diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb new file mode 100644 index 00000000..9bfe9647 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb @@ -0,0 +1,709 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## ADVANCED PROJECT: CYLINDER FLOW\n", + "This .ipynb contains examples for invocation of the `01_script_cylinder_simulation.py` script, that conducts a simulation of the flow around a circular cylinder.\n", + "The code is ported from projects MP1, MP2 and Master’s Thesis of M.Bille. Some of the most prominent features implemented are:\n", + "- efficient bounce back boundary conditions (Fullway, Halfway, linear Interpolated) for solid bodies, that allow calculation of force on the solid body by Momentum exchange algorithm (MEA)\n", + "- simulation-class that allows:\n", + " - boundaries can be called after the streaming step\n", + " - boundaries can use post-collision populations for their bounce back algorithm (needed for HWBB and IBB algorithm)\n", + "- observables: coefficient of lift, coefficient of drag\n", + "- reporter: profile reporter for average velocity and reynolds stress profiles\n", + "- data-processing and -plotting utilities for the measured observables\n", + "\n", + "### Examples\n", + "1. 2D Simulation at Re = 200: periodic vortex shedding behind a circular cylinder, calculation of force coefficients (lift, drag) and Strouhal number\n", + "2. 3D Simulation at Re = 3900: turbulent vortex shedding behind a circular cylinder, calculation of average velocity and reynolds stress profiles and comparison to literature\n", + "\n", + "### Usage\n", + "The example command line calls and parameters below serve as examples for the use of `01_script_cylinder_simulation.py`. There are lots of parameters available through arguments (see script itself). The \"%run\" command is used here, to simulate a bash command line call of `python 01_script_cylinder_simulation.py `\n", + "\n", + "The script will conduct the simulation and write data to an individually named folder in the directory specified for the `--outdir` parameter. This contains plots, .txt files etc.\n", + "The output can be controlled through parameters (see script content (argument parser) itself). Plots etc. are named accordingly in the output data directory.\n", + "\n", + "You might want to output 2D or 3D vtk-data for further analysis and visualization. But beware the amount of storage you might need and set your parameters accordingly (see script parameters and code).\n", + "\n", + "### Note\n", + "- Some of the functionalities might not work flawlessly through this .ipynb-implementation (warnings etc.): just use a regular python console or call the script with parameters from a bash script.\n", + "- Some functionalities throw a warning, when the flow data con not be processed, because it doesn't meet certain physical characteristics (for example: peak finding in a periodic force coefficient timeseries). This is intended. The errors are captured and forwarded to the output, without crashing the script.\n", + "- The command line output of the script (including useful messages) might not appear in the notebook, depending on your configuration of Jupyter. See the .txt-output in the output directory for the full log and output, or execute the script in a python console." + ], + "id": "62eec9273a6c4271" + }, + { + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-11-14T15:40:54.515141Z", + "start_time": "2025-11-14T15:40:54.512769Z" + } + }, + "cell_type": "markdown", + "source": [ + "### Example 1:\n", + "#### 2D Simulation at Re = 200: periodic vortex shedding behind a circular cylinder, calculation of force coefficients (lift, drag) and Strouhal number\n", + "NOTE: these default parameters take approx. 2 min to run on an NVIDIA 2060super\n", + "\n", + "Defaults: `01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 100 --bbbc_type ibb1 --reynolds_number 200 --domain_height_y_in_d 5 --collision bgk --name 2D_simulation_cylinder_Re200 --mach_number 0.1 --stencil D2Q9`" + ], + "id": "d918fef7d168e9ab" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-17T16:04:29.456923Z", + "start_time": "2025-12-17T16:03:05.111157Z" + } + }, + "cell_type": "code", + "source": [ + "%matplotlib inline\n", + "%run 01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 100 --bbbc_type ibb1 --reynolds_number 200 --domain_height_y_in_d 5 --collision bgk --name 2D_simulation_cylinder_Re200 --mach_number 0.1 --stencil D2Q9" + ], + "id": "cdd448d543dd26be", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SCRIPT: Writing arguments to dictionary...\n", + "SCRIPT: Input arguments are: \n", + "{'name': '2D_simulation_cylinder_Re200', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 200.0, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 20, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': 5.0, 'domain_width_z_in_d': None, 'perturb_init': False, 'u_init_condition': 0, 'lateral_walls': 'periodic', 'n_steps': 100000, 't_target': 100.0, 'collision': 'bgk', 'stencil': 'D2Q9', 'eqlm': False, 'bbbc_type': 'ibb1', 'periodic_region_start_relative': None, 'periodic_region_start_pu': None, 'periodic_region_start_lu': None, 'calc_u_profiles': False, 'output_u_profiles_timeseries': False, 'profile_reference_path': '../profile_reference_data/', 'vtk_full_basic': False, 'vtk_full_basic_interval': None, 'vtk_3D': False, 'vtk_3D_fps': None, 'vtk_3D_step_interval': None, 'vtk_3D_t_interval': None, 'vtk_3D_step_start': None, 'vtk_3D_step_end': None, 'vtk_3D_t_start': None, 'vtk_3D_t_end': None, 'vtk_slice2D': False, 'vtk_slice2D_fps': None, 'vtk_slice2D_step_interval': None, 'vtk_slice2D_t_interval': None, 'vtk_slice2D_step_start': None, 'vtk_slice2D_step_end': None, 'vtk_slice2D_t_start': None, 'vtk_slice2D_t_end': None, 'count_tensors': False}\n", + "\n", + "SCRIPT: Creating timestamp, simulation ID and creating output directory...\n", + "outdir/simID = ./datafolder/251217_170307-2D_simulation_cylinder_Re200\n", + "outdir_DATA/simID = ./datafolder/251217_170307-2D_simulation_cylinder_Re200/251217_170307-2D_simulation_cylinder_Re200\n", + "SCRIPT: Writing input parameters to file: ./datafolder/251217_170307-2D_simulation_cylinder_Re200/input_parameters.txt\n", + "SCRIPT: Saving simulation script to outdir...\n", + "-> Saved simulation script to './datafolder/251217_170307-2D_simulation_cylinder_Re200/251217_170307-2D_simulation_cylinder_Re200_01_script_cylinder_simulation.py'\n", + "SCRIPT: Starting stdout-LOGGER (see outdir for log file)\n", + "SCRIPT: Processing parameters...\n", + "\n", + "(INFO) parameters set for simulation of 34641 steps, representing 100.000 seconds [PU]!\n", + "\n", + "SCRIPT: initializing solver components...\n", + "-> initializing context...\n", + "-> initializing flow...\n", + "OBST_Cylinder: self.resolution= [200, 100]\n", + "OBST_Cylinder: x_pos, y_pos, radius: 50.5 50.5 10.0\n", + "-> initializing collision operator...\n", + "\n", + "SCRIPT: initializing simulation object...\n", + "CYLINDER: the bc_type was given as: ibb1\n", + "IBB: calc force is: True\n", + "SIMULATION: creating no_collision_mask entries for: i, boundary = 3 \n", + "collision index is: 0\n", + "-> initializing reporters...\n", + "\n", + "SCRIPT: spacial and temporal dimensions:\n", + "domain shape (LU): [200, 100]\n", + "t_target (PU) with 34641 steps (LU): 100.0 seconds\n", + "steps to simulate 1 second PU: 346.41 steps\n", + "steps to simulate 100.000 (t_target, PU) seconds: 34641.000 steps\n", + "\n", + "#################################################\n", + "\n", + "SCRIPT (2025-12-17 17:03:07): running simulation for 34641 steps...\n", + "\n", + "#################################################\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "***** SIMULATION FINISHED AT 2025-12-17 17:04:26 *****\n", + "\n", + "### STATS ###\n", + "MLUPS: 8.869\n", + "simulated PU-Time: 99.99995337498915 seconds\n", + "simulated LU-steps: 34641\n", + "runtime (WALLTIME) of simulation(num_steps): 78.118 seconds (= 1.3 minutes )\n", + "\n", + "### HARDWARE UTILIZATION ###\n", + "current GPU VRAM (MB) usage: 9.747\n", + "max. GPU VRAM (MB) usage: 15.871\n", + "CPU % avg. over last 1 min, 5 min, 15 min; 2.66 1.05 0.58\n", + "current total RAM usage [MB]: 10744.05 of 64219.7 MB\n", + "\n", + "SCRIPT: processing, plotting and saving data...\n", + "\n", + "DRAG STATS:\n", + "mean_simple = 1.693653928340924\n", + "mean_periodcorrected = 1.6928978755967055\n", + "min_simple = 1.5952873956700555\n", + "max_simple = 1.7953284200403388\n", + "max_mean = 1.7873442402797335\n", + "min_mean = 1.617481548579985\n", + "frequency_fit = 0.1999999999241945\n", + "frequency_fft = 0.0\n", + "fft_resolution = 0.01999827742257103\n", + "\n", + "LIFT STATS:\n", + "mean_simple = -0.013755539770956112\n", + "mean_periodcorrected = -0.0005787575118849577\n", + "min_simple = -0.7790682792632936\n", + "max_simple = 0.7791679323475318\n", + "max_mean = 0.7675340372781098\n", + "min_mean = -0.7712608122909177\n", + "frequency_fit = 0.20000000029835424\n", + "frequency_fft = 0.21998105164828133\n", + "fft_resolution = 0.01999827742257103\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" 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" 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" 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" 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Q4FqemJioRYsWWdocAACAL/L62t7SpUu1aNEidevWTTabzbU8JiZGhw8ftrQ5AAAAX+T1GaxTp04pLCys3PKCggK3wAUAAHCt8jpgde7cWcuXL3fNl4Wqt99+m0E5AQAAVIlLhFOmTFGfPn20d+9eFRcX6/XXX9fevXu1adMmrVu3rip6BAAA8Clen8G67bbbtGPHDhUXFys2NlafffaZwsLClJGRoU6dOlVFjwAAAD7FqzNYRUVFeuSRR/Tcc8/prbfeqqqeAAAAfJpXZ7Dq1q2rv/3tb1XVCwAAQK3g9SXCAQMGaOnSpVXQCgAAQO3g9U3ubdq00UsvvaSNGzeqU6dOql+/vtv6J554wrLmAAAAfJHXL3tu0aLFpTdms+nIkSM/uSlfxcueAQDwPdX2smen0+n6wqysLEu+GAAAoLby6B6sRo0a6eTJk5Kku+66S3l5eVXZEwAAgE/zKGA1aNBA3333nSQpPT1dRUVFVdoUAACAL/PoEmFiYqJ69uypG2+8UZJ07733ym63V1i7Zs0a67oDAADwQR4FrPfff1/z58/X4cOHtW7dOsXExKhevXpV3RsAAIBP8vopwp49e2rJkiUKCQmpopZ8F08RAgDge6rtKcKLrV271pIvBgAAqK08usl96tSpOnfunEcb/OKLL7R8+fKf1BQAAIAv8yhg7d27V82aNdNjjz2mTz/9VKdOnXKtKy4u1s6dO/Xmm2/q1ltv1aBBg9SwYcMqaxgAAKCm8+gS4XvvvacdO3bojTfe0AMPPCCn0yl/f385HA7Xma0OHTpo1KhRGj58uAICAqq0aQAAgJrM65vcS0tLtXPnTn311Vc6f/68QkNDFRcXp9DQ0Krq0WdwkzsAAL6nRtzk7ufnp7i4OMXFxVnSAAAAQG3j0T1YkvTOO+9cdv2ZM2c0atSon9wQAACAr/M4YKWkpOiee+5RdnZ2uXWrVq1STEyMvvzyS0ubAwAA8EUeB6wdO3aooKBAMTEx+vDDDyX9cNZq5MiR6t+/v379619ry5YtVdYoAACAr/D4HqzmzZtr7dq1eu211/Twww/rgw8+0K5du9SgQQNt3LhRXbp0qco+AQAAfIbXN7k/8sgjWr9+vZYuXar69etr2bJlio2NrYreAAAAfJLHlwglaePGjbrlllu0f/9+rVy5Un369FFCQoJef/31quoPAADA53gcsJ566inddddd6t+/v7Zt26ZevXrpo48+0jvvvKOXX35Zd955p7KysqqyVwAAAJ/gccD65JNP9M9//lOvvPKK20jtgwYN0u7duxUcHKybb765SpoEAADwJR7fg7Vz507Vq1evwnXh4eH65JNP9L//+7+WNQYAAOCrPD6DdalwdbGhQ4f+pGYAAABqA4/OYB07dqxSGw8JCbHsnT4AAAC+wqOA1bx5c9lsNnnzXmibzabJkyfr+eefr3RzAAAAvsijgFVaWlrVfQAAANQaXo2DBQAAgCsjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWq3TAKiws1IEDB1RcXGxlPwAAAD7P64B17tw5jRw5UvXq1VNMTIxrENLHH39cU6dOtbxBAAAAX+N1wJo0aZJ27Nih9PR0t5c+JyYmatGiRZY2BwAA4Is8ftlzmaVLl2rRokXq1q2bbDaba3lMTIwOHz5saXMAAAC+yOszWKdOnVJYWFi55QUFBW6BCwAA4FrldcDq3Lmzli9f7povC1Vvv/22EhISrOsMAADAR3l9iXDKlCnq06eP9u7dq+LiYr3++uvau3evNm3apHXr1lVFjwAAAD7F6zNYt912mzIzM1VcXKzY2Fh99tlnCgsLU0ZGhjp16lQVPQIAAPgUmzHGVHcTtYXT6VRwcLCOH89XZGRQdbcDAAA8UPb7nZ+fr6Aga36/PTqD5XQ6PZ68tX79evXv319RUVGy2WxaunSp2/rhw4fLZrO5Tb1793aryc3N1YMPPqigoCCFhIRo5MiROnv2rFvNzp07dfvttysgIEDR0dGaNm1auV4WL16sdu3aKSAgQLGxsVqxYoXX+wMAAODRPVghISEePyFYUlLiVQMFBQW65ZZb9NBDD+m+++6rsKZ3796aO3eua97hcLitf/DBB3XixAmtXr1aRUVFGjFihEaPHq0FCxZI+iEg9urVS4mJiZozZ4527dqlhx56SCEhIRo9erQkadOmTRoyZIhSU1N1zz33aMGCBRowYIC2bdumm266yat9AgAA1zaPLhFefPP60aNH9fTTT2v48OGupwYzMjI0f/58paamatiwYZVvxmbTkiVLNGDAANey4cOHKy8vr9yZrTL79u1T+/bt9eWXX6pz586SpJUrV6pv37765ptvFBUVpdmzZ+uZZ55Rdna27Ha7JOnpp5/W0qVLtX//fknSoEGDVFBQoGXLlrm23a1bN8XFxWnOnDke9c8lQgAAfE9VXCL06AzWHXfc4frzSy+9pFdffVVDhgxxLfvFL36h2NhY/fWvf/1JAetS0tPTFRYWpkaNGumuu+7Syy+/rOuuu07SD+EuJCTEFa6kH0aV9/Pz0xdffKF7771XGRkZ6tGjhytcSVJSUpL++Mc/6vTp02rUqJEyMjKUkpLi9r1JSUmXDHaSdOHCBV24cME1X5lLpAAAoPbx+inCjIwMtzBTpnPnztq8ebMlTV2sd+/eeu+995SWlqY//vGPWrdunfr06eO6FJmdnV1u4NM6deqocePGys7OdtWEh4e71ZTNX6mmbH1FUlNTFRwc7Jqio6N/2s4CAIBaweuAFR0drbfeeqvc8rfffrtKAsbgwYNdZ8gGDBigZcuW6csvv1R6errl3+WtSZMmKT8/3zV9/fXX1d0SAACoAbweaHT69OkaOHCgPv30U8XHx0uSNm/erH//+9/629/+ZnmDP9ayZUuFhobq0KFDuvvuuxUREaGTJ0+61RQXFys3N1cRERGSpIiICOXk5LjVlM1fqaZsfUUcDke5G+4BAAC8PoPVt29fHTx4UP3791dubq5yc3PVv39/HTx4UH379q2KHt188803+u677xQZGSlJSkhIUF5enrZu3eqqWbNmjUpLS10BMCEhQevXr1dRUZGrZvXq1Wrbtq0aNWrkqklLS3P7rtWrV/P6HwAA4LVqH2j07NmzOnTokCSpQ4cOevXVV9WzZ081btxYjRs31osvvqiBAwcqIiJChw8f1oQJE3TmzBnt2rXLdfaoT58+ysnJ0Zw5c1zDNHTu3Nk1TEN+fr7atm2rXr16aeLEidq9e7ceeughTZ8+3W2YhjvuuENTp05Vv379tHDhQk2ZMsWrYRp4ihAAAN9TFU8Reh2w1q9ff9n1PXr08KqB9PR09ezZs9zyYcOGafbs2RowYIC2b9+uvLw8RUVFqVevXvr973/vdkN6bm6uxo4dq3/84x/y8/PTwIEDNWPGDDVo0MBVs3PnTiUnJ+vLL79UaGioHn/8cU2cONHtOxcvXqxnn31WR48eVZs2bTRt2jSvzsoRsAAA8D01ImD5+ZW/qnjxIKTeDjRamxCwAADwPdX2qpyLnT592m06efKkVq5cqS5duuizzz6zpCkAAABf5vVThMHBweWW/fznP5fdbldKSorbzeYAAADXIq/PYF1KeHi4Dhw4YNXmAAAAfJbXZ7B27tzpNm+M0YkTJzR16lTFxcVZ1RcAAIDP8jpgxcXFyWaz6cf3xnfr1k3vvvuuZY0BAAD4Kq8DVlZWltu8n5+fmjRpooCAAMuaAgAA8GVe34O1bt06RUREqFmzZmrWrJmio6MVEBCgwsJCvffee1XRIwAAgE/xOmCNGDFC+fn55ZafOXNGI0aMsKQpAAAAX+Z1wDLGuA0sWuabb76pcAgHAACAa43H92B16NBBNptNNptNd999t+rU+f8fLSkpUVZWlnr37l0lTQIAAPgSjwPWgAEDJEmZmZlKSkpye8+f3W5X8+bNNXDgQMsbBAAA8DUeB6zJkydLkpo3b65Bgwbx1CAAAMAleD1Mw7Bhw6qiDwAAgFrDo4DVuHFjHTx4UKGhoWrUqFGFN7mXyc3Ntaw5AAAAX+RRwJo+fboaNmzo+vPlAhYAAMC1zmZ+/M4bVJrT6VRwcLCOH89XZGRQdbcDAAA8UPb7nZ+fr6Aga36/PTqD5XQ6Pd6gVY0BAAD4Ko8CVkhIyBUvC5YNQFpSUmJJYwAAAL7Ko4C1du3aqu4DAACg1vAoYN1xxx1V3QcAAECt4fU4WJJ0+vRpvfPOO9q3b58kqX379hoxYoQaN25saXMAAAC+yOuXPa9fv17NmzfXjBkzdPr0aZ0+fVozZsxQixYttH79+qroEQAAwKd4PUxDbGysEhISNHv2bPn7+0v64WXPjz32mDZt2qRdu3ZVSaO+gGEaAADwPVUxTIPXZ7AOHTqkp556yhWuJMnf318pKSk6dOiQJU0BAAD4Mq8DVseOHV33Xl1s3759uuWWWyxpCgAAwJd5fZP7E088od/+9rc6dOiQunXrJkn6/PPPNWvWLE2dOlU7d+501d58883WdQoAAOAjvL4Hy8/v8ie9bDbbNTvoKPdgAQDge6rtVTkXy8rKsuSLAQAAaiuvA1azZs2qog8AAIBao1IDjR4/flwbNmzQyZMnVVpa6rbuiSeesKQxAAAAX+V1wJo3b54eeeQR2e12XXfddW4vgbbZbAQsAABwzfM6YD333HN6/vnnNWnSpCve8A4AAHAt8johnTt3ToMHDyZcAQAAXILXKWnkyJFavHhxVfQCAABQK3g9DlZJSYnuuecenT9/XrGxsapbt67b+ldffdXSBn0J42ABAOB7asQ4WKmpqVq1apXatm0rSeVucgcAALjWeR2wXnnlFb377rsaPnx4FbQDAADg+7y+B8vhcKh79+5V0QsAAECt4HXA+u1vf6uZM2dWRS8AAAC1gteXCDdv3qw1a9Zo2bJliomJKXeT+8cff2xZcwAAAL7I64AVEhKi++67ryp6AQAAqBW8Dlhz586tij4AAABqDUuGY3c6nZo9e7Y6d+5sxeYAAAB8mtdnsC62du1avfvuu/r4448VHByse++916q+AAAAfJbXAes///mP5s2bp7lz5yovL0+nT5/WggULdP/99zPQKAAAgLy4RPi3v/1Nffv2Vdu2bZWZmalXXnlFx48fl5+fn2JjYwlXAAAA/+XxGaxBgwZp4sSJWrRokRo2bFiVPQEAAPg0j89gjRw5UrNmzVLv3r01Z84cnT59uir7AgAA8FkeB6y//OUvOnHihEaPHq0PP/xQkZGR+uUvfyljjEpLS6uyRwAAAJ/i1TANgYGBGjZsmNatW6ddu3YpJiZG4eHh6t69ux544AFGcQcAANBPGAerTZs2mjJlir7++mu9//77OnfunIYMGWJlbwAAAD7JZowxVm3s5MmTCgsLs2pzPsfpdCo4OFjHj+crMjKoutsBAAAeKPv9zs/PV1CQNb/flozkXqYy4Wr9+vXq37+/oqKiZLPZtHTpUrf1xhg9//zzioyMVGBgoBITE/Xvf//brSY3N1cPPviggoKCFBISopEjR+rs2bNuNTt37tTtt9+ugIAARUdHa9q0aeV6Wbx4sdq1a6eAgADFxsZqxYoVXu8PAACApQGrMgoKCnTLLbdo1qxZFa6fNm2aZsyYoTlz5uiLL75Q/fr1lZSUpO+//95V8+CDD2rPnj1avXq1li1bpvXr12v06NGu9U6nU7169VKzZs20detW/elPf9ILL7ygv/71r66aTZs2aciQIRo5cqS2b9+uAQMGaMCAAdq9e3fV7TwAAKidTA0iySxZssQ1X1paaiIiIsyf/vQn17K8vDzjcDjMhx9+aIwxZu/evUaS+fLLL101n376qbHZbOY///mPMcaYN9980zRq1MhcuHDBVTNx4kTTtm1b1/z9999v+vXr59ZPfHy8eeSRRzzuPz8/30gyx4/ne/wZAABQvcp+v/Pzrfv99vgM1vr161VcXFxVOa9CWVlZys7OVmJiomtZcHCw4uPjlZGRIUnKyMhQSEiI24umExMT5efnpy+++MJV06NHD9ntdldNUlKSDhw44BrPKyMjw+17ymrKvqciFy5ckNPpdJsAAAA8Dlg9e/ZUbm5uVfZSTnZ2tiQpPDzcbXl4eLhrXXZ2drl7v+rUqaPGjRu71VS0jYu/41I1ZesrkpqaquDgYNcUHR3t7S4CAIBayOOAZax72LDWmDRpkvLz813T119/Xd0tAQCAGsCrm9yv9gudIyIiJEk5OTluy3NyclzrIiIidPLkSbf1xcXFys3NdaupaBsXf8elasrWV8ThcCgoKMhtAgAA8Phlz5I0fPhwORyOy9ZYOZp7ixYtFBERobS0NMXFxUn64YnAL774Qo8++qgkKSEhQXl5edq6das6deokSVqzZo1KS0sVHx/vqnnmmWdUVFSkunXrSpJWr16ttm3bqlGjRq6atLQ0jRs3zvX9q1evVkJCgmX7AwAArg1eBayGDRsqMDDQ0gbOnj2rQ4cOueazsrKUmZmpxo0bq2nTpho3bpxefvlltWnTRi1atNBzzz2nqKgoDRgwQJJ04403qnfv3nr44Yc1Z84cFRUVaezYsRo8eLCioqIkSQ888IBefPFFjRw5UhMnTtTu3bv1+uuva/r06a7v/e1vf6s77rhDr7zyivr166eFCxdqy5YtbkM5AAAAeMTTxw1tNpvJycmx7PHFMmvXrjWSyk3Dhg0zxvwwVMNzzz1nwsPDjcPhMHfffbc5cOCA2za+++47M2TIENOgQQMTFBRkRowYYc6cOeNWs2PHDnPbbbcZh8Nhfvazn5mpU6eW6+Wjjz4yN9xwg7Hb7SYmJsYsX77cq31hmAYAAHxPVQzT4PGrcvz9/XXixIlr+lU4V8KrcgAA8D3V+qocD3MYAADANc/jgPXWW2+5bggHAADApXkcsEaPHu0a9VySBg0aVG5YAwAAAPyES4QrVqxQQUGB5Q0BAAD4Oq8GGgUAAMCVeRywbDZbuZHcr/bI7gAAAL7A44FGjTFuI7l///33GjNmjOrXr+9WZ+VI7gAAAL7I44D1m9/8xu2M1a9//esqaQgAAMDXeRyw5s2bV4VtAAAA1B4e34N15MgRBhsFAADwgMcBq02bNjp16pRrnnGwAAAAKsY4WAAAABZjHCwAAACLMQ4WAACAxRgHCwAAwGIeB6xhw4a5zTMOFgAAQMU8Dlhz586tyj4AAABqDW5yBwAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAi9X4gPXCCy/IZrO5Te3atXOt//7775WcnKzrrrtODRo00MCBA5WTk+O2jWPHjqlfv36qV6+ewsLCNH78eBUXF7vVpKenq2PHjnI4HGrdurXmzZt3NXYPAADUQjU+YElSTEyMTpw44Zo2bNjgWvfkk0/qH//4hxYvXqx169bp+PHjuu+++1zrS0pK1K9fPxUWFmrTpk2aP3++5s2bp+eff95Vk5WVpX79+qlnz57KzMzUuHHjNGrUKK1ateqq7icAAKgdbMYYU91NXM4LL7ygpUuXKjMzs9y6/Px8NWnSRAsWLNCvfvUrSdL+/ft14403KiMjQ926ddOnn36qe+65R8ePH1d4eLgkac6cOZo4caJOnTolu92uiRMnavny5dq9e7dr24MHD1ZeXp5Wrlzpca9Op1PBwcE6fjxfkZFBP23HAQDAVVH2+52fn6+gIGt+v33iDNa///1vRUVFqWXLlnrwwQd17NgxSdLWrVtVVFSkxMREV227du3UtGlTZWRkSJIyMjIUGxvrCleSlJSUJKfTqT179rhqLt5GWU3ZNi7lwoULcjqdbhMAAECND1jx8fGaN2+eVq5cqdmzZysrK0u33367zpw5o+zsbNntdoWEhLh9Jjw8XNnZ2ZKk7Oxst3BVtr5s3eVqnE6nzp8/f8neUlNTFRwc7Jqio6N/6u4CAIBaoE51N3Alffr0cf355ptvVnx8vJo1a6aPPvpIgYGB1diZNGnSJKWkpLjmnU4nIQsAANT8M1g/FhISohtuuEGHDh1SRESECgsLlZeX51aTk5OjiIgISVJERES5pwrL5q9UExQUdNkQ53A4FBQU5DYBAAD4XMA6e/asDh8+rMjISHXq1El169ZVWlqaa/2BAwd07NgxJSQkSJISEhK0a9cunTx50lWzevVqBQUFqX379q6ai7dRVlO2DQAAAG/U+ID1P//zP1q3bp2OHj2qTZs26d5775W/v7+GDBmi4OBgjRw5UikpKVq7dq22bt2qESNGKCEhQd26dZMk9erVS+3bt9fQoUO1Y8cOrVq1Ss8++6ySk5PlcDgkSWPGjNGRI0c0YcIE7d+/X2+++aY++ugjPfnkk9W56wAAwEfV+HuwvvnmGw0ZMkTfffedmjRpottuu02ff/65mjRpIkmaPn26/Pz8NHDgQF24cEFJSUl68803XZ/39/fXsmXL9OijjyohIUH169fXsGHD9NJLL7lqWrRooeXLl+vJJ5/U66+/ruuvv15vv/22kpKSrvr+AgAA31fjx8HyJYyDBQCA77lmx8ECAADwJQQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsALVOQYFks/0wFRRUdzcArkUELAAAAIsRsAD4DM5MAfAVBKwfmTVrlpo3b66AgADFx8dr8+bN1d0SAADwMQSsiyxatEgpKSmaPHmytm3bpltuuUVJSUk6efJkdbcGAAB8iM0YY6q7iZoiPj5eXbp00RtvvCFJKi0tVXR0tB5//HE9/fTTV/y80+lUcHCwDh3KV0REUFW3C9QaBQVSePgPf87JkerXr1l1AMqrTX9/nE6noqKClZ+fr6Aga36/61iylVqgsLBQW7du1aRJk1zL/Pz8lJiYqIyMjAo/c+HCBV24cME1n5+fL0lq3dpZtc0CtVjZP9g1tQ5Aeb7/9+eH320rzzkRsP7r22+/VUlJicJ/9P+S8PBw7d+/v8LPpKam6sUXX6xgTXQVdAgAAKrSd999p+DgYEu2RcD6CSZNmqSUlBTXfF5enpo1a6Zjx45ZdoBQOU6nU9HR0fr6668tO92LyuFY1Bwci5qDY1Gz5Ofnq2nTpmrcuLFl2yRg/VdoaKj8/f2Vk5PjtjwnJ0cREREVfsbhcMjhcJRbHhwczF+YGiIoKIhjUUNwLGoOjkXNwbGoWfz8rHv2j6cI/8tut6tTp05KS0tzLSstLVVaWpoSEhKqsTMAAOBrOIN1kZSUFA0bNkydO3dW165d9dprr6mgoEAjRoyo7tYAAIAPIWBdZNCgQTp16pSef/55ZWdnKy4uTitXrix34/ulOBwOTZ48ucLLhri6OBY1B8ei5uBY1Bwci5qlKo4H42ABAABYjHuwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsLw0a9YsNW/eXAEBAYqPj9fmzZsvW7948WK1a9dOAQEBio2N1YoVK65Sp7WfN8firbfe0u23365GjRqpUaNGSkxMvOKxg+e8/XtRZuHChbLZbBowYEDVNngN8fZY5OXlKTk5WZGRkXI4HLrhhhv4d8oi3h6L1157TW3btlVgYKCio6P15JNP6vvvv79K3dZe69evV//+/RUVFSWbzaalS5de8TPp6enq2LGjHA6HWrdurXnz5nn/xQYeW7hwobHb7ebdd981e/bsMQ8//LAJCQkxOTk5FdZv3LjR+Pv7m2nTppm9e/eaZ5991tStW9fs2rXrKnde+3h7LB544AEza9Yss337drNv3z4zfPhwExwcbL755pur3Hnt4+2xKJOVlWV+9rOfmdtvv9388pe/vDrN1nLeHosLFy6Yzp07m759+5oNGzaYrKwsk56ebjIzM69y57WPt8figw8+MA6Hw3zwwQcmKyvLrFq1ykRGRponn3zyKnde+6xYscI888wz5uOPPzaSzJIlSy5bf+TIEVOvXj2TkpJi9u7da2bOnGn8/f3NypUrvfpeApYXunbtapKTk13zJSUlJioqyqSmplZYf//995t+/fq5LYuPjzePPPJIlfZ5LfD2WPxYcXGxadiwoZk/f35VtXjNqMyxKC4uNrfeeqt5++23zbBhwwhYFvH2WMyePdu0bNnSFBYWXq0WrxneHovk5GRz1113uS1LSUkx3bt3r9I+rzWeBKwJEyaYmJgYt2WDBg0ySUlJXn0Xlwg9VFhYqK1btyoxMdG1zM/PT4mJicrIyKjwMxkZGW71kpSUlHTJenimMsfix86dO6eioiJLX+x5LarssXjppZcUFhamkSNHXo02rwmVORZ///vflZCQoOTkZIWHh+umm27SlClTVFJScrXarpUqcyxuvfVWbd261XUZ8ciRI1qxYoX69u17VXrG/2fVbzcjuXvo22+/VUlJSblR3cPDw7V///4KP5OdnV1hfXZ2dpX1eS2ozLH4sYkTJyoqKqrcXyJ4pzLHYsOGDXrnnXeUmZl5FTq8dlTmWBw5ckRr1qzRgw8+qBUrVujQoUN67LHHVFRUpMmTJ1+NtmulyhyLBx54QN9++61uu+02GWNUXFysMWPG6He/+93VaBkXudRvt9Pp1Pnz5xUYGOjRdjiDhWvO1KlTtXDhQi1ZskQBAQHV3c415cyZMxo6dKjeeusthYaGVnc717zS0lKFhYXpr3/9qzp16qRBgwbpmWee0Zw5c6q7tWtOenq6pkyZojfffFPbtm3Txx9/rOXLl+v3v/99dbeGSuIMlodCQ0Pl7++vnJwct+U5OTmKiIio8DMRERFe1cMzlTkWZf785z9r6tSp+uc//6mbb765Ktu8Jnh7LA4fPqyjR4+qf//+rmWlpaWSpDp16ujAgQNq1apV1TZdS1Xm70VkZKTq1q0rf39/17Ibb7xR2dnZKiwslN1ur9Kea6vKHIvnnntOQ4cO1ahRoyRJsbGxKigo0OjRo/XMM8/Iz4/zIVfLpX67g4KCPD57JXEGy2N2u12dOnVSWlqaa1lpaanS0tKUkJBQ4WcSEhLc6iVp9erVl6yHZypzLCRp2rRp+v3vf6+VK1eqc+fOV6PVWs/bY9GuXTvt2rVLmZmZrukXv/iFevbsqczMTEVHR1/N9muVyvy96N69uw4dOuQKuZJ08OBBRUZGEq5+gsoci3PnzpULUWXB1/DK4KvKst9u7+6/v7YtXLjQOBwOM2/ePLN3714zevRoExISYrKzs40xxgwdOtQ8/fTTrvqNGzeaOnXqmD//+c9m3759ZvLkyQzTYBFvj8XUqVON3W43//d//2dOnDjhms6cOVNdu1BreHssfoynCK3j7bE4duyYadiwoRk7dqw5cOCAWbZsmQkLCzMvv/xyde1CreHtsZg8ebJp2LCh+fDDD82RI0fMZ599Zlq1amXuv//+6tqFWuPMmTNm+/btZvv27UaSefXVV8327dvNV199ZYwx5umnnzZDhw511ZcN0zB+/Hizb98+M2vWLIZpuBpmzpxpmjZtaux2u+natav5/PPPXevuuOMOM2zYMLf6jz76yNxwww3GbrebmJgYs3z58qvcce3lzbFo1qyZkVRumjx58tVvvBby9u/FxQhY1vL2WGzatMnEx8cbh8NhWrZsaf7whz+Y4uLiq9x17eTNsSgqKjIvvPCCadWqlQkICDDR0dHmscceM6dPn776jdcya9eurfDf/7L//sOGDTN33HFHuc/ExcUZu91uWrZsaebOnev199qM4dwjAACAlbgHCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAH6CHj16aMGCBZZuc+/evbr++utVUFBg6XYBXD0ELAA+Zfjw4bLZbOWmQ4cOXfVe/v73vysnJ0eDBw92LWvevLlee+21crUvvPCC4uLiPNpu+/bt1a1bN7366qsWdQrgaiNgAfA5vXv31okTJ9ymFi1alKsrLCys0j5mzJihESNGyM/P+n9KR4wYodmzZ6u4uNjybQOoegQsAD7H4XAoIiLCbfL399edd96psWPHaty4cQoNDVVSUpIkaffu3erTp48aNGig8PBwDR06VN9++61rewUFBfrNb36jBg0aKDIyUq+88oruvPNOjRs37pI9nDp1SmvWrFH//v0rtQ8VnYVr3ry5a/3Pf/5z5ebmat26dZXaPoDqRcACUKvMnz9fdrtdGzdu1Jw5c5SXl6e77rpLHTp00JYtW7Ry5Url5OTo/vvvd31m/PjxWrdunT755BN99tlnSk9P17Zt2y77PRs2bFC9evV04403VqrPi8++HTp0SK1bt1aPHj1c6+12u+Li4vSvf/2rUtsHUL3qVHcDAOCtZcuWqUGDBq75Pn36aPHixZKkNm3aaNq0aa51L7/8sjp06KApU6a4lr377ruKjo7WwYMHFRUVpXfeeUfvv/++7r77bkk/hLTrr7/+sj189dVXCg8Pr/Dy4MSJE/Xss8+6LSssLFT79u1d8xEREZIkY4wGDhyo4OBg/eUvf3H7TFRUlL766qvL9gGgZiJgAfA5PXv21OzZs13z9evXd/25U6dObrU7duzQ2rVr3QJZmcOHD+v8+fMqLCxUfHy8a3njxo3Vtm3by/Zw/vx5BQQEVLhu/PjxGj58uNuyGTNmaP369eVqf/e73ykjI0NbtmxRYGCg27rAwECdO3fusn0AqJkIWAB8Tv369dW6detLrrvY2bNn1b9/f/3xj38sVxsZGVnppw9DQ0N1+vTpS677cX+NGzcuV/f+++9r+vTpSk9P189+9rNy63Nzc9WqVatK9QegenEPFoBarWPHjtqzZ4+aN2+u1q1bu03169dXq1atVLduXX3xxReuz5w+fVoHDx687HY7dOig7OzsS4asK8nIyNCoUaP0l7/8Rd26dauwZvfu3erQoUOltg+gehGwANRqycnJys3N1ZAhQ/Tll1/q8OHDWrVqlUaMGKGSkhI1aNBAI0eO1Pjx47VmzRrt3r1bw4cPv+LQCx06dFBoaKg2btzodU/Z2dm69957NXjwYCUlJSk7O1vZ2dk6deqUq+bo0aP6z3/+o8TERK+3D6D6EbAA1GpRUVHauHGjSkpK1KtXL8XGxmrcuHEKCQlxhag//elPuv3229W/f38lJibqtttuK3cv14/5+/trxIgR+uCDD7zuaf/+/crJydH8+fMVGRnpmrp06eKq+fDDD9WrVy81a9bM6+0DqH42Y4yp7iYAoKa58847FRcXV+Go7GWys7MVExOjbdu2WRqECgsL1aZNGy1YsEDdu3e3bLsArh7OYAFAJUVEROidd97RsWPHLN3usWPH9Lvf/Y5wBfgwniIEgJ9gwIABlm+z7CZ8AL6LS4QAAAAW4xIhAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGCx/wfwLZLo36D7WgAAAABJRU5ErkJggg==" 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" 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" 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" 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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Strouhal number is: 0.20000000029835424\n", + "\n", + "♬ THE END ♬\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Example 2:\n", + "#### 3D Simulation at Re = 3900: turbulent vortex shedding behind a circular cylinder, calculation of average velocity and reynolds stress profiles and comparison to literature\n", + "Note: This simulation may take very long (hours) to finish (reasons: 3D stencil, high Reynolds number,...), depending on your settings\n", + "\n", + "The following settings take 1:30 minutes to run on an RTX 2060super:\n", + "\n", + "`01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 1 --bbbc_type ibb1 --reynolds_number 3900 --domain_height_y_in_d 5 --domain_width_z_in_d 3 --collision kbc --name 3D_simulation_cylinder_Re3900 --mach_number 0.1 --stencil D3Q27 --calc_u_profiles --profile_reference_path \"./profile_reference_data/\" --show_u_profiles`\n", + "\n", + "as a showcase to see, if the code works. For physically meaningful data, you need to increase `--t_target` to >10²-10³ seconds..." + ], + "id": "fc77a746f992e219" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-17T16:45:15.690110Z", + "start_time": "2025-12-17T16:43:45.533416Z" + } + }, + "cell_type": "code", + "source": [ + "%matplotlib inline\n", + "%run 01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 1 --bbbc_type ibb1 --reynolds_number 3900 --domain_height_y_in_d 5 --domain_width_z_in_d 3 --collision kbc --name 3D_simulation_cylinder_Re3900 --mach_number 0.1 --stencil D3Q27 --calc_u_profiles --profile_reference_path \"./profile_reference_data/\" --show_u_profiles" + ], + "id": "a25110bac2f3c90a", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SCRIPT: Writing arguments to dictionary...\n", + "SCRIPT: Input arguments are: \n", + "{'name': '3D_simulation_cylinder_Re3900', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 3900.0, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 20, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': 5.0, 'domain_width_z_in_d': 3.0, 'perturb_init': False, 'u_init_condition': 0, 'lateral_walls': 'periodic', 'n_steps': 100000, 't_target': 1.0, 'collision': 'kbc', 'stencil': 'D3Q27', 'eqlm': False, 'bbbc_type': 'ibb1', 'periodic_region_start_relative': None, 'periodic_region_start_pu': None, 'periodic_region_start_lu': None, 'calc_u_profiles': True, 'show_u_profiles': True, 'output_u_profiles_timeseries': False, 'profile_reference_path': './profile_reference_data/', 'vtk_full_basic': False, 'vtk_full_basic_interval': None, 'vtk_3D': False, 'vtk_3D_fps': None, 'vtk_3D_step_interval': None, 'vtk_3D_t_interval': None, 'vtk_3D_step_start': None, 'vtk_3D_step_end': None, 'vtk_3D_t_start': None, 'vtk_3D_t_end': None, 'vtk_slice2D': False, 'vtk_slice2D_fps': None, 'vtk_slice2D_step_interval': None, 'vtk_slice2D_t_interval': None, 'vtk_slice2D_step_start': None, 'vtk_slice2D_step_end': None, 'vtk_slice2D_t_start': None, 'vtk_slice2D_t_end': None, 'count_tensors': False}\n", + "\n", + "SCRIPT: Creating timestamp, simulation ID and creating output directory...\n", + "outdir/simID = ./datafolder/251217_174345-3D_simulation_cylinder_Re3900\n", + "outdir_DATA/simID = ./datafolder/251217_174345-3D_simulation_cylinder_Re3900/251217_174345-3D_simulation_cylinder_Re3900\n", + "SCRIPT: Writing input parameters to file: ./datafolder/251217_174345-3D_simulation_cylinder_Re3900/input_parameters.txt\n", + "SCRIPT: Saving simulation script to outdir...\n", + "-> Saved simulation script to './datafolder/251217_174345-3D_simulation_cylinder_Re3900/251217_174345-3D_simulation_cylinder_Re3900_01_script_cylinder_simulation.py'\n", + "SCRIPT: Starting stdout-LOGGER (see outdir for log file)\n", + "SCRIPT: Processing parameters...\n", + "\n", + "(INFO) parameters set for simulation of 346 steps, representing 1.000 seconds [PU]!\n", + "\n", + "SCRIPT: initializing solver components...\n", + "-> initializing context...\n", + "-> initializing flow...\n", + "OBST_Cylinder: self.resolution= [200, 100, 60]\n", + "OBST_Cylinder: x_pos, y_pos, radius: 50.5 50.5 10.0\n", + "-> initializing collision operator...\n", + "\n", + "SCRIPT: initializing simulation object...\n", + "CYLINDER: the bc_type was given as: ibb1\n", + "IBB: calc force is: True\n", + "SIMULATION: creating no_collision_mask entries for: i, boundary = 3 \n", + "collision index is: 0\n", + "-> initializing reporters...\n", + "ProfileReporters at x-positions: p1: 71.2 p2: 80.8 p3: 90.4\n", + "ProfileReporter: requested position is off grid! Interpolating u(71.2) = 0.7999999999999972 * u(71) + 0.20000000000000284 * u(72)\n", + "ProfileReporter: requested position is off grid! Interpolating u(80.8) = 0.20000000000000284 * u(80) + 0.7999999999999972 * u(81)\n", + "ProfileReporter: requested position is off grid! Interpolating u(90.4) = 0.5999999999999943 * u(90) + 0.4000000000000057 * u(91)\n", + "\n", + "SCRIPT: spacial and temporal dimensions:\n", + "domain shape (LU): [200, 100, 60]\n", + "t_target (PU) with 346 steps (LU): 0.999 seconds\n", + "steps to simulate 1 second PU: 346.41 steps\n", + "steps to simulate 1.000 (t_target, PU) seconds: 346.410 steps\n", + "\n", + "#################################################\n", + "\n", + "SCRIPT (2025-12-17 17:43:46): running simulation for 346 steps...\n", + "\n", + "#################################################\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "***** SIMULATION FINISHED AT 2025-12-17 17:45:12 *****\n", + "\n", + "### STATS ###\n", + "MLUPS: 4.857\n", + "simulated PU-Time: 0.9988159656980528 seconds\n", + "simulated LU-steps: 346\n", + "runtime (WALLTIME) of simulation(num_steps): 85.493 seconds (= 1.42 minutes )\n", + "\n", + "### HARDWARE UTILIZATION ###\n", + "current GPU VRAM (MB) usage: 577.019\n", + "max. GPU VRAM (MB) usage: 1845.631\n", + "CPU % avg. over last 1 min, 5 min, 15 min; 4.18 5.31 5.19\n", + "current total RAM usage [MB]: 11127.89 of 64219.7 MB\n", + "\n", + "SCRIPT: processing, plotting and saving data...\n", + "\n", + "(WARNING!) peak finding for drag didn't work... This might just be because there is no converged periodic region!\n", + "Analyze Periodic Timeseries (verbose):-> see Python Stack Trace below:\n", + "\n", + "--- Python Stack Trace ---\n", + "Traceback (most recent call last):\n", + " File \"/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py\", line 129, in analyze_periodic_timeseries\n", + " if peaks_min[0][0] < peaks_max[0][0]:\n", + " ~~~~~~~~~~~~^^^\n", + "IndexError: index 0 is out of bounds for axis 0 with size 0\n", + "\n", + "--------------------------\n", + "\n", + "DRAG STATS:\n", + "mean_simple = 0.1732521376257104\n", + "mean_periodcorrected = None\n", + "min_simple = -0.724629230527121\n", + "max_simple = 1.1230686001217727\n", + "max_mean = None\n", + "min_mean = None\n", + "frequency_fit = 0.19893275083624243\n", + "frequency_fft = 1.9794866372215736\n", + "fft_resolution = 1.9794866372215736\n", + "\n", + "LIFT STATS:\n", + "mean_simple = 0.00042755743250013793\n", + "mean_periodcorrected = -0.002531614794975356\n", + "min_simple = -0.02564869868377675\n", + "max_simple = 0.025483797803982536\n", + "max_mean = 0.01726717693382942\n", + "min_mean = -0.02071004755938259\n", + "frequency_fit = 0.202221662229376\n", + "frequency_fft = 1.9794866372215736\n", + "fft_resolution = 1.9794866372215736\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" 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" 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" 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" 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" 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" 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" 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" 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DwDHeC0DjrwPQ+t8LrQJDLKJfv37MSy+9xF/WarVMcHAwk5iYaMVRWc7SpUuZqKgog7cVFhYyzs7OzA8//MBfd/nyZQYAk5KS0kIjtDwAzM6dO/nLOp2OkcvlzIcffshfV1hYyEgkEua7775jGIZhLl26xABgTp48yR/z+++/MwKBgLlz506Ljd2car8ODMMwkydPZp544ol679MaX4ecnBwGAHPo0CGGYZr2e7Bnzx5GKBQyCoWCP+azzz5jZDIZo1QqW/YJmEnt14FhGOahhx5iXn311Xrv0xpfB4ZhGG9vb+aLL75w2PcCh3sdGMZx3wv2hjKLFqBSqZCamoqYmBj+OqFQiJiYGKSkpFhxZJZ19epVBAcHIzw8HM888wy/BV5qairUarXe69GlSxe0a9euVb8eGRkZUCgUes/b09MT/fv35593SkoKvLy80LdvX/6YmJgYCIVCHD9+vMXHbEnJyckICAhA586dMWvWLOTl5fG3tcbXoaioCAD4fcOb8nuQkpKCHj16IDAwkD8mNjYWxcXFepkYe1L7deBs3boVfn5+6N69OxYtWoTy8nL+ttb2Omi1Wmzbtg1lZWWIjo522PdC7deB40jvBXtFO7hYwL1796DVavXe3AAQGBiIK1euWGlUltW/f39s2bIFnTt3RlZWFt566y08+OCDuHjxIhQKBcRicZ1mz4GBgVAoFNYZcAvgnpuh9wF3m0KhQEBAgN7tTk5O8PHxaVWvTVxcHMaOHYuwsDBcu3YNb7zxBkaMGIGUlBSIRKJW9zrodDrMmTMHAwcO5Pc7bsrvgUKhMPh+4W6zN4ZeBwB4+umn0b59ewQHB+P8+fNYsGAB0tLSsGPHDgCt53W4cOECoqOjUVlZCXd3d+zcuRORkZE4e/asQ70X6nsdAMd5L9g7ChaJWdTcAqxnz57o378/2rdvj++//x6urq5WHBmxBRMmTOD/v0ePHujZsyc6dOiA5ORkDBs2zIojs4yXXnoJFy9e1Fu364jqex1qrkXt0aMHgoKCMGzYMFy7dg0dOnRo6WFaTOfOnXH27FkUFRXhxx9/xOTJk3Ho0CFrD6vF1fc6REZGOsx7wd7RNLQF+Pn5QSQS1alsy87Ohlwut9KoWpaXlxc6deqE9PR0yOVyqFQqFBYW6h3T2l8P7rk19D6Qy+V1ip40Gg3y8/Nb9WsTHh4OPz8/pKenA2hdr8Ps2bOxe/duHDx4EG3atOGvb8rvgVwuN/h+4W6zJ/W9Dob0798fAPTeD63hdRCLxYiIiECfPn2QmJiIqKgorFmzxuHeC/W9Doa01veCvaNg0QLEYjH69OmDpKQk/jqdToekpCS9dRqtWWlpKa5du4agoCD06dMHzs7Oeq9HWloaMjMzW/XrERYWBrlcrve8i4uLcfz4cf55R0dHo7CwEKmpqfwxBw4cgE6n4/9otka3b99GXl4egoKCALSO14FhGMyePRs7d+7EgQMHEBYWpnd7U34PoqOjceHCBb3Aef/+/ZDJZPy0na1r7HUw5OzZswCg936w99fBEJ1OB6VS6TDvhfpwr4MhjvJesDvWrrBprbZt28ZIJBJmy5YtzKVLl5gZM2YwXl5eehVdrcm8efOY5ORkJiMjgzl69CgTExPD+Pn5MTk5OQzDMMzMmTOZdu3aMQcOHGBOnTrFREdHM9HR0VYetelKSkqYM2fOMGfOnGEAMKtWrWLOnDnD3Lx5k2EYhnnvvfcYLy8v5ueff2bOnz/PPPHEE0xYWBhTUVHBnyMuLo7p3bs3c/z4cebIkSNMx44dmYkTJ1rrKRmlodehpKSEmT9/PpOSksJkZGQwf/75J3PfffcxHTt2ZCorK/lz2PvrMGvWLMbT05NJTk5msrKy+J/y8nL+mMZ+DzQaDdO9e3dm+PDhzNmzZ5m9e/cy/v7+zKJFi6zxlIzS2OuQnp7OLF++nDl16hSTkZHB/Pzzz0x4eDgzePBg/hyt4XVYuHAhc+jQISYjI4M5f/48s3DhQkYgEDD79u1jGMYx3gsM0/Dr4CjvhdaAgkULWrt2LdOuXTtGLBYz/fr1Y/7++29rD8lixo8fzwQFBTFisZgJCQlhxo8fz6Snp/O3V1RUMC+++CLj7e3NSKVSZsyYMUxWVpYVR2weBw8eZADU+Zk8eTLDMGz7nMWLFzOBgYGMRCJhhg0bxqSlpemdIy8vj5k4cSLj7u7OyGQyZurUqUxJSYkVno3xGnodysvLmeHDhzP+/v6Ms7Mz0759eyY+Pr7OFyd7fx0MPX8AzObNm/ljmvJ7cOPGDWbEiBGMq6sr4+fnx8ybN49Rq9Ut/GyM19jrkJmZyQwePJjx8fFhJBIJExERwbz22mtMUVGR3nns/XV4/vnnmfbt2zNisZjx9/dnhg0bxgeKDOMY7wWGafh1cJT3QmsgYBiGabk8JiGEEEIIsSe0ZpEQQgghhNSLgkVCCCGEEFIvChYJIYQQQki9KFgkhBBCCCH1omCREEIIIYTUi4JFQgghhBBSLwoWLUipVGLZsmX1dqp3FPQ6sOh1YNHrwKLXgV4DDr0OLHodbBf1WbSg4uJieHp6oqioCDKZzNrDsRp6HVj0OrDodWDR60CvAYdeBxa9DraLMouEEEIIIaReFCwSQgghhJB6OVl7ANamVqtx5MgRBAQEQCg0b+xcWloKAEhLS4O7u7tZz21P6HVg0evAoteBRa8DvQYceh1Y5noddDodcnJyMGjQIDg7O5treA7N4dcsHjx4EEOHDrX2MAghhBBiRgcOHMDDDz9s7WG0Cg6fWQwPDwfAvqnkcrmVR0Mak1uqwtObz8JZJMCeF++39nBatee+PgdFkRJrnoxEZJDjZjsIIfZFoVBg6NCh/Oc7MZ3DB4sikQgA0LFjR7Rp08bKoyGNkRVWwEl2G2InIbp27Wrt4bRqvgG5uMeUILBtGLp29LP2cAghpEk8PDwAVH++E9NRgQuxKzodu2pCILDyQByAq5j9Q1uh1lp5JIQQQqyJgkVilwSgaNHSXJ3ZYLFcpbHySAghhFgTBYvErnDlWEKKFS1OWpVZrKTMIiGEODSHX7PYFAzDQKPRQKulD01jiUQiODk5QWDi/LGO4aahKVq0NBcxl1mk9z0hhDgyChYboVKpkJWVhfLycmsPxe5JpVIEBQVBLBYbfQ6uzxOFipYndaY1i4QQQihYbJBOp0NGRgZEIhGCg4MhFospo2UEhmGgUqmQm5uLjIwMdOzY0egG6AxDBS4thS9wocwiIYQ4NAoWG6BSqaDT6dC2bVtIpVJrD8euubq6wtnZGTdv3oRKpYKLi4tR56kqhqagvQVQsEgIIQSgApcmMfc2gI7KPK8jGy1SgYvl8dXQNA1NCCEOjaIgYlcos9hy5DI2+3vjXpmVR0IIIcSaKFgkdoVrnUOhouX1aucFADh3qxBanUNvIU8IIQ6NgkViVxhQ65yW0jHAA25iEcpUWlzNKbH2cAghxCIOHz6Mxx57DMHBwRAIBNi1a1ej90lOTsZ9990HiUSCiIgIbNmypc4x69atQ2hoKFxcXNC/f3+cOHHC/INvIRQstlJTpkyBQCCAQCCAs7MzAgMD8cgjj2DTpk3Q6XTWHp7RuKFTrGh5IqEAUW29AABnMgutOhZCCLGUsrIyREVFYd26dU06PiMjA6NGjcLDDz+Ms2fPYs6cOZg+fTr++OMP/pjt27cjISEBS5cuxenTpxEVFYXY2Fjk5ORY6mlYFAWLrVhcXByysrJw48YN/P7773j44Yfx6quv4tFHH4VGY3gLN7Va3cKjbB4+s2jlcTiK3lVT0WcyC6w7EEIIsZARI0bg7bffxpgxY5p0/Pr16xEWFoaVK1eia9eumD17Np588kl8/PHH/DGrVq1CfHw8pk6disjISKxfvx5SqRSbNm2y1NOwKAoWm4lhGJSrNFb54XoMNpVEIoFcLkdISAjuu+8+vPHGG/j555/x+++/8ylzgUCAzz77DI8//jjc3NzwzjvvQKvVYtq0aQgLC4Orqys6d+6MNWvW6J1bo9HglVdegZeXF3x9fbFgwQJMnjwZo0ePNtMrbVj1dn8ULraE3m29AVBmkRBif0pKSlBcXMz/KJVKs5w3JSUFMTExetfFxsYiJSUFANt2LzU1Ve8YoVCImJgY/hh7Q30Wm6lCrUXkkj8aP9ACLi2PhVRs2j/Z0KFDERUVhR07dmD69OkAgGXLluG9997D6tWr4eTkBJ1OhzZt2uCHH36Ar68vjh07hhkzZiAoKAhPPfUUAOD999/H1q1bsXnzZnTt2hVr1qzBrl278PDDD5v8PBvCF7hQrNgiIoNlAICMe2XQ6hiIqGcRIcROREZG6l1eunQpli1bZvJ5FQoFAgMD9a4LDAxEcXExKioqUFBQAK1Wa/CYK1eumPz41kDBogPq0qULzp8/z19++umnMXXqVL1j3nrrLf7/w8LCkJKSgu+//54PFteuXYtFixbxaftPP/0Ue/bssfjYaRq6ZQXKXCASCqDRMcgtUULuaVwzdUIIaWmXLl1CSEgIf1kikVhxNPaNgsVmcnUW4dLyWKs9tjkwDKNXTdy3b986x6xbtw6bNm1CZmYmKioqoFKp0KtXLwBAUVERsrOz0a9fP/54kUiEPn36WLx4hqE+iy1KJBRALnPBncIK3C2qoGCREGI3PDw8IJPJzH5euVyO7Oxsveuys7Mhk8ng6uoKkUgEkUhk8Bi5XG728bQEWrPYTAKBAFKxk1V+zBUgXb58GWFhYfxlNzc3vdu3bduG+fPnY9q0adi3bx/Onj2LqVOnQqVSmeXxTaGjvaFbXLAXGyDeLayw8kgIIcT6oqOjkZSUpHfd/v37ER0dDQAQi8Xo06eP3jE6nQ5JSUn8MfaGgkUHc+DAAVy4cAHjxo2r95ijR49iwIABePHFF9G7d29ERETg2rVr/O2enp4IDAzEyZMn+eu0Wi1Onz5t0bED3GZ/VODSkoK9XAFQsEgIaZ1KS0tx9uxZnD17FgDbGufs2bPIzMwEACxatAiTJk3ij585cyauX7+O119/HVeuXMH//d//4fvvv8fcuXP5YxISErBx40Z89dVXuHz5MmbNmoWysrI6S77sBU1Dt2JKpRIKhQJarRbZ2dnYu3cvEhMT8eijj+q98Wvr2LEjvv76a/zxxx8ICwvDN998g5MnT+plI19++WUkJiYiIiICXbp0wdq1a1FQUGDx6WGGMostLsiTCxYrrTwSQggxv1OnTukVZyYkJAAAJk+ejC1btiArK4sPHAF2Hf9vv/2GuXPnYs2aNWjTpg2++OILxMZWL1EbP348cnNzsWTJEigUCvTq1Qt79+6tU/RiLyhYbMX27t2LoKAgODk5wdvbG1FRUfjkk08wefJkCIX1J5VfeOEFnDlzBuPHj4dAIMDEiRPx4osv4vfff+ePWbBgARQKBSZNmgSRSIQZM2YgNjYWIpF51lXWh7b7a3khNA1NCGnFhgwZ0mBrOkO7swwZMgRnzpxp8LyzZ8/G7NmzTR2eTaBgsZXasmWLwTd4bYZ+QSQSCTZv3ozNmzfrXZ+YmMj/v5OTE9auXYu1a9cCYNdjdO3ala+WthSahm55/DR0EQWLhBDiiChYJEa5efMm9u3bh4ceeghKpRKffvopMjIy8PTTT1v0cXU6Si22NJqGJoQQx0YFLsQoQqEQW7Zswf3334+BAwfiwoUL+PPPP9G1a1eLPi6XWaRYseWEVGUW88tUqFRrrTwaQgghLY0yi8Qobdu2xdGjR1v8cWm7v5Ync3WCm1iEMpUWdwsrEO7vbu0hEUIIaUGUWSR2haqhW55AIEBgVTNuRTFNRRNCiKOhYJHYFSpwsQ4PF2cAQJmSpqEJIcTRULBI7IqugfYGxHI8JOyKlVKl2sojIYQQ0tIoWCR2hfaGtg43Cds/s5Qyi4QQ4nAoWCR2pXoa2qrDcDhuVZnFMqXGyiMhhBDS0ihYJHZFRwUuVuFOwSIhhDgsChYdkEAgwK5du/jLV65cwQMPPAAXFxf06tXLauNqEr4nN0WLLYkLFksqKVgkhBBHQ8FiKzVlyhSMHj3a4G1ZWVkYMWIEf3np0qVwc3NDWloakpKSsGXLFnh5ebXMQJuJqYoWaRq6ZdE0NCGEOC5qyu2A5HK53uVr165h1KhRaN++vZVG1HQ6XdX/0Dx0i+KnoVUULBJCiKOhYLG5GAZQl1vnsZ2lZgmSBAIBdu7cidGjR/NVxampqVi+fDkeeughHDp0iD8OYDOPy5YtM/lxzYG2+7MON751DlVDE0KIo6FgsbnU5cC7wdZ57DfuAmI3s54yKysLMTExiIuLw/z58yGVSrF582YsWbIEaWlpAAB3d9vZ3o3bwYWmoVuWe1XrHJqGJoQQx0PBooOTy+VwcnKCu7s7Pz3t6ekJgUBQZ7raFuioz6JVuEvYHVxKqcCFEEIcDgWLzeUsZTN81npsh0eZRWuobspNwSIhhDgaChabSyAw+1QwaTodtc6xCipwIYQQx0Wtc0gdYrEYWq1tFjIwVOFiFTVb5zC0PzchhDgUyiy2YkVFRTh79qzedb6+vo3eLzQ0FKWlpUhKSkJUVBSkUimkUtuYAqc+i9bh7sL+qVBrGSg1Org4i6w8IkIIIS2FgsVWLDk5Gb1799a7btq0aY3eb8CAAZg5cybGjx+PvLw8m2qdQ9PQ1uEmrv5TUabUULBICCEOhILFVmrLli3YsmWLwdu++OILvcu1s48A8Nlnn+Gzzz6zwMhMw9De0FYhEgrg6ixChVqLMqUWvrbTTYkQQoiF0ZpFYpeEFC22uOrG3FTkQgghjoSCRWJXdJRZtBq+MTdVRBNCiEOhYJHYFW5vaMostjyuyIUacxNCiGOhYJHYFS1t92c1XJELTUMTQohjoWCxCaivnHmY43Ws3huaosWW5l6j1yIhhBDHQcFiA5yd2f1wy8vLrTyS1oF7HbnX1Rhc6xwhpRZbHBW4EEKIY6LWOQ0QiUTw8vJCTk4OAEAqlUJAGa1mYxgG5eXlyMnJgZeXF0Qi43v0aXU0DW0t/JpFChYJIcShULDYCLlcDgB8wEiM5+Xlxb+exuKmoUUULbY4moYmhBDHRMFiIwQCAYKCghAQEAC1Wm3t4dgtZ2dnkzKKHC6zSBneludatWtLhdo29w0nhBBiGRQsNpFIJDJLsENMw61ZFFGw2OIkzuwS50q1zsojIYQQ0pKowIXYFR21zrEaFyf2y5JSQ8EiIYQ4EgoWiV3RUescq6nOLNI0NCGEOBIKFoldodY51kOZRUJIa7Vu3TqEhobCxcUF/fv3x4kTJ+o9dsiQIRAIBHV+Ro0axR8zZcqUOrfHxcW1xFOxCFqzSOwKtc6xHpeqAhfKLBJCWpPt27cjISEB69evR//+/bF69WrExsYiLS0NAQEBdY7fsWMHVCoVfzkvLw9RUVH4z3/+o3dcXFwcNm/ezF+WSCSWexIWRplFYleodY71SJzYPxeUWSSE2IOSkhIUFxfzP0ql0uBxq1atQnx8PKZOnYrIyEisX78eUqkUmzZtMni8j48P5HI5/7N//35IpdI6waJEItE7ztvb2+zPsaVQsEjsirYqTqHWOS2PyywqKbNICLEDkZGR8PT05H8SExPrHKNSqZCamoqYmBj+OqFQiJiYGKSkpDTpcb788ktMmDABbm5uetcnJycjICAAnTt3xqxZs5CXl2faE7IimoYmdoUrcKHWOS2PK3ChzCIhxB5cunQJISEh/GVD08D37t2DVqtFYGCg3vWBgYG4cuVKo49x4sQJXLx4EV9++aXe9XFxcRg7dizCwsJw7do1vPHGGxgxYgRSUlLssg0fBYvErlDrHOvhClxozSIhxB54eHhAJpNZ9DG+/PJL9OjRA/369dO7fsKECfz/9+jRAz179kSHDh2QnJyMYcOGWXRMlmBT09CHDx/GY489huDgYAgEAuzatavB45OTkw1WJCkUipYZMGlxXLBI09Atj1rnEEJaGz8/P4hEImRnZ+tdn52d3ej2tGVlZdi2bRumTZvW6OOEh4fDz88P6enpJo3XWmwqWCwrK0NUVBTWrVvXrPulpaUhKyuL/zFUvURaB34HF0ottjhqnUMIaW3EYjH69OmDpKQk/jqdToekpCRER0c3eN8ffvgBSqUSzz77bKOPc/v2beTl5SEoKMjkMVuDTU1DjxgxAiNGjGj2/QICAuDl5WX+ARGbo6PWOVZTM7PIMAxldwkhrUJCQgImT56Mvn37ol+/fli9ejXKysowdepUAMCkSZMQEhJSp0Dmyy+/xOjRo+Hr66t3fWlpKd566y2MGzcOcrkc165dw+uvv46IiAjExsa22PMyJ5sKFo3Vq1cvKJVKdO/eHcuWLcPAgQPrPVapVOqVz5eUlLTEEImZ8GsWKVpscVxmUccAGh0DZxH9GxBC7N/48eORm5uLJUuWQKFQoFevXti7dy9f9JKZmQmhUH8iNi0tDUeOHMG+ffvqnE8kEuH8+fP46quvUFhYiODgYAwfPhwrVqyw216Ldh0sBgUFYf369ejbty+USiW++OILDBkyBMePH8d9991n8D6JiYl46623WnikxFy41jm03V/L4zKLAJtddBbZ1CoWQggx2uzZszF79myDtyUnJ9e5rnPnznzf39pcXV3xxx9/mHN4VmfXwWLnzp3RuXNn/vKAAQNw7do1fPzxx/jmm28M3mfRokVISEjgL9+5cweRkZEWHysxD2qdYz1cU26AXbfoYcWxEEIIaTl2HSwa0q9fPxw5cqTe2yUSiV4auLi4uCWGRcyEodY5ViMQCCBxEkKp0VFFNCGEOJBWN4909uxZu602Io3TUuscq+Kyi5VqqogmhBBHYVOZxdLSUr0eRBkZGTh79ix8fHzQrl07LFq0CHfu3MHXX38NAFi9ejXCwsLQrVs3VFZW4osvvsCBAwcMLjglrQO1zrEuF2cRiis1UGoos0gIIY7CpoLFU6dO4eGHH+Yvc2sLJ0+ejC1btiArKwuZmZn87SqVCvPmzcOdO3cglUrRs2dP/Pnnn3rnIK0Ltc6xrur2OZRZJIQQR2FTweKQIUPqrS4CgC1btuhdfv311/H6669beFTEllDrHOuqbsxNmUVCCHEUrW7NImndqHWOdbk4VwWLlFkkhBCHQcEisSsMtc6xKq7AhTKLhBDiOChYJHZFx1dDW3kgDorLLNKaRUIIcRwULBK7oq1a0krT0NZBmUVCCHE8FCwSu8Lv4EIFLlZBmUVCCHE8FCwSu0Ktc6yruik3ZRYJIcRRULBI7Aq1zrEuCVcNraHMIiGEOAoKFoldodY51kWZRUIIcTwULBK7Qq1zrMuFMouEEOJwKFgkdoVa51iXizNlFgkhxNFQsEjsCrXOsS6JE2UWCSHE0VCwSOwKQ61zrIoyi4QQ4ngoWCR2RaujaWhroswiIYQ4HgoWiV2hptzWRZlFQghxPBQsEruio9Y5VsVnFmkHF0IIcRgULBK7wjflpmDRKrjMIu0NTQghjoOCRWJXqoNFKw/EQdHe0IQQ4ngoWCR2hVrnWBe3gwtlFgkhxHFQsEjsCrXOsS7KLBJCiOOhYJHYFWqdY12UWSSEEMdDwSKxK1WxImUWrYQyi4QQ4ngoWCR2RaejamhrqplZ5JYEEEIIad0oWCR2hVrnWJekKrOoYwC1loJFQghxBBQsErtCrXOsi+uzCACVtG6REEIcAgWLxK5waxaFFC1ahVgk5IuLaBcXQghxDBQsErtC09DWJRAI+HWLtD80IYQ4BgoWiV3R6mga2tr4/aE1lFkkhBBHQMEisSsMtc6xOm7dImUWCSGtxbp16xAaGgoXFxf0798fJ06cqPfYLVu2QCAQ6P24uLjoHcMwDJYsWYKgoCC4uroiJiYGV69etfTTsBgKFold0VLrHKujzCIhpDXZvn07EhISsHTpUpw+fRpRUVGIjY1FTk5OvfeRyWTIysrif27evKl3+wcffIBPPvkE69evx/Hjx+Hm5obY2FhUVlZa+ulYBAWLxK7QmkXr4zKLSsosEkJsWElJCYqLi/kfpVJp8LhVq1YhPj4eU6dORWRkJNavXw+pVIpNmzbVe26BQAC5XM7/BAYG8rcxDIPVq1fjzTffxBNPPIGePXvi66+/xt27d7Fr1y5zP80WQcEisSvV1dDWHYcjo8wiIcQeREZGwtPTk/9JTEysc4xKpUJqaipiYmL464RCIWJiYpCSklLvuUtLS9G+fXu0bdsWTzzxBP755x/+toyMDCgUCr1zenp6on///g2e05Y5WXsAhDQHZRatj9YsEkLswaVLlxASEsJflkgkdY65d+8etFqtXmYQAAIDA3HlyhWD5+3cuTM2bdqEnj17oqioCB999BEGDBiAf/75B23atIFCoeDPUfuc3G32hoJFYlcoWLQ+fn9oaspNCLFhHh4ekMlkZj9vdHQ0oqOj+csDBgxA165d8fnnn2PFihVmfzxbQJN5xK5Q6xzr4/eHpqbchBA75+fnB5FIhOzsbL3rs7OzIZfLm3QOZ2dn9O7dG+np6QDA38+Uc9oaChaJXaHWOdbH7Q9N09CEEHsnFovRp08fJCUl8dfpdDokJSXpZQ8botVqceHCBQQFBQEAwsLCIJfL9c5ZXFyM48ePN/mctoamoYldodY51sdnFqnAhRDSCiQkJGDy5Mno27cv+vXrh9WrV6OsrAxTp04FAEyaNAkhISF8gczy5cvxwAMPICIiAoWFhfjwww9x8+ZNTJ8+HQBbKT1nzhy8/fbb6NixI8LCwrB48WIEBwdj9OjR1nqaJqFgkdgVbs0ixYrWw69ZpGloQkgrMH78eOTm5mLJkiVQKBTo1asX9u7dyxeoZGZmQlijBUdBQQHi4+OhUCjg7e2NPn364NixY4iMjOSPef3111FWVoYZM2agsLAQgwYNwt69e+s077YXAobhJvYc0+3bt9G2bVvcunULbdq0sfZwSCM6/fd3qLQ6pCwaiiBPV2sPxyG99es/2Hz0Bl4c0gGvx3Wx9nAIIUQPfa6bH61ZJHZFS9XQVkeZRUIIcSwULBK7Qq1zrM+Fb8pNBS6EEOIIKFgkdoNhGL4amoqhrUfCN+WmzCIhhDgCChaJ3dDVWF1LrXOsx6WqGpqachNCiGOgYJHYDW2NaFFA09BWw/VZpKbchBDiGChYJHZDV6NwnxKL1sPtDU1rFgkhxDGYNVjU6SjTQCyHoWlomyBxoswiIYQ4EpODxYKCAjz99NPw9PSERCJBREQEFi5ciMLCQjMMj5BqWr3MIgWL1sJlFmnNIiGEOAaTg8WFCxeiXbt2uH37NsrKyrBnzx4AQHR0NBQKhckDJISjo2DRJlBmkRBCHIvJ2/0dP34cZ8+e5S936tQJ7733HqKiorBs2TKsX7/e1IcgBACg09GaRVtAmUVCCHEsJmcWa+6XWNPEiRNx6tQpU09PCI9a59gGyiwSQohjMTlYzMnJwY8//ojLly9Dq9XPNFB7E2JO1DrHNlBmkRBCHIvJ09Dz5s3D77//jg8//BBXr15FcHAwunXrhsjISOTk5JhjjIQAYHdwAWgK2tq4zGKlmoJFQghxBCYHi3PnztW7nJGRgYsXL+LixYsYNGiQqacnhMclFmkK2rokfJ9FHRiGoSwvIYS0cs0OFi9duoTvvvsO8+bNg5eXV53bw8LCEBYWhscee8wc4yOEx7XOoeDEulyqdnBhGECl1fGZRkIIIbZt/Pjx2L59e7Pv1+w1i4mJibh48aLBQLGyshJXrlxp9iAIaQquGlpEwaJVSZyq/2woNVTkQggh9uLkyZNG3a/ZweLff/+NV155xeBtLi4uiI+PR2JiolGDIaQhOlqzaBPEIiG4eJ3WLRJCiG158803sW3bNly8eBEajcYs52x2sHj79m1ERETUe/vMmTPxyy+/mDQoQgzh1iwKKVq0KoFAABdqn0MIITbJ19cX+/fvx7Rp0xAQEIDu3btj/PjxWLFiBUpLS406Z7PXLPr4+CArKwtt27Y1eHu/fv2Qnp5u1GAIaUh1ZpGCRWuTOAtRodZCSe1zWtztgnIIBQIEe7laeyiEEBtQu5akocLjRx55xKjHaHawOHjwYGzZsgX9+vUzeLtQKERlZaVRgyGkIdyaRUosWh+bWVSjQkWZxZZUVKHGoPcPwlvqjONvxEDsZHKrXEKInUtMTERpaanBWhKlUgmlUonHHnvMpMLjZv+lmT9/PjZu3IgNGzYYvD0lJQXh4eFGD4iQ+lDrHNvh6y4GAOSW0hfDlnTgSjYAoKBcjdxSpZVHQwixBQ3VkkgkErPUkjQ7WOzTpw/+7//+Dy+++CIeeeQR7Nq1C5mZmcjPz8fPP/+MBQsW4OmnnzZpUIQYwu3gQq1zrC/Ik50CvVtIwWJL+v2Cgv//3BIKFgkhLVNLYlRT7vj4eHTt2hUJCQkYO3Ys/+HNMAyGDx9eZ76cEHPg1ixS6xzrC/ZyAQBkFVVYeSSOo1SpQfK/ufzlnGIK1AkhLVNLYvQOLoMGDcKJEydw5coVnD59GuXl5ejevTseeOABkwZESH2odY7t4DKLWZRZbDGH/82FqkZfS5qGJoQALVNLYvJ2f126dEGXLl1MPQ0hjaLWObaDyyzepcxiizmTWaB3OaeYgkVCCFtL8sADD6BXr16YMWNGndvNUUtCpXTEblDrHNshl3HT0JRZbCnnbhUBANp4s1ldyiwSQoCWqSWhYJHYDWqdYzu4Hn9ZRZVgqoJ4YjkarQ4X7rDBYkzXQABU4EIIqRYfH4/k5GQUFRVh7NixCAsLg7+/P8aMGYMePXqYXEti8jQ0IS2FpqFtR6DMBQIBoNLokF+mgq+7xNpDatXSc0tRodbCXeKEB8J9sOXYDeRQsEgIqcGStSQULBK7odXRNLStEDsJ4ecuQW6JEllFlRQsWti5W4UAgB4hngisWgJwj4JFQogBlqglMTpYTEhIMHi9QCCAi4sLIiIi8MQTT8DHx8fowRFSE0Otc2xKsKcLckuUuFtYge4hntYeTqt2tmq9Ys+2nvD3YAPz3BIlGIahvqOEEIszOlg8c+YMTp8+Da1Wi86dOwMA/v33X4hEInTp0gX/93//h3nz5uHIkSOIjIw024CJ49IyXFNuKw+EAADkni44d7sIdwupItrSuNe4g587HyyqtDoUVajhJRVb5DHPZBZg55k7mDe8MzxdnS3yGIQQ+2B0gcsTTzyBmJgY3L17F6mpqUhNTcXt27fxyCOPYOLEibhz5w4GDx5MDbqJ2dB2f7alg787AODTg+m4oii28mhat5JKNQBA5uoEiZOID94sWeSy+s+r+DrlJn45e8dij0EIsQ9GB4sffvghVqxYAZlMxl/n6emJZcuW4YMPPoBUKsWSJUuQmppqloESQq1zbEv8g+GIDJLhXqkKC3+6YO3htGollRoAgIcLGyRy2UVLFrncyi8HAFzNKW3wOIZhsO5gOn6/kGWxsRBCGpeZmWmwOwXDMMjMzDTp3EYHi0VFRcjJyalzfW5uLoqL2SyDl5cXVCqV8aMjpAZqnWNbvN3EWPfMfQCAS3eL+QIkYn7VwSK7csjfvXrdoiUwDIM7VVPf13IbDhav5Zbiwz/S8PpP56mNErFb69atQ2hoKFxcXNC/f3+cOHGi3mM3btyIBx98EN7e3vD29kZMTEyd46dMmQKBQKD3ExcXZ9HnEBYWhtzc3DrX5+fnIywszKRzmzQN/fzzz2Pnzp24ffs2bt++jZ07d2LatGkYPXo0AODEiRPo1KmTSQMkhEOtc2xPOx8pJE5CqLQ6PhNFzI+bhuYyiz5u7DrFgnLLfBnPLVVCWbW1YHojmUWuMXtJpQZ5ZZQcIPZn+/btSEhIwNKlS3H69GlERUUhNjbWYEIMAJKTkzFx4kQcPHgQKSkpaNu2LYYPH447d/SXbMTFxSErK4v/+e677yz6POoreCstLYWLi4tJ5za6wOXzzz/H3LlzMWHCBGg07LdeJycnTJ48GatWrQLAlm9/8cUXJg2QEA61zrE9IqEA4f7uuJxVjPScUoT6uVl7SK2OVsegTKUFUJ1Z9JKyQWNBudoij3mnoLpoKbtYiZJKNR+o1lYzu3kzrwx+1EaJ2JlVq1YhPj4eU6dOBQCsX78ev/32GzZt2oSFCxfWOX7r1q16l7/44gv89NNPSEpKwqRJk/jrJRIJ5HK5ZQeP6u40AoEAixcvhlQq5W/TarU4fvw4evXqZdJjGB0suru7Y+PGjfj4449x/fp1AEB4eDjc3d35Y0wdHCE1Uesc29TB3w2Xs4pxLbcUMQi09nBandKqKWigOlj0rqqALrRQZvFOrQr367lliGrrZfBY/WCxHH3aU7s0YhtKSkr4ZXEAG7xJJPpfZlQqFVJTU7Fo0SL+OqFQiJiYGKSkpDTpccrLy6FWq+u0CkxOTkZAQAC8vb0xdOhQvP322/D19TXhGRl25swZAOxn5IULFyAWV3dIEIvFiIqKwvz58016DJObcmdmZuLu3btQqVS4ceMGf/3jjz/e7HMdPnwYH374IVJTU5GVlYWdO3fyU9r1SU5ORkJCAv755x+0bdsWb775JqZMmdLsxya2j1rn2KaIAPYLYmPTlcQ4xVVT0BInISROIgAtm1kE2HWJTQkWb+TRUgRiO2q37Vu6dCmWLVumd929e/eg1WoRGKj/RTcwMBBXrlxp0uMsWLAAwcHBiImJ4a+Li4vjt927du0a3njjDYwYMQIpKSkQiUTGPSEDzp8/jz///BMikQhTp07FJ598Ag8PD7Odn2N0sHj9+nWMGTMGFy5cgEAg4LM+3Hy5Vqtt9jnLysoQFRWF559/HmPHjm30+IyMDIwaNQozZ87E1q1bkZSUhOnTpyMoKAixsbHNfnxi26h1jm3igsXGCiGIcYprrVcELJ9ZvF0rWGzoi0BuaXWwmJlXZpHxEGKMS5cuISQkhL9cO6toDu+99x62bduG5ORkvXWBEyZM4P+/R48e6NmzJzp06IDk5GQMGzbMbI/fu3dvKBQK+Pv749ChQxYrKja6wOXVV19FWFgYcnJyIJVKcfHiRRw+fBh9+/ZFcnKyUeccMWIE3n77bYwZM6ZJx69fvx5hYWFYuXIlunbtitmzZ+PJJ5/Exx9/bNTjE9vGUOscm8T1W0zPKaVqWAvgKqFlLtXf7b3duMyiZaehOwWy/7b/ZpfUeyxlFomt8vDwgEwm438MBYt+fn4QiUTIzs7Wuz47O7vR9YYfffQR3nvvPezbtw89e/Zs8Njw8HD4+fkhPT29+U+kAV5eXvxSwBs3bkCn05n1/Byjg8WUlBQsX74cfn5+EAqFEIlEGDRoEBITE/HKK6+Yc4wNjqFm2hcAYmNjG1xnoFQqUVxczP+UlNT/R5DYFq7AhWJF2xLm5wahACiu1OhlmYh51G6bA4DftaWgzLLT0I/2DAYAnMjIr7c1Uu0CF0LsiVgsRp8+fZCUlMRfp9PpkJSUhOjo6Hrv98EHH2DFihXYu3cv+vbt2+jj3L59G3l5eQgKCjLLuDnjxo3DQw89hLCwMAgEAvTt2xfh4eEGf0xh9DS0Vqvl58X9/Pxw9+5ddO7cGe3bt0daWppJg2oqhUJhcJ1BcXExKioq4OrqWuc+iYmJeOutt1pkfMS8aBraNrk4i+DjJsa9UhXyy1QI8DCtRQPRV7ttDmDZaeiaPRZHdJdj41/XUVypwcU7RQbXLdb8glBQrkZRhZq2ByR2JSEhAZMnT0bfvn3Rr18/rF69GmVlZXx19KRJkxASEoLExEQAwPvvv48lS5bg22+/RWhoKBQKBQC28Nfd3R2lpaV46623MG7cOMjlcly7dg2vv/46IiIizL5EbsOGDRg7dizS09PxyiuvID4+3rbWLHbv3h3nzp1DWFgY+vfvjw8++ABisRgbNmwwOYK1pEWLFvFl5gBw584d2rvaTuiodY7NcnFmF2xXqJq/Vpk0jJ+Gdq0xDV1V4FKm0kKl0UHsZPQkUR2F5WqUKtnHbOsjRXS4L/ZdysbRa/fqBItKjRaFVUU2Ls5CVKp1SM8poYpoYlfGjx+P3NxcLFmyBAqFAr169cLevXv5ZFRmZiaEwurfsc8++wwqlQpPPvmk3nm4AhqRSITz58/jq6++QmFhIYKDgzF8+HCsWLHCIusmuWbfqampePXVV20rWHzzzTdRVsZOOSxfvhyPPvooHnzwQfj6+mL79u1mG2BD5HK5wXUGMpnMYFYRqFs6X7Osntg22u7PdrlywaKagkVzK66oyixKqrN1MhdnCAVstr2wXIUAmfmyuTeqppKDPF3g4izCwAg/NlhMv4cXh0ToHXuvlM1sOosEiOkaiN3ns/C/vzMpWCR2Z/bs2Zg9e7bB22rXYdTs/GKIq6sr/vjjDzONrOk2b95ssXMbHSzWTKVGRETgypUryM/Ph7e3t8EO4pYQHR2NPXv26F23f//+BtcZEPulZWi7P1slFVNm0VJKlHXXLAqFAni6OqOgXI2CcrVZg8WbVUUq7X3Zxr4DI/wAACdvFECrY/SWgXDrFf3dJXhhcAfsPp+FX87dRcIjndDWRwpCiGUlJCRgxYoVcHNz05s1NYTbMMUYRgWLarUacXFxWL9+PTp27MhfX7shZXOVlpbqVQplZGTg7Nmz8PHxQbt27bBo0SLcuXMHX3/9NQBg5syZ+PTTT/H666/j+eefx4EDB/D999/jt99+M2kcxDbRmkXb5UKZRYsxtGYRYNctssGiedct8sGiD7sbT2hV0KjS6FBYroJvjR1a+GDRQ4IebTwxKMIPR9Lv4YfU20h4hLZ6JcTSzpw5A7Vazf+/pRgVLDo7O+P8+fPmHgtOnTqFhx9+mL/MRcmTJ0/Gli1bkJWVhczMTP72sLAw/Pbbb5g7dy7WrFmDNm3a4IsvvqAei60Utc6xXZRZtJxiA9XQQHVjbnMXuXAVze392CDRSSSEp6sziirYLGZ9wSIAPBDugyPp95BdtV80IcSyDh48aPD/zc3oaehnn30WX375Jd577z2zDWbIkCEN9mnbsmWLwftYMpomtoNa59guVzFlFi3FUOscoLoi2ty7uHBrFrnMIgD4uImrgkX9wPRuVdW0f1UFvKyqCpprJE4Isb7bt29j+fLl2LBhg9HnMDpY1Gg02LRpE/7880/06dMHbm5uerebMjdOiCE0DW27qBracrhpaFmtdjR8r0UzZxYz8/XXLLKPxT52fpn+Y128WwQAiAxiqy9lLhQsEmJr8vLy8OWXX1onWLx48SLuu+8+AMC///6rd1tLFbgQx0Ktc2wXNw1dTsGi2dWfWeSmoc0XmJVUqvkK55rBog/fBLw6WGQYBhfvsMFitxBPANXtfYorNGYbEyHE+owOFi05N06IIdQ6x3ZxrXMqaRra7LjWObLaBS5udQM4U3HFLb5uYv0m4G51p7yzi5W4V6qCSChAZJBMb4yUWSSkdTFfJ1dCLIxa59gu6rNoOfVlFrldUooqzBeY3S5gg8XabW983OpOeXNZxQh/d34ZAr9m0YxjIoRYn1GZRZ1Ohy1btmDHjh24ceMGBAIBwsLC8OSTT+K5556jaWhiEQytWbRZrmL2TwlNQ5uXWqvjA/DarXOkFigq4qaPueCQY2jN4oWqYLF71RQ0UB3AFldqwDAMfRYQ0gLGjh3b4O2FhYUmP0azg0WGYfD4449jz549iIqKQo8ePcAwDC5fvowpU6Zgx44d2LVrl8kDI6Q2HV8NTR9AtsbVmZ2koMyieZVWVq/9q51ZdLVAURHXANxdov9YhtYs/nOXCxZl/HXcNLRWx6BcpYWbxOiVToSQJvL09Gz09kmTJpn0GM3+Td6yZQsOHz6MpKQkvZ6IAHDgwAGMHj0aX3/9tckDI6Q2moa2XdKqzGIlZRbNipuCdnUWwVmkv2rI1QJFRVxw6l67mMbANDS3vrFjQPU+tC7OQjiLBFBrGRRXqilYJKQFWHKbP06z1yx+9913eOONN+oEigAwdOhQLFy4EFu3bjXL4AipiVrn2C4Xqoa2iGJ+95a6QRcXoJszm1umMpxZNNTT8V6pfkNugM36c9lFc66lJIRYV7ODxfPnzyMuLq7e20eMGIFz586ZNChCDKHWObaLClwso+FgkQvQzdemhstk1pmGdtNfs6jR6vjA0dddf31jdZELtc8hpLVodrCYn5+PwMDAem8PDAxEQUGBSYMixBBqnWO7uMCFWueYV3UltHOd2yzRCL20njWLXGaxqEINjVaH/KrpaKGg+jaOzIXrtUiZRUJai2YHi1qtFk5O9a9DEYlE0GjoGyUxP1qzaLu4wIWmoc2rvrY5gGWqoUurMpm1g0XPGrvHFFWokVfVuNvHTVxnWQht+UdIyzl//jx0Op3FH8eoaugpU6ZAIpEYvF2pVJo8KEIM4VrnCClatDk0DW0Z/FZ/BjKLXLCo1jJQa3V1CmCMUaZk//1qF7g4iYTwdHXm94fmgkVft7qfA3xjbsosEmJxvXv3RlZWFgICAhAeHo6TJ0/C19fX7I/T7GBx8uTJjR5DldDEEmjNou3is1yUWTQrLrPIbaNXE5fNBdgg3RzBYn2tcwA2i1hUoUZ+mZovbqm9XrHmWIsr659hYhgGy3dfQqVai3fH9KB2WIQYycvLCxkZGQgICMCNGzcslmVsdrDYEiXahBhC09C2y7XGlCg1YzafEr7ApW5mUeIkhFDAdgmoUGkNZh+bq1RZNQ1tYNrbW+qMDAB3CsurM4vuzcsspt4swIbD1yCXueCrlJsAgFkPRaCdr7TOsYSQxo0bNw4PPfQQgoKCIBAI0LdvX4hEIoPHXr9+3ejHoSZYxG7QDi62i8tyaXUM1FoGYif6NzIHfs2igUyfQCCAVOyEUqXGbGtFS+uphgaA/uG+OJ1ZiM1Hb2BABz8AgJ/BzGL9axa/TrmBP/7J1rsuu6SSgkVCjLRhwwaMHTsW6enpeOWVVxAfHw8PD4/G79hMFCwSu6GlHVxsFjcNDbBZLrETbTtvDg21zgHYjC4bLJqnqJBfs2ggWJw+KAxbjt7A+dtFyCqqBAD4GcwsctXQdceUVVhZ5zpFUd3rCCFNx7UzTE1NxauvvmqRYJH+ohO7wbXOEVGwaHOcRUI4VWV8qcjFfBpqnQNUFxaZo2WRUqOFSsuudzI0De3rLsFz0e0BALklVWsW3erPLBpqys3tALN6fC+M7CEHAGQXU7BIiDls3rwZWq0WK1euxPTp0zF9+nR8/PHHKCoqMvnczQoWW6pEmxBDdLRm0aa5WqCVi6MrbqB1DlCzMbfpr3nNfajdxIYf74lewXqXG1yzaGAamiuM6RokQ7CnKwAKFgkxl1OnTqFDhw74+OOPkZ+fj/z8fKxatQodOnTA6dOnTTp3s4LF3r174969ewCA8PBw5OXlmfTghDQH9z2FWufYJldn8+8o4uj41jmu9WQWzRksVlVCS8WietcFd5XL9NYpNrRmsaBMpXe9usauL/4eEsg9XQAA2cXUbo0Qc5g7dy4ef/xx3LhxAzt27MCOHTuQkZGBRx99FHPmzDHp3M0KFrkSbQAWLdEmxBDawcW2udIuLmbXUFNuwLzT0PXt3lKTUCjAwAg//rKhNYthfm4AgLtFlXywC4CvoBYJBfBydUaAjA0WFZRZJMQsTp06hQULFuhtnOLk5ITXX38dp06dMunczSpwaakSbUIModY5to1vzK2iL5Hm0lBTbsAy09CG1ivW1Le9N34+exeA4T6LPm5iBMokyC5WIk1Rgr6hPgCq1zn6uYshFAogrwoWcyhYJMQsZDIZMjMz0aVLF73rb926ZXLRS7OCxZYq0SbEEGqdY9uqp0RpGtocVBodKtVs4F1/NTR7fWPB4r1SJbKLK9Et2LPeY5qSWQSAmMhALP75H/i5SyCtZ21j1yAZsotzcblGsMitV+SykYEy9r+K4krqzUmIGYwfPx7Tpk3DRx99hAEDBgAAjh49itdeew0TJ0406dzNbp3TEiXahBjSaOucgpvA7ZNA5GhAWQwInQAXWcsN0MHRln/mVXMKt74ATspncxsO0Cdu+BtXc0qx++VB6B5iOGBsarAY5OmKQ68N0dtBprauQTIkp+XiclYxf11unWCRzSxWqnUortTo7T9NCGm+jz76CAKBAJMmTYJGw/4+Ozs7Y9asWXjvvfdMOrfRfRY3b96MwsJCrFy5EpcvXwYAdOvWDc8//zw8Pev/9kqIsapb5xi4sTQH+PIRoDQbOLWJDRplwcDMI4CEvtC0BNryz7y49YpSsQhO9Wzl19QK9Ks5pQCAbScz8XZID4PHNDVYBID2vm4N3t41iP2SphcsVk1D+3uwwaKLs4jfbzq7uJKCRUJMJBaLsWbNGiQmJuLatWsAgA4dOkAqNb3pvdF9Fi1Zok2IIXyBS+1paHUF8NM0NlAEgJtHAa0KKLgB7JwJ7J4L5FwBrh0EjqwGKosBVRmgo6DGnFwos2hW/L7QDWzj15Rq6JrBe3pV0GhIU9csNkVkEPsFLU1Rwu/pXnsaGgC/bpEacxNiPlKpFD169ECPHj3MEigCJmQWuRLtjRs38pU3Go0G06dPx5w5c3D48GGzDJAQDld8rzcNXVEAfDMWuHsacJYCg18DTmwE2vYDLu0Cruxmj7vyG1CeB+g0wOEPAVUp0CkOeHp7iz+P1oqmoc2rpJHdW4Ca09D1v+ZckAYAl+4WQ6djDLafak5msTGhvm6QOAlRrtIiM78coX5uuFdVDV2z3U6ATIK07BLcKig3+TEJIZZjUmbRUiXahBiiqYoWnWt+0P21ig0UXb2BZ34EHkwA5l0GnvoK6D8L8GwHyELYrKNOAzi5sIEiAPy7F/j3D+DKHkBLRRmmomlo8ypRNp7pa8o0dG6NYLG4UoPLimKDx5U0sC90czmJhGjjzTbd5rYGzC1h/8tNQwPAfe28AQDfn7wFhqtgI4TYHKODRa5EuzZzlGgTYohay36Y8Ou3lCVA6lfs/4/+DAgdqH+HEe8Bcy8Az/4EeAQBoQ8CCZeB5/8AevyHPebbp4BtE4G9C1voWbReLrYSLFYUAsc+ZTPMOZf1b1NXAMfWAl88Avw6B7h9qrrMXqcDrh8Ckt8Hfl8A7F8C3DpZfV+dDji6Bvjjv0Duv8C57UDhLf3zp/0OpCeZ5WlwvRNr7rtdW1Omoe+V6De9/r/ka3rFM+ySDB3KmhCcNge3u0teGfv4XGbRv8Y09KTo9nBxFuLc7SKkXKNNHgixVUb/VbBkiTYhhnDV0E5CAaC4wH7oK4sA3wigY2z9dwzoCsy5wFZHCwRAuwcAqR9w4UcAVYHCqS+B+yYBQT3ZD8/DHwEdHwHaD7D8E2slpM7snxOzTUMzDBvciWutuSm4CSS/B+RcYqvde44Huo0BnFzZ9aq/vgrkX6s+PigKeHA+UHQLOPoJUKpgr799AkjdDPh3ASJi2ECv5v0ANujs9TRQlst+Obl5lL0+5VP2vx7BQM//ANeT2fOcr1rW0Ps5ds3s/dMAiQzITAE6DAX+fAvwDgUeWw0IRICTuPq5CgRAWR7g4gmInPig28Wp/mCxKdlcLkjj/HY+C9GFu/Fs0F1A5Ayc2QpEDEOheh4AmK3QhJtuzitVQaPV8esSa2YWfd0lGN+3Lb5KuYlv/r6JATUafhNCmiczMxNt27at0zGEYRjcunUL7dq1M/rcRgeLlizRJsQQtZadhg7MOw78OgPQVWVHBrwCCBtJkotqfQD6RQAjPwSyLwLFWcDVP4DPBwPhDwHeYWwQcepLNgi59DPQ4yl2ittDboFn1jq4itl/g2ZlFsvz2UCsPB/w7wxIfdgM3tV9wB+LgPzrgLwHG2D5dwE0SrbaXVWjUCPjMLDndUAoYlsmAYCsDeDfCcj4C8g6B3z/XPXxnu2AB2YCWefZf9vcK+wPAIg9gC6jAFkQm5X8dy9w5pvq+wqd2S8nuZfZ4LTkLpttBNjH4XD3uXm0KnPJsGtlAeDW3+z7raIQ6Ps8kP0P+zw7DgfOfQsEdgPGbYJnznkIIUE4cxO4LQTa9Kn7mjtzfRbrX0bBrVmc2K8tojv44ZNtv2JizsdATo3m6Vf34WGpP/7ECAR4uNR7LgDsko2D77DPqd8MtuuAAb5uVZnFUiX+vp6PUqUGPm5ifocXTkxkIL5KuYlrufUX35BWSF0JlOUAXsYHMOa0bt06fPjhh1AoFIiKisLatWvRr1+/eo//4YcfsHjxYty4cQMdO3bE+++/j5EjR/K3MwyDpUuXYuPGjSgsLMTAgQPx2WefoWPHjhZ7DmFhYcjKykJAQIDe9fn5+QgLC4NWa/wXeaODRUuWaBNiiFbHYIjwDPoeX88GiqEPAn2mAN3HGXfCfvHsf4vuAN9NABTn2QwRktnrK4uA1C3s/5/4HLjwAzvd3TnOpOfRWhkscKksZgM8RscGglzQXnQH+GU2cO1A9bFiD+D+54HLu/UzfIoL7M/lX6uvazcAiH4JuJcGnP4GKMioPkf3scDQxYC7P5upO7qaDd58woHezwK9nq3O6I38APhnJ3DjKNA+ml2ewLVaYhjg+Ho2mJP3ZLPYETFAUC82KK0sAjaPZDOOfaey752oiYBWzY7V1Rv493f2XLI2QPFtIPg+IO8aW5gFsF9IOGf/V/18192PEQB+FEcg6mYG8IUOGLKQzaZGTQQ6j2Bfc37NYv275lTvnCLBfW3c8ZrT9xBBB0beE4LAboCbP3DsEzxdvhWDxPtQqf4GQGC958P+JcDf69j/P/oJm4GPfRfw7aB3GLe7y70yFX67kAUBdIiLDKjTBijEi13beKegwjaac6srgaLb7Psl5xLg5kdfEjkMwxYKit3Z311GBwT3Bm78xX6JyrsG3DrOZvyv/gmInAD3QODmMSC4F6BRsa+t1If9ghYUBUz/09rPCtu3b0dCQgLWr1+P/v37Y/Xq1YiNjUVaWlqdwAsAjh07hokTJyIxMRGPPvoovv32W4wePRqnT59G9+7dAQAffPABPvnkE3z11VcICwvD4sWLERsbi0uXLsHFpZEvZEaq7/entLTU5McUMA6+qvj27dto27Ytbt26hTZt2pj35Ge/Y3+ZaqrzD2ngD6PFjmnK7QLbOabgJpvBkciArHMounkOnkxV5qhdNPDcLsDZjL90d1KBLY8C6nIgpA+bWdIo2Q/py7+wH+IQAI+vBe57rtHTOZSyPOw+exOzf83CkM7+2PJMd3Y6+ML31cdIfYF+LwAlWex0rbqqAtbFCxCJ2SwDR+zBBmB9plRnf++eZlsi9fgP0GlEdTZZpwOyLwAiCeATBjjV3a/YYtSV7PvW0GPqdMDJL9igsftYNugM6Mp+WOZcYqfY98xnP2Q7xQJpe9lM9rG1QKkCOogghIFMgLMbW8BVUYBT7g/hyQ2nEOorRfJrDxsc4qz/peL3iwpsHFCImH8WQqAshpYRQDXjCFxDugMMA93BRJQd+gQeggqoA6LgPPMgm6mtqTQX2L8YOPcdezm4N3D3DPv//l2BFw7pvQ7f/H0Ti3ddREzXAFy7eQsfa1aguzgHTj3HsZ0IImIAJzEq1Vp0WbwXAHB2ySPwklYF8loN+wXt3r/AoLkt02C/PB/46nH2/eTsBqjL2GxytzHs6yHvwX4ZEDoBvZ9h/21tSWUR+55rF214CY1Oy67TFQjYYE1dAURNYJc+AED2JeDij+xzPr+dDQw7DAUKb7KFggU32dcGAvBLeEwhawO8eo4NKs2ouZ/r/fv3x/33349PP2WXl+h0OrRt2xYvv/wyFi6su559/PjxKCsrw+7du/nrHnjgAfTq1Qvr168HwzAIDg7GvHnzMH/+fABAUVERAgMDsWXLFkyYMMFMz5SVkJAAAFizZg3i4+P1knZarRbHjx+HSCTC0aNHjX4M8/4LEX2/vFw9VUpM5glAzYiQ1XUK2o1927yBIsAGiOP/x05zxr7DfovWaQC/jsDAV9kP9tNfsxkxjyCgY4x5H9+W6XSAqqT6QwVgg4eTG4EbR4DMFIyECC+JxsCpqAOw8eXqqV1OeR6Q/G715ZC+wJj17OurVbPrEDNTgB5PstP+Enf2uFoZqzqEQjZDYQ0NvQeFQqD/jOrLQT3Z//qEsT8Au9uQUMR+eA9+jb2u97OAsgTf7PsbvS6+i9LgQRgo17FT1O6BbLC99UkAQFi3KRgi9AdT6QfAcLB4r1QJMdQYdOVtCJTFKGVcsUYzBlPdOsAVAAQC5N0/DyP3tUeSZD5kOefYAp9BcwDPqg9arQbYOq56qn3oYmDwfLZ/6VePsl/q/nwLGL6CDzL93Nig7/TldHwlfg89hDcADYDTX7E/YYOB536Gi7MIfu4S3CtV4nZBBbzUOcCe19j3ApeBvXsaePqH6oxwTcpSNpu1dxG7lGHUKjarXJuqDChRAH9/Blw/WP3aS33Z+535H5sBK77DHq8uY7/EaFXVX3q4QBlg36++4UCvZ4D+L9R9vJvH2NfRtwO7RrroNvt3peb7uaKQHdeh9wDFRXYta+33cmUxsO1p9stscG+2o0OPJ9m1tBythv1S9cvL7AwJwAZ3AiEw4gP2/VZZBBz5mF1aUdOBd9ikhsSD/cKmq7Wk4dKuqn+DMzWuZAD3qmxrqQIIiATyMwBnV3YNsaoEaNOPPa40BwgdxAapzq5AYHeg/B57H5/wxpMaJigpKUFxcXX1v0QigUSi/8VOpVIhNTUVixYt4q8TCoWIiYlBSkqKwfOmpKTwARonNjYWu3btAgBkZGRAoVAgJqb6M8LT0xP9+/dHSkqK2YPFM2fYfxuGYXDhwgWIxdW/J2KxGFFRUXzQaiwKFi2p4yP6v3h1krgGvplZ9Zim3M603DGu3uwfTmUJ4BOOWUck+CtXivV9Hka72kUP5hIxjP2pzUkCPPYJ+/+nv2arp8OOVX946XTA0Y8Bz7ZsFuLqPjZ75OrDvg9sZF1OvXLT2KCv1zPVARDDsB9Q6nL2w7vgBvDkJvaDr+QuOx1ZWN0RQQgdXnP+HiisukLqBzz1NZvh0GnZAOH6QTbQ7jIKCHuo+oNC5AwMW9ySz9g2GMqouHoBrl64LinCUtXbeDk8AgOHdwZGrWQDmfWD+Kys7z9bsEUMqDROQN4jBgPrdoUnMcYpGa7ldwF3OeIqV+G2EhhdrkJw1RRwTkklcuGFz0RPY4HuC/ZLwLltwNQ9bJCRvp8NFF28gGd3VK+fDOgCjPwI+GEyOzV962+2hZXUB77uEoQKsvC183toJ8xFsdALsjEr2XWc57axa01TPgUGvIwQb1fcK1UiPbsI4b8+D6mCrULXuvpApFGyU/xr+wAPvcYWogHsF4wfplT3UgXYZQnpfwJt+gJD3gBC7mODvcu72eb8morqY//6yPC/iVsA8MwPbADl35kNWm+dYLOJd06zfwvu/cv+fmedq/7xCWcDpUs/AxmH2E0AdOrq4A1gA7qHFrDnvfFXVaBWI3u8eWTV0pqxbDD1zy7g+GfVQTpXYHUtiQ2Qg6IAeRSwayY7JoCdiVGWVAe922oVnIok7NS6ewB7XF46e72qhP1vRAwbQAdFsZnwzOPsF7qCG2zw3OtpdsbFvWp6tqKQDc61arZoq77141YoFoyMjNS7vHTpUixbtkzvunv37kGr1SIwUH/pRWBgIK5cqfWFt4pCoTB4vEKh4G/nrqvvGHM6ePAgAGDq1KlYs2YNZDLzZ+EpWLSkid81fgxpsn+PJKMUZXAyuN9fCxAIgOFvs1WzeVeB3XPYzMS9dLYJ+NHV7HFH17AfChyxOzA9if1grU1ZwgZSrl4t8ATqobhYtfauiA0YH/+ELd44sLy6eIOz7Wn9y95hbNY1fAjST+5F6dGNcHfSIaL/KODBeezaJIANiu6fxv6QJuHWfvJ7MDu7sMHgrKNsJunct2ymCIBYoAGz/VkInKXs6x7ci19GsbJySfVf+odeh/SIO1BaisLy6lmPnGJ2XeMh2RNYMLQ/cOwTdtnFhof0l9I88lbdQptuo4HyVWxm8U4q8PNsYMJW+IlV+NL5I7QT5uKmLgB7uq3BrB4j2ayYvAcbvO1fDKRuRozLLIxwOoTOu85DKsyEWiTFNPVruKbuioP/cYJ453SgKJPNnDEM++Xi2oHqQFEgYoOYu2fY372Mw+x7WegMaGu0DhJJgMBIoOcENqBz8WKzlnnX2OnYro8D7frrZ9AjYtifmnRaNghM+x049D5wdit7/YG3ofdFuMujgFd7QFPJPl5eOjszUZtfJ/bLVeYxNoBOWccGdGW57O0uXux6aWUx+++S8mn1emqO2J2dfo57jx1DiQI4v43NmIokbFZa7MZmL9s9wN5Ho2L/zVy9gcpCNhAO7q1/3g5D6463Ji6LW7uI0AZcunQJISEh/OXaWcXWZvPmzRY7t9HBoiVLtAkxhGud42ytYBFgP0SGvw3sfKH6AwJg/8hzsi+y650iH2czEffS2Gmm/2zRP5eqDFj/IJsBeGAW296l9rqsEgVQfJfNFkh9qoOv2sryAMU5dmq39jlunWQ/WAUCIPIJNqsBAHfPAvveZDMnXAb8nx3sj4snm1EC2CxDm35sYHxlN7ue0L8zW6Ec9x7/YVEaORGjD7ZDiNQVR2Mb+YAhjaqsKlrhg0WOTzj732FLUdn+YTy96TS2iVdAnHOJvX7nTPZDvywHjEAEAYBUXUd0HzASkvsmwSuV3TShoLy6pU5OVcPsQE8XoOdTbHC0cShbOOTixU5Hh9wH9J5keLD3T2OzeV/EAGm/AW8HIEzoBIGwHHcZH4xTvYV57btXH99nKrs+7vTXQP51vIzX+E8jHSPAG8pJOKzpDKh0OOXUGwPmXWH7W576Evj1Ff3HfuobNnvv7Mpm+O+lsV9yzn2nHygOeBmIWV6d+XpgJvtfrm1RcwhFbFAV3Jv9Xbh2kF2Tevc0+/sxZAGbmWs/qPrxiu4AyYns73Pm3+w0d8wydvMAJwn7O3jpZ/Z37J+dbKAoa8MGwb2fYX/fADaobXM/m+3N/ocNkAO6Ac/t0C/E8evITv/eH89OQ9cMgDlOYrawq5Xy8PBoNMvm5+cHkUiE7Oxsveuzs7MhlxsubJLL5Q0ez/03OzsbQUFBesf06tWruU+jWZKSkpCUlIScnBzodDq92zZt2mT0eY0OFi1Zok2IIVxTblFjbXIsLWoCOwVz8F3223zxXfYDSt6T/clMAcZuYD88FReB9QPZ6aSKJ4CujwH3T2fPk/J/1VW8R9ew/e4eXcUGdADb+Hnj0OrpIQiAIYvYDyKOTse2MTm2lv1gdPMHhr8DRI1nb7+4A/hpevVU18F3gPCH2Q//c9uq19S2i2Yfd+8iAEx1oDjgFXYdGlC9OF7enc1Q1MJVQ1fSdn9mUZ1ZrOf9LhBAEjEYF0XleFPzPJaHp8GlMpddP1gVIwkYLa7qQjBFtxjnYx8HBAJ4SdkMkKHMIt82R+rDNq+/nsxWXjeluCQoip2S3jMf0Kog0KpQyTjjJdWruAdPdPB31xs7Rn0ExCwFdrwApP2GAsYd72iewRldBK4x1dmgw//ew4AOfsCI99mMfsZfbLDkHsBO10Y+Xn1eoZCdOh2zHhg4hw3q3APYKVK3eno4mrpmrvs49kenZb+UBUQCniF1j/MMAZ6o6s9ZWcwGgzWXDYicq9brPskGePf+Zf/WOLvWPVe30ewPw7BtlzzbGC6yEgjYLDOpl1gsRp8+fZCUlITRo0cDYAtckpKSMHu2gSwwgOjoaCQlJWHOnDn8dfv370d0NBt4h4WFQS6XIykpiQ8Oi4uLcfz4ccyaNctiz+Wtt97C8uXL0bdvXwQFBZm1s4DRwaIlS7QJMYTb7s/JwL62La7D0OrpmdJcdkqwx3/q9pyTd2eDsEs/sx+815PZhee+EdVTvA+8xK5xzLsKfD+JLXRoFw3sW8wGihIZAAE7TZz8LjtNFj6EzUzueb265YpExn4A7ZzBZiY0FVWtgMAGiE4SdnvD6werx9flUTYY5LJVPceza7Nun2QXpvd8qvpYoYh97HpIm7CbCGk6Luh2rZ1ZrEEgEMBHKsb3xQ/j2dg30VOcxe5O4yIDHvsEd07vQfzZSPj4yPi/195V1caFNTKL2VWZxQBZjYDDI7D6S0dT9ZnMBnAVhYC6HCPXncV1hg0Sw/3rfsGAxAN46mucO7AdM5J0yEbdzPnW4zfBMAz+07cNIp7dyWbkDGXJajO07MOShCI2w9kULrKGA/DQgXV3pDJEIGi8AIw0KiEhAZMnT0bfvn3Rr18/rF69GmVlZZg6dSoAYNKkSQgJCUFiYiIA4NVXX8VDDz2ElStXYtSoUdi2bRtOnTqFDRs2AGB/L+fMmYO3334bHTt25FvnBAcH8wGpJaxfvx5btmzBc8+Zv1tHs4NFrgJIIBBg8eLFBku0LZ1mJY6J38HFmtPQhrj7s+v26vPoarYv4O0TwMWf2HV/Qid24X1QL3Za+5G3gD/eAE5sqG7eDLCZwplH2Q/uX+ewzcJ/nMa2Evn7/9gpbIGQbefT4yngyCq2SpPr7wcB2/h55Ifsh1nBDXbqT1UGdB7JVqTW/NLHTXMbKvJphEuNPos20S/PzjUlWAQAbzcxFMWVKChXA226sq1IxG6Aswt+ywrDjTNXMCKoOjDhMosFhjKLMjN80Zd48L0qbyjT+at93QxUMgOAyAni7o8hO+kvAGzz8N3nsvD0A+3w+aHrKKnU4PPD13GnsAKfPn0fIGpCoEhIM4wfPx65ublYsmQJFAoFevXqhb179/IFKpmZmRDWmNEaMGAAvv32W7z55pt444030LFjR+zatYvvsQgAr7/+OsrKyjBjxgwUFhZi0KBB2Lt3r0WTaSqVit9Rz9yaHSy2RIk2IYbwe0Nbexq6uaQ+7PoozfOAqpwN5LQqtqn42I1Va5qqWlwERbEBZcENdiePwa+xgSLAtt24ewbIOgvs+y97nVc7YNTH1W18hixks44Zf7Hb5HUeWd2mBWDXPQ1bYpmnWWMPY6VGV3etHWmWetcs1uLNBX9lVZlCN1/+tn/usm1DuodUB1hefGaxRrBYwk1Dm7cAQFej1qOhLw9tvKunWl8cEoHEsWyboRMZ+TiTWQgA/H8JsYTZs2fXO+2cnJxc57r//Oc/+M9//lPv+QQCAZYvX47ly5eba4iNmj59Or799lssXmz+zhLNDhZbokSbEEM0Vdv9WbXAxRROYuDpbWyD6bIctt9YzcbHAgHbY6/3s4bvL3YDJv/KtinJPM72wRvwct01Te0eqK52bEE1g5pylZaCRRPVqYauh3dVxi6/TFXntot32LWn3YKr/05782sWaxS4FFdNQ5s5WGwqDxdnrHoqCjoGaOtTPVu18j9R2HMhCx/t+xdZRRWoVNP7ipD6VFZWYsOGDfjzzz/Rs2dPODvrV6ivWrXK6HMbvWbRkiXahBii0XEFLnYaLHJkQeyPMVxkbJ87ndbsux6YSiQUQOwkhEqj09/yjxiF22O73gKXKoaCPwAoU2pw/V4ZAKBbcM3MIjcNzR7PMAxyS804DV3D8ie6YcnP/+DDJ3s2euzY++rutBHu746XHo7ApqM3kF+mwtXsUvRoQ9PQhBhy/vx5fhngxYsX9W4zdVmQSZ82lirRJsQQDd86x86moc1NILC5QJEjFYvYYJGKXEym1FStWRQ3nEnzqZpWzq8VLF5RFINhgECZBP41Mob8NHQFOw1dXKnhl3jUu67QSJOiQzGqRxB83Y3PWAoEAnSRe+DYtTxcVhRTsEhIPbiZX0sw+lP3rbfewvDhw5GUlIR79+6hoKBA74cQc2IYhi9wsfvMYivGFWNQsGg6PrPo1LRp6JoFKwBw8U7VesVg/eDKu9aaRW762k0sssgUrymBIqeLnJ1Gv5JV0siRhBBLMDo9YckSbUJq09RYKe9sbwUuDsS1RkU0MR7DMKjUsLM1jWUWueCvoNaaxdsF7JaAtVvWeNWYttbpGOSXsVPQPu7mzSqaUxc5W119RVHcyJGEOLa//voLn3/+Oa5du4Yff/wRISEh+OabbxAWFoZBgwYZfV6jP3UtWaJNSG0abXWwaHOtcwiPC2woWDSNWludSW9qZrF2gQuXafRx08/sccGijgFKKjXIK1UZPM6WdAlig8XLWcVgGtvTnhAH9dNPPyE2Nhaurq44c+YMlEr2i2BRURHeffddk85tdLDIlWgT0hI0NdbE0jS07aqehtZYeST2rVJTHWy7iJta4KI/Dc1lGn3c9CsiJU4ivs1RfrmKDzLNvV7RnDrLPeDiLERBuRrpOaXWHg4hNuntt9/G+vXrsXHjRr1K6IEDB+L06dMmndvoaWhLlmgTUlvNzKLDF7jYMMosmkdl1XpFgQAQN/J+965R4FKzGTpX8MLdXlO4vxsu3inGuVuFyOODStsNFiVOIvRp742j6XlIuZ6HjoEe1h4SITYnLS0NgwcPrnO9p6cnCgsLTTq30cGiJUu0CalNXSOzSIlF28VlFmnLP9NwDbldnUWN/j3lgjyuZZFUzP5ZL2ggCBwY4YeLd4pxJP0ePF3ZL/q2nFkEgOhwXzZYvJaHSdGh1h4OITZHLpcjPT0doaGhetcfOXIE4eHhJp3b6GDRkiXahNSm5dvmCOjLiA3jM4sULJqkqQ25AbZdkVgkhEqrQ36Zig8WuellLwOZxUERfvj80HUcuXoP0R3YHV9sObMIgB/n39fzoNMxENK3RkL0xMfH49VXX8WmTZsgEAhw9+5dpKSkYP78+Sbv6mKbzdoIqUVjr1v9ORhuLVwlTUObpKn7QgPsTI63mzOyi5UoKFOjjTeg1upQXMmuGzUUBN4f6gOxkxCK4kqcyMiv9zhb0iPEC67OIhSUq/FvTgnfTocQwlq4cCF0Oh2GDRuG8vJyDB48GBKJBPPnz8fLL79s0rlN+uT966+/8OyzzyI6Ohp37twBAHzzzTc4cuSISYMipDaudY4TZRNsmgtNQ5sFl1mUNLJ7C8e7VmNurthFIAA/zVyTi7MI94d6AwDuFFYAAHxtuHUOAIidhOhRtcf1v9lU5EJIbQKBAP/973+Rn5+Pixcv4u+//0Zubi5WrFhh8rmNDhYtWaJNSG3cvtDUNse2UZ9F82hOZhGozgpy6xS5rfy8XJ3r7R4QHe5b6xy22zqH086X3Tc6M6/MyiMhxHaJxWJERkaiX79+cHd3N8s5jZ6G5kq0J02ahG3btvHXDxw4EG+//bZZBkcIh9uOzIkqoW0aTUObR2Uz1iwCgF/VLin3qvZ45tYrejcwtdw31Efvsq0XuABAO5+qYDG/3MojIcQ2JCQkYMWKFXBzc0NCQkKDx5rSpcboYNGSJdqE1KalaWi7QNPQ5lGzGropuCnke1UNtvlKaAPFLZyoNl56l219zSIAtK/KLN7Mo2CREAA4c+YM1Go1//+WYnSwaMkSbUJq41rn0DS0baNqaPOoroZuWia9TmaxvPHMoqtYBD93MR9gShvZVtAWtK3KLN6izCIhAPQ701iyS43Rc3pcifbx48f5Eu2tW7di/vz5mDVrljnHSAhVQ9sJKTXlNovmT0OzQWFeVbDYlMwiAIT7Va9nsoeWVO2rgsWs4kooNfQeI6SmxMREbNq0qc71mzZtwvvvv2/SuY3+5F24cCGefvppDBs2DKWlpRg8eDCmT5+OF154weQSbUJq47b7o2lo21a93R99kJuiopkFLr5uXGaRK3Bhp6UayiwCwOjeIQDY/qX2wMdNDDexCAwD3MqvsPZwCLEpn3/+Obp06VLn+m7dumH9+vUmndvoaWiuRPu1115Deno6SktLERkZabbKG0Jq0lCBi11woWpos+DWLDY5s+jBBot1Motuddvm1DTh/rbQMQz6tPc2dqgtSiAQoJ2vGy5nFeNWfjkiAujzhhCOQqFAUFBQnev9/f2RlZVl0rlN/uS1RIk2IbVRgYt94HYPocyiafjWOU1cR8hVMt8rZfeHbmhf6JqEQgGefaA9ugbZT4Prdj6uAICbTWyfU1CmwvaTmTRtTVq9tm3b4ujRo3WuP3r0KIKDg006d7Myiy1Vok1IbWrqs2gXqM+ieXDBtotT8wpcVFodSpQa5JawGcbGgkV71N7XDQBwPCMfUwaGNXp87OrDyClRQq1l8OwD7S09PEKsJj4+HnPmzIFarcbQoUMBAElJSXj99dcxb948k87drGCxpUq0CamNdnCxD65U4GIWfIFLEzOLrmIR3MQilKm0OHE9H//cLYZAAHSWe1hymFYxqkcQvvjrOn6/qMDu83cxKMIPW49n4pn+7ersg516Mx85VYHz4X9zKVgkrdprr72GvLw8vPjii1Cp2NkFFxcXLFiwAIsWLTLp3M0KFluqRJuQ2qqDRVqzaMu4YJH6LJqGb53j1PR2Nn4eEpTllWPl/n8BADFdA/lWM61JVFsvvDgkAp8eTMeK3ZcwrGsgvj2eibxSFZY8Fskfp9Ux+Hj/Vf4yY43BEtKCBAIB3n//fSxevBiXL1+Gq6srOnbsCInE9N2ZjP7ktWSJNiG10XZ/9oGbhlZpdPw6U9J8fFPuZvQ+5NYtXs4qBgBMHRhq9nHZipeHRcBNLEJ2sRI/pt4GAJy8kc/fzjAMFv50HkfS7/HX3S6g6mniGNzd3XH//feje/fuZgkUAROqoT///HN8++23da7v1q0bJkyYgAULFpg0MEJqqu6zSMGiLavZ2LlSrYWbxOg/MQ6tsplNuYHqdYsA0EXuUWfv59ZE4iTC4E7++P2iAioNG1hfyipGmVIDN4kTUq7n4YfU2xAJBXjp4Qh8knQVdwqokTdpfWx+uz9LlmgTUhs/DU2tc2yapEZBRrmKgkVjVTazzyIA+NYIFp8fFGYXTbZNMaxrIH6/qOAva3UMzt0uxIAOfjh7qxAAENddjhcGh+OTpKsortSgqEINT9eG2wkRYk9q1pKcPn263t97U/8eGP2XnCvRDgvTr0YzR4k2IbVRU277IBAI4OosQoVaywc8pPm4NYuSZgSL/lW7uPi6ifF4VOv/G/xwZ38IBADDAGInIVQaHU7fLMCADn64dJediu8e7Ak3iRN83MTIL1PhTkEFBYukVVmzZg1kMrb1VXJyssUex6Tt/ubMmYPNmzfj5s2buHnzJjZt2oS5c+ciPj7enGMkhJpy2xHa8s90xmQWH+rsD3eJE+YN79zkZt72zNddgjG9QxDs6YL4B9mkxfZTt7DlaAYfLEYGsx+iIV5sb8Y7hbRukbQuvXv3xr177Nrc8PBw5OXlWeRxjM4sWrJEm5DauMyiM2UWbR4XqFBFtPGau4MLAPRp74MLy4a3+unnmlY91QsAcDW7BOsOXsOt/Aos+/USf3tkVbPxNt6uuHCnCLdp3SJpZby8vJCRkYGAgADcuHEDuqrPSnMzabs/S5VoE1KbuiqzKKJg0ebxvRYpWDSaMZlFwPR1SfaqY6AHdr88CJ8kXcW+S9kAgAAPCfyrtkHkMotUEU1am3HjxuGhhx5CUFAQBAIB+vbtC5HI8N+N69evG/04Jq8+50q0CbEkLRW42I3qaWiNlUdivyqMqIZ2dN1DPLFgRBc+WPRwqf54a+NdNQ1NwSJpZTZs2ICxY8ciPT0dr7zyCuLj4+HhYf5m/LTdH7ELfJ9FyizaPG7qtEJlmemQ1o5hGKMzi46ug787fN3EyCtTIbpDdeugdr5sc/KMe03bT5oQe3H+/HkMHz4ccXFxSE1Nxauvvmr9YLGlSrQJqU3NZxbpvWXrqMDFNCqtDlw/8+ZUQxPWLy8Pwv/+vokXBofz13UMYD88r98rhVqrgzPNUJBWonfv3sjKykJAQAAOHTrE15CYW7OCxZYq0SakNm4amv7I2z5XPrNI09DG4IpbAMosGiPEyxUL4rrUuY7bO/tmXhkiAlrfntmkZeTn5+Pll1/Gr7/+CqFQiHHjxmHNmjVwd3ev9/ilS5di3759yMzMhL+/P0aPHo0VK1bA09OTP85Qku27777DhAkTGhyPTRa41Ixgw8PDcfLkSfj6tt5dAojtUFdNQ1OBi+3jg0XKLBqFm4IWCgBnyqSbhVAoQESgB87dKkSaopSCRWK0Z555BllZWdi/fz/UajWmTp2KGTNmGNzRDgDu3r2Lu3fv4qOPPkJkZCRu3ryJmTNn4u7du/jxxx/1jt28eTPi4uL4y15eXo2OxyYLXFoqgl23bh0+/PBDKBQKREVFYe3atejXr5/BY7ds2YKpU6fqXSeRSFBZWWmRsRHr4DOLFCzavOpqaFqzaIya6xVpSY/5dA50Z4PF7BKMQt3dxwhpzOXLl7F3716cPHkSffv2BQCsXbsWI0eOxEcffWRwQ5Lu3bvjp59+4i936NAB77zzDp599lloNBo4OVWHYV5eXpDL5c0ak00WuLREBLt9+3YkJCRg/fr16N+/P1avXo3Y2FikpaUhICDA4H1kMhnS0tL4y/QHtvVRU1Nuu8FlFsupGtooXEbWVUxT0ObUKZD9AP1XUWLlkZCWUlJSguLiYv6yRCIxqb1fSkoKvLy8+EARAGJiYiAUCnH8+HGMGTOmSecpKiqCTCbTCxQB4KWXXsL06dMRHh6OmTNnYurUqU2KZ7hspM0UuLREBLtq1SrEx8fz2cL169fjt99+w6ZNm7Bw4UKD9xEIBM2Oxol90dA0tN3ggpxK6rNoFK4/pcSJgkVz4oPFHAoWHUVkZKTe5aVLl2LZsmVGn0+hUNRJWjk5OcHHxwcKhaKee+m7d+8eVqxYgRkzZuhdv3z5cgwdOhRSqRT79u3Diy++iNLSUrzyyitNHt/mzZvx119/4fPPP8f169fxww8/ICQkBN988w3CwsIwaNCgJp+rtmYFi5Yu0VapVEhNTdXbAUYoFCImJgYpKSn13q+0tBTt27eHTqfDfffdh3fffRfdunUzeKxSqYRSqeQvl5TQHw57UF3gQsGirXOlamiTcAUulFk0r85y9rPqxr0yVKq1DrEloqO7dOkSQkJC+Mv1ZRUXLlyI999/v8FzXb582eTxFBcXY9SoUYiMjKwTtC5evJj//969e6OsrAwffvhhs4LFn376Cc899xyeeeYZnD59mo91ioqK8O6772LPnj1Gj71Zc3o19yC0RIn2vXv3oNVqERgYqHd9YGBgvVF7586dsWnTJvz888/43//+B51OhwEDBuD27dsGj09MTISnpyf/U/ubB7FNXOsckZCmoW2dK233Z5JKashtEQEeErg6i6BjgKwiWtPuCDw8PCCTyfif+oLFefPm4fLlyw3+hIeHQy6XIycnR+++Go0G+fn5jc5ulpSUIC4uDh4eHti5cyecnZ0bPL5///64ffu2XnKrMW+//TbWr1+PjRs36p1/4MCBOH36dJPPY4hNFrg0R3R0NKKjo/nLAwYMQNeuXfH5559jxYoVdY5ftGiRXkPxO3fuUMBoB7hpaMos2j4uWKykzKJRqCG3ZQgEAgR5ueB6bhmyiioQ5udm7SERG+Hv7w9/f/9Gj4uOjkZhYSFSU1PRp08fAMCBAweg0+nQv3//eu9XXFyM2NhYSCQS/PLLL3BxcWn0sc6ePQtvb+9mrbFMS0vD4MGD61zv6emJwsLCJp/HEJsqcPHz84NIJEJ2drbe9dnZ2U1ek+js7IzevXsjPT3d4O21F7jWXPxKbJeGa8pNmUWbx02fUmbRONVb/VGwaG5BnlXBYiFlFknzde3aFXFxcYiPj8f69euhVqsxe/ZsTJgwga+EvnPnDoYNG4avv/4a/fr1Q3FxMYYPH47y8nL873//Q3FxMR93+Pv7QyQS4ddff0V2djYeeOABuLi4YP/+/Xj33Xcxf/78Zo1PLpcjPT0doaGhetcfOXIE4eHhhu/URDZV4CIWi9GnTx8kJSVh9OjRAACdToekpCTMnj27SefQarW4cOECRo4cabZxEeuj7f7sB/VZNA23ZpGCRfOTy9g9ohXFFCwS42zduhWzZ8/GsGHD+Kbcn3zyCX+7Wq1GWloaysvLAbC73R0/fhwAEBERoXeujIwMhIaGwtnZGevWrcPcuXPBMAwiIiL4Yt/miI+Px6uvvopNmzZBIBDg7t27SElJwfz58/XWRBqjWcEiYPkS7YSEBEyePBl9+/ZFv379sHr1apSVlfHV0ZMmTUJISAgSExMBsBVEDzzwACIiIlBYWIgPP/wQN2/exPTp0806LmJdGtruz25IxeyflQrKLBqFMouWE+zFTv9lFVUYvL1CpYWzSEAtuki9fHx86m3ADQChoaFgGIa/PGTIEL3LhsTFxek14zbWwoULodPpMGzYMJSXl2Pw4MGQSCSYP38+Xn75ZZPO3exgkWOpEu3x48cjNzcXS5YsgUKhQK9evbB3716+6CUzMxPCGlORBQUFiI+Ph0KhgLe3N/r06YNjx47ROsRWRqPlClwoWLR1rmL295Myi8apXrNIAYu5yT2rgkUD09AFZSqM/OQv+LlL8MvsgdSvl9gdgUCA//73v3jttdeQnp6O0tJSREZG1rsVYXMYHSxaskR79uzZ9U47196T+uOPP8bHH39s9GMR+6DRcQUu9AFq61z4vaEpWDRGJWUWLSbYk52GNlQNveloBrKKKpFVVIncEiUCZI0XIRBii8RisdkTZkZ/8lqyRJuQ2qoLXOjbvq3jp6Eps2gUqoa2HD6zWGsaulKtxbfHM/nL/2aXtui4CDGXwsJCrFy5EtOnT8f06dOxatUqFBUVmXxeo4NFS5ZoE1KbRktrFu2FK2UWTcIF2RIKFs2OyywWlKv5oLxCpcX8H84hr6y6b/C/2bRZA7E/p06dQocOHfDxxx8jPz8f+fn5+Pjjj9GhQweTk3hGB4tciXZt5ijRJqQ2NV8NTdPQto4LFjU6hv93I03H7+BCwaLZyVyd+Nf1TiGbXXxj5wXsPp8FJ6EAHfzZ3otXaUtAYofmzp2Lxx9/HDdu3MCOHTuwY8cOZGRk4NFHH8WcOXNMOrfRn7xcifbx48f5Eu2tW7di/vz5mDVrlkmDIqQ2LU1D242a29TRVHTzVdAOLhYjEAjg6y4GAAxbeagqULwLANg05X68MqwjACBNQcEisT+nTp3CggUL4ORUXY7i5OSE119/HadOnTLp3EYXuFiyRJuQ2qpb59AHqK1zFgkgFgmh0uqQU6yEzKXhba2IPiWtWbSo9r5S3C5gs4rcOsUeIZ4Y3MkfVxRss+Sr2aVgGIYqooldkclkyMzMRJcuXfSuv3XrlsltDo3+5OVKtPPz83Hx4kX8/fffyM3NNbjFHiGm4qqhac2i7RMIBOgX5gMA2HfJ8J7upH7UZ9GynnugPfq290avtl78dU/2aQMACPNzg0goQIlSQ427id0ZP348pk2bhu3bt+PWrVu4desWtm3bhunTp2PixIkmndvozCLHEiXahNTGF7jQNLRdGNUzCEfS7+G381l4cUhE43cgPNrBxbLiugchrnsQruWWIm71YTgJhXg8it2qTeIkQqivFNdyy3A1uxRBVQUxhNiDjz76CAKBAJMmTYJGowHAboE8a9YsvPfeeyad26RgsbCwEF9++SUuX74MAIiMjMS0adPg6elp0qAIqU2tpb2h7UlsNzne3HUR/9wtRsa9MoT5uVl7SHaDqyKnNYuW1cHfHTtfHAihQABvNzF/fZifO67lluFmfrkVR0dI84nFYqxZswaJiYm4du0aAKBDhw6QSqUmn9vov0aWLNEmpDYtTUPbFR83MQZ08AUA7Dx928qjsS+VGlqz2FK6h3giMlimd107H/aDNTOvzBpDIqTZDhw4gMjISBQXs2tupVIpevTogR49ekCtVqNbt27466+/THoMo4NFS5ZoE1IbTUPbn6f6tgUAbDt5i1roNENlVWaxZlU5aTntfNip50zKLBI7sXr1asTHx0Mmk9W5zdPTEy+88AJWrVpl0mOYlFm0VIk2IbWpabs/uxPbTQ5fNzFySpRIupxj7eHYDSpwsa52vlWZxfyKRo4kxDacO3cOcXFx9d4+fPhwpKammvQYRn/yciXatZmjRJuQ2rg+iyLKLNoNsZMQ/6nKLu6gqegmo6bc1sVNQ9/KLwfDMFYeDSGNy87O1tt2uTYnJyfk5uaa9BhGB4uWLNEmpCaGYaoLXGjNol0ZGMGuW8y4R+u/moJhmBrb/VEW3RraeLPBYqlSg4JytZVHQ0jjQkJCcPHixXpvP3/+PIKCgkx6DKOroS1Zok1ITUpN9Xo3mpqzL8Fe7Pqvu4UV1OS4CWq+1ymzaB0uziLIZS5QFFfiZl4ZfGpUShNii0aOHInFixcjLi4OLi4uerdVVFRg6dKlePTRR016DKODRUuWaBNSk1JdI1h0og9QexJc1aeuTKVFcaUGnq60m0tDKmtsj0hfjKynnY8UiuJKZOaXo3c7b2sPh5AGvfnmm9ixYwc6deqE2bNno3PnzgCAK1euYN26ddBqtfjvf/9r0mM0e56jJUq0CamJm5YTCQVwpmlou+IqFsFLygaIWUVUMNAYbr2ik1BAxVxW1LbGukVCbF1gYCCOHTuG7t27Y9GiRRgzZgzGjBmDN954A927d8eRI0cQGBho0mM0O7PY1BLtBx980KSBEcLhsi0uTkKaxrRDQZ6uKCxXI6uwEl3kdf9ukGpUCW0buCIXbg9pQmxd+/btsWfPHhQUFCA9PR0Mw6Bjx47w9jZPZrzZX11bokSbkJq4JsX0AWqfgj3ZNTR3KbPYqEoKFm2Cjzu7TrGgXGXlkRDSPN7e3rj//vvRr18/swWKgBHBYkuUaBNSU/X2Z/QBao+4Ipeswkorj8T2VWcWaQramri1tUUVVA1NCGBEsNgSJdqE1MSt46IPUPsU5EWZxabiMotUCW1dXLBYSK1zCAFgRLDIlWhXVtbNEpirRJuQmmga2r5xFdF3CylYbAxNQ9sGr6pgsZgyi4QAMKLApSVKtAmpSUnZFrsWVLVmMauIpqEbQ7u32AaahiZEX7ODRa5Ee9asWVi0aBG/HZJAIEBsbCzWrVtncok2ITVRhah949csFlVSY+5GcOtzafcW6+KCxTKVFmqtjtoYEYdnVFNuS5doE1ITrVm0bwEyCQBApdGhuEIDTyk15q4Pt+SCMovWJavRPL6oQg0/d4kVR0OI9Rm9gwtQXaJNiCXROi77JnESQSwSQqXVoUxFwWJDqPLfNoiEAni4OKGkUkPBIiEwosCFkJZG09D2Typh/+3KVRorj8S2cXtDU2bR+mjdIiHVKFgkNo+moe2fm5idxChTahs50rFVZxbpvW5t3DaVRdQ+hxAKFonto2po+ycVs/92ZZRZbBC/5EJM73Vro8wiIdUoWCQ2j6ah7Z9UwmYWucwZMayCvhjZDAoWCalGwSKxeVTgYv+kzlxmkYLFhtAXI9vh6cruD027uBBCwSKxA9VrFukD1F65cQUuSpqGboiSmnLbDMosElKNgkVi86qzLfR2tVdSrsCFMosNove67aBgkRiSn5+PZ555BjKZDF5eXpg2bRpKS0sbvM+QIUMgEAj0fmbOnKl3TGZmJkaNGgWpVIqAgAC89tpr0Ghs58u1SX0WCWkJ/DS0E2Vb7BVlFpuGllzYDr4aukJl5ZEQW/LMM88gKysL+/fvh1qtxtSpUzFjxgx8++23Dd4vPj4ey5cv5y9LpVL+/7VaLUaNGgW5XI5jx44hKysLkyZNgrOzM959912LPZfmoGCR2Dx+ao4qRO0WZRabhtYs2g7KLJLaLl++jL179+LkyZPo27cvAGDt2rUYOXIkPvroIwQHB9d7X6lUCrlcbvC2ffv24dKlS/jzzz8RGBiIXr16YcWKFViwYAGWLVsGsVhskefTHDTXQWwetwUaTc3ZLzcxNeVuikpas2gzKFi0fyUlJSguLuZ/lEqlSedLSUmBl5cXHygCQExMDIRCIY4fP97gfbdu3Qo/Pz90794dixYtQnl5ud55e/TogcDAQP662NhYFBcX459//jFpzOZCmUVi8/hGxTQNbbe41jnUlLthNA1tO7hgkaqh7VdkZKTe5aVLl2LZsmVGn0+hUCAgIEDvOicnJ/j4+EChUNR7v6effhrt27dHcHAwzp8/jwULFiAtLQ07duzgz1szUATAX27ovC2JgkVi8/jMIk1D2y3KLDZNJfVZtBm+7uzUX36ZCjodA6FQYOURkea6dOkSQkJC+MsSieE9vhcuXIj333+/wXNdvnzZ6HHMmDGD//8ePXogKCgIw4YNw7Vr19ChQwejz9uSKFgkNo9vnUOZRbtFaxabhqqhbYe/uwRCAaDRMbhXpkSAh4u1h0SaycPDAzKZrNHj5s2bhylTpjR4THh4OORyOXJycvSu12g0yM/Pr3c9oiH9+/cHAKSnp6NDhw6Qy+U4ceKE3jHZ2dkA0KzzWhIFi8TmVVYFGFTgYr+oGrppaBradjiJhAjwcIGiuBKKokoKFlsxf39/+Pv7N3pcdHQ0CgsLkZqaij59+gAADhw4AJ1OxweATXH27FkAQFBQEH/ed955Bzk5Ofw09/79+yGTyepMpVsLfX0lNo8KXOwfl1ksp8xivRiGoQb0NkbuyQaIWUWVVh4JsQVdu3ZFXFwc4uPjceLECRw9ehSzZ8/GhAkT+EroO3fuoEuXLnym8Nq1a1ixYgVSU1Nx48YN/PLLL5g0aRIGDx6Mnj17AgCGDx+OyMhIPPfcczh37hz++OMPvPnmm3jppZfqnTpvafTpS2yaRquDWssAoGloe8ZnFmnNYr2UGh3//5RFtw1BVcGigoJFUmXr1q3o0qULhg0bhpEjR2LQoEHYsGEDf7tarUZaWhpf7SwWi/Hnn39i+PDh6NKlC+bNm4dx48bh119/5e8jEomwe/duiEQiREdH49lnn8WkSZP0+jJaG01DE5tWSR+grQKtWWxcRY3XxsWJvsfbAsosktp8fHwabMAdGhoKhmH4y23btsWhQ4caPW/79u2xZ88es4zREugvErFp3BouAJDQB6jdcuOmoWnNYr245RbOIgGcRPRetwXVmcUKK4+EEOuiv0jEpnHBosRJCIGAWlfYKy4rXK7WQqdjGjnaMVE/UdsTKKPMIiEABYvExvF952gK2q5xaxYZpjqDRvTxxS30XrcZQZ6uAABFMQWLxLFRsEhsGvVYbB1cnETgEsO0i4th1GPR9gTVWLNYcx0aIY6G/ioRm1ZJH6CtglAogNSZKqIbQru32J4AGdu2RKXR0bZ/xKHRJzCxadR3rvWg/aEbVlzBBiMeLs5WHgnhSJxE8Kva9u8uFbkQB0bBIrFpFbSjRatB+0M3rLiSDRY9XSlYtCUh3lIAwI175VYeCSHWQ8EisWmF5SoAgJeUPkDtHe3i0rCiCgoWbVFXuQcA4Iqi2MojIcR6KFgkNq2gKlj0loqtPBJiKtrFpWFcsChzob0SbEmXqmDx/O0izP/hHNYdTKf2T8Th0F8lYtPyy9gPUAoW7R+XWSylNYsGUWbRNnUJkgEADv2by1/3z90irJ14H0RC6v1KHANlFolNKyhjM4s+bvQBau/c+QIXyiwaUlTBvi4yChZtCpdZrGnPBQV+u5BlhdEQYh0ULBKbxk9Du1Fm0d5x09ClFCwaRJlF2+QlFfP9FgFgcCd/AMCXRzKo9yJxGBQsEpvGBYs+NA1t99wos9ggChZtF5dd9PeQ4KP/9ITYSYhztwqRerPAyiMjpGVQsEhsWn4ZZRZbC5qGblgJBYs267523gCAUT2CEODhgkd7BgEA9l/OtuawCGkxVOBCbFpBORW4tBZcZrGEgkWD+GpoChZtTvzgcLTxcUVcNzZI5DKNiiLaM5o4BgoWic3S6hi+z6I3FbjYPcos1o9hGJqGtmEuziKM6d2GvxwoY9cwUrBIHAVNQxObVVyhBtfOjDKL9s+dtvurV7lKC03Vm52CRdsnrwoWs4spWCSOgYJFYrO44hYPFyc4i+itau+4aWiqhq6Lyyo6CQWQimlrS1sX5OkKAMgqqqSKaOIQ6BOY2Cy+EpqKW1oFap1Tv5pT0AIBNXq2dQEyCQBAqdHx/3aEtGYULBKbRbu3tC60ZrF+xbRe0a64OIvgXbVfvYKmookDoGCR2Cxu9xbujzKxbzQNXT8uO+VBwaLdkNeYiiaktaNgkdisfNq9pVXxqJFZpHVe+qgS2v7Iq6aisylYJA6AgkVis2j3ltaFyyzqGKBCTRXRNVGwaH/kVVsA0jQ0cQQULBKbda+EMoutiVQsAle7QVPR+qrXLFLrW3shl7HT0NQ+hzgCChaJzUrPLQUAhPq6WXkkxBwEAgHcxNRr0ZDiSjZ4lrlQZtFeyD3ZaWhas0gcAQWLxCbpdAyuZpcAADpXba1F7B/XPocqovUVV1YVuFCwaDe4Apc7BRVWHgkhlkfBIrFJtwsqUK7SQuwkRKiv1NrDIWbiThXRBnHBs7sLTUPbi65VX2Kv5ZaipJJ6LZLWjYJFYpOuKIoBABH+7nCi3VtaDT5YrKRgsSYueOYqxontC5C5oK2PK3QMcPZWobWHQ4hF0acwsUn/Vk1Bd6Ep6FaFq4guU1GwWFNp1RpOdwoW7Uqfdt4AgFM3Cqw8EkIsi4JFYpOuKGi9YmtEjbkNK62axnSjYNGu9An1AQCk3qRgkbRuFCwSm5RWFSx2omCxVaEt/wzjp6FpzaJd6duezSyeySyAVsfgt/NZuJVfbuVREWJ+9JeJ2Jwb98pwNacUAgHQLVhm7eEQM6I1i4aV0TS0XeoU6AEPiRNKlBpsOHwd7++9AomTEO+N64G/rt7DohFd4e8hsfYwCTEZZRaJzfnuRCYA4KFO/gjwcLHyaIg5VU9DU59Fjk7H8JlFmoa2LyKhAL3aeQEA1h+6BgBQanSYu/0cdpy+gx9Sb1lxdISYj00Gi+vWrUNoaChcXFzQv39/nDhxosHjf/jhB3Tp0gUuLi7o0aMH9uzZ00IjJeZWqdbih9TbAIBn+re38miIublTn8U6ahb70DS0/enbnl23yG3ZWNPlrJKWHg6xsPz8fDzzzDOQyWTw8vLCtGnTUFpaWu/xN27cgEAgMPjzww8/8McZun3btm0t8ZSaxOaCxe3btyMhIQFLly7F6dOnERUVhdjYWOTk5Bg8/tixY5g4cSKmTZuGM2fOYPTo0Rg9ejQuXrzYwiMnpipTavDi1tPIL1MhyNMFD3f2t/aQiJnxmUWqhuZxU9BOQgEkTjb3J5k0ok/VukXO2om9MXVgKAAgraoFGGk9nnnmGfzzzz/Yv38/du/ejcOHD2PGjBn1Ht+2bVtkZWXp/bz11ltwd3fHiBEj9I7dvHmz3nGjR4+28LNpOpv7Grtq1SrEx8dj6tSpAID169fjt99+w6ZNm7Bw4cI6x69ZswZxcXF47bXXAAArVqzA/v378emnn2L9+vUtOvbaVu5Lg1bHAACYGtczjP5xTM1b69xW835Mjf9v6Lh6zm3gfvq31Th/E8fb1HHUfmI1b9MxDHJKlDiZkY8ylRYuzkJ8+GQU9Vdshbyl7D7f90qUVh6J7ShVVldCC7jNs4nd6NXOC0IBoGOAQJkEj/YMQt9Qb2w+egPXcsug1GghcRJZe5jEDC5fvoy9e/fi5MmT6Nu3LwBg7dq1GDlyJD766CMEBwfXuY9IJIJcLte7bufOnXjqqafg7u6ud72Xl1edY22FTQWLKpUKqampWLRoEX+dUChETEwMUlJSDN4nJSUFCQkJetfFxsZi165dBo9XKpVQKqs/qEpKLDdNsP7QNai1DURnpI5QXyneH9cT/cN9rT0UYgFtvKu2SCukLdI4JVXFPlTcYp/cJU7oGiTDP3eL0TfUBwKBAHKZCzxdnVFUoUZ6Tim6BXtae5gOqaSkBMXF1dldiUQCicT4gqOUlBR4eXnxgSIAxMTEQCgU4vjx4xgzZkyj50hNTcXZs2exbt26Ore99NJLmD59OsLDwzFz5kxMnTrVZr5A2tRfp3v37kGr1SIwMFDv+sDAQFy5csXgfRQKhcHjFQqFweMTExPx1ltvmWfAjZgUHQpdjRSaANX/6DX//Wu/FfRuq3FB77had2rKuWu/55o0nlp3qu989Z2rwfvUuODjJkZnuQd6t/WymV8OYn4hVcGioqgSGq2Osseonoam9Yr2K66bHP/cLcaI7mxWSCAQoGuQB/6+no/LWSUULFpJZGSk3uWlS5di2bJlRp9PoVAgICBA7zonJyf4+PjUG3PU9uWXX6Jr164YMGCA3vXLly/H0KFDIZVKsW/fPrz44osoLS3FK6+8YvR4zcnh/jotWrRILxN5586dOm8oc1n8qGXOS4i9CvBwgZNQAI2OQXaJEiFertYektXVnIYm9unFhyMwuncI2vpU72PfRS7D39fzcSWL1i1ay6VLlxASEsJfri+ruHDhQrz//vsNnuvy5csmj6eiogLffvstFi9eXOe2mtf17t0bZWVl+PDDDylYNMTPzw8ikQjZ2dl612dnZ9c7jy+Xy5t1fO00dM0UNSHEskRCAYK9XJGZX46M3DIAQLCni0Nnk2ka2v6JhAK9QBEAugaxGwqkZtLuLtbi4eEBmazxXr3z5s3DlClTGjwmPDwccrm8TrGtRqNBfn5+k9Ya/vjjjygvL8ekSZMaPbZ///5YsWIFlEqlSVPn5mJTf53EYjH69OmDpKQkvgpIp9MhKSkJs2fPNnif6OhoJCUlYc6cOfx1+/fvR3R0dAuMmBDSXCFVweLkzSeg1THoHiLDe2N7onuIY07VcW2E3GkaulV5qFMAhALgTGYhbtwrQ6ifm7WHROrh7+8Pf//Gu29ER0ejsLAQqamp6NOnDwDgwIED0Ol06N+/f6P3//LLL/H444836bHOnj0Lb29vmwgUARtsnZOQkICNGzfiq6++wuXLlzFr1iyUlZXx1dGTJk3SK4B59dVXsXfvXqxcuRJXrlzBsmXLcOrUqXqDS0KIdXFFLlyngIt3irHh8HVrDsmquIbc7mIKFlsTuacLHuzIBgU/nb5t5dHYHo1WZ+0hNFvXrl0RFxeH+Ph4nDhxAkePHsXs2bMxYcIEvhL6zp076NKlS53+0Onp6Th8+DCmT59e57y//vorvvjiC1y8eBHp6en47LPP8O677+Lll19ukefVFDYXLI4fPx4fffQRlixZgl69euHs2bPYu3cvX8SSmZmJrKws/vgBAwbg22+/xYYNGxAVFYUff/wRu3btQvfu3a31FAghDeCKXGpSFFVaYSS2oYQyi63Wk33aAAB+Sr0NnY46Y3CKytXo8/afeOnb06hU29duTlu3bkWXLl0wbNgwjBw5EoMGDcKGDRv429VqNdLS0lBerr9H+KZNm9CmTRsMHz68zjmdnZ2xbt06REdHo1evXvj888+xatUqLF261OLPp6kEDNNQ573W7/bt22jbti1u3bqFNm3aWHs4hLR6P6bexvwfzgFg+9JlFyvR3leKQ689bOWRWcebuy7gf39n4tVhHTH3kU7WHg4xo0q1Ft2X/gGNjkHKoqEI8qSCLgDYeeY25m4/h06B7tg39yGzn58+183P5jKLhJDWrWYF9Jje7B/ynGIlHPV7aykVuLRaLs4iBFe932/lU29Rzh8X2aLU2G622YCa1EXBIiGkRbXxrhkssm0tKtRafjrW0ZTSNHSrxr3fbxeUN3Jk65N6Mx+fJF1FcWX1vtkVKi2S/2UriilYtB/014kQ0qLaeLtifN+2cHEWorPcAx4uTiip1CCnWAmZi7O1h9fi+GCRMoutUltvKYA8h8ws/nfnRVxRlGDD4evYNuMBdA/xxOGruahU6xDi5YpuwY23tSG2gf46EUJalEAgwPtP9uQvB3hIqoLFSkQEuDdwz9aJgsXWzVEzi0qNFlcU7Ha6pUoNJm78G1893w/JaWxWcXi3QIfur2pvaBqaEGJVAR4uAICcEmUjR7ZO/JpFmoZulbhm3bccLFhMzykFAAgFwP2h3iip1OClradxND0PADAows+awyPNRMEiIcSqAmVs09nsYsdsn1NcFSzS3tCtU3Vm0bGmodOqsop9Q33w1fP94OnqjKyiSmTml7MBZJiPlUdImoOCRUKIVQXIHDezqNLokF+mAgD4u9vGTg3EvLjMYlZRpV02ojYWNwXdRe4BqdgJsd0C+dt6hHg65Ppke0bBIiHEqgI8HDezeK+UDZCdhAJ4S8VWHg2xBH93CcROQmh1DLJacfN5RVElcmt84asOFtkilkd7BvO3RXegKWh7Q8EiIcSqHDmzyD1nfw8JhEJa7N8aCYUCtOF6LbbSdYvfn7yFwR8exMhP/oJSw+7IciWrGADQWe4BABjQwRe+buwXooERvtYZKDEaLZIhhFhVYFVmMccBM4vcc+ayq6R1ausjxfV7ZbiZV44BHaw9GvP64dQtvP7TeQBAbokSx9LzEOzlyn8R4oJFJ5EQ65/rg0t3i6m4xQ5RsEgIsSous5jr0JlFFyuPhFhSxwB3HPo3ly/6aC0URZVYvvuS3nU/nr7NZxWHdgnQawl1f6gP7g+lwhZ7RNPQhBCr8nRlF7qXqbQOVQAAVAeLATLKLLZmXHattQWLK/eloaRSg6i2XvhmWj8AwG/ns3AttwxymQs+rNFPldg3ChYJIVZVM/NQptRacSQtL7eEpqEdAVfkkZZd0qr2QD92je2ZOH94J0SH669D/Py5PvClCv9Wg4JFQohViZ2EkDixf4pq7iHrCHKKqzKLNA3dqnUMdIdQAOSXqZBbarvLLSr+v707D4uy3P8H/p6FYdgXgREMF0AE9y0R0lSkY8dyOS1aeVxywRK+LdYpS3OrrONPyzSXtFLLreVoesxcwrQ03KFUEFFA8gQoIKuyDHP//hhmFGWUIWZh5v26rrmu5pn7eebz3NLMZ+61qgYnLxXqdxW6myulFfhf0Q1IJED3QE/IZVLMeiQcoSpXbJ0WhW6BnqYPmMyGYxaJyOLclHJUllU16EvKlui7odmyaNOUDjK0beGCjPxypOWWWuWPg/3n8vDat6eRX1YJpYMUrzzUAVMeDNK/vjQhHTnFFZg/ohMcZFIkZxcB0I7HdKtdM3Fy/yBM7h9U3+WpmWPLIhFZnO7LprTC3pLF2m5ojlm0edY+bnHGf7SJopODDBXVGnz80wVoNNou8/8V3cAH+85j87FsrPjpIm5U1SD5jyIAQI9ALwtGTebCZJGILE43brGs0n66oWs0Avll2t1brLGliZqWLlk8Z4XJYkFZpb6VO/GNaDg5yFB8oxoZ+dr9nXefydWX/fDH8wifvRsrDlwEAHRv7Wn2eMn8mCwSkcXp9kW2p5bFwvIq1GgEJBLAx5W7t9i6dj4uAIDLVrgwd/oVbVIY6O0ET2cFugV6AABOZF0DAPxwOsfgud05NtEucMwiEVmcrmXRnpJFXRd0CxcF5DL+brd1Kt1ORSXWN8ElPU/b2tneT9v62auNF45kFOLkpWuIDG6Bk9napPGX1wahsLwKMqkEM7edhkIuRajKzWJxk/kwWSQii9ONWbSnCS7F17Vd7twT2j7oksXckgoIISCRWM/2jufztC2L7VWuAIDebbwBXMQ3Jy/j0IV8CAFEtPNGoLczAr2dAQDb4/tZKlyyAP6cJSKLu9kNbT9jFktqW1F19062TVU7iel6VY3V/Sg6X9uyGFrbstiz9c1JKznFFQj2dcHiUd0sEhtZB35KEZHF6Se42FE3tC4x1rWqkm1zVsjhppSjtEKNvJJKq/p3141Z1HUpezg74Mle9yExowB/79wScYNC4MkWcLvGZJGILM4eJ7iUsmXR7qjclSitKMOVkgqE+Lma5T3LK9VwVsgMdnvnl1WisLwKEgnqxPT/nmRLIt3EbmgisjhXXbJoZd1zplTClkW7o+uKzqud3GRqB89fRZe5e/DBvvMGy3x1/A8AQIivK5wUMrPERc0Pk0Uisjj9BBc7bFl0Z8ui3VDVrqeZW2yeGdHLEtKhEcCaXzKQ8mcJfjp3BVVqDXb89ieSsq8hv6wSK2vXS4wbFGKWmKh54qcUEVmcm27pHDtalFs3ZtHdiS2L9kLloU0W80pM37J4+nIxTlzSLnlTUa3B0KW/AND+v1ZaqYbSQYrB4SqUVarRuZU7hncLMHlM1HyxZZGILE43bs8eWxY5ZtF+qGr3AL9ihm7o9YlZAIBWnk51juuGelRUa/D979rFtt8cGg6p1HqW8iHrw08pIrI4V05wITugW2sxz8QLc6trNNiXkgcAWPRkN+w5mwsHmQQT+7XDwbSrUMilmP71bwCA6DA/RAX7mDQeav74KUVEFqcbs2hPE1z0S+c4shvaXpirG/rkpWsovlENL2cH9GnnjcjgFvrXnurTGgDwY2oeDqXn442/h5k0FrINTBaJyOJ06yxWqTWoVNfAUW77szLZsmh/bt3yr7pGAweZFMXXq/Hf3/9EYkYBCsuq8MrfQtG7rfdfep+Ec1cAAIM6+EFmoHv546d7okYIOHCrSWoAfkoRkcXpkkVAO27R0dX2k8WbO7iwZdFe+Lsr4eOqQH5ZFY5kFKB7oCeiFx9AQXmVvszivecR01GFY5kFeO+xrvB2MX4x7B9TtV3Qg8NVBstIpRJIwXGK1DD8SUFEFieTSuBSu8abtW2FZio311nkb3Z7IZVK8FDHlgCA3WdykZRdhILyKng4OeDp2u7hI5kFeG9XKvaczcNzX55EpbrGqPcoLK9CxtVySCRA/1CORaSmwWSRiKyCPU1yqVTXoEqtAcClc+zN3ztrk8U9Z/OQlF0EABgQ6ov3HuuCnq09IQSg1ggAwLGsQkz4/Djyyxo+ISa78DoA7ZqO7my1pibCZJGIrIKuO1bX4mbLbk2Ib+2CJ9vXN6gF3JVy5JdV4tNDGQCAboGeAIBht6x1+PzAYDgrZEjMKMDLXyU3+Pp/1CaLgd5O9yhJjfHuu+8iKioKzs7O8PT0bNA5QgjMnj0b/v7+cHJyQkxMDNLT0+uUKSwsxJgxY+Du7g5PT09MmjQJZWVlJriDxmGySERWwbO2ha3ouv0ki66OcoMTEMg2KeRSPFzbuqj7O+ge6AEAGN4tAH5ujujf3gevDemADZMjAADHswqhqW1tvFV1jQbrf81CWm6p/tgf12qTRS9nk96HvaqqqsKTTz6J559/vsHnLFy4EEuXLsWqVatw9OhRuLi4YMiQIaiouDkrfsyYMTh79iz27duHnTt34ueff0ZsbKwpbqFR+JOWiKxCC1ftQP4CI7rcmqtSjle0a0/3aY2vT1zWP+/or00WW7g64sgbgwEAEokEXVt5QC6VoKJagyullWhZu/SOzoJdqVh7OAudAtzx/Qv9AQB/FN4AANznzWTRFObNmwcAWLduXYPKCyGwZMkSzJo1CyNGjAAAfPHFF1CpVPjuu+/w1FNPITU1Fbt378bx48fRu3dvAMCyZcswdOhQLFq0CAEBlt9dhy2LRGQVfFy1u1tcLau6R8nmj8vm2Lfutd3OOk6Km7P/pVKJfjcVuUyK+7y03clZBeV1zrlSWoG1h7MAAGf/LIG6RjsG9rK+ZZHd0KWlpSgpKdE/KivN/0M0MzMTubm5iImJ0R/z8PBAREQEEhMTAQCJiYnw9PTUJ4oAEBMTA6lUiqNHj5o95vowWSQiq9CiNlm0r5ZFTkCwRxKJBEtGdwcA/F90yF3LtmnhAgC4dFuyuPLAxTrPL17Vvq4bs3gfu6HRsWNHeHh46B/vvfee2WPIzc0FAKhUdZcxUqlU+tdyc3Ph5+dX53W5XA5vb299GUvjz1oisgq++m5o229ZLLnBlkV7N7JHK/Ro7YkAz7u3ALZt4YyDALIKruuPaTQCO2v3ddY5879ihPi54n9F2m5oTnABUlJS0KpVK/1zR0fHesvNmDED//73v+96rdTUVISF2e9uN/ykIiKroGtZNGaZkOZKN+ObS5vYN12rYUPK3NqyeDL7Gq6WVsJNKcfwbgHYeDQbZ/8sQWRwC1TXCMilEvh7MFl0c3ODu7v7Pcu98sormDBhwl3LBAUFNSqGli21k5ny8vLg7++vP56Xl4fu3bvry1y5cqXOeWq1GoWFhfrzLY3JIhFZBd2YxVt3s7BVHLNIDdXWR9udnJV/s2Xxh9ParsmHwlXo0doLG49m48yfxfou6ABPJ86yN4Kvry98fX1Ncu127dqhZcuWSEhI0CeHJSUlOHr0qH5GdWRkJIqKinDy5En06tULALB//35oNBpERESYJC5jccwiEVkF3Wzo/FLbb1m8UnuPLRqxlRvZl1tbFoXQLp+zN0WbLD7cuSU6BWhbzlL+LMGGo9m153C8oqlkZ2cjOTkZ2dnZqKmpQXJyMpKTk+usiRgWFoZt27YB0I5Pfemll/DOO+9gx44dOH36NMaNG4eAgACMHDkSABAeHo6HH34YU6ZMwbFjx3D48GHEx8fjqaeesoqZ0ABbFonISvi4aFsWSyvVqKiugdLBdveHzsrXdik2pBuS7Nt9Xk6QSoDyqhpcLauEQibF5WvacYlRIT5wlEvh6+aIq6WV+O9vf0ImleD5AcEWjtp2zZ49G+vXr9c/79GjBwDgp59+wsCBAwEAaWlpKC4u1pd57bXXUF5ejtjYWBQVFaFfv37YvXs3lMqbSyFt3LgR8fHxGDx4MKRSKR5//HEsXbrUPDfVAEwWicgquDvJ4SCToLpGoLC86p4D/5sz3TIobX2YLNLdOcplaOvjgoyr5TiRdQ0qd22C4e+h1O/+s+7Z+/HqN7/jXG4J5g7vhKgQ7gltKuvWrbvnGou6FmAdiUSC+fPnY/78+QbP8fb2xqZNm5oiRJNgskhEVkEikaCFiyNySyqQX1Zps8nijaoa5BRrd24IYrJIDRATrsLqqxnYezZXnwgG+7rqX+8U4IGd/9cPheVV8HWrf8Yv0V/BMYtEZDV83Gx/+ZxLhdpWRQ8nB3hxzCI1wN86atfoSzh3BedytFv7BfvW/aEhk0qYKJLJMFkkIqvRwkW3i0slzueVYveZnHuc0fxkXmUXNBmnR2sv+Lg6orRCjQ1HLwEAQvxc73EWUdNhskhEVkM3I/ri1TI8tfoInttwCgmpeRaOqmll1o5XbMcZq9RAMqkEf+ukbV2sUmu39bu1G5rI1JgsEpHV8K1da3HtoSwU1q63uOrgxbud0uzoZkKzZZGMMbZvmzrPg9mySGbEZJGIrEZEkDckEqCqRtt6IpNKcDzrGk5eKrRwZE1Ht7hyOyaLZIRwf3f4e9xcasWP4xPJjJgsEpHViA5T4b/x/fBIF3/8a0gHjOiuXZB2z1nb6YrWdUO35RqLZKTFT3YDAAwO84NEwh1ayHy4dA4RWZXOrTywfExPADe7oG1lv+iySjWu1u7ewm5oMlZUiA/2vzKAs57J7JgsEpHV8nbWTni5ZiP7RevGK3q7KODh5GDhaKg5CuLEFrIAdkMTkdXSrUNYeL3awpE0Df3OLZwJTUTNiN23LGo02oH0OTm2t54bUXNXXXIN6pJ85PyvHJcvX7Z0OH9ZUmom1CX5aCGR28T9EFkj3fe57vud/jqJuH0TQztz/Phx9OnTx9JhEBERURM6duwY7r//fkuHYRPsPllUq9VISkqCSqWCVNq0vfKlpaXo2LEjUlJS4Obm1qTXprpY1+bF+jYf1rX5sK7Nx5R1rdFokJeXhx49ekAut/sO1CZh98miKZWUlMDDwwPFxcVwd3e3dDg2jXVtXqxv82Fdmw/r2nxY180LJ7gQERERkUFMFomIiIjIICaLJuTo6Ig5c+bA0ZELqJoa69q8WN/mw7o2H9a1+bCumxeOWSQiIiIig9iySEREREQGMVkkIiIiIoOYLBIRERGRQUwWiYiIiMggJot/0fLly9G2bVsolUpERETg2LFjdy3/zTffICwsDEqlEl26dMGuXbvMFGnzZ0xdr1mzBv3794eXlxe8vLwQExNzz38bqsvYv22dLVu2QCKRYOTIkaYN0IYYW9dFRUWIi4uDv78/HB0dERoays+SBjK2rpcsWYIOHTrAyckJgYGBePnll1FRUWGmaJuvn3/+GcOGDUNAQAAkEgm+++67e55z4MAB9OzZE46OjggJCcG6detMHic1kKBG27Jli1AoFOLzzz8XZ8+eFVOmTBGenp4iLy+v3vKHDx8WMplMLFy4UKSkpIhZs2YJBwcHcfr0aTNH3vwYW9fPPPOMWL58uUhKShKpqaliwoQJwsPDQ1y+fNnMkTdPxta3TmZmpmjVqpXo37+/GDFihHmCbeaMrevKykrRu3dvMXToUHHo0CGRmZkpDhw4IJKTk80cefNjbF1v3LhRODo6io0bN4rMzEyxZ88e4e/vL15++WUzR9787Nq1S8ycOVNs3bpVABDbtm27a/mMjAzh7Owspk+fLlJSUsSyZcuETCYTu3fvNk/AdFdMFv+CPn36iLi4OP3zmpoaERAQIN577716y48aNUo88sgjdY5FRESIqVOnmjROW2BsXd9OrVYLNzc3sX79elOFaFMaU99qtVpERUWJTz/9VIwfP57JYgMZW9crV64UQUFBoqqqylwh2gxj6zouLk5ER0fXOTZ9+nTxwAMPmDROW9OQZPG1114TnTp1qnNs9OjRYsiQISaMjBqK3dCNVFVVhZMnTyImJkZ/TCqVIiYmBomJifWek5iYWKc8AAwZMsRgedJqTF3f7vr166iuroa3t7epwrQZja3v+fPnw8/PD5MmTTJHmDahMXW9Y8cOREZGIi4uDiqVCp07d8aCBQtQU1NjrrCbpcbUdVRUFE6ePKnvqs7IyMCuXbswdOhQs8RsT/j9aN3klg6gucrPz0dNTQ1UKlWd4yqVCufOnav3nNzc3HrL5+bmmixOW9CYur7d66+/joCAgDs+jOhOjanvQ4cO4bPPPkNycrIZIrQdjanrjIwM7N+/H2PGjMGuXbtw4cIFTJs2DdXV1ZgzZ445wm6WGlPXzzzzDPLz89GvXz8IIaBWq/Hcc8/hzTffNEfIdsXQ92NJSQlu3LgBJycnC0VGACe4kB14//33sWXLFmzbtg1KpdLS4dic0tJSjB07FmvWrIGPj4+lw7F5Go0Gfn5+WL16NXr16oXRo0dj5syZWLVqlaVDszkHDhzAggULsGLFCpw6dQpbt27F999/j7ffftvSoRGZFVsWG8nHxwcymQx5eXl1jufl5aFly5b1ntOyZUujypNWY+paZ9GiRXj//ffx448/omvXrqYM02YYW98XL15EVlYWhg0bpj+m0WgAAHK5HGlpaQgODjZt0M1UY/62/f394eDgAJlMpj8WHh6O3NxcVFVVQaFQmDTm5qoxdf3WW29h7NixmDx5MgCgS5cuKC8vR2xsLGbOnAmplO0tTcXQ96O7uztbFa0A/9IbSaFQoFevXkhISNAf02g0SEhIQGRkZL3nREZG1ikPAPv27TNYnrQaU9cAsHDhQrz99tvYvXs3evfubY5QbYKx9R0WFobTp08jOTlZ/xg+fDgGDRqE5ORkBAYGmjP8ZqUxf9sPPPAALly4oE/IAeD8+fPw9/dnongXjanr69ev35EQ6pJ0IYTpgrVD/H60cpaeYdOcbdmyRTg6Oop169aJlJQUERsbKzw9PUVubq4QQoixY8eKGTNm6MsfPnxYyOVysWjRIpGamirmzJnDpXMayNi6fv/994VCoRDffvutyMnJ0T9KS0stdQvNirH1fTvOhm44Y+s6OztbuLm5ifj4eJGWliZ27twp/Pz8xDvvvGOpW2g2jK3rOXPmCDc3N7F582aRkZEh9u7dK4KDg8WoUaMsdQvNRmlpqUhKShJJSUkCgPjggw9EUlKSuHTpkhBCiBkzZoixY8fqy+uWzvnXv/4lUlNTxfLly7l0jhVhsvgXLVu2TLRu3VooFArRp08fceTIEf1rAwYMEOPHj69T/uuvvxahoaFCoVCITp06ie+//97METdfxtR1mzZtBIA7HnPmzDF/4M2UsX/bt2KyaBxj6/rXX38VERERwtHRUQQFBYl3331XqNVqM0fdPBlT19XV1WLu3LkiODhYKJVKERgYKKZNmyauXbtm/sCbmZ9++qnez2Bd/Y4fP14MGDDgjnO6d+8uFAqFCAoKEmvXrjV73FQ/iRBsSyciIiKi+nHMIhEREREZxGSRiIiIiAxiskhEREREBjFZJCIiIiKDmCwSERERkUFMFomIiIjIICaLRERERGQQk0UiIiIiMojJIhEZpW3btliyZInJrj9w4EC89NJLTXa9CRMmYOTIkUafZ+r7JCJqLuSWDoCI6FZbt26Fg4OD2d5v3bp1eOmll1BUVFTn+PHjx+Hi4mK2OG43b948pKenY8OGDRaLgYgIYMsiEVkZb29vuLm5WToM+Pr6wtnZ2WLvv337dgwfPtxi709EpMNkkYj0Bg4ciPj4eMTHx8PDwwM+Pj546623cPsW8tevX8fEiRPh5uaG1q1bY/Xq1frXoqOjER8fX6f81atXoVAokJCQAABYsWIF2rdvD6VSCZVKhSeeeKJODLd2Q1dWVuL1119HYGAgHB0dERISgs8++wwAUFNTg0mTJqFdu3ZwcnJChw4d8NFHHzX4fg8cOIBnn30WxcXFkEgkkEgkmDt3LoA7u6ElEgk++eQTPProo3B2dkZ4eDgSExNx4cIFDBw4EC4uLoiKisLFixfrvMf27dvRs2dPKJVKBAUFYd68eVCr1XeN648//sDZs2fx8MMPG4y7T58+cHFxgaenJx544AFcunSpwfdNRGQMJotEVMf69eshl8tx7NgxfPTRR/jggw/w6aef1imzePFi9O7dG0lJSZg2bRqef/55pKWlAQAmT56MTZs2obKyUl9+w4YNaNWqFaKjo3HixAm88MILmD9/PtLS0rB79248+OCDBuMZN24cNm/ejKVLlyI1NRWffPIJXF1dAQAajQb33XcfvvnmG6SkpGD27Nl488038fXXXzfoXqOiorBkyRK4u7sjJycHOTk5ePXVVw2Wf/vttzFu3DgkJycjLCwMzzzzDKZOnYo33ngDJ06cgBCiTqL8yy+/YNy4cXjxxReRkpKCTz75BOvWrcO7775717h27NiBgQMHwt3d/Y7X1Go1Ro4ciQEDBuD3339HYmIiYmNjIZFIGnTPRERGE0REtQYMGCDCw8OFRqPRH3v99ddFeHi4/nmbNm3EP//5T/1zjUYj/Pz8xMqVK4UQQty4cUN4eXmJr776Sl+ma9euYu7cuUIIIf7zn/8Id3d3UVJSYjCGF198UQghRFpamgAg9u3b1+B7iIuLE48//rj++fjx48WIESMMll+7dq3w8PC443ibNm3Ehx9+qH8OQMyaNUv/PDExUQAQn332mf7Y5s2bhVKp1D8fPHiwWLBgQZ3rfvnll8Lf3/+u9/DQQw+Jjz/+uN7XCgoKBABx4MCBu16DiKipsGWRiOro27dvnVaqyMhIpKeno6amRn+sa9eu+v+WSCRo2bIlrly5AgBQKpUYO3YsPv/8cwDAqVOncObMGUyYMAEA8NBDD6FNmzYICgrC2LFjsXHjRly/fr3eWJKTkyGTyTBgwACD8S5fvhy9evWCr68vXF1dsXr1amRnZzf6/u/m1vtWqVQAgC5dutQ5VlFRgZKSEgDAb7/9hvnz58PV1VX/mDJlCnJycgzec0lJCQ4ePGhwvKK3tzcmTJiAIUOGYNiwYfjoo4+Qk5PTVLdIRHQHJotEZLTbZytLJBJoNBr988mTJ2Pfvn24fPky1q5di+joaLRp0wYA4ObmhlOnTmHz5s3w9/fH7Nmz0a1btztmIwOAk5PTXePYsmULXn31VUyaNAl79+5FcnIynn32WVRVVf31m6zHrfetS6jrO6ari7KyMsybNw/Jycn6x+nTp5Geng6lUlnve/zwww/o2LEjAgMDDcaxdu1aJCYmIioqCl999RVCQ0Nx5MiRv3x/RET1YbJIRHUcPXq0zvMjR46gffv2kMlkDb5Gly5d0Lt3b6xZswabNm3CxIkT67wul8sRExODhQsX4vfff0dWVhb2799f73U0Gg0OHjxY7/scPnwYUVFRmDZtGnr06IGQkJA7Jpjci0KhqNNq2pR69uyJtLQ0hISE3PGQSuv/+N2+fTtGjBhxz2v36NEDb7zxBn799Vd07twZmzZtaurwiYgAcJ1FIrpNdnY2pk+fjqlTp+LUqVNYtmwZFi9ebPR1Jk+ejPj4eLi4uOAf//iH/vjOnTuRkZGBBx98EF5eXti1axc0Gg06dOhwxzXatm2L8ePHY+LEiVi6dCm6deuGS5cu4cqVKxg1ahTat2+PL774Anv27EG7du3w5Zdf4vjx42jXrl2D42zbti3KysqQkJCAbt26wdnZucmWzJk9ezYeffRRtG7dGk888QSkUil+++03nDlzBu+8884d5dVqNX744Ye7TrLJzMzE6tWrMXz4cAQEBCAtLQ3p6ekYN25ck8RMRHQ7tiwSUR3jxo3DjRs30KdPH8TFxeHFF19EbGys0dd5+umnIZfL8fTTT9fpcvX09MTWrVsRHR2N8PBwrFq1Cps3b0anTp3qvc7KlSvxxBNPYNq0aQgLC8OUKVNQXl4OAJg6dSoee+wxjB49GhERESgoKMC0adOMijMqKgrPPfccRo8eDV9fXyxcuNDoezVkyJAh2LlzJ/bu3Yv7778fffv2xYcffqjvkr/dwYMH4erqip49exq8prOzM86dO4fHH38coaGhiI2NRVxcHKZOndpkcRMR3UoixG0LqBGR3Ro4cCC6d+/eJNvcZWVlITg4GMePH79r8kM3vfDCC1Cr1VixYoWlQyEi0mM3NBE1qerqahQUFGDWrFno27cvE0UjdO7cGZGRkZYOg4ioDiaLRNSkDh8+jEGDBiE0NBTffvutpcNpVhrT3U9EZGrshiYiIiIigzjBhYiIiIgMYrJIRERERAYxWSQiIiIig5gsEhEREZFBTBaJiIiIyCAmi0RERERkEJNFIiIiIjKIySIRERERGfT/AW3QI9u8fwA4AAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Strouhal number is: 0.202221662229376\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" 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tm5sbBw4c4NSpU4wdO5bs7GwGDRpEhw4dyM3NVfPVrFlTbf387zMZcvf7YGlpiY2NTZHXpKWlFWj9K46pU6cSEBBA06ZNMTExoWvXrgwaNAgArTYv5Jk9ezbVqlXD29sbnU7HqFGjePXVV9Xz+WrUqEFwcDAHDx5kxIgRDBo0iJCQEL085ubmpKamlrieZY1McBFCCCHuwdLSUu91cnIyXbp04bPPPiuQ183NTf3exMRE75xGo1EDL3Nz81Kv58mTJ9XgqH79+mzcuJH27dsTFxdHYGAgr7766j3LqFWrFrVq1eLNN99k+PDhtGjRgr///ps2bdoART+TISW9xsnJiVu3bt2zrv9lbm7O4sWL+e6774iNjcXNzY1FixZhbW2tTrxxdnZm/fr1pKenc/PmTcqXL897771H5cqV9crS6XRUrVoVyPtj4fDhw8yePZvvvvtOzRMfH18qE3qedBIsCiGEeGRCQ0NLlP6w+fj4sGbNGhRFUVsX9+3bh7W1NRUqVDB4Xf369VmzZg0VK1bE2Pj+Pkrr1KnD1atXOXfuXKGtiz4+Puzbt08vbd++fVSvXl2vZe9u7u7u7Nmzh0mTJgF5LZHr1q2jc+fOODg4MGrUqBLV0dfXF4CUlJQSXfeg6tWrV6AVryRMTEzUn99vv/1G586dC7QcmpmZ4e7uTlZWFmvWrCkw3vS/cnNzycjI0Es7ffq0OoP6aSbBohBCiIfOyckJCwsL+vfvbzCPhYWFwS7SB5WQkEBwcLBemqOjI2+++SazZs1i9OjRjBo1irCwMD744APGjx9fILi428iRI/n+++/p06cP7777Lg4ODly4cIHffvuNH374wWAwd7dWrVrRsmVLunfvztdff03VqlU5e/YsGo2GDh06MGHCBBo1asSMGTPo1asXBw4cYO7cucyfP99gme+88w4dO3Zk5MiRjBgxgqysLP7++290Oh03btxg06ZN9OrVq9BrR4wYQfny5Wnbti0VKlQgOjqajz76CGdn5wITOx629u3b8/rrr5OTk6P3XoaEhJCZmUl8fDxJSUnqz7Ru3boAnDt3jkOHDtGkSRNu3brF119/zenTp1m2bJlaxsGDB4mKiqJu3bpERUUxffp0cnNzeffdd9U8kyZNIiAgAE9PT5KSklixYgVBQUH88ccfevXcs2cPM2bMeHhvxBNCgkUhhBAPnaenJ6GhocTFxRnM4+Tk9NDWWAwKCirQAjRkyBB++OEHtm7dyjvvvIOfnx8ODg4MGTKEKVOmFFle+fLl2bdvHxMnTqRdu3ZkZGTg5eVFhw4digwy/2vNmjW8/fbb9OnTh5SUFKpWrcqnn34K5LVe/v7770ybNo0ZM2bg5ubGhx9+yODBgw2W16FDB3bs2MEHH3xA8+bN0Wq1vPDCCxw6dIi1a9cyePBgPDw8aNasWYFrX3jhBRYvXsyCBQu4efMmTk5O+Pv7s2PHDhwdHYv9TKUhICAAY2Nj/vrrL9q3b6+md+zYUW+2dv7PVFEUIG/m8ldffUVYWBgmJia0adOG/fv3U7FiRfWa9PR0pkyZwqVLl7CysqJjx478/PPP2NnZqXmuX7/OwIEDiY6OxtbWljp16vDHH3/w4osvqnkOHDhAQkICr7zyykN6F54cGiX/HRbFlpiYiK2tLQkJCdjY2Dzu6gghxEOXnp5OeHg4lSpVuq+JB0KU1Lx589i4cWOB1rwnRa9evfDz8+P9999/3FVRFfV7+iCxi7QsCiGEEOKJ88Ybb3D79m2SkpLULf+eFJmZmdSuXZu33nrrcVflkZBgUQghhBBPHGNjYyZPnvy4q1EonU53z6EKTxNZZ1EIIYQQQhhU5oPF3bt306VLF8qXL49Go2H9+vX3vCYoKIj69etjampK1apV77lNkRBCCCHEs6rMB4spKSn4+fkxb968YuUPDw+nU6dOtGnThuDgYMaNG8frr7/+xA6gFUIIIYR4nMr8mMWAgAACAgKKnX/hwoVUqlSJr776Cshb9HTv3r188803etPzhRBCCCHEU9CyWFIHDhzghRde0Etr3749Bw4cMHhNRkYGiYmJeocQQgghxLPgmQsWY2JiKFeunF5auXLlSExMJC0trdBrZs6cia2trXp4eHg8iqoKIYQQQjx2z1yweD8mTZpEQkKCely5cuVxV0kIIYQQ4pF45oJFV1dXYmNj9dJiY2OxsbHB3Ny80GtMTU2xsbHRO4QQQogHNX36dHVf46fhPo/L0qVL9bbrexADBgzgk08+KZWySlvTpk1Zs2bNI7/vMxcs5u9zebft27c/8k3ShRBCPBqDBw+mW7duemmrV6/GzMxMnewo7s/y5cvx9vbGzMyMihUrMmPGjGJd17p1azQajboP9t06deqERqNh+vTpxa5Hr169OHfuXLHzG3LixAm2bt3KmDFjAMjKymLixInUrl0bS0tLypcvz8CBA7l27ZredfHx8fTr1w8bGxvs7OwYMmQIycnJenlOnjxJixYtMDMzw8PDg88//7zA/VetWqW+n7Vr12br1q1656dMmcJ7771Hbm7uAz9rSZT5YDE5OZng4GCCg4OBvKVxgoODiYyMBPK6kAcOHKjmHz58OJcuXeLdd9/l7NmzzJ8/n99///2Z2bJHCCGedT/88AP9+vVjwYIFTJgwodA8mZmZj7hWZU9ERAQDBw6kW7duhIaG8vvvv1OpUqViX+/h4VFgneOoqCh27NiBm5tbiepibm6Oi4tLia4pzJw5c+jRowdWVlYApKamcuzYMaZOncqxY8dYu3YtYWFhvPTSS3rX9evXjzNnzrB9+3Y2b97M7t27GTZsmHo+MTGRdu3a4eXlxdGjR/niiy+YPn06ixYtUvPs37+fPn36MGTIEI4fP063bt3o1q0bp0+fVvMEBASQlJTEtm3bHvhZS0Qp43bt2qUABY5BgwYpiqIogwYNUlq1alXgmrp16yo6nU6pXLmysmTJkhLdMyEhQQGUhISE0nkIIYR4wqWlpSkhISFKWlpagXPJyclKcnKykpubq6ZlZGQoycnJSnp6eqF5c3Jy1LTMzEwlOTm5QNmF5b0fgwYNUrp27aooiqJ89tlnipmZmbJ27Vq9PK1atVJGjhypjB07VnF0dFRat26tKIqifPXVV0qtWrUUCwsLpUKFCsqIESOUpKQkRVHyPgvMzMyUrVu36pW1du1axcrKSklJSVEURVGuXLmi9O7dW7G3t1csLCyUBg0aKP/884+iKIrywQcfKH5+fspPP/2keHl5KTY2NkqvXr2UxMREtbycnBzlk08+USpWrKiYmZkpderUUVatWqWez/8c/Ouvv5QGDRoo5ubmir+/v3L27Fk1T/598l24cEGpVKmSMnLkSCU3N1eJj49XBgwYoNjZ2Snm5uZKhw4dlHPnzhX5vkZERCharVYJCwsrzo+hwPs9YsQIxdHRUdm7d6+a/vHHHytdunRR/Pz8lA8++EBNv1f9lixZotja2hZ43qLe1//Kzs5WbG1tlc2bNxdZ90OHDimAcvnyZUVRFCUkJEQBlMOHD6t5tm3bpmg0GiUqKkpRFEWZP3++Ym9vr2RkZKh5Jk6cqNSoUUN93bNnT6VTp05692rSpInyxhtv6KW9+uqrSv/+/QutW1G/pw8Su5T5lsXWrVujKEqBI/+vlaVLlxIUFFTgmuPHj5ORkcHFixcZPHjwI6+3EEI8LaysrLCysiIuLk5N++KLL7CysmLUqFF6eV1cXLCyslJ7fwDmzZuHlZUVQ4YM0ctbsWJFrKysCA0NLZV6Tpw4kRkzZrB582ZefvnlAueXLVuGTqdj3759LFy4EACtVsu3337LmTNnWLZsGTt37uTdd98FwMbGhs6dO7NixQq9cn755Re6deuGhYUFycnJtGrViqioKDZu3MiJEyd499139boRL168yPr169m8eTObN2/m77//1uuenTlzJj/99BMLFy7kzJkzvPXWW/Tv35+///5b776TJ0/mq6++4siRIxgbG/Paa68V+j6cPHmS5557jr59+zJ37lw0Gg2DBw/myJEjbNy4kQMHDqAoCh07diQrK8vg++nu7k7Dhg0ZNWoU6enp93j3C9LpdPTr148lS5aoaUuXLi203vdTv3u9r/918uRJEhISaNiwYZH1TkhIQKPRqGMkDxw4gJ2dnd51L7zwAlqtloMHD6p5WrZsiU6nU/O0b9+esLAwbt26peYpztJ+jRs3Zs+ePUXWsbSV+UW5hRBCiHvZtm0bGzZsYMeOHbRt27bQPNWqVSswjmzcuHHq9xUrVuSjjz5i+PDhzJ8/H8jrfhwwYACpqalYWFiQmJjIli1bWLduHQArVqzgxo0bHD58GAcHBwCqVq2qd4/c3FyWLl2KtbU1kDfBYseOHXz88cdkZGTwySef8Ndff6lj6ytXrszevXv57rvvaNWqlVrOxx9/rL5+77336NSpE+np6ZiZmal59u/fT+fOnZk8ebLaBX/+/Hk2btzIvn37aNasGZAX8Hp4eLB+/Xp69OhR6Ps1dOhQFEWhcuXKBAQEsGHDBnUCaJcuXfDy8mLu3LmFXpvvtddeo0WLFsyePZujR4+SkJBA586d9cYr3m/9inpfC3P58mWMjIyK7M5OT09n4sSJ9OnTR33WmJiYAtcYGxvj4OBATEyMmue/XfT5y/jFxMRgb29vcGm//DLylS9fnitXrpCbm4tW+2ja/CRYFEII8UDyB/JbWFioae+88w7jxo3D2Fj/Y+b69esAeqtPjBw5kqFDh2JkZKSXNyIiokDe+1WnTh3i4uL44IMPaNy4sTom7W4NGjQokPbXX38xc+ZMzp49S2JiItnZ2aSnp6vBYceOHTExMWHjxo307t2bNWvWYGNjo7YQBQcHU69ePTVQLEzFihXVgAbAzc1NfZ8uXLhAamoqL774ot41mZmZ1KtXr8Az3l0G5L3fnp6eAERGRvLiiy/y8ccf6wXBoaGhGBsb06RJEzXN0dGRGjVqGGzVDQkJYenSpZw5cwYfHx8GDx5M69atCQwMxMXFhdOnT9O/f3+Dz5zPz8+PatWqsXr1anbt2sWAAQMK/Ju5n/pB0e9rYdLS0jA1NUWj0RR6Pisri549e6IoCgsWLLjnsz0s5ubm5ObmkpGRUSq/G8VR5ruhhRBCPF6WlpZYWlrqfcjqdDosLS0xNTUtNO/dLSImJiZYWlrqtYAZynu/3N3dCQoKIioqig4dOpCUlFToc9wtIiKCzp07U6dOHdasWcPRo0eZN28ecGcCjE6n45VXXlG7olesWEGvXr3UgKc4H+YmJiZ6rzUajdpNnR+Ib9myRZ3MGRwcTEhICKtXrzZYTv7P4u7ubmdnZxo3bsyvv/76wDuRnTx5ElNTU3x9fdFoNCxevJjKlSvTvHlzvv/+e5KSkgpMAjHktddeY968eaxevdpg1/n9KOp9LYyTkxOpqamFTm7KDxQvX77M9u3b9ZbQc3V1LRCEZmdnEx8fj6urq5qnsGX78s8VlSf/fL74+HgsLS0fWaAIEiwKIYR4Rnh5efH3338TExNjMGC829GjR8nNzeWrr76iadOmVK9evcCSKZDXFR0YGMiZM2fYuXMn/fr1U8/VqVOH4OBg4uPj76vOvr6+mJqaEhkZSdWqVfWOku4mZm5uzubNmzEzM6N9+/bq8/v4+JCdna2OrwO4efMmYWFh+Pr6FlqWu7s7GRkZ6jVGRkasWLGCKlWqMGzYMCZPnlzsYKZv376cOnWKWrVqFXq/+6nf/chfhzIkJEQvPT9QPH/+PH/99ReOjo565/39/bl9+zZHjx5V03bu3Elubq7aGurv78/u3bv1xlhu376dGjVqYG9vr+YpztJ+p0+fLtCq/LBJsCiEEOKZ4eHhQVBQENevX6d9+/ZFtrBVrVqVrKws5syZw6VLl/j555/ViS93a9myJa6urvTr149KlSrpdZf26dMHV1dXunXrxr59+7h06RJr1qwpMGnBEGtra95++23eeustli1bxsWLFzl27Bhz5sxh2bJlJX5+S0tLtmzZgrGxMQEBASQnJ1OtWjW6du3K0KFD2bt3LydOnKB///64u7vTtWvXQst57rnnaNasGb169WL9+vVcvHiRwMBArl27hqWlJStWrCA1NbVYdbK3tyc6OrpAoJTvfup3P5ydnalfvz579+5V07KysnjllVc4cuQIv/zyCzk5OcTExBATE6O2QPr4+NChQweGDh3KoUOH2LdvH6NGjaJ3796UL18eyAuIdTodQ4YM4cyZM6xcuZLZs2czfvx49V5jx44lMDCQr776irNnzzJ9+nSOHDlSYJLYnj17aNeuXak9d3FIsCiEEOKZUqFCBYKCgoiLiysyYPTz8+Prr7/ms88+o1atWvzyyy/MnDmzQD6NRkOfPn04ceKEXqsi5HVT//nnn7i4uNCxY0dq167Np59+WmB8ZlFmzJjB1KlTmTlzphqYbNmypURrGt7NysqKbdu2oSgKnTp1IiUlhSVLltCgQQM6d+6Mv78/iqKwdevWAl25dz9zYGAgr7zyCuPHj8fX15dJkyYxZMgQzp07R0xMDP369Sv24tF2dnYFhgHcraT1u1+vv/46v/zyi/o6fxb71atXqVu3Lm5ubuqxf/9+Nd8vv/yCt7c3zz//PB07duS5557TW0PR1taWP//8k/DwcBo0aMCECROYNm2a3lqMzZo1Y8WKFSxatAg/Pz9Wr17N+vXrqVWrll599u/fz6uvvlqqz30vGkVRlEd6x6dAYmIitra2JCQkyNZ/QohnQnp6OuHh4VSqVKnA2EIhnhZpaWnUqFGDlStXPpE7u02cOJFbt27pBaJ3K+r39EFiF5kNLYQQQghB3rjOn376SW/N0CeJi4uLXtf1oyLBohBCCCHEv1q3bv24q2CQoe0pHzYZsyiEEEIIIQySYFEIIYQQQhgkwaIQQgghhDBIgkUhhBBCCGGQBItCCCGEEMIgCRaFEEIIIYRBEiwKIYQQQgiDJFgUQggh/iMoKAiNRsPt27cfqJyIiAg0Gg3BwcHFvmb69OnUrVv3ge77JFi6dCl2dnbq6+I+19SpU/W2wXuSvPfee4wePfpxV+ORk2BRCCHEU2vhwoVYW1uTnZ2tpiUnJ2NiYlJg8eX8APHixYs0a9aM6OhobG1tH+j+Hh4eREdH6+3v+7AsX74cb29vzMzMqFixIjNmzCjWda1bt0aj0aDRaDAzM6N69erMnDmT0t4N+O2332bHjh1F5omJiWH27NlMnjxZTZs5cyaNGjXC2toaFxcXunXrRlhYmN516enpjBw5EkdHR6ysrOjevTuxsbF6eSIjI+nUqRMWFha4uLjwzjvv6P27gLw9nv38/LCwsMDNzY3XXnuNmzdv6j3DsmXLuHTp0v2+DWWSBItCCCGeWm3atCE5OZkjR46oaXv27MHV1ZWDBw+Snp6upu/atQtPT0+qVKmCTqfD1dUVjUbzQPc3MjLC1dUVY+OHu2FaREQEAwcOpFu3boSGhvL7779TqVKlYl8/dOhQoqOjCQsLY9KkSUybNo2FCxeWah2trKxwdHQsMs8PP/xAs2bN8PLyUtP+/vtvRo4cyT///MP27dvJysqiXbt2pKSkqHneeustNm3axKpVq/j777+5du0a//vf/9TzOTk5dOrUiczMTPbv38+yZctYunQp06ZNU/Ps27ePgQMHMmTIEM6cOcOqVas4dOgQQ4cOVfM4OTnRvn17FixYUBpvSdmhiBJLSEhQACUhIeFxV0UIIR6JtLQ0JSQkRElLSytwLjk5ucRHVlaWen1WVpaSnJyspKam3rPc++Hm5qbMnDlTff3uu+8qI0eOVHx8fJRdu3ap6S1btlQGDRqkKIqi7Nq1SwGUW7duKYqiKBEREUrnzp0VOzs7xcLCQvH19VW2bNmiKIqixMfHK3379lWcnJwUMzMzpWrVqsrixYsVRVGU8PBwBVCOHz+uV+5ff/2lNGjQQDE3N1f8/f2Vs2fPqvX44IMPFD8/P/X1hQsXlEqVKikjR45UcnNzC33GiIgIRavVKmFhYSV+f1q1aqWMHTtWL61+/frKyy+/rL6Oj49XBgwYoNjZ2Snm5uZKhw4dlHPnzulds2TJEsXDw0MxNzdXunXrpnz55ZeKra2twecqTM2aNZW5c+cWmef69esKoPz999+KoijK7du3FRMTE2XVqlVqntDQUAVQDhw4oCiKomzdulXRarVKTEyMmmfBggWKjY2NkpGRoSiKonzxxRdK5cqV9e717bffKu7u7nppy5YtUypUqFBkHR+Xon5PHyR2kZZFIYQQD8TKyqrEx7p169Tr161bh5WVFQEBAXrlVqxYscB196NNmzbs2rVLfb1r1y5at25Nq1at1PS0tDQOHjxImzZtCi1j5MiRZGRksHv3bk6dOsVnn32m1mfq1KmEhISwbds2QkNDWbBgAU5OTkXWafLkyXz11VccOXIEY2NjXnvttULznTx5kueee46+ffsyd+5cgy2d7u7uNGzYkFGjRum1lpaUoijs2bOHs2fPotPp1PTBgwdz5MgRNm7cyIEDB1AUhY4dO5KVlQXAwYMHGTJkCKNGjSI4OJg2bdrw0Ucfleje8fHxhISE0LBhwyLzJSQkAODg4ADA0aNHycrK4oUXXlDzeHt74+npyYEDBwA4cOAAtWvXply5cmqe9u3bk5iYyJkzZwDw9/fnypUrbN26FUVRiI2NZfXq1XTs2FHv/o0bN+bq1atERESU6PnKsofbLi6EEEI8Zm3atGHcuHFkZ2eTlpbG8ePHadWqFVlZWWpX64EDB8jIyDAYLEZGRtK9e3dq164NQOXKlfXO1atXTw1yKlaseM86ffzxx7Rq1QrImzTRqVMn0tPTMTMzU/Ps37+fzp07M3nyZCZMmFBkeUOHDkVRFCpXrkxAQAAbNmzAxsYGgC5duuDl5cXcuXMNXj9//nx++OEHMjMzycrKwszMjDFjxgBw/vx5Nm7cyL59+2jWrBmQN7bPw8OD9evX06NHD2bPnk2HDh149913AahevTr79+8nMDDwnu9FvsjISBRFoXz58gbz5ObmMm7cOJo3b66OA42JiUGn0+lNpgEoV64cMTExap67A8X88/nnAJo3b84vv/xCr169SE9PJzs7my5dujBv3jy96/Lrd/ny5WL9rJ8G0rIohBDigSQnJ5f4ePnll9XrX375ZZKTk9m2bZteuREREQWuux+tW7cmJSWFw4cPs2fPHqpXr46zszOtWrVSxy0GBQVRuXJlPD09Cy1jzJgxfPTRRzRv3pwPPviAkydPqudGjBjBb7/9Rt26dXn33XfZv3//PetUp04d9Xs3NzcArl+/rqZFRkby4osvMm3atHsGiiEhISxdupSlS5eyYMECPD09ad26tVre6dOnadGiRZFl9OvXj+DgYPbt20dAQACTJ09WA8PQ0FCMjY1p0qSJmt/R0ZEaNWoQGhqq5rn7POS11JVEWloagF7A/F8jR47k9OnT/PbbbyUquzhCQkIYO3Ys06ZN4+jRowQGBhIREcHw4cP18pmbmwOQmppa6nV4UkmwKIQQ4oFYWlqW+Lh7woexsTGWlpbqh3BR5d6PqlWrUqFCBXbt2sWuXbvUFr3y5cvj4eHB/v372bVrF23btjVYxuuvv86lS5cYMGAAp06domHDhsyZMweAgIAALl++zFtvvcW1a9d4/vnnefvtt4usk4mJifp9ftdybm6umubs7Ezjxo359ddfSUxMLLKskydPYmpqiq+vLxqNhsWLF1O5cmWaN2/O999/T1JSEi+99FKRZdja2lK1alUaNWrE77//zty5c/nrr7+KvKa05Xfd37p1q9Dzo0aNYvPmzezatYsKFSqo6a6urmRmZhZY5ig2NhZXV1c1z39nR+e/zs8zc+ZMmjdvzjvvvEOdOnVo37498+fPZ/HixURHR6vXxcfHA3k/o2eFBItCCCGeem3atCEoKIigoCC9JXNatmzJtm3bOHTokMEu6HweHh4MHz6ctWvXMmHCBL7//nv1nLOzM4MGDWL58uXMmjWLRYsWPVB9zc3N2bx5M2ZmZrRv356kpCSDed3d3cnIyODgwYNA3gzsFStWUKVKFYYNG8bkyZMLBOJFsbKyYuzYsbz99tsoioKPjw/Z2dlq+QA3b94kLCwMX19fAHx8fPTOA/zzzz8leWSqVKmCjY0NISEheumKojBq1CjWrVvHzp07C8zybtCgASYmJnrL8oSFhREZGam2bvr7+3Pq1Cm91tvt27djY2OjPkNqaiparX5YZGRkpNYh3+nTpzExMaFmzZoler6yTIJFIYQQT702bdqwd+9egoOD1ZZFgFatWvHdd9+RmZlZZLA4btw4/vjjD8LDwzl27Bi7du3Cx8cHgGnTprFhwwYuXLjAmTNn2Lx5s3ruQVhaWrJlyxaMjY0JCAgw2A3/3HPP0axZM3r16sX69eu5ePEigYGBXLt2DUtLS1asWFHiLtM33niDc+fOsWbNGqpVq0bXrl0ZOnQoe/fu5cSJE/Tv3x93d3e6du0K5HXTBwYG8uWXX3L+/Hnmzp1bovGKAFqtlhdeeIG9e/fqpY8cOZLly5ezYsUKrK2tiYmJISYmRu22trW1ZciQIYwfP55du3Zx9OhRXn31Vfz9/WnatCkA7dq1w9fXlwEDBnDixAn++OMPpkyZwsiRIzE1NQXyxnauXbuWBQsWcOnSJfbt28eYMWNo3Lix3jjKPXv20KJFixIF4GXeg0zRflbJ0jlCiGdNUUtylAX5S9h4e3vrpUdERCiAUqNGDb30/y6dM2rUKKVKlSqKqamp4uzsrAwYMECJi4tTFEVRZsyYofj4+Cjm5uaKg4OD0rVrV+XSpUt69/3v0jn55SqKohw/flwBlPDwcEVRCi4xk5SUpDRr1kxp2bKlweWDEhMTlQkTJiiVKlVSdDqd4uvrq8yaNUuJiopSKlSooHTr1k3Jyckp9NrCls5RFEV54403lJo1ayo5OTnq0jm2traKubm50r59+wJL5/z4449KhQoVFHNzc6VLly73tXTO1q1bFXd3d726AoUeS5YsUfOkpaUpb775pmJvb69YWFgoL7/8shIdHa1XdkREhBIQEKCYm5srTk5OyoQJE/SWcFKUvKVyfH19FXNzc8XNzU3p16+fcvXqVb08NWrUUH799dcin+NxeVhL52gUpZSXaH8GJCYmYmtrS0JCgjrbTAghnmbp6emEh4dTqVKlIicgCPEgFEWhSZMmvPXWW/Tp0+dxV6eAbdu2MWHCBE6ePPnQF1q/H0X9nj5I7CLd0EIIIYR4Img0GhYtWlRgG74nRUpKCkuWLHkiA8WH6dl6WiGEEEI80erWrUvdunUfdzUK9corrzzuKjwW0rIohBBCCCEMkmBRCCGEEEIYJMGiEEIIIYQwSIJFIYQQQghhkASLQgghhBDCIAkWhRBCCCGEQRIsCiGEEEIIgyRYFEIIIf6l0WhYv359sfO3bt2acePGqa8rVqzIrFmzSr1eD0NERAQajYbg4OASX9uyZUtWrFhR+pUqBb179+arr7563NV4qkiwKIQQ4qk2ePBgNBoNGo0GExMTypUrx4svvsjixYvJzc3VyxsdHU1AQECRZXXr1s3g+cOHDzNs2LDSqvp91eFh27hxI7GxsfTu3RuA+Ph4Ro8eTY0aNTA3N8fT05MxY8aQkJCgd11kZCSdOnXCwsICFxcX3nnnHb2dWqKjo+nbty/Vq1dHq9XqBeF3mzVrlnovDw8P3nrrLdLT09XzU6ZM4eOPPy5wf3H/JFgUQgjx1OvQoQPR0dFERESwbds22rRpw9ixY+ncubNewOLq6oqpqel938fZ2RkLC4vSqPIT69tvv+XVV19Fq80LIa5du8a1a9f48ssvOX36NEuXLiUwMJAhQ4ao1+Tk5NCpUycyMzPZv38/y5YtY+nSpUybNk3Nk5GRgbOzM1OmTMHPz6/Qe69YsYL33nuPDz74gNDQUH788UdWrlzJ+++/r+apVasWVapUYfny5Q/pHXj2SLAohBDigaRmZhs80rNySjXv/TI1NcXV1RV3d3fq16/P+++/z4YNG9i2bRtLly5V85W0G/q//tsNHRkZSdeuXbGyssLGxoaePXsSGxtbZBlXrlyhZ8+e2NnZ4eDgQNeuXYmIiABg+vTpLFu2jA0bNqitpUFBQYWWExgYyHPPPYednR2Ojo507tyZixcv3vezAdy4cYOdO3fSpUsXNa1WrVqsWbOGLl26UKVKFdq2bcvHH3/Mpk2b1ED8zz//JCQkhOXLl1O3bl0CAgKYMWMG8+bNIzMzE8h772bPns3AgQOxtbUt9P779++nefPm9O3bl4oVK9KuXTv69OnDoUOH9PJ16dKF33777YGeVdwhe0MLIYR4IL7T/jB4rk0NZ5a82lh93WDGX6T9JyjM16SSAyvf8FdfP/fZLuJTMvXyRHza6QFre0fbtm3x8/Nj7dq1vP7666VWbr7c3Fw1UPz777/Jzs5m5MiR9OrVy2CAl5WVRfv27fH392fPnj0YGxvz0Ucf0aFDB06ePMnbb79NaGgoiYmJLFmyBAAHB4dCy0pJSWH8+PHUqVOH5ORkpk2bxssvv0xwcLDaKlhSe/fuxcLCAh8fnyLzJSQkYGNjg7FxXphx4MABateuTbly5dQ87du3Z8SIEZw5c4Z69eoV6/7NmjVj+fLlHDp0iMaNG3Pp0iW2bt3KgAED9PI1btyYjz/+mIyMjAdqKRZ5nopgcd68eXzxxRfExMTg5+fHnDlzaNy4caF5ly5dyquvvqqXZmpqqjfeQQghxLPB29ubkydPPpSyd+zYwalTpwgPD8fDwwOAn376iZo1a3L48GEaNWpU4JqVK1eSm5vLDz/8gEajAWDJkiXY2dkRFBREu3btMDc3JyMjA1dX1yLv3717d73XixcvxtnZmZCQEGrVqnVfz3T58mXKlStXZLAZFxfHjBkz9MZuxsTE6AWKgPo6Jiam2Pfv27cvcXFxPPfccyiKQnZ2NsOHD9frhgYoX748mZmZxMTE4OXlVezyReHKfLC4cuVKxo8fz8KFC2nSpAmzZs2iffv2hIWF4eLiUug1NjY2hIWFqa/zfyGFEEKUXMiH7Q2e0/7n/9ejU18odt69E9s8WMWKQVGUh/YZEBoaioeHhxooAvj6+mJnZ0doaGihweKJEye4cOEC1tbWeunp6ekl7kI+f/4806ZN4+DBg8TFxamTeSIjI+87WExLS8PMzMzg+cTERDp16oSvry/Tp0+/r3sUJSgoiE8++YT58+fTpEkTLly4wNixY5kxYwZTp05V85mbmwOQmppa6nV4FpX5YPHrr79m6NChamvhwoUL2bJlC4sXL+a9994r9BqNRnPPv8iEEEIUj4Wu+B8lDyvv/QoNDaVSpUoP/T7FlZycTIMGDfjll18KnHN2di5RWV26dMHLy4vvv/+e8uXLk5ubS61atdQxgvfDycmJW7duFXouKSmJDh06YG1tzbp16zAxMVHPubq6FhhXmD92sySfx1OnTmXAgAHqsIHatWuTkpLCsGHDmDx5striGR8fD5T8PROFK9MTXDIzMzl69CgvvHDnL1WtVssLL7zAgQMHDF6XnJyMl5cXHh4edO3alTNnzhR5n4yMDBITE/UOIYQQZdvOnTs5depUge7a0uLj48OVK1e4cuWKmhYSEsLt27fx9fUt9Jr69etz/vx5XFxcqFq1qt6RP+lDp9ORk1P4uM98N2/eJCwsjClTpvD888/j4+NjMMgriXr16hETE1OgrMTERNq1a4dOp2Pjxo0FWh/9/f05deoU169fV9O2b9+OjY2NwfeiMKmpqQW6wI2MjIC8VuJ8p0+fpkKFCjg5ORW7bGFYmQ4W4+LiyMnJKXQchKExEDVq1GDx4sVs2LCB5cuXk5ubS7Nmzbh69arB+8ycORNbW1v1uLtLQQghxJMvIyODmJgYoqKiOHbsGJ988gldu3alc+fODBw48KHc84UXXqB27dr069ePY8eOcejQIQYOHEirVq1o2LBhodf069cPJycnunbtyp49ewgPDycoKIgxY8aon1MVK1bk5MmThIWFERcXR1ZWVoFy7O3tcXR0ZNGiRVy4cIGdO3cyfvz4e9bZ29ubdevWGTxfr149nJyc2Ldvn5qWHyimpKTw448/kpiYSExMDDExMWpQ265dO3x9fRkwYAAnTpzgjz/+YMqUKYwcOVJvAkpwcDDBwcEkJydz48YNgoODCQkJUc936dKFBQsW8NtvvxEeHs727duZOnUqXbp0UYNGgD179tCuXbt7Pq8oJqUMi4qKUgBl//79eunvvPOO0rhx42KVkZmZqVSpUkWZMmWKwTzp6elKQkKCely5ckUBlISEhAeqvxBClBVpaWlKSEiIkpaW9rirUmKDBg1SAAVQjI2NFWdnZ+WFF15QFi9erOTk5OjlBZR169YVWVbXrl3V161atVLGjh2rvvby8lK++eYb9fXly5eVl156SbG0tFSsra2VHj16KDExMUXWNzo6Whk4cKDi5OSkmJqaKpUrV1aGDh2qfuZcv35defHFFxUrKysFUHbt2lVoOdu3b1d8fHwUU1NTpU6dOkpQUJDe84WHhyuAcvz4cb3nX7JkSZH1e/fdd5XevXurr3ft2qW+v/89wsPD1XwRERFKQECAYm5urjg5OSkTJkxQsrKy9MourAwvLy/1fFZWljJ9+nSlSpUqipmZmeLh4aG8+eabyq1bt9Q8aWlpiq2trXLgwIEin+NpVNTvaUJCwn3HLhpFuavdtozJzMzEwsKC1atX661mP2jQIG7fvs2GDRuKVU6PHj0wNjbm119/LVb+xMREbG1t1aUBhBDiaZeenk54eDiVKlUqcoKDePrFxMRQs2ZNjh079kTONF6wYAHr1q3jzz//fNxVeeSK+j19kNilTHdD63Q6GjRowI4dO9S03NxcduzYgb+/fxFX3pGTk8OpU6dwc3N7WNUUQgghnhqurq78+OOPREZGPu6qFMrExIQ5c+Y87mo8Vcr8bOjx48czaNAgGjZsSOPGjZk1axYpKSnq7OiBAwfi7u7OzJkzAfjwww9p2rQpVatW5fbt23zxxRdcvnz5oSzIKoQQQjyNHufe1Pcin+elr8wHi7169eLGjRtMmzaNmJgY6tatS2BgoDrpJTIyUm/m1K1btxg6dCgxMTHY29vToEED9u/fX6LZWEIIIYQQz4oyPWbxcZExi0KIZ42MWRTiySdjFoUQQjx20r4gxJPrYf1+SrAohBDinvJ345Dt04R4cuXvznP3mpOlocyPWRRCCPHwGRkZYWdnp+7AYWFh8dD2VBZClFxubi43btzAwsICY+PSDe8kWBRCCFEs+Xv43r1lmxDiyaHVavH09Cz1P+QkWBRCCFEsGo0GNzc3XFxcCt1iTgjxeOl0ugJ7Z5cGCRaFEEKUiJGRUamPiRJCPLlkgosQQgghhDBIgkUhhBBCCGGQBItCCCGEEMIgCRaFEEIIIYRBEiwKIYQQQgiDJFgUQgghhBAGSbAohBBCCCEMkmBRCCGEEEIYJMGiEEIIIYQwSIJFIYQQQghhkASLQgghhBDCIAkWhRBCCCGEQRIsCiGEEEIIgyRYFEI8kRITE7ly5Qrx8fFqWm5uLmlpaY+xVkII8eyRYFEI8UTIyMhAURT19bx58/D09OSdd95R05KTk/Hw8ODgwYOPo4pCCPFMkmBRCPFYXb16lcmTJ1OhQgX27NmjphsZGWFqaoqxsbGaFhMTw82bN+natSuXL19+HNUVQohnjka5+095USyJiYnY2tqSkJCAjY3N466OEGVOTk4Oe/fuZd68eaxdu5acnBwAhg0bxnfffWfwuqSkJJo0aUJoaChVq1blxx9/xMrKSi+Pk5MTnp6eD7X+QghR1jxI7GJ87yxCCPHgkpKS+PPPP9m0aRNbtmwhLi5OPde6dWtGjx7NSy+9VGQZt27dIjw8HIALFy7QqlWrAnksLCwIDQ2VgFEIIUqJBItCiIcmIiKCTZs2sWnTJoKCgsjKylLP2dra0rNnT0aNGkWdOnWKVV5cXBzp6en83//9H5988gkZGRn06tWLd999F4DQ0FD69+9PXFycBItCCFFKJFgUQjwUP/30E4MGDdJLq1atGl26dKFz584899xzmJiYFHptZGSkXstjvtDQUAAaNGjAihUr6N69OytXrqRFixaMHDmyQL7CSDe1EEKUjIxZvA8yZlEIfTExMaxatYoqVarQsWNHAKKioqhUqRL+/v506dKFLl26UKNGjXuWFRkZiY+PD6mpqQbzmJmZERYWxq+//sp7772HVqtly5Yt5OTk0Llz5yLLl25qIcSzSMYsCiEeOUVR0Gg0ACxevJjJkyfTrl07NVh0d3fnxo0b2NralqjcuLg4UlNTWb58OT4+PgDcSsvBzkzL2bNn6d+/P+np6cTFxfHuu+9y7tw5Fi9eTM+ePXn//fcBmDFjhlqPu0k3tRBClJwEi0KIYsnIyGD37t1s27aNbdu28d5776ndzL169WLTpk0FJqiUNFBMz8rhVGwGVn7t8fHxoX79+gD0XHiA6MQ06jiWx8TJk6y4SAA0Gg0LFizg0qVLBAUF8eWXXwJQqVIl9VohhBAPRoJFIUQBOTk5XLp0iZCQEEJCQti/fz87d+7U6xretm2bGixWqVKFAwcO3Ne9rt5KZVfYDYLOXmffxTjSs3JxaPcmaVm5ap4KDuYciojnSjyUHzKftEtHOBWbQX1Ap9OxZs0amjZtyvnz5wHIzMy8/4d/DAyN0cz3sMdZHj9+nNu3b+Pr60u5cuUe2n2EEGWTBItCCPbu3cuuXbvU4DAsLIyMjIwC+dzc3AgICKBDhw68+OKLD3TP1Uevsmj3Rc7FJuulO5hruXL0b9Kye6hpM7rWon1NV5bsOsOByBTMKzfkg7/j2RO5kyH1bDE11vD555/Tt29f0tLSiI2NfaC6PUrFHaO5evVq3NzcCpwrSSC5Z88eRo4cSfny5QkMDFTTx48fT1BQEL/99hu9evUC8sY3ffPNNwwdOpTy5cuX8KmEEE8TCRaFeIYoisJnn33G6dOnmTt3LnZ2dgCsXbuWb775Ri+vubk53t7e+Pr6UqdOHdq3b0+dOnXUcYolEZecQVDYDVpUc6KcjRkAUbE3OBebjFYDNRxNaOBmRn03U1KvXWDA9C/Y39AKDycbatSogaWpMe1ruuJjnYlv45aY1gnAql5H/gpPY+3vv3Jr5w9696tdu/b9vUGPQWFjNPNFR0fTvXt30tPTDU7cMTRh5+rVq3z88ceMGTNGLdfc3JxTp05x/fp1vbwVK1akZs2aeoPef/75Z6ZPn86aNWs4efJkaTyqEKKMkmBRiKdITk4O4eHhagthSEgIVlZWzJ8/H8gb4zd//nyuXLnCiBEjaN68OZC3KPatW7fw9fVVDy8vL7Taku8ImpaZw7nYJM7GJBIancTxK7c5efU2igIfdatF/6ZeREZGMmVQZ3Kdq5J+6SjhGSkE/qecDz74AEVR+OCDD9Q0T09PVv44j86dOzO2e2v2JNgz5aM3sDYdAdyZwJLfApeVlaW3PE90dLSarzCPc1mdu8do5jt27JjawltYMFnUhJ3x48ezatUq4uPjWblyJQC+vr5s2bKF6tWr6+VdsmRJgfpUrVqVFi1a0LNnT7308PBwKlWqdH8PKYQokyRYFKIMiYmJ4fTp01y/fr3AERkZydmzZwt0H5crV04NFgFGjRpFTk4O7u7uatpLL710z91T/ktRFK7eSkOr1eBuZw7AiSu36TZ/H4UtyFU+N5GNs7+mymoTTLOySI67xofVvJiWkYKHhQXrmzSBjAxCb92i/7/BnOuSJRxbtQqAwykpBCYl0cDeHoDO65YwtXx5NJd0oNOBqSnExwMQOn8+u3Ny+HLTJhZ88AHufn5EJyXRvXt3APr371/oMxXV3ZsfaBZ2Dh5+oFlYMHm3zMxMtFqtupf21KlTuX79ut76kxYWFoXOEi9M+/btad++PXevrvbnn38SEBDA6NGj+eabb+6rlVkIUfZIsCjEE2rZsmUcPHiQ0aNHqy1KmzZtYtiwYUVeZ2ZmpnYf+/j44Ovrq7fMTf5uJ8WSlQU3b5IcfZ2wyJuExiRx9lYmZ1MUwrJ0JGFM48v7+SB0CzWTk6mUmILy2g8oKbfgejjDbkTgcz2c5pdPMDY5npWAv1ZLK0tLABpdvQpATnY23jodFvb2ODk7YxoWRkZuLsMvXy5QpfU3b2JhZIRTYiKaxETIzITMTH5yb8RxM3ssgP4//qjmf2nMGL3rdRoNa7t3x83bGypUyDtcXNizfz/jxo275zqNhhS1fmNRE1iKWkC8uEJDQxk0aBADBgxQf761a9cmKCjogcu+OyDcuXMnubm5aDQaCRSFeIZIsCjEI5aZmUlkZCTh4eF6R0JCAtu2bVPzLV26lKCgIJo2baoGi56entSqVQsXF5cCh5ubGz4+PlSsWBEjIyPDFUhOhthY/SMmBmJjyYmJJTwhnds3b+EdEYJVYiI3zW1oMGbFvxcbAeZqUZqcbHbcTqKpiQk1u3fHxsmJLco5an02HiOtlj7r1nE7N5coMzNq7dmDcUQEVq1bE2phAf37kzVvHomtW2Ntba2W6Qms2bKFzp0763W9Hj9+nLVr1zJw4ED8/f31grKzMYl8MHsPigLTN/ThebdcYsLDWf3777zRoQMm168TGhxM/1WrWFurFp0OHYI1a1CbQHU6Qh0dAZjh70/Hli3B0xN8fcHGRu3uhZJ3BxdnAgvcabm8H+fPn1fHoY4ZMwYzMzNiE9O5eD2ZW6lZ3ErN5HZqJrkK1HC1pmU1Z8x1RfwbMeDTTz+lffv2ei2ciqKQkZGBmZnZfddfCPFkk2BRiIfg1q1bHDlyhMuXLxMREaF+jYiIICoqCkMbJ6WmpmJhYQFAnz598Pf3p1atWur5/K5BPYoCt29DdDRERsKhQ/qB4PXreceNG3lfU1NRgHVVGrPCrhxxdq5Uc/Hkin1DLldzJE1rQuqFQ4xxs2VKr144ODphf0jhxq14sm5EMKZfV2p5OeHtasOmFT+w4/R5KrzzDvw7ts07J4fg9m1RFIVazZqRlpamV91fVqxQv+/ZsydhYWF6wSLc6eq9u+u1fv36DBkyRC/fmjVraNOmDd6uDkzu6MNHW0JZcCCGVsP86dioER3vGm+XdfAgrFqF29KlUL8+ZGTA5ctw8WLesXkzREdTKTyc+keO5LWqAnh7Q7Vqajk+3t4Gu4Ojo6M5duyYXlpoaCipqanMmDGDevXqFejG3rp1K1OnTuX27duFlmnI3ftsd+nSBUVRePPNNzEzM2PkimNsOWk4+Hy3WhwV3Zxp0qQJEclatBrwq2CHVnvv1sI2bdqo32dkZPD6669z8+ZNNm7cqHaBCyGeLvKbLUQxKIpCbGwsFy9e5Nq1a/TocWdZl08//ZTAwEDGjBnD//73PwAOHjxIQECAwfLMzc2pVKmSelSsWJFKlSrdmVCSk8Owl1++E+CtWpX39e7gLyYmL0CMjs4LfIBrwDtaY/bYubCsWh2i3SpyxbUpWx2NCLO7zLBql/hw8GA05coxPSidxH/jjYi76qZVckBRSKlXD159FQ2wu10W/zdlElXaVGHQi9XUgLbG+Ld4e/xbes9mZGSEn58fx44dIy0trciWuPydWO5nrN+aNWvo0aMHPj4+7Nu3jyHPVSIkOpG1x6IY8+txto5tgYOlDoDc3FymTZsG3BVkmZpC9ep5B4C9PfzxB3z5JfTunRdAHjwI//wDu3bduXH79hAQAC++CC+8AHctK/PKK6+Qnp5eaH2nTp1aaFd1fjd0eHh4oYGmoWf//PPP+fbbbwENLw2fzLUjfzB48GAAKtiZAwpZN6+Sk5pIbloiuelJoNFiZGHHyM+mA3lDFnxHLuCmsTNutma0r+lK+5quNKpoj7HRvSc3hYWFsWbNGjIzM/nnn3947rnn7nmNEKLskWBRPPOys7OJj4/n5s2bxMXFcfPmTaKiorh48SKXLl1Sv97djZiSkqIGTGFhYfz99996LX6VK1fG19eXihUr4uXllffV3R0va2sqmZvjkp2NJj/4i42F48chMBA++CDv9c2bkJurX1GdDlxcyHEpR2z5ihyv2oATlXJ5ztGIli1agKsrg/amce52BlqNllf/85xGOnvS7W2gb18A2tw4zqEjx3G1NqZjqyZUdbXH09GCchZadCYd0Ol06rXWZibq7iglda+JGferRo0alC9fnpYtW2Jra4tGo2FG11oEX7nNpRspvL3qBD8OaohGo+HgwYNs374dgNGjR/PHH39g/+9EmUIZGd0JJAcMgGPHoEGDvHOdOsGpU7B8ed7rWrWgXj0A0tPTCwTH+YHxjBkzmDp1aoHgOH/5oqlTpzJ16tRCq6PT6dRWyytXrqiB9qzlG3Ed8BUm5atjkZTMhQsXqFSpEkNaVKJTFTOSbzqQkJDwn8lQl7jeoQPh4eGEhYVxOew0FtWaEJ0AS/dHsHR/BOVsTBnVthq9GnqgMzYcNNapU4dffvkFKysrCRSFeIpJsCjKjKysLKKiooiIiCAyMrLQRaPz5Y+jGjFihNo19t1337Fjxw769u1Lt27dgLzFqFu0aFGs+2u1Wjw8PPDy8srrLjY1hZs3eb1NG9qXL099nQ5mzYLr16l+/TpnqlTJawEMCyPr+nXSkpK4e+v234Akc3NecnPDsYIHt908OeFRg/0padR3saDT88+DszPfRhmx60IU0YlpmNi5EpOUSVZOXjd2Zlwkh6/8Sct/WzpNju1Bk5CFTgteTlZ4OFjgYW+OvS4XR115AvxeUe8/u3c96F2v2O//w1JY61lxJn3UqlWLI0eO4OzsrE62sDQ1Zm6f+nSbv4+dZ6/z3e5LDG9VBX9/f77++mveeustDh8+TNOmTdm8eTPV7upeLraxY/O6sW/cgB078lojN29WT/t89x31+/WDzp3hrhnnhpabye+WNrTOYn5r5d0Tb4ysnXB6cRQHLJthaglmRjDv+yW82CLveVyszXCpUQkwvMSNoijs3r2bL7/8ks3ffot5xXo812cUscbliE3MYOr60wSdvc6PgxsV+Xa8/PLLBcqVyS9CPF0kWBSPTE5ODlFRUer4vbtnh44aNUpdD++PP/7gzJkztGvXTh2vt23bNjp37kzuf1vb7mHAgAFqy83Ro0dZtWoVderUUYPF/HMajQZ7OzscbW1xtLLC1dqaKvb2VLa0pIpOR2VFwSsjA118PNy4wS1fXw7GxWGmKDS/637vGRsTZWbGe961sPSszM061fntdjLzjh+nU41cVv3f/0G5crx3JpPfdh8hS2vKrHIVSMm6M4YxKz4K75CVdJozJ+/9+HYPZxLNADO4nRcgG2s12JvCbSUNb29v9dq5fethbWaCk5Xuif7AdnJywszMjPT0dIPL2FhYWODk5FRkOa6urnqvFUXBt7wNUzv7Mm3DaazN7vwX17JlSwAcHR05d+4cjRo14osvvqDBvy2G4eHhJXsIZ+e87ureveHoUWjYMC89JQVGjoThw/OCyvz0ezDUArt3715mzZrF6tWrychWsGnSHbumr4CxDg3gXw6GNHbB3jypQDd2Ucv5aDQaWrVqRatWrTh79izffvstH73VCUsbW1YevsKs7WG0q2pd6LWGxMXF0bt3bz755BMaN25comuFEE8uCRbLoOzsbFJTU0lJSSE1NZXs7GwcHBxwcHAoehbsXa5du8b+/fu5fPnyPWdp3u39999X77F27Vo1qGvSpAkAV65cYenSpUDerN8rV66oEzyuXr1KdnZ2oeUOGzZMDRZ//fVXli1bxpdffqkGi+XKlSM3NxdTU1M8PT3x8vLC8t/lV1SKQnpKCiaAUW4uphoNW2bM4MfAQBqVL08vDw/qtG1L00OHULp2RRMfj/fNm9xwdsbm1m2yktPITs3ENiNCLTLYw5cfre25pDOnT6UqmFTxJr2uHTvib7Mu+Cgvmt9m7ddfQ7lyDPgrhj1nLqPoLNhjdNevlgPY2zUgJvQn+HeNu9P794BTZUxADRQ1GrA01pBrbqIulg0w0N+Ln35bjTYrjd5dXqRVw1q42pj9O6ZMf828ys5Wxf1Rlor7XRLG09OT1atXF5jxfLeSrFt45coVpkyZgre3N5MmTaJ/E0/quNvi52GnV56FhQU3b94EICEhodBliOLi4oocO1hoS+jZs3defP89VKyYN6xg40bIn9Dz9tt5Xy9dygsii3D16lXWr1/PunXr+Pvvv8nJyQGgymtfk+2cN8ayjpsFf38zml8vn+FXA+UUtZzP3by9vfXW4uzqa89XI6fw3tKbNPjzT6pWrcqO0FisTI1pUtnRYDlTp05lx44dDBw4kDNnzhT7/yMhxJPtqQgW582bxxdffEFMTAx+fn7MmTOnyL9qV61axdSpU4mIiKBatWp89tlnxV6o1pDo6GjOnj2rBnD5X+/+/r9fTUxMWLdunVrGa6+9xt69e/niiy/o2rUrAH/99RfDhw8nLS1NvfbuWZB302q1ODg44OzszKFDh7CyygscPvvsM4KCgpg4cSKKonD9+nUSExPvuV5fYV5++WVWrlyJs7Mz//zzD7/++is2NjYEBwcTHh5O1apV1YkEhTExMcHDw4OKFStSrly5vAkd2dmkRUYSEhJCVlIS/k5OZLdpg1NULL+Me5fYm/HUcXRm7+iJWOcoRN1K4NDlS3heDuFVc3NISOA317p8mGvENWMn+pqY4mqsI83ElM1hcMyxPuk7fuEzR0eed3SkT+vRHNGYk+VpgoW5FdlaYzLJG5eliY+kS9Juvv38c3B0ZNy8g0TczAump9z9IK7g0LwKDhn7wN8fgNsZ18Dchvz2PEudEQ5WOmzNjHGtXpNFC/9WL5/wYg0ysnNxsNSph625CUaFzEbt1ciTXo3Gl/hn9bA96JIwhc14vl/79u3jp59+wtrampEjR2JjY6MXKN5KySQqy5LQ0FDi4uJIT0/nww8/5I8//gCgRYsWjBgxgiFDhjBu3Lgi71XUgt7q5BYHh7yxoX375k2SadqUcFdXiIkhtEcPqFIFOnSAgABC/50FHR4eTmBgIOvXr+fw4cN3lazB3NyctLQ0Olcz5zDmvN/RB9esazQcd8ZgsF3Ucj73cuvWLZIT4klLTUWr1XI6KoE3fzmGAszuVZeA2oUvTP7FF18QGxvLxx9/LIGiEE+RMh8srly5kvHjx7Nw4UKaNGnCrFmzaN++PWFhYbi4uBTIv3//fvr06cPMmTPp3LkzK1asoFu3bhw7dkxviZLiys3N5fr162zYsIERI0aU6FpLS0uOHj3K7Nmzady4MVFRUZw/f56kpCReeeUVQkNDee2117h48WKh12s0GiwtLUlPTyc7O5vc3Fzi4uK4ffs2f//9NxMnTsTPz4/09HQCAwPp2LEjCxcuJCQkhHkLFtKkeUs8PL24lZjEzr+242Rjwcsvv0wuGpKMbPh7915uJSbRuKk/ji6u5KJlzeEIvliyBh97I0b27YtlZhYRSfbs3B1M5M142iQ589zAD8lVNKTl5HLh3DHMT//B2qZN8VQUxvr05mxGFuc1WhKNdei0xmQYmdDiu5PEXT5B+fUzOQG8AXjXG0m6mRm4/+fBrSFdOYn96U95dcAAsLXl89zGpCrG2AFb/5Pd1zSTt8d2g39377j6+U6y4vOWc/lvmJOpaNkdGgoeHgBUdbEi6XY8Rko27q4uONhYYaYzwszYCCfrykwKuDONZOb/aqPRgKOlKXYWJpiZGP6wbONd8N/mk8pQ62H+kjCzZs0qdNxn/pIwx48fL3TXk9JYjDpfr1692LNnD4MGDdLb3xggPiWT3osOEHEzlaWDG9Hs38B027ZtLFy4kLfeeos9e/YQHh7Ozz//bHBs4b12cImOji50QW8nNzcsLCyYGhwMQH/Im2k9b17eARhrtbzyyp3xpBqNhkbPd8Hcvy9t61Skc2UToqKi6NKlC5nZueiMtRw7llefhzGBqGLFiuzbt4/r169TuXJl0rNyaFXdmT9DYnlzxTE+7FqLAU29ClxnZWXF2rVrS7UuQjwMubm5xMfHY2trq/ZqnTp1iiNHjhRo2Pnv8l93s7W15eOPP1Zfr1y5ktu3b9OhQwe8vPJ+R8LDwzl69CgpKSl6R3JyMimpqSSnpJGSlk5aaippyQnqEmq/rN9Gdk4u2bkKMz76mNCz53h96DAaNm6Mhc6YzBsRfPfddwDc1NqjaApORmvdujVNGtSlnmcRE/qKQaMYWvCtjGjSpAmNGjVi7ty5QN4/AA8PD0aPHs17771XIH+vXr1ISUlh810D0ps2bUrdunVZuHBhoffIyMjQm0yRmJiIh4cHI5fsZeOWraSmptKoUWPOnz+HkZEx2dlZXPtnE7apUXkzZK2cibD05sKFi2RkpFOzZk3s7OwwNjZGUSDwh09pUsGcuXPncjE6nj+umfDn9u3cio+nVevWWFpYYGRkxM2bN9nx82xq22UTFBREfHouU9afYdffe0jLyMTbtybGOjMysrLJzM7lbOAyqmRc4JNPPiH0cjSLrpQjPSsbRVMwiCl/+SAB57cytUYNbqdlU7f6QIPvufuZIP63+UsmAJlaY6q/s95g3tRzB6iyfTYH/P3BxoaqVQaRXcg/aIDcqDP4XlhH4NKlYGVFnUWnSMzIQZObjZnOGHNTHTojLVpySL0ayouWV/niiy8AmLT2FLeSUrCxMMPSLC9QM//3KGdrxkt+d5Y3OXr5FrGxsdy+eZ2aNarh7GCHuYkRKUm3Cb9wDlOdTsZb/as4rYdmZmaEhYUVaL3a8u/C2kUpbjfpg8jKyWXE8mP8FRqLzljL9C416dPYQx3TGRwcTK9evTh37hxGRkZ8+OGHvPfeeyXeF/vYsWM0aNDA4ESV48ePM3XqVJYvX463tzeZt29jeuAAbNtG9IEDdFcU2jo749vieTaZ1SDToyEKGuwsTPhn0vMF/vjIv9/Ro0cLDRbvdb6kNm3ewk8hGRyONwVgckcfhrasXOQ1ISEhzJ8/nzlz5jzRY2jFwxEeHs7OnTuxt7dXlxWDvD/UsrKy8Pf3x97BEQUw+XepprTMHC7eSCYjO4eMrFzSs3PIzM4lM0chKzuXGq7W1HK3BfL+EPz1UCSZ2blk5eQfCilpGdxKTMTDOAWn1LxhUCbWjkSWb63mPXLsONm5ULlqNYxMdHSpU56Uf35j+vTpaE0tcR+x2OBzpZzdS3xg3pjySlWqYdlvjnouOTmZnNwcLCwsMTE2pq2PC74Jhxg+fDgAFcb8isbYBI3WCLRGaO76PEy7dITrq6YDef83VpywhpTMnELr0LiiAy9ZXWTAgAEAuI/8CWMrh0Lz+rrZsHVsCxITE7G1tSUhIaHAH9X3UqZbFjMzMzl69CiTJk1S07RaLS+88AIHDhwo9JoDBw4wfrx+l1779u1Zv369wfvMnDmT//u//yuQvvHENbRVmmEFhKYDHg3JH5HXpaob3Wva0atXL45F3uJ/8/djUt0NE+AycDnxTjmd+79BO/ccatasidYhkTF/7QH3+li5w9GbwM3Mf3NaMOXr75nUMe+DKCslhZ1nr0O5GpgBEckAmWq5/+v3KpPaVaF69eo0SMpg9sd/QSGBIkALNEzVauHaNXRWtrhlp2CCgrEGdFrQGWn+PbQ0f74pI4duACsrTCwt6XUqHZ2ZKTozHTpzU3Q6E3TGWkyNtVToV5fGP4zP65YDFoTEcj02muyMdCqUd8PR3hadkRadsRZrs+cpZ3NnK7ojU90wMTK0rVg7/Z/R/2oX/sMrRAMve/CyB7z10u0tXajgWnZa/B6FuLg4UlNT72utxKJm+eZ7WPsp37p1C1tbW7RaLSZGWub2rceoFcf5KzSW99ed4p9LN/nkf7WxMjWmbt26HDlyhBEjRvDLL78wb9483njjDRwdDY/NM/QsFhYWBrupIe8DwMbGhtGjR9OgQQPmzJkDU6bAjRtELV/BL4eu8a1bI7J1ebvkvGibzXsDmhbZSn0vRbXgFvf9P3v2LL169iAtLY0+Hy9nf6Idn2wLpbqrNa2qOxd6TXp6Os8//zwxMTE0atSIQYMG3fcziKKlp6ejKAo6nU7t/o+Li+PIkSOYm5vTqlUrNa+fnx+xsbFs2bJFndy1d+9evvnmG+rXr8/kyZPVvKNHj+Zq1DWmfPgx9uUq4GCp46+tG3njjTdo1KodvSZ8QnpWLilpGXzx9SyyFA2t2r5AxSrVeKlueX7+bAZLlizBvnJtJv2dCFoTcjRaMrMVMDJBs/cAGq0RmYdXEb/vNzIyMnCoWhfr7h8ZfNaRbaqw+vtv+Oqrr7B0rYRZ95kG8yYe2cCtHd8D4ORSActX7/Qeap0qoQOuJmYD2dxY/jvWf+S19FsAWlPLggX+q069BlTzGMrGjRvR6XQkZdw1Ft/EDC2QngPpOdmkZeaQnp6Oubk5pqamGJtZFtoCCFCzVh3eafwF77zzDqampliY5oVoRloNKUmJZKSnYWtjjYO9Lc7WpjjY532umpiYUMHamPR/+8oSExNJT0/HytKKcq7lqOhkYfBZiqtMB4txcXHk5ORQrlw5vfRy5cpx9u4B53eJiYkpNH9MTIzB+0yaNEkvwMxvWRz/YjUUjRFmZmYFWiGaVm6ujplytzNnUoB+YHK3hhX9aeCV90N3tjYtMq/egH0rUz5/pQ46Iy3GRhpMjLR637vZmuHlmPcP3sFSx+532mBirMFYq0WnBRMTI0yMtBhrNWg0nYC8gNgCKDzULkgDfNakmJmBF33LgW+5e2eEItd3E6WrqK5myJs1fr+tUw9rnUVDli1bxvjx4/nqq6/URarNTIxYNKAB3++5xOd/hLHxxDVORyUwr199fNxssLa25ueff+b555+ncuXKaqAYHx/Phx9+SP/+/Wl4j1nNnp6e6phIQ5ycnLhw4QIHDhwgJCSETz75BEtLS3ZcS2FqWi1ivKoCYJ90jY9ObaDTni3wmW3eEIq+faF167w1IIshv9v8XsFrcVp2q1evzrBhw5g9eza/Tu5PwAfLCUm3Y8yvx9k8+jk8HAp+GJmZmTFu3DiCgoJo27Ztser8LLp16xbnzp3D1NSUunXrqum//fYbiYmJvPTSS+qs/8DAQL788kvq1aun9qoA1K1bl7CwMHbv3q0OCVm9ejUjRoygY8eOtGrViozsHBLSsohOhURjRw5fS+eScoUGXvacOnWKtWvXEpNlzon5u7mdmkVCejYxJi3RVLeg+/KLwEVeckvm9tEtxMfHc+LSNc5uClHrYN6gK+bA8UQ4fjwKb1drdWJWSloGNuZ3/qjQ3lnCFYDUzCy19y47OYFyxrmYKjmYKTmcvnaF3OxMqllaUElngteSP9m2YwXJycmkRl3G7sQfkJOFkpuDkpOtfm+Wk4179DlqAnsBbfx1Zu1aiLHOBBOdMR/ExnAiOZGJVSrTxcsDF0sNt9u3Z966dVRycmKF82UwNwdLC3rPncvRCxf5dsIEAoa+joWFKZFhNfn+++8xMTFhz9ut1Wd57bXX2LNnD59/8Tkvd3sZC50RN66YM27cOMzMzAh6ty1aTd5n9Mcffcihf/5hwvi36NwpAGOtliuXw1mzZg0ODg5smfyCWu7UqVP5a/9fvPXWW/Tsmff7FBUVRdOmTbGwsGDHzD5q3n379hEVFUX9+vWpWrVqSf9JFqpMB4uPiqmpKaampgXSX3uucrGacsvZmPFGqyrFupeTlWmx81qaGtOzoUex8hppNXg6PvhfF6JsKmrWcnR0ND169ChyXM4rr7xSaFfzk+jGjRvEx8fzyy+/qMEigFar4Y1WVWhY0Z5RK45zKS6FJfvC+fwVPyBvnOCrr+ovZb5q1Spmz55NUFAQwf+OOVQUhdzc3EIncHh6eqrv0c2bN9m2bRubNm3Czs5OHVvk6enJrFmz6NGjB7GxsQwePBgTB3fiag3C3c6c9wK86VynIxrNUDh7Nm829YoVsHgxuLnlbavYuzf8O87KkPztA2fMmFHoBL6STIDRarV88803WFpa8sknn7Dto8E0fPcXWvvWwNm64P+N+d555x3efffdZ64LOjw8nN27d2NnZ6dOVgR49dVXiYyMZMmSJep7vnLlSkaMGMFLL73Ehg0b1LxTp07lwoUL1KxZUw0WExIS2LFjBxmZWVxPTOdmSia3UjLJca+LtWU11p5LJzDuJE1cIC0tDVdXV+JM3fCdFkjqv92ZFt0/wQL4dN9t4DafvFybyMhIAI6dPE2Ub5JaB43uzmdGbnoyS5b9RPLxvFHhWQk3+F8la8yUbMyyM7kSGYG1Jpcq5mZYZWfSZMMh2iYn06tZM26mZnDu73nkJCdimZqETUoyVhlp2GVnYpudgUNmOlrgOmAWE85zH78ExsZgY8MPwA1jY3qUL09VV1ewscHL3x+7q1cxt7CgvvNtUoyMyDAy4nJiMuh09OzYGb/69cHKimuJiazfsQMbe3u63fWHU7VLl8jKysLd3V2dDOoBFDYdb/v/OhIVFYWrqyt2dnld4GbVq7Nt2zbs7e2p6HSnFXLR1x8TGxtL1apVKfdvuk2VKuzYsQN3d3e1AQdgzhefFLhXlSpVCu0ZnTFjBjNmzNBLc3d3LzTv3atplJYyHSw6OTlhZGREbGysXnpsbGyB9dfyubq6lii/EGXBgwaDQKETVUpjW75HbcyYMTg6Oqpjef6rgZcDW8e04J3VJxny3J0xdxdvJBN9O53mVR3V4KZ27dr07t1bb3eS8PBwvL298fLy0tuysVKlSpQvX55//vmHTZs2sW/fPnVdUFtbW+bOnYuJiQmKotC86wC2RyTSyC6TI0eOYGwczI/jJ9G1eW39Lmdvb/jwQ/i//8vb83vFCvj9d5g9G/79Pyt0y5a8/cH/E5DlrxtZqVKlUmnZ1Wg0fPzxx1hYWDBlyhSOfjmIFya8hekrdQxe898el+TkZPWDuazKzs7W2wP7u+++49SpU0ydOlXttdq2bRsjR46kU6dOesHi3r17uXDhAhEREervkoeHB56entg4uXI88hbxKZnEp2RSpdMb2KdksjQ0myUXDjPkuco0a9aMn376iRvmHjT+ZMedSjUagAOw7nwmcIVvA+eSfCIQAIuYa+RUzwsUtRpQMlLJTL6Fd2VPPFwccDbOxe3f3Zoyb0QQt+lLjLLT0GWlYZ6ZhnlWKpYZKVjmZOObk4OtiQn1s7JoEhFKy+GtKND/Y2EBtrZ5h50d1WxtoUIFsLO7k25rC9bWYGOT9zX/sLICS8u8rzodaDS8XsjPoMm/R3GUB9709S2QXrly0eNt72Ztba23ni2AjY0NHTp0KJC3atWqBVrzzMzMynzrepkOFnU6HQ0aNGDHjh3qIsu5ubns2LGDUaNGFXqNv78/O3bs0FseY/v27fj/u/yJEGVNcZewudesZScnp/sOKu53F5aHQafT6bUQ5ubmcu7cOb3/7O0tdfwwSL9bed6uC6w9FoWPmw2D/L3oUMuVZs2a0axZM7184eHhZGVlceHCBS5cuFBkXWrXrk2XLl3o0qUL2Qr8eTKaRbsvcuJqAhoNbB79HL/++isNGjQoOhDXaKBJk7zj669hzx6cfvgBi19+of+0aVDEclX5C8+XlsmTJ2NhYcH48eP59NOZpKam8PXX33AyKsHgjMvs7GxmzpzJV199xbfffsvAgYYn0D1KoaGhbN68GVdXV70/LgICArhw4QLLly9X15D9448/6NGjB82bN2fbtm1q3tmzZxMaGkrXrl158cUXAahXrx5t2rRR/8jIzVW4npTBsPc/40ZqDntvWbFl9Ul6NfagU6dOXL58mQ3BUbw8f/+dypn5gBlsv5AMJNO6hgvJSedYunQp0TnWKI2HkJuWSE5aArmpieSkJZKbmkBOWiLahChqVqlCJScnqtgb8ZbdeexuRGMdG0VWTAy6uDjYGocmLg4yMngBGA2QmgAhQWhMTMDR8d/D7a7vHfPGnzs45O2lbm9/Jwi0s8sL/u7R4i3KpjIdLAKMHz+eQYMG0bBhQxo3bsysWbNISUlRPywGDhyIu7s7M2fmDYIdO3YsrVq14quvvqJTp0789ttvHDlyhEWLFj3OxxDivhU1CQXuHQzeb1BXWruwPGwzZsxg5syZ/Pjjj/Tr16/QPIqi4GChw9zEiNDoRN5be4op60/zXDUnOtV2o11NV2zN8z4EW7duTUREBOHh4XpHREQEV65coUaNGnTp0oXOnTtj7uBK4OkYFp29wYEtO0jLymvhMTHS0L1+BaxMjQtsl3dPRkbQujWerVsT+uGHxG3fDrt35x1Xr4KZGTRqRGjlyvRftqzIpX7A8M+/qMkvb731Fubm5owYMYI587/jsEUjrmscWPlGU3X89X/t3buXhIQEkpOTS/a89+m/2w6OGTOGQ4cOsWzZMmrUqAHkzYR/9913adWqlV6wGBkZyYULF/Ra49PS0khKSiqwbmi/fv1ISEzE3KEcxyJv4W5njr+/Pzt37mT/hTjafBlE1K00MnNygX+77K/klXF4+1qMLuUtUaQ4eOHefhwOljrsLXUc2buLmMsX6NW1Iy18q9E4J57gvXvZuXMnaLTw90Z0Wg0+VlbU1OmopSjUSk+nVkoKXpDX4pe/7Nre3VCuHJQrh6mLC1StmrcD0b+HxskpLwDMDwitrAq0VItnW5kPFnv16sWNGzeYNm0aMTEx1K1bl8DAQLU7IDIyUq8rpFmzZqxYsYIpU6bw/vvvU61aNdavX39faywK8agUZ6cUQxNJSquFr7ByPv30U8aNG1cqu7A8DLm5uZw4caLA8lf/pdFomNLZl1Ftq/Lb4StsCL5GaHQiQWE3CAq7wZJ9EWwd2+LfvFpO3jahnFcd2tZuhIu1GamZ2Vy5lcbVW6lUdLRUl/b4KySWDzaeUe9jlJnMzcOb6F7HiU+7z3rg5/OsXBnPN96AN97I64o+exY2bYJt22D58rxMXbvmHS++CG3agI0NkZGR6rqORQX6RU1+GT58OCYmJrz++uucDzmFpU9L3vzlGJtHtygwjtHY2JitW7eyatUqevXqpaanpaVhbm5+389/7tw5rl+/To0aNXB2zptAsWPHDkaMGEHlypUJDAxU8x48eJBDhw5x+vRpNVj09fWlb9++1Kunv0f6zz//TFpamt7nQsuWLTl79iwWdk4Eno7hwvUkzl9P5pxVS8LTk/l91RXgCjO61qSLjx3BwcHsOhVBeFxevZTcHHISb5CdEEt2wnWyE2LZd+kImTF5rdMmRmfIqF8NTcgNiI1l9NGjHE9MpOuuH+nw7wp3ZsBywFlngoejI9XKl8fYxSUv6HNyAhcX/cPZOS9INDO77/dYCHgKgkXI21fYULdzUFBQgbQePXrQo0ePh1wrIfQVFfBB3nqehU2kio6O5pVXXrmzO4gBhnZKeRDFbT1s0aLFEzmeUavVsnr1anVR+nw3b94sdGkcOwsdw1tVYXirKly8kcyWk9FsORlN+5p3xjTHp2Yy+tfjBu85tEUlNVhsUsmeZlUcaVHNmVbVnTFJieVXuzC95b5KjUYDPj55x7vvwp490LIlNG0KW7fmLQBuZARNmhBXs6b676moZZHuNU51yJAhnDlzhlnz5mBarjKxVGD0r8dYPqTJv9tR3mFkZETv3r3V19nZ2bRp04aEhAQCAwPVBYwLc/jwYaZOnYqjoyO//PKLmj506FB2797N77//rv6fbmlpyfnz5wuM0X3//ffJysrSG/zv5+enV16+On51uRyfyoErSZw/cp2GFR3wr+KIg4MDh8LjGb78aIFrNICblQlcuULQ1h95+aOP0JiYoXOtRnZCDDlJN0HJpbxGQ21FwQeoCLjkH+bmKBs3onFxAVdX5vTsmRfoubqqrYIVypWjn4tLXsufEI/QUxEsCvEo3WsyCRTc5aO4AV9RdDoda9euLVB2fjdz/uzX0lTaezg/DlqtVi9QzMjIoGnTpnh7e7No0SKD3bRVnK0Y83w1xjxfjeycXDU9PSuHxhUduJ6UTmxihtq1XM7GFA97C8rb5bWUhYaG8sYbb/DSSy8xovW/+0JjU+R2mKXq373TQ7t1g0mT8rqoDx6Ef/4hNL/VEfD5+GPqt2mTF1TWrQslGPgP8Pnnn3PixAl2r/kI98Gz+OdSPF/8GcakgMLX1swXFBTE4cOHMTEx0fsZfPrpp6xcuZIvv/yS559/Hshr+f3jjz9wdHTU61728vKiWrVq6g4ckDdOdMeOHWrrYb67J5r8162UTDYER3HiagKh0YlcupHyb7dxngEeGhKvH+Tg8eMci4ym1ovjqJ4aR9X4KDaeOcyOq+eYkXCd93Lz1ts7BVQDqpBDzdRr+Li74OvVAJ9q1bDz8soL/u5uAXRyypvQIcQTSoJFIUqguJNJDLlXwFfYMif5rTyZmZm4ubkV6GourW7m8PBwjh07ViA9Pwh91OslPiz79+/n8uXLJCcnY2lpeOHdu93dSlbB3oLfh+dNiFMUheSMbEyMtAUWzj5z5gx79uzh7NmzvPnmm1hYPNqlq4qzSLiZsTFOvr6wYwfMn3/3xXlfp0/Pm1RTpcqdw0F/TKKxsTErV66kYcOG1M8O4ZhJfb77+xL1POzpUMvwKhMvvPAC0dHRnD17Ft1dgdLu3bsJDg5m3759arBYu3Zt5s+fry4ine+nn34qUK6lpWXhM08zM1Fu3uTy5esEX47HISOZltyCuDjS45KYnqs/4ckkKx2zuCukxEXy9fpDfBS2Tz0XmvYh3l5e4OxMOU9L/F18aN14EDRvDuXKUdvVlXMuLhIAiqeGBItClEBxdjSBgl17xQ34SmuZk8IYCgbzW0mnTp3K1KlTC732SZioUlratGnDyZMniYqKUtdJVRSFDz/8kJdeeqnA+LWiaDQarM3yWrX27NlDTk4OrVu3BuB///sfY8eO5a233nrkgSIUvUh4/r/H1evX49mpU17irVsQGgoXLuR1Yf/wQ94EiX37ID7+zsV2dlCpkt7hVL48x+bMwaFiRT46k8YPwTeYsu4UrWs4F7n7jIuLCy4u+rsmzZkzhxMnTlCzZk01zVSnY8Srr0JSEkREQHIypKZCSsqd4/btvGfI/xofz+34RIKzzAk2dSTY3osTbtW4ZZE3ROCF84dpuXZG3k5U9vbUb66QlBBDxNWzRESeJTvhOnBnN9xqlSvTxN+fxk2a4NCrV16LIND930OIp5kEi0Lch3u1sj2OVrgHCQbNzMxYvXq1wS7ZJ72ruaS8vb31ltI5ePAg06dPZ+bMmcTExJR4uZlt27bRsWNHateuTXBwMFqtFq1Wy6xZs0q34iV09yLhhdH7edvbQ7NmeUetWnnB4s8/Q/36eQHYpUt5wePFixAennds2JAXvGVnk9/eOFFrRFzAWJ4/sgGz/4vIa13LP8zN8yZbmJnlfW/8n48gRaFKRgZV0tMhLQ3S0/OCwuRkyM6mSEZGZDg4YWprDXZ25Dg44t9wJGla/XvotFDT0ZRaTbuR/dNEGjZvzokTJ+C3O1u6ajQaGjZsQNu2bWndujVNmjTBwaHwWd5CPAskWBSijMsPbCQYvH82Njb07t0ba2trvUCxUaNGKIrCsmXL1Jau3377jWXLljFw4ED69Omj5jMzM6NZs2akpaUVu3u7zLCzywsaC/sDKCdHbcnj1i0SIyI4+eGH/Jwcw4kZM6hgaQmZmXmBX0bGnSAwLS3v2v/KDybvDirzF2zO//rv4s2KhQVnUrXsvJrKjku3yVVg0+jn1Hr5fneA+NRs6nrYYZ15k7Xff42tksS6PbvV22VmZgJQq1Yt2rZtS9u2bWnZsiX29oWvGSnEs0iCRSHKuPwA0NAEFJBg8F58fX359ddfUZQ73Y5ZWVkEBweTnZ2tt63n6dOnCQwMxNHRUQ0WnZyciIqKKnOtT4bGu5ZoHKyRUd4Yx3+HKVjVrUvC55+TAZyuV4+bNZtyMiqBAU0Nz3YuifSsHA5cvMlfobHsPHuZ6IQ7k8Y0Ggi7fI11v/7EwoULee31YUyb8j4AZ8+eZcbArVhYWJCTk6Nu1/jzzz9ToUIFdbk1IURBEiwKUYYUtVPK0zIB5XG6exFnIyMjTp48SVhYGO7u7mp6jx49cHR0pH379nrXlqVAsViTX8zMiI6OLnRoQ34Zhf0BYmpqyu+//05OTg7G9uXpMHsPWTm5lLM2pV1NwxNeiuv9dadYeyzqTj1NtDxX1YlKumROBq6gTvX/qa2F69asUoNFb29vVq9eTYsWLfT29f7vpBkhREESLArxiBUW8OXv41vYuMPo6GhMTU3JyMh4ondKedpotVp8fHwKtNb6+fnh5+f3mGpVOoqa/AJ39hPv3LmzwTKKWrS7SpUq6vf/q+fOb4ev8Mbyo0x4sTpvtq6KVnvv3UFup2by55lYNp+KZlpnX6q65K0t2LqGCwcu3qSttwsN3UwJ27OJnz77kR/v+r1q1KgRb775pt4C4ADduz8bU1Hutaar9DSIkpJgUYj7cK+9kP97vjgBH9z/uEP5z1+UVFGTX44dO0ZaWprBoQ3FXbQb4EWHeNZfPUx6hUZ8+ec5gq/cpmdDD7xdbfB0zJslrih5eydfupHChRvJ7AyNZe+FOLJy8oYFbDkZzdgXqgHQztsJ7ZV0Fi/+gM83bSL734kvFhYW9OzZkzfffJNGjRrd9/tSVhgKCIuzpuu9ducR4r8kWBSiBIrTfQeGt1ArKuAztIPL3feW/9zFo/SgQxtyc3OZ8NY4wk6coM7Lw0n16cJfodf5K/Q6L/iU44dBeWsbXryRwgtf/13gem9XazrXcaOLX3mSkpL49NNPWbp0KdeuXVPzNGnShCFDhtCrVy+9saVPs+Ks92poTdeSBPpC5JNgUYgSKE73HRTcwSWfBHziWaLVavn9999p3LgxJ9ctpIezJW5t+nHxRjK+5e8Edp4OFuiMtZS3NaOSkyV1PezpVMeVqi7Wap6srCyWL1/OtWvXcHJyYsCAAQwZMkRvPcayqKgWwtu3b2NnZ1dowHev9V4NrekqxP2QYFGIErrX2nVCiDuqV6/OypUr6dixI6sWfcWc2hWZPXqUXh6dsZYz/9cek7t2yjlx4gSjPvyeb775BhMTE0xMTJg9ezY5OTl06dJFb9eXsupBd4Sys7OTYFA8EhIsCiGEKJH8FnRDS+z8twW9ffv2fPbZZ7zzzjuMGzcOX1/fAlvy3R0oZmVl0b59e2JjY2ncuDEDBw4EoFu3bqX8JA/uQSaTGNoRKr91cMSIESxYsKDA+Ye5H7wQhZFgUQghRLFFRkbyyiuvAIbH5hY2gWLChAmcOHGC5cuX061bN15//XX69+9PbGwsu3fvZu/evWzZsgUbGxtMTEx4++23OXLkyBPdclaclsGixinnB92GxoY2b96cBQsWFDhfWvvBC1FcEiwKIYQolKFZ//kzbYsaM/ffCRQajYZFixYRHh7Ovn37+Oabb/jmm2/0rj1w4IC6fuXbb79d2o9TpPtpISxqr/jo6Gi6d+9Oenq6wSWIzMzMHrziQjwCEiwKIYTQU9xFu1u0aFGi8bvm5uYEBQURGBjIjz/+yJYtW/D09KRFixa0bNmSunXrlkLtS+5+WwiLWhD/2LFjZGRkAEUH1Q/TvZb4EqK4JFgUQgihp6hZ//lBzurVq+9ropexsTGdO3emc+fOKIqit2vO42KohfDuNQuLaiHM70425GHtriSL+ItHRYJFIYQQBdxr1r+h5aFK4lEGikV1MxtqITx27Bjp6enMmDGDqVOnPjETTezs7ABZxF88OhIsCiGEeKoVd4kaQy2ElSpVAgoGk6XVpfvfcvJf79u3r9Dz+cGpoR12QAJCUbokWBRCCFHm3avl0NBEFHi8S9GYmZkZ7C5esGABUPiscwsLixKPGRXifkmwKIQQosQMtao9rAkURQWD0dHR9OjRg7S0tCLLMLSI9YPWubCxg8Uts6hldQzt4ALScigeLQkWhRBCFFtxZkqX9gSK4nYjz5o1ixYtWhRIf1gth8UZO6jT6YiOjjYYTMqWfKIseOBgMTc3l6VLl/Laa6+VRn2EEEI8wYq7P3pcXFyhee6nRayo9QzhTjDo5OT0QC2H/20hzL8uPDy80Pz5LX6G1lnMn0ltaBa1zEoWZcUDB4tarZbvvvtOgkUhhHhGGJopHRkZSYsWLYpsASxsd5e7rze0XA88vG7ke7UQTp06FTMzswIthEWtswgQFhZ231sBCvEkKZVu6IYNGzJ37lxGjRp178xCCCGeSvdqATS0uwsUr6v5lVdeISwsrNQDLEMthMVZZ7Go1sF7LT8kRFlRKsHi1atX2bZtG19++SXNmjWjdu3a1K5d22DTuxBCiKfX/SxCXVSgmR9kpqenFxpoFldhE1HyyzdUb2kdFKKUgsVVq1YRFBSEmZkZRkZGhIaG8tdff0mwKIQQokQexm4nxZmIYqiFUFoHhSilYLF79+64ubmxdu1a7O3tSU1NpXbt2qVRtBBCCFEshloOZRFrIR5MqQSLkZGRbNq0iUOHDhEcHMy8efO4fPlyaRQthBDiKVPYhJT8tHvts1zYtfndxPdqOZRFrIW4P6USLJqZmQF560llZmYycuRImjVrVhpFCyGEeErkB4JFrdFY2CQWJycnzMzMSE9PN3htUXsh55chgaIQ96dUgsUxY8YQHx9P9+7dGT58OM2bNy9yQLAQQoinl6GlbI4fPw7AjBkz6NixY4FrDE1i8fT0ZPXq1XTu3NlgV7IEg0I8PKUSLPbr1w+AiRMnsnTpUs6cOcPq1atLo2ghhBBlRHF2dwGoV69ekZNYCgs288cdPowJMEKIopX6dn+DBw8u7SKFEEKUAffa3SW/9dDQXsf36mqWHU+EeDxKJVgMCQlhw4YN2NnZUbNmTWrXro29vX1pFC2EEKIMud+lZqSrWYgnV6kEiy+99BKjR48mJSWFH3/8kVOnTpGQkMDFixdLo3ghhBDPgPwWR+lqFuLJUirBoqurK2PHjtVLy8nJKY2ihRBCCCHEY/RAweL48eOpW7curVq14scff2TIkCHqOSMjoweunBBCiKePodnShtKFEI+XRlEU5X4v3rhxIydPnlSP3NxcGjZs+NTvDZ2YmIitrS0JCQnY2Ng87uoIIUSZEBkZiY+PD6mpqQbzWFhYEBoaKmMThShlDxK7PFCwmO/q1au4urqSnZ3N6dOnOXnyJKdOneKbb7550KKfSBIsCiHE/YmMjCxyHV6ZxCLEw/EgsUupjFns2LEje/bswdbWloYNG1KlShVq1apVGkULIYR4itzvbGkhxOOjLY1CjI2NsbW1VV/b2toyYsSI0ihaCCGEEEI8RqUSLFaoUIE9e/bcKVSrJTMzszSKLlJ8fDz9+vXDxsYGOzs7hgwZQnJycpHXtG7dGo1Go3cMHz78oddVCCGEEKIsKpVu6Llz59KxY0f8/f1p3Lgxp06deiTdDP369SM6Oprt27eTlZXFq6++yrBhw1ixYkWR1w0dOpQPP/xQfW1hYfGwqyqEEEIIUSaVSrDo6enJ8ePHWbduHadOnaJ69ep88sknpVG0QaGhoQQGBnL48GEaNmwIwJw5c+jYsSNffvkl5cuXN3ithYUFrq6uD7V+QgghhBBPgxLPho6Pj8fBweFh1afYFi9ezIQJE7h165aalp2djZmZGatWreLll18u9LrWrVtz5swZFEXB1dWVLl26MHXq1CJbFzMyMsjIyFBfJyYm4uHhIbOhhRBCCFEmPNLZ0E5OTri7u+Pn56d3VK9eHY1GU9Li7ltMTAwuLi56acbGxjg4OBATE2Pwur59++Ll5UX58uU5efIkEydOJCwsjLVr1xq8ZubMmfzf//1fqdVdCCGEEKKsKHGweOrUKYKDgzlx4gSHDx9m0aJFxMfHY2ZmRq1atTh48OADVei9997js88+KzLPg6zyP2zYMPX72rVr4+bmxvPPP8/FixepUqVKoddMmjSJ8ePHq6/zWxaFEEIIIZ52JQ4Wa9asSc2aNenXrx8AiqIQGBjI6NGjef755x+4QhMmTGDw4MFF5qlcuTKurq5cv35dLz07O5v4+PgSjUds0qQJABcuXDAYLJqammJqalrsMoUQQgghnhYPPMFFo9EQEBDA8uXLWbRo0QNXyNnZGWdn53vm8/f35/bt2xw9epQGDRoAsHPnTnJzc9UAsDiCg4MBcHNzu6/6CiGEEEI8zUplnUWApk2bsmvXrtIq7p58fHzo0KEDQ4cO5dChQ+zbt49Ro0bRu3dvdSZ0VFQU3t7eHDp0CICLFy8yY8YMjh49SkREBBs3bmTgwIG0bNmSOnXqPLK6CyGEEEKUFSVuWbSysqJ27dr4+flRp04d/Pz88Pb25vDhwyQlJT2MOhr0yy+/MGrUKJ5//nm0Wi3du3fn22+/Vc9nZWURFhamblqv0+n466+/mDVrFikpKXh4eNC9e3emTJnySOsthBBCCFFWlHjpnMDAQIKDgwkODub48eNcvHgRRVHQaDTMmDGDSZMmPay6PjEeZPq5EEIIIcSj9kiWzpk2bRpdu3alQ4cOdOjQQU1PTU0lPDwcR0dHWehaCCGEEOIpU+xg8erVqwQEBKDT6ejSpQtdu3albdu2WFhYULNmzYdZRyGEEEII8ZgUe4LL4sWLiYmJ4ddff8Xa2pqxY8fi5ORE9+7d+emnn4iPj3+Y9RRCCCGEEI9Biccs3i00NJRNmzaxYcMGjh49SuPGjXnppZfo06cP7u7upVnPJ4qMWRRCCCFEWfIgscsDBYt3u3HjBhs3bmTjxo20aNGCt99+uzSKfSJJsCiEEEKIsuSJCBafJRIsCiGEEKIseZDYpVQW5R4+fHiBrfeEEEIIIUTZVyrBYkBAAB07dmT69OmkpKSURpFCCCGEEOIJUCrBYteuXTl48CDlypWjWbNmLFy4kNzc3NIoWgghhBBCPEaltje0kZERnTp14q233mLKlCn4+vqyadOm0ipeCCEKlZ2drX6fkZHByJEj2bt3LzIcWwghSkepBIsdOnTAy8uLvn37cvLkSebMmcMvv/zC+vXrGTduXGncQggh9CxevJhy5coxYsQINc3U1JTff/+dFi1asH///sdYOyGEeHoUeweXfPHx8Tg4OOilffrpp9SuXRsjIyO99B9//BFvb+8Hq6EQQgBXrlzB0dERCwsLAMzNzbl+/TqhoaFqHkVRGDx4MDt37qRZs2Zq+p49e6hSpQrly5d/5PUWQoiyrsRL52i1Wtzd3fHz89M7qlevjkajKZD/0qVLVK5cudQq/CSQpXOEeLQmTpzIV199xbfffku7du24ffs2165d4+LFizg4OJCTk0NKSgqpqamYmppiZmaGlZUVFhYWtGvXjgYNGhAbG8tPP/1Ex44dMTYu8d/JQghRpj1I7FKs/zE//fRThg8fjp2dHadOnSI4OJgTJ05w+PBhFi1aRHx8PGZmZtSqVYuDBw/qXfu0BYpCiEfP3d2dnJwcJk75gAmfLyIn5RZZcZEAaM1tcP7fFHIzUshNTyY3PYWc5JukhASRk3iDkJAQbG1tSUxMZMuWLYwZM4ZPP/2U3r17P+anEkKIsqFYweInn3xCz549sbOzo2bNmtSsWZN+/foBed0+gYGBjB49mueff/6hVlYI8WxIS0tjy5YtuLq6Ur9xU5wbdaLTp5U4dSMHjbEJuSHbsTz/B5aWlpg6eRBVwbdAGfYtB2Aed5ZkYzsOHDjAxYsX+eKLL7h8+TKJiYn88ccftG3bFhMTk8fwhEIIUXYUK1gsqqdao9EQEBDA8uXLWbRoUalVTAjxbFIUhYCAAPYeOUndnuPI3pnK7dQsQIvGWIuHvRk9x45k9POzAEhKz2LfhZskpmWRmJ5FQloWRy/fYv/Fm6Q5+3IyNp1G1YyoXr063377LQ0bNqRy5cq8+OKL1KlThwkTJnDx4kVGjhyJi4vLY312IYR4EhVrzKK1tTUnTpy4Z5dypUqVCA8PL7XKPalkzKIQpUtRFNLS0rCwsODatWt4+/hiO2AuRlb2AHg6WNCtnjud67hRvZx1sco8cy2Bnw9cZkpnX6xM8/4uPnU1AUtTI07u+4vXX3+dbt26sXv3bs6fP4+xsTFdunRhyJAhtG/fXsY1CiGeKg99zOL777+vzoC2srKidu3a+Pn5UadOHfz8/PD29ubw4cMk/X979x3eVPn3cfydpBlN915QWvaUvYcgKMhwAU4URAG3DBUcuFAR8REUFy5QURFRHD+UJaAsUfYqBUppoXTvlaQZzx8haUubUkZpC9/XdZ2ryck5J3dOC/30nvn55196IcRVLTY2lkcefRRVcFNWf/MR4eHh/LluLXPXHMHi78eEvo25vnUIKmXFAXRVaRPuw5sjr3E+t1htPPXDXuLSC7i3ZzP+3XMQH3c3Nm7cyNy5c9m+fTsrVqxgxYoVhIeHM27cOMaPH0+TJk0u9UcWQoh65bxHQ69atYo9e/awZ88edu/eTVxcHDabDYVCwaxZs3j22Wdrqqx1htQsCnHxCgsLef3115n/zf/w7n03ush2zB0Wyei+7QAosVhRqy7ZugFkF5qY9sNe1h+2r2Pv465m8qBm3NczCpVSwR133MGyZcvQaDSYTCbnef3792fSpEncfvvtKJWXrjxCCHE5XUx2Oe+weLaioiLi4+MJCAggNDT0Yi5Vb0hYFOLCHT16lMWLF/PJ8tUo29+Ee+PO9hesZiZ28WdIUw8AAgMDiYyMvOTvv+loOq+vjOFwir0lpH+LIObf0YEfv/ua5557jqVLl5KZmcnnn3/O6tWrnX22e/bsyYcffkiHDh0ueZmEEKKm1WpYvBpJWBTi/NhsNrZu3cqDDz5IXKYR3773oG/Ww/6axUzBvrXkbvseS36G8xy9Xk9MTEyNBEaL1cZ3/yby2spDGEqsNPR3Z9mknvhq7JN9O/z999+sW7eOefPmUVBQgFKpZNq0abz11luXvExCCFGTarzPohBCXIjs7Gx+/fVX3nvvPXbt2gVA+IMfoQ5oiM1qoaNfCRN7NyT0rvHAeOd5MTExjBkzhoyMjAphMTExkYyMDFypTo2kSqlgTI9GdIz0ZdLXO4kK8CDYS1euX2RKSgp33HEHzZo1Y9u2bbz22mt8//33eHh4XMCdEEKI+kvCohDikrNYLAwePJg///wTt4AGmHNS0el0jBgxgojWHqRptCx78X5WrP2FTp06VTg/OTkZoNxSfo79o0aNwmAwuHxvnU7H8uXLCQsLq/T1smGyTbgP/3u8D4AzKBrNFpQKBTExMRQWFpKdnU3z5s1ZunQpDz30EN27d3dea/fu3Xh5edG0adPzuDtCCFG/SDP0BZBmaCHK27dvH19//TVDhgxh4MCBnMwq4oYHnyXXpymakMa0NR5iyvBO+PnZp8Jx1Bzu3LmzQlhMTEykRYsWVQZCjUbDTz/9VCEQbtq0icmTJ1dZ1qrCZGBgIB/tyOFoagEf3NOJvNSTFBUV0b59e8DenJ6QkEBUVBRms5muXbuSlJTEqlWrKg29QghRV0gztBDisjEajezYsYP27dvj6elJZmYm/fr1o8Dqxo58b96L0bL3VC40vwEN9j6J22ISWTn/mQrXctQglpWRkeEMikuWLKFVq1bO1xwh02QyERYWViGgOWoiZ82axdChQyu818iRIzEYDAwfPrzSz6YPakDDCR9jsMAN/7eBqT19aResdTahb9q0iWeeeYaXXnqJRx55BKVSiUqlcoZgIYS4EklYFEJUKScnhy1btrB582bWrVvH7t27sVgsrFy5kqFDh+Lv749W74X3XfOJV+vgVC5KBRTG7+Gu3s25tUs0XnfdD2/c77zm77//zsyZM8nJyanyvVu1anVBNXbR0dEVztu1axdGoxGoGEKhtFYy7qOJBN3yLLnB0by4Pp2cTUvI+2c5YEOlUmGxWLBYLPj6+rJx40aSkpKIjo4+7zIKIUR9IWFRCFFBZmYmP//8M8uWLWP9+vWYzWbna0p3b7xb9mHxAQNDh9qX/Fzzx/94b1cx+UYbw9uH0cCWzsDeL3DfMxWbmaFiX8TLrbIQ6ijTS1MfZuANPflkVy4bThTjd+1Yrr9zIl2tMcyY+gRPPfUUgwcPdtY2FhcXs3PnToKCgtizZw9Hjx5l2rRpl/0zCSFETZGwKIQA7M2/K1as4IcffmD9+vVYLBbna0079iKq1zBMgc1JKNaCQsm/WXAio5CoQA/at2/PwrZW3M5Mor1rV1a13jM+Pt4ZuhxqO0hGR0fTs1tnenS1sWzHSWb+cpAdyUa2pVoBePvtt3n77bcrnKfVagF7M/3p06eZO3euTOIthLgiSFgU4ipjs9k4ffo0ISEhzvWPp06dyrvvvovVanUeFxISQr5/C8IHT6JE68NRAAOggDbh3gy/JhwvXel/IW7nsdqKr68vADNnzmTmzJmVHqPT6QgMDDzfj3fJKBQK7ugaSZtwHyYs2saeZV8B5ZuwY2NjGTt2LGAPiU8++STvvvsu77zzDqmpqXzxxRdoNJpa+wxCCHEpSFgU4gpmNptJTU0lIiICsAfFqKgoEhMTOXjwIK1bt8ZsNvPRRx+h8AigSc+h3NanHRPuvAk3Nze+3xLLxwcsqFUKukX7M6BFMANbhRAdaJ9rMDExkYQjFec8dNQOVjaABXCORK6s76BjEMvy5curnC+xshrI+Pj4atyV89M2wof5g4PoMdNeA9qqVSvUwU1oGuxJp06daN26NStXruT555/nvvvuo3PnzowfP55vvvmG9PR0li9fjpeX1yUvlxBCXC4SFoW4QlitVo4cOcKOHTv477//2LFjB7t37yY6OpqDBw8CcPjwYfLz81G4afhrXzz7Cz2JSc6n0UOfYND6YwbCejSjWbNmADwW3pAOHTLo0ywQT235/y4SExNp1aoVRUVFLss0atQoYmNjXYa+qgaw5OTkVGiiTk5ORqvVYjQaGTNmjMv3zcjIuKTN22pV6WTdqQVmpv/6D+G+7nx4Tyfat2+PxWLh+eefB6BXr164ubnZ+3KuWcOAAQNYuXIlISEhF/z+QghRmyQsClGP5OTksG/fPhISEjhx4gQJCQnOx4mJiZhMpgrnJKZlE5eSTZNQP9zd3cmzqGk45Qfm7AH27LcfpPVHqYBOkX5E+uud52alnibYlMGRg6crXDcmJoaioqIqawcNBgObNm2q9HVXAgMD0ev1VYZBV3MlJicnM3r06CrnWtRoNCQnJ1cIk9Wtlcw1WtG4qTicks9N72/hrVHXEFrm9WXLlmEwGOjatSvx8fHs3LmT3r17s3r1apo0aVKt9xBCiLpEwqIQdZTNZuPIkSP4+vo6a6U2btzIrbfe6uIMBZ5hjWnW7ToCmrYnuVhJDp64eQXw7oYTvHeXH1FRUbzz2kw+SFGh16ppFeZN6zBvrmnoS9+mgfh5lPavq07NIdj7H55dOxgYGIhOp8NgMLgMfXq9vtI+iZGRkcTExFzwkn6HDx+u9Nyyq7+4mmcRzl0r2TxAw+9P9OGx73bzb3wWj3yzi0ENAOyrvgwePBi9Xk/Dhg1RKpU89thjxMXF0atXL/744w+ZvFsIUe9IWBSiDrDZbOTn5+Pu7o5arQZg7NixfP3118ydO5ennnoKsC+j5+/vT0hYOB179KVFZCjh4eE8+vgTBE/8ApW7F1lAFoCX/R+4Asg3lDjf64nHH+OeAiP+HhoUCsXZRXHKyMhwWXMIVc+VGBkZyfLlyxk+fLjL86sKfJGRkedc39mVqs6NjY11GUKrUyvpGHQT7K3j2we7M3d1LAv/Ps66U+A/5DHG3Hsf2KyVnpuWlsbQoUPZvXu3y6UIhRCiLpKwKMRlkJeXx8GDBzlw4ABHjhwhLS2twmYymdixYwedO3cG4NSpUygUCo7ExbMrMZuDSbn8ckyBZsSLFAVHURAZwIuP2dc13rZtG9vVVkwqBa3CfWgTbq8xbB3uTctQL/Sa8v/UAzzt07wkJia6DE+O2jRX/QrP1QfQEYgudGLtmnCuEOqqVtLRrP7mm2+SkZHhPGZwKGQ0KGR5og6v9oO5Y0g/HuzbuNy5mzdv5sknnwQgNTWVO++8075mtpv89yuEqB/kfyshakBeXh5vvPEG+/fv58CBAyQmJlZxtAKl3hu1bwSbj6RxJiuSk5ND0KiXWevTiTUfbj1zrBZtaFMATucasFptKJUKPv/8c9LyDPh7aKo9hU11m5ldjWi+ErkKk45+lK5qHfUt++B9zfVMmXgfzRpHVXrM0KFD2bRpE3///Tdff/01999/f6XHCSFEXSNhUYjzYLFYOHjwIA0aNMDf3x+AH3/8kZkzZ9KzZ08+//xzwN6EO3fuXGwqDSq9DwDh4eH4+flhiu5DYMuuKD0CKLSpyTXasGJvDv74kIInbDYUCgXPPfccXxxRcShPQaCnlrYR3rQJ96ZNuA9tw31o6O9erhk52FtXaZld1R46BqjMnz+fvn37Vni9ukvyXQ2q6kfpqHX8fu4MZ1C02WyUWGxo3EqD+6xZs4iLi+PYsWPcddddLF++nJEjR1bZFUAIIeoCCYviiuVYos7R3Ge1WikpKUGhUFRromSbzUZ8fDx79uxh+/btbNu2jW3btmE2m/n000958MEHAftkzPFGD2y5Pjz1w16Sc4tJSMsj4onvUGo90Kpg27Se+Pv7c//997MyLwiTKtQ+wTUACrDZ8FBZiQryxmi2olOrGDVqFF0yCtFrVC6D4LlUp/ZwxowZlU5vU92pZipbheV8zq8vztWEHR5ub3a32Wy88XsMMcn5fHJf53LHjB49GoDJkyfz7rvv8thjj7FgwYKaK7QQQlwCEhZFvWaz2UhPTycmJobDhw+X2xISEnjnnXecTYfbtm2jT58+NG3alKNHjzqv0bRpU1JSUggODsbb2xsPDw8yMzM5cuQoqHW4eQXg5huKm28oXtfej5tPCJ8l+PHAmRrAU6dO4dXhRoqbdmf5zlPO6yq19omr3VQqNB7egD0saHeewCsEBvbqRLC3jlBvHUFeWtSVNB9HnZn8+kJVNUil7PQ2GRkZ5z2gpDqrsLga8XwlO5VdzLfbEyk0WRj7xb882bHiHybh4eGoVCr27dvHsWPHaNq0aS2UVAghqkfCoqhRhYWFZGZmunxdoVAQFBSETle9mrOTJ0+ydetWtm3bxr///svhw4fJzs6u5MJKlBo9aYVm564//vgDbWQ7coOa88GGY+QbzOQbSshrcxv6jnqKNHriv3vWOZo1cMTTeLS+ttJypJRATlEJfh4acnNzsSTFEBkdwe1DryPMx51QHx0h3jpCfXTlJrMeOnQoQ4dW66Oel6qamqGS6W2sVjAaS5+np0NCApSUlG5n5h2M37CBXQUFYLHYzzuz5Zzph7nkySdp1aCB/To2W7n3D/TyInLHDti1C5TK0k2lKr+p1aDV2jeNxv5Vpyvd1GqoJ821Df31fP1gd8Z98S//ncjm5Tw1Sp1nuWOeeeYZ9u3bxzfffMOIESM4cOAAZrPZub60EELUJQqb7az/3cU55eXl4ePjQ25uLt7e3rVdnDrn1Vdf5ZdffiEhIaHKoFjWLbfcwooVKwB7v8C5732I2tOPPgOux2RTUmAw8/i0Z0jLzkPhpiXvnx+c5/r2HYO+cSc8fANRavVYlBpKbKW1dHFvDEWlVPDNN98wedk+PFr1c1mOd/qowFREamoqa3OC2JXrjpfWjcgAPZH+9q3hma/dov3RqVUVmrsviMUCRUX2rbDQvjkeO/afvRUXQ3ExienptFq2jCKz2eXldUCsry+RJSX2kGg2swtwNJLuBM4er7wScD0boZ0eiAEubJKb86BQ2EOjh0fFzcur/ObjA76+pZufH/j7l25npia6VHbt2kXnzp0r1N4ezy7hlb8yyTfZMKXFs+TB7lzXq6vz9ZSUFEaOHMncuXNp3bo1ffr0Yfz48UyZMkX6MQpRh2VlZbF69WqSkpKc05qB/Y/zzMxMmjRp4pwNoqSkhKNHj6JQKMr9/7Br1y6OHDlCu3btaNOmDWCvXJk3bx4Azz//vPP/gTVr1vDvv//Su3dvBgwY4LzunDlzAPsfn46uVRs2bGDLli107dqVwYMHO9/vtdde47bbbqNNmzYXlF0kLF6A2g6LVqsVg8FAYWEhRUVFzq9lt+DgYPr0sU+rYrPZmDlzJhaLhenTp+Pr60txcTH33nsv+/bt45ZbbkGlUgFw7NgxDh48SGRkJB07dnS+5++//47RaGT8+PE888wzACxatIgZM2ag0WiIi4vDqlCRbzBzw9ARHDoWj0KlxpgUg0Zjn89P2/Ja1AENUeo8UGr0KDTuoHZHoXHHx9eXmHfskze/8847zPmnAPfGnXFleOEqevboQVJSEnO3ZLoMgBoV7Jh5A946NRkZGby3aj9JJnf8vdzx0qnx0rnhpXPD+8zjAS2D0ant96LQaEalVKB1U5b/5V1SAvn59iBXUFB+y88v/9jxvOzjsuc5gqHBQCLgahpqx3jkMDc3e2hy1LxptcRYrYw5cYIlbdvSyt+/tIZOqyWmoIAxq1cDsPOxx+jUuLH9PI2GXcnJdD7TfLxz/nw6tWplD1Jntl3HjtF5zBiWvPUWrZo3t9cAlq0dVCoJDAggsmFDe5hzbGez2ey1kY6vjhpKs9n+2GKx31OjsXQzGMo/Nhjs4bioqPx9O/te5+dDbi5kZ9uvWRkvLwgMhKCg0i04uHQLCSn/9cy/DVeq6heqDowk5I7XUXn6EeIOrw8Kxt+99Hq2M10ZfvvtN15++WXCwsI4dOiQs4lfCFFzHP/+AIqLi9m5cydGo5GBAwc6j/n555/566+/uPXWW+nXz/57Zu/evXTo0AFvb29yc3Odx95///0sXryYN998k+nTpwP21rDIyEi0Wi0Gg7OjOg888ABffPEFr7/+Os899xwA6enpBAcHA/bf846yTZkyhfnz5/Pss8/yxhtvAFBUVISHh72bUkFBgfPx888/zxtvvMETTzzBu+++63w/hULB6tWrGTx48AVll3rdDP3666+zcuVK9uzZg0ajqdaoTZvNxksvvcSnn35KTk4OvXv35qOPPnKuhVvTHJMvp6enk56ejre3N61btwbs3/zJkydTUFDAV1995aypeuqpp1i+fHm5UFg1BTfceCOrf18JwDfffMOcBZ+iUGsZNHoc/oE2jCUWVu2Ox4oPC5b/iSG+dICCvmUfTmXp2bYxDoVKbd8Cu6FQqflsVy7PnDmuuLgYa68HoGFbWr20Fovjz47eTxLWG3QqG/883QdfX18+/fRTXliXgnt05fPtGcA5DYyfnx9WQzJWQwENQ4OcgS7u8CEUZgPdO7Xn/+6bj9ZNRXZ2No27HUfl4UeIn3e5AOilU6NRYg8V2RkEFhbyahvfMwEj2/41Pb98yCvz1aNsACn7WiVL6pW//Qrw9LRvZWu8PD3tAcTxmqNmzNOTRIOBVjNnUlS2abgyZnNpQDqL75tv0mnYsPI7d+2CM2GR+++Hsk3Ru3bBmbAYExhoD1BlOIantBo4sM7Mk1htNps9YObk2INjVhZkZpZ+zciwN72np8Phw7BpE6Sl2b/HZalUEBoK4eH2rUED+9awofNrZERElSvO7IlL5qWNGaTY/Llp/JMUH9lW4Rh3d3cmTZrEN998wxdffMHUqVNr4KYIcXXJyMjg66+/xmAw8Oyzzzr3T5gwga+++oq33nqr3Byoffv2xd3dvdzv2D/++INPPvkEHx8fZ1hs3rw5ffr0KVehAvYptpo2bVpuHXilUklgYGCFLiZt27ZlwIABNHB04cE+6f+ECRMqfI4ePXowYcIEunYtbZlQqVTOY8u2anXp0oUJEybQs2fPcteYMGECQUFB57hjrtXrmsWXXnoJX19fTp06xeeff16tsDhnzhxmz57Nl19+SXR0NDNnzmT//v0cOnSo2v3mHDWLy7fGovXwxGK1YTSVMGHiJKw20FgNWE7uBewjZdWtB6F298RNo8FstmCx2VAo7LU01zSN5K/PXgXg7bff5vX/HUTp7mVfCUKposRiZcWv/8NqU2DOSyNr9QfOcgTf8Tpq/zCUbpozoc4Nm0KFQuWG3pzPobfvBGDhwoW88q8ZTVBUpZ9Hay1mmNk+j19qaio7fPtj9Aqv9FgNFo68eRNgn39v6Fu/k60NLXeMGgsqWwkRgb6se+o6FAoF27ZtY86PW9EFNqRHlw72UKdVsea3FWjMJm7q04MbGgehLCqiJC8PRVERbkZj+WZYR82Sq61s023Zptxz0elKw9zZ4a6y546wV3ZzNId6eoJeX2kN27kmwB4zZkyVA1GASl93THGzZMkS7rnnnnKvOZpIAXbu3Fku9CUmJtKiRYtyf+2eTa/XExMTc8GrqdQ7xcX20JiaCikpkJwMSUlw+rT9a1ISnDxpD6FlBQeXhsiGDSEy0rntys2l2x1jmfrGAu7sWXEgi+P7O2nSJBYuXAjAu+++S1hYGHFxcUydOrVao/eFqE/K1uoB7Nmzh8zMTDp37uysWV+3bh2vv/46bdu2LTdrwPbt2wkMDKRx48bOa8TGxrJq1Sp69OhB9+7dATh+/DhNmjTB3d2dwsJC57ETJ07k008/ZdasWbzwwgsAZGdn061bN7y9vfnvv/9QKu1dmX744Qe2b9/OkCFDGDRoUI3fl5p0Ma2i9bpm8ZVXXgFg8eLF1TreZrMxf/58XnjhBW6++WYAvvrqK0JCQvj555+58847z+v9pyzbi1Krdz73G/IEAMUn9pJ14G/n/sC+Y1Cd1cHdIbsky/nYYrHg0fpa3LyD+GVfqnO/NroLAOF6+Oajaej1epYsWcI7hz1w8w4udz3HPz0f/wDnvmHDhvFTzn6Si0CnUaNxU9o3lf1roGcw74wb5Tx+3tojJGYVoVYp0GBDY7OgtlrQWs14Y7bXwhQWElZYyPI2OsxFKXgZCvAszsezKB+lo5kwvgiGvwOFhfQsLORnR6ibVxrsbq/kb5UKPcoUCnsAc2zu7hUfh4WV1tTp9c4auwqBrrJ9l7APW2JiIhmxsRX2l12XuCqVrbNcVmWroVR3iprKjnvzzTeZPHnyBS3Jd0Vyd4dGjexbVQoKSoPjqVP2r45twwZITCxXS2kB7pzzlL2pv3FjkiKbcywokms7NLL/7GL/yz8iIoIXX3yRKVOm4O3tTU5ODp6enjz22GM1+KGFuDgWi4WTJ09iMBho2bIlYP9927hxY06ePMnff/9Nr169AHv4uvPOO7n++utZtWqV8xp33nknsbGxbNy4kWuvtQ8sLCgoYOPGjRVa08aOHUtsbCx//PEHQ4YMAWDevHksXLiQJ554whkWw8LCuOOOOwgPD8dsNjuXUn311Vd54YUXCAgo/T3p5+dXbpYMh9GjRzunvLqa1euweL7i4+NJSUkp99eBj48P3bt3Z9u2bS7DotFoxFimeTAvLw+ATpG+aPWeqJQKVEoFebk52KwWWl9zLQ/MOgLY/1r5cHsmNpUad50WjZsKD72e4qJCVApoHdXSed0JEyag+PsYVqUab28vZ5jLycrETQFNI0OJjo4A7P0duiRm4eXtUxoAzxyvcVOiLTMZcIMGDVg9vbSqG4DZs+3NkI4A91FpgJtStjbPWvk6tw5NwN5/rbKBB44tPLxikHOEvbL7Xe3T6erFSNjqzGmo0Wj46aefKqwNXFMTYAcGBqLT6TAYDM7aybPp9Xr69u17dYXCi+XpCS1a2DdXcnPtofHPP2HKFOjXD4qKyP1vN/eb2xOXreb1Bc/TfN8aABR33skLzZuT2KoVn8XEYCgspFOLFky4+WZ7s3o9+DcgrhynT59m+/bteHh4cMMNNzj333nnnRw6dIhFixY5Wy1WrFjB6NGjGTx4sDMAKhQK/P39OXHiRLnr2mw2rFYr+Wd1+WjRogVubm7O/vMA3bt355tvvqFJkybOfWazGV9fXzQaTbk/nIcNG8aJEyecZQJ7946lS5dW+GyhoaGOwjh/11nz81EaDM7fg39u2cKJkyfRAx6Ah81m3yZOxMPXFw8PjwqDGrVaLZ6ens7PmZVlrwzy9/d31moWFBRgNBqx2WzOMQeVbTabDQ8PDzw8PIiIiKBDhw6AvbtWUkoqWXmF+AeFYLSAp9aN9MSjxMbGkltQhLZJN9y0es6uiikpLqxwL6rrqgqLKSkpAOX6EzieO16rzOzZs521mGV99UD3alXldutWvfL5+/vzzC2VHRxVYY+vry/XXkwn+KwsyMuzhzFf39JauspC3dn7zn6s0VxVv8guZEUUR1OjyWQiLCzsgmsHz1dkZCTLly9n+PDhUnt4ufn4QLt2pQNtpk6FTp1wN1tp++M+juxOYsaNT3DjjbfAnEeIiYqCvDweNBrZq1TyX0kJSbGx7IuMpKtej61RI8bk5tKneXMmDR+OsmlTiI62b15etflJRT2RnJzMf//9h5eXl3NULdibZffs2cPnn39Ou3btANi4cSP33HMP1157bbmwGBsby/79+8v9H+jp6YlarcZ6VuXCTz/9hFqtLjfX6ogRIzh9+nSFbl+//PJLhfKGhYZy9y232ANdQgIUFeFWVMQ/c+dSkpuLevNmZ5ejEYWFjOjdGw4dovjhh4lNSiImLY1TubkUFhdTaDRSaDRSYDIxSKnkvjMzUJwAumCv/S87CdvbgKPeU+GmRaE+sy37DYXKDYVKDSo3LPmZWPLt9+KusQ9w99RXKbFYKSgy8shjj6NQufH67Dko3dR0jPTl09eeZvHixSg9fPHtc4/9Wkr7tRRnNpRqimI3U7DnDwAGjhhFZpcJFJdYMJSUvceHARjTI5K89Z+yYMEClO7eNHzi20q//3rFOfrEV6HOhcUZM2Y4h4O7EhMT46zqvhyeffbZch3O8/LyaNiw4WV7/xoxd25tl6BeupgVUWqaq5VUHLWVlTVhi8tP46bk/25vT4SfOwvWH+MPIgkYOpkxq94Ha/npj1KBHgoFh554guMHD/Ltb7+xPDmZ67dto2nZwVB+fuX6STr7Tjr6UYaH20fIi6vGww8/zMaNG/n5559pcaYGfOvWrYwaNYrevXuzefNm57G7d+9mx44dxMfHO8NikyZN6N69u/O5w7vvvovJZLIP7rBYoLCQQa1bU7RnD24Gg7ObEoWFNKqkz7l7URHulfU/P3OsraCAlIICThQW4mM00vrM+1qAJdin67qF0u5KR4FtCgX7dXr2e/hyVKnmlEKFQq1DodGhVAeg8NRizD+COftMpVD/Gzk24B6KVBpyraDcewA3tY6brumAASWTrgmgz1/LUO7ZQ5Y2iOQ2d7u8z9l/f0XetmUAGFUePPbtbudrAYMfBeD//jxu/570L60hVard8epwo8vrhnsq8Pa2D2pt1qQxx4oqzu6gVinQa9xQq5TOQTfunt64h2gJ9POpMAWXm7mYC62WqHNhcdq0aYwbN67KYxo3bnxB13ZUPaemppZrBkxNTXVW8VZGq9XKZLkCqNkVUS6UrKRS/ygUCqbd0AJzbjof/peFZ7tBtO95La9c3wC92t6F5OTJk4wZM4aCggImbdvG77//zoIvvqC4uJimTz1lH4QTH2/fEhNLt7/+svejPLtLQ0hIxdHc4eEQEVH6VWoo67TNmzezdetWbrzxRmeI27t3Lw899BDBwcHlauf279/P4cOH+ffff+1h0WYjzM+Pbh070qZhQ9i3zznLwyvXX4+pRw+679sH+/dDfj7dCwr4p0UL++CuG25wzsLQr+x0VWf6YFcZJJRK0OuxuruTrdORrlaTrlaT7KYmSe3OaTctyQo1J20+JGt8SMo/SFG2vfn2ziGjaDzgLgoUarItsGb/IZQaPe82aYbBouDFQdEs//AVvv72W9zD2xA86kUAwiopRkfrEdrrc/Hw8EAf1Z65O0vDl76pvUVvX64VsJLq4cvzb71lv4+nchnxvj1YO7p4aVRK1ColapWCF9+azbhe3wFwKruIp5fvR+NW+rpaVdpF7JoIH57+/HM+//xz8opL+OqfROdxmrLXdVMSHdCHdg3sI7jNFivxGYXo1CrcNSp0bkrcNW6olGXDYJtz9m3Oy8tj4YNVHuJSnQuLQUFBFzW8uyrR0dGEhoby559/OsNhXl4e27dv5+GHH66R9xRXppqqpausdrBsE3VlzdWOmkNXzcwgTc11QWXfuwamBNJ+nE/QzdOJK3DnxxNuzLujAwCdOnVi/vz5PPjgg/z1118MGzaMn3/+GR8fH/vJoaEk22xMnj+f999/v+L/mwUF5QffnDpV+nzjRvvXMnPEAfbuJWfPMxkcXH4+ysBACAiwT3Du4XFVdUG5XMwGAwf++49Tx48zvFcv5/yiMx56iC0HDxIeF0e79u2hoADz0aP8888/NPDwgDvvdE7x9UJKCpbwcHo9/TQ8+igUFNDLZmM7wO7dUKYvn3NRKcfMEGfPAOHj4/xjolirJaaoiP25uRzMzORASgq9u/ej+8CbyVVqSDNZeePLb0DrwW2j76TIquTh/k14bfIDrFixAn2rfgQOnYLCrfKBhYrf3kaZm0aDBg1QtO3MN1mlFTW6hm0BOFVgASBPpcXD25v+/fsT3K4vO7Chc1PgoVPjodOg16jQa1S4a9y4p3tnBrexVxgl5RRj9ElEp1GhV6vQa9zKPFaVW2a1VZgXB14ZjLtadVY4qygywJPvJ/Ws8hgHXw8tTwys3pR9biolzUJq9w+5OhcWz0diYiJZWVkkJiZisVjYs2cPYF/r19HJtGXLlsyePZtbb70VhULB5MmTee2112jWrJlz6pzw8HBuueWW2vsg4qpXndpBQAap1EOBgYHo9XqX3zuA7B9fou/kBTx7Y/nuNY7WEJ1Ox8aNG+nWrRvvvfeeMxhOnjyZTZs2UVBQwMqVK8tf1NMTWra0b64UFdlrjhxTA6Wmlm5pabBnT+l8lJV1vdBo7KHRz6/8qjm+vq6nnCo7+E2vL7+0o1Zrr4mqKxwTxptM5bezJ4x3TBpfXFz+cdnnZSeWL9Msayso4EReHtriYsLPDLDYW1JCF8AXyKJ0loth2GvNmn7yiX0OUC8vmnp4sKJBA/w9Pe3zh3p6QmQkQ9q0Oec0Xxa9B3kad3JUOnKVGqJDfSjMTicxMZF/49LYcrKY7EITeQYzBUYLhSU2SiwalDpP0le9ieH4DgAK29/ApzuLgWIAVB3s06v9ciAdgOHXhDlHHrur3coFRZW1BI3Siodaibe7hlkL5nFHn9ZoNBriMwr5eXeSc+5cT619Hl0PrT3ghfu4M+ajj4CKU/FUJcLXnak3VDE4rQw3lRJPVR36mawl9Tosvvjii3z55ZfO544JMjds2ED//v0Be2fcsjOsP/PMMxQWFjJx4kRycnLo06cPq1atqvYci0JcrAupHUxOtq/hcvYoagepOay7IiMjXU7a7ei6sPTDtxg6dEC5X3Y7DsUxapR9SivHlEtHjhxxThUC9hDZsWNH3nvvvQsrnF4PTZvat3MpLLSHxoyMipOc5+SUbsnJEBNTflWd4uLql8nNrdwqRGg0pWuIu7mVPlYq7bWajhWFzmazld8cqwg5Vg1yrCZUdj30s7cLnYZYpbIPGHR3t4dgd3csOh0HbDaO2Wzc1qgRCj8/iIhgys6dvBsfz4v9+vHK8OGg19NOqyX06adpGxVF7qxZ+IaEgIcHz5YN21otKBT4YO/Dd7YSi5V9RxLY+98WNm9cz/r162nR/xaiBo4h+aSB0zl5ZBSklztn0f1d+XL2c3z55Zd4tB1I4LApZz4P9s6ClIaGtp260nNYT9q2bYumYTtWxFnw0Wvw1qmwFOXjqVHSJDIcL3c17Rv4ct077/D+++9jRkV2kQkvnRpP7dlNqeVFB3ow5frm1brlskRmzarXYXHx4sXnnGPx7DnHFQoFr776Kq+++moNlkxc7c4OhMnJyWi1WoxGo9QOXoUiIyOr/L6GhYWV+2X3y54kpi07jKpxDzi0kSVLluDl5cWSJUuYNm0aarXaGTQ//fTTclOLHDlyhGbNml36X56OkBIVdf7nms3lJ8svs8ylczMa7aGy7LKPRqO9Js8R8CwW+7XKLh/p2CrjWIKybLB0BE1H6CyzxCVqtT2Qnr1PoykNro6vjtrQsrWijoB4ZkqVsrVdpuJiOnp4YLPZSP3zT+eybo3mzUM9fTo5HTrA008DoAFOP/BAtb6HJ7OK2BaXSUJWIbFJ2RxJSic1vwSjQgMKJanL5jlX6IpW6Fh9MLXCNfRqJX4eWmw2G5GRkURFReEXrEeZsQcfnRv+njpC/Lxo0bgh17RsSuOIEIK8hjiXRgUYV80fBS3goa3X0eOqJN8xIS6R6sxpqNPpWL58eaU1hFI7KBz+ik3HbIXAEU+h8g6iZcuWdO7cmZtuusl5jGOakrKBYu/evfTu3ZuRI0fyySef1J2BeW5u9n5vjv6WV7ht27bx6quv0qhRIz7++GPAPudfx44d0Wq15ObmOsPihAkTeOSRRyp8rxQKBSazlVPZRSRkFZGYWURCZhGJWYU8MqApfpYcNmzYwMpDmex0a13mTBUo7SHOZimhWZv2DL39egYMGEBI07bsSyshzMedMB8diuJcvHVuhIeFOOc3vE4qU0QlJCwKcQFczYsoK6KIC3H2z9PdTW2knTSwOUOH37VjWbgzjw87WHE703fKarXy2muvAfaA6BhstWfPHgwGA6dPny43ubGoWSkpKajVame/PKPRyKpVq/Dz82PBggXOlUN27txZ4VxPT09yikyUGM14nqlx23A4lRd+PkhybjHWSlrCB7QMJm/XKh555BE0oU3x7Xcf5uzTlOSkEOGjo1vbZlzfuwtDB16Lv98t5c7t0KTss6sjvIuLJ2FRiPNQncEK0pwsqutcP09enYbjN2gia44XMfHrnSy4qyMeWjdMJpNzIQFHf1awL4PWqFEj2rdvX2F1CXFpOLo2OWp0p02bxrx583jjjTeYMWMGAH379mXOnDncfPPNzqAIUFRUxN7Dx9l+9DSHkvM5nmMmxeBGAToant6I+ehmUlJSMPlEoh7yDADuahWWvFRyEo8wqGcHhvbrRo/GAWQqOjNo0CDatm1L27bhtGlzPa1btz7vNX+FqA7530SI81DVYAUHqT0U1VWdwS/Tn3iIr44qWX84jTs+2cbyh3qh0+n4v//7P/r06VNuwAvgHNznsGDBAjw9Pbn33nslQJ4Hx3JsHh6l06i0b9+eI0eOcODAAWc/0WbNmmGz2Th27Bg2m43ExEQ8PDx4+umnMVttrF+/nhkzZpCQb0N93WO4eTnmOy0/qHJ/fDK5//wDgEKTyl9LO9Iiwp8gTy1ffvklJ/zU3Hx9Uzp2tK9b3iSoG2vXrq35GyEEEhaFOG/nGqwgxPk418/Tje3CGNI/molf7aBvsyDnoAJ3d/dyx50+fZqMjAyuueYa577Nmzfz5JNPYrPZ6NChg3PGCFG1lStX8uSTTzrXJnYoKirCYDCQlJREkyZNKCws5K677mL48OFovAPpd/cTHEjKpfP1t5Cj8OLOrpF0UFn577//UOp9aegViM1mRVmQjt6YSYCiiAi9lSYBWhqO6Uvw1JEEBwcTGRlJREQoyjOjvM+1UIUQNU3CohBC1GExMTG0agVzrvPFV1fonLT94KHSfo5JSUkMGDCArKws/ve//9GjRw8Aunfvzuuvv86hQ4fKBcUdO3bQunVr9Hr95f0wdVBmZibr16+nQ4cONGtmnyTZz8+PuLg4CgsLsVqtztC2YMECDhw4wOLFi3n44Yc5mpDEw5+sY09SPgmZe6HREHwawbFCABMHTucy/uZOrFixgkaNGpGnDqBz0zA8dZVPSC1EXaWwnT23jDinvLw8fHx8yM3Nlf4hQogaUeU65Co1Ibe/Ssmxrexe/j4+Pj5cf/319hospZLnnnuOmTNnotFoKpxaWFhIVFQU7u7urF271rlu8NXCZDKVuy+jR49m+fLlvPrqq84J8a1WKz///DNNmjRhx44dbPzrb/7eF0e2OgCsVvJ3/c9+skJJy+d+pdhsn5mnaaCe9g186NAogLYRPrQM9So3vYwQtelisovULAohRB1UVX/G1ccKWbgrD11kO745WMj0wQ1Zu3Ytjz76KN988w2vvfYav//+O1999RVt2rQpd+7x48dxd3fHx8fHOXr3alBUVMTs2bP56KOPiIuLcy6bOGjQIA4fPkxAQACnT59m7dq1rFq7nk2HTlLgHoK2QWu04cNRDtHjD6iKs3l4UGt69+5N9+7d+fukiUAvLZ0b+eEtNYbiCiU1ixdAahaFELXJZrPx7p9Hmb/uKABD24Xyzu0d0KlV/PDDDzz00ENkZWWh1Wp54403mDx5srMpFexNr2azmZCQkNr6CJed2WymRYsWHD9+nP/9738MGzYMq9WKQqGwz2loMuHr60txcTFh4z9AE9So3PnubtAlyp+eTYOY1K/JOdcJFqKuuZjsImHxAkhYFELUBSt2n+KZ5fsosdjoGOnLp/d1IdBTS3JyMg8++CC///47ANdeey2fffYZTV0s67dmzRr8/f3p0qXL5Sx+jbFarWzbto0NGzbw/PPPO6e5+fXXXzGbzbRu3ZoHH3yQIqOZ179YwfrDabxxWztuHjGc9PR0AgZOIFkbQa+mwfRsGkTXKH+ah3hJQBT1moTFy0zCohCitiUmJpKRkcHBdCNztmRTYLIR4qHi+b7+NPB2IyAggNWrVzN16lQKCwvRaDRMnz6dGTNmlBvY8s8//zBgwAC0Wi0HDhygQYMGtfipLo2SkhKCg4PJyclh3LhxZGVl0atXL6ZPn06RycxvO+J57K3PcW/cFaXGPoXNV+O70auxH25ubhSbLOjUynq73rDjZ8MVmd7r6iR9FoUQ4ipy9uAXN/8Igke9zOliD0be9gDm7NPo9XpiYmIYOHAgDz/8MGvXrmXWrFl8/fXXvP/++wwbNgyANm3a0K1bN7RaLREREbX5sS7IsWPHePXVV50jwUtKSvjuu++cQW/x4sUAZJRoSGiwkw2H0ykuseDRsi8ADfzcGdoujEh/vXMeSndN3R+U4ioQJicnM2rUKAwGg8tzHT8bEhhFdUlYFEKIeiYjI4OioqJyy0rmGiykF1loOvY354TeGRkZdOrUidWrV/PTTz8xZcoUTpw4wdGjR53X8vLyYu3ateTk5DgDlslkYs6cOTzxxBPOgSB1VXFxMd988w1Wq5XZs2ezcOFCEhISAPAJDGHIwP50796diDbdeWa9fdWbhv72gDisXRjtInzqZA1iVbWDycnJjB49muLiYpfnazQafvrppwrr0Jf92ZCwKKpLwqIQQtRTrVq1cq4LfTZddCeWHsinY0cbCoWCkSNHMmTIED7++GMee+wx53Hx8fFEREQQHBzs3Ddv3jxefPFFfvzxR3bv3l2nwpTFYuHgwYPOyccjIyO5+eab2bhxI8899xwKjZ7w/ncT1HMk/dtGMv9uez9Mm81GujqOfs2CaBvhXSc+08XUDgLMnz+fvn37ltvnCIMmk4mwsDCXPx9CnA8Ji0IIcYXJMVgIunkGyw4VYPx+D2+NugatmwoPDw+mTZvmPM5gMHDjjTeiVqtZvny5c87FLl260Lx5c6ZNm1YuVNlstloNWenp6Vx//fXEx8ezYcMGvv32Wz799FPy8vJQBzai0chJqJv1osSmJMcKm49nYzJb0bjZ+x8+OqDyAT61ocp5NM9wVTv4+++/M3PmTAIDAyUMistCwqIQQlxhfHUqstd/RtDQJ/hlz2mScwwsvLczfh7lJ+mOjY0lKysLNze3ctPoDBw4kP3796NWl84buH79el588UXeeustevXqddk+S1mBgYG4ubmhUCgYOnQoqampuDfuQvTt92ENaAxAiQ2ah3hyb88obu0YgcZNeY6r1oxzDTJJTk6u0JXA4Vy1gzExMQhxOUlYFEKIK1DBvjW8O/tl5m3P498TWdz20VY+va8LTYM9nce0b9+eQ4cOcfz4cXx9fQF77eGePXsqrCP98ssvs2XLFr777rvLEhZtNhvr1q1j8eLFLFq0iA0bNjBw4EC+/PJLQkJC+Pzzz1m9ejWtR0/jfwmgUioY3CaEe3tE0aOxf63WgFan1lCns4/CrqorgRB1hYRFIYS4QnUI1bL84V6MX/wf8RmFDHtvE9OHtGR8n2jnMYGBgQQGBjqff/3114wdO5YHH3yQF154gUaN7JNTf/fdd7z++uvOJfHA3oyt1WprJJgVFRVx5513kpWVxcGDB9m7dy9Ll36PZ6u+WAxuPP3000yfPp20fAON/0nk7m6RhProLnk5LkRlA5DKctQcClFfSFgUQoh6ylVzZNn9LUK9WPFIL6b9sJdNRzNIznU9grbsuZ999hmLFi1i1KhRPPXUU3Tp0oUPP/yw3LFTpkwhNjaWTz75xOWE3+ejqKiIrVu38ttvvzFnzhxeeOEFEhISsFgsJGQb+TBGScLuXbSL8OHnR3sDEOylY+r1zS/6vS9UZc3NZe9/bc5pWNnPhzRhiwshYVEIIeqZwMBA9Hp9lbVTer3eWWMY7K3jq/Hd+HlPEje2LR0skVNkwlunRllmZZLZs2czdOhQXn31VdatW8f333/P999/T79+/XjqqacYNmwYSqWS1NRUvv76awoLC0lKSrrgsGgymVi7di1vvvkmW7ZswbFOxA033MCUKVMoNJp5a+U+ArxuJMFgQ+Om5LqWwVistlpfUeVczc1jxoyp0TkN4+Pj2bVrV7l9ycnJaLVajEajy5+Psj8bQlSHhEUhhKhnIiMjiYmJOa9VOhQKBbd2LF2dxWK1MX7xf7hrVLw1qj0Rvu7O1/r27cvatWvZs2cP77zzDt999x1///03f//9N82bN2fMmDGMGDGCvXv38vvvv3PttdeeV/ktFgsbN25k6dKl/Pjjj2RnZztf02q13HvvvURERLByXzKvrTxEcq59CpnrWgbz0ojWNArwOK/3qymumpsdzcyzZs1i5syZFzWnYWU1gY7v+8yZM8t1CyhLp9OxfPnyCiOpQVZwEedPwqIQQtRDkZGRF/ULPyY5j0PJeRhKrAye9zczh7fi9i4Ny/U/7NChA1999RVvvPEGCxYsYOHChRw5coQXX3yRF198kYYNGzJ8+HByc3Px8fEhLy+Pt956i5kzZ6LVap3Xsdls5ObmcuDAAb7//nt++OEHUlNTna+HhoZy/fXXo9PpmD9/Pnq9nvWHU3l08Q7AvsrKyyPaMKh16Yjty6WqUc2OIOdqkEp0dHSFfa6uUVZ1agerCoMggVBcWrI29AWQtaGFEFeC+IxCnvphLzsT7DV7A1oE8ebIawjxrnygSH5+PsuWLePXX39l7dq1FBcXExAQQGpqKkqlkqFDh7Jq1SqioqLYsWMHAQEBAEydOpV58+ZVuF779u2ZN28e/fr1Q6Uqv8Se1Wrj7s/+oVt0AI/0b4JOffmX4KvOqGaA//3vf87lEwF27dpF586dWbJkCWPGjGHnzp3lwqTjdZ1OV+XE21I7KC4lWRtaCCHEeYsO9GDZpJ58vvk4b685wobYdK5/5y9eubkNt3SIqDDK2cvLiwceeIAHHniA4uJi1q9fT3p6ujPoTZkyhTVr1nDixAmSkpIICAjgrrvuYtWqVQAEBAQwbNgwoqOjmTdvHvfccw8DBgwAYP3hVD7+6ziL7++KXuOGUqng2wd7lOtPebmda1SzY3LsnJycSs+Pj48HKtYeOp5XVTMIEghF3SFhUQghrmIqpYKJ/ZowoEUwT/2wl72ncvl8czwjrgnHTeU6qLm7u5erTQP7oJTXXnsNNzc3goKCAHuTak5ODosWLWLcuHGAvVl6ypQp+Pj4kJZv4JXfDrFyXzIAn/4dz5ODmgFctqDoqqnZEep8fX0rbWZ2NbLYMQDJ0Z+wsqZkvV5Pu3btJAyKekHCohBCCJqFePHjw71Y+PdxBrUKwU1lX/kkJdeA0Wyp9qCSZ599ttzzt956C4vFUq5mTqFQ4OXlzbfbE5n9Rwz5BjMqpYIH+kQzod+5+/ldStVpah41ahSxsbHVDnaOAUibNm1izJgxldZMSq2hqE8kLAohxBXqXEvOnR1Y3FTKCusnL/w7jkVbTtC3WSD3dG/EwFbBqFXVX0KvW7duFfYdTc3n2Z/2s+NMX8l2ET7Mvq0dbSN8qn3d83GuQSrnWnbPYDBUOaK5silsypJVWkR9J2FRCCGuQNWpMavOHICZBSYUCth0NINNRzMI9tJyZ9eG3N61IQ389BdUtvl/HmVHQjZ6jYqnbmjB2F5RNTZnYnUHqbhqaj7XOVD1FDYyp6G4EkhYFEKIK1B1l5w71xyA793VkaduaMF3/yXyw46TpOUbeW/9Md5bf4xBrUL4bGwX57F7Tubg667GMS4mr9jMlrgM/opN596ejRjazj6YY+aw1iiAZ4e2Kje/Y0242EEqVXEMTnF1bZDmZnFlkLAohBBXsEvRBBoZoGf6kJZMGdScNYdS+OafRLYdzyTUp3QuxSKTmVs+2OLyGmG+OmdYDPXR8f7dl7dZ1tV9qO7yd1UtnSfNzOJKJ2FRCCFEtWjclAy/Jpzh14RTaDRjKLE4X0vNMxLuoyO3uMS5z02lpFOkL9c2D2JAy+AaLdu5RjQnJyef9zUDAwOdcyHK0nniaiZhUQghxHnz0LrhoS39FRId6MHWZwfWSllqYkQz2Ec1L1++nOHDh7tsapZmZnE1kLAohBCiXquqX+LFjmh29GWUpmZxNZOwKIQQ4opwIYFORjQLcW4SFoUQ4grmagBHdQd2XEkq+8yOmkMZ0SyEaxIWhRDiCuRYcs7VwAy4OmrMqjtIpW/fvhIIhXBBwqIQQlyBHEvOnc8KLnXZuVZhcUUGqQhx8SQsCiHEFSoyMtJlCHKEL1cBrC4FqOquwuJqehzH5NkySEWICyNhUQghrjKXainAy6W6q7Ds3r3bGQzLuhr7ZwpxKUlYFEKIq8ylWgrwcnNVM+ioUZQRzULUDAmLQghxlTpX+HJVI1eXmqhB1mgWoqZJWBRCCOGUmJjIqFGjAKocPVxXmqjLkj6JQtQMZW0X4GK8/vrr9OrVC71e75xY9VzGjRuHQqEotw0ZMqRmCyqEEPVERkYGBoMBsNfU7dy5s9y2ZMkSioqKqhxlLYS4stTrmkWTycTo0aPp2bMnn3/+ebXPGzJkCIsWLXI+12q1NVE8IYSo16SmTggB9TwsvvLKKwAsXrz4vM7TarWEhobWQImEEELUFFmNRojaUa/D4oXauHEjwcHB+Pn5cd111/Haa68REBDg8nij0YjRaHQ+z8vLuxzFFEKIGlVZyKqLwUtWoxGidl11YXHIkCHcdtttREdHExcXx3PPPceNN97Itm3bUKlUlZ4ze/ZsZy2mEELUd9UJXzqd7rKHr6pWaVm2bBlApfMogox2FqIm1bmwOGPGDObMmVPlMTExMbRs2fKCrn/nnXc6H7dr145rrrmGJk2asHHjRgYOHFjpOc8++yxTp051Ps/Ly6Nhw4YX9P5CCFHbqloK0DHH4vLlyy9r+KpvE4ULcTWpc2Fx2rRpjBs3rspjGjdufMner3HjxgQGBnLs2DGXYVGr1cogGCHEFaWqpQABcnJy2LVrV4X9NdVMXV8nChfialDnwmJQUBBBQUGX7f1OnTpFZmamy6YNIYS4mtR2/0AZgS1E3VPnwuL5SExMJCsri8TERCwWC3v27AGgadOmeHp6AtCyZUtmz57NrbfeSkFBAa+88gojR44kNDSUuLg4nnnmGZo2bcrgwYNr8ZMIIUTdUFUTNZSu7pKRkVHpMdJ3UIgrT70Oiy+++CJffvml83nHjh0B2LBhA/379wcgNjaW3NxcAFQqFfv27ePLL78kJyeH8PBwbrjhBmbNmiXNzEIIcYarJurExET69u17wf0KqxrAUhdHYQsh7Op1WFy8ePE551i02WzOx+7u7qxevbqGSyWEEFemi+lXWJ0BLFBacymEqDvqdVgUQghx+V1Iv8JzBc3ff/+dmTNnkpOTc4lKKYS4VCQsCiGEuKSqmuzb19e30qDpeD0+Pv6yjsIWQpybhEUhhBCXhKMJuaqR1KNGjSI2NrZCM7Wvry8AM2fOZObMmZWeK6u0CFE7JCwKIYS4JBxNyLNmzWLo0KHlXnP0ZzQYDJX2aXRMX+aqmRpkpLUQtUXCohBCiEsqOjr6gudKlHkWhah7JCwKIYQ4L676D8bHx1/mkgghLgcJi0IIIaqlOqu7QGn/Q1eqGgAjhKh7JCwKIYSolnOt7uLol1jZ8qmBgYHodDoMBoPLsCkDWISomyQsCiGEqDZXq7tU57zly5czfPhwl4NYZACLEHWThEUhhBCXlKsmZcdoaRnEIkT9ImFRCCHEJVGdPo3S1CxE/SNhUQghxCVxrj6NIE3NQtRHEhaFEEJcMhfap1EIUXcpa7sAQgghhBCi7pKwKIQQQgghXJKwKIQQQgghXJKwKIQQQgghXJKwKISot0wmE4cOHSI1NdW5z2q1cujQIXJzc7HZbLVYOiGEuDJIWBRC1AtWq5Vjx46V23fHHXfQpk0bli1b5tyXlpZGmzZt8Pf3x2KxOPevXr2ahQsXcvjw4ctWZiGEuBLI1DlCiDovISGBPn36kJeXR2pqKjqdDoCWLVvi6elJYWGh89jMzEx8fX1xd3fHza30v7jFixezdOlSnnzySe677z7n/qNHj9KsWTNA5gAUQojKKGzSTnPe8vLy8PHxITc3F29v79oujhBXnCNHjpCenk7v3r0Be61iZGQkeXl5bNy4kU6dOmGz2cgvLMbLwx2FQgFAYmYRafkGikssGI0m/Lz0eGrd0GtULJj3Nv/3xqtYrRaX76vX64mJiZHAKIS44lxMdpGaRSFEnfLDDz9w++23065dO/bu3cvJrGJiU/OZ9M4yipR63tiaS+LKVWQWWbDY4IdRoaiU9rD4+oZkdqa7unJn0Hmx5JP3adWqFSkFZn7/7Rc+njeHm2++mdtuu40xY8aQkZEhYVEIIcqQsCiEqDUlJSX8999/+Pj40Lp1a05kFmGJ6ICHhweRkZEUFBQw+48j/HEg5cwZ2RWu0b13X2ymYgB8+9+PvllPbGYjAAqNO8ozm8JNg81UjK+vL506deLJpbv5n6oXg+asoWNjH7TeZvs7ZGdzxx138Nprrzmbp4UQ4momYVEIcdnYbDZsNhtKpZICo5npcz5iyaotRHUegDIoieyiEgD+9+dmvNVWjh49SsaxvbgbVLQI98NHVcKPiz+ib4eWrP3le6ymYmdQBMjZuIicjYsqfW9vXz9sZiM5OTkAxJ20B9DD6cUcTrdfI+LhRUz/YTcH/z1Kwn33sW3rVmcTtxBCXK0kLAohapzVamPylMn8sGwZP/74I7uNwfzfmlistib49WtCLkBRCW4K8FcUcNPou8g/WX7UctlnG45uZuEHH3DjjTdisVgoKipix44djBkzBoC5c+cSFhbGyZMn2bdvH/v27UOj0bB7d2nN5M53xpNRaOLlhcs5UeLFX4dTwTuIDIKIGP44kzop2L17N2APuYGBgTRq1KiG75QQQtQ9EhaFEJdcUloWP/21E6tfFLtPZrM7MYewLAspKSmsW7eOa0aMx2oDb5UZXWEyOcd2kbj7b4ypccRZ7M3BS5YsoVWrVvz666+cOHGC66+/HoAxY8ZgNBrp2LEj4eHhzvcsOyL6uuuuo1OnTuXKtGTJEu69914ADAYDoaGhFBw7xrRbe5KWlsaSRwZCSHP0TbtjSjvO+Pf/AECh1hJy52yKj25j+l0DefaJSTV674QQoq6RsCiEuCg2mw2DwcCJnBI++fs4+05mcyy9AIVCCRxxHndNq27cd18+mzZt4p0FH1JgKMFSkFXuWlFRUTRv3pw1a9bQqlUrOnXqVC707dq164LLWbY5WafTsXv3bjIyMtDpdGRkZGAozIPjOzAc3wFAnz59mDhxImnukSzYUYA2vDkLT0Pson95fmgrmgR5oFAopJlaCHHFk7AohKi27EITB0/nceB0LgeScvHKOcaiV5/ghhtuYOrr77FidxKAPSgWZtKzaQg71ywncfdfLEk9DmWmrfHw8KD7ddfRs2dPunfvTrdu3QgJCWHXrl2sWbOmxj5DfHx8udCZmJhITEyM83nv3r3Ztm0bmzdvZvPmzdww7GZuvPY2VuxKQtfoGjbGprPpaAZ9Qiz8+8XLfPzeO1x77bU1Vl4hhKhtEhaFEC5lF5r4ZnsC+5Ny2X7kNDklqnKv9w3zIDU1lZ07d3LqwHZ0x/4k0hPefWkaLaPsTcTBr43BmJFBdHQ0vXr1olevXkRHRxMQEFBu0uykpCSSkpLKBbdLydfXF4CZM2cyc+bMSo/R6XR8++23FBcX88Ybb7BkyRLWrPwFVv4CwNsLvyJW14o1h1L5K1mJ5dppfPb1UgmLQogrmoRFIa5yNpuN5FwDB5JyOXA6j0h/PV0CzMTExNCj30DeXuNoSrYHRbUhG70xi7yEA0SH9WTr1q106NCBf//9l9gf51HUsCGtFr/jvP5vv/1GVFQUoaGhgL0mr1WrVhQVFVVZruTk5CpfPztUln1eWeB0jIJ29IU8+9wxY8awfPly5xyLX375JTNnzuT111/n66+/xmKx8NSk+7jhhht4afLLLDlowJiWynuvvOG8jtlsLheAhRDiSiD/qwlxlbFYbaw+mOIMh/tPZZNdZHa+HqHKZ+sbd6FUKunevTuW4B7kno7HlBKHKTUOm6k05BW38aHnk/cD0KVLF5YvX06XLl3KvV+PHj3KPc/IyKCoqKjS0Abw+++/M3PmTGe4KyswMBCdTofBYHCOfK6Mq9f0ej19+/Z1Oel2WFhYuedNmzZl0aJF3HLLLdxyyy2oVCrWrFnDmjVruPueMTz74iv4+fkBkJRTzKhXvyYk9R8Wf/COsyZTCCHqOwmLQlyhrFYbR1NyWbcrFpVay83XhHDo0CEaNWrEY1/uxeqmcx6rsFlxK8og+/he9p48eOZ8K9u2bQO2AfZm3K7dOtG2bVvn1q5dO+c1PDw8GDlyZLXL5xjAcjZHreDZfQsd3nzzTSZPnlxp2HTURp4d+hwudO3nhg0bArBixQqWLVvGkiVL+PabJTRt0pi2r7wCwEs/7iJZE8HpsBE8++0W/u/Bweg18l+sEKL+k//JhKjnrFYrWVlZ/L77BLuPneZgQioJeRaM7kGgtgdCDwxMvbUXxcXFbNy4kdx961CpNYwe1JPP3nqRm/t3QWmzsuvoLqKjo2nUYwJNmjQhKiqK6OhooqOjCQwMvCwjf6vTt/BcNYQXylV/Scf+iIgIvv76a6ZMmcKbb77JU0895Tzmnk5BJGcXcDAD/kiEmHc38cl9XWge4nVJyyiEEJebhEUh6rDTOcUk5RSTnGsgJbeYpOwiNu3Yz+mcYkrMZjK+fYaCggKsVishd89B17ANEA5n1oi3lhiwZCYSGuyBvm07cnOyUSgUzBvTk6CgIPr06cOcCcPx9PS85GVPTEwkIyOjwn5H8HLVJ9FRK+iqmRouvIbQlcDAQPR6fZVN2zqdjuTkZGdt54wZMzh69ChgD+xTHnwQhVLJK299wcL/MjmRWcTN72+mm+0wC194GJ1O5/LaQghRlylsNputtgtR3+Tl5eHj40Nubi7e3t61XRxRDxWZzKTkGkjJNdiDYJ6B5NxiDCVW3h7dHpvNRlZWFv1eWUGhvvImVZvVQuLbt4LNCkDooAfwimiGp7KE1NidtA7zYuHbs4huFIlCocBisaBSqSq91qVWnUEsOp2O2NjYCqFv165ddO7cmZ07d1baTF1TXIVbsAfb0aNHU1xcXOnrDt7e3uzZswef4Age/WYX245nAhBxYhVbli645GUWQojqupjsIjWLQlxCNpuNPIM9CB45lUZccibZBUZuiFRSVFREUVERH+y3caRA7eICVvjvG75dsoSvvvqKzIRYVKE2mkUEcfC/LfhobNw+/HrM+RlEh/gR++QTWC0Wxo8fT4cOHaos2+UKilD1IBbHyGODwUBGRsYlb0q+UJGRkS7LsmvXLoqLi13Wdjo+05w5c4iOjgbg6we6MenDP1h3MInXH7u7RssuhBA1ScKiEOdgNpvJzMwkJTWN+NPpHE/JQqHzYsqdgwF7E2S7+16i0DsK94Bwikus5c63WS28ek9pDWDgiKfwaN0fq7EIc34GlvwMLAWZmPMzifDzIE6dSXJyMjt37uTdu7sQERFB27ZtSUiIpnHjxnh5lekDd8fQy3YfLoSrQSwOlfURrKl5Fi+Fc32ebt26OR//uW4t/34yg+8XfUmPLvZzbDYbiRkFNAqSfoxCiPpDwqK4qpjMVnKLS5xbXnEJRxNPc/TEKdKycgjN3k9iYiKnTp3idFA3TAFNsal1KLWeKLX6M1dxB5uJx0ZZSEtNQa/Xk2OwoW4Q6gyKSnMxPmobaSeOoDLl4eHjS2FOFlFRUTQyHSYwIQVPrYpvvvmGoKAgPnjvPYKDgwkPDyc5OZlpk5+kY8eOeHh4OMt+pUzFUp3pb/R6PYGBgZe5ZJeOzWZj+vTpHDhwgOv69eGzzz7j7rvvZvbPO/lkcwKTu3oy+cwfG0IIUddJWBR1kslk4uTJk0RFRTmbT3fs2MHhw4fJKyjEWFzk7D+WYtaTb9VgxA2jTYXRpsJkU2GwKlFr3fnzxduc1+08eSH5npU1NSoAPxLe/j/nknSBI3rgEXDWsTYrqpIivNVWbh8zjp+/X8L777/Pq3f3A7WOxqH+DOzVBRUWjhmNKJVKAD78sBFbt27lnnvu4cYbb7RfymZj0aJFFUYYN23a9BLcwborMjKS5cuXM3z4cJfNupd6AMvlplAoWL16Nffeey9r167lnnvu4VTSaZanBII6iPm7jDRoksCoro1qu6hCCHFOEhbFZWe1WklJzyL+dCoJp9M4lngavZcvT40ZBkBubi6R149DHdyYITfdhsGiILe4hMSUDIoterBqOTn/Ief1gke/jHvjLpW/WRGUWKyoVUr7oJGUJNRNI9G7KXCzmVBZTXhpVKQkxuGldaNB336cjI9j8ODB9Bo2BKXeB53Syj2jb0VRUkR+Zhpajb2/4TPP7EalUpGRkcFLjz4K2JusF3/+CREREZQdO/bII4/wyCOPlCva5ZiGpiZVNSDkXE3JjhHP52rWrc9CQkJYtWoVTz/9NO+88w7Tn3max56czB7VNZxUBvPUjwfIMVh4sG/j2i6qEEJUScKiqJLNZsNotmIosVBcYsFoMhN1pr9VUVERU974gIzCEm6+bRS4aSg2Wdi5dz/HE06hxEozQyyZmZmkp6eTEDGQEr9o0LijUJUd4OGL0mLiqTMtkj4+Png27Yo6sj2bj+eUHqbUcaaijtF33InCZkWj0XDKz4tcUxpKs4HstCRsxiIaNwhFqzDTKDyEmS/M5KMP32fmzJm8fXsH3JQGOrS/hubNmwNgsVicNYBTppxky18b8Pf3Z9zNA52vv94wkPDwcAzFRWg1PgC88MILvPbaa2g0GmcR3dzcuO+++y79N6KWuAqEycnJjBo1CoPBUOX551qy70qnVCr5v//7P8LCwnj66ad5/9353HX33Vw3egZf/pPIaytjOJ1dyAvD26JU1u8/HoQQVy4Ji/WEzWYjNzeXtLQ055acmkZyWgYpGdkUGExYUWFRqLAq3GjdsgWvTxkPgNFoZOhjsyi0qrnp1pGU2BQYSixs3b6D+JOnMRfnwfYleHh44OHhQXG38Zi9wrCp1FgVblCmBkxjLmLL9P7s3r2bJk2a8GuCAm1Ee3auPFqmtGpQR2M1FrH2wyede4NH98PdvXS4vs1qQVFSjNpWgo+7mjfemM1vv/3Ko48+ytuPjiLXrEJpNvD801PAVMSG1SvxcVfjo1fz2KRlfP3VV7z55pt8PX0qAOnp6QQHBwNw1Gx2Nl9PnTqVvLw8Tp8+7ZxE2Wq1cueddxIcHIzZbHYGvnHjxtG/f39atmzpLKdKpWLv3r0VvidX+rRJ1Zn+RqPR8NNPP1VYMcWxZN/u3bsrXU2lLg9iqcq5Ju125amnniIkJITx48fz3bffMig9nSlPzWfe+ni+2JrI8aR0Fj00oN7XNgshrkz1NiyeOHGCWbNmsX79elJSUggPD2fMmDE8//zz5Wp6zmYwGJg2bRpLly7FaDQyePBgPvzwQ0JCQs67DDlFJooxYDJbMZotZOXmc+JkEtlpKZjST5CQkMCJEyeINXiTkZNHsbEEhZsaVGoUKg0KNzXd2jTlt3eewmq1kp+fT6txb6DU+3DtgEEYzVZMFisnEk+RV1iEKfMU6ctfcb5/xENf4ObTETywb2XEnMzhmTPh0t3dnRhrOJqgRnyyOaH0IEUIysgQlHnpJJWpAQq9pgStv56z2SwlWMwl3HbbbWzevJlPPvmEthHe5JFLRGgQmzauR6OEAf16k5majJvOilebNhw6eJAxY8Yw6LYeePn4obaZuHnoDbi7KSgsLHRef+LEifzzzz8MHTqUmWcGPqSmpjJp91oUCgVtI3xKP3t4OGq1ulyQCQgI4JVXXsHb2xur1eoMi1OmTGHixIk0aNDAeaxSqeS7776r8Bnbt29P+/btK/+G22xgNILBULo5nhuN5TeTqfTr2VtJScXNbC7dSkrAYrE/Lvv17M1qLb9ZLPYyWq2Vf3VsjuPPPE40mchwnFum6TympISioiLm+/vTV6u1/9FwZosxmRiTlobJZCLsscfo5OEBSqV9U6lIzs8HzrEKi0pF4EsvgY8PqNX2TaMp/VrZptWW33S6yjd3d9Dr7V8vwZRBFzJp99muvfZa/ve//zFy5EjWrV1LZsYYApv0JD3qBuL/+xPFw9dddDmFEKIm1NuwePjwYaxWKwsXLqRp06YcOHCACRMmUFhYyNtvv+3yvClTprBy5Up++OEHfHx8eOyxx7jtttvYsmXLeZehz5wNZUbIliqO30Pashedzxs+uRStzhNtJdc4XZLJo48+yieffMKzzz6LJrI9bl4BbD+RXXqQ2gc3Xx+sJgMajQaTyURERAQeOjVGQGGzYTEVYzMb8fHUo1bYCA/x5rXXXuPtt99m2rRp9Ituj0FRSOvmTflowXysZiNTH38UjUqBr0cQMW6Ps2DBAu644w6uHdyYwqJcjEX5vDzzOTRKWLJ4EU0aN6ZRo0bMmrWL9PR0fH19WfvmaAAyMzN5M2kN/v7+PDt5uLPov/YM49ChQwwcOJCuXbsC9j6Jb7zyImp1+bkGJ02axNChQ2nTpo1zX4C/P7H799tDVmamM4y9fNddvHHHHShMJvj7bzAaURqNvNi6tf2Yr792BrmGjgBXNty5elzVV6PxvH9GSr+H6vKByBGKHJubW+mmUtn3qVT2zbFPpbKf53isUpUGtLKbQlH69czjxMJCMgyGcvtQKEguLGTUr79iMJtdFn1GXh6xkyYR6e1dGiiTk+Grr+wH9OoFQUGlgdVqJSw1FY4eZUnfvrTy8iofcs1msFoJVCqJLC6G/PzyIbpsqC772BG+L+Teu7uX3/R6++bhUX7z9Cz9WmaL9PQk5rvvyDCZyl9DrbbfxzOTdg8fPtxlMfR6PTExMWzcuJGhQ4faa11TUujjZePLD/7v/D+XEEJcJlfUCi5z587lo48+4vjx45W+npubS1BQEN9++y2jRo0C7KGzVatWbNu2jR49elTrfRyzoDecvAw3nR6z0QDWEnRqNwyF+egKTmP86xMSExMZMWIEqr4TUOv0uClgzarfUdos3HvPXWhUChoFerJ72bt88sknvPzyyzTqNxKzxYZep+HB+8dis5SwYvkPKGwWwgJ9WfzubObNm8eMGTN4/uVZqFUKLCajc4qVvLw85zx8r7/+Om+99RaTJk3irbfecpb/0UcfxcfHh6eeegp/f3/7vcnOZtf27YQHBNCiYUMwGjl6+DDNhwwhNCCAXZ9+SpiHBxiN2IqLUZwdwM5+XrZmzVUN29mh4OywYDLZg8WFcnMrrX1y1ES5u5evkSpbQ3X247PPK1tzdXat1tk1XmVrws4Eipp0sauPVNac7JhoGqiwmopjlZXKXiv7+iVfhcVms/9cuAr0xcX2x8XFUFRk/1rZVlQEhYUVt4KC8o+t1qrL4+YGXl7s0mrpnJLCkmbNaBUYWBo6z2wxBQWM+fZbds6eTaf27YkvKGD4s89yKC4OvV7P3p07adqyJVmFJh5atJW5d3WlUYBH1e8thBDn4WJWcLmiwuILL7zAqlWr2LFjR6Wvr1+/noEDB5KdnV1uzrpGjRoxefJkpkyZUul5RqMRY5lapby8PBo2bEhmVjY2q4VHHnkEX19fPvzwQ2fT56ZNmzhx4gRdunRxTg1isVhIS0tDr9fj41PapFpcXIzJZEKr1TrXj3U0S4O9b5yjL1Nlx9psNvLy8iocW6U77oA//yz9ResilBkBDfaJZSp1dkgqG5YqC05lmxTPrmWrbJ9aXfGaGo3roFd2u4wrltSm6vQtBJg/fz59+/Ytt+9CA2GthcXLyWaz/9soKLBv+fmlj8/at+vIETp/8QU7hw+nk1pt35+XZ99yc9mVk0Pn4mJ2Ao67kQvcDkQBHwMKd3eGDp3KoaY90RvyWHb8N9pqTODtXX7z8qr6eRXdcIS44litFSsnKtvObk2qqnWpqoqQP/+0Vx7UQ7LcH3Ds2DEWLFhQZRN0SkoKGo2mwuTGISEhpKSkuDxv9uzZvPLKKxX2u6mUePv58v3331d4rW/fvhV+MatUqko7+7u7u+N+1g+fUqksFyirOlahUFR6bJVuugk6dqy6Zk2rRXt2bVzZGjWNBufwZFHjXNUexsTE2PsWVhIGoXSwSWBgYP0NbrVBoShtcg4KqvrYXbvgiy/glVegsnu8axd07gx//QVNmkBuLj75+azMzsaWm4uiuBhycwlc+hMmr2AIacIdLW9n4fGV9Dl8sFzwJD+/XN/SCjSaysOkp6f98VlN7OW2s5vjHZvbFfOrQlwIm81eoXChAauy187Vr/tcjx3nWywX9pkUioqtSGdXPDie6/Xg729/fIH1axarDaPZgrHEivHMOAdDiRWNm5LowNJWhFUHUig0mp3HGM1WjCVWiowmbEU5dPUtcg5yXZ3micHmhhUFVhTYzny1osADI90Vx3j88ccJDw/ngcX/kZqZXUUJq1bn/geYMWMGc+bMqfKYmJiYcqNVk5KSGDJkCKNHj2bChAmXvEzPPvssU6dOdT531CzWa/fcU9slEGe52KbkGTNmEBsbW2Ey65oeeVzfluyrVZ6eEBFh3yj/H7DZbCbtjz/QbvmYdo8sYF8K3N/iVt5+4WVu7hBReqDNZm8mL1t7WfZxZc/z8iAjA06cKF8rWlhYvb64Gk1pcHQMHCo7iKiy55V126jsj9OzBzRV1rrg6M97pY4Wt1orDmgrO+DN8bWqLjuV1a656rJRWc3aufpsn6tLRmUUCtetT2e3FjlajLy8qm5lOvtnpbJrVTUQTqvF7KamEBUoFPi42/vOmy1W/j6aXi7MOYKayWIlKsCDYdeEnfl22Xjqh73lQp9joGt+sRFN7kkapm4hISGBxMREioe9jkKtq/QWmU8fInfFq7z//vuMHTuWZ3/aR3ZRSaXHGpOPkPJVaRaxD3INrvRYU0YiGz5/k9tvv53w8HASsoo4nV5w/t/DM+pcWJw2bRrjxo2r8pjGjUsnsT19+jQDBgygV69efPLJJ1WeFxoaislkIicnp1ztYmpqKqGhoS7P0zpq2IS4SBc7b2FVTckGg4GMjIwaWfnk7PCXnJyMVqvFaDResUv2XUqO+SZdhejAwEDy8vL477//KC4uZuWNDflgZwEr9yXz5NI9pOcbSyfvVihKawIraak4byUlpeHT0VfT0XfT8bVsH09HX9CyfUJzcyElpXy/0LIhxGC44BqZcpTK0gFfZ391DPiqagBYmRH95bayys4KUHYWgapmGqjqa9mZCyqb1eBCa8aq4ghpjgB2dt/qsmHe2xuCgyuvZXNV81ZVjdzZfwhcZMC3WG1YrDY0bvZWLKPZwoGkXIpMFgqNFopMZgqNZgrOPG4d5s2N7ez/LnKLS3hg8X8UGLPJLzZRYDRjMNvnDgbwTD+I4a9PyM7OxoIS3wmLXZbDcGw7mb/M5uOPP+aBBx7gl72nsVgr/5kuPnGK1d9/5nzecIgZRZnxnDZLCTZzCTazCVNBLgUFBfz777+MHTuWHo0D2LnvICeOHcFmKeGeO2/HQ6fBajbx6Tx7+SIjI+nVqxcF2jSsJdls+msjNouZa/v2QaN2Q4kNtU8JoZMnE3SmVWTuqGvIzsllYNV1cS7VubAYFBTk/HDnkpSUxIABA+jcuTOLFi1yTqzsSufOnVGr1fz555+MHDkSgNjYWBITE+nZs+dFl11cHc5VAwhU2t3gQgeaQO00JVdnDWedTsfy5csr/bz1fcm+SyUxMdE5oK6qYB0TE0NMTAz//fcfrVs0Z150Cfv/3UyirgmfbjzCXd0i8dDWwH/ZajX4+tq3mmKzlY5oP7sp8uwR72Vrzxxfz65pq6wGrmwwc4SwsoHNEcrODoCVlbVsiCw7g0BlMw6cNfNAueeVBdayYfbsGQ/KfnVzK1+revb0UpXVyDoCYh2ohS02WTiUmENGbiHxSSmcSskgO78QkxVMVgWNPCy08jZz6623kmuCJ5fuJjk9i+z8IpRqHQq1liKTvXaveO8fZKz5CF9fX8Iat6Rg4HMu39cvJ5anN3/B+++/T7defdiR4LrpNTUzm4y4OOdz9+Qj9hBnMTnDnM1swmYpwZRyDLPZTNyZ458b2oplS79j66a/UFjNLP12CVo3JSXGYp6c9H+kATfddBOTJ0/GovHEbDIwcfz9eOh1fLnoC+dsIOvXp7DX816GDBkCwEdjOnPyZDDLlqUTFhbG6FHXOI99rNd80tKeIzg4mIgzrRM2m409ewYA0K5dO9xcdBnpGOlHnu+F9+Ovc2GxupKSkujfvz+NGjXi7bffJj093fmao5YwKSmJgQMH8tVXX9GtWzd8fHx44IEHmDp1Kv7+/nh7e/P444/Ts2fPao+EFle36g4mqUpVtYMmk4mwsLAKgfByNOtW9h5vvvkmkydPvmLXcL7UXDXJO2qMK7uPju99RkYGnTp1Ytgw+7KXCgUUb11CljWMRiFuWE29QetV8x+iJigUpaHGq55+hquIocRCXnEJucX2BROCve1NqGl5Br79N5Hc4hJyi0rIKTaRbzCTW1xCdoGRVm5phOXsJyEhgWNpBWR0e/isK5c2xebt+IXsPz9l3bp1tOnSm3+OZ515RQ8l2P8oOMOiUGG1WsnKyiI7fxdhnZKxlRiwlhiwmYqxmoqdX9OTDlMUc5DNmzdz7bXX8uE9nfjs4/f5/ZefUFnNHNq3C293LVZTEXff+R4nmjdnwoQJ3HTTTQBkZWXx8ssv4+3tzWtvveYsw/bt20lK6ucMdQ/0iWZI9FgOjOhMQEAAXdqV/sH896/fUVhYSHBwMJ6ens79ifHHKtzrjh07VtjXsGFDpk2bVmF/RESEMyQ6KBSKSq9xqdXbsLh27VqOHTvGsWPHyk22DDjX5C0pKSE2NrbcL/Z58+ahVCoZOXJkuUm5haiOjIwMioqKqvylD5WHgtoeaBIfH19hwujqNif37dtXQmEVqjtpd1X3sbKg2a5tW3YsWsTfwDXX7OK7777D6N+ELo38aqaWUVxRbDYbeQYz2YUmsopMZBeaSM7O58TuLbSP9Cc4OJgitQ/vbUunwGi1h8DiEmczLcD1YSZalMQxdepUcotLmL/uqMv3i9vxN9l/fgqAUu9LaPNbsRoKsBkLUVpL8PPyQGktQWktwZBjXyBi9+7d9Lm2P+/d1ZG1f/yPTz96H18Pd/7esA53tQo3LHz39XGyOj1Px44d8fb2JiMjg0OHEli2bBklJSV07dqVnKIc/AL8yFZqKQ7sS+fOnVEoFAxtF0aPWdPInDyOJk2alLZAemhYvXp1pZ9j1apVFfY5loctq7LwBlzQIh91Xb3932bcuHHn7NsYFRXF2TMD6XQ6PvjgAz744IMaLJ240rVq1arKwFfZ67VVO+hoMq9qNRVpTr44kZGRxMTEuBytPmbMGJYvX17pfXR0XagqaCoUCk6cOMGge58kaORM2jXwZdG4rgR4Sl/qq43RbCEtz0havpG0PAOpeQYyCkz0bhpIzyYBAOw/lcv9i/8jq8CAtZKJz3I2ryZ3y3cAuAU0IOLBjyscY7NasBoL+WHZr+RuXUpYWBjDbh3NXd0i2fTnKvbv3I6b1cRXny3Ex12NqTCH+5ZvIhu4+eabuffee4mKikKpVNKpUyc0Gg0ni4qc08stXryYXbta0K1bN7RuKm5qH87wdg/yf1PHVViFbeqTj1d6L2bNmlWte+bv7++cU1hcmHobFoUQdhfbt9BxDQmEFycyMrLKe+jq3ufk5AD2X3xDhw6t8LojbA4YMIAth5MwF+Wx75SCm9/fxLcTehEZUHEVKVH/lFispOUbSck1kJZnIC3fyDUNfOgY6QfA3pM5jF30LzkuRsp+/tmnpP75BTt27MDdM5iMAiOOGXKVFhPhgT74e2jQ2EpICA+gqHVr8vPzSc/OJf3n2ViL87EYCrCe2SgxEBERjvpMF4q4uDh89Rpm39aO/xoa6PbhM+h0OucIYQjk/rtGsXLlSm699VbnuACLxcLChQtp1qxZucqbyip8lEpllcv1itojYVGIeqSypmSQvoVXgujo6Cprq2+99VY2PPEEKd88Q8jtr3KKEIa/u4HP7u9Jt2ipNanLikxmUnINpOQaCPN1d86rF5uSz9PL95KcayCjwFhhvM3IlnoOqE5x+PBh9sSdJifKPlDKZi7BUpiFuSALS0EW1sJsUuN2YMjOZu7cucxf8D4rn+jDvn+3cPeoW8BSwvEyF58c9wvv/nCIuXPnMm3aNAoLC4mPj6dLly40iYpi+fJ1tGjRAo1Gw7p16zhx4gS9evVynt+xY0dOnDhRYWLnWbNmVajtU6lUTJw48RLeTVEbJCwKUcdcaFOy9C28svXu3ZuFCxcSHBzMC6/PJavdXeQFR3P7x1sYGFzEnHGDCAwIqO1iXvUSMgv5cEMcp3OL7QExz0C+oXSFrDEdA7g+1EjTpk3RaH3ZdyrX+ZrCZiXMV0+ojw4/nZKP35pBUczf9heVKtT+G7AUZIGpEKVSyY033kjjxo2Jjm7JX3+lsuL4Dvz8/NC6qWgT7kPkwL6EBQdWmPrN39+fqKgokpKSUCgUeHp60q5dO/Lz8yvU7A0aNKjCZ3Rzc6NRo0aX8K6Juk7CohCX2YUONJGm5PrNVZ/V+Pj4al/DUUMzePBgJj81nR8PxOPZ9jr+TPckqucwflzwCoMHD74k5RUV2Ww20guMHEkp4EhqPkfT8jmSWsCNbUK4IUpNQkICO2MT+f6Yb4VzraZiLPkZLHjnc17/dwV33XUXi776mk/v68I/6//g5elTsBblsisrEz8/e9Pz/549TBHQpk0bJkyYQMuWLWnWrBnt2rWjqKiIN998k9atWwNw++2389prr5X7/8HLy4vTp09XKMuLL77Iiy++WGG/NAELVyQsCnEBzrVqiQw0EQ7VGSkNVFiGtCru7u4898w0furSBU9lPkbPMIzxO+nWrZvzmB9//JH09HTuu+8+9Hrp13i+TGarcyLotHwDj32zm9iUPHLL1BI6bFr1CxN+fcv+RKHEp9edmHNTsRRkYs7PwJKfic1kn1/VEciysrLQuqm4vnUIYdYufBbojbt7iHMACMB7773Hd999x4033sikSZOc+5cvX050dHS5EbphYWEu/5AU4mJJWBTiPFT3F7/UDgqHqkZKQ+kAlvP9Rb9582YyMzMJjd/Gjs2bKZg7ET8/P9LyDLzy2yH+/fxzdmz4g7S0tEprkYSd1WojIauIfSez2XE8jX0nszmeZaSFVwkD9KcYMmQIDRtFsysxG7PVhs1qQWPKpX/HlrQI9aZ5qBdjhk0G7DV5Xbt2JSpKR6NG/fjoo4/Q++iZ/eFiunXrRlBQECaTCavVWm50btu2bUlISKhQtjvuuIM77rijwv4bb7yxxu6HEJWRsCjEeTjXL/6qVnABCYNXq3ONlAbXzdSu9t9zzz14enrSuXNnfHx88PHxAWDu6lhW7k9G2e0RWgRdwwOTHnGek5+fj9dVPCn2jh07OHHiBCdOnCA+IZGNtjYUagOhknV7t+yP48evp/Hiiy/yyiuv8OE9ncg6FcfYW4dgKi5g6Y4ddO7cAoCP5rzMmDFj6Nu3LytXrnRe47nnnquwooaHh0fNfkghaoDCdvZEhOKc8vLy8PHxITc3t8JoMCGEOB/VWRXIsRzguQLnd999h0Hjw8bcQP46Yl/VKthLy7NDW3Jz+3D69euHh4cHb731Fu3bt7+kn6MuKSgoYP36DZzKLqRhh34cSMpl36lcNm9Yy6mlpbWs4RM/Qe0Xbl+jNy0eU8oxVHmn8bHlo8hP42jMAZo1a8aRI0ec5wwcOJD169ezZMkS7rnnHgAMBgMGg+G8uhIIcbldTHaRsHgBJCwKIS4lV+uNJycnk5OTY18T9xz9WGNiYujcuTPFxcX8+eef2MLa8Or/DpGQaQ+hLQO1bP7waQrj96BQKBg1ahRPPPEEvXv3RlHL6whfLLPZTHFxMV5eXvy8O4nvNu5ly9FU3LyDyx2ntBgJ2fI20VFRNGrUCEVIC6IjgvG0FvDP1i3ccsstDBhgX2f35MmTREVF0b17d7Zu3eq8RlJSEn5+ftIPVNQ7EhYvMwmLQoiadr41jkajkalTpxIXF8fvv/+OUqnEaLbw+eZ43l9/jCKThfs7B3Jo+TssW7bMeY0OHTrw+OOPc9ddd+Hu7n45PtpFMxgM/Pvvv/zv7x1siTnJwZWLmDhxIm+99Rb3fr6dTUftwVths9I2wof2kX60i/ChbYQPLUO8SEg4QXR0tDMkP/nkk7z33ntMnDiRhQsXOt8nMzOTAJmOSFwhLia7SJ9FIYSog6pahxxKB8ZkZGQQGRmJVqvlgw8+wGQyOde/VWHDJ2k766bcyqeb45nYrzFho7/nueeeY+bCH/kvW8eRPX/wwMRJPPPMM0yYMIGHH364zvWrzcrKYvOWrfy+ZRf/xGWSbPVCE94KlUcLaNiCfPNitm/fDsDITg3o0NCX7tEBdGrki15T+mvOYrHQunUrjhw5wpEjR2jWrBkAw4YN49SpUxUGjkhQFMJOwqIQQtRh51qH/Gxl58p75513mD59OiNGjODXX3917m/fvj2+XYxoj6QT1Kg9NmMB+fvW8X+ffctbb71Fz549GTFiBCNHjqRp06aX9PNUR2JiIuvWrWPr1q1s3bqVk7rG+PYdg8q9MzQBR/2n0momQmdi3q9/cNO1XQG4pWMEAKdOnWLe3DkUFxfz2muvAfbVRMLDw4mPj+fAgQPOsHjDDTdwww03XPbPKUR9IWFRCCHqMVejpcE+ibSnpye33XZbhddeu7ktP+w8ybIdJ0nNA++ut+Dd9RaMSYfZs3cVW2bMwGQyOecENZlMKBQK1Gr1JS1/VnY2f/z9H8qASJKLFBxKzmd77CmOLpmH8eQBADxah6By90JlMxPlaeXaVuHc2Lkp7Rv6OedCPH78OFqtlogIe1hMTU3lhRdewMvLixdffNEZoj///HNCQkKu6FHJrvrAOsisDOJ8SVgUQoh6yDFNU1Vzfup0Oj755BNat27tXDXo6NGjWK1WWrRoweiWgTw58Dr+PprO0n9P8ufhNLQRLWnWuBEeEW7cdNNNAPyyJ4nEvVt54YkHefzxx501dQC5ubnOaXvOpaSkhCNHjtCmTRv+jc9izqrD7Dqegk2lBeLKHKmmVc/rGXLPcHr16kWr9l3IR0fbCB/UKmWF6zr6HD7//PPOsnXs2JF7772X3r17Y7FYnMc2bty4WmWtry7l6HohHCQsCiFEPZSTkwPArFmzGDp0aIXXN23axOTJk7nvvvtcXsMRGq5rGcl1LUNIyzfw484kgr20jHxlFACZBUaeXLoH0ON934esN5mYt/YI/h4a1FYT424ZRKDGTOvWrYlu0ZZ0dTDFJjPFJgsGsw2jxYbBzQuTRxC5/ywnZ9cfZGdno1Iq2JmQDSotWEoI00PfdtG0CvOmVZg3rcNvwFtXvhbTarUyffp01q5dy5o1awgMDATswdDNzY3MzEznsUqlkq+++uribnIdVVXNYUxMzHn1dRWiOiQsCiFEPRYdHV1pn0ZH83TZMJmfn8+sWbPYsWMHb7zxBo8++mi50BDspePh/k3KXSe7qISejQPYlZiN0cOX08C7fx51vu7Ruj/Jm74mOTkZ9b7jhI9fABXnuLbza4i3tzdxcXG0btued+/sQPNgD5qFeONWSY1hdnY2x44do2tXe39EpVLJH3/8wf79+1m/fj233347AKNHj2bUqFF4enqe172rq6oKg8nJyYwePZri4uIqr+Hr63tefV2FqIqERSGEqMNc9UmMj4+v1vlnh8lrr72WU6dOkZaW5tw3fvx4mjdvzsSJE8stQwfQNNiT7yb2wGi2sO9ULv/GZ3Equ5icIhPZRSZGjJhJU7dHOXToEHuOJLCdHHQqJTq1Ene1Er3WjSAPNU0CdHS76wm6tvvQOVr75g4RLsu9ZcsWBgwYQGhoKAkJCc5pbp5//nmsVisDBw50Hltf+x9WFgqTk5MZNWoUBoOhynPnz59P3759K+z//fffmTlzprPmWYhLQcKiEELUQdVdh/xCVg1p0KCBMyympqayaNEilEolDz30kPOYQ4cOkZ+fT5s2bfD09ETrpqJrlD9do/wrvWb37t3PuxwOmzdvZunSpXTq1Inx48cD0KlTJ7RaLb6+vqSkpDgnJa9sreT6qDp9CytbS94RBgMDA6usURbiUpKwKIQQddC51iF39D1ztQ55del0Ot577z0SEhLKBc9XXnmFZcuW8c477zBlyhTAPqnvihUraNOmDV26dDnnta1WK0VFReWah998802OHDnCSy+9RKNGjQDYs2cPH3zwAUOHDnWGRXd3d44ePUpoaOhFfb7adCF9Cx3f11mzZjFz5kzCwsLKhUIJg6I2SFgUQog6KjIyssYGIThGU58+fZrevXvTu3dv54hpsK+SEhoaSps2bZz7du/ezbhx44iOjub48ePO/YsXL+bkyZOMHz/eOXXN0qVLuffee7n++uv5/fffncd+/fXXHDp0iLvvvtsZFgcMGMDUqVPp379/uTJerqB4MVPNVLVUY3Wak131LYyOjj5HqYW4fCQsCiFEPXYhfRoTExMZNco+2tlVM7djpHTDhg2d+5RKJQMGDCi3D2DevHns27ePnj17OsOir68vZrOZpKSkcsdOmjSJvLw8oqKinPvatGnD//3f/7n+kJfAxYQ6vV7PunXr0Gq1532uRqPhp59+qlADXNN9C+Pj48uFfwepmRQXQsKiEELUQ9Xt05iRkVEhNMTExDgDTmVTrDim3dm0aVO51zw8PHj77bedU9Y43HzzzXTt2rXc8nj9+vUjMTGxQu3gE088Uf0PeR4udgSxq1DnaBa+7rrrqgyFZ/cvdJxnMpkqNCU7Xq8Jjq4EM2fOdE6ofja9Xl/heyhEVSQsCiFEPXSuPo2OgDR58mSX19DpdPTt27dcE2tiYiIzZswAzl3r6Djv1VdfrfQYvV5f3Y9zUaozWAQqH0F8rlDnYDAYzrt/YU1yVXPoqKl0Nc8iyAou4vxJWBRCiHrqXH0aDx8+XGmYdISc5cuXVzg/IyOjylrHujipc0ZGRpUTUZ9rBHF1uVqnuyb6Fzq6EZxdA+n4fp6r5vDsPwKEuBgSFoUQ4gp1rjB5rpHUrsJRXeWqvHW5n97ZNYTJycnodDpnEKysdreyKXXKkppDcalJWBRCCCEus+r0LXQVCiUMistNwqIQQohqc0y546q2rqaCzLnmLKzLKiuf9C0U9YmERSGEuEpVFmKqCl7nM+XOpQw51R3A4giyF+p874dDZf0Lk5OT0Wq1GI3GKu+V9C0U9YGERSGEuMpUZ9odnU5XYXqV2hr8Ut0BLOeas7CyEcTVCXU6nQ6DwVAhOF5s/0KpORT1hYRFIYS4ylQ17Y4j8L355ptkZGSUO6ZsWKqJwS+umpod7+tqtRPH666mk6nOCOKqQp3RaGTQoEHnDNfSv1BcqSQsCiHEVcjVSGlHreO55me81JM6V6epedSoUcTGxlYo98UMFnE4V6irak7L6pwvRH0mYVEIIYRTdWodK5uf8ezjXHEVqqpqana8r8FgqLSJ2xEAa3KwSE2u0y1EXSdhUQghRDkXOj+jY4BJVc215xoAczHN2/VtXkgh6gsJi0IIIS4JxwCTWbNmMXTo0AqvO2oIz15z2vEaXPyIZiHEpSdhUQghxHlx1czsmEImOjq60hq+6tQ8uuqXWNV71/V5FoWo7yQsCiGEqJbqTLkDpQNOzlZVzWNV/RIDAwOd09dUNWfhpR50I4Swk7AohBCiWqoa/AKlge9ca067qnms6n2XL1/O8OHDXQ5ikdHIQtQcCYtCCCGq7XKMCq5qeTwZxCLE5SdhUQghRK2TpmYh6i4Ji0IIIS6pcw2AqYw0NQtRd0lYFEIIcUlc7AAYR19HaWoWom6RsCiEEOKSqO4AmJycnErXcJYpcISom+ptWDxx4gSzZs1i/fr1pKSkEB4ezpgxY3j++efRaDQuz+vfvz9//fVXuX2TJk3i448/rukiCyHEFa+qATDVqXmUfolC1D31NiwePnwYq9XKwoULadq0KQcOHGDChAkUFhby9ttvV3nuhAkTePXVV53P9Xp9TRdXCCGueueqeQTplyhEXVRvw+KQIUMYMmSI83njxo2JjY3lo48+OmdY1Ov1hIaG1nQRhRBCnOVyTL0jhLi06m1YrExubi7+/v7nPO6bb75hyZIlhIaGMmLECGbOnFll7aLRaMRoNJZ7H4C8vLyLL7QQQgghRA1zZBabzXbe514xYfHYsWMsWLDgnLWKd999N40aNSI8PJx9+/Yxffp0YmNj+emnn1yeM3v2bF555ZUK+xs2bHjR5RZCCCGEuFwyMzPx8fE5r3MUtguJmDVoxowZzJkzp8pjYmJiaNmypfN5UlIS1157Lf379+ezzz47r/dbv349AwcO5NixYzRp0qTSY86uWczJyaFRo0YkJiae9w0XFyYvL4+GDRty8uRJvL29a7s4VwW557VD7vvlJ/f88pN7fvnl5uYSGRlJdna2y+mrXKlzNYvTpk1j3LhxVR7TuHFj5+PTp08zYMAAevXqxSeffHLe79e9e3eAKsOiVqtFq9VW2O/j4yM/5JeZt7e33PPLTO557ZD7fvnJPb/85J5ffkql8rzPqXNhMSgoiKCgoGodm5SUxIABA+jcuTOLFi26oBuwZ88egHMufC+EEEIIcTU6/3RVRyQlJdG/f38iIyN5++23SU9PJyUlhZSUlHLHtGzZkn///ReAuLg4Zs2axc6dOzlx4gS//vor9913H/369eOaa66prY8ihBBCCFFn1bmaxepau3Ytx44d49ixYzRo0KDca45umCUlJcTGxlJUVASARqNh3bp1zJ8/n8LCQho2bMjIkSN54YUXzuu9tVotL730UqVN06JmyD2//OSe1w6575ef3PPLT+755Xcx97zODXARQgghhBB1R71thhZCCCGEEDVPwqIQQgghhHBJwqIQQgghhHBJwqIQQgghhHBJwuJFuummm4iMjESn0xEWFsa9997L6dOna7tYV7QTJ07wwAMPEB0djbu7O02aNOGll17CZDLVdtGuaK+//jq9evVCr9ef9+z/ono++OADoqKi0Ol0dO/e3Tntl6gZf//9NyNGjCA8PByFQsHPP/9c20W64s2ePZuuXbvi5eVFcHAwt9xyC7GxsbVdrCvaRx99xDXXXOOcAL1nz5788ccf53UNCYsXacCAASxbtozY2Fh+/PFH4uLiGDVqVG0X64p2+PBhrFYrCxcu5ODBg8ybN4+PP/6Y5557rraLdkUzmUyMHj2ahx9+uLaLckX6/vvvmTp1Ki+99BK7du2iffv2DB48mLS0tNou2hWrsLCQ9u3b88EHH9R2Ua4af/31F48++ij//PMPa9eupaSkhBtuuIHCwsLaLtoVq0GDBrz55pvs3LmTHTt2cN1113HzzTdz8ODBal9Dps65xH799VduueUWjEYjarW6totz1Zg7dy4fffQRx48fr+2iXPEWL17M5MmTycnJqe2iXFG6d+9O165def/99wGwWq00bNiQxx9/nBkzZtRy6a58CoWCFStWcMstt9R2Ua4q6enpBAcH89dff9GvX7/aLs5Vw9/fn7lz5/LAAw9U63ipWbyEsrKy+Oabb+jVq5cExcssNzcXf3//2i6GEBfEZDKxc+dOBg0a5NynVCoZNGgQ27Ztq8WSCVGzcnNzAeT/78vEYrGwdOlSCgsL6dmzZ7XPk7B4CUyfPh0PDw8CAgJITEzkl19+qe0iXVWOHTvGggULmDRpUm0XRYgLkpGRgcViISQkpNz+kJCQckuYCnElsVqtTJ48md69DipkWgAABiBJREFUe9O2bdvaLs4Vbf/+/Xh6eqLVannooYdYsWIFrVu3rvb5EhYrMWPGDBQKRZXb4cOHncc//fTT7N69mzVr1qBSqbjvvvuQ1v3zd773Hezrfw8ZMoTRo0czYcKEWip5/XUh91wIIS6FRx99lAMHDrB06dLaLsoVr0WLFuzZs4ft27fz8MMPM3bsWA4dOlTt86XPYiXS09PJzMys8pjGjRuj0Wgq7D916hQNGzZk69at51XFK87/vp8+fZr+/fvTo0cPFi9ejFIpf/ucrwv5WZc+i5eeyWRCr9ezfPnycn3mxo4dS05OjrRWXAbSZ/Hyeuyxx/jll1/4+++/iY6Oru3iXHUGDRpEkyZNWLhwYbWOd6vh8tRLQUFBBAUFXdC5VqsVAKPReCmLdFU4n/uelJTEgAED6Ny5M4sWLZKgeIEu5mddXDoajYbOnTvz559/OsOK1Wrlzz//5LHHHqvdwglxCdlsNh5//HFWrFjBxo0bJSjWEqvVel45RcLiRdi+fTv//fcfffr0wc/Pj7i4OGbOnEmTJk2kVrEGJSUl0b9/fxo1asTbb79Nenq687XQ0NBaLNmVLTExkaysLBITE7FYLOzZsweApk2b4unpWbuFuwJMnTqVsWPH0qVLF7p168b8+fMpLCzk/vvvr+2iXbEKCgo4duyY83l8fDx79uzB39+fyMjIWizZlevRRx/l22+/5ZdffsHLy8vZJ9fHxwd3d/daLt2V6dlnn+XGG28kMjKS/Px8vv32WzZu3Mjq1aurfxGbuGD79u2zDRgwwObv72/TarW2qKgo20MPPWQ7depUbRftirZo0SIbUOkmas7YsWMrvecbNmyo7aJdMRYsWGCLjIy0aTQaW7du3Wz//PNPbRfpirZhw4ZKf6bHjh1b20W7Yrn6v3vRokW1XbQr1vjx422NGjWyaTQaW1BQkG3gwIG2NWvWnNc1pM+iEEIIIYRwSTp6CSGEEEIIlyQsCiGEEEIIlyQsCiGEEEIIlyQsCiGEEEIIlyQsCiGEEEIIlyQsCiGEEEIIlyQsCiGEEEIIlyQsCiGEEEIIlyQsCiGEEEIIlyQsCiFEHfHll1/Sp08fAPr3749CoUChUKDVaomIiGDEiBH89NNPtVxKIcTVRsKiEELUEb/88gs33XST8/mECRNITk4mLi6OH3/8kdatW3PnnXcyceLEWiylEOJqI2FRCCFq2FdffUVAQABGo7Hc/ltuuYV7770XAIPBwJo1a8qFRb1eT2hoKA0aNKBHjx7MmTOHhQsX8umnn7Ju3brL+hmEEFcvCYtCCFHDRo8ejcVi4ddff3XuS0tLY+XKlYwfPx6AP//8k4iICFq2bFnltcaOHYufn580RwshLhsJi0IIUcPc3d25++67WbRokXPfkiVLiIyMpH///kDFJmhXlEolzZs358SJEzVUWiGEKE/CohBCXAYTJkxgzZo1JCUlAbB48WLGjRuHQqHAZrPx22+/VSssAthsNhQKRU0WVwghnCQsCiHEZdCxY0fat2/PV199xc6dOzl48CDjxo0D4N9//8VsNtOrV69zXsdisXD06FGio6NruMRCCGHnVtsFEEKIq8WDDz7I/PnzSUpKYtCgQTRs2BCwN0EPGzYMlUp1zmt8+eWXZGdnM3LkyJourhBCAFKzKIQQl83dd9/NqVOn+PTTT50DWwB+/fXXSpugi4qKSElJ4dSpU/zzzz9Mnz6dhx56iIcffpgBAwZczqILIa5iCpvNZqvtQgghxNXivvvuY+XKlZw+fRqtVktcXBxt2rQhMzMTDw8P53H9+/fnr7/+AkCj0RAQEEDnzp0ZP348t956a20VXwhxFZJmaCGEuIySkpK455570Gq1gL0J+rrrrisXFAE2btxYC6UTQoiKJCwKIcRlkJ2dzcaNG9m4cSMffvihc3+DBg149tlna7FkQghRNWmGFkKIyyAqKors7GxmzpzJU089VdvFEUKIapOwKIQQQgghXJLR0EIIIYQQwiUJi0IIIYQQwiUJi0IIIYQQwiUJi0IIIYQQwiUJi0IIIYQQwiUJi0IIIYQQwiUJi0IIIYQQwiUJi0IIIYQQwqX/B4hstrf6qZ0KAAAAAElFTkSuQmCC" 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" 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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "♬ THE END ♬\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 3 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py new file mode 100644 index 00000000..eff600b4 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py @@ -0,0 +1,931 @@ + +""" + This script contains the code to run the efficient bounce back project with + the circular cylinder obstacle. + It is supposed to be invoked with parameters (arguments)! Examples are + given in the respective .ipynb file. + + Content: + - ARGUMENT PARSING: read all command line arguments or apply defaults + - PARAMETER PROCESSING: all relevant parameters are processed, missing + stuff ist calculated + - INITIALIZATION AND ASSEMBLING OF SOLVER COMPONENTS: stencil, context etc. + - SIMULATION CALL: running the simulation + - DATA POST-PROCESSING AND PLOTTING: does what it's name suggests... + + Work-In-Progress NOTE: + In some places there are TODOs and placeholders for future functionality! + This script still works, they are just future improvements! +""" + +#################### +# IMPORT + +import numpy as np +import torch + +import sys +import os +import psutil +import shutil +import resource + +import matplotlib.pyplot as plt + +import time +import datetime + +from pyevtk.hl import imageToVTK + +from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter + +# LETTUCE RELATED +import lettuce as lt +from obstacle_cylinder import ObstacleCylinder +from ebb_simulation import EbbSimulation +from reporter_ProfileReporter import ProfileReporter +from reporter_advanced_vtk_reporter import VTKReporterAdvanced, VTKsliceReporter +from observables_force_coefficients import DragCoefficient, LiftCoefficient + +# AUX. CODE +from helperCode import Logger +from data_processing_and_plotting import plot_force_coefficient, analyze_periodic_timeseries, draw_circular_mask, ProfilePlotter + +torch.autograd.set_detect_anomaly(True) + +#################### +# ARGUMENT PARSING: this script is supposed to be called with arguments, +# detailing all simulation- and system-parameters + +parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) + +# context and I/O +parser.add_argument("--name", default="cylinder_lowRe", help="name of the simulation, appears in output directory name") +parser.add_argument("--default_device", default="cuda", type=str, help="run on cuda or cpu") +parser.add_argument("--float_dtype", default="float64", choices=["float32", "float64", "single", "double", "half"], help="data type for floating point calculations in torch") +parser.add_argument("--t_sim_max", default=(72*60*60), type=float, help="max. wall time [s] to run the simulation(); default is 72 h. simulation will stops at 0.99*t_max_sim; IMPORTANT: this whole script may take longer to execute, depending on I/O etc.") + +parser.add_argument("--text_output_only", action='store_true', help="if you don't want pngs etc. to open, please use this flag; data is still saved to files") +parser.add_argument("--no_data", action='store_true', help="set, if you want no directories created and no date saved. Only direct output") +parser.add_argument("--outdir", default=os.getcwd(), type=str, help="directory to save output files to; default is CWD") +parser.add_argument("--outdir_data", default=None, type=str, help="directory to save large/many files to; if not set, everything os saved to outdir") + +# flow physics and geometry +parser.add_argument("--reynolds_number", default=200, type=float, help="Reynolds number") +parser.add_argument("--mach_number", default=0.1, type=float, help="Mach number (should stay < 0.3, and < 0.1 for highest accuracy. low Ma can lead to instability because of round of errors ") +parser.add_argument("--char_velocity_pu", default=1, type=float, help="characteristic velocity of the flow in physical units (PU)") + +parser.add_argument("--char_length_lu", default=1, type=int, help="characteristic length of the flow in lattice units. Number of gridpoints per diameter for a circular cylinder") +parser.add_argument("--char_length_pu", default=1, type=float, help="characteristic length of the flow in physical units. Diameter of the cylinder in PU") +parser.add_argument("--domain_length_x_in_d", default=None, type=float, help="domain length in x-direction (direction of flow) in number of cylinder-diameters") +parser.add_argument("--domain_height_y_in_d", default=None, type=float,help="domain height in y-direction (orthogonal to flow and cylinder axis) in number of cylinder-diameters") +parser.add_argument("--domain_width_z_in_d", default=None, type=float,help="domain width in z-direction (orthogonal to flow, parallel to cylinder axis) in number of cylinder-diameters; IMPORTANT: if not set, 2D-Simulation is performed") + +parser.add_argument("--perturb_init", action='store_true', help="perturb initial velocity profile to trigger vortex shedding") +parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") +parser.add_argument("--lateral_walls", default='periodic', help="OPTIONS: 'periodic' or 'bounceback'; add lateral walls, converting the flow to a cylinder in a channel. The velocity profile will be adjusted to be parabolic!") + +# LBM solver settings +parser.add_argument("--n_steps", default=100000, type=int, help="number of steps to simulate, overwritten by t_target, if t_target is >0") +parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, overwrites n_steps if t_target > 0") +parser.add_argument("--collision", default="bgk", type=str, choices=["kbc", "bgk", "reg", 'reg', "bgk_reg", 'kbc', 'bgk', 'bgk_reg'], help="collision operator (bgk, kbc, reg)") +parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions inferred from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") +parser.add_argument("--eqlm", action="store_true", help="use Equilibrium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") +#TODO: how to use EQLM in lettuce 2025? +parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1) for the solid obstacle") + +# reporter and observable settings +parser.add_argument("--periodic_region_start_relative", default=None, type=float, help="RELATIVE (0.0-1.0) assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") +parser.add_argument("--periodic_region_start_pu", default=None, type=float, help="ABSOLUTE PU-time; assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") +parser.add_argument("--periodic_region_start_lu", default=None, type=int, help="ABSOLUTE LU-steps; assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") + +parser.add_argument("--calc_u_profiles", action='store_true', help="calculate average velocity and reynolds stress profiles similar to [Di Ilio et al. 2018] and output plots and time-averages data for plots") +parser.add_argument("--show_u_profiles", action='store_true', help="plot average velocity and reynolds stress profiles") +parser.add_argument("--output_u_profiles_timeseries", default=False, help="output average velocity and reynolds stress profiles over time (full timeseries)") +parser.add_argument("--profile_reference_path", default="../profile_reference_data/", type=str, help="path to reference profiles from [Di Ilio et al. 2018]") + +parser.add_argument("--vtk_full_basic", action='store_true', help="output vtk files of full domain each interval steps") +parser.add_argument("--vtk_full_basic_interval", type=int, help="step interval for output of basic full vtk files") + +parser.add_argument("--vtk_3D", action='store_true', help="output vtk files of full domain each interval steps between start and end (if start and end are defined!)") +parser.add_argument("--vtk_3D_fps", type=float) +parser.add_argument("--vtk_3D_step_interval", type=float) +parser.add_argument("--vtk_3D_t_interval", type=float) +parser.add_argument("--vtk_3D_step_start", type=int) +parser.add_argument("--vtk_3D_step_end", type=int) +parser.add_argument("--vtk_3D_t_start", type=float) +parser.add_argument("--vtk_3D_t_end", type=float) + +parser.add_argument("--vtk_slice2D", action='store_true', help="toggle vtk-output of 2D slice of WHOLE DOMAIN (!) to outdir_data, if set True (1)") +parser.add_argument("--vtk_slice2D_fps", type=float) +parser.add_argument("--vtk_slice2D_step_interval", type=float) +parser.add_argument("--vtk_slice2D_t_interval", type=float) +parser.add_argument("--vtk_slice2D_step_start", type=int) +parser.add_argument("--vtk_slice2D_step_end", type=int) +parser.add_argument("--vtk_slice2D_t_start", type=float) +parser.add_argument("--vtk_slice2D_t_end", type=float) + +## FUTURE IMPROVEMENTS (which need arguments/parameters) +#TODO (optional): add NAN reporter (on/off, interval,...) +#TODO (optional): add watchdog reporter (on/off, interval,...) +#TODO (optional): add highMa reporter (on/off, interval,...) + +#TODO (optional): add 2D-mp4-animation-reporter... (fps_video, number of frames OR fps_pu) + +# Checkpointing +# TODO (optional): add checkpointing-utilities (read, write): +''' Checkpointing is NOT implemented in lettuce 2025 at the moment. +It was present in lettuce 0.2.3 +Relevant parameters and functionalities would/could include: + - checkpoint IN path (where to READ a checkpoint from) + - checkpoint OUT path (where to WRITE a checkpoint to) + - checkpoint i_start, or t_start (which timestep (in LU or PU) + does the checkpoint correspond to. + - should stuff continue on, or start from 0? (observables, time (step number), + statistics (mean, max, min of observables etc.) +''' + +# for debugging purposes: +parser.add_argument("--count_tensors", action="store_true", help="(for debugging: count all tensors, their sizes and memory consumption on the GPU, to find memory leaks") + + +########################################################### +# put arguments in dictionary +print("SCRIPT: Writing arguments to dictionary...") +args = vars(parser.parse_args()) + +# print all arguments +print(f"SCRIPT: Input arguments are: \n{args}\n") + +########################################################### + +# CREATE timestamp, sim-ID, outdir and outdir_data +print("SCRIPT: Creating timestamp, simulation ID and creating output directory...") +name = args["name"] +outdir = args["outdir"] +outdir_data = args["outdir_data"] + +default_device = args["default_device"] +float_dtype = args["float_dtype"] +t_sim_max = args["t_sim_max"] + +text_output_only = args["text_output_only"] +no_data_flag = args["no_data"] + +timestamp = datetime.datetime.now().strftime("%y%m%d_%H%M%S") +sim_id = str(timestamp) + "-" + name + +os.makedirs(outdir+"/"+sim_id) # create output dir +print(f"outdir/simID = {outdir}/{sim_id}") + +# save data to regular outdir, if data-dir is not specified +if outdir_data is None: + outdir_data = outdir + +# adding individual sim-ID to outdir path to get individual DIR per simulation +outdir = outdir+"/"+sim_id +outdir_data = outdir_data+"/"+sim_id +if not os.path.exists(outdir_data): + # create output dir for large/many files, if specified + os.makedirs(outdir_data) +print(f"outdir_DATA/simID = {outdir}/{sim_id}") + +# save input arguments/parameters to file in outdir: +print(f"SCRIPT: Writing input parameters to file: {outdir}/input_parameters.txt") +output_file = open(outdir+"/input_parameters.txt", "a") +for key in args: + output_file.write('{:30s} {:30s}\n'.format(str(key), str(args[key]))) +output_file.close() + +### SAVE SCRIPT: save this script to outdir +print(f"SCRIPT: Saving simulation script to outdir...") +temp_script_name = sim_id + "_" + os.path.basename(__file__) +shutil.copy(__file__, outdir+"/"+temp_script_name) +print(f"-> Saved simulation script to '{str(outdir+'/'+temp_script_name)}'") + +# START LOGGER -> get all terminal output into file +print(f"SCRIPT: Starting stdout-LOGGER (see outdir for log file)") +old_stdout = sys.stdout +sys.stdout = Logger(outdir) + +########################################################### + +# PROCESS AND SET PARAMETERS +print(f"SCRIPT: Processing parameters...") + +# calculate domain and obstacle geometry and infer dimensions (2D, 3D) +if args["domain_height_y_in_d"] is None or args["domain_height_y_in_d"] <= 1: + domain_height_y_in_d = 3 +else: + domain_height_y_in_d = args["domain_height_y_in_d"] + +if args["domain_length_x_in_d"] is None or args["domain_length_x_in_d"] <=1 : + # D/X = domain length in X- / flow-direction + domain_length_x_in_d = 2 * domain_height_y_in_d +else: + domain_length_x_in_d = args["domain_length_x_in_d"] + +if args["domain_width_z_in_d"] is None: # will be 2D + dims = 2 + domain_width_z_in_d = None +else: # will be 3D + dims = 3 + if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : + # if less than 1 lattice node... ->set to 1 lattice node + domain_width_z_in_d = 1/args["char_length_lu"] + print("(!) domain_width_z_in_d is less than 1 lattice node: " + "setting domain_width_z_in_d to 1 lattice node") + else: + domain_width_z_in_d = args["domain_width_z_in_d"] + +# CORRECT GPD for symmetry: +''' +if D/Y (height of the domain in number of cylinder diameters) is even, +the resulting GPD (gridpoints per diameter) can't be odd for a symmetrical +cylinder and symmetrical domain! + - if D/Y is even, GPD will be corrected to be even for symmetrical cylinder + => use odd D/Y value to use an odd GPD value! +''' +gpd_correction = False +if domain_height_y_in_d % 2 == 0 and args["char_length_lu"] % 2 != 0: + gpd_correction = True # gpd will be corrected + gpd_setup = args["char_length_lu"] # store old gpd for output + char_length_lu = int(gpd_setup / 2) * 2 # make gpd even + print("(!) domain_height_y_ind_d (DpY) is even, " + "gridpoints per diameter (GPD, char_length_lu) will be set to" + + str(char_length_lu) + ". Use odd domain_height_Y_in_D (DpY) " + "to enable use of odd GPD (char_length_lu)!") +else: + char_length_lu = args["char_length_lu"] + +char_length_pu = args["char_length_pu"] +char_velocity_pu = args["char_velocity_pu"] +reynolds_number = args["reynolds_number"] +mach_number = args["mach_number"] + +perturb_init = args["perturb_init"] +u_init_condition = args["u_init_condition"] + +# calculate lu-domain-resolution, +# ...total number of gridpoints and check correct stencil +if dims == 2: + resolution = [int(domain_length_x_in_d * char_length_lu), + int(domain_height_y_in_d * char_length_lu)] + number_of_gridpoints = char_length_lu ** 2 * domain_length_x_in_d * domain_height_y_in_d + if args["stencil"] == "D2Q9": + stencil = lt.D2Q9() + else: + print("(!) WARNING: wrong stencil choice for 2D simulation, D2Q9 is used") + stencil= lt.D2Q9() +else: + resolution = [int(domain_length_x_in_d * char_length_lu), + int(domain_height_y_in_d * char_length_lu), + int(domain_width_z_in_d * char_length_lu)] + number_of_gridpoints = (char_length_lu ** 3 * domain_length_x_in_d + * domain_height_y_in_d * domain_width_z_in_d) + if args["stencil"] == "D3Q15": + stencil = lt.D3Q15() + elif args["stencil"] == "D3Q19": + stencil = lt.D3Q19() + elif args["stencil"] == "D3Q27": + stencil = lt.D3Q27() + else: + print("(!) WARNING: wrong stencil choice for 3D simulation, D3Q27 is used") + stencil = lt.D3Q27() + +# read dtype +if float_dtype == "float32" or float_dtype == "single": + float_dtype = torch.float32 +elif float_dtype == "double" or float_dtype == "float64": + float_dtype = torch.float64 +elif float_dtype == "half" or float_dtype == "float16": + float_dtype = torch.float16 + +# OVERWRITE n_steps, if t_target is given +n_steps = args["n_steps"] +t_target = args["t_target"] +if args["t_target"] > 0: + n_steps = int(t_target * (char_length_lu / char_length_pu) + * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) +else: + t_target = n_steps / (char_length_lu/char_length_pu + * char_velocity_pu/(mach_number*1/np.sqrt(3))) + +# calculate relative starting point for observables that are calculated over a +# ...temporally converged or periodic region: +''' +- the temporal beginning of the periodic region depends on the time the flow +needs to converge into - for example - a von Karman vortex street. + => This time is also dependent on the domain size! +EXAMPLE: For an Re=100 in a reasonable domain size, the flow needs + t_PU = 75-100 seconds to reach it's periodic state +''' +#TODO (optional): change periodic_start parameter to abs. step-number, instead of relative start: +# - ease of use for reporters and processing +# - no round-off error in processing LU-parameter, if set +if args["periodic_region_start_lu"] is not None and args["periodic_region_start_lu"] >= 0: + periodic_start = args["periodic_region_start_lu"] / n_steps +elif args["periodic_region_start_pu"] is not None and args["periodic_region_start_pu"] >= 0: + periodic_start = args["periodic_region_start_pu"] / t_target +elif (args["periodic_region_start_relative"] is not None and + 0 <= args["periodic_region_start_relative"] < 1.0): + periodic_start = args["periodic_region_start_relative"] +else: + if args["reynolds_number"] > 1000: + periodic_start = 0.4 + else: + periodic_start = 0.9 + +# check EQLM parameter +if args["eqlm"]: + # TODO: use EQLM ( QuadraticEquilibriumLessMemory() ) how is this used in new lettuce? + print("SCRIPT: EQLM-parameter was set, but usage of EQLM is not implemented yet!" + "...using default equilibrium") + +# print temporal parameters: steps, T_PU +print(f"\n(INFO) parameters set for simulation of {n_steps} steps, " + f"representing {t_target:.3f} seconds [PU]!\n") + +########################################### +# INITIALIZE SOLVER COMPONENTS + +print("SCRIPT: initializing solver components...") + +# CONTEXT +print("-> initializing context...") +context = lt.Context(device=default_device, dtype=float_dtype,use_native=False) + +# FLOW +print("-> initializing flow...") +flow = ObstacleCylinder(context=context, resolution=resolution, stencil=stencil, + reynolds_number=reynolds_number, mach_number=mach_number, + char_length_pu=char_length_pu, char_length_lu=char_length_lu, + char_velocity_pu=char_velocity_pu, + bc_type=str(args["bbbc_type"]), calc_force_coefficients=True, + lateral_walls=args["lateral_walls"]) + +# COLLISION OPERATOR +print("-> initializing collision operator...") +collision_operator = None +if args["collision"].casefold() == "reg" or args["collision"].casefold() == "bgk_reg": + collision_operator = lt.RegularizedCollision(tau=flow.units.relaxation_parameter_lu) +elif args["collision"].casefold() == "kbc": + # if dims == 2: + # collision_operator = lt.KBCCollision2D(tau=flow.units.relaxation_parameter_lu) + # else: + # collision_operator = lt.KBCCollision3D(tau=flow.units.relaxation_parameter_lu) + collision_operator = lt.KBCCollision(tau=flow.units.relaxation_parameter_lu) +else: # default to bgk + collision_operator = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) + +# SIMULATION +print("\nSCRIPT: initializing simulation object...") +simulation = EbbSimulation(flow, collision_operator,reporter=[]) + + +# REPORTERS: initialize and append to simulation.reporter +print("-> initializing reporters...") + +# DRAG and LIFT (Force Coefficients) and respective reporters: +cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else ( + flow.char_length_pu*domain_width_z_in_d) + +DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[-1], + solid_mask=simulation.post_streaming_boundaries[-1].mask, + area_pu=cylinder_cross_sectional_area) +DragReporter = lt.ObservableReporter(DragObservable, interval=1, out=None) +simulation.reporter.append(DragReporter) + +LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[-1], + solid_mask=simulation.post_streaming_boundaries[-1].mask, + area_pu=cylinder_cross_sectional_area) +LiftReporter = lt.ObservableReporter(LiftObservable, interval=1, out=None) +simulation.reporter.append(LiftReporter) + +# VELOCITY- and REYNOLDS-STRESS profiles over space and time: +if args["calc_u_profiles"]: + # define positions + position_1 = flow.x_pos_lu - 0.5 + 1.06 * flow.radius_lu * 2 + position_2 = flow.x_pos_lu - 0.5 + 1.54 * flow.radius_lu * 2 + position_3 = flow.x_pos_lu - 0.5 + 2.02 * flow.radius_lu * 2 + print("ProfileReporters at x-positions:" + " p1: " + str(position_1) + " p2: " + str( + position_2) + " p3: " + str(position_3)) + + # create and append profileReporters + ProfileReporter1 = ProfileReporter(flow=flow, interval=1, position_lu=position_1, + i_start= int(n_steps * periodic_start)) + simulation.reporter.append(ProfileReporter1) + ProfileReporter2 = ProfileReporter(flow=flow, interval=1, position_lu=position_2, + i_start= int(n_steps * periodic_start)) + simulation.reporter.append(ProfileReporter2) + ProfileReporter3 = ProfileReporter(flow=flow, interval=1, position_lu=position_3, + i_start= int(n_steps * periodic_start)) + simulation.reporter.append(ProfileReporter3) + +# NAN REPORTER (if nan detected -> stop simulation) +# TODO (optional): add NaN reporter (see PR for that topic) +# - NEEDS breakable simulation (!) + +# HighMa Reporter (if Ma>0.3 detected -> report and/or stop simulation) +# TODO (optional): HighMa Reporter + +# Watchdog/Progress-Reporter +# TODO (optional): Progress-Reporter + +# VTK Reporter: for visualization etc. + +# BASIC lettuce vtk output (see also: advanced vtk reporter below) +if args["vtk_full_basic"]: + vtk_reporter = lt.VTKReporter(interval=args["vtk_full_basic_interval"], + filename_base=outdir_data+"/vtk/out") + + # export obstacle mask for visualization + mask_dict = dict() + if dims ==2: + mask_dict["mask"] = flow.obstacle_mask[...,None].astype(int) + else: + mask_dict["mask"] = flow.obstacle_mask.astype(int) + imageToVTK( + path=outdir_data+"/vtk/obstacle_point", + pointData=mask_dict, + ) + imageToVTK( + path=outdir_data+"/vtk/obstacle_cell", + cellData=mask_dict, + ) + + simulation.reporter.append(vtk_reporter) + + +# ADVANCED vtk output +# 3D +if args["vtk_3D"] and dims == 3: + if args["vtk_3D_t_start"] is not None: + #print("(vtk) overwriting vtk_step_start with {}, because vtk_t_start = {}") + vtk_3d_i_start = int(round(flow.units.convert_time_to_lu(args["vtk_3D_t_start"]))) + elif args["vtk_3D_step_start"] is not None: + vtk_3d_i_start = int(args["vtk_3D_step_start"]) + else: + vtk_3d_i_start = 0 + + if args["vtk_3D_t_end"] is not None: + #print("(vtk) overwriting vtk_step_end with {}, because vtk_t_end = {}") + vtk_3d_i_end = int(flow.units.convert_time_to_lu(args["vtk_3D_t_end"])) + elif args["vtk_3D_step_end"] is not None: + vtk_3d_i_end = args["vtk_3D_step_end"] + else: + vtk_3d_i_end = n_steps + + if args["vtk_3D_t_interval"] is not None and args["vtk_3D_t_interval"] > 0: + vtk_3d_interval = int(flow.units.convert_time_to_lu(args["vtk_3D_t_interval"])) + elif args["vtk_3D_step_interval"] is not None and args["vtk_3D_step_interval"] > 0: + vtk_3d_interval = args["vtk_3D_step_interval"] + elif args["vtk_3D_fps"] is not None and args["vtk_3D_fps"] > 0: + vtk_3d_interval = int(flow.units.convert_time_to_lu(1 / args["vtk_3D_fps"])) + else: + vtk_3d_interval = 1 + + if vtk_3d_interval < 1: + vtk_3d_interval = 1 + + vtk_3d_reporter = VTKReporterAdvanced(flow, + interval=int(vtk_3d_interval), + filename_base=outdir_data + "/vtk/out", + imin=vtk_3d_i_start, imax=vtk_3d_i_end) + simulation.reporter.append(vtk_3d_reporter) + vtk_3d_reporter.output_mask(flow.solid_mask, outdir_data + "/vtk", "solid_mask", + point=True) + + +# slice2D (2D slice at Z/2) +if args["vtk_slice2D"]: + if args["vtk_slice2D_t_start"] is not None and args["vtk_slice2D_t_start"] > 0: + #print("(vtk) overwriting vtk_step_start with {}, because vtk_t_start = {}") + vtk_slice2d_i_start = int(round(flow.units.convert_time_to_lu(args["vtk_slice2D_t_start"]))) + elif args["vtk_slice2D_step_start"] is not None and args["vtk_slice2D_step_start"] > 0: + vtk_slice2d_i_start = int(args["vtk_slice2D_step_start"]) + else: + vtk_slice2d_i_start = 0 + + if args["vtk_slice2D_t_end"] is not None and args["vtk_slice2D_t_end"] > 0: + #print("(vtk) overwriting vtk_step_end with {}, because vtk_t_end = {}") + vtk_slice2d_i_end = int(flow.units.convert_time_to_lu(args["vtk_slice2D_t_end"])) + elif args["vtk_slice2D_step_end"] is not None and args["vtk_slice2D_step_end"] > 0: + vtk_slice2d_i_end = int(args["vtk_slice2D_step_end"]) + else: + vtk_slice2d_i_end = n_steps + + if args["vtk_slice2D_t_interval"] is not None and args["vtk_slice2D_t_interval"] > 0: + vtk_slice2d_interval = int(flow.units.convert_time_to_lu(args["vtk_slice2D_t_interval"])) + elif args["vtk_slice2D_step_interval"] is not None and args["vtk_slice2D_step_interval"] > 0: + vtk_slice2d_interval = int(args["vtk_slice2D_step_interval"]) + elif args["vtk_slice2D_fps"] is not None and args["vtk_slice2D_fps"] > 0: + vtk_slice2d_interval = int(flow.units.convert_time_to_lu(1 / args["vtk_slice2D_fps"])) + else: + vtk_slice2d_interval = 1 + + if vtk_slice2d_interval < 1: + vtk_slice2d_interval = 1 + + if args["vtk_slice2D"]: + vtk_domainSlice_reporter = VTKsliceReporter(flow, + interval=int(vtk_slice2d_interval), + filename_base=outdir_data + "/vtk/slice_domain/slice_domain", + sliceXY=([0,resolution[0]-1],[0,resolution[1]-1]), + sliceZ=int(resolution[2]/2), + imin=vtk_slice2d_i_start, imax=vtk_slice2d_i_end) + simulation.reporter.append(vtk_domainSlice_reporter) + + +# DRAW CYLINDER-MASK in 2D (xy-plane) +try: + draw_circular_mask(flow, flow.char_length_lu, filebase=outdir_data, output_data=True) +except: + print("(!) Drawing of circular cylinder mask did not work...") + +################################################## +# PRINT relevant PARAMETERS prior to simulation: +print(f"\nSCRIPT: spacial and temporal dimensions:") +print("domain shape (LU):", flow.resolution) +print("t_target (PU) with", n_steps, "steps (LU):", + round(n_steps * (flow.char_length_pu / flow.char_length_lu) + * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 3), + "seconds") +print("steps to simulate 1 second PU:", + round((flow.char_length_lu / flow.char_length_pu) + * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3), "steps") +print(f"steps to simulate {t_target:.3f} (t_target, PU) seconds: {t_target + * round((flow.char_length_lu / flow.char_length_pu) + * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3):.3f} steps") + +################################################## +# RUN SIMULATION: +print(f"\n#################################################") +print(f"\nSCRIPT ({datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}): " + f"running simulation for {n_steps} steps...\n") +print(f"#################################################\n") + +t_start = time.time() +mlups = simulation(num_steps=n_steps) +t_end = time.time() +runtime = t_end - t_start + +print(f"***** SIMULATION FINISHED AT " + f"{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} *****\n") + +################################################## +# OUTPUT STATS: +print(f"### STATS ###") +print(f"MLUPS: {mlups:.3f}") +print(f"simulated PU-Time: {flow.units.convert_time_to_pu(n_steps)} seconds") +print("simulated LU-steps: ", n_steps) +print(f"runtime (WALLTIME) of simulation(num_steps): " + f"{runtime:.3f} seconds (= ", round(runtime / 60, 2), "minutes )") +print("") +# GPU VRAM +print("### HARDWARE UTILIZATION ###") +print(f"current GPU VRAM (MB) usage: " + f"{torch.cuda.memory_allocated(device=context.device)/1024/1024:.3f}") +print(f"max. GPU VRAM (MB) usage: " + f"{torch.cuda.max_memory_allocated(device=context.device)/1024/1024:.3f}") +# CPU +[cpuLoad1, cpuLoad5, cpuLoad15] = [x / psutil.cpu_count() * 100 for x in psutil.getloadavg()] +print("CPU % avg. over last 1 min, 5 min, 15 min; ", + round(cpuLoad1, 2), round(cpuLoad5, 2), round(cpuLoad15, 2)) +# RAM +ram = psutil.virtual_memory() +print("current total RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str( + round(ram.total / (1024 * 1024), 2)) + " MB") + +### export stats +if not no_data_flag: + output_file = open(outdir + "/stats.txt", "a") + output_file.write("DATA for " + timestamp) + output_file.write("\n\n### SIM-STATS ###") + output_file.write( + f"\nruntime: {runtime:.3f} seconds (= {round(runtime / 60, 2)} " + f"min = {round(runtime / 3600, 2)} h)") + output_file.write("\nMLUPS = " + str(mlups)) + output_file.write("\n") + output_file.write("\nVRAM_current [MB] = " + str( + torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = " + str( + torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\n") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str( + round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str( + round(cpuLoad15, 2)) + " %") + output_file.write("\nCurrent total RAM usage [MB]: " + str( + round(ram.used / (1024 * 1024), 2)) + " of " + str( + round(ram.total / (1024 * 1024), 2)) + " MB") + output_file.write("\nmaximum total RAM usage ('MaxRSS') [MB]: " + str( + round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024, + 2)) + " MB") + output_file.close() + +################################################## +# PLOTTING 2D IMAGE of VELOCITY +fig, axes = plt.subplots(1, 2, figsize=(10, 3)) +fig.subplots_adjust(right=0.85) +u = flow.u_pu.cpu().numpy() +if dims == 2: # 2D + im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") + im2 = axes[1].imshow(u[0, ...].T, origin="lower") +else: # 3D + im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask[:, :, + int(flow.solid_mask.shape[2] / 2)].T), origin="lower") + im2 = axes[1].imshow(u[0, ...][:, :, int(flow.solid_mask.shape[2] / 2)].T, origin="lower") +cbar_ax = fig.add_axes((0.88, 0.15, 0.04, 0.7)) +fig.colorbar(im2, cax=cbar_ax) +plt.show() +#TODO (optional): plot image of pressure + +################################################## +# PROCESS DATA: calculate and SAVE OBSERVABLES AND PLOTS: +print("\nSCRIPT: processing, plotting and saving data...\n") + +# DRAG +drag_timeseries = np.array(np.array(DragReporter.out)) +plot_force_coefficient(drag_timeseries, ylabel="Coefficient of Drag $C_{D}$", + ylim=(0.5, 1.6), + secax_functions_tuple=(flow.units.convert_time_to_lu, + flow.units.convert_time_to_pu), + filenamebase=outdir_data+"/drag", periodic_start=periodic_start, + adjust_ylim=True) +drag_stats = analyze_periodic_timeseries(drag_timeseries, periodic_start_rel=0.5, + name="drag", verbose=True, + pu_per_step=flow.units.convert_time_to_pu(1), + outdir=outdir_data) + +print(f"DRAG STATS:") #\n{drag_stats}") +for key, value in drag_stats.items(): + print(f"{key:<20} = {str(value)}") +print("") +''' +reminder: + STATS ARE: {"mean_simple": mean_simple, + "mean_periodcorrected": mean_periodcorrected, + "min_simple": min_simple, + "max_simple": max_simple, + "max_mean": max_mean, + "min_mean": min_mean, + "frequency_fit": frequency_fit, + "frequency_fft": freq_peak, + "fft_resolution": freq_res} +''' + +# LIFT +lift_timeseries = np.array(np.array(LiftReporter.out)) +plot_force_coefficient(lift_timeseries, ylabel="Coefficient of Lift$C_{L}$", ylim=(-1.1, 1.1), + secax_functions_tuple=(flow.units.convert_time_to_lu, + flow.units.convert_time_to_pu), + filenamebase=outdir_data+"/lift", periodic_start=periodic_start) +#OLD: lift_prominence = ((abs(lift_timeseries[2].max()) - abs(lift_timeseries[2].min())) * 0.5) +lift_stats = analyze_periodic_timeseries(lift_timeseries, periodic_start_rel=0.5, + name="lift", + verbose=True, pu_per_step=flow.units.convert_time_to_pu(1), + outdir=outdir_data) +print(f"LIFT STATS:") +for key, value in lift_stats.items(): + print(f"{key:<20} = {str(value)}") + +# plot DRAG and LIFT together: +try: + fig, ax = plt.subplots(layout="constrained") + drag_ax = ax.plot(drag_timeseries[:, 1], drag_timeseries[:, 2], color="tab:blue", + label="Drag") + ax.set_xlabel("physical time / s") + ax.set_ylabel("Coefficient of Drag $C_{D}$") + ylim_adjusted = (drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, 2].min() + * 0.5, drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, + 2].max() * 1.2) + ax.set_ylim(ylim_adjusted) + + secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, + flow.units.convert_time_to_pu)) + secax.set_xlabel("timesteps (simulation time / LU)") + + ax2 = ax.twinx() + lift_ax = ax2.plot(lift_timeseries[:, 1], lift_timeseries[:, 2], color="tab:orange", + label="Lift") + ax2.set_ylabel("Coefficient of Lift $C_{L}$") + ax2.set_ylim((-1.1, 1.1)) + + fig.legend(loc="upper left", bbox_to_anchor=(0, 1), bbox_transform=ax.transAxes) + + if not no_data_flag: + try: + plt.savefig(outdir_data + "/dragAndLift_coefficient.png") + except: + print("(!) saving dragAndLift_coefficient.png didn't work!") + plt.show() +except: + print("(!) plotting drag and lift together didn't work!") + +# STROUHAL number +# f = Strouhal for St=f*D/U and D=U=1 in PU +print("Strouhal number is: ", lift_stats["frequency_fit"] + * flow.char_length_pu/flow.char_velocity_pu) + +# AVERAGE VELOCITY and REYNOLDS STRESS PROFILES +if args["calc_u_profiles"] and args["profile_reference_path"] is not None: + profile_plotter = ProfilePlotter(flow, output_path=outdir_data, + reference_data_path=args["profile_reference_path"], + i_timeseries=ProfileReporter1.i_out, + u_timeseries1=ProfileReporter1.out, + u_timeseries2=ProfileReporter2.out, + u_timeseries3=ProfileReporter3.out) + + profile_plotter.process_data() + profile_plotter.plot_velocity_profiles(show_reference=True, save=True, + show=args["show_u_profiles"] + if args["show_u_profiles"] is not None else False) + profile_plotter.plot_reynolds_stress_profiles(show_reference=True, + save=True, show=args["show_u_profiles"] + if args["show_u_profiles"] is not None else False) + + +# EXPORT OBSERVABLES: +if not no_data_flag: + ### CUDA-VRAM-summary: + output_file = open(outdir_data + "/" + timestamp + "_GPU_memory_summary.txt", "a") + output_file.write("DATA for " + timestamp + "\n\n") + output_file.write(torch.cuda.memory_summary(context.device)) + output_file.close() + + if args["count_tensors"]: + try: + ### list present torch tensors: + output_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "a") + total_bytes = 0 + import gc + + for obj in gc.get_objects(): + try: + if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): + output_file.write("\n" + str(obj.size()) + ", " + + str(obj.nelement() * obj.element_size())) + total_bytes = total_bytes + obj.nelement() * obj.element_size() + except: + pass + output_file.close() + + ### count occurrence of tensors in list of tensors: + from collections import Counter + + my_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "r") + data = my_file.read() + my_file.close() + data_into_list = data.split("\n") + c = Counter(data_into_list) + output_file = open(outdir_data + "/" + timestamp + "_GPU_counted_tensors.txt", "a") + for k, v in c.items(): + output_file.write("type,size,bytes: {}, number: {}\n".format(k, v)) + output_file.write("\ntotal bytes for tensors:" + str(total_bytes)) + output_file.close() + except: + print("(!) counting tensors didn't work!") + +############################################ +# OUTPUT parameters, stats and observables +if not no_data_flag: + output_file = open(outdir_data + "/" + timestamp + "_parms_stats_obs.txt", "a") + output_file.write("DATA for " + timestamp) + output_file.write("\n\n### SIM-Parameters ###") + output_file.write("\n{:30s} {:30s}".format("Re", str(reynolds_number))) + output_file.write("\n{:30s} {:30s}".format("Ma", str(mach_number))) + output_file.write("\n{:30s} {:30s}".format("n_steps", str(n_steps))) + output_file.write("\n{:30s} {:30s}".format("t_target [s]", str(t_target))) + output_file.write("\n{:30s} {:30s}".format("gridpoints_per_diameter (GPD)", + str(flow.char_length_lu))) + if gpd_correction: + output_file.write("\n(!) gpd was corrected from: " + str(gpd_setup) + " to " + + str(flow.char_length_lu) + " because D/Y is even") + output_file.write("\nDpX (D/X) = ".ljust(31) + str(domain_length_x_in_d)) + output_file.write("\nDpY (D/Y) = ".ljust(31) + str(domain_height_y_in_d)) + if flow.stencil.d == 3: + output_file.write("\nDpZ (D/Z) = ".ljust(31) + str(domain_width_z_in_d)) + output_file.write("\nshape_LU: ".ljust(31) + str(flow.resolution)) + output_file.write(("\ntotal_number_of_gridpoints: ".ljust(31) + str(flow.rho(flow.f).numel()))) + output_file.write("\nbc_type = ".ljust(31) + str()) + output_file.write("\nlateral_walls = ".ljust(31) + str(flow.lateral_walls)) + output_file.write("\nstencil = ".ljust(31) + str(flow.stencil)) + output_file.write("\ncollision = ".ljust(31) + str(collision_operator)) + output_file.write("\n") + # output_file.write("\nMa = " + str(mach_number)) + output_file.write("\nrelaxation parameter tau [LU]".ljust(31) + + str(flow.units.relaxation_parameter_lu)) + output_file.write("\ngrid_reynolds_number (Re_g) = ".ljust(31) + + str(flow.units.characteristic_velocity_lu/ + ((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5)))) + output_file.write("\n") + output_file.write("\ncylinder diameter PU = ".ljust(31) + + str(flow.units.characteristic_length_pu)) + output_file.write("\ncharacteristic velocity PU = ".ljust(31) + + str(flow.units.characteristic_velocity_pu)) + output_file.write("\nperturb_init = ".ljust(31) + str(perturb_init)) + output_file.write("\n") + + output_file.write("\n\n### SIM-STATS ###") + output_file.write("\nruntime = ".ljust(31) + str(runtime) + + " seconds (=" + str(runtime / 60) + " minutes)") + output_file.write("\nMLUPS = ".ljust(31) + str(mlups)) + output_file.write("\n") + + output_file.write("\nVRAM_current [MB] = ".ljust(31) + + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = ".ljust(31) + + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\n") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: ".ljust(31) + + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + + " %, " + str(round(cpuLoad15, 2)) + " %") + output_file.write("\ntotal current RAM usage [MB]: ".ljust(31) + + str(round(ram.used / (1024 * 1024), 2)) + " of " + + str(round(ram.total / (1024 * 1024), 2)) + " MB") + + output_file.write("\n\n### OBSERVABLES ###") + output_file.write("\nCoefficient of drag between " + + str(round(drag_timeseries[int(drag_timeseries.shape[0] + * periodic_start - 1), 1], 2)) + " s and " + + str(round(drag_timeseries[int(drag_timeseries.shape[0] - 1), 1], 2)) + + " s:") + output_file.write("\nCd_mean, simple = ".ljust(31) + + str(drag_stats["mean_simple"])) + output_file.write("\nCd_mean, peak_finder = ".ljust(31) + + str(drag_stats["mean_periodcorrected"])) + output_file.write( + "\nCd_min = ".ljust(31) + str(drag_stats["min_mean"] if drag_stats["min_mean"] is not None + else drag_stats["min_simple"])) + output_file.write( + "\nCd_max = ".ljust(31) + str(drag_stats["max_mean"] if drag_stats["max_mean"] is not None + else drag_stats["max_simple"])) + output_file.write("\n") + output_file.write("\nCoefficient of lift:") + output_file.write("\nCl_min = ".ljust(31) + str(lift_stats["min_mean"] + if lift_stats["min_mean"] is not None + else lift_stats["min_simple"])) + output_file.write("\nCl_max = ".ljust(31) + str(lift_stats["max_mean"] + if lift_stats["max_mean"] is not None + else lift_stats["max_simple"])) + output_file.write("\n") + output_file.write("\nStrouhal number:") + output_file.write("\nFFT: St +- df = " + str(lift_stats["frequency_fft"]) + " +- " + + str(lift_stats["fft_resolution"]) + " Hz") + output_file.write( + "\nSINE-FIT: St = " + str(lift_stats["frequency_fit"])) + output_file.write("\n") + output_file.close() + +# export flow physics to file: +output_file = open(outdir+"/flow_physics_parameters.txt", "a") +output_file.write('\n{:30s}'.format("FLOW PHYSICS and units:")) +output_file.write('\n') +output_file.write('\n{:30s} {:30s}'.format("Ma", str(reynolds_number))) +output_file.write('\n{:30s} {:30s}'.format("Re", str(mach_number))) +output_file.write('\n') +output_file.write('\n{:30s} {:30s}'.format("Relaxation Parameter LU", + str(flow.units.relaxation_parameter_lu))) +output_file.write('\n{:30s} {:30s}'.format("l_char_LU", + str(flow.units.characteristic_length_lu))) +output_file.write('\n{:30s} {:30s}'.format("u_char_LU", + str(flow.units.characteristic_velocity_lu))) +output_file.write('\n{:30s} {:30s}'.format("viscosity_LU", + str(flow.units.viscosity_lu))) +output_file.write('\n{:30s} {:30s}'.format("p_char_LU", + str(flow.units.characteristic_pressure_lu))) +output_file.write('\n{:30s} {:30s}'.format("rho_char_LU", + str(flow.units.characteristic_density_lu))) +output_file.write('\n') +output_file.write('\n{:30s} {:30s}'.format("l_char_PU", + str(flow.units.characteristic_length_pu))) +output_file.write('\n{:30s} {:30s}'.format("u_char_PU", + str(flow.units.characteristic_velocity_pu))) +output_file.write('\n{:30s} {:30s}'.format("viscosity_PU", + str(flow.units.viscosity_pu))) +output_file.write('\n{:30s} {:30s}'.format("p_char_PU", + str(flow.units.characteristic_pressure_pu))) +output_file.write('\n{:30s} {:30s}'.format("rho_char_PU", + str(flow.units.characteristic_density_pu))) +output_file.write('\n') +output_file.write('\n{:30s} {:30s}'.format("grid reynolds number Re_g", + str(flow.units.characteristic_velocity_lu/ + ((1 / np.sqrt(3.0))**2 + * (flow.units.relaxation_parameter_lu - 0.5))))) +output_file.write('\n{:30s} {:30s}'.format("flow through time PU [s]", + str(domain_length_x_in_d + *flow.units.characteristic_length_pu/ + flow.units.characteristic_velocity_pu))) +output_file.write('\n{:30s} {:30s}'.format("flow through time LU", + str(domain_length_x_in_d + *flow.units.characteristic_length_lu/ + flow.units.characteristic_velocity_lu))) +output_file.write('\n') +output_file.close() + +## END OF SCRIPT +print(f"\n♬ THE END ♬") + +# reset stdout (from LOGGER, see above (beginning)) +sys.stdout = old_stdout + diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/__init__.py b/examples/advanced_projects/efficient_bounce_back_obstacle/__init__.py new file mode 100644 index 00000000..fa823920 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/__init__.py @@ -0,0 +1,11 @@ +from .solid_boundary_data import SolidBoundaryData +from .fullway_bounce_back_boundary import FullwayBounceBackBoundary +from .halfway_bounce_back_boundary import HalfwayBounceBackBoundary +from .linear_interpolated_bounce_back_boundary import LinearInterpolatedBounceBackBoundary + +__all__ = [ + 'FullwayBounceBackBoundary', + 'HalfwayBounceBackBoundary', + 'LinearInterpolatedBounceBackBoundary', + 'SolidBoundaryData' +] diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py new file mode 100644 index 00000000..9781d62e --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py @@ -0,0 +1,948 @@ +""" + THIS FILE CONTAINS THE UTILITIES TO PROCESS AND PLOT THE REPORTED DATA FROM + THE CYLINDER OBSTACLE SIMULATION SCRIPT + - plotting of force coefficients + - analysis of periodic (force coefficient) signals + - drawing of circular cylinder mask in 2D + - plotting- and analysis utility for average velocity and reynolds + stress profiles +""" + +import os +import numpy as np +import matplotlib.pyplot as plt +from typing import Any +import traceback +from scipy.signal import find_peaks +from scipy.optimize import curve_fit +from collections import Counter + + +def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[float, float], + secax_functions_tuple: tuple[Any,Any], filenamebase, + save_timeseries = False, periodic_start=0, adjust_ylim=False): + """ + - plot force coefficient timeseries + - (opt.) save png to outdir + - contains exception-capture functionality if data is not plot-abel, + save-able etc. + """ + + # PLOT + try: + fig, ax = plt.subplots(constrained_layout=True) + ax.plot(data_array[:, 1], data_array[:, 2]) + ax.set_xlabel("physical time / s") + ax.set_ylabel(str(ylabel)) + ax.set_ylim(ylim) # change y-limits + secax = ax.secondary_xaxis('top', functions=secax_functions_tuple) + secax.set_xlabel("timestep (simulation time / LU)") + except Exception as e: + print(f"(WARNING!) plotting of {ylabel} didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + # SAVE PNG + try: + plt.savefig(filenamebase + ".png") + except Exception as e: + print(f"(WARNING!) saving of {ylabel} PLOT to {filenamebase}.png didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + # PLOT with ylim automatically ADJUSTED to fit data (additionally) + if adjust_ylim: + try: + fig, ax = plt.subplots(constrained_layout=True) + ax.plot(data_array[:, 1], data_array[:, 2]) + ax.set_xlabel("physical time / s") + ax.set_ylabel(str(ylabel)) + ylim_2 = ( data_array[int(data_array.shape[0] * periodic_start - 1):,2].min() * 0.5, + data_array[int(data_array.shape[0] * periodic_start - 1):,2].max() * 1.2) + ax.set_ylim(ylim_2) # change y-limits + secax = ax.secondary_xaxis('top', functions=secax_functions_tuple) + secax.set_xlabel("timestep (simulation time / LU)") + except Exception as e: + print(f"(WARNING!) plotting of {ylabel} didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + # SAVE PNG + try: + plt.savefig(filenamebase + "_ylimadjusted.png") + except Exception as e: + print(f"(WARNING!) saving of {ylabel} PLOT " + f"to {filenamebase}_ylimadjusted.png didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + # SAVE .txt timeseries + #TODO (optional): make this a seperate function... + if save_timeseries: + try: + np.savetxt(filenamebase + ".txt", data_array, + header=f"stepLU | timePU | {ylabel}") + except Exception as e: + print(f"(WARNING!) saving of {ylabel} TIMESERIES to {filenamebase}.txt didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + + +def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: float,prominence: float = None, name: str = "periodic timeseries", outdir=None, pu_per_step = None, verbose=False): + """ + ANALYZE PERIODIC SIGNAL (verbose = plot peak-finding), + - outputs: min, max, mean (simple + corrected for integer number of periods) + - RETURN: dictionary mit: Name, min, max, mean_simple, mean_n_periodic, + mean-min, mean-max (v.a. für Cl, nach neuen Skripten) + - print error if peak-finding didn't work + - do FFT, print FFT spectrum + - detect frequency of signal by sine-functino fitting + (can be more accurate than FFT for strictly sinusoidal signals!) + """ + values_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 2] + steps_LU_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 0] + mean_periodcorrected = None + max_mean = None # mean maximum value from all high peaks + min_mean = None # mean minimum value from all low peaks + if prominence is None: + prominence = ((values_periodic.max() - values_periodic.min()) * 0.5) + # The prominence value is up for debate; + # The value given here only reliably catches all peaks, + # if the signal is simple and periodically converged + + + try: + peaks_max = find_peaks(values_periodic, prominence=prominence) + peaks_min = find_peaks(-values_periodic, prominence=prominence) + + if peaks_min[0].shape[0] - peaks_max[0].shape[0] > 0: + peak_number = peaks_max[0].shape[0] + else: + peak_number = peaks_min[0].shape[0] + + if peaks_min[0][0] < peaks_max[0][0]: + first_peak = peaks_min[0][0] + last_peak = peaks_max[0][peak_number - 1] + else: + first_peak = peaks_max[0][0] + last_peak = peaks_min[0][peak_number - 1] + + if verbose: + peak_max_y = values_periodic[peaks_max[0]] + peak_max_x = steps_LU_periodic[peaks_max[0]] + peak_min_y = values_periodic[peaks_min[0]] + peak_min_x = steps_LU_periodic[peaks_min[0]] + + plt.subplots(constrained_layout=True) + plt.plot(steps_LU_periodic, values_periodic) + plt.scatter(peak_max_x[:peak_number], peak_max_y[:peak_number]) + plt.scatter(peak_min_x[:peak_number], peak_min_y[:peak_number]) + plt.scatter(steps_LU_periodic[first_peak], values_periodic[first_peak]) + plt.scatter(steps_LU_periodic[last_peak], values_periodic[last_peak]) + + max_mean = values_periodic[peaks_max[0]].mean() + plt.axhline(y=max_mean, color="r", ls="--", lw=0.5) + min_mean = values_periodic[peaks_min[0]].mean() + plt.axhline(y=min_mean, color="r", ls="--", lw=0.5) + if outdir is not None: + plt.savefig(outdir + f"/{name}_peakfinder.png") + + mean_periodcorrected = values_periodic[first_peak:last_peak].mean() + except Exception as e: + print(f"(WARNING!) peak finding for {name} didn't work... " + f"This might just be because there is no converged periodic region," + f"so don't worry if you expected this!") + if verbose: + print("Analyze Periodic Timeseries (verbose):-> see Python Stack Trace below:") + + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + # simple mean, max, min calculation + mean_simple = values_periodic.mean() + min_simple = values_periodic.min() + max_simple = values_periodic.max() + + # sine-fit + # (for better statistics and frequency analysis of purely sinusoidal signals) + def sine_func(xx, a, b, c, d): + return a * np.sin(2 * np.pi * b * xx + c) + d + + frequency_fit = None + try: + coefficients, values = curve_fit(sine_func, steps_LU_periodic, values_periodic, + p0=(0.7, 0.2, 0.5, 0)) + fig, ax = plt.subplots(constrained_layout=True) + if verbose: + plt.plot(steps_LU_periodic, values_periodic, steps_LU_periodic, + sine_func(steps_LU_periodic, *coefficients)) + plt.legend(["timeseries", "sine-fit"]) + ax.set_xlabel("physical time / s") + ax.set_ylabel("timeseries") + # ax.set_ylim([-1, 1]) + if outdir is not None: + plt.savefig(outdir + f"/{name}_sine-fit.png") + frequency_fit = coefficients[1] + except Exception as e: + print(f"(WARNING!) sine-fitting for {name} didn't work...") + if verbose: + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + # FFT + # run FFT on signal and get dominant frequency + freq_peak = None + freq_res = None + + if pu_per_step is not None: + try: + X = np.fft.fft(values_periodic) # fft result (amplitudes) + N = len(X) # number of freqs + n = np.arange(N) # freq index + T = N * pu_per_step # total time measured (T_PU) + freq = n / T # frequencies (x-axis of spectrum) + + if verbose: + plt.figure() + # plot spectrum |X|(f) + plt.stem(freq, np.abs(X), 'b', markerfmt=" ", basefmt="-b") + plt.xlabel("Freq (Hz)") + plt.ylabel("FFT Amplitude |X(freq)|") + plt.xlim(0, 1) + # print("max. Amplitude np.abx(X).max():", np.abs(X).max()) # uncomment for debugging + + # ylim, where highes peak is on left half of full spectrum: + plt.ylim(0, np.abs(X[:int(X.shape[0] * 0.5)]).max()) + + if outdir is not None: + plt.savefig(outdir + f"/{name}_fft.png") + + freq_res = freq[1] - freq[0] # frequency-resolution + + # get |X| Amplitude for left half of full spectrum + X_abs = np.abs(X[:int(X.shape[0] * 0.4)]) + freq_peak = freq[np.argmax(X_abs)] # find frequency with the highest amplitude + except Exception as e: + print(f"(WARNING!) fft for {name} didn't work...") + if verbose: + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + + return {"mean_simple": mean_simple, "mean_periodcorrected": mean_periodcorrected, + "min_simple": min_simple, "max_simple": max_simple, "max_mean": max_mean, + "min_mean": min_mean, "frequency_fit": frequency_fit, "frequency_fft": freq_peak, + "fft_resolution": freq_res} + + +def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, + filebase=".", print_data=False): + """ + calculate and draw a 2D representation of: + - the circular cylinder + - the basic solid mask + """ + + grid_x = gridpoints_per_diameter + 2 + + if print_data: + print("GPD = " + str(gridpoints_per_diameter)) + # define radius and position for a symmetrical circular Cylinder-Obstacle + radius_lu = 0.5 * gridpoints_per_diameter + y_pos_lu = 0.5 * grid_x + 0.5 + x_pos_lu = y_pos_lu + + # get x,y,z meshgrid of the domain (LU) + + # tupel of list indizes (1-n (non-zero-based!)) + xyz = tuple(np.linspace(1, n, n) for n in (grid_x, grid_x)) + # meshgrid of x- and y- indizes -> * unpacks the tuple to be two values and now a tuple + x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') + + # define cylinder (LU) (circle) + obstacle_mask_for_visualization = np.sqrt( + (x_lu - x_pos_lu) ** 2 + (y_lu - y_pos_lu) ** 2) < radius_lu + + nx, ny = obstacle_mask_for_visualization.shape # number of x- and y-nodes (Skalar) + + # for all the solid nodes, neighboring fluid nodes + rand_mask = np.zeros((nx, ny), dtype=bool) + + # same, but including q-dimension + rand_mask_f = np.zeros((flow.stencil.q, nx, ny), dtype=bool) + + rand_xq = [] # list of all x-values (incl. q-multiplicity) + rand_yq = [] # list of all y-values (incl. q-multiplicity) + + # np.array: list of + # (a) x-coordinates and + # (b) y-coordinates of the obstacle_mask_for_visualization + a, b = np.where(obstacle_mask_for_visualization) + # ...to iterate over all boundary/object/wall nodes + for p in range(0,len(a)): + # for all True-nodes in obstacle_mask_for_visualization + for i in range(0,flow.stencil.q): + # for all stencil directions c_i (lattice.stencil.e) + try: + # try in case the neighboring cell does not exist + # (an f pointing out of the simulation domain) + if not obstacle_mask_for_visualization[ + a[p] + flow.stencil.e[i][0], b[p] + flow.stencil.e[i][1]]: + # if neighbor in +(e_x, e_y; e is c_i) is False, + # we are on the object-surface (self True with neighbor False) + rand_mask[a[p], b[p]] = 1 + rand_mask_f[flow.stencil.opposite[i], a[p], b[p]] = 1 + rand_xq.append(a[p]) + rand_yq.append(b[p]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + rand_x, rand_y = np.where(rand_mask) # list of all surface coordinates + x_pos = sum(rand_x) / len(rand_x) # x-coordinate of circle center + y_pos = sum(rand_y) / len(rand_y) # y-coordinate of circle center + + # calculate all radii and r_max and r_min + r_max = 0 + r_min = gridpoints_per_diameter + + # list of all radii (without q-dimension) in LU + radii = np.zeros_like(rand_x, dtype=float) + for p in range(0, len(rand_x)): # for all nodes + # calculate distance to circle center + radii[p] = np.sqrt((rand_x[p] - x_pos) ** 2 + (rand_y[p] - y_pos) ** 2) + if radii[p] > r_max: + r_max = radii[p] + if radii[p] < r_min: + r_min = radii[p] + + # calculate all radii (with q-multiplicity) + radii_q = np.zeros_like(rand_xq, dtype=float) + for p in range(0, len(rand_xq)): + radii_q[p] = np.sqrt((rand_xq[p] - x_pos) ** 2 + (rand_yq[p] - y_pos) ** 2) + + ### all relative radii in relation to gpd/2 + radii_relative = radii / (radius_lu - 0.5) + # (subtract 0.5 because "true" boundary location is 0.5LU + # further out than node-coordinates) + radii_q_relative = radii_q / (radius_lu - 0.5) + + # calc. mean rel_radius + r_rel_mean = sum(radii_relative) / len(radii_relative) + rq_rel_mean = sum(radii_q_relative) / len(radii_q_relative) + + ## AREA calculation + area_theory = np.pi * (gridpoints_per_diameter / 2) ** 2 # area = pi*r² in LU² + # area in LU = number of nodes, because every node has a cell of 1LU x 1LU around it + area = len(a) + + if output_data: + output_file = open(filebase + "/obstacle_mask_info.txt", "a") + output_file.write("GPD = " + str(gridpoints_per_diameter) + "\n") + output_file.write( + "\nr_rel_mean: " + str(sum(radii_relative) / len(radii_relative))) + output_file.write("\nrq_rel_mean: " + str( + sum(radii_q_relative) / len(radii_q_relative))) + output_file.write("\nr_rel_min: " + str(r_max / (radius_lu - 0.5))) + output_file.write("\nr_rel_max: " + str(r_min / (radius_lu - 0.5))) + output_file.write("\n\narea_rel: " + str(area / area_theory)) + + output_file.write("\n\nradii: " + str(Counter(radii))) + output_file.write("\nradii_q: " + str(Counter(radii_q)) + "\n\n") + output_file.close() + if print_data: + print("area_rel: " + str(area / area_theory)) + + ### PLOT Mask + plt.figure() + plt.imshow(obstacle_mask_for_visualization) + # plt.xticks(np.arange(gridpoints_per_diameter + 2), minor=True) + # plt.yticks(np.arange(gridpoints_per_diameter + 2), minor=True) + ax = plt.gca() + xmin, xmax = ax.get_xlim() + ymax, ymin = ax.get_ylim() + if gridpoints_per_diameter >= 10: + plt.xticks(np.arange(0, xmax, int(xmax / 10))) + plt.yticks(np.arange(0, ymax, int(ymax / 10))) + else: + plt.xticks(np.arange(0, xmax, 1)) + plt.yticks(np.arange(0, ymax, 1)) + plt.title("GPD = " + str(gridpoints_per_diameter)) + ax.set_xticks(np.arange(-.5, xmax, 1), minor=True) + ax.set_yticks(np.arange(-.5, ymax, 1), minor=True) + + # grid thickness, circle, node marker + x, y = np.meshgrid(np.linspace(0, int(xmax), int(xmax + 1)), + np.linspace(0, int(ymax), int(ymax + 1))) + if gridpoints_per_diameter < 30: + ax.grid(which="minor", color="k", axis='both', linestyle='-', + linewidth=2) + circle = plt.Circle((xmax / 2 - 0.25, ymax / 2 - 0.25), + gridpoints_per_diameter / 2, color='r', fill=False, + linewidth=1) + ax.add_patch(circle) + plt.plot(x, y, marker='.', linestyle='', color="b", markersize=1) + elif gridpoints_per_diameter < 70: + ax.grid(which="minor", color="k", axis='both', linestyle='-', + linewidth=1) + circle = plt.Circle((xmax / 2 - 0.25, ymax / 2 - 0.25), + gridpoints_per_diameter / 2, color='r', fill=False, + linewidth=0.5) + ax.add_patch(circle) + elif gridpoints_per_diameter < 100: + ax.grid(which="minor", color="k", axis='both', linestyle='-', + linewidth=0.5) + elif gridpoints_per_diameter < 150: + ax.grid(which="minor", color="k", axis='both', linestyle='-', + linewidth=0.25) + + if output_data: + plt.savefig(filebase + "/obstacle_mask_GPD" + str( + gridpoints_per_diameter) + ".png") + if print_data: + plt.show() + else: + plt.close() + + +class ProfilePlotter: + """ + utility to handle profile reporter data + - load reference values + - process profile reporter data + - plot and save data with or without references + """ + + + def __init__(self, flow, output_path, reference_data_path, i_timeseries, + u_timeseries1, u_timeseries2, u_timeseries3): + self.flow = flow + self.output_path = output_path + self.i_timeseries = i_timeseries + self.u_timeseries1 = u_timeseries1 + self.u_timeseries2 = u_timeseries2 + self.u_timeseries3 = u_timeseries3 + + os.makedirs(self.output_path + "/ProfileReporter_Data/") + + self.import_profile_reference_data(reference_data_path) + + def import_profile_reference_data(self, data_path): + # import reference data: + # (data is: first column Y/D, second column u_d/u_char) + + # ux + self.p1_LS1993_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos1_LS1993.csv', delimiter=';') + self.p2_LS1993_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos2_LS1993.csv', delimiter=';') + self.p3_LS1993_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos3_LS1993.csv', delimiter=';') + + self.p1_KM2000_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos1_KM2000.csv', delimiter=';') + self.p2_KM2000_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos2_KM2000.csv', delimiter=';') + self.p3_KM2000_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos3_KM2000.csv', delimiter=';') + + self.p1_WR2008_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos1_WR2008.csv', delimiter=';') + self.p2_WR2008_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos2_WR2008.csv', delimiter=';') + self.p3_WR2008_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos3_WR2008.csv', delimiter=';') + + self.p1_DI2018_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos1_DI2018.csv', delimiter=';') + self.p2_DI2018_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos2_DI2018.csv', delimiter=';') + self.p3_DI2018_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos3_DI2018.csv', delimiter=';') + + # uy + self.p1_LS1993_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos1_LS1993.csv', delimiter=';') + self.p2_LS1993_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos2_LS1993.csv', delimiter=';') + self.p3_LS1993_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos3_LS1993.csv', delimiter=';') + + self.p1_KM2000_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos1_KM2000.csv', delimiter=';') + self.p2_KM2000_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos2_KM2000.csv', delimiter=';') + self.p3_KM2000_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos3_KM2000.csv', delimiter=';') + + self.p1_WR2008_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos1_WR2008.csv', delimiter=';') + self.p2_WR2008_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos2_WR2008.csv', delimiter=';') + self.p3_WR2008_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos3_WR2008.csv', delimiter=';') + + self.p1_DI2018_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos1_DI2018.csv', delimiter=';') + self.p2_DI2018_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos2_DI2018.csv', delimiter=';') + self.p3_DI2018_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos3_DI2018.csv', delimiter=';') + + # uxux + self.p1_DI2018_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos1_DI2018.csv', delimiter=';') + self.p1_KM2000_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos1_KM2000.csv', delimiter=';') + self.p1_R2016_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos1_R2016.csv', delimiter=';') + self.p2_BM1994_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_BM1994.csv', delimiter=';') + self.p2_DI2018_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_DI2018.csv', delimiter=';') + self.p2_KM2000_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_KM2000.csv', delimiter=';') + self.p2_LS1993_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_LS1993.csv', delimiter=';') + self.p2_R2016_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_R2016.csv', delimiter=';') + self.p3_DI2018_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos3_DI2018.csv', delimiter=';') + self.p3_KM2000_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos3_KM2000.csv', delimiter=';') + self.p3_R2016_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos3_R2016.csv', delimiter=';') + + # uyuy + self.p1_DI2018_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos1_DI2018.csv', delimiter=';') + self.p1_R2016_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos1_R2016.csv', delimiter=';') + self.p2_BM1994_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos2_BM1994.csv', delimiter=';') + self.p2_DI2018_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos2_DI2018.csv', delimiter=';') + self.p2_LS1993_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos2_LS1993.csv', delimiter=';') + self.p2_R2016_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos2_R2016.csv', delimiter=';') + self.p3_DI2018_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos3_DI2018.csv', delimiter=';') + self.p3_R2016_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos3_R2016.csv', delimiter=';') + + # uxuy + self.p1_BM1994_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos1_BM1994.csv', delimiter=';') + self.p1_DI2018_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos1_DI2018.csv', delimiter=';') + self.p1_R2016_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos1_R2016.csv', delimiter=';') + self.p2_BM1994_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos2_BM1994.csv', delimiter=';') + self.p2_DI2018_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos2_DI2018.csv', delimiter=';') + self.p2_LS1993_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos2_LS1993.csv', delimiter=';') + self.p2_R2016_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos2_R2016.csv', delimiter=';') + self.p3_BM1994_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos3_BM1994.csv', delimiter=';') + self.p3_DI2018_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos3_DI2018.csv', delimiter=';') + self.p3_R2016_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos3_R2016.csv', delimiter=';') + + def process_data(self, save=False): + """CALCULATE temporal velocity averages""" + avg_u1_temporal = np.mean(np.array(self.u_timeseries1), axis=0) + avg_u2_temporal = np.mean(np.array(self.u_timeseries2), axis=0) + avg_u3_temporal = np.mean(np.array(self.u_timeseries3), axis=0) + + self.avg_u1_x = avg_u1_temporal[0] + self.avg_u1_y = avg_u1_temporal[1] + + self.avg_u2_x = avg_u2_temporal[0] + self.avg_u2_y = avg_u2_temporal[1] + + self.avg_u3_x = avg_u3_temporal[0] + self.avg_u3_y = avg_u3_temporal[1] + + if save: + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos1" + "_t-avg.npy", avg_u1_temporal) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos2" + "_t-avg.npy", avg_u2_temporal) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos3" + "_t-avg.npy", avg_u3_temporal) + + # Y_inD for y-axis and plotting + self.y_in_D = ((np.arange(self.avg_u1_x.shape[0]) + 1 - self.flow.y_pos_lu) + / self.flow.char_length_lu) + if save: + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_YinD.npy", + self.y_in_D) + + # CALCULATE turbulent reynolds stresses + # diff between timeseries and time_average -> u' + u1_diff = self.u_timeseries1 - avg_u1_temporal + u2_diff = self.u_timeseries2 - avg_u2_temporal + u3_diff = self.u_timeseries3 - avg_u3_temporal + + # square of diff -> u'^2 + u1_diff_sq = u1_diff ** 2 + u2_diff_sq = u2_diff ** 2 + u3_diff_sq = u3_diff ** 2 + + # ux'*uy' + u1_diff_xy = u1_diff[:, 0, :] * u1_diff[:, 1, :] + u2_diff_xy = u2_diff[:, 0, :] * u2_diff[:, 1, :] + u3_diff_xy = u3_diff[:, 0, :] * u3_diff[:, 1, :] + + # time_average of u'² and ux'uy' + self.u1_diff_sq_mean = np.mean(u1_diff_sq, axis=0) # time average + self.u2_diff_sq_mean = np.mean(u2_diff_sq, axis=0) # time average + self.u3_diff_sq_mean = np.mean(u3_diff_sq, axis=0) # time average + self.u1_diff_xy_mean = np.mean(u1_diff_xy, axis=0) # time average + self.u2_diff_xy_mean = np.mean(u2_diff_xy, axis=0) # time average + self.u3_diff_xy_mean = np.mean(u3_diff_xy, axis=0) # time average + + if save: # save reynolds stresses + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_1_ReStress_x.npy", + np.array([self.y_in_D, self.u1_diff_sq_mean[0]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_2_ReStress_x.npy", + np.array([self.y_in_D, self.u2_diff_sq_mean[0]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_3_ReStress_x.npy", + np.array([self.y_in_D, self.u3_diff_sq_mean[0]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_1_ReStress_y.npy", + np.array([self.y_in_D, self.u1_diff_sq_mean[1]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_2_ReStress_y.npy", + np.array([self.y_in_D, self.u2_diff_sq_mean[1]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_3_ReStress_y.npy", + np.array([self.y_in_D, self.u3_diff_sq_mean[1]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_1_ReShearStress.npy", + np.array([self.y_in_D, self.u1_diff_xy_mean])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_2_ReShearStress.npy", + np.array([self.y_in_D, self.u2_diff_xy_mean])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_3_ReShearStress.npy", + np.array([self.y_in_D, self.u3_diff_xy_mean])) + + def save_timeseries_to_files(self, basepath): + """save the FULL timeseries to files""" + # timeseries (i) + np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + + "_timeseries_steps.npy", np.array(self.i_timeseries)) + #timeseries (u) + np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos1" + "_timeseries_data.npy", np.array(self.u_timeseries1)) + np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos2" + "_timeseries_data.npy", np.array(self.u_timeseries2)) + np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos3" + "_timeseries_data.npy", np.array(self.u_timeseries3)) + + def plot_velocity_profiles(self, show_reference = False, save = False, show = False): + """plot tht average velocity profiles""" + cm = 1 / 2.54 + + if not show_reference: + # PLOT ux + fig, (ax_ux, ax_uy) = plt.subplots(1, 2, constrained_layout=True, + figsize=(30 * cm, 10 * cm)) + ax_ux.plot(self.y_in_D, self.avg_u1_x, + self.y_in_D, self.avg_u2_x, + self.y_in_D, self.avg_u3_x) + ax_ux.set_xlabel("y/D") + ax_ux.set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") + ax_ux.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + # PLOT uy + ax_uy.plot(self.y_in_D, self.avg_u1_y, + self.y_in_D, self.avg_u2_y, + self.y_in_D, self.avg_u3_y) + ax_uy.set_xlabel("y/D") + ax_uy.set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") + ax_uy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_velocity_noReference.png") + if show: + plt.show() + else: + plt.close() + + else: + # PLOT ux against references + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.avg_u1_x, + self.y_in_D, self.avg_u2_x - 1, + self.y_in_D, self.avg_u3_x - 2) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") + ref_LS = ax.plot(self.p1_LS1993_ux[:, 0], self.p1_LS1993_ux[:, 1], + self.p2_LS1993_ux[:, 0], self.p2_LS1993_ux[:, 1], + self.p3_LS1993_ux[:, 0], + self.p3_LS1993_ux[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_KM = ax.plot(self.p1_KM2000_ux[:, 0], self.p1_KM2000_ux[:, 1], + self.p2_KM2000_ux[:, 0], self.p2_KM2000_ux[:, 1], + self.p3_KM2000_ux[:, 0], + self.p3_KM2000_ux[:, 1]) + plt.setp(ref_KM, ls="dotted", lw=1.5, marker="", color="k", + label="Kravchenko & Moin (2000)") + ref_WR = ax.plot(self.p1_WR2008_ux[:, 0], self.p1_WR2008_ux[:, 1], + self.p2_WR2008_ux[:, 0], self.p2_WR2008_ux[:, 1], + self.p3_WR2008_ux[:, 0], + self.p3_WR2008_ux[:, 1]) + plt.setp(ref_WR, ls="dashdot", lw=1.5, marker="", color="k", + label="Wissink & Rodi (2008)") + ref_DI = ax.plot(self.p1_DI2018_ux[:, 0], self.p1_DI2018_ux[:, 1], + self.p2_DI2018_ux[:, 0], self.p2_DI2018_ux[:, 1], + self.p3_DI2018_ux[:, 0], + self.p3_DI2018_ux[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") + ax.set_ylim((-2.5, +2)) + ax.set_xlim((-3, 3)) + ax.legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], + ref_DI[0]], loc='best') + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_ux_withReference.png") + if show: + plt.show() + else: + plt.close() + + # PLOT uy against references + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.avg_u1_y, + self.y_in_D, self.avg_u2_y - 1, + self.y_in_D, self.avg_u3_y - 2) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", + label="lettuce") + ref_LS = ax.plot(self.p1_LS1993_uy[:, 0], self.p1_LS1993_uy[:, 1], + self.p2_LS1993_uy[:, 0], self.p2_LS1993_uy[:, 1], + self.p3_LS1993_uy[:, 0], + self.p3_LS1993_uy[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_KM = ax.plot(self.p1_KM2000_uy[:, 0], self.p1_KM2000_uy[:, 1], + self.p2_KM2000_uy[:, 0], self.p2_KM2000_uy[:, 1], + self.p3_KM2000_uy[:, 0], + self.p3_KM2000_uy[:, 1]) + plt.setp(ref_KM, ls="dotted", lw=1.5, marker="", color="k", + label="Kravchenko & Moin (2000)") + ref_WR = ax.plot(self.p1_WR2008_uy[:, 0], self.p1_WR2008_uy[:, 1], + self.p2_WR2008_uy[:, 0], self.p2_WR2008_uy[:, 1], + self.p3_WR2008_uy[:, 0], + self.p3_WR2008_uy[:, 1]) + plt.setp(ref_WR, ls="dashdot", lw=1.5, marker="", color="k", + label="Wissink & Rodi (2008)") + ref_DI = ax.plot(self.p1_DI2018_uy[:, 0], self.p1_DI2018_uy[:, 1], + self.p2_DI2018_uy[:, 0], self.p2_DI2018_uy[:, 1], + self.p3_DI2018_uy[:, 0], + self.p3_DI2018_uy[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") + ax.set_ylim((-2.5, +1.5)) + ax.set_xlim((-3, 3)) + ax.legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], + ref_DI[0]], loc='best') + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_uy_withReference.png") + if show: + plt.show() + else: + plt.close() + + def plot_reynolds_stress_profiles(self, show_reference=False, save=False, show = False): + """plot average reynolds stress profiles""" + cm = 1 / 2.54 + if not show_reference: + fig, (ax_xx, ax_yy, ax_xy) = plt.subplots(1, 3, + figsize=(40 * cm, 10 * cm), + constrained_layout=True) + ax_xx.plot(self.y_in_D, self.u1_diff_sq_mean[0], + self.y_in_D, self.u2_diff_sq_mean[0], + self.y_in_D, self.u3_diff_sq_mean[0]) + ax_xx.set_xlabel("y/D") + ax_xx.set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") + ax_xx.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + ax_yy.plot(self.y_in_D, self.u1_diff_sq_mean[1], + self.y_in_D, self.u2_diff_sq_mean[1], + self.y_in_D, self.u3_diff_sq_mean[1]) + ax_yy.set_xlabel("y/D") + ax_yy.set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") + ax_yy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + ax_xy.plot(self.y_in_D, self.u1_diff_xy_mean, + self.y_in_D, self.u2_diff_xy_mean, + self.y_in_D, self.u3_diff_xy_mean) + ax_xy.set_xlabel("y/D") + ax_xy.set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") + ax_xy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_reynoldsStresses_noReference.png") + if show: + plt.show() + else: + plt.close() + else: + # plot reynolds stresses against reference + # uxux - streamwise + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[0], + self.y_in_D, self.u2_diff_sq_mean[0] - 0.5, + self.y_in_D, self.u3_diff_sq_mean[0] - 1) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", + label="lettuce") + ref_LS = ax.plot(self.p2_LS1993_uxux[:, 0], self.p2_LS1993_uxux[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_R = ax.plot(self.p1_R2016_uxux[:, 0], self.p1_R2016_uxux[:, 1], + self.p3_R2016_uxux[:, 0], self.p3_R2016_uxux[:, 1], + self.p3_R2016_uxux[:, 0], self.p3_R2016_uxux[:, 1]) + plt.setp(ref_R, ls="--", lw=1.5, marker="", color="k", + label="Rajani et al. (2016)") + ref_KM = ax.plot(self.p1_KM2000_uxux[:, 0], self.p1_KM2000_uxux[:, 1], + self.p2_KM2000_uxux[:, 0], self.p2_KM2000_uxux[:, 1], + self.p3_KM2000_uxux[:, 0], self.p3_KM2000_uxux[:, 1]) + plt.setp(ref_KM, ls="dotted", lw=1.5, marker="", color="k", + label="Kravchenko & Moin (2000)") + ref_BM = ax.plot(self.p2_BM1994_uxux[:, 0], self.p2_BM1994_uxux[:, 1]) + plt.setp(ref_BM, ls="dashdot", lw=1.5, marker="", color="k", + label="Beaudan & Moin (1994)") + ref_DI = ax.plot(self.p1_DI2018_uxux[:, 0], self.p1_DI2018_uxux[:, 1], + self.p2_DI2018_uxux[:, 0], self.p2_DI2018_uxux[:, 1], + self.p3_DI2018_uxux[:, 0], self.p3_DI2018_uxux[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") + ax.set_ylim((-1.2, 0.8)) + ax.set_xlim((-3, 3)) + ax.legend( + handles=[my_data[0], ref_LS[0], ref_R[0], ref_KM[0], ref_BM[0], + ref_DI[0]], loc='best') + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_uxux_withReference.png") + if show: + plt.show() + else: + plt.close() + + # uyuy - cross-stream + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[1], + self.y_in_D, self.u2_diff_sq_mean[1] - 0.5, + self.y_in_D, self.u3_diff_sq_mean[1] - 1) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", + label="lettuce") + ref_BM = ax.plot(self.p2_BM1994_uyuy[:, 0], self.p2_BM1994_uyuy[:, 1]) + plt.setp(ref_BM, ls="dashdot", lw=1.5, marker="", color="k", + label="Beaudan & Moin (1994)") + ref_LS = ax.plot(self.p2_LS1993_uyuy[:, 0], self.p2_LS1993_uyuy[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_R = ax.plot(self.p1_R2016_uyuy[:, 0], self.p1_R2016_uyuy[:, 1], + self.p3_R2016_uyuy[:, 0], self.p3_R2016_uyuy[:, 1], + self.p3_R2016_uyuy[:, 0], self.p3_R2016_uyuy[:, 1]) + plt.setp(ref_R, ls="--", lw=1.5, marker="", color="k", + label="Rajani et al. (2016)") + ref_DI = ax.plot(self.p1_DI2018_uyuy[:, 0], self.p1_DI2018_uyuy[:, 1], + self.p2_DI2018_uyuy[:, 0], self.p2_DI2018_uyuy[:, 1], + self.p3_DI2018_uyuy[:, 0], self.p3_DI2018_uyuy[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") + ax.set_ylim((-1.2, 0.8)) + ax.set_xlim((-3, 3)) + ax.legend(handles=[my_data[0], ref_BM[0], ref_LS[0], ref_R[0], + ref_DI[0]], loc='best') + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_uyuy_withReference.png") + if show: + plt.show() + else: + plt.close() + + # uxuy - Reynolds shear stress + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.u1_diff_xy_mean, + self.y_in_D, self.u2_diff_xy_mean - 0.5, + self.y_in_D, self.u3_diff_xy_mean - 1) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", + label="lettuce") + ref_BM = ax.plot(self.p2_BM1994_uxuy[:, 0], self.p2_BM1994_uxuy[:, 1]) + plt.setp(ref_BM, ls="dashdot", lw=1.5, marker="", color="k", + label="Beaudan & Moin (1994)") + ref_LS = ax.plot(self.p2_LS1993_uxuy[:, 0], self.p2_LS1993_uxuy[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_R = ax.plot(self.p1_R2016_uxuy[:, 0], self.p1_R2016_uxuy[:, 1], + self.p3_R2016_uxuy[:, 0], self.p3_R2016_uxuy[:, 1], + self.p3_R2016_uxuy[:, 0], self.p3_R2016_uxuy[:, 1]) + plt.setp(ref_R, ls="--", lw=1.5, marker="", color="k", + label="Rajani et al. (2016)") + ref_DI = ax.plot(self.p1_DI2018_uxuy[:, 0], self.p1_DI2018_uxuy[:, 1], + self.p2_DI2018_uxuy[:, 0], self.p2_DI2018_uxuy[:, 1], + self.p3_DI2018_uxuy[:, 0], self.p3_DI2018_uxuy[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") + ax.set_ylim((-1.2, 0.8)) + ax.set_xlim((-3, 3)) + ax.legend(handles=[my_data[0], ref_BM[0], ref_LS[0], ref_R[0], + ref_DI[0]], loc='best') + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_uxuy_withReference.png") + if show: + plt.show() + else: + plt.close() + diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py new file mode 100644 index 00000000..02536b0e --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py @@ -0,0 +1,106 @@ +import torch + +from timeit import default_timer as timer +from typing import List + +from lettuce._simulation import * +from lettuce import Flow + +__all__ = ['EbbSimulation'] + +class EbbSimulation(Simulation): + """ + advanced simulation class, with + - (additional) post_streaming_boundaries (substeps: C -> S -> B -> R) + - post_collision population information for post_streaming_boundaries + """ + def __init__(self, flow: 'Flow', collision: 'Collision', + reporter: List['Reporter']): + flow.context.use_native = False + super().__init__(flow, collision, reporter) + + if hasattr(flow.__class__, 'post_streaming_boundaries'): + # list of boundaries that are applied AFTER the streaming step + self.post_streaming_boundaries = flow.post_streaming_boundaries + else: + self.post_streaming_boundaries = [] + + # adjust No_collision_ and no_streaming_mask for use of post_streaming_boundaries (!) + if len(self.post_streaming_boundaries) > 0: + + # create NCM and NSM, + # ...if there were no pre_ or post_boundaries that triggered + # ...their creation in super-class + if self.no_collision_mask is None: + # fill masks with value of self.collision_index (= number of pre-boundaries) + self.no_collision_mask = self.context.full_tensor( + flow.resolution, self.collision_index, dtype=torch.uint8) + if self.no_streaming_mask is None: + self.no_streaming_mask = self.context.full_tensor([flow.stencil.q, + *flow.resolution], + self.collision_index, + dtype=torch.uint8) + + + for i, boundary in enumerate(self.post_streaming_boundaries, + start=self.collision_index + 1 + + len(self.post_boundaries)): +# FOR DEBUGGING: print("SIMULATION: creating no_collision_mask entries for: i, boundary = ", i, boundary) +# FOR DEBUGGING: print("collision index is:", self.collision_index) + ncm = boundary.make_no_collision_mask( + [it for it in self.flow.f.shape[1:]], context=self.context) + if ncm is not None: + self.no_collision_mask[ncm] = i + nsm = boundary.make_no_streaming_mask( + [it for it in self.flow.f.shape], context=self.context) + if nsm is not None: + self.no_streaming_mask |= nsm + + # get indices of post_streaming_boundaries, that need f_collided to be stored + self.store_f_collided_post_streaming_boundaries_index = [] + for i, boundary in enumerate(self.post_streaming_boundaries): + if hasattr(boundary.__class__, "store_f_collided"): + # append boundary to lis of boundaries that need + # ...f_collided state between collision and streaming substep + self.store_f_collided_post_streaming_boundaries_index.append(i) + if hasattr(boundary.__class__, "initialize_f_collided"): + # initialize the f_collided storage in each of those boundaries + boundary.initialize_f_collided() + + # redefine collide_and_stream() to include the storage of f_collided + # ...for use in post_streaming_boundaries + def collide_and_stream(*_, **__): + self._collide() + # pass f to post_streaming_boundaries between collision and streaming substep + self._pass_f_collided() + self._stream() + + self._collide_and_stream = collide_and_stream + + + def _post_streaming_boundaries(self): + # runs all the post_streaming_boundaries; required for efficient BBBC + + for boundary in self.post_streaming_boundaries: + boundary(self.flow) + + def _pass_f_collided(self): + # passes f to post_streaming_boundaries, for later use + + for idx in self.store_f_collided_post_streaming_boundaries_index: + self.post_streaming_boundaries[idx].store_f_collided(self.flow.f) + + def __call__(self, num_steps): + beg = timer() + + if self.flow.i == 0: + self._report() + + for _ in range(num_steps): + self._collide_and_stream(self) + self._post_streaming_boundaries() + self.flow.i += 1 + self._report() + + end = timer() + return num_steps * self.flow.rho().numel() / 1e6 / (end - beg) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py new file mode 100644 index 00000000..b14b2cd8 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py @@ -0,0 +1,185 @@ +import torch +import numpy as np + +from typing import List, Optional +from lettuce import Boundary, Flow, Context + +__all__ = ["FullwayBounceBackBoundary"] + +class FullwayBounceBackBoundary(Boundary): + """ + FULLWAY BOUNCE BACK BOUNDARY CONDITION (FWBB): + - inverts populations on solid boundary nodes, + instead of colliding them + - (!) mind observable-artifacts on solid regions. They do not affect + the flow in the fluid region, but can mess with visualization and + non-local statistics (mean velocity etc.) + - able to calculate fluid force on boundary by momentum exchange + """ + + def __init__(self, context, flow, mask, global_solid_mask=None, + periodicity: tuple[bool,bool,bool|None] = None, calc_force=False): + self.context = context + self.flow = flow + + self.mask = mask + + if periodicity is None: + periodicity = (False, False, False if self.flow.stencil.d == 3 else None) + + # global_solid_mask (optional) to filter out all "fake" fluid neighbors, + # ...which are outside the FWBB but not in the fluid region + if global_solid_mask is None: + global_solid_mask = np.zeros_like(self.mask, dtype=bool) + + # exclude self.mask from global_solid_mask for "other" solid mask + other_solid_bc_mask = np.where(~self.mask, global_solid_mask, False) + + if calc_force: + # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.force_sum = torch.zeros_like(self.context.convert_to_tensor( + self.flow.torch_stencil.e[0])) + self.calc_force = True + else: + self.calc_force = False + + ### create f_index_fwbb, needed for force-calculation + # ...(marks all fs which streamed into the boundary in prior streaming step) + # ... in other words: marks all fs that need to be bounced + self.f_index_fwbb = [] + + if self.flow.stencil.d == 2: + nx, ny = mask.shape # domain size in x and y + ix_sp, iy_sp = np.where(mask) + # np.arrays: list of (ix_sp) x-indizes and (iy_sp) y-indizes in the boundary.mask + # ...to enable iteration over all boundary/wall/object-nodes + for sp_index in range(0, len(ix_sp)): # for all TRUE-nodes in boundary.mask + for q_index in range(0, self.flow.stencil.q): + # for all stencil-directions c_i (lattice.stencil.e in lettuce) + + # check for boundary-nodes neighboring the domain-border. + # ...they have to take the periodicity into account... + border = np.zeros(self.flow.stencil.d, dtype=int) + if (ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 + and periodicity[0]): # searching border on left + border[0] = -1 + elif (ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 + and periodicity[0]): # searching border on right + border[0] = 1 + if (iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 + and periodicity[1]): # searching border on left + border[1] = -1 + elif (iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 + and periodicity[1]): # searching border on right + border[1] = 1 + try: # try in case the neighboring cell does not exist + # (= an f pointing out of the simulation domain) + if (not mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0]*nx, + iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1]*ny] + and not other_solid_bc_mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0]*nx, + iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1]*ny]): + # if the neighbour of sp_index is False in the boundary.mask, + # ...sp_index is ix_sp solid node, neighbouring ix_sp fluid node: + # the direction pointing from the fluid neighbour to solid + # ...sp_index is marked on the solid sp_index + + # list of [q, nx, ny], marks all fs on the boundary-border, which point into the boundary/solid + self.f_index_fwbb.append([self.flow.stencil.opposite[q_index], ix_sp[sp_index], iy_sp[sp_index]]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + if self.flow.stencil.d == 3: # like 2D, but in 3D...guess what... + nx, ny, nz = mask.shape + ix_sp, iy_sp, iz_sp = np.where(mask) + for sp_index in range(0, len(ix_sp)): + for q_index in range(0, self.flow.stencil.q): + border = np.zeros(self.flow.stencil.d, dtype=int) + if (ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 + and periodicity[0]): # searching border on left + border[0] = -1 + elif (ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 + and periodicity[0]): # searching border on right + border[0] = 1 + if (iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 + and periodicity[1]): # searching border on left + border[1] = -1 + elif (iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 + and periodicity[1]): # searching border on right + border[1] = 1 + if (iz_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 2] == -1 + and periodicity[2]): # searching border on left + border[2] = -1 + elif (iz_sp[sp_index] == nz - 1 and self.flow.stencil.e[q_index][ 2] == 1 + and periodicity[2]): # searching border on right + border[2] = 1 + try: # try in case the neighboring cell does not exist + # (and f pointing out of simulation domain) + if (not mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0] * nx, + iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1] * ny, + iz_sp[sp_index] + self.flow.stencil.e[q_index][ 2] - border[2] * nz] + and not other_solid_bc_mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0] * nx, + iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1] * ny, + iz_sp[sp_index] + self.flow.stencil.e[q_index][ 2] - border[2] * nz]): + self.f_index_fwbb.append([self.flow.stencil.opposite[q_index], ix_sp[sp_index], iy_sp[sp_index], iz_sp[sp_index]]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + self.f_index_fwbb = torch.tensor(np.array(self.f_index_fwbb), + device=flow.context.device, + dtype=torch.int64) # the batch-index has to be integer + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, + device=flow.context.device, + dtype=torch.int64) # batch-index has to be ix_sp tensor + + + def __call__(self, flow): + # FULLWAY-BBBC: inverts populations on all boundary nodes + + # calc force on boundary:# + if self.calc_force: + self.calc_force_on_boundary(flow.f) + + # bounce (invert populations on boundary nodes) + # -> basically an efficient version of: + # f = torch.where(self.mask, f[self.flow.stencil.opposite], f) + + if self.flow.torch_stencil.d == 2: + flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2]] = flow.f[self.f_index_fwbb[:, 0], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2]] + if self.flow.torch_stencil.d == 3: + flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2], + self.f_index_fwbb[:, 3]] = flow.f[self.f_index_fwbb[:, 0], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2], + self.f_index_fwbb[:, 3]] + + def calc_force_on_boundary(self, f): + """calculate fluid force on all relevant boundary nodes by + momentum exchange""" + if self.flow.torch_stencil.d == 2: + self.force_sum = 2 * torch.einsum('i..., id -> d', f[self.f_index_fwbb[:, 0], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2]], self.flow.torch_stencil.e[self.f_index_fwbb[:, 0]]) + if self.flow.torch_stencil.d == 3: + self.force_sum = 2 * torch.einsum('i..., id -> d', f[self.f_index_fwbb[:, 0], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2], + self.f_index_fwbb[:, 3]], self.flow.torch_stencil.e[self.f_index_fwbb[:, 0]]) + + def make_no_collision_mask(self, f_shape: List[int], context: 'Context') -> Optional[torch.Tensor]: + return self.context.convert_to_tensor(self.mask, dtype=bool) + + def make_no_streaming_mask(self, shape: List[int], context: 'Context') -> Optional[torch.Tensor]: + """FWBB needs streaming to invert the populations on the solid itself!""" + pass + + def native_available(self) -> bool: + return False + + def native_generator(self, index: int): + # not implemented yet + pass \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py new file mode 100644 index 00000000..9e1d7b76 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py @@ -0,0 +1,254 @@ +import torch +import numpy as np + +from typing import List, Optional +from lettuce import Boundary, Flow, Context +from solid_boundary_data import SolidBoundaryData + +__all__ = ["HalfwayBounceBackBoundary"] + +class HalfwayBounceBackBoundary(Boundary): + + """ + HALFWAY BOUNCE BACK BOUNDARY CONDITION (HWBB): + - inverts populations on fluid nodex, neighboring boundary + - improved temporal accuracy in comparison to FWBB + - able to calculate fluid force on boundary by momentum exchange + """ + + def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, + global_solid_mask=None, periodicity: tuple[bool,bool,bool|None] = None, + calc_force: bool = False): + + self.context = context + self.flow = flow + + self.mask = solid_boundary_data.solid_mask + + # global_solid_mask to filter out all "fake" fluid neighbors, + # which are outside this HWBB but not in the fluid region + if global_solid_mask is None: + global_solid_mask = self.mask + + if periodicity is None: + periodicity = (False, False, False if self.flow.stencil.d == 3 else None) + + if calc_force: + # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.force_sum = torch.zeros_like(self.context.convert_to_tensor( + self.flow.stencil.e[0])) + self.calc_force = True + else: + self.calc_force = False + + self.f_index = [] + self.f_collided = None + + # COMPATIBILITY to unified Solid Boundary Data object! + # -> combine f_index_lt and f_index_gt (needed for IBB1) to self.f_index + + # if solid_boundary_data contains batch_indices, use them + if ((hasattr(solid_boundary_data, "f_index_gt") + or hasattr(solid_boundary_data, "f_index_lt")) + and len(solid_boundary_data.f_index_lt.shape) + == len(solid_boundary_data.f_index_gt.shape)): + print("HWBB: SBD has got attributs f_index_gt and f_index_lt!") + self.f_index = np.concatenate((solid_boundary_data.f_index_lt, + solid_boundary_data.f_index_gt), axis=0) + elif (hasattr(solid_boundary_data, "f_index_gt") + and solid_boundary_data.f_index_lt.shape[0] == 0): + print("HWBB: SBD has got attributf_index_gt (NO f_index_lt)!") + self.f_index = solid_boundary_data.f_index_gt + elif (hasattr(solid_boundary_data, "f_index_lt") + and solid_boundary_data.f_index_gt.shape[0] == 0): + print("HWBB: SBD has got attributf_index_lt (NO f_index_gt)!") + self.f_index = solid_boundary_data.f_index_lt + else: #else do extra neighbour_search below + print("(INFO) HWBB didn't find solid_boundary_data, " + "doing legacy neighbour_search on mask...") + # searching boundary-fluid-interface and append indices to f_index + if self.flow.stencil.d == 2: + nx, ny = self.mask.shape # domain size in x and y + + # x- and y-index of boundaryTRUE nodes for iteration over boundary area + a, b = np.where(self.mask) + + for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask + for i in range(0, self.flow.stencil.q): + # for all stencil-directions c_i (lattice.stencil.e in lettuce) + # check for boundary-nodes neighboring the domain-border. + # ...they have to take the periodicity into account... + border = np.zeros(self.flow.context.d, dtype=int) + + if (a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 + and periodicity[0]): # searching border on left [x] + border[0] = -1 + elif (a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 + and periodicity[0]): # searching border on right [x] + border[0] = 1 + + if (b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 + and periodicity[1]): # searching border on left [y] + border[1] = -1 + elif (b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 + and periodicity[1]): # searching border on right [y] + border[1] = 1 + + try: # try in case the neighboring cell does not exist + # (= an f pointing out of the simulation domain) + if (not self.mask[a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny] + and not global_solid_mask[ + a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny]): + # if the neighbour of p is False in the boundary.mask, + # p is a solid node, neighbouring a fluid node: + # ...the direction pointing from the fluid neighbour + # to solid p is marked on the neighbour + + self.f_index.append([self.flow.stencil.opposite[i], + a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + if self.flow.stencil.d == 3: # like 2D, but in 3D...guess what... + nx, ny, nz = self.mask.shape + a, b, c = np.where(self.mask) + + for p in range(0, len(a)): + for i in range(0, self.flow.stencil.q): + border = np.zeros(self.flow.context.d, dtype=int) + # x - direction + if (a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 + and periodicity[0]): # searching border on left + border[0] = -1 + elif (a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 + and periodicity[0]): # searching border on right + border[0] = 1 + # y - direction + if (b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 + and periodicity[1]): # searching border on left + border[1] = -1 + elif (b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 + and periodicity[1]): # searching border on right + border[1] = 1 + # z - direction + if (c[p] == 0 and self.flow.stencil.e[i][ 2] == -1 + and periodicity[2]): # searching border on left + border[2] = -1 + elif (c[p] == nz - 1 and self.flow.stencil.e[i][ 2] == 1 + and periodicity[2]): # searching border on right + border[2] = 1 + + try: # try in case the neighboring cell does not exist + # (an f pointing out of simulation domain) + if (not self.mask[a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, + c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz] + and not global_solid_mask[ + a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, + c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz]): + + self.f_index.append([self.flow.stencil.opposite[i], + a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, + c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + # convert relevant tensors: + self.f_index = torch.tensor(np.array(self.f_index), + device=self.flow.context.device, + dtype=torch.int64) # the batch-index has to be integer + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, + device=self.flow.context.device, + dtype=torch.int64) # batch-index has to be a tensor + + def __call__(self, flow): + # HWBB: bounce populations on neighboring fluid nodes + + # calc force on boundary: + if self.calc_force: + self.calc_force_on_boundary() + + # bounce (invert populations on fluid nodes neighboring solid nodes) + # efficient version of: + # f = torch.where(self.f_mask[self.flow.stencil.opposite], + # f_collided[self.flow.stencil.opposite], f) + + if self.flow.torch_stencil.d == 2: + flow.f[self.opposite_tensor[self.f_index[:, 0]], + self.f_index[:, 1], + self.f_index[:, 2]] = self.f_collided[:, 0] + if self.flow.torch_stencil.d == 3: + flow.f[self.opposite_tensor[self.f_index[:, 0]], + self.f_index[:, 1], + self.f_index[:, 2], + self.f_index[:, 3]] = self.f_collided[:, 0] + + def make_no_collision_mask(self, f_shape: List[int], context: 'Context' + ) -> Optional[torch.Tensor]: + # INFO: for the halfway bounce back boundary, + # a no_collision_mask ist not necessary, because the no_stream_mask + # prevents interaction between nodes inside and outside the boundary region. + # INFO: pay attention to the initialization of observable/moment-fields + # (u, rho,...) on the boundary nodes, in the initial solution of your flow, + # especially if visualization or post-processing uses the field-values + # in the whole domain (including the boundary region)! + return self.context.convert_to_tensor(self.mask, dtype=bool) + + def make_no_streaming_mask(self, f_shape: List[int], context: 'Context' + ) -> Optional[torch.Tensor]: + # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), + # ...but CAN be (x,y,z) (z optional). + # ...in the latter case, torch.where() broadcasts the mask to (q,x,y,z), + # ...so ALL q populations of a lattice-node are marked equally + return self.context.convert_to_tensor(self.mask, dtype= bool) + + def calc_force_on_boundary(self): + """calculate the fluid force on boundary by momentum exchange""" + # calculate force on boundary by momentum exchange method (MEA, MEM) + # according to Kruger et al., 2017, pp.215-217: + # momentum (f_i*c_i - f_i_opposite*c_i_opposite = 2*f_i*c_i for a resting boundary) + # is summed for all populations pointing at the surface of the boundary + self.force_sum = 2 * torch.einsum('i..., id -> d', self.f_collided[:, 0], + self.flow.torch_stencil.e[self.f_index[:, 0]]) + + def store_f_collided(self, f_collided): + """store populations between collision and streaming, because they are + needed for calculation of bounce and force!""" + if self.flow.torch_stencil.d == 2: + self.f_collided[:, 0] = torch.clone( + f_collided[self.f_index[:, 0], # q + self.f_index[:, 1], # x + self.f_index[:, 2]]) # y + self.f_collided[:, 1] = torch.clone( + f_collided[self.opposite_tensor[self.f_index[:, 0]], # q + self.f_index[:, 1], # x + self.f_index[:, 2]]) # y + if self.flow.torch_stencil.d == 3: + self.f_collided[:, 0] = torch.clone( + f_collided[self.f_index[:, 0], # q + self.f_index[:, 1], # x + self.f_index[:, 2], # y + self.f_index[:, 3]]) # z + self.f_collided[:, 1] = torch.clone( + f_collided[self.opposite_tensor[self.f_index[:, 0]], # q + self.f_index[:, 1], # x + self.f_index[:, 2], # y + self.f_index[:, 3]]) # z + + def initialize_f_collided(self): + """initialize the tensors to store post-collision populations""" + f_collided = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) + f_collided_opposite = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) + self.f_collided = torch.stack((f_collided, f_collided_opposite), dim=1) + + def native_available(self) -> bool: + return True + + def native_generator(self, index: int): + # not implemented yet + pass \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py b/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py new file mode 100644 index 00000000..9be701c7 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py @@ -0,0 +1,21 @@ +import os +import sys + + +class Logger(object): + """logging functionality proposed by P.Spelten to log stdout to file""" + def __init__(self, outdir): + self.terminal = sys.stdout + self.filename = os.path.join(outdir, "log") + + def write(self, message): + self.terminal.write(message) + f = open(self.filename, "a") + f.write(message) + f.close() + + def flush(self): + # this flush method is needed for python 3 compatibility. + # This handles the flush command by doing nothing. + # you might want to specify some extra behavior here. + pass \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py new file mode 100644 index 00000000..4954504b --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py @@ -0,0 +1,209 @@ +import torch + +from lettuce import Boundary, Flow, Context +from solid_boundary_data import SolidBoundaryData + +__all__ = ["LinearInterpolatedBounceBackBoundary"] + +class LinearInterpolatedBounceBackBoundary(Boundary): + """Interpolated Bounce Back Boundary Condition (IBB or IBB1) + first introduced by Bouzidi et al. (2001), as described in Kruger et al. (2017) + - linear interpolation of populations to retain the true boundary location between fluid- + and solid-node + - improved accuracy in comparison to FWBB and HWBB + - able to calculate fluid force on boundary by momentum exchange + - (!) RELIES on the SolidBoundaryData object to be provided, + which supplies the f_index and interpolation constants. + """ + + def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, calc_force: bool =False): + self.context = context + self.flow = flow + + self.mask = solid_boundary_data.solid_mask + + if calc_force: + # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.force_sum = torch.zeros_like(self.context.convert_to_tensor( + self.flow.stencil.e[0])) + self.calc_force = True + else: + self.calc_force = False + + # convert relevant data to tensors: + ### TODO (QUESTION): fix batch-index-datatype (integer)? + self.f_index_lt = torch.tensor(solid_boundary_data.f_index_lt, + device=self.context.device, + dtype=torch.int64) # the batch-index has to be integer + self.f_index_gt = torch.tensor(solid_boundary_data.f_index_gt, + device=self.context.device, + dtype=torch.int64) # the batch-index has to be integer + + self.d_lt = self.context.convert_to_tensor(solid_boundary_data.d_lt) + self.d_gt = self.context.convert_to_tensor(solid_boundary_data.d_gt) + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, + device=self.context.device, + dtype=torch.int64) # batch-index has to be a tensor + # TODO (optional): replace self.opposite_tensor with flow.torch_stencil.opposite + + self.f_collided_lt = None + self.f_collided_gt = None + # will be populated in initialize_f_collided() method + + + def __call__(self, flow): + """ IBB1: interpolate bounced populations from two fluid nodes""" + ## reminder: f_collided_lt = [f_collided_lt, f_collided_lt.opposite] (!) in compact storage-layout + + if self.flow.stencil.d == 2: + # BOUNCE + # if d <= 0.5 + if len(self.f_index_lt) != 0: + flow.f[self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]] = (2 * self.d_lt * self.f_collided_lt[:, 0] + + (1 - 2 * self.d_lt) * flow.f[ + self.f_index_lt[:, 0], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]]) + # if d > 0.5 + if len(self.f_index_gt) != 0: + flow.f[self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2]] = ((1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1]) + + if self.flow.stencil.d == 3: + # BOUNCE + # if d <= 0.5 + if len(self.f_index_lt) != 0: + flow.f[self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]] = (2 * self.d_lt * self.f_collided_lt[:, 0] + + (1 - 2 * self.d_lt) * flow.f[ + self.f_index_lt[:, 0], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]]) + # if d > 0.5 + if len(self.f_index_gt) != 0: + flow.f[self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2], + self.f_index_gt[:, 3]] = ((1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1]) + + # CALC. FORCE on boundary (MEM, MEA) + if self.calc_force: + self.calc_force_on_boundary(flow.f) + + def make_no_streaming_mask(self, f_shape, context: Context): + # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), + # but CAN be (x,y,z) (z optional). + # in the latter case, torch.where broadcasts the mask to (q,x,y,z), + # so ALL q populations of a lattice-node are marked equally + return self.context.convert_to_tensor(self.mask, dtype=bool) + + def make_no_collision_mask(self, f_shape, context: Context): + # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) + # on the boundary nodes, in the initial solution of your flow, + # especially if visualization or post-processing uses the field-values + # in the whole domain (including the boundary region)! + return self.context.convert_to_tensor(self.mask, dtype=bool) + + def calc_force_on_boundary(self, f_bounced): + """calulate the fluid force on the boundary by Momentum Exchange""" + ### basically: force = e * (f_collided + f_bounced[opp.]) + if self.flow.stencil.d == 2: + self.force_sum = torch.einsum('i..., id -> d', + self.f_collided_lt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]], + self.flow.torch_stencil.e[self.f_index_lt[:, 0]]) \ + + torch.einsum('i..., id -> d', + self.f_collided_gt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2]], + self.flow.torch_stencil.e[self.f_index_gt[:, 0]]) + if self.flow.stencil.d == 3: + self.force_sum = torch.einsum('i..., id -> d', + self.f_collided_lt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]], + self.flow.torch_stencil.e[self.f_index_lt[:, 0]]) \ + + torch.einsum('i..., id -> d', + self.f_collided_gt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2], + self.f_index_gt[:, 3]], + self.flow.torch_stencil.e[self.f_index_gt[:, 0]]) + + + def store_f_collided(self, f_collided): + """store populations between collision and streaming, because they are + needed for calculation of bounce and force!""" + if self.flow.stencil.d == 2: + if len(self.f_collided_lt) != 0: + self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2]]) # y + self.f_collided_lt[:, 1] = ( + torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2]])) # y + if len(self.f_collided_gt) != 0: + self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2]]) # y + self.f_collided_gt[:, 1] = ( + torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2]])) # y + if self.flow.stencil.d == 3: + if len(self.f_collided_lt) != 0: + self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2], # y + self.f_index_lt[:, 3]]) # z + self.f_collided_lt[:, 1] = ( + torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2], # y + self.f_index_lt[:, 3]])) # z + if len(self.f_collided_gt) != 0: + self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2], # y + self.f_index_gt[:, 3]]) # z + self.f_collided_gt[:, 1] = ( + torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:, 0]], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2], # y + self.f_index_gt[:, 3]])) # z + + def initialize_f_collided(self): + """initialize the tensors to store post-collision populations""" + + # float-tensor with number of (x_b nodes with d<=0.5) values + f_collided_lt = torch.zeros_like(self.d_lt) + + # float-tensor with number of (x_b nodes with d>0.5) values + f_collided_gt = torch.zeros_like(self.d_gt) + + f_collided_lt_opposite = torch.zeros_like(self.d_lt) + f_collided_gt_opposite = torch.zeros_like(self.d_gt) + self.f_collided_lt = torch.stack((f_collided_lt, f_collided_lt_opposite), dim=1) + self.f_collided_gt = torch.stack((f_collided_gt, f_collided_gt_opposite), dim=1) + + def native_available(self) -> bool: + return False + + def native_generator(self, index: int): + # not implemented yet + return None diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py new file mode 100644 index 00000000..2d886acf --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py @@ -0,0 +1,74 @@ +"""Observables: force coefficients calculated from the force on an obstacle + - coefficient of drag + - coefficient of lift +""" + +import torch +from lettuce import Flow, Observable + +__all__ = ['DragCoefficient', 'LiftCoefficient'] + +# TODO (optional): write this as "force"-coefficient and make +# X, Y, Z as 0,1,2 direction choosable (parameter) to unify drag and lift +class DragCoefficient(Observable): + """The coefficient of drag of an obstacle, calculated using momentum exchange method (MEM, MEA) + - calculates the density, + - gets the force in x direction on the obstacle boundary, + - calculates the coefficient of drag + """ + + def __init__(self, flow, obstacle_boundary, solid_mask, area_pu): + super().__init__(flow) + self.obstacle_boundary = obstacle_boundary + self.solid_mask = solid_mask + + # cross-sectional area of obstacle in LU + # (! mind length-dimension in 2D -> area-dimension = self.lattice.D-1) + self.area_lu = (area_pu * (self.flow.units.characteristic_length_lu/ + self.flow.units.characteristic_length_pu) + ** (self.flow.stencil.d-1)) + self.nan_tensor = self.context.convert_to_tensor(torch.nan) + self.solid_mask = self.context.convert_to_tensor(self.solid_mask, dtype=bool) + + def __call__(self, f: torch.Tensor = None): + rho_tmp = torch.where(self.solid_mask, self.nan_tensor, self.flow.rho(f)) + rho_mean = torch.nanmean(rho_tmp) + + # get current force on obstacle in x direction + force_x_lu = self.obstacle_boundary.force_sum[0] + + # calculate drag_coefficient in LU + drag_coefficient = force_x_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu) + return drag_coefficient + + +class LiftCoefficient(Observable): + """The coefficient of lift of an obstacle, calculated using momentum exchange method (MEM, MEA) + - calculates the density, + - gets the force in y direction on the obstacle boundary, + - calculates the coefficient of lift + """ + + def __init__(self, flow, obstacle_boundary, solid_mask, area_pu): + super().__init__(flow) + self.obstacle_boundary = obstacle_boundary + self.solid_mask = solid_mask + + # cross-sectional area of obstacle in LU + # (! mind length-dimension in 2D -> area-dimension = self.lattice.D-1) + self.area_lu = (area_pu * (self.flow.units.characteristic_length_lu/ + self.flow.units.characteristic_length_pu) + ** (self.flow.stencil.d-1)) + self.nan_tensor = self.context.convert_to_tensor(torch.nan) + self.solid_mask = self.context.convert_to_tensor(self.solid_mask, dtype=bool) + + def __call__(self, f: torch.Tensor = None): + rho_tmp = torch.where(self.solid_mask, self.nan_tensor, self.flow.rho(f)) + rho_mean = torch.nanmean(rho_tmp) + + # get current force on obstacle in y direction + force_y_lu = self.obstacle_boundary.force_sum[1] + + # calculate lift_coefficient in LU + lift_coefficient = force_y_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu) + return lift_coefficient \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py new file mode 100644 index 00000000..de63c8f6 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -0,0 +1,584 @@ +from typing import Union, List, Optional + +import numpy as np +import torch + +from lettuce.ext._flows import ExtFlow +from lettuce import UnitConversion, Context, Stencil, Equilibrium +from lettuce.util import append_axes +from lettuce.ext import (EquilibriumBoundaryPU, EquilibriumOutletP) +from examples.advanced_projects.efficient_bounce_back_obstacle import SolidBoundaryData, FullwayBounceBackBoundary, HalfwayBounceBackBoundary, LinearInterpolatedBounceBackBoundary + +__all__ = ['ObstacleCylinder'] + + +class ObstacleCylinder(ExtFlow): + """ + FLOW AROUND A CIRCULAR CYLINDER in 2D or 3D + + Flow: + - inflow (EquilibriumBoundaryPU) at x=0, outflow (EquilibriumOutletP) at x=xmax + - further boundaries depend on parameters: + - lateral (y direction): periodic, no-slip wall, slip wall + - lateral (z direction): periodic (only if 3D) + - obstacle: cylinder obstacle centered at (y_lu/2+x_offset, y_LU/2+y_offset), + with radius=char_length, uniform symmetry in z-direction + - boundary condition for obstacle can be chosen: hwbb, fwbb, ibb1 + - FWBB: fullway bounce back + - HWBB: halfway bounce back + - IBB1: linear interpolated bounce back + - initial perturbation (trigger Von Kármán vortex street for Re>46) can be initialized in y and z direction + - initial velocity can be 0, u_char or a parabolic profile (parabolic if lateral_walls are True) + - inlet/inflow velocity can be uniform u_char or parabolic + """ + + + def __init__(self, context: Context, resolution: Union[int, List[int]], + reynolds_number, mach_number, + char_length_pu, char_length_lu, char_velocity_pu=1, + lateral_walls='periodic', bc_type='fwbb', + perturb_init=True, u_init=0, + x_offset=0, y_offset=0, + calc_force_coefficients=False, + stencil: Optional[Stencil] = None, + equilibrium: Optional[Equilibrium] = None): + + self.char_length_pu = char_length_pu + self.char_length_lu = char_length_lu + self.char_velocity_pu = char_velocity_pu + + # domain "shape" in LU. + # If only one INT, a square/cube-shaped domain is assumed + self.resolution = self.make_resolution(resolution, stencil) + + # flow and boundary settings + self.perturb_init = perturb_init + # toggle: introduce asymmetry in initial solution to trigger + # v'Karman Vortex Street + + self.u_init = u_init + # toggle: initial solution velocity profile type + + self.lateral_walls = lateral_walls + # toggle: lateral walls to be bounce back (bounceback), + # slip wall (slip) or periodic (periodic) + + self.bc_type = bc_type + # toggle: bounce back algorithm: + # - halfway (hwbb), + # - fullway (fwbb), + # - linearly interpolated (ibb1) + + self.calc_force_coefficients = calc_force_coefficients + self.periodicity = (False, False, True if len(self.resolution) == 3 else None) + + + # initialize masks (init with zeros) + self.solid_mask = np.zeros(shape=self.resolution, dtype=bool) + # marks all solid nodes (obstacle, walls, ...) + + self.wall_mask = np.zeros_like(self.solid_mask) + # marks lateral (top+bottom) walls + + self._obstacle_mask = np.zeros_like(self.solid_mask) + # marks all obstacle nodes (for fluid-solid-force_calculation) + + # initialize super class with unit conversion, equilibrium, context etc. + ExtFlow.__init__(self, context, resolution, reynolds_number, + mach_number, stencil, equilibrium) + + self.in_mask = np.zeros(self.grid[0].shape, dtype=bool) + # marks all inlet nodes + + + # cylinder geometry in LU + self.x_offset = x_offset + self.y_offset = y_offset + self.radius_lu = char_length_lu / 2 + self.y_pos_lu = self.resolution[1] / 2 + 0.5 + self.y_offset + # y_position of cylinder-center in 1-based indexing + self.x_pos_lu = self.y_pos_lu + self.x_offset + # keep symmetry of cylinder in x and y direction + + # MESHGRID of x, y, (z) index (LU) + xyz = tuple(np.linspace(1, n, n) for n in self.resolution) + # Tupel of index-lists (1-n (one-based!)) + if self.stencil.d == 2: + # meshgrid of x-, y-index + x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') + elif self.stencil.d == 3: + # meshgrid of x-, y- and z-index + x_lu, y_lu, z_lu = np.meshgrid(*xyz, indexing='ij') + else: + x_lu, y_lu, z_lu = 1,1,1 + print("CYLINDER OBSTACLE -> WARNING: something went wrong in LU-gird-index generation," + " lattice.D must be 2 or 3!") + + # Options for obstacle definition: + # 1) basic mask (no interpolation) -> see right below + # 2) IBB-index and mask -> see method make_ibb_index_lists below + # TODO: 3) OCC-enabled -> read externally supplied SolidBoundaryData + + # Opt1: basic mask (no interpolation) + condition = np.sqrt((x_lu - self.x_pos_lu) ** 2 + + (y_lu - self.y_pos_lu) ** 2) < self.radius_lu + self.obstacle_mask[np.where(condition)] = 1 + self.solid_mask[np.where(condition)] = 1 + + print("CYLINDER OBSTACLE: x_pos, y_pos, radius:", self.x_pos_lu, self.y_pos_lu, self.radius_lu) + + # MASKS for solid boundaries (lateral walls, obstacle, solid (= obstacle and walls), inlet) + # (INFO): indexing doesn't need z-Index for 3D, everything is broadcasted along z! + if self.lateral_walls == 'bounceback' or self.lateral_walls == 'slip': + # if top and bottom are link-based BC + self.wall_mask[:, [0, -1]] = True # don't mark wall nodes as inlet + self.solid_mask[np.where(self.wall_mask)] = 1 # mark solid walls + self.in_mask[0, 1:-1] = True + # inlet on the left, except for top and bottom wall (y=0, y=y_max) + else: # if lateral_wals == 'periodic', no walls + self.in_mask[0, :] = True # inlet on the left (x=0) + + # generate parabolic velocity profile for inlet BC if lateral_walls + # (top and bottom) are bounce back walls (== channel-flow) + self.u_inlet = self.units.characteristic_velocity_pu * self._unit_vector() + # u = [ux,uy,uz] = [1,0,0] in PU // uniform characteristic velocity in x-direction + + if self.lateral_walls == 'bounceback': + """ + parabolic velocity profile, zeroing on the edges + ## How to parabola: + ## 1.parabola in factorized form (GER: "Nullstellenform"): y = (x-x1)*(x-x2) + ## 2.parabola with a maximum and zero at x1=0 und x2=x0: y=-x*(x-x0) + ## 3.scale parabola, to make y_s(x_s)=1 the maximum: y=-x*(x-x0)*(1/(x0/2)²) + ## (4. optional) scale amplitude with 1.5 to have a mean velocity of 1, + also making the integral of a homogeneous velocity profile with u=1 + and the parabolic profile being equal + """ + + (nx, ny, nz) = self.resolution # number of gridpoints in y direction + parabola_y = np.zeros((1, ny)) + y_coordinates = np.linspace(0, ny, ny) + # linspace() creates n points between 0 and ny, including 0 and ny: + # top and bottom velocity values will be zero to agree with wall-boundary-condition + + parabola_y[:, 1:-1] = - 1.5 * np.array(self.u_inlet).max() * y_coordinates[1:-1] * ( + y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2 # parabolic velocity profile + # scale with 1.5 to achieve a mean velocity of u_char! + + if self.stencil.d == 2: + # in 2D u1 needs Dimension 1 x ny (!) + velocity_y = np.zeros_like(parabola_y) # y-velocities = 0 + + # stack/pack u-field + self.u_inlet = np.stack([parabola_y, velocity_y], axis=0) + elif self.stencil.d == 3: + ones_z = np.ones(nz) + parabola_yz = parabola_y[:, :, np.newaxis] * ones_z + parabola_yz_zeros = np.zeros_like(parabola_yz) + # create u_xyz inlet yz-plane and stack/pack u-field + self.u_inlet = np.stack([parabola_yz, parabola_yz_zeros, parabola_yz_zeros], axis=0) + + def make_units(self, reynolds_number, mach_number, resolution: List[int] + ) -> 'UnitConversion': + return UnitConversion( + reynolds_number=reynolds_number, mach_number=mach_number, + characteristic_length_lu=self.char_length_lu, + characteristic_length_pu=self.char_length_pu, + characteristic_velocity_pu=self.char_velocity_pu + ) + + def make_resolution(self, resolution: Union[int, List[int]], + stencil: Optional['Stencil'] = None) -> List[int]: + if isinstance(resolution, int): + return [resolution] * (stencil.d or self.stencil.d) + else: + return resolution + + @property + def obstacle_mask(self): + return self._obstacle_mask + + @obstacle_mask.setter + def obstacle_mask(self, m): + assert isinstance(m, np.ndarray) and m.shape == self.resolution + self._obstacle_mask = m.astype(bool) + + def initial_pu(self): + """initial flow field in physical units: + - considers perturbation and dimensionality + """ + p = np.zeros_like(self.grid[0], dtype=float)[None, ...] + u_max_pu = self.units.characteristic_velocity_pu * self._unit_vector() + u_max_pu = append_axes(u_max_pu, self.stencil.d) + + """ + initial velocity field: "u_init"-parameter + # 0: uniform u=0 + # 1: uniform u=1 or parabolic + # depends on lateral_walls: + # - bounceback => parabolic; + # - slip, periodic => uniform + """ + u = (1 - self.solid_mask) * u_max_pu + if self.u_init == 0: + u = u * 0 # uniform u=0 + else: + if self.lateral_walls == 'bounceback': + # parabolic along y, uniform along x and z (similar to poiseuille-flow) + + ny = self.resolution[1] # number of gridpoints in y direction + ux_factor = np.zeros(ny) # vector for one column (u(x=0)) + + # multiply parabolic profile with every column of the velocity field: + y_coordinates = np.linspace(0, ny, ny) + ux_factor[1:-1] = (- y_coordinates[1:-1] * (y_coordinates[1:-1] - ny) + * 1 / (ny / 2) ** 2) + if self.stencil.d == 2: + u = np.einsum('k,ijk->ijk', ux_factor, u) + elif self.stencil.d == 3: + u = np.einsum('k,ijkl->ijkl', ux_factor, u) + else: # lateral_walls == periodic or slip + # initial velocity u_PU=1 on every fluid node + u = (1 - self.solid_mask) * u_max_pu + + ### perturb initial velocity field-symmetry (in y and z) to trigger 'von Karman' vortex street + if self.perturb_init: # perturb initial solution in y + # overlays a sine-wave on the second column of nodes x_lu=1 (index 1) + ny = self.grid[1].shape[1] + if u.max() < 0.5 * self.units.characteristic_velocity_pu: + # add perturbation for small velocities + amplitude_y = (np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) + * self.units.characteristic_velocity_pu * 0.1) + if self.stencil.d == 2: + u[0][1] += amplitude_y + elif self.stencil.d == 3: + nz = self.grid[2].shape[2] + + # plane of ones + plane_yz = np.ones_like(u[0, 1]) + + # plane of amplitude in y + u[0][1] = np.einsum('y,yz->yz', amplitude_y, plane_yz) + + # amplitude in z + amplitude_z = (np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) + * self.units.characteristic_velocity_pu * 0.1) + u[0][1] += np.einsum('z,yz->yz', amplitude_z, plane_yz) + else: + # multiply scaled down perturbation if velocity field is already near u_char + factor = 1 + np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * 0.1 + if self.stencil.d == 2: + u[0][1] *= factor + elif self.stencil.d == 3: + nz = self.grid[2].shape[1] + plane_yz = np.ones_like(u[0, 1, :, :]) + u[0][1] = np.einsum('y,yz->yz', factor, u[0][1]) + + # perturbation in z-direction + factor = 1 + np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * 0.1 + u[0][1] = np.einsum('z,yz->yz', factor, u[0][1]) + return p, u + + def make_solid_boundary_data(self, x_center, y_center, radius): + print("(!) CYLINDER OBSTACLE WARNING: make_solid_boundary_data() is not implemented yet!") + # NOT YET IMPLEMENTED; OCC-version for index lists; + # CURRENTLY only make_ibb_index_lists is used with analytic function (see below) + # TODO (future): make reading of externally supplied SolidBoundaryData object a thing + + def make_ibb_index_lists(self, x_center, y_center, radius): + """calculate interpolation constants and population indices for + HWBB and IBB1 BBBC + """ + # INFO: this method relies on self.obstacle_mask too! + f_index_lt = [] # indices of relevant populations with d<=0.5 + f_index_gt = [] # indices of relevant populations with d>0.5 + d_lt = [] # distances between node and boundary for d<0.5 + d_gt = [] # distances between node and boundary for d>0.5 + + # searching boundary-fluid-interface and append indices to f_index, distance to boundary to d + if self.stencil.d == 2: + # TODO (optional): the 2D and 3D options could be condensed/unified + nx, ny = self.obstacle_mask.shape # domain size in x and y + a, b = np.where(self.obstacle_mask) + # x- and y-index of boundaryTRUE nodes for iteration over boundary area + + for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask + for i in range(0, self.stencil.q): + # for all stencil-directions c_i (lattice.stencil.e in lettuce) + # check for boundary-nodes neighboring the domain-border. + # ...they have to take the periodicity into account... + border = np.zeros(self.stencil.d, dtype=int) + + if a[p] == 0 and self.stencil.e[i][0] == -1: # searching border on left [x] + border[0] = -1 + elif a[p] == nx - 1 and self.stencil.e[i][0] == 1: # searching border on right [x] + border[0] = 1 + + if b[p] == 0 and self.stencil.e[i][1] == -1: # searching border on left [y] + border[1] = -1 + elif b[p] == ny - 1 and self.stencil.e[i][1] == 1: # searching border on right [y] + border[1] = 1 + + try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) + if not self.obstacle_mask[a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]: + # if the neighbour of p is False in the boundary.mask, + # p is a solid node, neighbouring a fluid node: + # the direction pointing from the fluid neighbour + # to solid p is marked on the neighbour + + # calculate intersection point of boundary surface and link -> + # ...calculate distance between fluid node and boundary surface on the link + px = a[p] + self.stencil.e[i][0] - border[0] * nx + # fluid node x-coordinate + py = b[p] + self.stencil.e[i][1] - border[1] * ny + # fluid node y-coordinate + cx = self.stencil.e[self.stencil.opposite[i]][ 0] + # link-direction x to solid node + cy = self.stencil.e[self.stencil.opposite[i]][ 1] + # link-direction y to solid node + + # pq-formula + h1 = ((px * cx + py * cy - cx * x_center - cy * y_center) + / (cx * cx + cy * cy)) # p/2 + h2 = (px * px + py * py + x_center * x_center + y_center * y_center + - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( + cx * cx + cy * cy) # q + + d1 = - h1 + np.sqrt(h1 * h1 - h2) + d2 = - h1 - np.sqrt(h1 * h1 - h2) + + # distance from fluid node to the "true" boundary location + # choose correct d and assign d and f_index + if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 + + if d1 <= 0.5: + d_lt.append(d1) + f_index_lt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) + else: # d>0.5 + d_gt.append(d1) + f_index_gt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) + + elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 + + if d2 <= 0.5: + d_lt.append(d2) + f_index_lt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) + else: # d>0.5 + d_gt.append(d2) + f_index_gt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) + else: # neither d1 or d2 is real and between 0 and 1 + print("IBB WARNING: d1 is", d1, "; d2 is", d2, + "for boundaryPoint x,y,ci", a[p], + b[p], self.stencil.e[i][0], self.stencil.e[i][1]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + if self.stencil.d == 3: # like 2D, but in 3D...guess what... + nx, ny, nz = self.obstacle_mask.shape + a, b, c = np.where(self.obstacle_mask) + + for p in range(0, len(a)): + for i in range(0, self.stencil.q): + border = np.zeros(self.stencil.d, dtype=int) + # x - direction + if a[p] == 0 and self.stencil.e[i][0] == -1: # searching border on left + border[0] = -1 + elif a[p] == nx - 1 and self.stencil.e[i][0] == 1: # searching border on right + border[0] = 1 + # y - direction + if b[p] == 0 and self.stencil.e[i][1] == -1: # searching border on left + border[1] = -1 + elif b[p] == ny - 1 and self.stencil.e[i][1] == 1: # searching border on right + border[1] = 1 + # z - direction + if c[p] == 0 and self.stencil.e[i][2] == -1: # searching border on left + border[2] = -1 + elif c[p] == nz - 1 and self.stencil.e[i][2] == 1: # searching border on right + border[2] = 1 + + try: # try in case the neighboring cell does not exist + # (an f pointing out of simulation domain) + if not self.obstacle_mask[a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]: + + # calculate intersection point of boundary surface and link -> + # ...calculate distance between fluid node and boundary surface on the link + px = a[p] + self.stencil.e[i][0] - border[0] * nx + # fluid node x-coordinate + py = b[p] + self.stencil.e[i][1] - border[1] * ny + # fluid node y-coordinate + # Z-coordinate not needed for cylinder ! + + cx = self.stencil.e[self.stencil.opposite[i]][0] + # link-direction x to solid node + cy = self.stencil.e[self.stencil.opposite[i]][1] + # link-direction y to solid node + # Z-coordinate not needed for cylinder ! + + # pq-formula + h1 = ((px * cx + py * cy - cx * x_center - cy * y_center) + / (cx * cx + cy * cy)) # p/2 + h2 = (px * px + py * py + x_center * x_center + y_center * y_center + - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( + cx * cx + cy * cy) # q + + d1 = - h1 + np.sqrt(h1 * h1 - h2) + d2 = - h1 - np.sqrt(h1 * h1 - h2) + + # distance from fluid node to the "true" boundary location + # choose correct d and assign d and f_index + if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 + + if d1 <= 0.5: + d_lt.append(d1) + f_index_lt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) + else: # d>0.5 + d_gt.append(d1) + f_index_gt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) + + elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 + + if d2 <= 0.5: + d_lt.append(d2) + f_index_lt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) + else: # d>0.5 + d_gt.append(d2) + f_index_gt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) + else: # neither d1 nor d2 is real and between 0 and 1 + print("IBB WARNING: d1 is", d1, "; d2 is", d2, + "for boundaryPoint x,y,z,ci", a[p], + b[p], c[p], self.stencil.e[i][0], self.stencil.e[i][1], + self.stencil.e[i][2]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + # output as np.array: f_index_lt, f_index_gt, d_lt, d_gt + return [np.array(f_index_lt), np.array(f_index_gt), np.array(d_lt), np.array(d_gt)] + + + @property + def grid(self): + xyz = tuple(self.units.convert_length_to_pu(np.linspace(0, n, n)) + for n in self.resolution) # tuple of lists of x,y,(z)-values/indices + return np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- (und z-)values/indices + + @property + def post_boundaries(self): + + # inlet ("left side", x[0],y[1:-1], z[:]) + inlet_boundary = EquilibriumBoundaryPU(flow=self, context=self.context, + mask=self.in_mask, + velocity=self.u_inlet) + + # outlet ("right side" (positive x-direction), x[-1],y[:], (z[:])) + if self.stencil.d == 2: + outlet_boundary = EquilibriumOutletP(direction=[1, 0], flow=self) + else: # self.stencil.d == 3: + outlet_boundary = EquilibriumOutletP(direction=[1, 0, 0], flow=self) + + # create and return boundary-list + return [ + inlet_boundary, + outlet_boundary, + ] + + @property + def post_streaming_boundaries(self): + """walls that should use the efficient bounce back boundaries""" + + # LATERAL WALLS (optional) ("top and bottom walls", x[:], y[0,-1], z[:]) + lateral_boundary = None + # stays None if lateral walls are not specified... + if self.lateral_walls == 'bounceback': + if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': # use halfway bounce back + print("(!) OBST. CYLINDER: lateral walls can currenlty only be " + "FWBB, HWBB requirest Solid Boundary Data which is not " + "implemented yet for lateral walls!") + lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, + periodicity=self.periodicity) + # TODO (optional, low prio): implement ibb- and hwbb-option for lateral walls + else: # else use fullway bounce back + lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, + periodicity=self.periodicity) + elif self.lateral_walls == 'slip' or self.bc_type == 'SLIP': + # use slip-wall (symmetry boundary) + print("(!) OBST. CYLINDER: lateral walls can currenlty only be " + "FWBB, slip boundary is not implemented yet!") + lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, + periodicity=self.periodicity) + # TODO (optional, low prio): implement slip-option for lateral walls + + # obstacle + # (for example: obstacle "cylinder" with radius centered at position x_pos, y_pos) + # (!) the obstacle_boundary should always be the last boundary + # in the list of boundaries to correctly calculate forces on the obstacle + + solid_boundary_data = SolidBoundaryData() + solid_boundary_data.solid_mask = self.obstacle_mask + + # load index-lists for circular cylinder into SBD object + # [f_index_lt, f_index_gt, d_lt, d_gt]: + (solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt, + solid_boundary_data.d_lt, solid_boundary_data.d_gt) = self.make_ibb_index_lists( + x_center=self.x_pos_lu-1, y_center=self.y_pos_lu-1, radius=self.radius_lu) + + print("CYLINDER: the bc_type was given as:", self.bc_type) + if self.bc_type.casefold() == 'fwbb' or self.bc_type == 'FWBB': + obstacle_boundary = FullwayBounceBackBoundary(self.context, self, + self.obstacle_mask, + periodicity = self.periodicity, + calc_force=self.calc_force_coefficients) + elif self.bc_type.casefold() == 'hwbb' or self.bc_type == 'HWBB': + obstacle_boundary = HalfwayBounceBackBoundary(self.context, self, + solid_boundary_data, + periodicity = self.periodicity, + calc_force=self.calc_force_coefficients) + elif self.bc_type.casefold() == 'ibb1' or self.bc_type == 'IBB1': + obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, + solid_boundary_data, + calc_force=self.calc_force_coefficients) + else: # use basic mask Bounce Back + print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted... " + "- will fall back to using FullwayBounceBackBoundary") + obstacle_boundary = FullwayBounceBackBoundary(self.context, self, + self.obstacle_mask, + periodicity = self.periodicity, + calc_force=self.calc_force_coefficients) + + # create and return boundary-list + if lateral_boundary is None: + # if lateral boundary is periodic... + # don't include the lateral_boundary object in the boundaries-list + return [ + obstacle_boundary + ] + else: + return [ + lateral_boundary, + obstacle_boundary + ] + + def _unit_vector(self, i=0): + return np.eye(self.stencil.d)[i] \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_DI2018.csv new file mode 100644 index 00000000..0cc4ba74 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_DI2018.csv @@ -0,0 +1,67 @@ +-2.984375000000001; 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1.252873563218391 +1.1015625000000004; 1.2298850574712645 +1.2109375000000004; 1.2068965517241381 +1.3671875000000004; 1.1800766283524906 +1.4999999999999996; 1.1609195402298853 +1.6328125000000004; 1.1455938697318009 +1.7968750000000004; 1.1302681992337167 +1.9531250000000004; 1.1149425287356323 +2.0937500000000004; 1.1034482758620692 +2.2500000000000004; 1.0957854406130272 +2.4218750000000013; 1.0842911877394639 +2.5937500000000004; 1.0766283524904217 +2.7968750000000004; 1.0651340996168583 +2.9687500000000004; 1.0613026819923372 +2.9960937500000004; 1.0613026819923372 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_KM2000.csv new file mode 100644 index 00000000..5e0bc661 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_KM2000.csv @@ -0,0 +1,125 @@ +-2.984375000000001; 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-1.014308426073132 +1.0290037831021435; -1.0047694753577106 +1.172761664564943; -1.001589825119237 +1.3089533417402262; -0.9999999999999999 +2.9356872635561153; -0.9999999999999999 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py new file mode 100644 index 00000000..3d11c209 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py @@ -0,0 +1,54 @@ +import torch +import numpy as np +import os + +from typing import List + +from lettuce._simulation import Reporter, Simulation + +class ProfileReporter(Reporter): + """ reporter to save average velocity profiles + -> use the ProfilePlotter in post-processing to further process + and plot data! + """ + + + def __init__(self, interval: int, flow, position_lu, i_start): + super().__init__(interval) + self.flow = flow + self.i_start = i_start + self.x_position = int(round(position_lu, 0)) + + # linear interpolation of u if x_pos is off grid + # ... positions x_pos# and weights w# + self.interpol = False + if position_lu % 1 != 0: + self.interpol = True + self.x_pos1 = int(np.floor(position_lu)) + self.x_pos2 = int(np.ceil(position_lu)) + self.w2 = position_lu - self.x_pos1 + self.w1 = 1 - self.w2 + print("ProfileReporter: requested position is off grid! " + "Interpolating u(" + str(position_lu) + ") = " + + str(self.w1) + " * u(" + str(self.x_pos1) + ") + " + + str(self.w2) + " * u(" + str(self.x_pos2) + ")") + + self.i_out = [] + self.out = [] + + def __call__(self, simulation: 'Simulation'): + if simulation.flow.i % self.interval == 0 and simulation.flow.i >= self.i_start: + # calculate or interpolate velocity profile in y-direction at position + if self.interpol: + u = self.flow.u(self.flow.f)[:, self.x_pos1] * self.w1 + \ + self.flow.u(self.flow.f)[:, self.x_pos2] * self.w2 + else: + u = self.flow.u(self.flow.f)[:, self.x_position] + u = self.flow.units.convert_velocity_to_pu(u).cpu().numpy() + self.i_out.append(self.flow.i) + if self.flow.stencil.d == 2: + self.out.append(u) + elif self.flow.stencil.d == 3: + # average over z-axis (axial cross stream for cylinder obstacle) + self.out.append(np.mean(u, axis=2)) + diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py new file mode 100644 index 00000000..4f0166d2 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py @@ -0,0 +1,266 @@ +import os +import numpy as np +import pyevtk.hl as vtk + +from lettuce._simulation import Reporter, Simulation + +__all__ = [ + "write_vtk", "VTKReporterAdvanced", "VTKsliceReporter" +] + +def write_vtk(point_dict, id=0, filename_base="./data/output", + origin: tuple[float, float, float] = (0.0, 0.0, 0.0)): + vtk.imageToVTK( + path=f"{filename_base}_{id:08d}", + origin=origin, + spacing=(1.0, 1.0, 1.0), + cellData=None, + pointData=point_dict, + fieldData=None, + ) + +class VTKReporterAdvanced(Reporter): + """ + Advanced VTK reporter. Adds functionality: + - take solid_mask and pin observable values inside the solid to 0 + -> useful for suboptimal initialization or use of FWBB boundary + """ + + def __init__(self, flow, interval=50, filename_base="./data/output", + solid_mask=None, imin=0, imax=None): + super().__init__(interval) + self.flow = flow + if interval < 0: + self.interval = 1 + else: + self.interval = interval + self.filename_base = filename_base + self.imin=imin + if imax is None: + self.imax = 1e15 + elif imax <= 0: + self.imax = 1 + else: + self.imax = imax + if solid_mask is not None and flow.stencil.d == 2: + self.solid_mask = solid_mask[..., None] + else: + self.solid_mask = solid_mask + directory = os.path.dirname(filename_base) + if not os.path.isdir(directory): + os.makedirs(directory) + self.point_dict = dict() + + def __call__(self, simulation): + if self.flow.i % self.interval == 0 and self.imin <= self.flow.i <= self.imax: + u = self.flow.units.convert_velocity_to_pu(self.flow.u(self.flow.f)) + p = self.flow.units.convert_density_lu_to_pressure_pu(self.flow.rho(self.flow.f)) +#if you want density as well: rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) + if self.flow.stencil.d == 2: + if self.solid_mask is None: + self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ..., None]) + else: + self.point_dict["p"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(p[0, ..., None])) + for d in range(self.flow.stencil.d): + if self.solid_mask is None: + self.point_dict[f"u{'xyz'[d]}"] = ( + self.flow.context.convert_to_ndarray(u[d, ..., None])) + else: + self.point_dict[f"u{'xyz'[d]}"] = ( + np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(u[d, ..., None]))) +#if you want density as well: self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) + else: + if self.solid_mask is None: + self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) + else: + self.point_dict["p"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(p[0, ...])) + for d in range(self.flow.stencil.d): + if self.solid_mask is None: + self.point_dict[f"u{'xyz'[d]}"] = ( + self.flow.context.convert_to_ndarray(u[d, ...])) + else: + self.point_dict[f"u{'xyz'[d]}"] = ( + np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(u[d, ...]))) + +#if you want density as well: self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) + write_vtk(self.point_dict, self.flow.i, self.filename_base) + + def output_mask(self, mask, outdir=None, name="mask", point=False, no_offset=False): + """ + output the mask as a vtk-file for visualizatione etc. + + UPDATE 28.08.2024 (MBille: outputs mask as cell data. cell data represents the approx. + location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, + if translated by (-0.5,-0.5,-0.5) LU. + Attention: point data is misleading, looking at masks rendered as solid objects or + point-clouds! + USE: in Paraview use Filter: + Threshold -> Above Upper Threshold (Upper Threshold 0.9) + -> Solid Color -> Volume/Wireframe,... + """ + + if outdir is None: + filename_base = self.filename_base + else: + filename_base = outdir+"/"+str(name) + + mask_dict = dict() + + mask_dict["mask"] = mask.astype(int) if len(mask.shape) == 3 \ + else mask[..., None].astype(int) + # extension to pseudo-3D is needed for Paraview + + if point: + vtk.imageToVTK( + path=filename_base +"_point", + pointData=mask_dict + ) + if no_offset: + vtk.imageToVTK( + path=filename_base +"_cell_noOffset", + cellData=mask_dict + ) + vtk.imageToVTK( + path=filename_base + "_cell", + cellData=mask_dict, + origin=(-0.5, -0.5, -0.5), + spacing=(1.0, 1.0, 1.0) + ) + +class VTKsliceReporter(Reporter): + ''' + reports a certain specified area portion of the domain as vtk-file + ''' + def __init__(self, flow, interval=50, filename_base="./data/output", + solid_mask=None, sliceXY=None, sliceZ=None, imin=0, + imax=None): + super().__init__(interval) + self.flow = flow + self.interval = interval + self.filename_base = filename_base + self.imin = imin + if imax is None: + self.imax = 1e15 + elif imax <= 0: + self.imax = 1 + else: + self.imax = imax + + if solid_mask is not None and self.flow.stencil.d == 2: + self.solid_mask = solid_mask[..., None] + else: + self.solid_mask = solid_mask + + directory = os.path.dirname(filename_base) + if not os.path.isdir(directory): + os.makedirs(directory) + + self.point_dict = dict() + + + if sliceXY is not None: + if sliceZ is None: + sliceZ = 0 + self.xmin = sliceXY[0][0] + self.ymin = sliceXY[1][0] + self.xmax = sliceXY[0][1] + self.ymax = sliceXY[1][1] + self.z_index = sliceZ + else: + self.z_index = None + # TODO (OPTIONAL): + # check if xmin == xmax (and y... and z...) and if so, add a "None" + # dimension and take only ONE value, so this becomes a slice. + # -> Probably best to do this in call() from a tuple: + # ([xmin, xmax],[ymin, ymax],[zmin, zmax]) + + + + def __call__(self, simulation): + if self.flow.i % self.interval == 0 and self.imin <= self.flow.i <= self.imax: + if self.z_index is not None: + f = self.flow.f[:,self.xmin:self.xmax+1, self.ymin:self.ymax+1, self.z_index, None] + # (!) None is needed because single-slice omits the last dimension and will result + # in bad dimension in conversion of u and rho/p below! + else: + f = self.flow.f + u = self.flow.units.convert_velocity_to_pu(self.flow.u(f)) + p = self.flow.units.convert_density_lu_to_pressure_pu(self.flow.rho(f)) + # if you want the density: rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) + if self.flow.stencil.d == 2: + if self.solid_mask is None: + self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ..., None]) + else: + self.point_dict["p"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(p[0, ..., None])) + for d in range(self.flow.stencil.d): + if self.solid_mask is None: + self.point_dict[f"u{'xyz'[d]}"] = ( + self.flow.context.convert_to_ndarray(u[d, ..., None])) + else: + self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(u[d, ..., None])) + # if you want the density: self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) + else: + if self.solid_mask is None: + self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) + else: + self.point_dict["p"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(p[0, ...])) + for d in range(self.flow.stencil.d): + if self.solid_mask is None: + self.point_dict[f"u{'xyz'[d]}"] = ( + self.flow.context.convert_to_ndarray(u[d, ...])) + else: + self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(u[d, ...])) + + # # if you want the density: self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) + write_vtk(self.point_dict, self.flow.i, self.filename_base, + origin=(self.xmin, self.ymin, self.z_index)) + # origin added to show slice at correct position when superimposing on 3D vtk-data! + + def output_mask(self, mask, outdir=None, name="mask", point=False, no_offset=False): + """ + output the mask as a vtk-file for visualizatione etc. + + UPDATE 28.08.2024 (MBille: outputs mask as cell data. cell data represents the approx. + location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, + if translated by (-0.5,-0.5,-0.5) LU. + Attention: point data is misleading, looking at masks rendered as solid objects or + point-clouds! + USE: in Paraview use Filter: + Threshold -> Above Upper Threshold (Upper Threshold 0.9) + -> Solid Color -> Volume/Wireframe,... + """ + + if outdir is None: + filename_base = self.filename_base + else: + filename_base = outdir + "/" + str(name) + + mask_dict = dict() + + mask_dict["mask"] = mask.astype(int) if len(mask.shape) == 3 else mask[..., None].astype( + int) # extension to pseudo-3D is needed for vtk-export to work + + if point: + vtk.imageToVTK( + path=filename_base + "_point", + pointData=mask_dict + ) + if no_offset: + vtk.imageToVTK( + path=filename_base + "_cell_noOffset", + cellData=mask_dict + ) + vtk.imageToVTK( + path=filename_base + "_cell", + cellData=mask_dict, + origin=(-0.5, -0.5, -0.5), + spacing=(1.0, 1.0, 1.0) + ) \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/solid_boundary_data.py b/examples/advanced_projects/efficient_bounce_back_obstacle/solid_boundary_data.py new file mode 100644 index 00000000..021cd40a --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/solid_boundary_data.py @@ -0,0 +1,15 @@ +import numpy as np + +__all__ = ['SolidBoundaryData'] + +# index lists for Bounce Back Boundaries: populations for FWBB, HWBB; IBB. +# - d and the differentiation of lt (less than 0.5) +# and gt (greater than 0.5) is only for IBB +class SolidBoundaryData(dict): + f_index_lt: np.ndarray + f_index_gt: np.ndarray + d_lt: np.ndarray + d_gt: np.ndarray + points_inside: np.ndarray + solid_mask: np.ndarray + not_intersected: np.ndarray = np.ndarray([]) \ No newline at end of file diff --git a/lettuce/_simulation.py b/lettuce/_simulation.py index 1ea64fcc..0a3251b3 100644 --- a/lettuce/_simulation.py +++ b/lettuce/_simulation.py @@ -77,21 +77,31 @@ def __init__(self, flow: 'Flow', collision: 'Collision', if len(self.pre_boundaries) + len(self.post_boundaries) > 0: + # fill masks with value of self.collision_index (= number of pre-boundaries) self.no_collision_mask = self.context.full_tensor( flow.resolution, self.collision_index, dtype=torch.uint8) self.no_streaming_mask = self.context.full_tensor( [flow.stencil.q, *flow.resolution], self.collision_index, dtype=torch.uint8) + # iterate over pre-boundaries for i, boundary in enumerate(self.pre_boundaries): + # if the i-th boundary returns a non-None no_collision_mask, + # ...write its index (i) to every entry in the global no_collision_mask + # ...that is True in the boundaries no_collision_mask ncm = boundary.make_no_collision_mask( [it for it in self.flow.f.shape[1:]], context=self.context) if ncm is not None: self.no_collision_mask[ncm] = i + + # if the i-th boundary returns a non-None no_streaming_mask, + # ...add its True-entries to the global no_streaming_mask nsm = boundary.make_no_streaming_mask( [it for it in self.flow.f.shape], context=self.context) if nsm is not None: self.no_streaming_mask |= nsm + # iterate over post-boundaries + # ...(similar to pre-b., but start with index collision_index+1) for i, boundary in enumerate(self.post_boundaries,start=self.collision_index+1): ncm = boundary.make_no_collision_mask( [it for it in self.flow.f.shape[1:]], context=self.context) @@ -212,19 +222,33 @@ def _stream(self): return self.flow.f def _collide(self): - if self.no_collision_mask is None: + # runs collision and all pre- and post-boundaries (which are pre- or post-collision, but pre-streaming) + if self.no_collision_mask is None: # COLLISION EVERYWHERE + # run pre-boundaries for boundary in self.pre_boundaries: self.flow.f = boundary(self.flow) + # run collision self.flow.f = self.collision(self.flow) + # run post-boundaries for boundary in self.post_boundaries: self.flow.f = boundary(self.flow) - else: + else: # SELECTIVE COLLISION (no_collision_mask (which is not a boolean mask!)) + # boundary-indices in no_collision_mask: + # pre_boundaries have indices: 0 to len(pre_boundaries)-1 + # collision_index: len(pre_boundaries) + # post_boundaries have indices: len(pre_boundaries)+1 to len(pre_boundaries)+len(post_boundaries) + # run pre-boundaries for i, boundary in enumerate(self.pre_boundaries): + # where the collision-int equals the boundary-index, apply the boundary, everywhere else, do nothing torch.where(torch.eq(self.no_collision_mask, i), boundary(self.flow), self.flow.f, out=self.flow.f) + # run collision + # ... where the collision-int equals the number of pre-boundaries, + # ...apply collision, everywhere else, do nothing torch.where(torch.eq(self.no_collision_mask, self.collision_index), self.collision(self.flow), self.flow.f, out=self.flow.f) + # run post-boundaries for i, boundary in enumerate(self.post_boundaries, start=self.collision_index+1): torch.where(torch.eq(self.no_collision_mask, i), boundary(self.flow), self.flow.f, out=self.flow.f) diff --git a/lettuce/ext/_boundary/equilibrium_outlet_p.py b/lettuce/ext/_boundary/equilibrium_outlet_p.py index 13bae89d..61cbc911 100644 --- a/lettuce/ext/_boundary/equilibrium_outlet_p.py +++ b/lettuce/ext/_boundary/equilibrium_outlet_p.py @@ -65,18 +65,17 @@ def __call__(self, flow: 'Flow'): other = tuple([slice(None)] + self.neighbor) rho = flow.rho() u = flow.u() - rho_w = self.rho_outlet * torch.ones_like(rho[here]) - u_w = u[other] + rho_w = self.rho_outlet * torch.ones_like(rho[tuple(here)]) + u_w = u[tuple(other)] feq = flow.f - feq[here] = flow.equilibrium(flow, rho_w[..., None], u_w[..., None])[ + feq[tuple(here)] = flow.equilibrium(flow, rho_w[..., None], u_w[..., None])[ ..., 0] return torch.einsum("q...,q...->q...", [feq, torch.ones_like(flow.f)]) def make_no_streaming_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: no_streaming_mask = context.zero_tensor(shape, dtype=bool) - no_streaming_mask[tuple([np.setdiff1d(np.arange(shape[0]), self.velocities)] - + self.index)] = 1 + no_streaming_mask[tuple([np.setdiff1d(np.arange(shape[0]), self.velocities)] + self.index)] = 1 return no_streaming_mask def make_no_collision_mask(self, shape: List[int], context: 'Context'): diff --git a/pyproject.toml b/pyproject.toml index 727f7d90..5a62935d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,7 @@ dependencies = [ "h5py==3.11.0", "matplotlib>=3.9.2", "mmh3>=4.1.0", + "notebook>=7.4.7", "numpy>=2.1.0", "packaging>=24.1", "pyevtk>=1.6.0", diff --git a/uv.lock b/uv.lock index 697ed9d2..0efdf8fb 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = "==3.12.*" resolution-markers = [ "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 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