e-16 1.414128e-14 4.423765e-04 --2.677208e-16 1.409941e-14 1.690663e-01 --2.247875e-16 1.403297e-14 1.246861e-01 --1.666187e-16 1.419270e-14 5.218478e-02 --2.793924e-16 1.424698e-14 1.926184e-01 --1.307772e-16 1.400070e-14 -1.267451e-02 --2.959258e-16 1.423490e-14 2.042926e-01 --1.252995e-16 1.401834e-14 -2.613979e-02 --1.307746e-16 1.407533e-14 1.031250e-02 --2.612655e-16 1.418409e-14 1.673809e-01 --1.545043e-16 1.404397e-14 4.147916e-02 --2.220909e-16 1.421259e-14 1.350299e-01 --2.054998e-16 1.395129e-14 1.184666e-01 --1.814427e-16 1.422741e-14 5.730113e-02 --1.115828e-16 1.413511e-14 -7.122348e-02 --3.586240e-16 1.408114e-14 2.563257e-01 --1.887821e-16 1.404564e-14 8.889140e-02 --1.912664e-16 1.418454e-14 8.754107e-02 --1.528202e-16 1.415899e-14 3.183085e-02 --2.430281e-16 1.406458e-14 1.447364e-01 --1.230267e-16 1.415089e-14 -7.055360e-02 --2.479133e-16 1.397098e-14 1.515348e-01 --3.542980e-16 1.406719e-14 2.378602e-01 --1.627049e-16 1.422294e-14 2.505250e-02 --2.095435e-16 1.415648e-14 1.085220e-01 --1.701903e-16 1.409001e-14 6.712804e-02 --1.643744e-16 1.395886e-14 5.098667e-02 --2.094557e-16 1.424561e-14 1.256407e-01 --1.112955e-16 1.409512e-14 -7.308439e-02 --3.539498e-16 1.418113e-14 2.498659e-01 --2.023184e-16 1.399282e-14 1.116494e-01 --1.831932e-16 1.420857e-14 6.508711e-02 --1.198989e-16 1.412933e-14 -3.583408e-02 --1.538141e-16 1.410112e-14 2.512272e-02 --2.442101e-16 1.413904e-14 1.503785e-01 --1.332832e-16 1.415340e-14 -1.308575e-02 --3.020321e-16 1.409875e-14 2.054638e-01 --1.535116e-16 1.419223e-14 2.071833e-02 --2.611810e-16 1.402867e-14 1.556205e-01 --3.156386e-16 1.327861e-14 5.537117e-02 --5.268141e-16 1.391220e-14 3.097192e-02 --3.649326e-16 1.375425e-14 -3.056171e-03 -1.643483e-17 1.282083e-14 4.573610e-02 --1.264190e-17 1.299691e-14 1.794650e-02 --6.729208e-17 1.380103e-14 8.407251e-02 --3.981121e-16 1.368180e-14 3.391319e-02 --3.100135e-16 1.305147e-14 1.523730e-01 --7.113703e-16 1.411247e-14 6.994570e-02 --2.473649e-16 1.361549e-14 2.687556e-02 --5.055498e-16 1.399058e-14 -6.591761e-02 --1.335135e-16 1.325905e-14 5.104251e-02 --4.068094e-16 1.371120e-14 1.320326e-01 --3.489744e-16 1.313563e-14 2.841650e-02 --4.830458e-16 1.389354e-14 9.042348e-02 --5.751090e-16 1.410691e-14 2.482841e-02 --1.219756e-15 1.166321e-14 7.851609e-02 --1.199082e-16 1.306360e-14 -6.441004e-02 --4.962449e-16 1.431137e-14 5.488240e-02 --2.585262e-16 1.345428e-14 -1.732366e-02 -6.195758e-17 1.315216e-14 -6.816676e-02 --3.569195e-16 1.397732e-14 5.121852e-02 --1.670021e-16 1.275273e-14 1.157752e-01 --3.592152e-16 1.383792e-14 -5.085674e-02 -8.183953e-17 1.368965e-14 1.418744e-01 --4.034897e-16 1.353324e-14 1.397815e-02 --7.367439e-16 1.412801e-14 1.723187e-02 -2.413934e-16 1.248941e-14 1.531141e-01 --7.883089e-16 1.414644e-14 -2.838495e-02 --6.863329e-16 1.320033e-14 3.989750e-03 --1.138302e-16 1.369902e-14 -1.088111e-01 --5.280900e-16 1.405060e-14 1.536876e-01 --1.235737e-16 1.290824e-14 2.305690e-02 --1.138542e-17 1.342437e-14 -1.091878e-01 -1.667267e-16 1.259014e-14 1.449319e-01 --3.237643e-16 1.293897e-14 7.297368e-02 --2.791673e-16 1.283778e-14 1.655337e-01 --3.993277e-16 1.358080e-14 1.728587e-01 --9.529959e-16 1.186161e-14 8.291014e-02 --2.450783e-16 1.311148e-14 -8.100817e-02 -2.379160e-17 1.307951e-14 -4.899673e-02 --4.860251e-16 1.309570e-14 -2.683874e-03 --3.081220e-16 1.359142e-14 -1.874991e-02 -7.670685e-17 1.249251e-14 1.361245e-01 -1.542876e-16 1.345062e-14 1.360484e-01 --1.562619e-16 1.317973e-14 -9.536805e-03 --6.744636e-17 1.397131e-14 1.559332e-01 --1.781876e-16 1.371570e-14 4.885094e-02 --1.545211e-16 1.407176e-14 1.425741e-01 --1.883714e-16 1.288061e-14 3.554258e-02 --6.329921e-16 1.392608e-14 -4.973582e-02 --2.447473e-16 1.389102e-14 -9.530992e-02 --1.254734e-16 1.290072e-14 2.465313e-02 --3.151983e-16 1.374837e-14 3.117808e-02 --4.620093e-16 1.431885e-14 6.117867e-02 --3.015722e-16 1.396145e-14 8.719768e-03 --4.894386e-16 1.407294e-14 -7.555047e-02 --2.960373e-16 1.414285e-14 4.257034e-02 --6.809042e-16 1.328355e-14 1.124220e-01 --5.136460e-16 1.440499e-14 7.172359e-02 --7.334554e-16 1.326775e-14 -2.498105e-02 --4.526629e-16 1.388171e-14 1.544556e-02 --4.861080e-16 1.413871e-14 4.342166e-02 --1.395960e-15 1.224905e-14 -7.488592e-03 -2.241483e-16 1.243484e-14 -1.774726e-02 --4.057614e-16 1.370579e-14 8.424664e-02 -2.426947e-17 1.297764e-14 1.381353e-01 --5.753637e-16 1.267265e-14 5.468143e-02 --5.990834e-16 1.411428e-14 1.619441e-01 --4.282323e-16 1.389469e-14 1.827144e-01 --9.280439e-17 1.239284e-14 1.657897e-01 -1.803323e-16 1.044715e-14 5.441435e-02 -1.663904e-17 1.277294e-14 1.646412e-01 --6.596187e-16 1.262555e-14 5.786063e-02 --3.173773e-16 1.303170e-14 1.212559e-01 -7.966916e-17 1.322217e-14 1.155696e-01 --1.745722e-16 1.371213e-14 8.233788e-02 --4.392428e-16 1.430987e-14 7.374554e-02 -1.654591e-16 1.330700e-14 1.311554e-01 --2.234416e-16 1.283587e-14 1.356399e-01 --4.082121e-16 1.355995e-14 1.201485e-01 --9.640711e-16 1.417317e-14 -1.343628e-02 -5.490007e-17 1.329383e-14 -9.085241e-02 --3.278696e-16 1.281929e-14 -9.289403e-03 --4.889044e-16 1.420598e-14 -4.901788e-02 -1.052744e-15 1.074956e-14 -4.744290e-02 --3.835029e-16 1.419256e-14 1.411738e-01 --2.454987e-16 1.379924e-14 -8.557795e-02 --3.195804e-16 1.331412e-14 6.242456e-02 --5.235980e-16 1.394386e-14 -2.276573e-02 --1.005200e-16 1.379131e-14 1.246806e-01 --8.180508e-16 1.340355e-14 -2.218199e-02 --4.349913e-16 1.306179e-14 6.754854e-02 --1.565667e-15 9.562924e-15 4.356379e-02 --6.079816e-16 1.422602e-14 1.198429e-01 --3.102156e-16 1.309490e-14 -4.692820e-02 --6.297460e-16 1.402840e-14 4.276306e-02 --4.964795e-16 1.404998e-14 1.916356e-01 --1.800418e-16 1.249831e-14 1.666665e-01 --2.542947e-16 1.310211e-14 -7.282397e-02 --6.246017e-16 1.363680e-14 -3.252615e-02 --8.335656e-16 1.264286e-14 4.583064e-02 --2.113238e-16 1.274963e-14 5.992850e-02 --5.214074e-16 1.369119e-14 1.473038e-01 --2.179553e-15 1.092361e-14 -3.645216e-02 --3.262877e-16 1.376317e-14 7.126333e-02 -5.672350e-17 1.283665e-14 3.777757e-02 --4.222233e-16 1.375722e-14 7.683173e-02 --2.541603e-16 1.347431e-14 9.425306e-02 --4.851037e-16 1.311076e-14 -1.027504e-02 --4.789428e-16 1.424726e-14 5.499121e-02 --5.065132e-16 1.386755e-14 1.734004e-01 --4.497105e-16 1.425190e-14 1.953776e-02 --2.473395e-16 1.421681e-14 -1.478071e-02 --1.711904e-16 1.395840e-14 -1.139179e-01 --2.576605e-16 1.420566e-14 2.273916e-03 --7.624059e-16 1.418216e-14 -6.039536e-02 --2.709132e-16 1.410149e-14 7.091454e-03 --6.349797e-16 1.292080e-14 3.792931e-02 --8.811639e-16 1.210707e-14 6.174465e-02 --2.155129e-16 1.271387e-14 2.303803e-02 --1.167889e-16 1.366899e-14 5.420362e-02 --8.326447e-16 1.407432e-14 -5.866261e-02 --9.502476e-16 1.345259e-14 -4.252538e-02 --5.155365e-16 1.392707e-14 8.112864e-02 --9.400959e-16 1.278851e-14 6.251022e-02 --4.273264e-16 1.401626e-14 9.890629e-02 --6.562198e-16 1.381926e-14 -5.923838e-02 --2.883581e-16 1.398478e-14 5.463996e-02 --7.905560e-17 1.362242e-14 -1.157386e-01 --4.538831e-16 1.396855e-14 1.824703e-01 --3.969255e-16 1.337770e-14 3.489513e-02 --6.989867e-16 1.324443e-14 8.025481e-03 -1.981297e-17 1.235273e-14 1.648340e-01 --1.266141e-15 1.042833e-14 6.327421e-02 --2.733228e-16 1.275694e-14 -8.212086e-03 --3.806204e-16 1.402983e-14 5.482776e-02 --5.423758e-16 1.439215e-14 9.412480e-02 --4.048762e-16 1.386590e-14 1.829307e-01 --3.757037e-16 1.357131e-14 5.739114e-02 --6.078962e-17 1.352161e-14 3.036758e-02 --4.563082e-16 1.387957e-14 1.022536e-01 --3.773643e-16 1.369624e-14 1.820930e-02 --6.966968e-16 1.342547e-14 -1.081598e-03 --9.687514e-17 1.298608e-14 -3.497787e-02 --2.473644e-16 1.302491e-14 -4.967612e-02 --1.981341e-16 1.296096e-14 6.871646e-02 --9.805802e-16 1.265380e-14 1.091902e-01 --3.936554e-16 1.383957e-14 4.344252e-02 -1.696013e-16 1.257390e-14 1.779878e-01 --4.751107e-16 1.435008e-14 4.053764e-02 --1.046031e-15 1.424878e-14 -5.683955e-02 --5.371524e-16 1.431722e-14 -1.896831e-02 --7.511477e-17 1.312110e-14 -1.067787e-01 --8.976796e-16 1.341574e-14 -3.771143e-02 --3.224531e-17 1.272124e-14 1.100065e-01 --5.696693e-17 1.389213e-14 1.170658e-01 --7.160598e-16 1.420920e-14 -5.913989e-02 --2.116573e-16 1.278064e-14 9.182605e-02 --2.398179e-16 1.292876e-14 -3.131891e-02 --5.055896e-16 1.396890e-14 1.883527e-01 - -VECTORS u_22 float -1.937343e-02 9.609459e-02 8.505404e-15 -9.342511e-03 -4.417858e-02 8.376449e-15 -1.937343e-02 9.609459e-02 8.505404e-15 -9.342511e-03 -4.417858e-02 8.376449e-15 -8.917136e-02 2.203542e-02 8.546298e-15 -6.189748e-02 3.807478e-02 9.555734e-15 --3.132061e-02 3.622791e-02 9.432554e-15 -1.852257e-02 -8.487776e-02 9.529634e-15 -6.189748e-02 3.807478e-02 9.555734e-15 --3.132061e-02 3.622791e-02 9.432554e-15 --2.650156e-02 7.220248e-03 1.034421e-14 -1.100360e-01 1.084117e-02 1.033731e-14 --3.139713e-02 2.652183e-02 1.017648e-14 -1.044098e-01 3.050448e-02 1.030194e-14 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -1.845538e-02 8.217181e-02 8.498748e-15 -1.750961e-02 6.824609e-02 8.491094e-15 -1.653121e-02 5.428129e-02 8.533385e-15 -1.553522e-02 4.027353e-02 8.432780e-15 -1.451098e-02 2.621383e-02 8.471246e-15 -1.345987e-02 1.213346e-02 8.509491e-15 -1.239822e-02 -1.948521e-03 8.486534e-15 -1.133815e-02 -1.602054e-02 8.458802e-15 -1.030942e-02 -3.009099e-02 8.472219e-15 -4.936086e-03 9.384619e-02 8.537962e-15 --8.751292e-03 8.862243e-02 8.516700e-15 --2.113998e-02 8.070719e-02 8.544050e-15 --3.156720e-02 7.048805e-02 8.572065e-15 --3.945337e-02 5.835072e-02 8.584665e-15 --4.430585e-02 4.475326e-02 8.542317e-15 --4.652489e-02 3.001713e-02 8.566661e-15 --4.606866e-02 1.499811e-02 8.521220e-15 --4.305944e-02 4.447918e-04 8.537698e-15 --3.711255e-02 -1.286433e-02 8.504483e-15 --2.838357e-02 -2.439143e-02 8.479340e-15 --1.731569e-02 -3.372975e-02 8.527992e-15 --4.557705e-03 -4.046698e-02 8.448978e-15 -1.845538e-02 8.217181e-02 8.498748e-15 -1.750961e-02 6.824609e-02 8.491094e-15 -1.653121e-02 5.428129e-02 8.533385e-15 -1.553522e-02 4.027353e-02 8.432780e-15 -1.451098e-02 2.621383e-02 8.471246e-15 -1.345987e-02 1.213346e-02 8.509491e-15 -1.239822e-02 -1.948521e-03 8.486534e-15 -1.133815e-02 -1.602054e-02 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9.477124e-15 -2.766626e-02 5.458474e-02 9.460535e-15 -1.519397e-02 5.555863e-02 9.457340e-15 -2.746360e-03 5.387625e-02 9.434671e-15 --9.469916e-03 4.998537e-02 9.416765e-15 --2.096271e-02 4.397087e-02 9.399596e-15 --2.542063e-02 -8.232487e-03 1.032320e-14 --2.072077e-02 -2.265269e-02 1.030984e-14 --1.247249e-02 -3.536213e-02 1.035307e-14 --1.242402e-03 -4.586711e-02 1.031631e-14 -1.220791e-02 -5.363258e-02 1.031314e-14 -2.708630e-02 -5.843702e-02 1.033127e-14 -4.256526e-02 -5.992090e-02 1.034632e-14 -5.796541e-02 -5.785221e-02 1.035038e-14 -7.254310e-02 -5.233736e-02 1.034163e-14 -8.560593e-02 -4.380292e-02 1.039384e-14 -9.644697e-02 -3.272840e-02 1.036750e-14 -1.043916e-01 -1.957766e-02 1.033109e-14 -1.089442e-01 -4.846790e-03 1.031194e-14 -1.079360e-01 2.660082e-02 1.032744e-14 -1.028054e-01 4.164140e-02 1.030493e-14 -9.472914e-02 5.505235e-02 1.033630e-14 -8.375285e-02 6.611092e-02 1.034284e-14 -7.056474e-02 7.427303e-02 1.037918e-14 -5.584040e-02 7.918041e-02 1.041040e-14 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9.268802e-02 1.020073e-14 -4.921085e-02 9.758751e-02 1.014703e-14 -3.364498e-02 9.917850e-02 1.015489e-14 -1.822603e-02 9.715094e-02 1.022244e-14 -3.712691e-03 9.162632e-02 1.019942e-14 --9.080250e-03 8.288801e-02 1.021951e-14 --1.935633e-02 7.143675e-02 1.023811e-14 --2.655558e-02 5.779197e-02 1.021050e-14 --3.040919e-02 4.253260e-02 1.013546e-14 -1.314042e-02 6.869130e-03 6.789159e-15 -2.513737e-02 1.722036e-02 8.209721e-15 -3.624615e-02 2.909069e-02 9.133671e-15 -4.537399e-02 4.133460e-02 9.699944e-15 -4.859296e-02 5.129990e-02 1.002953e-14 -4.742465e-02 5.681227e-02 1.019327e-14 -4.325213e-02 5.329988e-02 1.028715e-14 -4.116973e-02 3.948471e-02 1.032313e-14 -3.981513e-02 1.789315e-02 1.031734e-14 -3.764403e-02 -2.613290e-03 1.028935e-14 -3.890855e-02 -1.255358e-02 1.018827e-14 -4.302307e-02 -1.356646e-02 1.008261e-14 -5.317233e-02 -1.490194e-03 9.803373e-15 --1.622075e-02 1.552579e-03 7.925604e-15 --1.045008e-03 1.533191e-02 6.726217e-15 --6.839841e-03 2.178932e-02 8.666907e-15 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5.998170e-15 -3.173557e-02 5.169364e-02 8.988789e-15 -3.839651e-02 1.871067e-02 9.310418e-15 -4.018391e-02 -6.241036e-03 9.606257e-15 -3.871134e-02 -2.256211e-02 9.785329e-15 -3.333862e-02 -2.769737e-02 9.787004e-15 -2.233815e-02 -2.576612e-02 9.853005e-15 -8.076019e-03 -1.269740e-02 9.786258e-15 --4.677034e-03 2.512482e-03 9.602932e-15 --8.793837e-03 1.737046e-02 9.285170e-15 --6.839841e-03 2.178932e-02 8.666907e-15 --1.045008e-03 1.533191e-02 6.726217e-15 --1.686389e-02 3.901047e-02 8.535107e-15 --1.037616e-02 7.620178e-03 8.497366e-15 --1.085107e-02 6.006129e-02 8.538636e-15 --7.031935e-03 -1.389381e-02 8.453812e-15 --2.125600e-02 2.059779e-02 8.510231e-15 --7.840943e-03 2.051988e-02 8.525437e-15 --2.029605e-02 5.318354e-02 8.570635e-15 -1.288963e-03 7.383306e-02 8.529039e-15 -2.449870e-03 3.711026e-02 8.447156e-15 --3.324965e-02 3.447835e-02 8.529649e-15 --1.896408e-02 -1.208328e-02 8.472513e-15 -2.065234e-03 9.644348e-03 8.527340e-15 --2.480733e-02 2.428690e-03 8.500746e-15 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4.779925e-02 8.504152e-15 -5.322780e-02 6.608975e-02 8.541590e-15 -2.732463e-02 4.273943e-02 8.446394e-15 -4.901808e-02 3.210595e-02 8.495318e-15 -5.013769e-02 -1.974251e-02 8.474058e-15 -4.573517e-02 3.812499e-03 8.439911e-15 -2.623234e-02 1.779883e-02 8.525899e-15 -7.575281e-02 3.369500e-02 8.536958e-15 -3.109420e-02 8.131032e-02 8.522765e-15 -2.822977e-02 5.609999e-02 8.496770e-15 -7.160937e-02 7.427983e-03 8.580650e-15 -7.620180e-02 4.609729e-02 8.513775e-15 -3.843283e-02 2.361611e-02 8.484439e-15 -4.375599e-02 7.781457e-02 8.530559e-15 -5.810876e-02 2.192845e-02 8.542746e-15 -4.039056e-02 5.860931e-02 8.477402e-15 -5.993004e-02 1.003060e-02 8.543253e-15 -2.861941e-02 -2.850188e-03 8.466172e-15 -2.566476e-02 3.068175e-02 8.476941e-15 -5.631606e-02 3.871225e-02 8.465098e-15 -4.104701e-02 4.298751e-02 8.448015e-15 -6.717644e-02 6.125372e-02 8.531145e-15 -6.486597e-02 -9.934426e-03 8.495661e-15 -2.062267e-02 -3.095062e-02 8.458542e-15 -3.771977e-02 -4.194091e-03 8.429495e-15 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5.709461e-02 9.038929e-15 --7.907835e-03 -3.837677e-02 9.682219e-15 -4.811193e-02 -1.008591e-02 9.827405e-15 -1.381225e-02 8.225370e-03 8.987052e-15 -2.755966e-02 9.685433e-03 9.430147e-15 -5.709868e-02 5.251159e-02 9.834911e-15 --6.843451e-04 -1.792880e-02 7.123037e-15 -1.069664e-02 1.966197e-02 9.728964e-15 -2.980424e-02 3.058234e-03 9.088072e-15 -2.193410e-02 2.045941e-02 9.807337e-15 -1.768253e-02 2.339864e-02 9.567421e-15 --5.010672e-03 -8.318962e-03 8.878151e-15 -5.351722e-02 1.693289e-02 9.788484e-15 -5.515772e-02 6.031543e-02 1.009609e-14 -4.137895e-02 5.671545e-03 1.003225e-14 -5.098592e-02 -5.152186e-03 1.027529e-14 -4.738403e-02 -4.826771e-02 9.747291e-15 -5.890013e-02 7.147771e-04 1.027262e-14 -5.850605e-03 -2.426013e-02 1.019273e-14 -6.944580e-02 2.261339e-03 1.011897e-14 -5.437774e-02 1.561360e-02 9.967332e-15 -3.121835e-02 2.797360e-02 8.141961e-15 -4.193085e-02 -1.220215e-03 9.125283e-15 -2.685746e-02 2.107180e-02 9.596073e-15 -4.158287e-02 -1.915486e-02 1.017098e-14 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9.269593e-15 -4.718047e-02 9.398547e-03 9.845475e-15 -3.019771e-02 5.634792e-02 8.853857e-15 -3.914695e-02 1.732877e-02 9.903732e-15 -1.992330e-02 -1.756364e-02 1.027639e-14 -1.739384e-02 -1.654508e-02 9.996869e-15 --9.652223e-03 -4.762200e-02 9.503780e-15 -7.599452e-02 -1.655196e-02 1.006352e-14 -3.114354e-02 3.159945e-02 9.062815e-15 -3.908580e-02 4.434213e-02 9.737886e-15 -5.694103e-03 -2.907036e-02 1.011815e-14 -2.837552e-02 2.654424e-02 9.424294e-15 --2.335449e-02 -2.010467e-02 9.653271e-15 -5.258772e-02 6.506065e-02 1.022351e-14 -1.937343e-02 9.609459e-02 8.505404e-15 -9.342511e-03 -4.417858e-02 8.376449e-15 -1.937343e-02 9.609459e-02 8.505404e-15 -9.342511e-03 -4.417858e-02 8.376449e-15 -8.917136e-02 2.203542e-02 8.546298e-15 -6.189748e-02 3.807478e-02 9.555734e-15 --3.132061e-02 3.622791e-02 9.432554e-15 -1.852257e-02 -8.487776e-02 9.529634e-15 -6.189748e-02 3.807478e-02 9.555734e-15 --3.132061e-02 3.622791e-02 9.432554e-15 --2.650156e-02 7.220248e-03 1.034421e-14 -1.100360e-01 1.084117e-02 1.033731e-14 --3.139713e-02 2.652183e-02 1.017648e-14 -1.044098e-01 3.050448e-02 1.030194e-14 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -1.845538e-02 8.217181e-02 8.498748e-15 -1.750961e-02 6.824609e-02 8.491094e-15 -1.653121e-02 5.428129e-02 8.533385e-15 -1.553522e-02 4.027353e-02 8.432780e-15 -1.451098e-02 2.621383e-02 8.471246e-15 -1.345987e-02 1.213346e-02 8.509491e-15 -1.239822e-02 -1.948521e-03 8.486534e-15 -1.133815e-02 -1.602054e-02 8.458802e-15 -1.030942e-02 -3.009099e-02 8.472219e-15 -4.936086e-03 9.384619e-02 8.537962e-15 --8.751292e-03 8.862243e-02 8.516700e-15 --2.113998e-02 8.070719e-02 8.544050e-15 --3.156720e-02 7.048805e-02 8.572065e-15 --3.945337e-02 5.835072e-02 8.584665e-15 --4.430585e-02 4.475326e-02 8.542317e-15 --4.652489e-02 3.001713e-02 8.566661e-15 --4.606866e-02 1.499811e-02 8.521220e-15 --4.305944e-02 4.447918e-04 8.537698e-15 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9.511261e-15 --5.077619e-02 -2.709168e-02 9.497115e-15 --5.226972e-02 -1.293944e-02 9.427727e-15 --5.109780e-02 1.006629e-03 9.461289e-15 --4.726482e-02 1.416028e-02 9.408393e-15 --4.053875e-02 2.604641e-02 9.398293e-15 -4.620763e-02 3.773940e-02 9.506341e-15 -3.077946e-02 3.750986e-02 9.485555e-15 -1.541948e-02 3.717788e-02 9.525754e-15 --3.738404e-05 3.679166e-02 9.449807e-15 --1.556302e-02 3.643496e-02 9.422060e-15 -5.132137e-02 4.549680e-02 9.524115e-15 -3.983705e-02 5.115946e-02 9.477124e-15 -2.766626e-02 5.458474e-02 9.460535e-15 -1.519397e-02 5.555863e-02 9.457340e-15 -2.746360e-03 5.387625e-02 9.434671e-15 --9.469916e-03 4.998537e-02 9.416765e-15 --2.096271e-02 4.397087e-02 9.399596e-15 --2.542063e-02 -8.232487e-03 1.032320e-14 --2.072077e-02 -2.265269e-02 1.030984e-14 --1.247249e-02 -3.536213e-02 1.035307e-14 --1.242402e-03 -4.586711e-02 1.031631e-14 -1.220791e-02 -5.363258e-02 1.031314e-14 -2.708630e-02 -5.843702e-02 1.033127e-14 -4.256526e-02 -5.992090e-02 1.034632e-14 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9.445946e-15 -6.603749e-02 6.447517e-03 9.513421e-15 -4.818558e-02 -2.235423e-02 9.546489e-15 -7.062090e-02 -2.433442e-02 9.574260e-15 -7.288887e-02 -1.063813e-02 9.575654e-15 --9.271826e-03 -6.507279e-02 9.491393e-15 -4.291871e-02 2.137275e-02 9.510994e-15 --1.134283e-02 1.017349e-02 9.478533e-15 --2.843721e-03 -7.142307e-02 9.481665e-15 -4.172062e-02 -4.600040e-02 9.557596e-15 -7.924681e-03 2.304210e-02 9.483810e-15 --2.503193e-02 4.586891e-03 9.427821e-15 --3.123156e-02 -4.908565e-02 9.481084e-15 --2.022871e-02 -1.083872e-02 9.443813e-15 -3.942857e-02 1.025387e-02 9.508981e-15 -9.125149e-03 -5.267004e-02 9.503162e-15 --4.610379e-03 -4.798702e-02 9.459135e-15 -5.317525e-02 -1.091528e-02 9.544211e-15 -5.203265e-02 3.988354e-03 9.543424e-15 -3.675515e-03 -3.257249e-02 9.496690e-15 -5.893423e-02 2.161028e-02 9.533871e-15 --8.458448e-03 2.126449e-02 9.461084e-15 --2.376113e-02 2.099559e-02 9.418184e-15 -1.832522e-02 -4.294016e-02 9.529959e-15 -2.456318e-02 9.360451e-03 9.479648e-15 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4.671490e-02 1.037457e-14 -6.610056e-02 -1.292001e-02 1.035441e-14 -1.717777e-02 3.360739e-02 1.035421e-14 -7.227568e-03 -1.281790e-02 1.034612e-14 -7.540560e-02 3.253092e-02 1.035281e-14 -8.101199e-02 -7.553068e-03 1.034837e-14 -2.033543e-03 2.699409e-02 1.036017e-14 -5.368960e-02 -3.573635e-02 1.034275e-14 -2.829660e-02 5.594851e-02 1.039150e-14 -5.737072e-02 2.119505e-02 1.034616e-14 -2.567176e-02 -1.022825e-03 1.034504e-14 -6.242652e-02 5.503046e-03 1.034540e-14 -2.075547e-02 1.480168e-02 1.033679e-14 -4.249188e-02 -1.083343e-02 1.036165e-14 -4.095247e-02 3.217205e-02 1.035258e-14 -9.040674e-02 2.721013e-02 1.032111e-14 --7.928123e-03 -7.816482e-03 1.031333e-14 -1.471391e-02 -3.293135e-02 1.031096e-14 -6.792531e-02 5.319051e-02 1.038056e-14 -7.592233e-02 -2.818912e-02 1.038707e-14 -5.301539e-03 4.732707e-02 1.036249e-14 -9.261164e-02 8.751576e-03 1.033604e-14 --9.163353e-03 1.018264e-02 1.033114e-14 -3.275231e-02 -4.144013e-02 1.034058e-14 -4.856250e-02 6.193825e-02 1.037304e-14 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-6.674950e-02 5.604475e-02 1.020508e-14 -5.952228e-03 2.083324e-03 1.019132e-14 --7.184738e-03 4.544276e-02 1.022147e-14 -8.023747e-02 1.209765e-02 1.029650e-14 -5.480389e-02 -1.467658e-02 1.025200e-14 -1.769981e-02 1.435110e-02 1.022757e-14 -5.528872e-02 4.312056e-02 1.024528e-14 -2.143765e-02 3.524214e-02 1.023109e-14 -5.167428e-02 2.213182e-02 1.027883e-14 -8.102171e-02 5.226213e-02 1.021435e-14 --8.363257e-03 5.587647e-03 1.019180e-14 -1.810820e-02 -1.630517e-02 1.025265e-14 -5.450962e-02 7.462371e-02 1.025705e-14 -3.193601e-02 4.293337e-02 1.027027e-14 -4.092804e-02 1.339922e-02 1.025658e-14 --1.761576e-02 2.432785e-02 1.022186e-14 -8.978520e-02 3.302668e-02 1.027331e-14 -3.565066e-02 -2.413705e-02 1.027883e-14 -3.713092e-02 8.186335e-02 1.022334e-14 -8.117589e-02 -3.310397e-03 1.028502e-14 --8.554150e-03 6.091822e-02 1.024432e-14 -6.895763e-02 -1.410110e-02 1.030174e-14 -1.047533e-03 7.091246e-02 1.025039e-14 -4.690514e-02 -1.359512e-03 1.026541e-14 -2.701355e-02 5.663052e-02 1.026441e-14 -5.242205e-03 4.051053e-02 1.020029e-14 -6.775191e-02 1.719429e-02 1.029457e-14 -7.976668e-02 6.624641e-02 1.018845e-14 --6.646779e-03 -7.507770e-03 1.021660e-14 -7.032287e-02 7.003705e-02 1.020271e-14 -2.543242e-03 -1.183676e-02 1.026224e-14 -1.898349e-02 1.249715e-03 1.027291e-14 -5.388437e-02 5.681026e-02 1.022861e-14 -8.560916e-03 1.171438e-02 1.020130e-14 -6.445337e-02 4.588481e-02 1.028206e-14 --1.768483e-02 3.746672e-02 1.016807e-14 -9.028134e-02 1.968049e-02 1.028017e-14 -5.324007e-02 -2.593651e-02 1.028015e-14 -2.167982e-02 8.663245e-02 1.023257e-14 -1.087987e-02 2.826069e-02 1.024707e-14 -6.212241e-02 2.931232e-02 1.029599e-14 -5.769115e-02 9.838212e-03 1.024764e-14 -1.543656e-02 4.782133e-02 1.020825e-14 -6.352983e-02 -2.536709e-02 1.023427e-14 --1.606807e-02 4.901700e-02 1.021908e-14 -1.245709e-02 7.995490e-02 1.020598e-14 -8.927952e-02 8.482735e-03 1.027750e-14 -4.664743e-02 3.616995e-02 1.023258e-14 -2.648849e-02 2.118985e-02 1.025789e-14 --1.835246e-02 1.402828e-02 1.015820e-14 -9.106273e-02 4.340773e-02 1.027710e-14 -2.628129e-02 -2.739942e-02 1.029864e-14 -4.655345e-02 8.514146e-02 1.024802e-14 --5.526528e-03 3.558790e-02 1.021949e-14 -7.863789e-02 2.200335e-02 1.029324e-14 -4.619288e-02 -1.393059e-02 1.025105e-14 -2.984863e-02 6.705780e-03 1.027524e-14 -4.306058e-02 5.061018e-02 1.025641e-14 -5.598758e-02 -5.774292e-03 1.026250e-14 -2.068801e-02 6.904201e-02 1.023984e-14 -7.337954e-02 6.449656e-03 1.026794e-14 --5.204518e-04 5.104885e-02 1.023331e-14 -3.347444e-02 1.102293e-02 9.403961e-15 -1.941171e-02 8.465262e-03 9.992613e-15 -2.803266e-02 -7.150597e-03 9.759757e-15 -2.544885e-02 6.496806e-03 9.108976e-15 -3.293549e-02 -4.645286e-03 9.223594e-15 -3.206389e-02 3.271039e-02 9.683873e-15 -4.008735e-02 4.860709e-03 9.668471e-15 -3.305744e-02 5.076350e-02 9.346226e-15 -1.489728e-02 2.750465e-02 1.019491e-14 -2.850179e-02 1.059179e-02 9.602949e-15 -2.426195e-02 -2.946681e-02 9.985400e-15 -2.169358e-02 7.828186e-03 9.477101e-15 -1.782287e-02 3.984323e-02 9.791261e-15 -1.916246e-02 7.301485e-03 9.051995e-15 -1.908307e-02 2.739654e-02 9.980025e-15 -4.622351e-02 8.960894e-03 1.012454e-14 -3.632822e-02 3.518249e-02 8.404563e-15 -4.657256e-03 -3.268403e-02 9.439261e-15 -3.861647e-02 1.989427e-02 9.968798e-15 -3.172404e-02 -1.455406e-02 9.481926e-15 -1.444219e-02 -3.435332e-02 9.332968e-15 -5.508160e-02 1.483657e-02 9.987617e-15 -4.489226e-02 3.163443e-02 9.093957e-15 -3.123976e-02 -2.325178e-02 9.822171e-15 -2.430573e-02 5.822884e-02 9.439658e-15 -2.602693e-02 -9.273362e-03 9.354861e-15 -3.818004e-02 6.509100e-03 1.016496e-14 -4.205127e-02 4.518117e-02 8.877516e-15 -6.079756e-03 -7.185590e-03 1.019392e-14 -5.750128e-02 -5.133933e-05 9.709401e-15 -3.481799e-02 -4.491175e-02 9.588188e-15 -1.825223e-02 5.278075e-02 1.015409e-14 -1.918165e-02 8.362305e-03 8.756875e-15 -2.546357e-02 -4.771865e-02 9.410024e-15 -3.417055e-02 4.284156e-02 8.920412e-15 -3.458681e-02 1.600268e-02 9.140282e-15 -3.330905e-02 6.384387e-02 9.210182e-15 -1.445984e-02 5.732100e-02 9.787638e-15 -4.117724e-02 3.882165e-02 9.267454e-15 -2.200129e-03 -4.114757e-02 9.592289e-15 -1.250379e-02 -2.754402e-02 9.383682e-15 -4.677576e-03 -5.896794e-03 8.995731e-15 -3.013959e-02 -1.377181e-02 9.557290e-15 -4.987759e-02 3.727509e-02 8.908405e-15 -6.540883e-03 5.126938e-02 9.170777e-15 -3.320166e-02 -1.350759e-02 9.269773e-15 -5.418929e-02 6.178458e-02 9.752827e-15 -3.099577e-03 1.231693e-02 9.793073e-15 -5.678578e-02 5.051282e-02 9.936145e-15 -2.743965e-02 4.020210e-03 9.268891e-15 -4.251865e-02 -1.451317e-02 1.001533e-14 -4.040147e-02 -4.028500e-02 9.772743e-15 -2.699184e-02 -6.295260e-03 9.392016e-15 -4.456704e-02 -1.523193e-03 9.598281e-15 -2.563285e-02 2.196414e-02 1.019483e-14 -6.432100e-02 -4.798274e-03 9.910366e-15 -2.385059e-02 -3.959238e-02 9.944339e-15 -6.075174e-02 1.577831e-02 1.018142e-14 -5.160639e-02 4.326545e-02 9.287307e-15 -3.437205e-02 3.209292e-02 9.946598e-15 -5.814737e-02 -1.350893e-02 9.853114e-15 -2.429339e-02 8.982390e-04 9.970137e-15 -5.756486e-02 7.731899e-03 9.730371e-15 --2.727586e-03 -1.072837e-02 8.330550e-15 -1.119202e-02 -4.202773e-03 8.229160e-15 -4.118138e-02 2.476403e-02 9.716954e-15 -2.130357e-02 4.888358e-02 9.058833e-15 -3.168976e-02 1.079966e-02 8.934454e-15 -2.601525e-02 5.874790e-02 1.014191e-14 -1.136774e-02 5.836549e-02 1.001006e-14 -5.472825e-02 5.130048e-02 8.885231e-15 --2.245704e-02 1.064378e-02 7.079270e-15 -2.846428e-02 5.662084e-02 8.988639e-15 -4.807280e-02 2.584295e-02 9.770637e-15 -3.657273e-02 3.622789e-02 9.273368e-15 -1.204642e-02 4.032399e-02 9.143195e-15 -3.283200e-02 2.855271e-02 9.461027e-15 -2.460877e-02 2.622610e-02 1.030557e-14 -1.659568e-03 4.638885e-02 9.073050e-15 -2.948178e-02 4.843984e-02 9.394371e-15 -2.379650e-02 3.576092e-02 9.704125e-15 -8.870092e-03 -1.849257e-04 1.017008e-14 -1.993909e-02 -4.239331e-02 9.388192e-15 -5.093451e-03 -4.205289e-03 8.798219e-15 -2.330706e-02 -3.135099e-02 9.949209e-15 -6.350044e-03 -1.392347e-02 7.023278e-15 -5.988859e-02 4.834653e-02 1.017310e-14 -3.523810e-02 -3.627872e-02 9.734498e-15 -2.966731e-02 2.010084e-02 9.251549e-15 -2.134963e-02 -1.100294e-02 9.998907e-15 -3.934025e-02 5.101847e-02 9.537761e-15 -7.470965e-02 -1.344248e-02 1.011175e-14 -4.723366e-02 2.358571e-02 9.468673e-15 -1.691238e-02 2.109373e-02 6.652381e-15 -4.057784e-02 4.336882e-02 1.024640e-14 -1.855674e-02 -2.649844e-02 9.596457e-15 -1.521916e-02 1.642978e-02 1.008788e-14 -4.824641e-02 6.614636e-02 1.027001e-14 -4.971887e-02 5.709461e-02 9.038929e-15 --7.907835e-03 -3.837677e-02 9.682219e-15 -4.811193e-02 -1.008591e-02 9.827405e-15 -1.381225e-02 8.225370e-03 8.987052e-15 -2.755966e-02 9.685433e-03 9.430147e-15 -5.709868e-02 5.251159e-02 9.834911e-15 --6.843451e-04 -1.792880e-02 7.123037e-15 -1.069664e-02 1.966197e-02 9.728964e-15 -2.980424e-02 3.058234e-03 9.088072e-15 -2.193410e-02 2.045941e-02 9.807337e-15 -1.768253e-02 2.339864e-02 9.567421e-15 --5.010672e-03 -8.318962e-03 8.878151e-15 -5.351722e-02 1.693289e-02 9.788484e-15 -5.515772e-02 6.031543e-02 1.009609e-14 -4.137895e-02 5.671545e-03 1.003225e-14 -5.098592e-02 -5.152186e-03 1.027529e-14 -4.738403e-02 -4.826771e-02 9.747291e-15 -5.890013e-02 7.147771e-04 1.027262e-14 -5.850605e-03 -2.426013e-02 1.019273e-14 -6.944580e-02 2.261339e-03 1.011897e-14 -5.437774e-02 1.561360e-02 9.967332e-15 -3.121835e-02 2.797360e-02 8.141961e-15 -4.193085e-02 -1.220215e-03 9.125283e-15 -2.685746e-02 2.107180e-02 9.596073e-15 -4.158287e-02 -1.915486e-02 1.017098e-14 -7.741386e-02 -1.207422e-02 1.000240e-14 -1.835035e-02 2.561430e-02 1.000935e-14 --3.673831e-03 1.283465e-02 9.121130e-15 -5.096959e-02 3.228203e-02 1.007137e-14 -4.324863e-02 -1.820730e-02 9.955451e-15 -4.283178e-02 2.070703e-02 9.937318e-15 -3.055920e-02 -4.864300e-02 9.548836e-15 -1.561916e-02 5.949698e-02 1.007585e-14 -2.831692e-02 6.286476e-03 9.320220e-15 -6.565485e-02 -1.257051e-03 1.008382e-14 -5.699085e-02 4.793630e-02 8.867933e-15 -1.994114e-02 2.976717e-02 8.148757e-15 --2.483110e-02 -8.781654e-03 9.553846e-15 -3.208001e-02 1.322527e-02 9.933462e-15 -1.967052e-02 3.539448e-02 1.009835e-14 -8.307958e-03 5.780646e-02 9.991911e-15 -2.734851e-02 1.299039e-02 9.631505e-15 -4.965797e-03 2.266832e-03 9.691338e-15 -2.551709e-02 2.986051e-02 9.763136e-15 -4.142024e-02 -3.550510e-03 9.592279e-15 -5.854028e-02 2.322726e-03 9.792809e-15 -1.212554e-02 -2.271058e-02 9.307283e-15 --1.697932e-02 -2.867194e-02 9.690774e-15 -4.043323e-02 1.407699e-02 9.202490e-15 -5.688619e-02 4.509478e-02 9.269593e-15 -4.718047e-02 9.398547e-03 9.845475e-15 -3.019771e-02 5.634792e-02 8.853857e-15 -3.914695e-02 1.732877e-02 9.903732e-15 -1.992330e-02 -1.756364e-02 1.027639e-14 -1.739384e-02 -1.654508e-02 9.996869e-15 --9.652223e-03 -4.762200e-02 9.503780e-15 -7.599452e-02 -1.655196e-02 1.006352e-14 -3.114354e-02 3.159945e-02 9.062815e-15 -3.908580e-02 4.434213e-02 9.737886e-15 -5.694103e-03 -2.907036e-02 1.011815e-14 -2.837552e-02 2.654424e-02 9.424294e-15 --2.335449e-02 -2.010467e-02 9.653271e-15 -5.258772e-02 6.506065e-02 1.022351e-14 - -CELL_DATA 603 -SCALARS mat_id int 1 -LOOKUP_TABLE default -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 diff --git a/example/Inference/More advanced examples with FE models - Sfepy/output/homogenization_opt.py b/example/Inference/More advanced examples with FE models - Sfepy/output/homogenization_opt.py deleted file mode 100644 index 421bec137..000000000 --- a/example/Inference/More advanced examples with FE models - Sfepy/output/homogenization_opt.py +++ /dev/null @@ -1,171 +0,0 @@ -from __future__ import absolute_import -import numpy as nm - -import sfepy.discrete.fem.periodic as per -from sfepy.discrete.fem.mesh import Mesh -from sfepy.mechanics.matcoefs import stiffness_from_youngpoisson -from sfepy.homogenization.utils import define_box_regions -import sfepy.homogenization.coefs_base as cb -from sfepy import data_dir - -# material function -def get_mat(coors, mode, pb): - if mode == 'qp': - cnf = pb.conf - # get material coefficients - if hasattr(cnf, 'opt_data'): - # from optim. - E_f, nu_f, E_m, nu_m = cnf.opt_data['mat_params'] - else: - # given values - E_f, nu_f, E_m, nu_m = 160.e9, 0.28, 5.e9, 0.45 - - nqp = coors.shape[0] - nel = pb.domain.mesh.n_el - nqpe = int(nqp / nel) - out = nm.zeros((nqp, 6, 6), dtype=nm.float64) - - # set values - matrix - D_m = stiffness_from_youngpoisson(3, E_m, nu_m) - Ym = pb.domain.regions['Ym'].get_cells() - idx0 = (nm.arange(nqpe)[:,nm.newaxis] * nm.ones((1, Ym.shape[0]), - dtype=nm.int32)).T.flatten() - idxs = (Ym[:,nm.newaxis] * nm.ones((1, nqpe), - dtype=nm.int32)).flatten() * nqpe - out[idxs + idx0,...] = D_m - - # set values - fiber - D_f = stiffness_from_youngpoisson(3, E_f, nu_f) - Yf = pb.domain.regions['Yf'].get_cells() - idx0 = (nm.arange(nqpe)[:,nm.newaxis] * nm.ones((1, Yf.shape[0]), - dtype=nm.int32)).T.flatten() - idxs = (Yf[:,nm.newaxis] * nm.ones((1, nqpe), - dtype=nm.int32)).flatten() * nqpe - out[idxs + idx0,...] = D_f - - return {'D': out} - -def optimization_hook(pb): - cnf = pb.conf - out = [] - yield pb, out - - if hasattr(cnf, 'opt_data'): - # store homogenized tensor - pb.conf.opt_data['D_homog'] = out[-1].D.copy() - - yield None - -def define(is_opt=False): - filename_mesh = data_dir + '/meshes/3d/matrix_fiber_rand.vtk' - - mesh = Mesh.from_file(filename_mesh) - bbox = mesh.get_bounding_box() - - regions = { - 'Y' : 'all', - 'Ym' : ('cells of group 7', 'cell'), - 'Yf' : ('r.Y -c r.Ym', 'cell'), - } - - regions.update(define_box_regions(3, bbox[0], bbox[1])) - - functions = { - 'get_mat': (lambda ts, coors, mode=None, problem=None, **kwargs: - get_mat(coors, mode, problem),), - 'match_x_plane' : (per.match_x_plane,), - 'match_y_plane' : (per.match_y_plane,), - 'match_z_plane' : (per.match_z_plane,), - } - - materials = { - 'mat': 'get_mat', - } - - fields = { - 'corrector' : ('real', 3, 'Y', 1), - } - - variables = { - 'u': ('unknown field', 'corrector'), - 'v': ('test field', 'corrector', 'u'), - 'Pi': ('parameter field', 'corrector', 'u'), - 'Pi1': ('parameter field', 'corrector', '(set-to-None)'), - 'Pi2': ('parameter field', 'corrector', '(set-to-None)'), - } - - ebcs = { - 'fixed_u' : ('Corners', {'u.all' : 0.0}), - } - - epbcs = { - 'periodic_x' : (['Left', 'Right'], {'u.all' : 'u.all'}, 'match_x_plane'), - 'periodic_y' : (['Near', 'Far'], {'u.all' : 'u.all'}, 'match_y_plane'), - 'periodic_z' : (['Top', 'Bottom'], {'u.all' : 'u.all'}, 'match_z_plane'), - } - - all_periodic = ['periodic_%s' % ii for ii in ['x', 'y', 'z'][:3]] - - options = { - 'coefs': 'coefs', - 'requirements': 'requirements', - 'volume': { 'variables' : ['u'], 'expression' : 'd_volume.5.Y( u )' }, - 'output_dir': 'output', - 'coefs_filename': 'coefs_le', - } - - equation_corrs = { - 'balance_of_forces': - """dw_lin_elastic.5.Y(mat.D, v, u) - = - dw_lin_elastic.5.Y(mat.D, v, Pi)""" - } - - coefs = { - 'D' : { - 'requires' : ['pis', 'corrs_rs'], - 'expression' : 'dw_lin_elastic.5.Y(mat.D, Pi1, Pi2 )', - 'set_variables': [('Pi1', ('pis', 'corrs_rs'), 'u'), - ('Pi2', ('pis', 'corrs_rs'), 'u')], - 'class' : cb.CoefSymSym, - }, - 'vol': { - 'regions': ['Ym', 'Yf'], - 'expression': 'd_volume.5.%s(u)', - 'class': cb.VolumeFractions, - }, - 'filenames' : {}, - } - - requirements = { - 'pis' : { - 'variables' : ['u'], - 'class' : cb.ShapeDimDim, - }, - 'corrs_rs' : { - 'requires' : ['pis'], - 'ebcs' : ['fixed_u'], - 'epbcs' : all_periodic, - 'equations' : equation_corrs, - 'set_variables' : [('Pi', 'pis', 'u')], - 'class' : cb.CorrDimDim, - 'save_name' : 'corrs_le', - 'dump_variables' : ['u'], - }, - } - - solvers = { - 'ls' : ('ls.scipy_direct', {}), - 'newton' : ('nls.newton', { - 'i_max' : 1, - 'eps_a' : 1e-4, - 'problem': 'linear', - }) - } - - if is_opt: - options.update({ - 'parametric_hook': 'optimization_hook', - 'float_format': '%.16e', - }) - - return locals() diff --git a/example/Inference/More advanced examples with FE models - Sfepy/output/matrix_fiber_rand.vtk b/example/Inference/More advanced examples with FE models - Sfepy/output/matrix_fiber_rand.vtk deleted file mode 100644 index cc7079d4d..000000000 --- a/example/Inference/More advanced examples with FE models - 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-1.852999e-02 8.536742e-15 -6.385615e-02 -2.863439e-02 8.441010e-15 -5.150161e-02 -3.637280e-02 8.549300e-15 -3.784766e-02 -4.191231e-02 8.454237e-15 -2.365306e-02 -4.468369e-02 8.403614e-15 -4.620763e-02 3.773940e-02 9.506341e-15 -3.077946e-02 3.750986e-02 9.485555e-15 -1.541948e-02 3.717788e-02 9.525754e-15 --3.738404e-05 3.679166e-02 9.449807e-15 --1.556302e-02 3.643496e-02 9.422060e-15 -7.158211e-02 2.830137e-02 9.543819e-15 -7.879123e-02 1.687341e-02 9.472384e-15 -8.319452e-02 4.078854e-03 9.541023e-15 -8.460906e-02 -9.623136e-03 9.572658e-15 -8.358132e-02 -2.377902e-02 9.587168e-15 -8.063725e-02 -3.769229e-02 9.625521e-15 -7.539633e-02 -5.071885e-02 9.615844e-15 -6.758519e-02 -6.218965e-02 9.597196e-15 -5.745542e-02 -7.157350e-02 9.622423e-15 -4.555950e-02 -7.870730e-02 9.599637e-15 -3.235207e-02 -8.318306e-02 9.546254e-15 -4.580312e-03 -8.369951e-02 9.474856e-15 --8.892775e-03 -7.971173e-02 9.487543e-15 --2.122265e-02 -7.312766e-02 9.500936e-15 --3.196273e-02 -6.427557e-02 9.456040e-15 --4.058003e-02 -5.333911e-02 9.500389e-15 --4.687215e-02 -4.076555e-02 9.511261e-15 --5.077619e-02 -2.709168e-02 9.497115e-15 --5.226972e-02 -1.293944e-02 9.427727e-15 --5.109780e-02 1.006629e-03 9.461289e-15 --4.726482e-02 1.416028e-02 9.408393e-15 --4.053875e-02 2.604641e-02 9.398293e-15 -4.620763e-02 3.773940e-02 9.506341e-15 -3.077946e-02 3.750986e-02 9.485555e-15 -1.541948e-02 3.717788e-02 9.525754e-15 --3.738404e-05 3.679166e-02 9.449807e-15 --1.556302e-02 3.643496e-02 9.422060e-15 -5.132137e-02 4.549680e-02 9.524115e-15 -3.983705e-02 5.115946e-02 9.477124e-15 -2.766626e-02 5.458474e-02 9.460535e-15 -1.519397e-02 5.555863e-02 9.457340e-15 -2.746360e-03 5.387625e-02 9.434671e-15 --9.469916e-03 4.998537e-02 9.416765e-15 --2.096271e-02 4.397087e-02 9.399596e-15 --2.542063e-02 -8.232487e-03 1.032320e-14 --2.072077e-02 -2.265269e-02 1.030984e-14 --1.247249e-02 -3.536213e-02 1.035307e-14 --1.242402e-03 -4.586711e-02 1.031631e-14 -1.220791e-02 -5.363258e-02 1.031314e-14 -2.708630e-02 -5.843702e-02 1.033127e-14 -4.256526e-02 -5.992090e-02 1.034632e-14 -5.796541e-02 -5.785221e-02 1.035038e-14 -7.254310e-02 -5.233736e-02 1.034163e-14 -8.560593e-02 -4.380292e-02 1.039384e-14 -9.644697e-02 -3.272840e-02 1.036750e-14 -1.043916e-01 -1.957766e-02 1.033109e-14 -1.089442e-01 -4.846790e-03 1.031194e-14 -1.079360e-01 2.660082e-02 1.032744e-14 -1.028054e-01 4.164140e-02 1.030493e-14 -9.472914e-02 5.505235e-02 1.033630e-14 -8.375285e-02 6.611092e-02 1.034284e-14 -7.056474e-02 7.427303e-02 1.037918e-14 -5.584040e-02 7.918041e-02 1.041040e-14 -4.035199e-02 8.049940e-02 1.038716e-14 -2.493889e-02 7.818391e-02 1.037434e-14 -1.032279e-02 7.241646e-02 1.039513e-14 --2.602815e-03 6.367119e-02 1.039089e-14 --1.307612e-02 5.219635e-02 1.033963e-14 --2.047119e-02 3.843508e-02 1.034352e-14 --2.485392e-02 2.305022e-02 1.030890e-14 --2.965309e-02 1.094371e-02 1.019394e-14 --2.455631e-02 -3.547390e-03 1.016313e-14 --1.602903e-02 -1.624385e-02 1.018973e-14 --4.506361e-03 -2.667265e-02 1.031469e-14 -9.039851e-03 -3.482775e-02 1.016456e-14 -2.365569e-02 -4.010129e-02 1.033277e-14 -3.897672e-02 -4.158559e-02 1.030193e-14 -5.427648e-02 -3.941392e-02 1.020149e-14 -6.884872e-02 -3.374320e-02 1.022686e-14 -8.181935e-02 -2.512513e-02 1.026152e-14 -9.232149e-02 -1.385541e-02 1.024208e-14 -9.965468e-02 -4.155381e-04 1.028335e-14 -1.037052e-01 1.465075e-02 1.021584e-14 -1.021659e-01 4.630066e-02 1.022249e-14 -9.705414e-02 6.105942e-02 1.016577e-14 -8.879527e-02 7.406271e-02 1.017856e-14 -7.763728e-02 8.472391e-02 1.015251e-14 -6.412705e-02 9.268802e-02 1.020073e-14 -4.921085e-02 9.758751e-02 1.014703e-14 -3.364498e-02 9.917850e-02 1.015489e-14 -1.822603e-02 9.715094e-02 1.022244e-14 -3.712691e-03 9.162632e-02 1.019942e-14 --9.080250e-03 8.288801e-02 1.021951e-14 --1.935633e-02 7.143675e-02 1.023811e-14 --2.655558e-02 5.779197e-02 1.021050e-14 --3.040919e-02 4.253260e-02 1.013546e-14 -1.314042e-02 6.869130e-03 6.789159e-15 -2.513737e-02 1.722036e-02 8.209721e-15 -3.624615e-02 2.909069e-02 9.133671e-15 -4.537399e-02 4.133460e-02 9.699944e-15 -4.859296e-02 5.129990e-02 1.002953e-14 -4.742465e-02 5.681227e-02 1.019327e-14 -4.325213e-02 5.329988e-02 1.028715e-14 -4.116973e-02 3.948471e-02 1.032313e-14 -3.981513e-02 1.789315e-02 1.031734e-14 -3.764403e-02 -2.613290e-03 1.028935e-14 -3.890855e-02 -1.255358e-02 1.018827e-14 -4.302307e-02 -1.356646e-02 1.008261e-14 -5.317233e-02 -1.490194e-03 9.803373e-15 --1.622075e-02 1.552579e-03 7.925604e-15 --1.045008e-03 1.533191e-02 6.726217e-15 --6.839841e-03 2.178932e-02 8.666907e-15 --8.793837e-03 1.737046e-02 9.285170e-15 --4.677034e-03 2.512482e-03 9.602932e-15 -8.076019e-03 -1.269740e-02 9.786258e-15 -2.233815e-02 -2.576612e-02 9.853005e-15 -3.333862e-02 -2.769737e-02 9.787004e-15 -3.871134e-02 -2.256211e-02 9.785329e-15 -4.018391e-02 -6.241036e-03 9.606257e-15 -3.839651e-02 1.871067e-02 9.310418e-15 -3.173557e-02 5.169364e-02 8.988789e-15 -3.603143e-03 -1.781481e-02 5.998170e-15 --1.622075e-02 1.552579e-03 7.925604e-15 -5.317233e-02 -1.490194e-03 9.803373e-15 -4.302307e-02 -1.356646e-02 1.008261e-14 -3.890855e-02 -1.255358e-02 1.018827e-14 -3.764403e-02 -2.613290e-03 1.028935e-14 -3.981513e-02 1.789315e-02 1.031734e-14 -4.116973e-02 3.948471e-02 1.032313e-14 -4.325213e-02 5.329988e-02 1.028715e-14 -4.742465e-02 5.681227e-02 1.019327e-14 -4.859296e-02 5.129990e-02 1.002953e-14 -4.537399e-02 4.133460e-02 9.699944e-15 -3.624615e-02 2.909069e-02 9.133671e-15 -2.513737e-02 1.722036e-02 8.209721e-15 -1.314042e-02 6.869130e-03 6.789159e-15 -3.603143e-03 -1.781481e-02 5.998170e-15 -3.173557e-02 5.169364e-02 8.988789e-15 -3.839651e-02 1.871067e-02 9.310418e-15 -4.018391e-02 -6.241036e-03 9.606257e-15 -3.871134e-02 -2.256211e-02 9.785329e-15 -3.333862e-02 -2.769737e-02 9.787004e-15 -2.233815e-02 -2.576612e-02 9.853005e-15 -8.076019e-03 -1.269740e-02 9.786258e-15 --4.677034e-03 2.512482e-03 9.602932e-15 --8.793837e-03 1.737046e-02 9.285170e-15 --6.839841e-03 2.178932e-02 8.666907e-15 --1.045008e-03 1.533191e-02 6.726217e-15 --1.686389e-02 3.901047e-02 8.535107e-15 --1.037616e-02 7.620178e-03 8.497366e-15 --1.085107e-02 6.006129e-02 8.538636e-15 --7.031935e-03 -1.389381e-02 8.453812e-15 --2.125600e-02 2.059779e-02 8.510231e-15 --7.840943e-03 2.051988e-02 8.525437e-15 --2.029605e-02 5.318354e-02 8.570635e-15 -1.288963e-03 7.383306e-02 8.529039e-15 -2.449870e-03 3.711026e-02 8.447156e-15 --3.324965e-02 3.447835e-02 8.529649e-15 --1.896408e-02 -1.208328e-02 8.472513e-15 -2.065234e-03 9.644348e-03 8.527340e-15 --2.480733e-02 2.428690e-03 8.500746e-15 --1.443676e-03 -2.688971e-02 8.483093e-15 --8.298773e-05 5.602739e-02 8.539343e-15 --2.443509e-02 6.252831e-02 8.562212e-15 -1.525318e-03 -3.118935e-03 8.488857e-15 --1.201789e-02 7.094192e-02 8.557220e-15 --1.289404e-02 -2.124495e-02 8.479005e-15 --3.186313e-02 4.773170e-02 8.573772e-15 --3.493758e-02 2.027150e-02 8.494980e-15 -8.630181e-03 8.363075e-02 8.496267e-15 --9.561214e-03 -4.651960e-03 8.491336e-15 --3.192270e-02 5.627930e-02 8.556052e-15 -2.857866e-03 2.320807e-02 8.495242e-15 -5.346599e-04 -1.558846e-02 8.444616e-15 --3.449896e-02 7.710843e-03 8.523953e-15 --6.993546e-03 3.254760e-02 8.485531e-15 -4.310896e-02 1.269717e-02 8.451154e-15 -5.470744e-02 4.853527e-02 8.504890e-15 -4.044485e-02 -1.068229e-02 8.459449e-15 -6.458045e-02 3.529009e-02 8.495612e-15 -3.495597e-02 3.187451e-02 8.491648e-15 -3.772591e-02 6.865224e-02 8.505040e-15 -5.776109e-02 -3.293189e-03 8.502609e-15 -7.432878e-02 2.078549e-02 8.576293e-15 -3.550028e-02 -2.174320e-02 8.474091e-15 -6.560822e-02 4.779925e-02 8.504152e-15 -5.322780e-02 6.608975e-02 8.541590e-15 -2.732463e-02 4.273943e-02 8.446394e-15 -4.901808e-02 3.210595e-02 8.495318e-15 -5.013769e-02 -1.974251e-02 8.474058e-15 -4.573517e-02 3.812499e-03 8.439911e-15 -2.623234e-02 1.779883e-02 8.525899e-15 -7.575281e-02 3.369500e-02 8.536958e-15 -3.109420e-02 8.131032e-02 8.522765e-15 -2.822977e-02 5.609999e-02 8.496770e-15 -7.160937e-02 7.427983e-03 8.580650e-15 -7.620180e-02 4.609729e-02 8.513775e-15 -3.843283e-02 2.361611e-02 8.484439e-15 -4.375599e-02 7.781457e-02 8.530559e-15 -5.810876e-02 2.192845e-02 8.542746e-15 -4.039056e-02 5.860931e-02 8.477402e-15 -5.993004e-02 1.003060e-02 8.543253e-15 -2.861941e-02 -2.850188e-03 8.466172e-15 -2.566476e-02 3.068175e-02 8.476941e-15 -5.631606e-02 3.871225e-02 8.465098e-15 -4.104701e-02 4.298751e-02 8.448015e-15 -6.717644e-02 6.125372e-02 8.531145e-15 -6.486597e-02 -9.934426e-03 8.495661e-15 -2.062267e-02 -3.095062e-02 8.458542e-15 -3.771977e-02 -4.194091e-03 8.429495e-15 -1.843317e-02 -2.046158e-02 8.425989e-15 -4.888895e-02 -7.647279e-03 8.469787e-15 -2.911871e-02 7.044225e-02 8.480058e-15 -7.470621e-02 5.740881e-02 8.526606e-15 -1.689286e-02 -3.068989e-02 9.541061e-15 --1.517755e-02 -3.785630e-04 9.469139e-15 -4.315655e-02 -4.083006e-03 9.497784e-15 --9.049644e-03 -3.528770e-02 9.479618e-15 -5.624465e-02 -3.672780e-02 9.596180e-15 -2.017346e-02 -5.627346e-02 9.523097e-15 -1.274148e-02 7.777882e-03 9.485747e-15 --2.266474e-02 -2.293630e-02 9.458891e-15 -6.219324e-02 -1.049200e-02 9.550233e-15 -1.807920e-03 -5.812376e-02 9.506207e-15 -3.219329e-02 1.457737e-02 9.507842e-15 --1.995703e-02 1.153153e-02 9.438969e-15 -3.779614e-02 -5.644116e-02 9.540181e-15 -5.143327e-02 1.670581e-02 9.521284e-15 -1.050373e-03 -2.037125e-02 9.499128e-15 -3.616141e-02 -1.929481e-02 9.492881e-15 -6.512331e-03 -4.402753e-02 9.525652e-15 --3.163952e-02 -5.891565e-03 9.412358e-15 --4.084611e-05 1.802973e-02 9.475178e-15 -3.169485e-02 -1.440460e-03 9.488652e-15 --3.370262e-02 -3.782773e-02 9.479972e-15 -2.992131e-02 -4.329092e-02 9.544583e-15 -6.748425e-02 -3.790436e-02 9.607596e-15 --1.688039e-02 -5.704950e-02 9.480530e-15 -1.262481e-02 -6.897156e-02 9.534757e-15 -5.604340e-02 -5.072296e-02 9.600437e-15 --2.623089e-03 -6.439746e-03 9.482476e-15 -3.167617e-02 -6.846525e-02 9.541563e-15 -1.953992e-02 2.371128e-02 9.498420e-15 -1.459352e-02 -4.960204e-03 9.487259e-15 --3.464322e-02 1.039299e-02 9.398645e-15 --1.174649e-02 -2.265313e-02 9.466594e-15 --3.732903e-02 -1.919808e-02 9.445946e-15 -6.603749e-02 6.447517e-03 9.513421e-15 -4.818558e-02 -2.235423e-02 9.546489e-15 -7.062090e-02 -2.433442e-02 9.574260e-15 -7.288887e-02 -1.063813e-02 9.575654e-15 --9.271826e-03 -6.507279e-02 9.491393e-15 -4.291871e-02 2.137275e-02 9.510994e-15 --1.134283e-02 1.017349e-02 9.478533e-15 --2.843721e-03 -7.142307e-02 9.481665e-15 -4.172062e-02 -4.600040e-02 9.557596e-15 -7.924681e-03 2.304210e-02 9.483810e-15 --2.503193e-02 4.586891e-03 9.427821e-15 --3.123156e-02 -4.908565e-02 9.481084e-15 --2.022871e-02 -1.083872e-02 9.443813e-15 -3.942857e-02 1.025387e-02 9.508981e-15 -9.125149e-03 -5.267004e-02 9.503162e-15 --4.610379e-03 -4.798702e-02 9.459135e-15 -5.317525e-02 -1.091528e-02 9.544211e-15 -5.203265e-02 3.988354e-03 9.543424e-15 -3.675515e-03 -3.257249e-02 9.496690e-15 -5.893423e-02 2.161028e-02 9.533871e-15 --8.458448e-03 2.126449e-02 9.461084e-15 --2.376113e-02 2.099559e-02 9.418184e-15 -1.832522e-02 -4.294016e-02 9.529959e-15 -2.456318e-02 9.360451e-03 9.479648e-15 -4.283867e-02 -6.993083e-02 9.586311e-15 -4.448032e-02 -3.427525e-02 9.552394e-15 -6.766548e-04 8.514180e-03 9.469112e-15 -4.419891e-02 -9.926282e-03 9.504720e-15 -1.463377e-02 -1.817774e-02 9.518011e-15 -2.032457e-02 -7.421075e-02 9.552717e-15 -3.165914e-02 2.436416e-02 9.516027e-15 --1.268488e-02 -1.387195e-02 9.475031e-15 -6.685447e-02 -4.622569e-02 9.617374e-15 --2.091067e-02 -3.707960e-02 9.460960e-15 -3.140143e-02 -3.195022e-02 9.507450e-15 --3.937426e-02 2.316593e-03 9.440815e-15 -2.994197e-02 -5.180007e-02 9.540669e-15 -7.478002e-02 1.505812e-04 9.532608e-15 -4.829785e-02 -6.270839e-02 9.634357e-15 -5.906387e-02 -2.432808e-02 9.561326e-15 -1.811476e-02 4.693177e-02 9.463228e-15 -1.314487e-02 4.512653e-02 9.476939e-15 --1.197992e-02 4.345630e-02 9.407876e-15 -4.246741e-02 4.466635e-02 9.491943e-15 -4.178010e-02 1.064259e-02 1.033588e-14 -7.939580e-02 1.386162e-02 1.032262e-14 -3.397007e-03 4.848425e-03 1.030927e-14 -3.186851e-02 -2.627542e-02 1.036251e-14 -5.064498e-02 4.671490e-02 1.037457e-14 -6.610056e-02 -1.292001e-02 1.035441e-14 -1.717777e-02 3.360739e-02 1.035421e-14 -7.227568e-03 -1.281790e-02 1.034612e-14 -7.540560e-02 3.253092e-02 1.035281e-14 -8.101199e-02 -7.553068e-03 1.034837e-14 -2.033543e-03 2.699409e-02 1.036017e-14 -5.368960e-02 -3.573635e-02 1.034275e-14 -2.829660e-02 5.594851e-02 1.039150e-14 -5.737072e-02 2.119505e-02 1.034616e-14 -2.567176e-02 -1.022825e-03 1.034504e-14 -6.242652e-02 5.503046e-03 1.034540e-14 -2.075547e-02 1.480168e-02 1.033679e-14 -4.249188e-02 -1.083343e-02 1.036165e-14 -4.095247e-02 3.217205e-02 1.035258e-14 -9.040674e-02 2.721013e-02 1.032111e-14 --7.928123e-03 -7.816482e-03 1.031333e-14 -1.471391e-02 -3.293135e-02 1.031096e-14 -6.792531e-02 5.319051e-02 1.038056e-14 -7.592233e-02 -2.818912e-02 1.038707e-14 -5.301539e-03 4.732707e-02 1.036249e-14 -9.261164e-02 8.751576e-03 1.033604e-14 --9.163353e-03 1.018264e-02 1.033114e-14 -3.275231e-02 -4.144013e-02 1.034058e-14 -4.856250e-02 6.193825e-02 1.037304e-14 -6.425156e-02 -4.153949e-02 1.035051e-14 -1.806665e-02 6.171681e-02 1.037462e-14 -4.672938e-02 -2.055839e-02 1.034168e-14 -3.583181e-02 4.104956e-02 1.036563e-14 --9.746939e-03 -1.954638e-02 1.029037e-14 -9.212497e-02 3.864278e-02 1.030798e-14 -7.238884e-02 2.813127e-04 1.033541e-14 -1.062672e-02 1.948315e-02 1.032118e-14 --2.817881e-06 -2.815593e-02 1.031109e-14 -8.246573e-02 4.753944e-02 1.033777e-14 -8.791370e-02 -1.921018e-02 1.033095e-14 --4.779144e-03 3.857506e-02 1.033910e-14 -6.878928e-02 1.840274e-02 1.033939e-14 -1.394786e-02 5.184482e-04 1.034666e-14 -3.503795e-02 6.716959e-02 1.039408e-14 -4.704139e-02 -4.662406e-02 1.035161e-14 -9.521234e-02 -1.168801e-02 1.031969e-14 --1.158366e-02 3.073722e-02 1.034592e-14 -6.247187e-02 3.459783e-02 1.036588e-14 -2.038062e-02 -1.457153e-02 1.033349e-14 -7.411559e-02 -1.563857e-02 1.035501e-14 -8.524051e-03 3.540504e-02 1.035563e-14 -9.799967e-02 1.439593e-02 1.032479e-14 --1.489376e-02 4.294542e-03 1.034162e-14 -1.966335e-02 -4.297368e-02 1.033692e-14 -6.269573e-02 6.346837e-02 1.037296e-14 -4.501751e-02 -3.255047e-02 1.035140e-14 -3.707749e-02 5.295717e-02 1.038485e-14 -5.618145e-02 -6.661793e-03 1.036351e-14 -2.713021e-02 2.781740e-02 1.035171e-14 -5.187552e-02 9.219271e-03 1.033713e-14 -3.323103e-02 -1.227573e-02 1.036106e-14 -4.989506e-02 3.234364e-02 1.037215e-14 -3.118965e-02 1.201275e-02 1.034296e-14 -4.875996e-02 1.120839e-03 1.035170e-14 -3.663541e-02 2.374254e-02 1.035091e-14 -5.637175e-02 -4.888308e-02 1.033365e-14 -2.606875e-02 6.917759e-02 1.036945e-14 -6.035558e-02 -2.429081e-02 1.036076e-14 -2.200919e-02 4.448884e-02 1.036117e-14 -3.708161e-02 -6.999026e-04 1.033072e-14 -4.647713e-02 2.200758e-02 1.034782e-14 -3.648301e-02 2.853684e-02 1.021299e-14 --2.028606e-03 2.127137e-02 1.022265e-14 -7.480440e-02 3.628590e-02 1.024696e-14 -3.282907e-02 -8.118464e-03 1.023737e-14 -3.947092e-02 6.597388e-02 1.023080e-14 -6.357122e-02 -7.521765e-04 1.023826e-14 -9.065811e-03 5.947246e-02 1.024095e-14 -6.674950e-02 5.604475e-02 1.020508e-14 -5.952228e-03 2.083324e-03 1.019132e-14 --7.184738e-03 4.544276e-02 1.022147e-14 -8.023747e-02 1.209765e-02 1.029650e-14 -5.480389e-02 -1.467658e-02 1.025200e-14 -1.769981e-02 1.435110e-02 1.022757e-14 -5.528872e-02 4.312056e-02 1.024528e-14 -2.143765e-02 3.524214e-02 1.023109e-14 -5.167428e-02 2.213182e-02 1.027883e-14 -8.102171e-02 5.226213e-02 1.021435e-14 --8.363257e-03 5.587647e-03 1.019180e-14 -1.810820e-02 -1.630517e-02 1.025265e-14 -5.450962e-02 7.462371e-02 1.025705e-14 -3.193601e-02 4.293337e-02 1.027027e-14 -4.092804e-02 1.339922e-02 1.025658e-14 --1.761576e-02 2.432785e-02 1.022186e-14 -8.978520e-02 3.302668e-02 1.027331e-14 -3.565066e-02 -2.413705e-02 1.027883e-14 -3.713092e-02 8.186335e-02 1.022334e-14 -8.117589e-02 -3.310397e-03 1.028502e-14 --8.554150e-03 6.091822e-02 1.024432e-14 -6.895763e-02 -1.410110e-02 1.030174e-14 -1.047533e-03 7.091246e-02 1.025039e-14 -4.690514e-02 -1.359512e-03 1.026541e-14 -2.701355e-02 5.663052e-02 1.026441e-14 -5.242205e-03 4.051053e-02 1.020029e-14 -6.775191e-02 1.719429e-02 1.029457e-14 -7.976668e-02 6.624641e-02 1.018845e-14 --6.646779e-03 -7.507770e-03 1.021660e-14 -7.032287e-02 7.003705e-02 1.020271e-14 -2.543242e-03 -1.183676e-02 1.026224e-14 -1.898349e-02 1.249715e-03 1.027291e-14 -5.388437e-02 5.681026e-02 1.022861e-14 -8.560916e-03 1.171438e-02 1.020130e-14 -6.445337e-02 4.588481e-02 1.028206e-14 --1.768483e-02 3.746672e-02 1.016807e-14 -9.028134e-02 1.968049e-02 1.028017e-14 -5.324007e-02 -2.593651e-02 1.028015e-14 -2.167982e-02 8.663245e-02 1.023257e-14 -1.087987e-02 2.826069e-02 1.024707e-14 -6.212241e-02 2.931232e-02 1.029599e-14 -5.769115e-02 9.838212e-03 1.024764e-14 -1.543656e-02 4.782133e-02 1.020825e-14 -6.352983e-02 -2.536709e-02 1.023427e-14 --1.606807e-02 4.901700e-02 1.021908e-14 -1.245709e-02 7.995490e-02 1.020598e-14 -8.927952e-02 8.482735e-03 1.027750e-14 -4.664743e-02 3.616995e-02 1.023258e-14 -2.648849e-02 2.118985e-02 1.025789e-14 --1.835246e-02 1.402828e-02 1.015820e-14 -9.106273e-02 4.340773e-02 1.027710e-14 -2.628129e-02 -2.739942e-02 1.029864e-14 -4.655345e-02 8.514146e-02 1.024802e-14 --5.526528e-03 3.558790e-02 1.021949e-14 -7.863789e-02 2.200335e-02 1.029324e-14 -4.619288e-02 -1.393059e-02 1.025105e-14 -2.984863e-02 6.705780e-03 1.027524e-14 -4.306058e-02 5.061018e-02 1.025641e-14 -5.598758e-02 -5.774292e-03 1.026250e-14 -2.068801e-02 6.904201e-02 1.023984e-14 -7.337954e-02 6.449656e-03 1.026794e-14 --5.204518e-04 5.104885e-02 1.023331e-14 -3.347444e-02 1.102293e-02 9.403961e-15 -1.941171e-02 8.465262e-03 9.992613e-15 -2.803266e-02 -7.150597e-03 9.759757e-15 -2.544885e-02 6.496806e-03 9.108976e-15 -3.293549e-02 -4.645286e-03 9.223594e-15 -3.206389e-02 3.271039e-02 9.683873e-15 -4.008735e-02 4.860709e-03 9.668471e-15 -3.305744e-02 5.076350e-02 9.346226e-15 -1.489728e-02 2.750465e-02 1.019491e-14 -2.850179e-02 1.059179e-02 9.602949e-15 -2.426195e-02 -2.946681e-02 9.985400e-15 -2.169358e-02 7.828186e-03 9.477101e-15 -1.782287e-02 3.984323e-02 9.791261e-15 -1.916246e-02 7.301485e-03 9.051995e-15 -1.908307e-02 2.739654e-02 9.980025e-15 -4.622351e-02 8.960894e-03 1.012454e-14 -3.632822e-02 3.518249e-02 8.404563e-15 -4.657256e-03 -3.268403e-02 9.439261e-15 -3.861647e-02 1.989427e-02 9.968798e-15 -3.172404e-02 -1.455406e-02 9.481926e-15 -1.444219e-02 -3.435332e-02 9.332968e-15 -5.508160e-02 1.483657e-02 9.987617e-15 -4.489226e-02 3.163443e-02 9.093957e-15 -3.123976e-02 -2.325178e-02 9.822171e-15 -2.430573e-02 5.822884e-02 9.439658e-15 -2.602693e-02 -9.273362e-03 9.354861e-15 -3.818004e-02 6.509100e-03 1.016496e-14 -4.205127e-02 4.518117e-02 8.877516e-15 -6.079756e-03 -7.185590e-03 1.019392e-14 -5.750128e-02 -5.133933e-05 9.709401e-15 -3.481799e-02 -4.491175e-02 9.588188e-15 -1.825223e-02 5.278075e-02 1.015409e-14 -1.918165e-02 8.362305e-03 8.756875e-15 -2.546357e-02 -4.771865e-02 9.410024e-15 -3.417055e-02 4.284156e-02 8.920412e-15 -3.458681e-02 1.600268e-02 9.140282e-15 -3.330905e-02 6.384387e-02 9.210182e-15 -1.445984e-02 5.732100e-02 9.787638e-15 -4.117724e-02 3.882165e-02 9.267454e-15 -2.200129e-03 -4.114757e-02 9.592289e-15 -1.250379e-02 -2.754402e-02 9.383682e-15 -4.677576e-03 -5.896794e-03 8.995731e-15 -3.013959e-02 -1.377181e-02 9.557290e-15 -4.987759e-02 3.727509e-02 8.908405e-15 -6.540883e-03 5.126938e-02 9.170777e-15 -3.320166e-02 -1.350759e-02 9.269773e-15 -5.418929e-02 6.178458e-02 9.752827e-15 -3.099577e-03 1.231693e-02 9.793073e-15 -5.678578e-02 5.051282e-02 9.936145e-15 -2.743965e-02 4.020210e-03 9.268891e-15 -4.251865e-02 -1.451317e-02 1.001533e-14 -4.040147e-02 -4.028500e-02 9.772743e-15 -2.699184e-02 -6.295260e-03 9.392016e-15 -4.456704e-02 -1.523193e-03 9.598281e-15 -2.563285e-02 2.196414e-02 1.019483e-14 -6.432100e-02 -4.798274e-03 9.910366e-15 -2.385059e-02 -3.959238e-02 9.944339e-15 -6.075174e-02 1.577831e-02 1.018142e-14 -5.160639e-02 4.326545e-02 9.287307e-15 -3.437205e-02 3.209292e-02 9.946598e-15 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9.949209e-15 -6.350044e-03 -1.392347e-02 7.023278e-15 -5.988859e-02 4.834653e-02 1.017310e-14 -3.523810e-02 -3.627872e-02 9.734498e-15 -2.966731e-02 2.010084e-02 9.251549e-15 -2.134963e-02 -1.100294e-02 9.998907e-15 -3.934025e-02 5.101847e-02 9.537761e-15 -7.470965e-02 -1.344248e-02 1.011175e-14 -4.723366e-02 2.358571e-02 9.468673e-15 -1.691238e-02 2.109373e-02 6.652381e-15 -4.057784e-02 4.336882e-02 1.024640e-14 -1.855674e-02 -2.649844e-02 9.596457e-15 -1.521916e-02 1.642978e-02 1.008788e-14 -4.824641e-02 6.614636e-02 1.027001e-14 -4.971887e-02 5.709461e-02 9.038929e-15 --7.907835e-03 -3.837677e-02 9.682219e-15 -4.811193e-02 -1.008591e-02 9.827405e-15 -1.381225e-02 8.225370e-03 8.987052e-15 -2.755966e-02 9.685433e-03 9.430147e-15 -5.709868e-02 5.251159e-02 9.834911e-15 --6.843451e-04 -1.792880e-02 7.123037e-15 -1.069664e-02 1.966197e-02 9.728964e-15 -2.980424e-02 3.058234e-03 9.088072e-15 -2.193410e-02 2.045941e-02 9.807337e-15 -1.768253e-02 2.339864e-02 9.567421e-15 --5.010672e-03 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9.424294e-15 --2.335449e-02 -2.010467e-02 9.653271e-15 -5.258772e-02 6.506065e-02 1.022351e-14 -1.937343e-02 9.609459e-02 8.505404e-15 -9.342511e-03 -4.417858e-02 8.376449e-15 -1.937343e-02 9.609459e-02 8.505404e-15 -9.342511e-03 -4.417858e-02 8.376449e-15 -8.917136e-02 2.203542e-02 8.546298e-15 -6.189748e-02 3.807478e-02 9.555734e-15 --3.132061e-02 3.622791e-02 9.432554e-15 -1.852257e-02 -8.487776e-02 9.529634e-15 -6.189748e-02 3.807478e-02 9.555734e-15 --3.132061e-02 3.622791e-02 9.432554e-15 --2.650156e-02 7.220248e-03 1.034421e-14 -1.100360e-01 1.084117e-02 1.033731e-14 --3.139713e-02 2.652183e-02 1.017648e-14 -1.044098e-01 3.050448e-02 1.030194e-14 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -0.000000e+00 0.000000e+00 0.000000e+00 -1.845538e-02 8.217181e-02 8.498748e-15 -1.750961e-02 6.824609e-02 8.491094e-15 -1.653121e-02 5.428129e-02 8.533385e-15 -1.553522e-02 4.027353e-02 8.432780e-15 -1.451098e-02 2.621383e-02 8.471246e-15 -1.345987e-02 1.213346e-02 8.509491e-15 -1.239822e-02 -1.948521e-03 8.486534e-15 -1.133815e-02 -1.602054e-02 8.458802e-15 -1.030942e-02 -3.009099e-02 8.472219e-15 -4.936086e-03 9.384619e-02 8.537962e-15 --8.751292e-03 8.862243e-02 8.516700e-15 --2.113998e-02 8.070719e-02 8.544050e-15 --3.156720e-02 7.048805e-02 8.572065e-15 --3.945337e-02 5.835072e-02 8.584665e-15 --4.430585e-02 4.475326e-02 8.542317e-15 --4.652489e-02 3.001713e-02 8.566661e-15 --4.606866e-02 1.499811e-02 8.521220e-15 --4.305944e-02 4.447918e-04 8.537698e-15 --3.711255e-02 -1.286433e-02 8.504483e-15 --2.838357e-02 -2.439143e-02 8.479340e-15 --1.731569e-02 -3.372975e-02 8.527992e-15 --4.557705e-03 -4.046698e-02 8.448978e-15 -1.845538e-02 8.217181e-02 8.498748e-15 -1.750961e-02 6.824609e-02 8.491094e-15 -1.653121e-02 5.428129e-02 8.533385e-15 -1.553522e-02 4.027353e-02 8.432780e-15 -1.451098e-02 2.621383e-02 8.471246e-15 -1.345987e-02 1.213346e-02 8.509491e-15 -1.239822e-02 -1.948521e-03 8.486534e-15 -1.133815e-02 -1.602054e-02 8.458802e-15 -1.030942e-02 -3.009099e-02 8.472219e-15 -3.383183e-02 9.518791e-02 8.526505e-15 -4.781812e-02 9.127247e-02 8.524566e-15 -6.066436e-02 8.458243e-02 8.526041e-15 -7.178201e-02 7.538816e-02 8.579916e-15 -8.054529e-02 6.404534e-02 8.562581e-15 -8.651165e-02 5.104454e-02 8.467455e-15 -8.924460e-02 3.682610e-02 8.500289e-15 -8.677272e-02 7.342559e-03 8.658936e-15 -8.191665e-02 -6.389612e-03 8.623669e-15 -7.411698e-02 -1.852999e-02 8.536742e-15 -6.385615e-02 -2.863439e-02 8.441010e-15 -5.150161e-02 -3.637280e-02 8.549300e-15 -3.784766e-02 -4.191231e-02 8.454237e-15 -2.365306e-02 -4.468369e-02 8.403614e-15 -4.620763e-02 3.773940e-02 9.506341e-15 -3.077946e-02 3.750986e-02 9.485555e-15 -1.541948e-02 3.717788e-02 9.525754e-15 --3.738404e-05 3.679166e-02 9.449807e-15 --1.556302e-02 3.643496e-02 9.422060e-15 -7.158211e-02 2.830137e-02 9.543819e-15 -7.879123e-02 1.687341e-02 9.472384e-15 -8.319452e-02 4.078854e-03 9.541023e-15 -8.460906e-02 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9.524115e-15 -3.983705e-02 5.115946e-02 9.477124e-15 -2.766626e-02 5.458474e-02 9.460535e-15 -1.519397e-02 5.555863e-02 9.457340e-15 -2.746360e-03 5.387625e-02 9.434671e-15 --9.469916e-03 4.998537e-02 9.416765e-15 --2.096271e-02 4.397087e-02 9.399596e-15 --2.542063e-02 -8.232487e-03 1.032320e-14 --2.072077e-02 -2.265269e-02 1.030984e-14 --1.247249e-02 -3.536213e-02 1.035307e-14 --1.242402e-03 -4.586711e-02 1.031631e-14 -1.220791e-02 -5.363258e-02 1.031314e-14 -2.708630e-02 -5.843702e-02 1.033127e-14 -4.256526e-02 -5.992090e-02 1.034632e-14 -5.796541e-02 -5.785221e-02 1.035038e-14 -7.254310e-02 -5.233736e-02 1.034163e-14 -8.560593e-02 -4.380292e-02 1.039384e-14 -9.644697e-02 -3.272840e-02 1.036750e-14 -1.043916e-01 -1.957766e-02 1.033109e-14 -1.089442e-01 -4.846790e-03 1.031194e-14 -1.079360e-01 2.660082e-02 1.032744e-14 -1.028054e-01 4.164140e-02 1.030493e-14 -9.472914e-02 5.505235e-02 1.033630e-14 -8.375285e-02 6.611092e-02 1.034284e-14 -7.056474e-02 7.427303e-02 1.037918e-14 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8.472391e-02 1.015251e-14 -6.412705e-02 9.268802e-02 1.020073e-14 -4.921085e-02 9.758751e-02 1.014703e-14 -3.364498e-02 9.917850e-02 1.015489e-14 -1.822603e-02 9.715094e-02 1.022244e-14 -3.712691e-03 9.162632e-02 1.019942e-14 --9.080250e-03 8.288801e-02 1.021951e-14 --1.935633e-02 7.143675e-02 1.023811e-14 --2.655558e-02 5.779197e-02 1.021050e-14 --3.040919e-02 4.253260e-02 1.013546e-14 -1.314042e-02 6.869130e-03 6.789159e-15 -2.513737e-02 1.722036e-02 8.209721e-15 -3.624615e-02 2.909069e-02 9.133671e-15 -4.537399e-02 4.133460e-02 9.699944e-15 -4.859296e-02 5.129990e-02 1.002953e-14 -4.742465e-02 5.681227e-02 1.019327e-14 -4.325213e-02 5.329988e-02 1.028715e-14 -4.116973e-02 3.948471e-02 1.032313e-14 -3.981513e-02 1.789315e-02 1.031734e-14 -3.764403e-02 -2.613290e-03 1.028935e-14 -3.890855e-02 -1.255358e-02 1.018827e-14 -4.302307e-02 -1.356646e-02 1.008261e-14 -5.317233e-02 -1.490194e-03 9.803373e-15 --1.622075e-02 1.552579e-03 7.925604e-15 --1.045008e-03 1.533191e-02 6.726217e-15 --6.839841e-03 2.178932e-02 8.666907e-15 --8.793837e-03 1.737046e-02 9.285170e-15 --4.677034e-03 2.512482e-03 9.602932e-15 -8.076019e-03 -1.269740e-02 9.786258e-15 -2.233815e-02 -2.576612e-02 9.853005e-15 -3.333862e-02 -2.769737e-02 9.787004e-15 -3.871134e-02 -2.256211e-02 9.785329e-15 -4.018391e-02 -6.241036e-03 9.606257e-15 -3.839651e-02 1.871067e-02 9.310418e-15 -3.173557e-02 5.169364e-02 8.988789e-15 -3.603143e-03 -1.781481e-02 5.998170e-15 --1.622075e-02 1.552579e-03 7.925604e-15 -5.317233e-02 -1.490194e-03 9.803373e-15 -4.302307e-02 -1.356646e-02 1.008261e-14 -3.890855e-02 -1.255358e-02 1.018827e-14 -3.764403e-02 -2.613290e-03 1.028935e-14 -3.981513e-02 1.789315e-02 1.031734e-14 -4.116973e-02 3.948471e-02 1.032313e-14 -4.325213e-02 5.329988e-02 1.028715e-14 -4.742465e-02 5.681227e-02 1.019327e-14 -4.859296e-02 5.129990e-02 1.002953e-14 -4.537399e-02 4.133460e-02 9.699944e-15 -3.624615e-02 2.909069e-02 9.133671e-15 -2.513737e-02 1.722036e-02 8.209721e-15 -1.314042e-02 6.869130e-03 6.789159e-15 -3.603143e-03 -1.781481e-02 5.998170e-15 -3.173557e-02 5.169364e-02 8.988789e-15 -3.839651e-02 1.871067e-02 9.310418e-15 -4.018391e-02 -6.241036e-03 9.606257e-15 -3.871134e-02 -2.256211e-02 9.785329e-15 -3.333862e-02 -2.769737e-02 9.787004e-15 -2.233815e-02 -2.576612e-02 9.853005e-15 -8.076019e-03 -1.269740e-02 9.786258e-15 --4.677034e-03 2.512482e-03 9.602932e-15 --8.793837e-03 1.737046e-02 9.285170e-15 --6.839841e-03 2.178932e-02 8.666907e-15 --1.045008e-03 1.533191e-02 6.726217e-15 --1.686389e-02 3.901047e-02 8.535107e-15 --1.037616e-02 7.620178e-03 8.497366e-15 --1.085107e-02 6.006129e-02 8.538636e-15 --7.031935e-03 -1.389381e-02 8.453812e-15 --2.125600e-02 2.059779e-02 8.510231e-15 --7.840943e-03 2.051988e-02 8.525437e-15 --2.029605e-02 5.318354e-02 8.570635e-15 -1.288963e-03 7.383306e-02 8.529039e-15 -2.449870e-03 3.711026e-02 8.447156e-15 --3.324965e-02 3.447835e-02 8.529649e-15 --1.896408e-02 -1.208328e-02 8.472513e-15 -2.065234e-03 9.644348e-03 8.527340e-15 --2.480733e-02 2.428690e-03 8.500746e-15 --1.443676e-03 -2.688971e-02 8.483093e-15 --8.298773e-05 5.602739e-02 8.539343e-15 --2.443509e-02 6.252831e-02 8.562212e-15 -1.525318e-03 -3.118935e-03 8.488857e-15 --1.201789e-02 7.094192e-02 8.557220e-15 --1.289404e-02 -2.124495e-02 8.479005e-15 --3.186313e-02 4.773170e-02 8.573772e-15 --3.493758e-02 2.027150e-02 8.494980e-15 -8.630181e-03 8.363075e-02 8.496267e-15 --9.561214e-03 -4.651960e-03 8.491336e-15 --3.192270e-02 5.627930e-02 8.556052e-15 -2.857866e-03 2.320807e-02 8.495242e-15 -5.346599e-04 -1.558846e-02 8.444616e-15 --3.449896e-02 7.710843e-03 8.523953e-15 --6.993546e-03 3.254760e-02 8.485531e-15 -4.310896e-02 1.269717e-02 8.451154e-15 -5.470744e-02 4.853527e-02 8.504890e-15 -4.044485e-02 -1.068229e-02 8.459449e-15 -6.458045e-02 3.529009e-02 8.495612e-15 -3.495597e-02 3.187451e-02 8.491648e-15 -3.772591e-02 6.865224e-02 8.505040e-15 -5.776109e-02 -3.293189e-03 8.502609e-15 -7.432878e-02 2.078549e-02 8.576293e-15 -3.550028e-02 -2.174320e-02 8.474091e-15 -6.560822e-02 4.779925e-02 8.504152e-15 -5.322780e-02 6.608975e-02 8.541590e-15 -2.732463e-02 4.273943e-02 8.446394e-15 -4.901808e-02 3.210595e-02 8.495318e-15 -5.013769e-02 -1.974251e-02 8.474058e-15 -4.573517e-02 3.812499e-03 8.439911e-15 -2.623234e-02 1.779883e-02 8.525899e-15 -7.575281e-02 3.369500e-02 8.536958e-15 -3.109420e-02 8.131032e-02 8.522765e-15 -2.822977e-02 5.609999e-02 8.496770e-15 -7.160937e-02 7.427983e-03 8.580650e-15 -7.620180e-02 4.609729e-02 8.513775e-15 -3.843283e-02 2.361611e-02 8.484439e-15 -4.375599e-02 7.781457e-02 8.530559e-15 -5.810876e-02 2.192845e-02 8.542746e-15 -4.039056e-02 5.860931e-02 8.477402e-15 -5.993004e-02 1.003060e-02 8.543253e-15 -2.861941e-02 -2.850188e-03 8.466172e-15 -2.566476e-02 3.068175e-02 8.476941e-15 -5.631606e-02 3.871225e-02 8.465098e-15 -4.104701e-02 4.298751e-02 8.448015e-15 -6.717644e-02 6.125372e-02 8.531145e-15 -6.486597e-02 -9.934426e-03 8.495661e-15 -2.062267e-02 -3.095062e-02 8.458542e-15 -3.771977e-02 -4.194091e-03 8.429495e-15 -1.843317e-02 -2.046158e-02 8.425989e-15 -4.888895e-02 -7.647279e-03 8.469787e-15 -2.911871e-02 7.044225e-02 8.480058e-15 -7.470621e-02 5.740881e-02 8.526606e-15 -1.689286e-02 -3.068989e-02 9.541061e-15 --1.517755e-02 -3.785630e-04 9.469139e-15 -4.315655e-02 -4.083006e-03 9.497784e-15 --9.049644e-03 -3.528770e-02 9.479618e-15 -5.624465e-02 -3.672780e-02 9.596180e-15 -2.017346e-02 -5.627346e-02 9.523097e-15 -1.274148e-02 7.777882e-03 9.485747e-15 --2.266474e-02 -2.293630e-02 9.458891e-15 -6.219324e-02 -1.049200e-02 9.550233e-15 -1.807920e-03 -5.812376e-02 9.506207e-15 -3.219329e-02 1.457737e-02 9.507842e-15 --1.995703e-02 1.153153e-02 9.438969e-15 -3.779614e-02 -5.644116e-02 9.540181e-15 -5.143327e-02 1.670581e-02 9.521284e-15 -1.050373e-03 -2.037125e-02 9.499128e-15 -3.616141e-02 -1.929481e-02 9.492881e-15 -6.512331e-03 -4.402753e-02 9.525652e-15 --3.163952e-02 -5.891565e-03 9.412358e-15 --4.084611e-05 1.802973e-02 9.475178e-15 -3.169485e-02 -1.440460e-03 9.488652e-15 --3.370262e-02 -3.782773e-02 9.479972e-15 -2.992131e-02 -4.329092e-02 9.544583e-15 -6.748425e-02 -3.790436e-02 9.607596e-15 --1.688039e-02 -5.704950e-02 9.480530e-15 -1.262481e-02 -6.897156e-02 9.534757e-15 -5.604340e-02 -5.072296e-02 9.600437e-15 --2.623089e-03 -6.439746e-03 9.482476e-15 -3.167617e-02 -6.846525e-02 9.541563e-15 -1.953992e-02 2.371128e-02 9.498420e-15 -1.459352e-02 -4.960204e-03 9.487259e-15 --3.464322e-02 1.039299e-02 9.398645e-15 --1.174649e-02 -2.265313e-02 9.466594e-15 --3.732903e-02 -1.919808e-02 9.445946e-15 -6.603749e-02 6.447517e-03 9.513421e-15 -4.818558e-02 -2.235423e-02 9.546489e-15 -7.062090e-02 -2.433442e-02 9.574260e-15 -7.288887e-02 -1.063813e-02 9.575654e-15 --9.271826e-03 -6.507279e-02 9.491393e-15 -4.291871e-02 2.137275e-02 9.510994e-15 --1.134283e-02 1.017349e-02 9.478533e-15 --2.843721e-03 -7.142307e-02 9.481665e-15 -4.172062e-02 -4.600040e-02 9.557596e-15 -7.924681e-03 2.304210e-02 9.483810e-15 --2.503193e-02 4.586891e-03 9.427821e-15 --3.123156e-02 -4.908565e-02 9.481084e-15 --2.022871e-02 -1.083872e-02 9.443813e-15 -3.942857e-02 1.025387e-02 9.508981e-15 -9.125149e-03 -5.267004e-02 9.503162e-15 --4.610379e-03 -4.798702e-02 9.459135e-15 -5.317525e-02 -1.091528e-02 9.544211e-15 -5.203265e-02 3.988354e-03 9.543424e-15 -3.675515e-03 -3.257249e-02 9.496690e-15 -5.893423e-02 2.161028e-02 9.533871e-15 --8.458448e-03 2.126449e-02 9.461084e-15 --2.376113e-02 2.099559e-02 9.418184e-15 -1.832522e-02 -4.294016e-02 9.529959e-15 -2.456318e-02 9.360451e-03 9.479648e-15 -4.283867e-02 -6.993083e-02 9.586311e-15 -4.448032e-02 -3.427525e-02 9.552394e-15 -6.766548e-04 8.514180e-03 9.469112e-15 -4.419891e-02 -9.926282e-03 9.504720e-15 -1.463377e-02 -1.817774e-02 9.518011e-15 -2.032457e-02 -7.421075e-02 9.552717e-15 -3.165914e-02 2.436416e-02 9.516027e-15 --1.268488e-02 -1.387195e-02 9.475031e-15 -6.685447e-02 -4.622569e-02 9.617374e-15 --2.091067e-02 -3.707960e-02 9.460960e-15 -3.140143e-02 -3.195022e-02 9.507450e-15 --3.937426e-02 2.316593e-03 9.440815e-15 -2.994197e-02 -5.180007e-02 9.540669e-15 -7.478002e-02 1.505812e-04 9.532608e-15 -4.829785e-02 -6.270839e-02 9.634357e-15 -5.906387e-02 -2.432808e-02 9.561326e-15 -1.811476e-02 4.693177e-02 9.463228e-15 -1.314487e-02 4.512653e-02 9.476939e-15 --1.197992e-02 4.345630e-02 9.407876e-15 -4.246741e-02 4.466635e-02 9.491943e-15 -4.178010e-02 1.064259e-02 1.033588e-14 -7.939580e-02 1.386162e-02 1.032262e-14 -3.397007e-03 4.848425e-03 1.030927e-14 -3.186851e-02 -2.627542e-02 1.036251e-14 -5.064498e-02 4.671490e-02 1.037457e-14 -6.610056e-02 -1.292001e-02 1.035441e-14 -1.717777e-02 3.360739e-02 1.035421e-14 -7.227568e-03 -1.281790e-02 1.034612e-14 -7.540560e-02 3.253092e-02 1.035281e-14 -8.101199e-02 -7.553068e-03 1.034837e-14 -2.033543e-03 2.699409e-02 1.036017e-14 -5.368960e-02 -3.573635e-02 1.034275e-14 -2.829660e-02 5.594851e-02 1.039150e-14 -5.737072e-02 2.119505e-02 1.034616e-14 -2.567176e-02 -1.022825e-03 1.034504e-14 -6.242652e-02 5.503046e-03 1.034540e-14 -2.075547e-02 1.480168e-02 1.033679e-14 -4.249188e-02 -1.083343e-02 1.036165e-14 -4.095247e-02 3.217205e-02 1.035258e-14 -9.040674e-02 2.721013e-02 1.032111e-14 --7.928123e-03 -7.816482e-03 1.031333e-14 -1.471391e-02 -3.293135e-02 1.031096e-14 -6.792531e-02 5.319051e-02 1.038056e-14 -7.592233e-02 -2.818912e-02 1.038707e-14 -5.301539e-03 4.732707e-02 1.036249e-14 -9.261164e-02 8.751576e-03 1.033604e-14 --9.163353e-03 1.018264e-02 1.033114e-14 -3.275231e-02 -4.144013e-02 1.034058e-14 -4.856250e-02 6.193825e-02 1.037304e-14 -6.425156e-02 -4.153949e-02 1.035051e-14 -1.806665e-02 6.171681e-02 1.037462e-14 -4.672938e-02 -2.055839e-02 1.034168e-14 -3.583181e-02 4.104956e-02 1.036563e-14 --9.746939e-03 -1.954638e-02 1.029037e-14 -9.212497e-02 3.864278e-02 1.030798e-14 -7.238884e-02 2.813127e-04 1.033541e-14 -1.062672e-02 1.948315e-02 1.032118e-14 --2.817881e-06 -2.815593e-02 1.031109e-14 -8.246573e-02 4.753944e-02 1.033777e-14 -8.791370e-02 -1.921018e-02 1.033095e-14 --4.779144e-03 3.857506e-02 1.033910e-14 -6.878928e-02 1.840274e-02 1.033939e-14 -1.394786e-02 5.184482e-04 1.034666e-14 -3.503795e-02 6.716959e-02 1.039408e-14 -4.704139e-02 -4.662406e-02 1.035161e-14 -9.521234e-02 -1.168801e-02 1.031969e-14 --1.158366e-02 3.073722e-02 1.034592e-14 -6.247187e-02 3.459783e-02 1.036588e-14 -2.038062e-02 -1.457153e-02 1.033349e-14 -7.411559e-02 -1.563857e-02 1.035501e-14 -8.524051e-03 3.540504e-02 1.035563e-14 -9.799967e-02 1.439593e-02 1.032479e-14 --1.489376e-02 4.294542e-03 1.034162e-14 -1.966335e-02 -4.297368e-02 1.033692e-14 -6.269573e-02 6.346837e-02 1.037296e-14 -4.501751e-02 -3.255047e-02 1.035140e-14 -3.707749e-02 5.295717e-02 1.038485e-14 -5.618145e-02 -6.661793e-03 1.036351e-14 -2.713021e-02 2.781740e-02 1.035171e-14 -5.187552e-02 9.219271e-03 1.033713e-14 -3.323103e-02 -1.227573e-02 1.036106e-14 -4.989506e-02 3.234364e-02 1.037215e-14 -3.118965e-02 1.201275e-02 1.034296e-14 -4.875996e-02 1.120839e-03 1.035170e-14 -3.663541e-02 2.374254e-02 1.035091e-14 -5.637175e-02 -4.888308e-02 1.033365e-14 -2.606875e-02 6.917759e-02 1.036945e-14 -6.035558e-02 -2.429081e-02 1.036076e-14 -2.200919e-02 4.448884e-02 1.036117e-14 -3.708161e-02 -6.999026e-04 1.033072e-14 -4.647713e-02 2.200758e-02 1.034782e-14 -3.648301e-02 2.853684e-02 1.021299e-14 --2.028606e-03 2.127137e-02 1.022265e-14 -7.480440e-02 3.628590e-02 1.024696e-14 -3.282907e-02 -8.118464e-03 1.023737e-14 -3.947092e-02 6.597388e-02 1.023080e-14 -6.357122e-02 -7.521765e-04 1.023826e-14 -9.065811e-03 5.947246e-02 1.024095e-14 -6.674950e-02 5.604475e-02 1.020508e-14 -5.952228e-03 2.083324e-03 1.019132e-14 --7.184738e-03 4.544276e-02 1.022147e-14 -8.023747e-02 1.209765e-02 1.029650e-14 -5.480389e-02 -1.467658e-02 1.025200e-14 -1.769981e-02 1.435110e-02 1.022757e-14 -5.528872e-02 4.312056e-02 1.024528e-14 -2.143765e-02 3.524214e-02 1.023109e-14 -5.167428e-02 2.213182e-02 1.027883e-14 -8.102171e-02 5.226213e-02 1.021435e-14 --8.363257e-03 5.587647e-03 1.019180e-14 -1.810820e-02 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2.911289e-03 -6.750313e-06 1.471467e-05 3.203104e-03 -6.732673e-06 1.225267e-05 3.494890e-03 -6.819588e-06 9.487808e-06 3.786765e-03 -6.400920e-06 6.481327e-06 4.078343e-03 -8.343057e-06 3.373825e-06 4.371311e-03 -0.000000e+00 0.000000e+00 4.653401e-03 -0.000000e+00 0.000000e+00 0.000000e+00 -8.296151e-06 -2.920595e-06 2.843724e-04 -6.418576e-06 4.059182e-06 5.770025e-04 -6.817817e-06 7.207069e-06 8.688196e-04 -6.735089e-06 1.007077e-05 1.160724e-03 -6.751776e-06 1.184029e-05 1.452606e-03 -6.748224e-06 1.281693e-05 1.744485e-03 -6.748780e-06 1.302006e-05 2.036356e-03 -6.748465e-06 1.254033e-05 2.328221e-03 -6.748486e-06 1.144957e-05 2.620080e-03 -6.747681e-06 9.828310e-06 2.911931e-03 -6.750863e-06 7.738523e-06 3.203776e-03 -6.733784e-06 5.312230e-06 3.495611e-03 -6.818798e-06 2.408260e-06 3.787457e-03 -6.402285e-06 -5.769796e-08 4.079184e-03 -8.347598e-06 -5.280111e-06 4.371932e-03 -0.000000e+00 0.000000e+00 4.653741e-03 -0.000000e+00 0.000000e+00 0.000000e+00 -4.116842e-06 1.445580e-05 2.832544e-04 -3.230194e-06 1.712855e-05 5.771599e-04 -3.397112e-06 2.135120e-05 8.683397e-04 -3.369242e-06 2.394983e-05 1.160189e-03 -3.373283e-06 2.578483e-05 1.451892e-03 -3.372972e-06 2.674614e-05 1.743635e-03 -3.373020e-06 2.695360e-05 2.035378e-03 -3.373126e-06 2.647323e-05 2.327129e-03 -3.373175e-06 2.538410e-05 2.618887e-03 -3.373198e-06 2.375932e-05 2.910650e-03 -3.373994e-06 2.168643e-05 3.202427e-03 -3.368642e-06 1.919368e-05 3.494175e-03 -3.402298e-06 1.655900e-05 3.786093e-03 -3.214945e-06 1.302156e-05 4.077322e-03 -4.167040e-06 1.208364e-05 4.371360e-03 -0.000000e+00 0.000000e+00 4.654772e-03 -0.000000e+00 0.000000e+00 0.000000e+00 -4.119047e-06 5.761069e-06 2.829907e-04 -3.228631e-06 1.058289e-05 5.773948e-04 -3.399438e-06 1.428227e-05 8.684843e-04 -3.369579e-06 1.700630e-05 1.160483e-03 -3.374227e-06 1.881156e-05 1.452242e-03 -3.373646e-06 1.977971e-05 1.744062e-03 -3.373675e-06 1.998548e-05 2.035867e-03 -3.373695e-06 1.950547e-05 2.327675e-03 -3.373678e-06 1.841580e-05 2.619484e-03 -3.373625e-06 1.679261e-05 2.911288e-03 -3.374346e-06 1.471248e-05 3.203109e-03 -3.368952e-06 1.225018e-05 3.494868e-03 -3.402470e-06 9.487104e-06 3.786861e-03 -3.215512e-06 6.481083e-06 4.077985e-03 -4.162782e-06 3.374957e-06 4.372203e-03 -0.000000e+00 0.000000e+00 4.656146e-03 -0.000000e+00 0.000000e+00 0.000000e+00 -4.122039e-06 -2.934742e-06 2.832177e-04 -3.229688e-06 4.034088e-06 5.773797e-04 -3.400317e-06 7.211634e-06 8.687244e-04 -3.370447e-06 1.006070e-05 1.160745e-03 -3.375005e-06 1.183656e-05 1.452602e-03 -3.374366e-06 1.281165e-05 1.744486e-03 -3.374315e-06 1.301592e-05 2.036356e-03 -3.374270e-06 1.253642e-05 2.328221e-03 -3.374168e-06 1.144638e-05 2.620080e-03 -3.374093e-06 9.824958e-06 2.911930e-03 -3.374562e-06 7.737681e-06 3.203781e-03 -3.369664e-06 5.306190e-06 3.495590e-03 -3.401753e-06 2.414312e-06 3.787551e-03 -3.216412e-06 -5.869030e-08 4.078829e-03 -4.166799e-06 -5.335834e-06 4.372826e-03 -0.000000e+00 0.000000e+00 4.656486e-03 -0.000000e+00 0.000000e+00 0.000000e+00 --1.464351e-17 1.445612e-05 2.830298e-04 --5.147535e-17 1.712859e-05 5.771457e-04 --1.074128e-16 2.134849e-05 8.683641e-04 --1.742584e-16 2.394774e-05 1.160179e-03 --2.453821e-16 2.578331e-05 1.451895e-03 --3.182133e-16 2.674442e-05 1.743634e-03 --3.719356e-16 2.695223e-05 2.035378e-03 --4.206704e-16 2.647192e-05 2.327129e-03 --4.652057e-16 2.538303e-05 2.618887e-03 --4.852347e-16 2.375825e-05 2.910651e-03 --4.691343e-16 2.168588e-05 3.202425e-03 --4.255494e-16 1.919281e-05 3.494183e-03 --3.452100e-16 1.655751e-05 3.786078e-03 --2.117447e-16 1.302469e-05 4.077291e-03 --8.983334e-17 1.209869e-05 4.371707e-03 -0.000000e+00 0.000000e+00 4.655474e-03 -0.000000e+00 0.000000e+00 0.000000e+00 --2.077779e-17 5.759462e-06 2.827661e-04 --7.083445e-17 1.057992e-05 5.773810e-04 --1.435218e-16 1.427995e-05 8.685087e-04 --2.300660e-16 1.700460e-05 1.160473e-03 --3.209459e-16 1.880975e-05 1.452245e-03 --4.144346e-16 1.977812e-05 1.744061e-03 --4.897782e-16 1.998405e-05 2.035867e-03 --5.586216e-16 1.950420e-05 2.327675e-03 --6.182832e-16 1.841468e-05 2.619483e-03 --6.431996e-16 1.679165e-05 2.911289e-03 --6.223320e-16 1.471176e-05 3.203106e-03 --5.658688e-16 1.224936e-05 3.494877e-03 --4.653294e-16 9.486578e-06 3.786846e-03 --3.007204e-16 6.482187e-06 4.077955e-03 --1.375700e-16 3.375557e-06 4.372551e-03 -0.000000e+00 0.000000e+00 4.656850e-03 -0.000000e+00 0.000000e+00 0.000000e+00 --2.692630e-17 -2.938239e-06 2.829934e-04 --9.012535e-17 4.028072e-06 5.773662e-04 --1.796870e-16 7.209731e-06 8.687485e-04 --2.855916e-16 1.005937e-05 1.160735e-03 --3.968060e-16 1.183447e-05 1.452605e-03 --5.109562e-16 1.281019e-05 1.744485e-03 --6.075663e-16 1.301444e-05 2.036356e-03 --6.973065e-16 1.253518e-05 2.328221e-03 --7.707359e-16 1.144521e-05 2.620079e-03 --8.018723e-16 9.824094e-06 2.911931e-03 --7.753342e-16 7.736779e-06 3.203778e-03 --7.074277e-16 5.305430e-06 3.495598e-03 --5.857412e-16 2.414727e-06 3.787536e-03 --3.902010e-16 -5.960063e-08 4.078799e-03 --1.854579e-16 -5.349697e-06 4.373176e-03 -0.000000e+00 0.000000e+00 4.657190e-03 -0.000000e+00 0.000000e+00 0.000000e+00 --4.116842e-06 1.445580e-05 2.832544e-04 --3.230194e-06 1.712855e-05 5.771599e-04 --3.397112e-06 2.135120e-05 8.683397e-04 --3.369242e-06 2.394983e-05 1.160189e-03 --3.373283e-06 2.578483e-05 1.451892e-03 --3.372972e-06 2.674614e-05 1.743635e-03 --3.373020e-06 2.695360e-05 2.035378e-03 --3.373126e-06 2.647323e-05 2.327129e-03 --3.373175e-06 2.538410e-05 2.618887e-03 --3.373198e-06 2.375932e-05 2.910650e-03 --3.373994e-06 2.168643e-05 3.202427e-03 --3.368642e-06 1.919368e-05 3.494175e-03 --3.402298e-06 1.655900e-05 3.786093e-03 --3.214945e-06 1.302156e-05 4.077322e-03 --4.167040e-06 1.208364e-05 4.371360e-03 -0.000000e+00 0.000000e+00 4.654772e-03 -0.000000e+00 0.000000e+00 0.000000e+00 --4.119047e-06 5.761069e-06 2.829907e-04 --3.228631e-06 1.058289e-05 5.773948e-04 --3.399438e-06 1.428227e-05 8.684843e-04 --3.369579e-06 1.700630e-05 1.160483e-03 --3.374227e-06 1.881156e-05 1.452242e-03 --3.373646e-06 1.977971e-05 1.744062e-03 --3.373675e-06 1.998548e-05 2.035867e-03 --3.373695e-06 1.950547e-05 2.327675e-03 --3.373678e-06 1.841580e-05 2.619484e-03 --3.373625e-06 1.679261e-05 2.911288e-03 --3.374346e-06 1.471248e-05 3.203109e-03 --3.368952e-06 1.225018e-05 3.494868e-03 --3.402470e-06 9.487104e-06 3.786861e-03 --3.215512e-06 6.481083e-06 4.077985e-03 --4.162782e-06 3.374957e-06 4.372203e-03 -0.000000e+00 0.000000e+00 4.656146e-03 -0.000000e+00 0.000000e+00 0.000000e+00 --4.122039e-06 -2.934742e-06 2.832177e-04 --3.229688e-06 4.034088e-06 5.773797e-04 --3.400317e-06 7.211634e-06 8.687244e-04 --3.370447e-06 1.006070e-05 1.160745e-03 --3.375005e-06 1.183656e-05 1.452602e-03 --3.374366e-06 1.281165e-05 1.744486e-03 --3.374315e-06 1.301592e-05 2.036356e-03 --3.374270e-06 1.253642e-05 2.328221e-03 --3.374168e-06 1.144638e-05 2.620080e-03 --3.374093e-06 9.824958e-06 2.911930e-03 --3.374562e-06 7.737681e-06 3.203781e-03 --3.369664e-06 5.306190e-06 3.495590e-03 --3.401753e-06 2.414312e-06 3.787551e-03 --3.216412e-06 -5.869030e-08 4.078829e-03 --4.166799e-06 -5.335834e-06 4.372826e-03 -0.000000e+00 0.000000e+00 4.656486e-03 -0.000000e+00 0.000000e+00 0.000000e+00 --8.287326e-06 1.446053e-05 2.844101e-04 --6.418092e-06 1.711437e-05 5.767848e-04 --6.812222e-06 2.137092e-05 8.684335e-04 --6.732373e-06 2.394996e-05 1.160167e-03 --6.748417e-06 2.579196e-05 1.451896e-03 --6.745416e-06 2.675036e-05 1.743634e-03 --6.746194e-06 2.695807e-05 2.035378e-03 --6.746177e-06 2.647697e-05 2.327129e-03 --6.746494e-06 2.538758e-05 2.618887e-03 --6.745912e-06 2.376177e-05 2.910651e-03 --6.749636e-06 2.168998e-05 3.202423e-03 --6.732021e-06 1.919260e-05 3.494197e-03 --6.819263e-06 1.656647e-05 3.785998e-03 --6.400131e-06 1.302108e-05 4.077678e-03 --8.347826e-06 1.202571e-05 4.370470e-03 -0.000000e+00 0.000000e+00 4.652033e-03 -0.000000e+00 0.000000e+00 0.000000e+00 --8.291403e-06 5.770520e-06 2.841466e-04 --6.416758e-06 1.058833e-05 5.770180e-04 --6.815844e-06 1.428987e-05 8.685784e-04 --6.733465e-06 1.701138e-05 1.160462e-03 --6.750159e-06 1.881699e-05 1.452246e-03 --6.746812e-06 1.978446e-05 1.744061e-03 --6.747486e-06 1.998979e-05 2.035867e-03 --6.747324e-06 1.950929e-05 2.327675e-03 --6.747490e-06 1.841914e-05 2.619483e-03 --6.746785e-06 1.679552e-05 2.911289e-03 --6.750313e-06 1.471467e-05 3.203104e-03 --6.732673e-06 1.225267e-05 3.494890e-03 --6.819588e-06 9.487808e-06 3.786765e-03 --6.400920e-06 6.481327e-06 4.078343e-03 --8.343057e-06 3.373825e-06 4.371311e-03 -0.000000e+00 0.000000e+00 4.653401e-03 -0.000000e+00 0.000000e+00 0.000000e+00 --8.296151e-06 -2.920595e-06 2.843724e-04 --6.418576e-06 4.059182e-06 5.770025e-04 --6.817817e-06 7.207069e-06 8.688196e-04 --6.735089e-06 1.007077e-05 1.160724e-03 --6.751776e-06 1.184029e-05 1.452606e-03 --6.748224e-06 1.281693e-05 1.744485e-03 --6.748780e-06 1.302006e-05 2.036356e-03 --6.748465e-06 1.254033e-05 2.328221e-03 --6.748486e-06 1.144957e-05 2.620080e-03 --6.747681e-06 9.828310e-06 2.911931e-03 --6.750863e-06 7.738523e-06 3.203776e-03 --6.733784e-06 5.312230e-06 3.495611e-03 --6.818798e-06 2.408260e-06 3.787457e-03 --6.402285e-06 -5.769796e-08 4.079184e-03 --8.347598e-06 -5.280111e-06 4.371932e-03 -0.000000e+00 0.000000e+00 4.653741e-03 - -CELL_DATA 128 -SCALARS mat_id int 1 -LOOKUP_TABLE default -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 diff --git a/example/Inference/More advanced examples with FE models - Sfepy/sfepy_log.txt b/example/Inference/More advanced examples with FE models - Sfepy/sfepy_log.txt deleted file mode 100644 index 568596271..000000000 --- a/example/Inference/More advanced examples with FE models - Sfepy/sfepy_log.txt +++ /dev/null @@ -1,1152 +0,0 @@ -sfepy: left over: ['K', 'mu_nh', 'mu_mr', 'kappa', 'lam', 'mu', 'UserMeshIO', 'stiffness_from_lame', 'mesh_hook', 'gen_block_mesh', 'verbose', '_filename'] -sfepy: reading mesh (function:mesh_hook)... -sfepy: ...done in 0.00 s -sfepy: creating regions... -sfepy: Omega -sfepy: Bottom -sfepy: Top -sfepy: ...done in 0.01 s -sfepy: using solvers: - ts: ts - nls: newton - ls: ls -sfepy: equation "Mooney-Rivlin": -sfepy: dw_tl_he_neohook.i.Omega(solid.mu_mr, v, u) - + dw_tl_he_mooney_rivlin.i.Omega(solid.kappa, v, u) - + dw_tl_bulk_penalty.i.Omega(solid.K, v, u) - = dw_surface_ltr.isurf.Top(load.val, v) -sfepy: updating variables... -sfepy: ...done -sfepy: setting up dof connectivities... -sfepy: ...done in 0.00 s -sfepy: matrix shape: (28, 28) -sfepy: assembling matrix graph... -sfepy: ...done in 0.00 s -sfepy: matrix structural nonzeros: 688 (8.78e-01% fill) -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: ====== time 0.000000e+00 (step 1 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.00 s -sfepy: initial residual: 0.000000e+00 -sfepy: ====== time 4.000000e-02 (step 2 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.00 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 1.582679e-02 (rel: 3.165359e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 2.416768e-05 (rel: 4.833536e-05) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 4.511520e-11 (rel: 9.023041e-11) -sfepy: ====== time 8.000000e-02 (step 3 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 1.695369e-02 (rel: 3.390738e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 2.746872e-05 (rel: 5.493744e-05) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 6.033706e-11 (rel: 1.206741e-10) -sfepy: ====== time 1.200000e-01 (step 4 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 1.827341e-02 (rel: 3.654683e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 3.117504e-05 (rel: 6.235008e-05) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 8.079191e-11 (rel: 1.615838e-10) -sfepy: ====== time 1.600000e-01 (step 5 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 1.982590e-02 (rel: 3.965181e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 3.530749e-05 (rel: 7.061497e-05) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 1.082080e-10 (rel: 2.164159e-10) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 3.610893e-15 (rel: 7.221785e-15) -sfepy: ====== time 2.000000e-01 (step 6 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 2.165949e-02 (rel: 4.331897e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 3.987726e-05 (rel: 7.975453e-05) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 1.448999e-10 (rel: 2.897998e-10) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 4.972743e-15 (rel: 9.945485e-15) -sfepy: ====== time 2.400000e-01 (step 7 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.00 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 2.383234e-02 (rel: 4.766468e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 4.488461e-05 (rel: 8.976922e-05) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 1.938219e-10 (rel: 3.876439e-10) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 3.878240e-15 (rel: 7.756479e-15) -sfepy: ====== time 2.800000e-01 (step 8 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.00 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: 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-sfepy: nls: iter: 2, residual: 5.618894e-05 (rel: 1.123779e-04) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 3.444450e-10 (rel: 6.888901e-10) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 5.871600e-15 (rel: 1.174320e-14) -sfepy: ====== time 3.600000e-01 (step 10 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.00 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 3.314695e-02 (rel: 6.629390e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 6.252643e-05 (rel: 1.250529e-04) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 4.569152e-10 (rel: 9.138304e-10) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 3.665418e-15 (rel: 7.330836e-15) -sfepy: ====== time 4.000000e-01 (step 11 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 3.750477e-02 (rel: 7.500954e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 6.949123e-05 (rel: 1.389825e-04) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 6.035902e-10 (rel: 1.207180e-09) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 4.192470e-15 (rel: 8.384941e-15) -sfepy: ====== time 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-sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 9.796763e-02 (rel: 1.959353e-01) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 2.197194e-04 (rel: 4.394388e-04) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 3.087749e-09 (rel: 6.175497e-09) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 1.092315e-14 (rel: 2.184630e-14) -sfepy: ====== time 7.200000e-01 (step 19 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 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-sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 1.748135e-01 (rel: 3.496271e-01) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 4.777325e-04 (rel: 9.554650e-04) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 2.285583e-09 (rel: 4.571167e-09) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 1.716794e-14 (rel: 3.433588e-14) -sfepy: ====== time 9.200000e-01 (step 24 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 1.905525e-01 (rel: 3.811050e-01) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 5.122174e-04 (rel: 1.024435e-03) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 1.520361e-09 (rel: 3.040723e-09) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 1.909067e-14 (rel: 3.818134e-14) -sfepy: ====== time 9.600000e-01 (step 25 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 2.054888e-01 (rel: 4.109776e-01) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 5.346598e-04 (rel: 1.069320e-03) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 8.243733e-10 (rel: 1.648747e-09) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 9.431016e-15 (rel: 1.886203e-14) -sfepy: ====== time 1.000000e+00 (step 26 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: load -sfepy: solid -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 2.193273e-01 (rel: 4.386546e-01) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 5.450684e-04 (rel: 1.090137e-03) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 4.938122e-10 (rel: 9.876244e-10) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 4, residual: 1.095020e-14 (rel: 2.190040e-14) -sfepy: solved in 26 steps in 1.65 seconds -sfepy: equation "Mooney-Rivlin": -sfepy: dw_tl_he_neohook.i.Omega(solid.mu_mr, v, u) - + dw_tl_he_mooney_rivlin.i.Omega(solid.kappa, v, u) - + dw_tl_bulk_penalty.i.Omega(solid.K, v, u) - = dw_surface_ltr.isurf.Top(load.val, v) -sfepy: updating variables... -sfepy: ...done -sfepy: setting up dof connectivities... -sfepy: ...done in 0.00 s -sfepy: matrix shape: (28, 28) -sfepy: assembling matrix graph... -sfepy: ...done in 0.00 s -sfepy: matrix structural nonzeros: 688 (8.78e-01% fill) -sfepy: updating materials... -sfepy: solid -sfepy: load -sfepy: ...done in 0.01 s -sfepy: ====== time 0.000000e+00 (step 1 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: solid -sfepy: load -sfepy: ...done in 0.01 s -sfepy: initial residual: 0.000000e+00 -sfepy: ====== time 4.000000e-02 (step 2 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: solid -sfepy: load -sfepy: ...done in 0.00 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 1.669274e-02 (rel: 3.338548e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 2.487308e-05 (rel: 4.974616e-05) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 4.571924e-11 (rel: 9.143849e-11) -sfepy: ====== time 8.000000e-02 (step 3 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: solid -sfepy: load -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] 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-sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 4.566197e-12 (rel: 9.132395e-12) -sfepy: ====== time 4.000000e-01 (step 11 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: solid -sfepy: load -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, residual: 5.000000e-01 (rel: 1.000000e+00) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 1, residual: 1.093191e-02 (rel: 2.186382e-02) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 2, residual: 7.683692e-06 (rel: 1.536738e-05) -sfepy: residual: 0.00 [s] -sfepy: matrix: 0.00 [s] -sfepy: solve: 0.00 [s] -sfepy: nls: iter: 3, residual: 3.467507e-12 (rel: 6.935014e-12) -sfepy: ====== time 4.400000e-01 (step 12 of 26) ===== -sfepy: updating variables... -sfepy: ...done -sfepy: updating materials... -sfepy: solid -sfepy: load -sfepy: ...done in 0.01 s -sfepy: nls: iter: 0, 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a/example/Reliability/SubsetSimulation/SubsetSimulation_Example1/SubsetSimulation_Example1.ipynb b/example/Reliability/SubsetSimulation/SubsetSimulation_Example1/SubsetSimulation_Example1.ipynb deleted file mode 100644 index 0720d9e43..000000000 --- a/example/Reliability/SubsetSimulation/SubsetSimulation_Example1/SubsetSimulation_Example1.ipynb +++ /dev/null @@ -1,139 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Subset Simulation Example 1\n", - "Author: Michael D. Shields\n", - "Date: May 23, 2018, Last modified by Dimitris Giovanis on 4/8/2019." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example runs subset simulation with the component-wise modified Metropolis-Hastings algorithm for MCMC to estimate the probability of failure of a system with linear performance function and 2-dimensional standard unit normal random variables, $u_i$ given by:\n", - "\n", - "\n", - "$g(\\textbf{U}) = -\\frac{1}{\\sqrt{d}}\\sum_{i=1}^{d} u_i + \\beta$\n", - "\n", - "\n", - "The probability of failure in this case is $P(F) ≈ 10^{−3}$ for $\\beta$ = 3.0902" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from UQpy.Reliability import SubsetSimulation\n", - "import matplotlib.pyplot as plt\n", - "import time\n", - "import numpy as np\n", - "from UQpy.RunModel import RunModel" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we use 1000 samples per subset and call the performance function using a Python script (provided) called 'pfn.py' that evaluates $g(U)$ as defined above. The conditional probabilities for each level are " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "__init__() missing 1 required positional argument: 'runmodel_object'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m x_ss = SubsetSimulation(dimension=2, nsamples_ss=1000, pdf_proposal_scale=1, pdf_target_type=\"marginal_pdf\", \n\u001b[0;32m 3\u001b[0m \u001b[0mpdf_target\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'normal'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'normal'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpdf_target_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m algorithm='MMH', model_script='pfn.py',model_object_name=\"RunPythonModel\", p_cond=0.1)\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mt_run\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Probability of failure: {0}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_ss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'runmodel_object'" - ] - } - ], - "source": [ - "t = time.time()\n", - "x_ss = SubsetSimulation(dimension=2, nsamples_ss=1000, pdf_proposal_scale=1, pdf_target_type=\"marginal_pdf\", \n", - " pdf_target=['normal', 'normal'], pdf_target_params = [[0, 1], [0, 1]],\n", - " algorithm='MMH', model_script='pfn.py',model_object_name=\"RunPythonModel\", p_cond=0.1)\n", - "t_run = time.time()-t\n", - "print(\"Probability of failure: {0}\".format(x_ss.pf))\n", - "print(\"thresholds: {0}\".format(x_ss.g_level))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot\n", - "dx, dy = 0.05, 0.05\n", - "y, x = np.mgrid[slice(-4, 5 + dy, dy),\n", - " slice(-4, 5 + dx, dx)]\n", - "z = np.zeros_like(y)\n", - "for i in range(x.shape[0]):\n", - " for j in range(x.shape[1]):\n", - " pq = np.array([y[i, j], x[i, j]]).reshape(1, -1)\n", - " z[i, j] = RunModel(samples=pq.reshape(1, -1), model_script='pfn.py', model_object_name=\"RunPythonModel\").qoi_list[0][0]\n", - " \n", - "plt.figure() \n", - "plt.contour(x, y, z,[0],colors='r') # LSF\n", - "for j in range(len(x_ss.samples)):\n", - " plt.scatter(x_ss.samples[j][:,0],x_ss.samples[j][:,1],marker='.')\n", - "\n", - "plt.axis('equal')\n", - "plt.grid(True)\n", - "plt.xlabel(r'$U_1$')\n", - "plt.ylabel(r'$U_2$')\n", - "plt.xlim([-3, 5])\n", - "plt.ylim([-3, 5])\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/example/Reliability/SubsetSimulation/SubsetSimulation_Example1/pfn.py b/example/Reliability/SubsetSimulation/SubsetSimulation_Example1/pfn.py deleted file mode 100644 index 7a2663597..000000000 --- a/example/Reliability/SubsetSimulation/SubsetSimulation_Example1/pfn.py +++ /dev/null @@ -1,13 +0,0 @@ -import numpy as np -import sys - - -def RunPythonModel(samples): - - qoi = list() - beta = 3.0902 - for i in range(samples.shape[0]): - qoi.append(-1/np.sqrt(2) * (samples[0, 0] + samples[0, 1]) + beta) - return qoi - - \ No newline at end of file diff --git a/src/UQpy/SampleMethods/AKMCS.py b/src/UQpy/SampleMethods/AKMCS.py index 38a109977..411dff9c3 100644 --- a/src/UQpy/SampleMethods/AKMCS.py +++ b/src/UQpy/SampleMethods/AKMCS.py @@ -2,11 +2,11 @@ import scipy.stats as stats from UQpy.SampleMethods.LHS import LHS - - +######################################################################################################################## ######################################################################################################################## # Adaptive Kriging-Monte Carlo Simulation (AK-MCS) ######################################################################################################################## + class AKMCS: """ Adaptively sample for construction of a Kriging surrogate for different objectives including reliability, @@ -35,18 +35,14 @@ class AKMCS: * **nsamples** (`int`): Total number of samples to be drawn (including the initial samples). - If `nsamples` is provided when instantiating the class, the ``run`` method will automatically be called. If - `nsamples` is not provided, ``AKMCS`` can be executed by invoking the ``run`` method and passing `nsamples`. + If `nsamples` and `samples` are provided when instantiating the class, the ``run`` method will automatically be + called. If either `nsamples` or `samples` is not provided, ``AKMCS`` can be executed by invoking the ``run`` + method and passing `nsamples`. * **nlearn** (`int`): Number of samples generated for evaluation of the learning function. Samples for the learning set are drawn using ``LHS``. - * **nstart** (`int`): - Number of initial samples, randomly generated using ``LHS``. - - Either `samples` or `nstart` must be provided. - * **qoi_name** (`dict`): Name of the quantity of interest. If the quantity of interest is a dictionary, this is used to convert it to a list @@ -103,15 +99,14 @@ class AKMCS: """ - def __init__(self, dist_object, runmodel_object, krig_object, samples=None, nsamples=None, nlearn=10000, - nstart=None, qoi_name=None, learning_function='U', n_add=1, random_state=None, verbose=False, - **kwargs): + def __init__(self, dist_object, runmodel_object, krig_object, samples=None, nsamples=None, nlearn=None, + qoi_name=None, learning_function='U', n_add=1, random_state=None, verbose=False, **kwargs): # Initialize the internal variables of the class. self.runmodel_object = runmodel_object self.samples = np.array(samples) self.nlearn = nlearn - self.nstart = nstart + self.nstart = None self.verbose = verbose self.qoi_name = qoi_name @@ -131,10 +126,11 @@ def __init__(self, dist_object, runmodel_object, krig_object, samples=None, nsam self.kwargs = kwargs # Initialize and run preliminary error checks. - if self.samples is not None: - self.dimension = np.shape(self.samples)[1] - else: - self.dimension = len(self.dist_object) + self.dimension = len(dist_object) + + if samples is not None: + if self.dimension != self.samples.shape[1]: + raise NotImplementedError("UQpy Error: Dimension of samples and distribution are inconsistent.") if type(self.learning_function).__name__ == 'function': self.learning_function = self.learning_function @@ -184,21 +180,13 @@ def __init__(self, dist_object, runmodel_object, krig_object, samples=None, nsam else: raise NotImplementedError("UQpy: krig_object must have 'fit' and 'predict' methods.") - # If the initial sample design does not exists, run the initial calculations. - if self.samples is None: - if self.nstart is None: - NotImplementedError("UQpy: User should provide either 'samples' or 'nstart' value.") - if self.verbose: - print('UQpy: AKMCS - Generating the initial sample set using Latin hypercube sampling.') - self.samples = LHS(dist_object=self.dist_object, nsamples=self.nstart, random_state=random_state).samples - if self.verbose: print('UQpy: AKMCS - Running the initial sample set using RunModel.') # Evaluate model at the training points - if len(self.runmodel_object.qoi_list) == 0: - self.runmodel_object.run(samples=self.samples) - else: + if len(self.runmodel_object.qoi_list) == 0 and samples is not None: + self.runmodel_object.run(samples=self.samples, append_samples=False) + if samples is not None: if len(self.runmodel_object.qoi_list) != self.samples.shape[0]: raise NotImplementedError("UQpy: There should be no model evaluation or Number of samples and model " "evaluation in RunModel object should be same.") @@ -207,9 +195,11 @@ def __init__(self, dist_object, runmodel_object, krig_object, samples=None, nsam if self.nsamples <= 0 or type(self.nsamples).__name__ != 'int': raise NotImplementedError("UQpy: Number of samples to be generated 'nsamples' should be a positive " "integer.") - self.run(nsamples=self.nsamples) - def run(self, nsamples, samples=None, append_samples=True): + if samples is not None: + self.run(nsamples=self.nsamples) + + def run(self, nsamples, samples=None, append_samples=True, nstart=None): """ Execute the ``AKMCS`` learning iterations. @@ -226,6 +216,11 @@ def run(self, nsamples, samples=None, append_samples=True): * **samples** (`ndarray`): Samples at which to evaluate the model. + * **nstart** (`int`): + Number of initial samples, randomly generated using ``LHS`` class. + + Either `samples` or `nstart` must be provided. + * **append_samples** (`boolean`) Append new samples and model evaluations to the existing samples and model evaluations. @@ -243,19 +238,32 @@ def run(self, nsamples, samples=None, append_samples=True): """ self.nsamples = nsamples + self.nstart = nstart if samples is not None: # New samples are appended to existing samples, if append_samples is TRUE if append_samples: - self.samples = np.vstack([self.samples, samples]) + if len(self.samples.shape) == 0: + self.samples = np.array(samples) + else: + self.samples = np.vstack([self.samples, np.array(samples)]) else: - self.samples = samples + self.samples = np.array(samples) self.runmodel_object.qoi_list = [] if self.verbose: print('UQpy: AKMCS - Evaluating the model at the sample set using RunModel.') self.runmodel_object.run(samples=samples, append_samples=append_samples) + else: + if len(self.samples.shape) == 0: + if self.nstart is None: + raise NotImplementedError("UQpy: User should provide either 'samples' or 'nstart' value.") + if self.verbose: + print('UQpy: AKMCS - Generating the initial sample set using Latin hypercube sampling.') + self.samples = LHS(dist_object=self.dist_object, nsamples=self.nstart, + random_state=self.random_state).samples + self.runmodel_object.run(samples=self.samples) if self.verbose: print('UQpy: Performing AK-MCS design...')