diff --git a/CHANGELOG.md b/CHANGELOG.md index 5925914dc..2eaf67229 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -12,6 +12,7 @@ This changelog is effective from the 2025 releases. ## [Unreleased] ### Added +* Function `plot_energy_landscape` to visualize Energy Landscapes of completed AMS jobs * MultiJob now supports generic Job types for the self.children attribute * Function `view_orbital` to visualize orbitals of completed AMS jobs diff --git a/examples/EnergyLandscape/EnergyLandscape.ipynb b/examples/EnergyLandscape/EnergyLandscape.ipynb new file mode 100644 index 000000000..fce409c6f --- /dev/null +++ b/examples/EnergyLandscape/EnergyLandscape.ipynb @@ -0,0 +1,866 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "intro-rewritten", + "metadata": {}, + "source": [ + "Here we introduce several options for plotting and analyzing an energy landscape obtained from an AMS calculation.\n", + "\n", + "In particular, `plot_energy_landscape()` can display molecular structures on top of the landscape by using two different molecule-plotting backends: `view` and `plot_molecule`. In this notebook we show how to use both approaches, how to compare layout options, and how to control the orientation of all molecules or only selected states.\n", + "\n", + "A useful practical difference between the two backends is that `plot_molecule` uses explicit Euler-like rotation strings such as `\"90x,0y,0z\"`, while `view` specifies the viewing direction through `ViewConfig`, for example with `direction`, `normal`, and `normal_basis`. Depending on whether you want direct angle-based control or a view-oriented description, one backend may be more convenient than the other.\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-imports", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "This cell imports PLAMS, the energy-landscape plotting tools, the `ViewConfig` helper used by the `view` backend, and Matplotlib for figure creation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "imports", + "metadata": {}, + "outputs": [], + "source": [ + "import scm.plams as plams\n", + "from scm.plams.tools.plot import plot_molecule, plot_energy_landscape\n", + "from scm.plams.tools.view import ViewConfig\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "md-molecule", + "metadata": {}, + "source": [ + "## Build the molecular system\n", + "\n", + "This cell defines the small HCNO molecule used throughout the example and guesses its bonds so that the different visualization backends can draw it properly.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c59cbea9-b1b1-4db0-8d3a-03aecfabae94", + "metadata": {}, + "outputs": [], + "source": [ + "molecule = plams.Molecule()\n", + "molecule.add_atom(plams.Atom(symbol=\"H\", coords=(0.26799604, 1.56164318, 0.80172174)))\n", + "molecule.add_atom(plams.Atom(symbol=\"O\", coords=(0.70317302, 1.15034441, 0.02980438)))\n", + "molecule.add_atom(plams.Atom(symbol=\"N\", coords=(0.07385403, -1.26509181, -0.02723174)))\n", + "molecule.add_atom(plams.Atom(symbol=\"C\", coords=(0.33895691, -0.13354579, 0.04083563)))\n", + "\n", + "molecule.guess_bonds()" + ] + }, + { + "cell_type": "markdown", + "id": "md-view-preview", + "metadata": {}, + "source": [ + "## Preview the molecule with `view`\n", + "\n", + "This cell displays the molecule with the `view` interface. This is the first of the two backends that can also be used later inside the energy-landscape plot.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f42910a0-84be-432e-9d26-9f6358ccae41", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plams.view(molecule)" + ] + }, + { + "cell_type": "markdown", + "id": "md-plotmol-preview", + "metadata": {}, + "source": [ + "## Preview the molecule with `plot_molecule`\n", + "\n", + "This cell displays the same molecule with `plot_molecule`, which is the second backend supported in the landscape examples below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6ddca846-8bbe-4a3f-a9e4-d895688eede4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zUautHVc7EJEgof2MyL9i3LQntPLqO8itsg+97wDa3vnTuJXqbpZWbIlbxUD5UPClF0R0mfBHXwyVzRhXeenfhUIjZjr2ZSY78clHT5ufHF2fYjt5D8AF1eoWgiu3gRCRQqTcT0JdoYZqtFGTfxwtq7wFwp99549ZIt6zC90frYnHul8XJPwvSmvgJ8x8suDECsDt+2N8AP7Ep5Q+aFvxWWqwKhEuv9wfKr88pARGgYQGZhO2eRaJ/oNGvKcxkYx9ECQR6JTWwBOAbPCytCMfvLyKSQMwFcBM4Y/OIaGOxrk9WgNsn7XNs28Dci+A/czc40kSBTCo9+ANZYb/y9U0DAuThmFh0jAsTBqGhUnDsDBpGBYmDcPCpOH/AGuUFc/Q5IKnAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_molecule(molecule);" + ] + }, + { + "cell_type": "markdown", + "id": "md-settings", + "metadata": {}, + "source": [ + "## PES exploration settings\n", + "\n", + "This cell prepares the AMS settings used to generate the energy landscape. The setup follows the same HCNO PES exploration used in the related plotting tests.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "settings", + "metadata": {}, + "outputs": [], + "source": [ + "settings = plams.Settings()\n", + "settings.input.ams.UseSymmetry = \"No\"\n", + "settings.input.ams.Task = \"PESExploration\"\n", + "settings.input.ams.PESExploration.RandomSeed = 1\n", + "settings.input.ams.PESExploration.Job = \"ProcessSearch\"\n", + "settings.input.ams.PESExploration.NumExpeditions = 500\n", + "settings.input.ams.PESExploration.NumExplorers = 4\n", + "settings.input.ams.PESExploration.SaddleSearch.MaxEnergy = 6.0\n", + "settings.input.ams.PESExploration.SaddleSearch.MinEnergyBarrier = 0.1\n", + "settings.input.ams.PESExploration.StructureComparison.UseCovalent = \"Yes\"\n", + "settings.input.MOPAC.Model = \"AM1\"" + ] + }, + { + "cell_type": "markdown", + "id": "md-load", + "metadata": {}, + "source": [ + "## Run/Load a precomputed landscape\n", + "\n", + "This cell presents two options: rerunning the AMS job or reusing previously generated results. If the `job.run()` line is active, the notebook will perform the calculation. However, by commenting out `job.run()` and uncommenting the subsequent line, the notebook will load an existing RKF file and extract the energy landscape object." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "load-or-run", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All stationary points:\n", + "======================\n", + "State 1: CHNO local minimum @ -0.02420235 Hartree (found 1 times, results on State1_MIN)\n", + "State 2: CHNO local minimum @ -0.00530947 Hartree (found 1 times, results on State2_MIN)\n", + "State 3: CHNO local minimum @ 0.06366600 Hartree (found 1 times, results on State3_MIN)\n", + "State 4: CHNO local minimum @ 0.08915027 Hartree (found 2 times, results on State4_MIN)\n", + "State 5: CHNO local minimum @ 0.12945368 Hartree (found 1 times, results on State5_MIN)\n", + "State 6: CHNO transition state @ 0.14087766 Hartree (found 105 times, results on State6_TS_1-5)\n", + " +- Reactants: State 1: CHNO local minimum @ -0.02420235 Hartree (found 1 times, results on State1_MIN)\n", + " Products: State 5: CHNO local minimum @ 0.12945368 Hartree (found 1 times, results on State5_MIN)\n", + " Prefactors: 1.193E+13:1.542E+13 s^-1\n", + " Barriers: 4.492:0.311 eV\n", + "State 7: CHNO transition state @ 0.16456046 Hartree (found 80 times, results on State7_TS_3-5)\n", + " +- Reactants: State 3: CHNO local minimum @ 0.06366600 Hartree (found 1 times, results on State3_MIN)\n", + " Products: State 5: CHNO local minimum @ 0.12945368 Hartree (found 1 times, results on State5_MIN)\n", + " Prefactors: 3.551E+10:3.179E+13 s^-1\n", + " Barriers: 2.745:0.955 eV\n", + "State 8: CHNO local minimum @ 0.17404504 Hartree (found 1 times, results on State8_MIN)\n", + "State 9: CHNO transition state @ 0.18873463 Hartree (found 106 times, results on State9_TS_3-8)\n", + " +- Reactants: State 3: CHNO local minimum @ 0.06366600 Hartree (found 1 times, results on State3_MIN)\n", + " Products: State 8: CHNO local minimum @ 0.17404504 Hartree (found 1 times, results on State8_MIN)\n", + " Prefactors: 5.770E+10:5.107E+13 s^-1\n", + " Barriers: 3.403:0.400 eV\n", + "State 10: CHNO transition state @ 0.19838369 Hartree (found 45 times, results on State10_TS_2-4)\n", + " +- Reactants: State 2: CHNO local minimum @ -0.00530947 Hartree (found 1 times, results on State2_MIN)\n", + " Products: State 4: CHNO local minimum @ 0.08915027 Hartree (found 2 times, results on State4_MIN)\n", + " Prefactors: 2.750E+13:1.564E+13 s^-1\n", + " Barriers: 5.543:2.972 eV\n", + "State 11: CHNO transition state @ 0.22701818 Hartree (found 6 times, results on State11_TS_4-8)\n", + " +- Reactants: State 4: CHNO local minimum @ 0.08915027 Hartree (found 2 times, results on State4_MIN)\n", + " Products: State 8: CHNO local minimum @ 0.17404504 Hartree (found 1 times, results on State8_MIN)\n", + " Prefactors: 5.378E+13:1.036E+14 s^-1\n", + " Barriers: 3.752:1.441 eV\n", + "State 12: CHNO local minimum @ 0.22743859 Hartree (found 1 times, results on State12_MIN)\n", + "State 13: CHNO transition state @ 0.23108474 Hartree (found 4 times, results on State13_TS_4-5)\n", + " +- Reactants: State 4: CHNO local minimum @ 0.08915027 Hartree (found 2 times, results on State4_MIN)\n", + " Products: State 5: CHNO local minimum @ 0.12945368 Hartree (found 1 times, results on State5_MIN)\n", + " Prefactors: 2.268E+13:4.421E+13 s^-1\n", + " Barriers: 3.862:2.766 eV\n", + "State 14: CHNO transition state @ 0.23640722 Hartree (found 2 times, results on State14_TS_3-12)\n", + " +- Reactants: State 3: CHNO local minimum @ 0.06366600 Hartree (found 1 times, results on State3_MIN)\n", + " Products: State 12: CHNO local minimum @ 0.22743859 Hartree (found 1 times, results on State12_MIN)\n", + " Prefactors: 3.851E+11:1.114E+12 s^-1\n", + " Barriers: 4.701:0.244 eV\n", + "State 15: CHNO local minimum @ 0.24187016 Hartree (found 1 times, results on State15_MIN)\n", + "State 16: CHNO transition state @ 0.25836414 Hartree (found 5 times, results on State16_TS_5-15)\n", + " +- Reactants: State 5: CHNO local minimum @ 0.12945368 Hartree (found 1 times, results on State5_MIN)\n", + " Products: State 15: CHNO local minimum @ 0.24187016 Hartree (found 1 times, results on State15_MIN)\n", + " Prefactors: 3.165E+13:6.697E+12 s^-1\n", + " Barriers: 3.508:0.449 eV\n" + ] + } + ], + "source": [ + "job = plams.AMSJob(name=\"HCNO\", molecule=molecule, settings=settings)\n", + "\n", + "# job.run()\n", + "job = plams.AMSJob.load_external(\"plams_workdir/HCNO/ams.rkf\")\n", + "\n", + "energy_landscape = job.results.get_energy_landscape()\n", + "print(energy_landscape)" + ] + }, + { + "cell_type": "markdown", + "id": "md-default-plot", + "metadata": {}, + "source": [ + "## Default energy-landscape plot\n", + "\n", + "This cell produces the basic landscape plot with the default layout and without molecule thumbnails, which is a good starting point for understanding the connectivity between states.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fe96f792-e7fa-4383-8a37-d38bd9f2e0d1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=120)\n", + "ax = plot_energy_landscape(energy_landscape, ax=ax)\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "md-bfs-plot", + "metadata": {}, + "source": [ + "## Change the layout\n", + "\n", + "This cell redraws the same landscape with the `bfs` layout so you can compare how the ordering of states changes while keeping the same data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7f33e4bf-ad02-4def-8591-38ca6fa81020", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=120)\n", + "ax = plot_energy_landscape(energy_landscape, ax=ax, layout=\"bfs\")\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "md-view-landscape", + "metadata": {}, + "source": [ + "## Show molecules with the `view` backend\n", + "\n", + "This cell adds molecule thumbnails above the states while using `molecule_plot_backend=\"view\"`. This backend is convenient when you want to control the visualization through `ViewConfig` or other `view()` options, that is, by specifying the viewing direction rather than explicit Euler angles.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "all-layouts", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DgbVr12IwGGjQoAE9evTAvwTDM8qaTP4kyYZWL1uGKSkJdDpwdAQ3N3B2RjEaccjIwPjrr5j69Stx65/BYODvv/+mRYsW5Odbu3ecnJwwmUy2eBqSHej1ehSLin/MZvKyswnJyaFAq6X+hAkMHz68WNe0WCx89tlnzJo1C5VKRW5uLo6Ojjz22GM2mUhS3pnNZpYu3cKMGSlkZSn4+bkzZkwd+vfvUSpDMMqD2rWrYzDsITPTkYyMHJn8lYKcnBz++usvCgoK6N69O/Xq1bN3SEUmkz9JsqEzZ85gAVItFtLy8wnKzERVUAC5uRjNZkyxseTn5eHm4VGi+wghyMrK4sSJE+zfvx9FUXB2diYtLY3Q0FDbPBmpzKSnp/Pl869hOnSehsP6cz4thRSNhp6DBtHjoYeKfV2VSsW4ceNwd3dnw4YNODk5MXDgQPr372/D6Mun/Px8ZszYxPz5agwGDXXrOvDWW03o0KH01rwuD2rUCMFs3ohe70RSUpas9WkjcXFxrFmzBicnJ1JSUlCpVDz88MMXZ1hXPDL5kyQbcvH05JgQhJhMuObkYDSb8dJoEAUFpBYUEOfoSJhWW+L7ODg40LRpU1avXo2iKERHR+Pi4kKXLl1s8CykspSZmclHL7zJqXV7uX/SOJ5+aSyKoqAoik26Zp2cnHjuued4+umnAWw25rS82rfvGIsWpREd7cC+fX4IkUOrVkamTu1MaGhte4dX6vz8/NDpCigs1BIbm0OrVvaOqOIzGo188sknqFQq2rRpg9Fo5Omnn8bLy8veoRVb5X4VkKQy1r1HD1YuXIi70Yi5oIA0oxEvRUExm0kXAsvdd9tkcHlCQgKHDx/G29ub1NRUYmJiePjhh2nSpIkNnoVUVrKzs/lg3LscWbGP/m8+ydMvjUVrgw8HN1LZkz6Ac+cieeGFjRiNj+Hj44NGk0HXrlv56KOeVKtWNeoaenh44OEBubkKUVEF9g6nUjh48CiRkRm4u8O5c+fQaDQVvjRQ5X81kKQy1KVLF3o+9BBr/viDDLOZGCHwVBQsQpBvsTAgOLjE9zCbzXz11VcAtG3blrfeegtPT89KO4apssrNzeW9/01h99L9PPjqUJ5/dXypJX5VxdGjx0hMTCcgIB8XF4Grq+DZZ5tUmcQPrL0CQUFOhIebiI62DhGpaiV9bMVisbBhww5+/nk3FksN0tLOs2fPHsaMGVPh/1Zl8idJNqTT6Rg9ejTr/1jMGbOJFG9PvNxcUWm16A4fJvrzzznZogWN7r672PfYtGkTu3btokaNGgwbNgxvb28bPgOpNF0qt2I0Gpn86qds/uMgw14fxPjXX6zwbyblQZ06tXBxmUVKyiqyspri65tKcHDx/9YqIpVKRc2avhw4oCc+3hOj0YhOp7N3WBWO0WhkwYL1/PprLCaTls6dqzN48CO4ublWiooKMvmTJBvbtGEjbknZNAoO4p0/fiO4enUKCgp48uGH2XHwILpXXsHjt98IKsYLSHp6Ol9//TUeHh507dqVli0r9+D1ysJisbBq1S4WLDBgMLjhrGzi0MYjjHi5F+Nff1G+OdtI06ZNef/9x/n119U4Op7nueceJCCg6q1fXLduMAZDFikpweTlGeTvVxHp9Xq++mo9f/1VgFbrxJAhHjz33AOVph4kyORPkmwqPz+fNX8swcGipUW/3oQ1bXq5y+XLH39k9KBBuBw/jvqNN+j/00+4ubvf8bUtFgvfffcdWVlZ3HXXXTz88MOyq7eC2LZtD2+8kYKDw33otBYunFxB935teeWtcfKN2YZUKhVDhgzmgQcGoihKlRjneCO1a1fHZIogM9OVlJRsvLwq9prFZSU1NZ3z55OZNess//zjhKenwrPPujN8eK9K97sk3zkkyYYOHTpE3OEzaJ1cuXf4kKvG2jRq1IgPZ8xgr6srhzZvZuNnnxWpLt/evXtZuXIlPj4+jBgxokLPNKtqli/fQWGhN4GBDtSq7YJ3rVfJLexUZQotlzWtVlvp3qyLIiQkCLU6g7w8DWfOZGE2m+0dUrl34sRpHn54Ay+84M/mze3w8NDw/vu+PP74/ZXyd0kmf5JkI0IIlv6+CE2BQs22zWna4q7rjunWrRtjp0xhu8XCvgUL2Pb77whxuxVa/1s/0snJiXvuuYe2bduWwjOQSoui5GMynSMtLZXERCMFBgMODqlyIH4lYzKZOHv2LEeOHOHs2bNERkZiMBjKNIbCwkJ27z6HShVCQUEekyY58OKLx4mIiC3TOCqalSvXERUl8PV1wc/PkxdecOf++ztW2r/RypfOSpKdpKSksGP1FrQqJ7oPf/CGKygoisLwESOIiopi47RpOHzxBd61a3NXp043va4QgtmzZxMTE0OzZs0YMWKE7O6tYB544B7Wrv2JxMRCFCUYnS6Jhx8OlS1/lUh+fj5Lly4lODiYli1b4ubmhtlsJjY2Fh8fH9yLMMSjuMxmM99++wfffmsAOuLmVhchtGzdKjh6NJkZM2Jo1qxGqcdREfn7e2E2b+bcOS+0WicaNPCxd0ilSr6DSJKNbFi/gezYbDxCArmnb6+bHqdWq3n1tddoMGAAuy5cYM177xEbFXXT448fP878+fPx8vJi1KhR+PhU7helyqhTp458++0oeveOpGfPPXz9dS369ets77AkG1q+fDkJCQl4enri7Ox8uUh3rVq1yMzMxGKxlHoMERERzJy5G5PJG2dnbwICtNSurRASoiIz058vv8yjsLCw1OOoiAYNeoBnnw2lefPVvPxyCm3bNrB3SKVKtvxJkg0YjUaW/vYXaosj7QZ0xc/P75bHOzk58fFnn/FEXBz/Hj+O0/vv8+TXX1/XOlBQUMDHH3+Moij06tWLu0tQIkayH0VR6N69O927d7d3KFIpKCgoYPXq1dSrV4/c3FwKCwtRq9UoioIQApVKRU5ODh4lXNbxdrZv30VWlgm12oxWa8HZGdzdQaWCtDQ4fNiFxMRMatS49etTVeTq6sobb7yK2WyuEi3ysuVPkmzg5MmThO8+ic7ZjQEjHrijcSK+vr58/f33pPn5sWvbNhZ/+y1Go/GqY/7880+OHDlC7dq1eeKJJ6rEi5IkVTQFBQXExcVx4cIFzp8/z/nz58nLy8NoNKLX68nIyCA3N7fU48jMzAAyECKVwsIkcnIMpKcLsrIEBoMZgyEPvV62/N1KVXmNlS1/kmQDSxf9TWGOhvr3NKJJsztfYi00NJRPvv2WcSNH8s8ff+BXuzZ9Hn4Yi8VCbGwsM2bMwN3dndGjR1OtWtWrVyZJFYGjoyNOTk6cPHkSNzc3LBYLycnJuLu7U1hYSFxcHD169Cj1OOrUqYVKtRyLJRK93ovkZBVZWQGYTGry8jLx8zuLj0/HUo9DKv9k8idJJZSVlcXqvzahUrky4LG+Ra7b1rFjR1798EOmvv46S77/noPHjpEvBOHh4eTm5jJo0CC6detWOsFLklRijo6ODBgwgE8//RSwvibExMTg6uqKxWwmyN8fT0/PUo+jW7du1K79M+fOHcVoVJGTk01enh9CaBAil6Agj0pZtkQqOtntK0klYDQa2bplB7FnsvGrGUCP+7oW6zqDBw+m59ChHDh/nohdu1Aursfp5+dH79695Qu2JJVzI0aMYNCgQcTGxrJ9+3Y2bNjA/j17aFuvHu0bNMBYBhMtAgMDGTfuJVwLm+BiUSMs2zCZ/gHWodFEkpnZli+/jCMjI7vUY5HKN/mOIknFkJWVxdy529m0ScfJY39iNrvR6f721y0gL4TAYrFgNBrJzy8gN1dPVlYO6ek5pKdnkZGRTUZGLllZOSQmqsAjhNSCArrXqEHb9u05f/48hw4don379rK8iySVY87Oznz44Yf069ePnTt3kpyczMnDh7FkZxMUGkr0iRPUb9GiVGMQQnDiSCY+5v6oHAuo32MNnt5OtG/fhm3bDrB16xKOHBnKV1/F8dJLwXh7l+4EFKn8qhTJn6Io9YAPgE6ANxAD/AZ8LoTQ2zM2qfLJzc1l/PgFbN7cDEfHhmSkLkSoqrF7XwuWLTtEfn4hycn5pKSYSE83k5lpJi8PCgvNGI0WhDChKIWo1fkoSi6KkoMQuVgsWQSF1KR6dTeEoqAoCjqdjqSkJAoLC3F0dCxZ4FOnwqFDcPAgnDtnnQJ4uxVGzp6FKVNgwwZISQFPT2jeHD7+GFq1Klk8knRRWmoqp3fuRCcEdTp1wsvX9/YnlUM6nY4uXbrQpUsXjEYjr77yCis3bqR2tWr4urmRlpKCTymO3Y2Ojmfz78l4qMNo2ieRj7774vKs4z597mPixI/Yvft3XFwe4fvvE3j+efDykglgVVThkz9FUaoDe4Es4FsgHegAvAe0AgbaLzqpMlq9ejObN5txdq5OUJAv9UI/IyU5g4TkEMaNu4CiqFCr81Crs1GUDBQlCyEyUZQcFEWPWm1AozGh0wkcHBQcHMDBwUJubhIGQxwuLjU4c+YMKSkp5OTk4OzsjFarLXngEydak7cWLSA315rM3crmzdC/PwQHwzPPQEgIpKdbk8eEhJLHI0lAXEwM/zzzDD0iI/FSFM7Xq4f5p5/wDQy0d2glotVqeXnCBEY/9hjbdu3iwe7dSY6IwKNTp1IZxiGEYNH8dTil10LtEs8jTze96j6urq58/PGbTJ78Gbt3/46r6zB+/jmFp55S8PEp/QLUUvlS4ZM/4DHAE+gkhIi4uO0nRVFUwOOKongJITLsFp1U6ezZcwCLxQeNBlxdwcvLG7PFm8xsE3p9PGbzGTQaFTqdBp1OjUajQqv1Qq12Qa3OR63ORavNRqfT4+xciIeHGh8fZ9Tq+qxde5oLFy5QUFBwuVDsc889Z5vyA2fPQt261u+7dbt18peWBg8/DO3awapVUNJWR0m6iY1//EHNM2cIrFMHlVZL3TNnOLxqFb6jR9s7tBKrUaMGI556ihVz5tDI35/Gdepw4cwZajRqZPN7xccnsH1BNJ7qTtTolkCzu64vUuzq6sr777/Ou+9OZf/+Bbi6Dmf+/HQeewyZAFYxlSH5u/Qbm3TN9gTAAsiiRpJNabUCIS5gMMSQluZDQYELWVkWTKY01OpUnJyccHHJxN09Bx+fQqpVUxMY6ExwsBdBQdXx96+Gr683np6euLi4oNPpUKvVmM1mOnRow9y5c8nOzsbNzY3hw4fTtWvxJpFc51Lidyd++MGaHH7xhTXxKygARYEbLFknSSWRmJaGK2Awm3FUqcixWMivRKtQDBkyhA1r17J61y6qu7vjUFBAbnAwrjZe7u2v39bikhyC2imZQWOa3bR10dnZmcmTX+eDDz7n8OEFuLqOYPHiRAYNMlGtmrdNY5LKr8qQ/G0BXgdmKYryLpAG3A08B0wTQuTZMTapEurRoxN//jmNggIfEhML0Wj8KSw0YzJF88IL+Qwa1BpPTw9cXV1xcHC4PObmdtRqNYMHD6Zfv37o9XpcXFxuuD5wmVi1CtzcID8f2raFffus25s2hXffhYcesk9cUqVz9/3388eKFSjR0VQDzvv60rVnT3uHZTNOTk68+sYbvDJ6NM0DA+l1113E//svLj173tHrwp1ITExi59wI/DRtcOmQRvN2Ybc83sXFhcmTX+ejj77k8OH5uLqOYNWqdPr1U/D19bJJTFL5VuGnDwoh1gLvAD2BQ1gneywEpgsh/nercxVF8VMUJezKB1CE5hGpKurWrRtPPtkBrXYzBQUryc1dhsXyNwMHnuPFFwfQoEF9/P39cXFxQaPRFPkF3tHREW9vb/slfgAnToDZDD17Qr16sGgRfPstpKbC4MHw66/2i02qVDp17syQb7/l+IABHHrkEdrMnEmtevXsHZZNNW3alJ6DB7Ns716ioqIIiI4m4cwZm13/74Wr8EzwBXU69z/b4o5qjTo6OvLmmy/TokUe+/bNJS/Pi40bM0hKus1YYKlSUIQQ9o6hxBRFGQGMAJZgbfnrC4wCxgkhvr3FeZOBd2+0Lzw8nLCwW396kqouk8nErl272LZtN2YztGvXkm7dOpd8Rm5Z6dYNduy4+Wxfjcaa/A0bBr/99t/2U6egSRPw94fYWGtXsCRJt5WRkcGoRx6hh5sbj7VrR46nJ34jRuDg5FSi66ampvJaj0nUuBAG7Qy8vug5nIpwzYKCAr78cjoREQr33vsEbm4ZdO7sib+/XFGoginSi3GFT/4URXkE+AWoL4SIu2L7bGAoUEMIkXaTc/2Aa3/D6wLLZPInVWq3S/7c3Kwzgtevt7b+XalrV9i2zdo62LBhqYcqSZXFP//8w5cvv8wr7dpxd+3axLdqRZ1evUp0zTkzZhPx1glcdV50/fluug0o+hhhg8HAF198w+nTgr59n8TFJYuWLd0JCPArUWxSmSpS8lfhu32B54FDVyZ+Fy0HnIGbVtUUQiQLISKufADnSjFWSaoYqle3fr1RuY1L29LTyy4eSaoEunXrRqN77mHFsWMkJifjtW8fqXHXvnXdufT0dA78sp1gjRvapkba9GhdrOs4ODgwYcJ4GjRQWL/+F8Cd8PBcEhKunUdZPpjNZrIyMzEajfYOpcKqDMmfP3CjOhiXCqNVhkktklS22re3fo2NvX7fpW3+/mUXjyRVAmq1mpcmTCDCYmHrqVPoUlPJWr682EnMP3+txjdSjUrJo8VTrXBxcSl2bNYE8CUaNNCycuUstFoXzp/Xl7sEMD0tjWX/+x9Rffty+KmniI+MtHdIFVJlSP5OAy0URal/zfZhWEu9HC37kCSpghs50vp1xgy4cmjIwYOwe7e1u7copWMkSQIgJCSEJ8aPZ/m5cxyPiyMoIoILl2bTF0FWVhb7fl5HLbUbxvom7u7TqcSx6XQ6xo0bS+3aOpYu/Rmdzpno6HSWL1/B5s2bOXLkCHq9fRfNWvnzz9TdsIFmzs60PHaMmOnTqejD1+yhMrSKfQbcD2xXFOVbrBM++l3cNlMIccGewUlSuTF/PkRHW7+PjrYmdR9++N/+t9/+7/uuXeGpp2DWLLjvPhg4EJKSYPp00GqtSaEkScXy4KBBrFu5klXHj1NDq8VhyRKyGzbE3fvO6+xtWr4a/9MFqNV6Gj15Hx4etlmmTafTMX78i/zww4/89tsPtGjRkFq1gmjevDkODg6cO3eOunXr2q0aQdS5czTSaLA4O2PW6chPSsJisdimEH4VUuEnfAAoitIWmIx1fJ8PcB6YC3wqhLjN4qXXXSsMCJcTPqRKp1s32Lr15vuvfS0wm63lXWbOhDNnrMWeO3Wy1vlr06ZUQ5Wkyi48PJz/DRnCC97e9A4KIvbee6n79NOoVLfvkMvJyeHTvmNoc8KBCzUdeXj9x3h527Y+X0FBAU8/PYbGjRszaNCD1K9v7VwzGo3Ex8dTu3Ztm97vTi36/XfCJ02ii0pFAaB9+216PfaYXWIpZ4o04aMytPwhhNgL9LF3HJJUrm3ZUrTj1WoYP976kCTJpsLCwuj79NMsnzaN+jodwWvXctDJCVNeHg46HW6NGxPSvDmOzs7XnfvvmvUEh6ehVXsT8MQ9Nk/8LsnNzSE/X49er8disaBSqVCpVFgsllK53514YPBgXFxdCT94kCatW9OlhLOlq6pKkfxJkiRJUkWiKAqjnnqKUatXszoqiiaJiTi8/Ta1FAVflQrFwYHEJk0Qr79O7Sta2vPy8jj44x+0VzsSEyjoPeS+UolPp9MRGBjI6dOnOX78ON7e3vj4+JCWlkahHZff02q19OnfH/r3t1sMlYFM/iSpijh9+jQ1a9bEYebMYo/Z29GsGTMdHAgLC7v8qFGjhs2Wqarqpk6dyqFDhzh48CDnzp1DpVJhukktxi1bttC9e/cb7vPx8SE1NbU0Q5VswM3NjXZ3303K8ePoFYXaWi1Bzs7oXFxArcbl9GlyXnyRuG++IaRdOwD2bvyHwMMxOCqeuI/oSbVSmnWvUqkYNWoUr7/+Ops2bSIzMxNfX18sFgsDBgwolXtKZUcmf5JUBaSmptKjRw8CAgLY3LUrrsePF+s6cWYzc0+dumqbq6srjRs3JiwsjBdffJEWLW5aWlO6jYkTJ+Lp6UmLFi3Izc0lJeX2S22NGTOGzp07X7Wtwqw0U8VFRESwb8kSWqlUOKtUuDg6ovX0RPHxAQcHyM3FLTGRjClTiP3mGxRg/4y59FK0RPkptH30wVKNr02bNnzzzTcsXryYqKgo3N3d6du3b4lKykjlg0z+JKmSs1gsjBgxgri4OKpXr45TjRrQuHGxrtWhb1++r1OHiIiIy4/k5GT27t3L3r17GT58+C3Pz87Oxs3NTbYU3sTZs2epe7GETrdu3e4o+evQoQMjRowo7dCkUrDsr78ozMwEwKwoWNRqa9Ln4gIuLljUag4kJHB0/348BgzARwjaZuRiUbuiGtyHgODgUo+xadOmNG3a9PKYP6lykMmfJFVyH330EevWrcPHx4c//vgDdfXqMG5csa5VE3j2mm0pKSmXE8Hbtfrdc889REZG0rhxY5o0aXJV97Gfn1+VTwrrFrN2ol6vR1GUIq3pKtmX2Wwm4vhxCoBcIEcIsiwW/IxGHAwGFLWajXFxHE1Npa1KRWh2Nl4qFYpGkK3KpkCfhtFoRKfTlUm8MvGrXGTyJ0mVXO3atXFzc+PXX3+l+qVl22yoWrVqdOvWjW7dut3yOCEEycnJZGRksHPnTnbu3HnVfh8fHw4fPkxISIjNY6zMxo8fz6hRowBrAeERI0bwzjvv4HyDWaJS+SGEwGw2k4G1OG2KECQUFuKWm0ugSkViaiprYmLoqCjUcnDA39UVlaMjGI1Uy8nB/NdfnO7Rgyb332/vpyJVQDL5k6RKbvjw4fTu3RsfHx+7xqEoCtHR0SQmJl7VbXzpkZubS+CN1hK+wqxZs6hXrx5hYWF2fz72ptVq6du3L3369KF69eqkpKTw119/MXXqVDZu3MjWrVsrVwI4dSocOmRdZebcOVCp4CaTYa5z4YJ1qENWFnzwwdUFze1ErVZTNzSUA7t2EScEHhYLjkYjSm4ueqORDXl5aM1mPLRaPFxcUPn7o7i7Q34+QlHwSksjctUqRO/eVb7FXCo6mfxJUhVQXhIlRVEIDAwkMDCQe++99/J2IQQpKSm3rNKfmprK6NGjL//b39//qm7jpk2bcvfdd5dq/OVJx44dWbly5VXbnnzySd544w0++eQTpk2bxhtvvGGn6ErBxIng6QktWkBuLtzBeMjLnnvOWrS8HFEUhf79+7Nk8WKi9XochQCzGYPBQIbRyKGCAvw0GoRK9d84QHd30GggIwOhVmOOj8dkMqHVam9/Q0m6guzElyTJ7hRFwc/P75bHGAwGnnjiCdq0aYOLiwtJSUls2rSJ6dOn8+yzz8pJDxe98847qFSq6xLDCu/sWcjIgE2boEGDOz9v4UJYtQree6/0Yiumtm3b8tjjj5OtVnMCiBCCcIuFoyYTKRYLmUCGxUKm0YjIz0fk5CBycxEGA9lmM3keHnJZM6lYZMufJEkVQnBwMLNnzwasM5hjYmIudxmHh4fj6+t7y/MjIiKYMGECYWFhlyebNG7cGFdX17IIv8y4uLjg5+dHcnKyvUO5JYPBwAcffHB5vOil/9PQ0NAbn1CcyTCpqdbJTePHQ8uWJQu4FGg0Gvr3789PP/1EuhDkq1QkAJ6AXq3GCERZLPjm5aFJTsYrOxvFYCArO5vTQqDp1ElOxJCKpdjJn6IozkBPoCPQGPAFBJAKnAB2AhuFEHk2iFOSJOkylUpFrVq1qFWrFn379r2jcw4dOsS6detYt27dVdtr1qxJWFgYAwYM4JlnnimNcMtUdnY2SUlJ1KtXz96h3NKFCxc4cuQITZo0IT8/HxcXF2y+1vz48dbu0vffh337bHttG7BYLMycOZPExEQ0Gg0DHnoI/zp1cHJ0JLRePf7+4w+O/PsvaoOBLJMJX5UKlRCkGY0ktmzJkAdLt86fVHkVOflTFKUpMAEYBLgC+UAskIF1YeH6QA/gFSBPUZQlwBdCiGO2ClqSKoSiDFDfuxc++wwOH4akJOv4pBo14P774ZVXICjotrcrLCxk+fLlPPTQQ3IA+A307NmTpUuXXjXJ5NSpU0RHRxMdHU2NGjVueX5OTg4ajabclFNJS0u7biynEILXXnsNIQQPPPCAfQK7DSEE585FsmHDdkwmha1bt+Lg4EBAQABms9l2Sevq1fDbb7BmjTUBLIciIiJYtGgRAHVDQnj7o4+ofsXvYdu2bfnwnXfYvmMH0YWFeAFCp8O1UyeGfvAB3uVkLK9U8RQp+VMU5Q/gIWA/MBnYABwXQpivOU6NtTWwFzAYOKQoyiIhxDBbBC1JFUJRBqifOQM5OTBsGAQGWhPFo0fhxx+tb2AHD942AXz11VeZNm0ar776Kp9++qltn0sl4O/vz4MPPsiDV7SWmEwmzp49S0RExG3L4Hz77be8/fbb1KlT56qJJmFhYTRs2BAHB4cSxzh//nyio6MBiI6ORgjBhx9+eHn/21fMUu3duzf+/v60bt2akJAQUlJSWLZsGXv27KFLly688MILJY7H1rKysli/fhsJCUY0Gk8aNWrC8eMHWbhwIW3btr1qQk+JZGfDM89Y/55697bNNW3MbDYzY8YM0tPTUatUPPfCC1clfmBtlZ7244/s2bOH40eOoFIUmjZtSot27SrXTG6p7Akh7vgB/A7cVZRzLp53F/B7Uc+zxwMIA0R4eLiQpBI5e/a/77t2FUKtLvo1/vhDCBDivfduediiRYsEILRarfj333+Lfh/ptl555RWhVqsF1uEtVz1UKpX47rvvSnyPrl273vD6lx5Xmjp1qujYsaPw8/MTWq1WuLq6ijZt2oivvvpKFBYWljgWWzIajWL79t1iypQF4pNP1oi33vpW/PrrHyInJ0dkZmaKxMREYTQa7/yCt/t7euYZIby9hUhK+m/b5s3Wv6UPPiju07AJs9ksCgoKxNGjR4Wfn5/QaDSifatWIjU11a5xSRVekXKdIrX8iWK23AkhDgOy1U+qWoq5WsNVatWyfr24BNSNnDlzhieffBKAzz//nHYXF4CXbOuzzz7jww8/5PTp01d1HYeHh3Pu3DmCb7PU1urVq9Hr9YSFhREaGnrD8hxbtmy543hef/11Xn/99aI+jTIXHR3L4sXbSUlxx2QCH5+zjB7dj1qXfrcBDw8P293w4EH46SdrLb/sbOsDID7e+jU93Tpz2M/PWjqljBiNRnbs2MGOHTvIysriwIEDpKen46DV8tKECcUvx1SU4SUJCfDdd9ZjDx2CxETo0QM2biz+EyvHpk6dyqFDhzh48CDnzp1DpVJhusnPZuvWrSxevJht27Zdbn2vW7cuw4cP57nnnis3wz1sRRFFHGCrKMpYYKEQIrV0QrIvRVHCgPDw8HDCwsLsHY5UWXTrBjt23L4orV5vfeTnw/Hj8MYb1nGAGzdaX6SvkZ+fT4cOHThy5AiDBw/mzz//lOP97KCgoABFUW7Z9dutWze2bt0KWAs0N2jQ4Kqu43vuuQdPT88yirj05eXlsWrVdrZuzcVs1qHTnWPQoDZ06tQejaaEhSZu9ff0999wJxMhvv8enr12scLSYTKZmD59Ops2bcLb2xuLxcJff/2FwWCgaePG/Llkyc1nOd+Oovw3vOTECevwkpu9zmzZAt27Q3AwtGoFy5dX6uRPURQ8PT1p0aIFJ06cICUl5abJX/v27YmJieHBBx+kWbNmFBYWsmzZMv755x9atmzJzp07cXR0LONnUCRFeuEvzl/gNOBLRVHWA78Cy4QQ+mJcR5Kka3366dX1yGrXhnnzbpj4AYwbN44jR44QGhrKzJkzZeJnJ3fyptCrVy9cXV2JiIggKiqK8PBwwsPDL+/fv38/rVq1AuC7775jxowZxY7n+eeft9uYP4vFwr59R1iw4ASpqd6oVGl07erGww8/adsWvptp2xYuTqK4SkQETJ4MjzwCDz1UpqVftmzZwtKlS6lWrRpubm7s3buXgoICHHQ6WrVuzcaNG6lVq1bxkuKzZ//rZejW7dZji1u1sk4ou1RTs5K/Xpw9e/byetndunUj5RY/m6lTp9KpU6er/g9efPFFHn30UX7//Xd++eUXnn/++VKPuawUJ/m7D3gUeADog3VG799YE8H1QgiLzaKTpKrm8cehUydrV9WBA7BiBaSl3fDQpUuXMnPmTBwcHFi8eHHZvLFKxfbmm29e/j43N5cTJ05c1X3cqFGjy/tTUlI4fvx4se91qzc5WzGbzSQkpGAwmAgK8sXJyZELFxL5+efN7N3rhhAKYWFnGTPmfurVs8EQiPnz4WJ3HNHRIARcMRnm8pJtQUEwePD151+qAxkWduP9pcRisfD3339jMplQFAWtVosQApVKxd0dOtDsrruIiYkhKSnptkMHbqgow0vc3KyPKqJuEX42N1ub/JFHHuH333/n6NGjNoqqfChy8ieE2ABsUBTlWaAf1kTwoYtfUy/OCP5VCLHHppFKUlVQp471ATBoEAwcCHffDQUF1i7gK9x///089dRTtG/fnubNm9shWKm4XF1dadOmDW3atLnh/mrVqtG4ceNiX79atWp3dJzFYkEIUeRVItLT0/nwwxVs2hSKxVKL0NAounaNYfXqDLKy3AgOPsPTTzenR4/Btlt6bNYsuNhtftk77/z3fTlYr/dGTCYTCQmpqFSOGI1GCgsL6devHz169KB+/fpkZ2ej1+tJTU0tXvInlar4i2NF/f397RyJbRV5zN8NL6IoblhLujwKdMO6bNx5rK2BvwkhTpX4JmVEjvmTSsWdjvm7kSZNrKVioqJsHZVUReXl5bHnzz8RGzfibjBgbt2auiNHUi0w8LbnWiwW3n77GxYsCMTTsze+vh7k50NqajyenusYNkzDE0/0x9vbG7BWlKiqwxGMRiPz5y/i77/34OCgIj09HH9/P4KCAvH19UWlUpGRkUF6ejqvv/46dS598Cuuor7OKEqlHvN3pW7durFjx46bjvm7kZycHJo1a0Z8fDzh4eHUr1+/FCMssVIf83cdIUQOMBuYrSiKP/AwMAJ4G3jLVveRpCopP9+6pqkk2UB+fj6/vfYaTdasoZmDA05aLeYjRzi1bRvMnEm1oCCEEJjNZgoLC8nX55OTlUNWeg5pydlciM5iy6I8tMINFxc1gYGQlweZmV6MHh3KqFEd0Ol0mEwmNm7cyIEDB3Bzc6NTp060aNGiyiSCer2eb7+dy7Ztcbi5BZOUdAqTyRcIJj4+ifT0dFQqFQaDgeDgYNnqdwdOnjzJkiVLGDJkCKGhoWRlZZGfn0/QHRTBLyqTycTDDz9MVFQUX331VXlP/IqsNJKyYKAGEIQ1Ey0shXtIUuWSmAgBAddvX74cIiPhvvvKPiapUtqxcSPG1aup7uyMc3AwiqsrSnIy9U6eZMcrE9EH1SM/MQ99koHcVMjN0pKV60RmgTPpRmcyhCOFIhjFLYO8vDiSkupiMKgwGrP45psE1q9fSMuWLuh0aWzduo769Wvh6urK+vXr0Wg0NGvWrGRP4LvvoJiTYXa3aMHPOh2enp54eXld9fD09KR+/fq3XSP6TmRkZPDRR7MIDzfi7OxHTEwEKpWGkJBm6PVppKVdwMlJQVEUvLy8GDFihE2KhFd2S5cuJScnB6PRCICnpyeJiYk2T/5MJhOPPvooa9as4bXXXuOll16y6fXLA5skf4qihGLt8h2GdXk3gO1YVwFZbIt7SFKFc6cD1AH69AEvL+v4vho1rN28e/bA4sXg7Q2ff162sUuV1r5du/A0mxEajXXZM1dX6+oyGg26f7aSnHmMFLUzKTpnslydyfd0RqnljmuQF02rq/Cv7sb6DZvZtSefwsxc9PpkhHDExSWSxo2PEBWVxvHjFkDBw8Ob+vVdqF27NikpKRw4cKBYyd9VXccpKdYySMWQpNUy+8iRm+7/8ccfGTNmzE33z507l4MHD16XNF76vmbNmmRlZTN58mxiYjzR6ZyJjj6Ko6Mr/v41yctLwmiMprAwGwcHN1q2bMlTTz1FgwYNivV8qhIhBGlpaURGRnLw4EGcnZ1xdnYmJyfHpvcxGo0MGzaMJUuWMHHiRD766CObXr+8KHbypyhKAPAI1qSvFdZWvnBgItbVPGJtEqEkVVRFGaD+9NOwdKn1nLQ0UKutBZ5ffNG6tq/sEpJsRGg0xAtBVEEBnikpOOfkYMnMJMVoJLJhLRxHjqJJ9WD8Avzx9vHB3d0dR0dH1Gr15QSsR+/OvPzyRI4cWYHReBg/Pw/efvs++vf/CL1eT1xcHOPHTyAm5gLp6S05e9aT3NxcvLy8ih6vEDz22GPcfffdPPfccyjVqkExJ8M0u/defh47lszMTDIyMq56ZGZmUrNmzVuev3btWhYuXHjT/R9//An79xvJyAhBUQqJjT2Mq6s/vr7BFBTEUaeOCUfH2jg7h+Hr60tgYCAXLlwgPz8fLy8vAgICZAvgDZhMJpYvX0VKip5Tp06zatUq4uLicHd3p8dNymAVR2FhIUOHDmXZsmW8++67TJ482WbXLm+KU+T5SawJX1dADcRhXfZtgRDimM0jLGNywodU3gghePnllxk1alTJu8ykKu+ff/5h2tNP09lkoplWi59ajdFkItpkwvHtt+l3i5avK+Xk5HDkyBHy8wto3LjRdWPW5s2bx2effYa/vz/e3t5oNBrGjRtH+/btixTv559/zquvvoqbmxsnT54slfFdd2rDhg2Eh4dflzRmZGSQkJBGUNAQNJrmWCxZpKYextOzJl5efggRzdixd/PTT9NZtmzZTa//xx9/MHTo0JvuX7hwIdHR0dd1WV9uhXzwQZRKNuEjJyeHOXN+58SJJEwmPV5eCunpKbi7uzNkyBDatWt3R+NIbzfho7CwkIceeoiVK1cyZcqUq0ozVRClPuFjJpAFzAEWANuELaYMS5J0Q1999RVff/01f/zxB5GRkeW9yrxUznXp0oXdTzzBP3PmEGMw4AMYNRrc+/ThuWF3vgrnpUkcNzNs2DBUKhVr1qzBxcWFhx56iLZt2xYp1k2bNl1ewm7evHl2TfwAevbsSc+ePa/aZrFYWL58I59/fgJFqYden0hmZgTe3k1wdfUEzvLGGz3p3LkdBQVZtGzZ8oatjhkZGbct0TNnzhzWrVt31bYRwKX2yjf9/HC+xfCSJUuWEDJ3Lk6Ojjg5OVEPKDxzBuPEidbW3Tp14LHHiv3zsbW4uDi+/fZXEhMLESKb/v078MADAy6XJrpd0jd//vzLS7VFR0cjhODDK342b1/xsxk+fDgrV66kY8eO1KhRgwULFlx1rbp169KhQwdbPTW7K07L34PAKiFEpZzIIVv+JJsrwQB1vV7PGzExTL9YKHbgwIE2Dk6qioxGI9u3b2fH5s1gNtOyXTt69OpVKuuXms1mFEVBpVIV6byYmBhatWpFamoqb7311lVv2uWF0Whk9uwV/PhjBjpdXfT6c+TmnsXXtylOTk54eJzmvff6cNddTW1yvzlz5hAREXFV4vjpvn20zs29+UlXvMd369aNLdcORblCRrNmeN1iTOTy5cvJzs6+4WQZW38oPXDgCN99t4zcXAUnpxxeeGEobdq0KtJs8SuXVLyRK/OfWrVqXU4Ub2TkyJHMmTPnju9tB0Vq+bNVnb9goAvgBywRQsQpiqIGPIAsIYS5xDcpIzL5k25n4cKFGAwGhg4dilar5fz589SrV+/mJ0yefPWSbUU0Gfi7eXMOHz5c7GtIUkVSUFBA586d2b9/P/fddx+rVq0qciHq0qbX6/nii0X8+aczjo7VyckJp6AgnmrVWqLVKgQFneLjjx+kXr0S1u6zoWnTpl2VPF459jEzM5N///33poXHwbr+7Z49N16/wdHRkU2bNt2ydWzt2rUYDIbrkkdnZ+fLSZ3ZbGb58vXMnbuXwkJB3boqXn11FDVqVC/Zk6/8yq7On2L93/oCGHvxWgI4hnUcoCsQBUwCvi7JfSSpvBBCsGfPHry9vUlMTCQwMBCDwXDrk4oxQF0AMdHR5OblkQocOXKENWvWcP/99xc7dkmqCIQQPP/88+zfv5/atWvz22+/lbvELyMjg3feWcw//9TEwaEaaWn7MJmy8PXtAhQQGnqKjz4aRkiIfbuprzVu3Lib7rNYbr8ya9++falTp84Nu6wLCgpwdXW95fkTJ0684YdYrVaLl5cXK1asYMeOE6xYEYWiGOjRI4Bx40bhdnFJui1btiCEuGqGtZubW5FblaUStvwpivIa8DHwCfAPsAG4Vwix6eL+OUBdIUTnkodaNmTLn3Qrp0+fZvv27Rw6dIigoCBq1qxJ3bp1izyI/XY++OADJk2ahI+PD2PGjOHjjz8mICCA8PBwfHx8bHovSSpPcnJy6NatGydOnGDXrl3cdddd9g7pKrGx8bz88koOH74LrdaF7OydgAUfn1ZAJnfffZ4PPxxcpf5OhRDo9frLs8JvZsKECZw+fZqMjAzS0tJJT88lOzufggLr+uUjR04mPFzBxSWHZ55pz9ChA9Bo/mujatiwIadOXb1gmEqlwtPTE09PT9auXXvrXpjKrey6fRVFOQPsEEKMUhTFB0jh6uTvZeB1IUSFWRRPJn/SjQgh2Lt3L3Fxcbi5uXH06FESEhLo2LEj/fv3v2r9UovFwsMPP8zgwYMZOnRokVc0SEtLo06dOuTk5LBmzRruvffeyzPVhgwZwh9//FFlVkmQqqb8/HwOHjxIx44d7R3KVcLDTzF+/A4iI+9Go4Hs7C2o1W54ed0FJNCnTyyTJg253FJVpkowttgiBKd69KDR9Ok2DurGzp+P5p13NnLwYFMUpTYhIYkYjctISBDUqJHD5MmDad++zXWvc6NGjSIyMvKq7urcK8Y7xsTEUL361d3D3333HTOK+XMBeP7553nhhReKfX4ZKtPl3aoDu26xPw9wL+E9JMmuzGYzGzZsIDc3F7PZjE6n4+WXX0ZRlBsmYQsWLGDx4sXs2LGDvn373rYr5Fo+Pj78+++/bNmyhfsuruwxd+5cmjdvzqJFi3jggQd49NFHbfLcJKk8cnJyKheJX0pKGgcOnCUurgCj0czMmQmkpPQC9GRnb0KrDcbdvQlmcyQjRiTxyiuP2m82fgmKX6uAP0+c4IlXXrltrcOSKigo4K23ZrJjx114eNSnWjUP9HoPLlyoR5s2p/jkk7HUrFnjhufOnj37um1Go5HMzEwyMzMJvMHa1CkpKRwv5s/l0vmVUUmTv2SsCeDNtAJiSngPSbKbgoICli5diqIoZGdn065du1t2Q2VnZ18uTfHJJ58UOfG7pFGjRjRq1Ojyv+vUqcNXX33F008/zQsvvECXLl0ICQkp1rUlSbo1i8XCsmUb+fLL08TE1MdiqQ64IERNLJZs8vJ24eDQCBeX+lgs4bz4Yh7PPvvwVT0ApUmv15OYmEidOldMJilB8eu4+HiSs7KYNWsW77///m2Pv2rFlSIwmUwcORzOvn+TURQ1Li5qAgLAaNSSmdmX++6re9PE72a0Wi3VqlW7aZmcatWq0biYP5dL51dGJe32/Rprwef2WGv/pQA9hBCbFUXpBawEPhVCvH3zq5QvsttXuiQjI4M///wTnU5HVlYWAwcOpHbt2rc857XXXuOzzz6jQ4cO7Nixw6YDkYUQDBgwgIiICBYvXkzLli1tdm1Jkv7z11/rmDhxD3l57VGrw9Dp/FCpNBiNAoMhB5MpBgeHQBwd9/PWW4Lhw3uV2aSUyMhIBg0aRG5uLvv27SvWqinX2rZtG127diUoKIjo6OirxtldYjKZOLpjB2mrV+OUmYm2cWNqDR6M/00+hJrNZrKzs0mKSybqeDxnDqVxPtzM+ZPOxKQ4k+YWh4dPRwICamM2q4mPz+W5544zbly7Ej+fKqpMx/x5ANuA2ljX8u2NddKHK9ABOAR0EULoi32TMiaTPwkgNjaWhQsX4uTkhMlkYvjw4bf9BHjq1CmaNm2KyWRi7969tG7d2uZxpaam4uDgYJ8xRZJUBaSlpTFo0FucO9cSrbY9bm4N8PTUodUq5OZCRoYgPz8FN7cDfPKJO/363V1mY3DXrl3Lo48+SkZGBqGhoaxevdomExyEEDRq1IhTp06xbNkyBgwYcNV+s9nMkunTcZ0xg7YWC546HZbCQs7XrInj9OmENGhAXm4uSXEJXDhxnoTD0aQcTSX7rCAv1YnMfGcyLCrSNQYMnnqS8mPJ0WjQ6Nrh6NgIcMVkSuDHH93o0UN+qC2mshvzJ4TIUhSlPTABGAwUYF327RzwHvCZECK/JPeQpCulpaQQtXcvpKfjWrcuNVu2LPEYG5PJRGRkJBkZGQQHB5Oens5ff/2FVqvFzc2NUaNG4eLicstrCCEYP348RqOR0aNHl0riB+Dr61sq15Ukezh16hR+fn42ab2ylcOHjxAdrQcc0ekc8fDQ4O+v4OAAmZlQWAgGgzMPPlh2iZ/FYuHjjz/mnXfeQQhBv379mD9/Pp6enja5vqIojBkzhgkTJvDjjz9el/wdPXiQmB9+oD/gHRiI4uGBOjub0JgYDrz4P4641UR1LhNLsgV9vgNpFh3JKCS7QGGIDq/GPnRtU4ewFk2pG1qbpUuX8uWX8ygszCI39yzgSN++nnTsONImz0e6vZKO+eNicvfhxYcklZo9mzcTN3kyrePjqaYomNRqTnboQPBHH1HtBgN974Rer+ebb77h7Nmz+Pr64uzsTF5eHhqNhvr16zN48OA7GsezcuVK1q1bh4eHB1OmTLnj+5vNZlQqlZy9K1U56enp3H///SiKwoYNG64ev2ZHKSkpGI2FqFSFKEoharVAowGt1vpQqUBRTOTmmsrk7zYrK4uRI0eybNkyFEXhvffe4+2337Z5bbvHH3+ciRMnsmbNGmJiYqhR47+xdzs2bUKTl4fW3R3FwwPF1xfUalTp6XiePEFSTiLRGicSqnlgbuOGX4sGtG/dhPqNGxAUFHRVEWeAZ599ltq1a7NixRouXDjFzp0R+Ps/T0JCAsHBweh0Ops+N+l6JU7+JKksxEZHs+PNN7kvJYUQX19UHh6Ql0eTHTs4MmUKXl9/fcNxKrezfPlytm7dSsOGDWnYsCFOTk6cPHmSsLAwHnrooTt+ge3QoQPPPvssjRs3xs/P747v/7///Y+0tDR+/PHHYk8OkaSKxmw2M3z4cM6fP0/Lli1vOEvTXtzd3VCrM7BYUjGZktHrA8jM9EarVcjJgYICIxZLCoGBxlKPxWKx0KNHDw4cOICnpye//vorffr0KZV7+fr68tBDD/H7778za9Ys3rtiVaKs7GwKhSDJZCIwNxdHtRpycigwmUhy1JH92mg6du5IjVq18PDwuO34R41GQ9++fenTpw8xMTF0796T335bTHb2aWrWDOHRRx/F37/CVIirkIr00UFRlB8VRbn1iPcbn1dXUZQfi3qeJF2ydf16VPHxODk4oPL2RgkMRPH1Re3sjMeWLSTdYk3GmxFCsHPnToxG4+X1R3U6HWq1Gp1OV6RP1r6+vnz//fe8+OKLd3zOokWLmD59OosWLbqucKkkVWbvvfcea9euxcfHh6VLl5bKmsLF1bx5c/z8jEAUhYXnyMo6RULCBeLjs0hOTkevj8LJ6Tjdu5d+wqpSqXjjjTdo1qwZ+/fvL7XE75Lx48fz+eefM3bs2Ku21w0L46SicCw/n/CkJC5ERZGQkMDJ/HzO16rJwKdGcVfLlnh7exdp4ouiKKxcuRInJy2pqadxcHBHq9WyfPlybLH0rHRzRW0qqQ6cUhTlH+AP4B8hROyNDlQUpRZwLzAU6A6sL0GcUhUXExtLoRCkmkwE5+fjkJ0Nej0mo5E8gwFjSgrBdesW6ZoWiwVHRyf0egOJiYlERETg7OxMamoqrVq1KqVnYnX69GmeeuopAL788ssS3+9SAmu3GmOSdIeWLVvGBx98gEqlYuHChaVeV66ogoKCGD36YT755FeMRi16fQEGQyLghhBmhEhh0CAVrVo1KJN4Bg8ezAMPPFCsno2iateuHe3aXT/b9r7evfnrt9/YfvgwGbm5BABqIEmno9FTTxW718JsNvPvv/8SEBCAg0MW/v7WZdsiIyPR6/W3HWstFV+RfpuEEH0URekIvAL8BKgVRUnDuoZvBtbZJl5YZ/96AWZgNdBdCLHDhnFLVUxgzZpsBGoUFOCQlERwVhaqwkLS8vM57exMuyJ0tYI18Vu9ehMJCWays/OIjY1Fr9ej0+nw8fEp1eQvPz+fIUOGkJOTw5AhQ0pcPf7kyZMMHz6cTp068c0339goSkmyvVOnTvHYY48B8PHHH3PvvffaOaLrKYrCqFFPYDQamTXrb1JTEzCZAgFnPDwEgwfX45VXHimTZOySsrzXjXh7ezN12jQ+nTKFLXv24Go04hkQQM/Ro+k1dGixr6tSqXBzcyM/P58mTRqTnZ1BVlY6JpPJ7s+5sit2qRdFUaoB/bCWdGkIXFrIMA04CewGVgkhkm0QZ5mRpV7Kp9jYWMY88ggh0dE0URQCFQWNEKQJgWngAzz9zZ2P+UtPz2DGjGWcP59IVpYBJ6dk9PpEcnJyqFevHi+88EKJioLezujRo5k1axb16tVj//79uLuXbBGcAwcO0L59e0wmExs3bqRHjx42ilSSbMdoNNKiRQsiIiIYPHgwf/75Z7me6CSEIDIykl27dpOUlIq3tw/t27emQYP6qD/7DA4dgoMH4dw56ywQk+nmFzOZ4Isv4JdfICoKfHxg4ED48EPr9xWE0WgkMTGR/Px8/P398fDwKPE1t2/fzv/+9z8cHR3x8fFBq9UycuRI+vfvb4OIq5Syq/NXGcnkr/zavn07n0yeTOGZM/iazdZZuNVr8vDzY+kzbMgdXWPVqm0cOxbDzp0pqFS5dOlSjaefti7JZDAYcHZ2LtVirXPnzuWJJ57A0dGRPXv20KxZM5tc94MPPmDSpEmEhIRw7Ngxm5WAkCRbWrJkCZ999hkbNmyo2LUqFQU8PaFFCzhxwrq02q2Sv8cegwULoF8/GDAAzp+Hr7+GunXh33/ZvHcv3bp1K9fJcGkRQrB7926WLFmCxWKhV69e9OpVdkWzK5Gi/fIIIeTjigcQBojw8HAhlT9paWli9uzZItQ3QDxSvZk4vGefmP31DyInJ+eW5yUnp4glS7aIkSN/Ej17/iwGD/5ArF+/SZjN5mLFYTAYinXeiy++KAAxa9asYp1/M0ajUbRr104AYsSIETa9tiTZUnH/5sqVs2f/+75rVyHU6psf+88/QoAQAwZcvX3xYiFALGvdWgDi66+/LpVQpSqjSLmObQsFSVIp8/b2ZtiwYdzVsQNxCWkcWr+DkAZ1OHn45A2Pt1gsHDt2mpUrD/HllzFER+to2DCLL74YSc+e3YtVKys9PZ169eoxZcoUTLf6tH8D06ZNY/v27Tz55JNFvu+taDQa5s2bh5OTEwsWLGDx4sU2vb4k2Yqt69PZRVEml82bZ/368stXbY5r144LDg403r8fBwcHm3ShStKdqgR/hVJV4+DgwLMvvkCCo2DLb5vxq+bH+XPRGAyGq47Lzc3l3Ll4Xn/9MD/9lI9Wa+DRR5349NMXqFGjerHv/+677xITE8PGjRuL1TXRqVOnYt/7VurXr8/nn38OwDPPPENCQkKp3EeSpCLYs8c6JrB9+8ubtmzZQqtWrdhiMBAK/Lt6NU888YTdQryZs2fPMnv2bHuHIZUCmfxJFVKnTp1o1r0tR85GcnRnOGonHeFHTmMymRBCkJ+fz/Tpu3jiicMkJfkSEBDDp5+2YvToISUqh3Ls2DFmzJiBSqVi2rRp5W6MznPPPcd9991Hbm4u//77r73DkSQpLg58fcHBASEEX331Fffeey/JycmoL5a5uascLtuYlZVFkyZNGD16NHFxcfYOR7IxOZdaqpAcHBx4ftwLPLXlWeZP20C2V3vy8zJo1jqfbt0srFiRTWyslsJCFX36nOeVVx4p8bq4QgjGjRuHxWJh7NixNG3a1EbPxnYUReGXX34hLS2tXMYnSVWOXg8X1y6eNWsWL1/s/n3jjTcYolbDlCnWY8oZDw8PBg4cyJ9//skvv/zCpEmT7B2SZEOy5U+qsDp37kzTjmGcio4iI6EGiq4TBw+24uOPQzh8OBC1Op1Jk8x8+OHIEid+AIsXL2bLli34+PhctfRReRMUFCQTP8mufvrpJ7755hu5SgOAszNcHJIyYsQIevTowZIlS/j4449RFRb+d0w5NGbMGABmzpyJ2Wy2czSSLZUo+VMU5YSiKG8qilK+SrRLVYIQApNwI5NkNMp5GjSsTUiICkWpRv3655k3rzmDB99nk2Kher2eCRMmADBlyhS8vb1vG9vZs2dLfF9JKpdSUuCFF6BmTdDpICgInn4aEhPZvXs3Y8eO5aWXXmLHDlnbn5AQSE0FgwFHR0c2bNjAoEGDrPsudaeGhNgvvlvo3r07devWJTY2lrVr19o7HMmGStryFwu8B5xTFGWboiijFUWRU5akMmEwGMjOVqFxb4WDTyvMZrBYAApp2dKNBg1CbXavH374gdjYWO666y5Gjx592+O/+OILmjZtyvz5820WgySVCykp0K4dfP89dO8O06fD44/DwoWY2rblmQcfxGg0Mn78eDp37mzvaO2vbVvrC9OePQBXjxPevds6c/g2HybtRaVS8fTTTwPW1lyp8ihR8ieE6AWEAK8CTliXfEtUFGWxoigDFUXR2iBGSbohZ2dnatVywsm5Nrn5hZw7l8iFC0kYjcdo1sy2EzHGjh3L559/zrfffnvbGb47d+7kjTfeoKCgoMSrd0hSufPRR9YixVOmwJw58MwzMHUqplWrUMXGMjYpic6dO/PZZ5/ZO9Ly4eJydnzxxdXbly61rvZxaX859cQTT6DRaFi5ciXx8fH2DkeyEZuu8KEoSgNgBDAM6/q+mcAfwAIhxC6b3agUlYcVPs6di2bp0jhSUtxp185C374NSjRDtTLbsWMn48ZNJTm5ASpVbRRFoWtXM99++wTu7mW/gkBKSgotWrQgPj6eV155pdy9AQohyt0MZamCad4cjh6FhAQICLi8efz48Tw/bRrBikJuZCQBtWrZL8bSNn8+REdbv581C2Ji4OI44MTERJKefprmzZv/d/yjj8Lvv1tX+Bg40Jo8f/UV1K5tbRF0dbXDk7hzQ4YMYfHixXz22We88sor9g5HujH7L++mKEoA8A1wac0tAUQCXwPfCyEspXDPlsBkoBPgePF+PwkhphXxOnZN/k6dOsfTT+8hI6M37u5e5OYaGTgwgkmTmpb5QtdCCC5cuEB2djZ+fn74lMM1KIUQhIeHs2TJclJScmnVqimDBvWxy/JmZrOZPn36sH79ejp27MjmzZutS9CVA3l5ebzyyiu4u7vzySef2DscqSJr2BBOnYKsLLjYsr1gwQIee+wxDgF3AezbB61b2zHIUtatG2zdetPdtWvVYv/+/f+9ZhqN8PnnMHu2NWn09rYu8zZlirUMTDl36NAhEhMT5bJr5VuRkj+bZROKorgAD2Jt+bvn4uaVwDygEBgDTAOaAc/Y6r4X790LWAEcAj4AcoG6WLukK5QFC9YQF1eDmjVdCA6GhAQtS5cGUr36Llq29CcoyBtPT1ccHBxuWynfYrFQUFCAWq3GwcGhyLFs3bqV6Oho+vfvj9lsJjY2lurVi18cuTQoikLTpk3LxezWKVOmsH79enx9fVm4cGG5SfwAjh8/zk8//YQQgr59+9KlSxd7hyRVVGFh1uRv0yZ44AGEEMyaNYsAoIlWa010YmIqd/K3ZctV/zQajbz22mt8/fXXADzepQvOV87g1Wph4kTrowJq0aKFvUOQbKxEyZ+iKGrgPqwJ3wDAGTgATAB+F0KkXnH4ckVRPgJewIbJn6Io7lgTzFXA4NJoVSxLCQlRmM0qsrOT0GqDyM42k5Fxntdf34yTkwteXloCAnTUqOFAaKg7det6Ubu2L0FB1fDy8sDJyQmVSkVkZCS///47mZmZuLu706JFC+67777rEhIhBCaTifz8fLKzsy8/jEYjhw8fxsPDA51Oh4uLC5GRkXb6qZR///zzD5MnT0ZRFH777TdCytnsvTZt2vDmm2/y4YcfMnLkSI4cOSLHI0rF8/LLsGwZPPccGAwo7duz7q23yIqJQX1p9mo5rFtXWpKSkhg6dCjbtm1Do9HwzTff8Nxzz8nhFVK5VtKWv0TAG4gHpgPzhBAnbnH8UcDWA7EeBfyBt4QQlostkPkVNQls2bIe69dvIy3NgaysehiNhbi7b2HkyECioy8QFZVBZqaec+c0bNmiR612xdFRhaenE9WqORIc7EpIiAdHjuxGq9XQoEED/Pz8iIiIwGQy0bx5c9LT00lPTycrK4vCwkLUajWOjo44Ojqi0+kutxhaLBYOHjxIrVq1qF27NmlpadQtypqWVUhISAhhYWE89NBD9OzZ097h3NA777zD6tWrOXjwIC+//DIzZ860d0hSRdSxIyxaBC++CI88AoBOUag2eDD07g0zZlzuDq7MUlNTyZk6lcJvvmGGyYRGo6F6SAjO330H33132/MFkPDAAwRNmVL6wUrSNUo05k9RlDnAfGCTsFM1T0VRFgM9gYeA74D6QN7FuP4nhCgo4vXsOuYvIyODV16ZyObNMRQWBuLp6cCrr97D8OHW7pXExES0Wi2bN+/Ezc2Z1at3YDYbOHs2gby8HPLzDZjNOXh6qmjQoAGtWrWiTp06xMfHc/LkSdzd3dFoNCiKgslkwmAwYDKZUBQFR0dHvL29CQgIICgoiJSUFL799lv8/f1p3Lgxw4cPp2bNqlHSMSoqilpFHLCu1+txcHAo12Nijh8/TsuWLTEYDCxbtowBAwbYOySpojKb4fhxyMiwlisJDoahQ62J4enTUK+evSMsVbGxsfzeoAGv5ecX+xpTHRx47Nw5goODbRiZVEXZf8JHWVIU5QhwqaDbLGAL0A14EVgohBh2i3P9gGrXbK4LLLPnbN+CggIOHjxIWlo6DRrUp169ejftQrBYLOTn52MwGIiMjMRisTBnzjz27NlN7dq1qV+rFtVr1yYrJ4fIyEg8PDwIDAwkODiY6tWr4+/vj4+PDy4uLpeTwislJSWRnJxMjRo18PCoGiUc9+/fT7t27Xjqqaf48ccfK133zVdffcXLL7+Mn58f4eHhVKt27Z+AJBWDwWBNAL29rclfFXBkzBj8liwhwN+/SK8TAoiJieHT3Fyi+vRh5cqVle51RipzZTfhQ1GUGrc5RAAFQGoptgy6Yh1r+IMQYtzFbUsVRdEBzyiKMkkIceYm5z4PvFtKcRWbo6Mjd9999x0dq1KpcHFxwcXF5fKqE6b8fE7s/ZfC2Fj02dkcjInB5OJCzZo1eeutt4o0EcHf3x9/f/9iPY+KyGKxXF6/19PTs1K+II8fP54VK1awefNm5syZw6uvvmrvkKTK4M03IS0NvvzS3pGUmeY//QTFKH6sANoLF/gtLIzM1auZO3cuTzzxhM3jk6SbKWm3rwVrgnc7BcB24AMhxM5i3/DGMYQDYUBXIcS2K7Z3AbYCI4UQ825ybrls+SuJxAsX2Lt6NdvWrsUlIYFok4lItRq/oCDefffdIs+Kzc/P58jKlbB9O86AuVMnGvTrd/VMtkpk/vz5PP744wQEBHDq1KlKOykiJiaGzZs38/jjj1fKBFcqHTk5ORQWFuLTsaO1VEloKOTnw19/WUufPP/8HY13k6wuvd64u7sTERFR7iaK3YjJZGLNmjWEh4czsYLOXq6kyq7bV1GUUcA4oDrwK3BpMdN6WCdiRAOzsXbLjsA62aO3EGJzsW96fQzrsY75ayiEOHXF9obACeAlIcQ3Rbie3Ys8F1daYiK7ly4lJjoaw+bNZPv44NSpE76BgfTs2bPIZVry8/NZPHEizVevppGLCxqtFqNeT0TXrjT49FOcXVxK6ZnYR05ODvXr1ycxMZG5c+fy+OOP2zskSSo3hBAMGTKE/fv3c7BZM7yPH4f4eOvavi1aWNf6HTLk9heqQFavXk3Xrl1xKaXXOiEEAwcOZMWKFfTu3ZvVq1eX+w9jiYmJVK9eHSEEMTExBAUF2TskyapIvzglXds3CNABoUKI8UKI6Rcf47BOvHACnIQQLwENgARs38164OLXa0fMXvqNTLHx/cqllMRE9v76K0mRkWhXr8bk4sKA997jtbfe4sknnyxWfb4d69ZhWL4cfycnNNWro4SGovX1peH69RxfvLgUnoV9ffjhhyQmJtK+fXtGjBhx0+PmzZtHTk5OGUYmSfb36aefsmTJEjIyMkj74gs4e9ba6peVZa17V4kSP7PZzMSJE+nbty+dOnXCYimd4hGKovDjjz/i5eXF2rVrmT17dqncx5YCAgIYMGAAZrO5QsQr3VhJk79ngZlCiMxrdwgh0oGZwNiL/04DfgFalfCe1/rz4tenrtk+GjBhnQBS6ej1erLS0ynIzyclIYFDs2eTnpqKy/LlmBwd6fTuu7Ro27ZE99i9dSsGkwk91k+oQghQqVAA/T//lNoL4s0kJCQQERFBYmKiza99+vRpvvrqKxRFYdq0aTctoP3nn38ycuRIOnXqhMlksnkcklQebdiwgTfffBOwruZRrxLP5M3IyKBfv35MnToVgMOHD18u3lwaAgMDmT59OhqNhoyMjFK7jy2NGTMGgJ9//vmO3weys7P54IMPaNq0KW5ubvj4+NCmTRu+++47jEZjaYYr3UBJ6/z5YJ1scTMuXD2mLpEiNk3ejhDikKIovwBPKoqiwTrOrxvWpeU+FkJcsOX97C0/P5/1s2ejWbqU6llZCC8vEjUaMsLCcF+0iGwXFxpPnkyHbt1KfK88s5lci4VTeXk4JiTgqdNhyc0l0Wgk89QpIjZvpm779mXS/Xvo0CH27dvHgw8+iE6nIzIykjp16tjs+r/99htGo5GnnnqKNm3a3PCY06dPM3r0aACefvrpMl9uT5LsISoqimHDhmGxWJg0aRL9+/cv0vkVaT3p8PBwHnjgAc6dO4eiKAghaNy4Mf369SvV+z766KO0b9++wtRR7dmzJzVr1iQ6OpoNGzZw33333fJ4k8lEjx49OHjwICNHjmTs2LEYDAaWLFnC2LFj2b17NwsWLCij6CUo+Zi/rUAjoIcQ4tg1+5oBG4HjQohuF7d9BdwvhGhY7JveOA4t8CYwCmt3bzTwnRDi62Jcq9yO+TObzcx6/308586li1aLn4sLihDk5eVxUq/ngqMjjp98Qs/Bg23yYvvHwoXMfO01OlosNNJo8FMUMJtJsFhIMBho7uCA0qQJmv79Ce3Th8CaNW+75FxRWSwW0tPT+e6773B3d+fxxx/H29ub6OjoItfhuxUhBMuXL6dDhw74+fldt1+v19O+fXuOHTvG0KFDWbhwYYV5Q5Ok4vrll19IfOcdBl64gKurKzVq1Cjyp/dvjEa+NBgIDg4mJCTkuq8tWrQotTF1RbF48WJGjhyJ/orVSYYMGcIvv/yCq6urHSMrn6ZMmcLbb7/NoEGDWLJkyS2P3bhxIz179mTChAl8/vnnl7ebzWZat27N0aNHyczMxM3N1mtAVCllurbvi8Bm4JCiKLv5b8JHKNAByMY6IQRFURyxtsjZfLCYEMIIvHfxUWmdOHGCU4sWMUBR8PT2RgkKQrFYcImJoZrJRMrrr3PvQw/ZLCnp268f69euZdPGjcQajVRTFCxCUNioEcGdO/PHunV4Hz5Mw/Bw0mfPRtWxIzUGDqR+27a4lODF0mKxkJqaSmRkJOnp6bi6utKuXTuWLFnCjh07aNiwISkpKTZN/hRFYeDAgTfd/+KLL3Ls2DHq1avHzz//XCkTPyEEv/76K7GxsXIWn0RERARPPfUU72Itp0BurrWocxEJDw9isrKIiYm54f4jR47QrFmzm56/b98+3NzcCA4OLrXkYP/+/Qy5YsyiSqXik08+YcKECZXyb90WRo0axbvvvsvy5ctJSEggMDDwpsdmZWUBXDc5RK1WExAQQEREBDqdrlTjla5WouRPCHFUUZSmwBtY1/i91F8WDcwAPhVCxF08tgCQq0OXwImICApycijUarFoteDkBEKAgwMarZa6LVvatOXN1dWVz7/6irlz5zL/p59QpWXSY8hDvPzaq9SoUYOcl19m3969rF+8mMTt2wn5+28SNm3iWMOG+N57L0179iSoZs3LK17k5+cTGxuLWq0mJCQEBweHy/cym80kJydz5swZ4uPjcXBwwMHBgbS0NI4fP46DgwMJCQnMmzePsLCwW07IsLU5c+bwyy+/4OjoyOLFiytt+ZcTJ05cLv3SrVs3OnToYO+QJDsKCwtj4sSJ5Hz1FQVBQTg6OhbrOi88/TQDBgwgPj6euLi4y18vfX+78iaDBg0i7uKawe7u7te1Hk6YMAFPT89ixXZJ69atadOmDfv27cPX15c//viDe+65p0TXrOyCgoIYMGAAf//9N5s3b+bRRx+96bEdO3bExcWFqVOnEhISQvv27SkoKODPP/9k3bp1vP/++1e9H0ilr9jdvoqiOGBN+KKEEEdtGpUdledu3+XLljF37Fj6qFR09vCgpo8PGiEwpKYSbjTiu2QJdZo0sfl9LRYLjw4aTM66Xby3YQmtO3W8ar8QgoSEBDZt2MDWv/6i4NQpQjQafIOC8GzZksY9e+Lq68ucuXMxGAx4enri4+PD4Ivd08ePHycqKgohBGq1mszMTHJzcwkJCaFdu3aEhobi5ORERkYG8fHxBAcH4+XlZfPneSPx8fHUq1eP/Px8fvnlF0aNGlUm97WX119/nU8//ZTQ0FAOHz5cLrrjJPvKz8/HycnJLvcWQtCrVy+ioqKIi4ujoOD61TqzsrJu+YHsvffew9PT86qEMTAw8Loxu4WFhYwbN44333yTGjVut36BBHDy5EkcHR3vqBfmn3/+4bnnnuPMmf/WXHB0dGTGjBmV/nW1jJRNnT/F2hZeAIwXQvxQrIuUQ+U5+YuLi+PJQYNoeOECbTQaQnU6XIQgq7CQyJYtGfLrr6VWfPnJEY8Ru2gT769eQIce3W96XGFhISdOnGDNsmUc/OcftJmZ+Pn4oLi7k+nkRJ2wMOrWrUtmZiYXLlxAp9OhKAq5ublYLBZCQ0Pp0KED9erVK3ZLg60tWLCAf//9l2+//dbeoZQ6g8FAmzZtOHbsGM899xwzZsywd0iSBFgTwYyMjKtaDRMTE3nnnXdueo7RaMTBwYFr3+dUKhX+/v6EhISwc+fOIq16VNZMJlOlmFy2f/9+3nvvPQIDA+nVqxd6vZ65c+eydetWZs6cKVc4KbkyLfIcjnX93A+LfZFypjwnfwBLlixh2rvvUiMzk1qAI2CsUYMHv/6a5iUs7XIrLz77PEd/Wcnkv3+me59bz+y6JCsri107d7L094WcPLCfmo0a0aRNGxo3bkxWVhZHjhzBYDDQpEkTOnXqRGhoqGz6LweOHDlCmzZtMBqNrFmzht69e9s7JEkqlvz8fL755pvrupuTkpIQQuDp6XnL8ioGg4G2bdvedLJKafZC6PV63nzzTSIiIli3bp3NJ9OVpSNHjtCuXTteeumlyyV0wNqr1LFjR44ePUpUVJRcZ7xkynTCx0fAl4qiLLpydQ2p9AwaNIjQ0FBWLFtGZlISAXXr0m/QIGracPLDjbi6uWFAhbGg8I7P8fDwoH6Dhrj71SJDdRTn3FxiY2MRQlBYWEhBQQGvvfZamXexmM1mVq5cyYABA+Rg7hto3rw5H3zwAW+88QZPPvkkx44dw8fHx95hSVKROTk58cYbbwCQmZnJvn376NmzJ0ajkYSEBNLT0295/oULFzh69ChHj954ZJOvry8pKTdfR8BkMpGSkoKfn9/lsc93Ki8vj99++42UlBR++OEHnn/++SKdX5588803GAyGqybVgLUFdvDgwfz777/s3buXvn372inCqqekyV97IA0IVxRlCxAF5F9zjBBCjC/hfaSLFEWhefPmNG/evEzv6+LmRiFqCgrurBhndnY2f/65ihUrdpOZmY7JpJCcnIyiKKSmpqIoCrVr177lDDGbmjwZ3rNOBlcDN5zXGxcHwdcuFFM1vfLKK6xYsYKdO3fy/vvv8803d7xColQBVZauxZuJiIhg4MCBREdHM3PmTEaOHEmNGjVu+8EzKCiIAwcO3HSyiq+v7y3Pj4yMpEGDBmg0GgIDA69rNaxfvz4DBgy44bnVqlVjxowZDBkyhNdee43evXvbtLZpWYqPjwesH7yvdalYviyaX7ZK+tc+9orve9zkGAHI5K+Cc3VzxWBRYTLcOvkzm83s2LGXn39eT1RUKvn5yWg04OUVglqdSUJCApmZmTRq1IjRo0eX3VibQYMgNJS8vDxeeeUVcnJzeXHsWNoFBMDbb0PLljLxu4JarWbevHl88cUXfPhhpRnVId1AQUEB7dq1o3r16ixfvrxCdy/eyNKlS3n88cfJy8sD4M0332TIkCF3ND7awcGBli1b0rJly2LdOzMzk2rVqpGSkkJsbCyxsbFX7W/duvVNkz+ALl260LZtW/bu3csjjzzChg0b8PDwKFYs9hQWFsb69euZM2cOba8YnmQ0Gvntt99Qq9W0bt3ajhFWPSUt9VK5XiWkm3Jzc6MQFbm3aPmLjo7lhx+Ws23bBfT6NMzmXJyd3QgKUjF69Cjat29LZGQkKpWKBg0alO1M0mbNoFkzJo4bxw+5uXTr1o2206bBpEnW/ReXK5L+U6dOHb777jt7hyGVsjFjxlzu2pw2bRovvfSSvUOyCbPZzLvvvsuUKVMub3N2duaLL74otYlx12rbti3JyckUFBRw4cKF61oQr617d60jR46wd+9ewFrv0NPTE3d398uth127duWtt94qi6dSIi+99BLz58/n+++/Jy4ujvvuuw+9Xs+CBQs4evQoL7/8MsHyw3eZqrzt/JJNubi6YERNfv71TfN5eXksWrSR+fOPk5ych9GYhIODI56erjzwQFOeeGIY3t7eAJe/2sOxY8eYMWMGKpWKadOmoVgsMHs2uLjAo4/yzTff4OzszOjRo+VYQKlK+Pnnn5k/fz4AjRs35tlnn7VzRLaRmZnJ8OHDWb169eVtoaGhLF26lKZNm5Z5PI6OjtSpU6fI3bbe3t4MHz6cw4cPExERAViH1GRnZ3PixInb1hzds2cPX3/99Q0nq9yo3E1JZGRk8OOPP5KSksIXX3xx1b4aNWqwb98+PvjgAzZs2MCaNWvQ6XQ0adKEmTNn8uSTT9osDukOCSFK/MA69m8i8BVQ7+I2Z6Al4GqLe5TVA2sxexEeHi6k/6xYsUIEa5uIaVN+urzNbDaLXbsOiqFDvxWNG38m6tR5WdSr95Jo3vxlMWbMWyIi4riwWCx2jPo/FotFdO/eXQBi7Nix1o0rVggBQjz5pNi+fbtQq9UCEPv377dvsJJUBvbt23f5d97V1VVER0fbOySbGTBggMA65EgAol+/fiIjI8PeYZXIsGHDBCDGjx8vjhw5IlavXi127dp1y3N++umnq34OVz4URRHPPPPMLc8vyut3XFycUKlUQqPRiMTExDs+T7KZIuU6JUr7FUXRAQuxjp9XLv5SrQDOABZg/cWEcMrNriFVDC4uLpgVFTn51tJA8fEJzJixkWXL8sjNzcRiycTR0Y1atfS88EI37r//3nK1XM+SJUvYvHkzPj4+vHdx4gc//wxA+uDBPPzww5jNZl599VVatWplx0glezEYDMTGxmIwGHB0dESn0+Hn51cpyw+lpqZy//33YzabURSF5cuXV6rCxq+88gqrV6/GbDYzefJk3n777Qo/lnH69On06tWLkSNHoijKLZfEu6RHjx7Mmzfvuu7mSzUSb7dm8Y8//shbb71101I3jRs3vvx7ExwcTL9+/Vi+fDlz5szh9ddft8nzlkpHSdt8PwD6Ac9hXeP3crkXIUSBoiiLsCaGMvmrwPLy8ti1K4ZC1Gzbkovjl6v4889k4uJMmEwJ6HSOeHk58+ijgYwePbhclgU5efIkKpWKKVOmWLueExJg1SpE06Y8/OWXXLhwgU6dOl01PkiqOlJTU1m3bh2hoaGEhYXh7OyM2WwmPj4ed3d3uw5XsDWz2czAgQNJTU0F4OOPP6Z795sXbq+IOnfuzLJlyxBCVJryIT4+PkUuhHyrrmaj0Uhh4a1Ld8XHx5Oenk56ejrHjh27bv/rr79+Vd2+MWPGsHz5cj7//HPmzZtXpFiv9Pzzz/PCCy8U+3zp9kqa/A0DvhdC/KQoyo3e8U8AQ26wXaogjEYjkyb9weLFrlhUHpw6U42jnztTWJiJSmXE2dmTLl3yeeWVITRtGlZux8q9/fbbDBo0iAYNGlg3zJ4NZjNrQ0LYuGYN1apVY+HCheW60n95cfz4cf79999KM07HaDSyYMECfHx88Pf3x8XFBUVRUKlU1KxZk7i4OFxcXCpNC2B2dvblJbb69+/Pa6+9ZueISkefPn3sHUK5ptVqb/t699577/Hiiy9e12p46eu1rY+9e/fmscceo7CwkD/++KPYsd2qdqJkGyVN/vyA6z8O/MeMdeyfVEEdP36SFSvS8PDoScMGrdE5+BMZaSE93UidOgeYMKEpffveUyHeGBs3bmz9RgiYNQuzgwOPrlmDoij8+uuvcrbZHUhISKB169YUFhbSrFmzSlGe4cSJExw9epRu3bphNpsRQlz1IcbLy4sLFy5Qu3ZtO0ZpO15eXkRGRvLmm2/y0UcfldsPbJL9qVQq/Pz88PPzu6NyN5dKRH333Xc3bCm8U3Klj9JX0uQvFmh4i/0dgbMlvIdkR7GxsRQU5ODomI+jUyigoFIZqV5dw8KFj1O9egVMmP75ByIj2RQQQGZiIu9OmkTPnj3tHVWFEBgYyDPPPMPXX3/NY489xsGDB3FycrJ3WCUSHh5OWloaaWlpJCYmUq1aNdzc3C7vNxqNpKenV5rkD8DV1ZVp06bZO4wSWbNmDZ06dbrq/0oqH1544QXZbVvOlXQE7G/AM4qidLhimwBQFOVpYChQ/I5/ye5q1qyBk9NZcnP3ExV1jvPnk8jNPUXTpkkEBQXYO7zimTkTgM4XixjfamF46XofffQRjRo14uTJk0ycONHe4ZSYEIKEhASio6M5c+YMx48f58KFC2RmZpKcnEx0dDQFBQX2DlO6yGKx8M4779CnTx/uuuuuG64aIUnSrSnCWt6keCdbZ/uuAO7BOr4vDGs3sDcQAqwGBgohKsxfp6IoYUB4eHg4YWFh9g7H7kwmE1OmfMqcOeGYTGGAO8HBOXz//UBatKiAP5/UVOtKHnXqwIkT9o6mwjpw4ADt27fHZDKxceNGevS42QI/5d/hw4cZPnw4ISEhNG3alLp16xIYGGid4W42k5SURJMmTeQs8HIgMzOTESNGsGrVqsvbZs2aVWnGnxbHokWLyMjIYIwsVF/VFWn8RklX+ChUFKU3MBwYjHXZVAfgKPA2MF+UJLuU7E6j0fDGGxNo2XIDe/cewctLzYABDxMaWtfeoRXPvHlQWAhPP23vSCq0Vq1aMWnSJCZNmsQTTzzBsWPH8PT0tHdYxdK4cWO6d+/OmjVrKCwsJD09HX9/f5ydnTEajTg6OjJ48GB7h1nlHT9+nAEDBnDu3LnL25599lmGDx9ux6js68CBAwwdOhRHR0e6dOlCw4a3GoUlSf8pUctfZSRb/iq+5ORkfvrpJyZMmFDhx6OVZyaTiY4dO7J3715GjRrFL7/8Yu+Qii0pKYk333yTLVu24ODgQKN69ejRuTMKcP9DD1Grgo73O3jwIJ6enkVeWaK8+fvvvxk+fDh6vR4AnU7H999/X6Vb/C4ZNWoUc+bMoX379uzYsQO1Wm3vkCT7KFLLX8WueilJN/DWW2/xzjvv8Pzzz9s7lEpNo9Ewf/587r77bl5++WV7h1Mi/v7+TJ8+nRkzZtCyRQua1arFw/fcw8DOnfGooC2aCQkJdOvWjfr16/P333/bO5xiO3LkCA8++ODlxK969ers3LlTJn4XffXVVwQHB/Pvv//y5Zdf2jscqYIoccufoij3AU8BdQAvrs8+hRCiwvQRypa/im3//v20bduWsYrCZ3Xq4HBxlRGLEKiKUNIivEsX6n39dYUoYWNv15ZGqeg2b97MgunTeWP0aKoHBhLv5ETdCtadZjQaadWq1eVyGwsWLKjQ3aMtW7bk0KFD9OjRg99//12WArnGmjVr6NOnDw4ODhw8ePC/slZSVVJ2Y/4URXkVmAokAXu5dc0/SSpVFouFxx9/HCEEjf38cDj7X5WhojZxLz5+nMMJCSxatEgWfr6NypT4AYSFhZGh1xMfG0ttPz8KUlKggiV/zz777OXErzKMi9uzZw8///wzY8aMQaMpaYWyyuf+++/nySef5JdffuGJJ55g165d8uck3VJJfzvGA5uAPkIIow3ikaRimzNnDicuzuCt2bo1nD+PobCQyMhILBYLgQEBd7RMV0FBAXkJCSxbtozhw4fz22+/yRfSKsTHx4dqwcGcj46mXYMGaHJyMBgMFaYVeN68eZfHX7Zs2ZLp06fbOaKS02q1chjHbXz55ZesX7+effv28e233/LSSy/ZOySpHCvpO5oXsFgmfpK95eTkMH78eMBaiLjH0qXoTSbatWtHuMXCI488wm+//QZ30ErlCDy8fz8/9ejBokWL0Ol0zJ07Vw6kriLUajV3tW7N2e3byU5Px1ejISs9Hb/AQHuHdlvHjh3jqaeeAqwreaxZs0Z+cKkiPDw8mDVrFgsXLizyGsBS1VPSCR97gQa2CESSSuLVV18lNzcXsLZ86HQ6xo4dS3h4OPXr1+enn34qUvdk69atWbt2La6urvz666+MGTMGi8VSWuFL5Uzbtm2JSk4mJTERN0UhPTra3iHdltFopGfPnphMJlQqFWvWrMHPz8/eYd2RrKws1q1bZ+8wKrxevXrxyy+/VNiyS1LZKWny9zwwSFGUR20RjCQVl0qlQlEUevbsyb333svs2bOZPXs2Tk5OLF68uFhLQHXo0IFVq1bh7OzML7/8wtixY5Glke5MXl5ehX4zr1evHga1mrgLF1AMBgqio8v9/71Wq+XZZ59Fo9Ewffp02rVrZ++Q7sjJkydp3bo1999/P7NmzbJ3OJJUJZR0hY+jWFfzCARygTjg2tU8hBCiebFvUsYqxWzf7Gz49FNYsgSiosDJCerVgxdfhBEj7B1dqYmMjMTFxYWkpCTatWtHQUEBs2fPLnEXyD///EO/fv0IDQ1lx44deHh42CbgSio3N5eWLVsSFRXFvn37aN68wvz5XyaEYMK4cdTNyeHxbt1INZnwf+QRnF1d7R3abSUlJeHv72/vMO7I8uXLGTZs2OUyLg0bNiQiIgKVSlYhk6QiKtM6f+nAGWAbcBBIBtKueaSX8B5SUcTHQ+vWMGMG3H8/fPstTJ4MbdpABei6Kok6derg7+9PVFQUarWaUaNG2WTsS48ePVi7di2bN2+Wid8dcHV15d5778VoNPLYY49hMBjsHVKRKYpCmw4dOBsfT1ZaGj4mE6lxcfYO645UhMTPYrEwefJkBg4ceDnxa9asGatXr5aJnySVAbnCxzUqfMtfjx5w/Djs3QvVq9s7Grs5deoU1atXx9nZ2d6hVEl5eXm0aNGCM2fO8Oqrr/Lpp5/aO6QiO336NG+PHMlrXbvSvHp1TtSqRbO+fe0dVoWXlZXFiBEjWLly5eVtjz/+OD/88INckUeSik+u8FFl7dwJmzbB669bEz+zGS5OgqhqGjRoIBM/O3JxcWHevHmoVCo+//xztm3bZu+QiqxGjRqovLyISUxEFBRAbCxm87WjWqSiujLxU6vVfPfdd8yZM0cmfqXo7NmzHD9+3N5hSOVIkZM/RVFmKIrS+op/axVFGaooynUl1xVFuVdRlE0lDVK6Q6tWWb/WrQsPPWQd6+fmBkFB8OGH1mRQkspI+/btmThxIkIIRo4cSXZ2tr1DKhJHR0fq33UX5xIT0efk4JOaSm5mpr3DqvAmTJiARqPBz8+PrVu38vzzz1e6QuHlyebNm2nWrBnDhw/HaJRV2SSr4rT8PQvUv+Lf7sDvQNMbHOsPdC3GPaTiuFjgmKeegrg4mDkT5s2DmjXhnXfguefsG18lJUvA3NykSZNo0aIFUVFR/O9//7N3OEXWoVMnzqamkp6ejld+PmlRUfYOCYBvvvmGgQMHVsjfvW7durFlyxYOHz5Mx44d7R1OpdemTRv8/f05fPgwH330kb3DkcoJW3X7yo9t5UFOjvWriwts2waPPw6PPQZbt1pbA2fOhFOn7BujDWzatImGDRty4MABe4dCXl4ePXv25KeffrJ3KOWSTqdj/vz5+Pn50aVLF3uHU2TNmjcnFbiQlobOZEJ/6QOWHe3evZv//e9/l2fKVkQdO3YksAIUza4MXF1dmT17NgAffvghhw8ftm9AUrkgx/xVJpfGzDz6KFy5FJVOB8OHgxCwebN9YrMRs9nMiBEjOHXqFA888ABHjhyxa/21VatWsWnTJp599lnmzp1rtzjKs7CwMKKiohg5cqS9QykyPz8/3GrWJColBVNBAdpTpzAWFtotnuTkZHr37o0QAp1Ox5QpU+wWi1RxdOvWjbFjx2IymRg5ciSFdvwdlsoHmfxVJpdm997oE/WlbekVu/LO1KlTSUhIAOB///sfrVq14vHHH7db99fQoUP55JNPEELw5JNPsnDhQrvEUd5V1MH8Wq2WZu3bczY1lZycHLxSUshKSbFLLCaTiZ49e14eO/nHH38QGhpql1huZ926dWTK8ZHlytSpU6lTpw5Hjx6VHxokmfxVKu3bW7/Gxl6/79K2ClAD7GbS09N5//33AWjXrh1ffPEFZrOZ4OBgu9YGe+2113j//fexWCyMGDGCJUuW2C0Wyfbad+zIeb2e1IwMPPR60u3U9fv8889z9OhRwDpp4oEHHrBLHLdyqX5f7969adSoESaTyd4hSRe5uLhc7v796KOPOHjwoJ0jkuypuO+YjyuKMk1RlGnAx4AAxl7adsW+x2wWqXR7AweCpyfMn//f+D+wlnuZOxe0WujVy27hldSl7gqVSoVOp+PChQt07tyZDz/80N6h8c477/DWW29hNpt55JFHWLFihb1DkmykYcOG5Lq5EZ+RgaqwEP2hQ2U+1ODXX3/l559/BqzLDpbHuonZ2dn079+f9957D4DExES2b99u56ikK3Xp0oVx48bRoEED1Gq1vcOR7EkIUaQHYCniw1zUe9jzAYQBIjw8XFRIc+cKAUI0aCDEp58K8dlnQjRqZN02ZUqZhoL1Q8ENH8eOHSvStfbu3Xv53I4dOwpA+Pn5ifj4+FKKvugsFouYMGGCAIROpxMnTpywd0iSDZjNZvH844+LH5s1E7kDB4ozTz4p8vPyyuz+FotF1K9fXwDC19dXZGRklNm979TJkydF7dq1L/+NOjk5ib///tveYUk3oNfrRUFBgb3DkGyvSLmOphjJouwqLs8efxyqVYOpU+G998BigaZN4fff4ZFHyjyczp07M2bMmOu2Vy/C6iNCCIYOHQqAu7s7u3btQlEUfvvtN4KCgmwWa0kpisJnn31GYWEhbm5uNGjQwN4hlWtCCE6dOkXDhg3tHcotqVQqWnXqxMmtW8nKycHLYCApIoKAJk3QOTqWeo06RVE4dOgQgwcP5pNPPsHT07NU71dUK1euZOjQoeTn5wMQGhrKypUr5e9/OVVRx99KtiWXd7tGhV/erRxRFIWRI0cyZ86cEl/r/fff56OPPsLBwYHs7Gzee+89Jk2aVPIgS8GlvylZuPbmTCYTjzzyCCtXruTAgQPl/m/t0IED/PDQQzyk1eJjsaDx8EAVEIDp7rsJHjYMv7p17R2iXURERNCkSZPL/37ggQeYN28ebm5udoxKkqokubybVL4YjUZyrhyDWAyTJk0iNTWVIUOG0KtXL95++20bRWd7iqLIxO82NBoNnp6eGAwGHnvssXJdeiIhPp5N33xDB7MZj/x8gi0WGmZn0/jMGZr+8guWxx7j5IoVdi05ZC9hYWG0bt0aRVH45JNPWLp0qUz8JKkCkC1/16hKLX+l3UKlKAouLi4UFBRgNpvx8PCgX79+fPjhh9SqVavY1zUYDDhcWcdQqpBycnJo3rw558+f56233ioXE3eulZ2dzdRnn8Vz61aaq1Q00GoJdnZG4+yMotFAYSEiL49MBwdyvvuOGlVwxQqTycSBAwdo166dvUORpKqsSG/kMvm7RoVL/r77DmbMKNapubm5zADazJlD9+7dbRsX1mWFHnroIerXr4/BYGDHjh38/PPPuLm5sXPnznI/1ksqfdu3b6dr164oisLOnTtpf6lcUTkxf/ZsNr/7Lm0sFpqo1TR0ccHXywvFywscHcFggNRURFoaZ5s2JWTePJxcXOwdtiQVWX5+Poqi4OjoaO9QpOKRyV9JVJTkb9GiRfz444985uJCi+XLi32dycB7QO/evfnkk09o1qyZrUK8oTVr1vy/vfsOj7LMHj7+vWcy6ZBOCb1D6CLgCrqYBEVELGBZurjICqiIC8JPEQUE3FUWVkBXBCkWBBVFwIJ0IYB0CJ0QSgIppPcp9/tHgBcRMMnMZFLO57rmwszMc58zCOHkucuhZ8+ePPDAA/z4449OjVUW7dq1C19fX8LCwlydSpnx6quv8q9//YvGjRuzf/9+fMpI8ZSfn8+QJ5+k6u7dtAfC3N0Jq1qVoOBgVEhIYRvF/HxITERfukRaXh6ZS5ZQ144CNj8/n8uXL5epjUyi4tu9ezcDBgzgkUce4Z133nF1OqJkZM1fZbBv3z7Wr1/PRYsFwsJK9LA1b85dvXpRpUoVfvzxR9q1a8egQYM4e/as0/J+8MEH6dy5M+vXrycvL89pccqi/fv3ExkZSUREBCdOnHB1OmXG5MmTad26NadOnWLs2LGuTuealJQUzp87RwGQB+QCuVqjbTa01QpWa2HLxCvLJlRBAVnR0XbF7N+/P3Xr1mXBggV25+8omZmZcm5lBWexWDh58iTvvvsuO3bscHU6ohQ4rPhTStVUSrVVSpWNH9sruDNnzgCQ9OSTEB1doofh6FF6fP89MTExjB49Gjc3N5YuXUrTpk355z//6bSWaQ0aNMBisZByi1ZzFoulQnYGaNasGR07duTSpUuEh4cTExPj6pTKBA8PD5YuXYq7uzsWi8VlrfpuZDabyS0oIF1r0oE0m41ks5ms3FzIyECnpUF6Ojo7G6vFQqbNhjU7u8Tx3n//fb7++musVis//PCDwz6HPU6cOEHr1q3p3bs3H3zwgavTEU5y11138corr2Cz2RgyZMi1Y3tExWV38aeUekQpdQy4AOwFOl95PlgptU8p9ai9McQfxcbGAoWFlL2Cg4P5z3/+w/Hjx+nfvz8FBQXExcU5rWXaiRMnMJlMBAUF3fT14cOHU6VKFaZNm+aU+K7i5eXFqlWr6Nq1K3FxcYSHh3Pu3DlXp1UmtG3blmPHjvHRRx+5tFXf9Xx8fHD39SVZKZKABJuNuPx8zmVmkpqSgjkxEUtSEvkZGVzOyyNeKXQJ2yfu2rWLl156CSj8O/3ZZ5858JOUzOrVq2nbtu21mYA1a9a4OCPhTJMnT6Z58+YcP368zB6jJRzHru+ySqmHgW+AZAqXjl2bc9ZaJwNxwDP2xBA3d/XOnyOKv6saNGjAp59+yt69e5k+fbpdY12+fPmmz3/xxRfs3buXHj163HTH7pkzZ1i4cCF5eXm88cYbHHVRH1Vn8fHxYc2aNXTu3JmzZ88SHh5OXFycq9MqExz5Z9kR/P39ad22LQnABa25YLNxzmzmdE4Ox9PTOZWSwpnUVGKysjhtsRBbtSqBHToUO87ly5fp3r07Wms8PT3ZsmWLS3ez22w23njjDR5++OFrSzNGjRrFt99+67KchPN5enqyePFiDAYD7733Htu3b3d1SsKJit3h4wZvAFu01vcppYIo3D9wvShguJ0xxA1ycnJISEjAZDI5ZWF4+/bt7R5j6tSpbNu2jfDwcOrWrUtBQQHbtm3j66+/pmbNmsyaNeum1z3yyCPX/rtv374Vckdw1apV+fHHH4mMjGTPnj1ERESwefNmqpfwrpFwDqPRyMCBA9myeTNnsrJw0xqrzUaO2UyqxYKvUpi0xqI1qUrh1bMnNYtZwFqtViIiIsjIyABg1apV1K5d2xkfp0gyMzPp27cvP//8MwAmk4lFixbRr18/l+UkSk+nTp0YN24cM2bMYMiQIezfvx9vb29XpyWcwN75lVbA8tu8ngBUszOGuMHVKd+6deu6pDm32Wz+00XB9913H0FBQXz22We88sorjB8/nkOHDvHyyy+zf/9+GjZs+IdrvvrqKw4dOgRAaGgo8+fPr7CHJfv7+/PTTz/Rpk0bLl68KNO/ZdTdd9/Nc8OHk+XuznGlOKo1R7Um2mYj2mrlkNbsA+LuvJPI0aOL/fdx1KhRHDhwACg8yLx79+5O+BRF99hjj10r/GrWrMmePXuk8Ktk3nzzTcLCwoiPj2ffvn2uTkc4ib13/nKA223waAjcfP5PlJgj1/uVxPz58xk5ciSPPPII06ZNu+mxJb1796Z3795FHtNmszFo0CCg8HDon376qcJ3CggKCmLdunXExcU55G6rcDyj0cioUaMICAjg/fff51hCAhcBf8ALMHl70+b++xk2YQLVa9Ys9vgNGjRAKUW3bt148803HZt8Cbz11lts3ryZe+65h2+++abM9REWzufh4cGyZcvw9fUtc0sxhOPYW/xtBAYrpWbd+IJSqgYwDFhtZwxxg7/+9a/s2rXLZXfF8vPz8fb25rvvvuP777/nmWee4a233qJWrVolHrN///7Xdpj9+9///l2/0IqsWrVqVKsmN8dvJzMz06U/CLi7uzN48GAOHjzExx/PJ93NRGiHjjzQ60G6dO1K6zZtcHd3L9HY48aN46GHHqJ+/fpl4i53ly5dOHr0KA0bNiwzG29E6WvdurWrUxBOZtchz0qpZsAOIBZYAUwB3gXMFK71U8CdWutYexMtLeXlkGdXu3TpEpMnT+ajjz7CarXi6enJmDFjSrQoXGt9bWNHkyZN5Aw8ART+uZg1axZTpkwhKiqKZs2auSSPrKxsDh48z6uv/pOoqJ/wNJj439yP6D9sgEvyEUKImyjWT4923fnTWh9XSnUFZlNY+Cng6imtm4CR5anwE0VXo0YN5s2bx+jRo3nttdc4e/Ysbm5uHDlypMRjenh4sGfPHgdmKcozpRT79u0jNTWVQYMGsW3bNtzc7J2sKDqtNdu27eXdd3dx6FA14uKOoxS44UPUb/5E9k6hevXAUstHCCEcxe7vpFrraCBSKRUANKZwE0mM1jrJ3rFLSin1GjAViNZaV475Qxdp2rQpK1asICcnh08++cSutmUjRoyo8Ov8ikNrjcViwWQyuToVl/nvf//Lxo0b2bVrF9OnT2fixImlFjsqag8vvriSxMQ7gABstjS01hjdQvlhXUdSsy8xd64Jf//y9Wf2xx9/JCwsjLp167o6FSGEi9g77RumtS75rR4nUErVBo4DGogtbvEn076iLNBaM378eA4fPsw333zj0nPfXG39+vVERkbi5ubGjh076FCCs/SKKzs7m/79/8nu3XUxGDoDWcTHPwVYCA14nGp1PyUtXTN2bAzDh5eP44i01rz++utMmzaN4OBg4uPjK/UPFkJUMKXa2/ewUuqgUur/lFKN7RzLUd6lcB3iblcnIkRJXbx4kYULF7J27VqeeuopzGazq1NymYiICF566SUsFgsDBgwoVuupixcvsnnzZg4ePEhWVlaRr4uOjubQoQuACaPRiMmkUUqhMBJSsxP+AW6YTCZWr7aSl5dfpDwGDx7ssraFWVlZREZGXuuak5ub69Qe3qLiuXz5MgMGDGDTpk2uTkU4gL3F3/NAEjAZOK6U2qOUGquUqmd/asWnlLoX6AuMdkV8IRwlNDSUdevWERAQwHfffUf//v0rZL/jopo+fTotWrTg2LFjTJgwoUjXWCwWli5dio+PD61ateLChQtFjnfu3Dny83PQOhfIwde3DY0aTaFpsxep36Q/BgMYDJqLF41kZOTddiyz2UzXrl1ZsmQJ7dq1K3IOjnLq1CmaNWvGhg0bgMIe08ePH6dx47Ly87ooD+bPn89nn33G0KFDi/WDlCib7Cr+tNb/01pHALWAl4BsYAYQo5SKUkq9pJRyfAuKm1BKGYH3gY+11oeKeE01pVTL6x9AI6cmKkQRtWvXjp9++omqVauyYsUKhgwZgtVqdXVaLuHl5cXSpUtxc3Njy5Yt19qOZWZmcvny5d/9vmRkZLBjxw7Wrl1Lu3btOHPmDNHR0cX6B8vd3R2lMoDLWK1JmM3pBAUNp27d6WRnVycjAwoKsnFzu4yb2+2/jfbr14+YmBgAXnzxxeJ/eDusWrWKli1bEh8fD8DTTz/NgQMH7DqWSVROY8aMufb3afz48a5OR9jJrjV/Nx1QqVrAE8CTQCdAa62dvrBEKTUSeBtoorVOUkptAoJvt+ZPKfUmMOlmr5XVNX9z5sxh1qxZvPTSS7zwwguuTkeUgu3bt3P//feTnZ3N0KFDmT9/fqU9g23jxo106dIFNzc3vvzyS6KioggICKBOnTp07NiRY8eOkZGRgVKKgoICAgMDiYqKolatWgwePLjILfSOHTvG44//jfT0hijVHpOpBZ6ejfDwqA54kpubT17eJf761zMsWNDrlmvn5s6dy6hRo4DCIvCzzz5z1G/Fnzp8+PC189oMBgPvv/8+I0aMKLX4ouI5cOAAd955JxaLhfXr1xMeHu7qlMT/V3pHvdzCRSAaOEph+7fbdQBxiCt9hScDU4q5y3gehecTXq8R8J2jcnO0EydOcPr0aQoKClydiigld999N2vXrqVHjx4sXLiQNm3a8NJLL7k6LZe47777ANi7dy+LFi2lRYtmNG3alLy8PJYvX47RaKRWrVp07dqVpk2bYjKZePLJJwvX6xXjEOXGjRtz//3d+OqrDYA3ZrPGas0iNzcY8MRmK8DDI4F+/VrfsvDbu3fvtTt9TZs2ZfHixfZ+/GJp1aoVf/nLXzhy5Ajr1q2jY8eOpRpfVDxt27Zl4sSJTJo0iWeffZaDBw/KCQ3llEOKP1X4XbUb8BTwGBAMpALLgC8dEeNPTAVSKJz2LTKtdSKQeP1zZeGU/ds5c+YM4LrWbsI17r33XlatWsXs2bP5+9//7up0XCY7O5u9e4+wfPlmDIaWZGZayMjIwGq1YjKZGDVqFMHBwb+7piR3Sd3c3Bg79p+cP3+OXbsOYrNlYrUmYLUGopQ7np55jBjRisjItje9Pj09nfDwcGw2G97e3mzdurVUzyi8auvWraSnpxMYKOcRCseYMGEC3377Lfv27WPcuHF88MEHrk5JlIC9R73cQ+H0bl+gGpABfEthwfeL1trpK9SVUk2AYxRu8vj+upeWAQHAA0CG1jqliOOV6aNeWrVqRXR0NHv27OGOO+5wdTpCOJzVasVgMFz7QcxisRATc4Gffopm69ZkEhIysFjiyM8/RpUqWdSpE4rBYKBLly4MGzbMobkkJyfz/vvv8/HHi7FafVDKj6ZNGzN27EAeeKDbLe/69evXjy+++AKlFFu2bKFr164OzUsIVzp06BAdOnTAbDZz6NChStOOs4wr1WnfzUAWhUXXl8CPWuvSno+sReHGlf9eedzoDIUdSEaXYk5OobUmNjYWkDt/ouJJT0/nk0+2sXOnH9Wre/Pkk94kJKTy3XcpREcbyM5OwmA4h49PGi1aeFK79h1s3ryOc+fO0bFjRx577DGH5xQcHEyfPn2YPXs2OTnxGI216N59KL16db/tdQsXLuTMmTP07t1bCj9R4bRu3ZqZM2dSp04dKfzKKXuLvyeANVrr25914FyHKZxqvtFUoAqFu5BPl2pGTpKcnEx2djZ+fn4EBAS4Oh0hHMZisTBp0mJWrWpPcHAnzp1z4+efU8nIyETrDNzd46lVK4uIiCAeffRRWrZsjslkIjV1FPn5+QQHBzvtwOK8vDzMZjM2mw2DAapW9f3Tazw9PYmKinJKPtfLysri66+/ZvDgwU6PJcT1rm5kEuWTvb19v3ZUInbkkEzhVPPvKKVGX3n9D6+VV7LeT1RU8fHxrP/lFO7uf6F6dSM+PpCdXRU3tzjuuOMiffo04f777yI4OPh363IDAwPZvn07PXv25KuvvqJRI8ee1JSTk8OePXuuHSVjMEB+fjYFBQW4u7s7NFZxnTx5kq5du5KYmEhqaiqjR492aT5CiPKjWMWfUuoNCtumva21tl35+s9orfWUEmUnfufqifxS/IkbJSUl8csvv/C3v/3N1akUS0FBAUcPn2TVksOYLv6FbN+zJCTUwt09gJycVP7+dzfGjRt520Jrzpw57N+/n8GDB7N582aMRqNDcsvIyGD69Ols27YNm80GgMGg2LFjO0FBiv79+7us7d5XX33F3/72t2sHfycnJ7skDyFE+VSsDR9KKRuFxZ+X1rrgytd/RmutHfPduBSU5Q0fWmsuX75MXl4etWvXdnU6oozIy8ujQ4cOHDlyhPnz55f53cBaay5dvMSuH3ezd/kFzuz1IFmZya12jlMp8RjcOqJUdYKCUvj003BatLj93byUlBRatWrFxYsXmTFjBq+++qpDcnz//fdZuXIlgYGB7Nu3j3PnzuPtXZt+/R7Hz8+N7t27ExkZaXes4ub18ssvM3v2bABMJhPLli3j8ccfL9U8hBBljvM2fGitDbf7WjiXUuoPx1gI4enpybBhw3j55Zd57rnncHd3Z9CgQS7LJysri+iNGzEfO4ZXvXo07t4dv4AAcnJyOPrbfg4viyJxXSJJSQZS62haD6/Lg0/+leCQIObPX8DOnTsJCanJc8/1+dPCDwqnfhcuXMiDDz7IxIkT6dGjB23b3vwIlqLKyMhg7dq1uLm54evrS4cOHTh//gJGI9SoEYzRaOG3337j3nvvBSiVKeCr/Xl37twJQI0aNfj1118dPtUthKj4Sv/gKSGEw40ePZr8/HzGjx/PM888g4eHB0899VSp55GWlsbKl17irm3baODujrZaOfb55yTe1Y3M1fsxH8nkkslEzl01uWfgfXQJ7/q7M+gmTnwNs9mM0Wgs1vl8PXr04B//+AcffvghAwcO5LfffrNrSjYjI4PLly9TtWpVbDYbbm5uKKUwmRS+vl4UFOSQkpLCunXr6NOnD++99x4jR44scbyiiIiIYNeuXUDhYddr1qzBy8vLqTGFKA6bzcbChQvp27cv/v7+rk5H3IZdd+6UUlalVL/bvP6UUqpyNiMVopS9+uqrvPXWW9hsNvr378/KlStLPYefV6zAb8sW6gUF4dGsGZ61atHi8GE8Z/6PS+mJZI28k97rJ/L6V//i4b69b3r4sMlkKtHBzO+++y6NGzfm0KFDvPFGUZYj39rVYi8vL4+cnBz8/Pxo1aolbdo0w2DQZGZmUlBQQN++fcnPz7/2++5Mc+bMwd3dnYkTJ7J+/Xop/ESZ889//pNhw4YxZswYV6ci/oS907Z/NsdspHCNoBCiFEycOJH/+7//w2q18tRTT7F69epSjX94717MgNXdHTw9wcMDm9FI2t3N6bdpKcOmTKBFq5ZOOZbFx8eHpUuXYjAY+Oijj0hJKdK57jcVFBRE8+bNycjIID09nYyMDO6++246dGjDhQvnSUhIICoqiry8PIxGI5s3b3Z6v+WOHTuSmprK5MmTy3wnIlE5Pffcc3h4ePDJJ5+wdu1aV6cjbsMR361uWtwppapS2F1DtqEJUUqUUkydOpUxY8ZgNps5f/58qcb3q12baIuF6ORkUk6fJjUujuN5eZjbtiEwKMjp8e+66y4WLlzI/v377Wpp5u7uzj/+8Q9MJhOXL18mISGB8+fPc+bMGc6dO8f58+fZv38/AJ988gktWrRw0Ce4PW9v71KJI0RJNG/enKlTpwIwbNgwUlNTXZyRuJVit3dTSk0CijqnooD/aq1HFzMvlynLu32FKCqtNVFRUdx9992lGvf06dP835Ah1I+NpYnBgMFmI7lBA55cuJD6jRuXai720lrzyy+/8N577xEfH4+7uzve3t54eXmxYcMGLBYLQ4cOZcGCBa5OVYgyw2q1cs899xAVFcXgwYNZtGiRq1OqLIo1HVCS4u9BoOeVQCOAdcCJG96mgWxgD/CN1tq5i2EcqKwWf3l5eZhMJoedYSaEs5w6dYplS5aQGBND3caNeXzgQBqW4x2paWlpREdHk5iYyLhx4zh16hQALVu25ODBgw6d7v3iiy+48847adKkicPGFKK0HT9+nHbt2pGXl8eqVat4+OGHXZ1SZeDc4u93Fyv1CfCh1npniQcpY8pq8TdlyhSmTJnC1KlTGTdunKvTEaLSmTlzJq+99tq1dX7JyckO29GotWb48OHMnz+fwMBA4uPjXXaAtBCOMHPmTF555RVq1qzJ4cOH7VqGIYqkWMWfXT+yaq2fqUiFX1l25swZzGYzfn5+rk5FiEonJiaGiRMnMmPGDAA6dOjgsMIvKyuLdu3aMX/+fAACAgLIy3Nlu3Qh7PfSSy9x7733MnDgQFmrWgY55Jw/pVRtoD3gx00KSq31EkfEqcykr69wpLy8PDw9PV2dRrkxbNgwWrZsyQsvvMDo0aOpUqWKQ8Y9ePAg9957L+np6QA88cQTfPHFF7K8Q5R7RqOR9evX4+YmxwmXRfae8+eplPoSiAW+AxYDi648PrnuIewkxZ9wlJUrV9KkSROOHj3q6lRKTXJyMoMGDbrWH7s45s+fz5YtW5g/f75D1/ctXLiQ9u3bk56ejsFg4P3332f58uVS+IkKQwq/ssve72TTgMeB14BuFM45DwbuB34ADgD29VkS147sUEpRt25dV6cjyjGtNQsWLODChQtERERw8uRJV6dUKsaPH8/SpUsZMmRIsQ5jjo+PZ+zYsbzyyit2t4y73m+//cazzz6LzWbD19eXnTt3MmrUKIeNL4QQt2Nv8dcX+ERr/Q4QfeW5OK31L1rrXkAa4NyeR5XAhQsXsNlshIaGyiJwYRelFMuXL+e+++7j4sWLhIeHX7urXJFNnz6datWqsWnTJmbPnl3k655//nmCg4OZNGmSQ/Pp2LEj99xzDy1atODChQvceeedDh1fCCFux97irxqw68p/51751ee617+m8M6gsINM+QpH8vb2ZtWqVXTp0oULFy4QHh5e6odBl7aQkBA+/vhjACZMmMCRI0f+9JpBgwaxatUqPvzwQ6e0Utu0aRPR0dGyiUsIUersLf4SgCAArXUOkAo0u+71qoCsKrdTamoqPj4+UvwJh/H19WXt2rV06tSJ2NhYwsPDiY+Pd3VaTvXwww/z7LPPkp+fz8CBAykoKLjleydOnMjSpUtxd3cnNDSUU6dOXXsA5ObmcurUKRISEkqcj8FgkDZtolI6ffq0q1Oo9Ow952854KW1fvjK14uAB4ExFBaW7wH7tNYP2J9q6Sir5/xprcnPz5cdmsKhUlNTiYiIYN++fURERPDLL7+4OiWnysjIoG3btsTGxvL6668zZcqUP7xnw4YNREREFGm8p556imXLljk6TSEqrLfffptJkybx5Zdf0qdPH1enU5GU3jl/wH+BGKXU1YVoEylc57eUwp2/6cCLdsYQFK7VksJPOFpAQADr1q3joYce4sMPP3R1Ok5XtWpVlixZglKK6dOn/2G9Y1JSEg899NC1937++eesWLHidw+A1q1bs2LFCl5++eVbxkpLS+Ptt9923ocRohzy9/fHarXy/PPPk5SU5Op0Ki277vzddEClDEBrwAoc01pbHBrAycrqnT8hhOPMmDGD1q1bXyv0AGw2G40bN+bMmTO4ublx7NgxGt2kLZ1S6k/vku7cuZPw8HBycnJ45513pCuPEFfYbDYiIyPZuHEjTzzxBMuXL3d1ShVFqd75+wOttU1rfUBrfbi8FX5CiMph/Pjxvyv8oHAK9+qdwM8///ymhV9RzJo1i7/85S/k5OTg5uYmxzMJcR2DwcCCBQvw8fFhxYoVUvy5SLHu/Cml7i1JEK31lpJc5wpy50+Iymf58uU89dRTQOHxLvPmzSv2GDabjT59+vDtt98CEBgYSFRUFE2bNnVkqkJUCB9++CHPP/88QUFBREdHU716dVenVN4V685fcYs/G1CceWIFaK11uTmyXoo/ISqfgoICunXrRm5uLnv37i32Ltzk5GQ6duxIbGwsAJ07d2bz5s1yLqcQt6C1pnv37qxfv57HH3+cr776Sna/26dYv3nF7b1yXzHfL4QoxywWC7Nnz2bkyJEVesORu7s7/fv3Z968ebRq1arY1586dera0TFjxozhvffec3SKQlQoSikWLFhAq1atSEpKIjs7G19fX1enVWkUq/jTWm92ViLi5i5duoTFYiE0NNShfUWFKIoXX3yRDz74gI0bN/LNN9/g7u7u6pScJikpqUiHP9+Ku7s7X375JY8++qjjkhKiAqtXrx5RUVGEhYXJv2+lzGFdl5VSNSns+HFKa53tqHEru5kzZ/Lvf/+bqVOn8tprr7k6HVHJPP/88yxfvpw1a9bw9NNP8+WXX2IymVydllOEhIQQFhZW4utHjBghhZ8QxVSSO+3CfnYXf0qpR4B3gCZXnuoObFBKBQPrgLe01t/aG6eyurqGqH79+i7NQ1ROrVu3Zt26dYSHh7Ny5UoGDhzIZ599htFYbpbxFtnIkSMZOVJakQshKj677rMqpR4GvgGSgbe4bsGh1joZiAOesSdGZSd9fYWrtW/fnp9++okqVarw5ZdfMnToUGw2m6vTEkIIUUL2TrK/AWzRWncF5t7k9SigvZ0xKjUp/kRZ0KlTJ3744Qd8fHxYsmQJw4cPL5cFoNlspkmTJsyaNcvVqQghhMvYW/y1Am53QmMChesARQlkZmZy+fJlPD09qVGjhqvTEZVcly5dWL16NV5eXuTk5JTL4q979+6cOnWKMWPGSGspIcowR3cfE79nb/GXA/jc5vWGwGU7Y1RaV+/61atXT84/EmVCt27d2LFjB0uWLMHNzWH7xUrFpEmT2Ly58MCCKVOmEBIS4uKMhBA3s3btWtq0aUN8fLyrU6mw7C3+NgKDlVJ/+FdAKVUDGAb8bGeMSuvqZg+Z8hVlSZs2bZyy4SM5OZmhQ4fStm1bgoKC8PT0pEGDBjz99NPs3bvXrrHXr1/P5MmTAYiMjJSd80KUUVpr5s6dy+HDhxk+fHiFvANosVj47LPP2LFjB2azGa01ly9f5tKlS6WWg73F32tAbeA3YDiF3T8eUEpNBQ5RuAHkLTtjVGotW7aUTiOiUkhLS+PYsWNERkYyadIk5s6dy4ABA9i2bRudO3fmp59+KtG4SUlJ9OrVC4AaNWrwww8/ODJtIYQDKaX46KOP8PPzY/Xq1SxdutTVKTlcdHQ0+/fvB7g2qxcYGEh6enqpLacpVnu3mw5Q2A5tNoXdP66fm9wEjNRaH7UrQCmT9m5ClC3x8fHUrVuXe++9lw0bNhTrWq01DRs2JDY2FpPJxKlTp6hbt66TMhVCOMrixYsZMmQIfn5+REdHU6tWLVen5DDffPMNn3/+Ob169aJLly7UrFkTq9XK0aNHad++fUnbQjq1vdsfaK2jgUilVADQmMK7iTFa6yQApZTSFfG+rRDiDzIzM6lSpYpDx6xevTpeXl6kpaUV+9rdu3dz9uxZAFasWCGFnxDlxKBBg1ixYgVr1qzhueeeY/Xq1RVm7XutWrWIiYlh69atZGVlUadOHQCycnLo1KlTqeTgsH4qWutUrfVvWuudWuskpZS7Uuo54LijYgghyq7jx4/TokUL5s2bZ9c4ZrOZ5ORkLl26xK5du+jXrx9ZWVnXpm6Lo2PHjuzcuZNp06bxyCOP2JWXEKL0XJ3+9ff3Z+3atSxatMjVKTlM+/btCQ8PJyoqiu+++44VK1aw8uuv8bFYyMvNLZUcSjTtq5RyB3oDjYBUYLXWOv7Ka97AKGA0UAM4rbVucouhyhyZ9hWiZBYuXMizzz4LwMcff3ztv4tr06ZN3Hfffde+9vPz4x//+AdTp04tdzuMhRD2Wbp0KYMGDaJx48YcPXq0wnwPyMnJYdWqVezevZuMjAx2bt3KkJ49ubt7d+7s3r0km+qcO+2rlAqlcD1fo+uC5SqlegMFwOdALWAX8AKFHUCEEBXc0KFDSU9PZ8yYMQwbNgx3d3cGDhxY7HHatm3LunXryM/P58SJEyxdupTMzEzy8/MrzDd+IUTRDBgwgISEBAYPHlyh/v57e3vz9NNP8/TTT1NQUMD48eP5bsMGgoxGvKpUoU2XLk6NX+w7f0qpT4ABwLvAVqABhZ0+soBgIBqYoLXe7NhUS4fc+RPCPjNmzGDChAkYDAY+//xznnrqKbvGS01NpW3btrRs2VJ26gohKqS0tDSeGTwYw6VLDOzWjfbPPku9pk2LM0Sx7vyVZM1fd+ATrfUErfVarfVcYASFReAWoGt5LfyEEPYbP348b775Jjabjf79+7Ny5Uq7xgsICKB37978+OOP186+FEKIisTf35/3/vMfLtps/LRtG8eXLCEtOdlp8UpS/FUHdtzw3NWvF2qty1/PpzLoyJEj/Prrr6SkpLg6FSGK7Y033mD8+PFYrVYGDhxodyu13CuLoFNTU2/5nl27dpGfn29XHCGEcJWGDRvyzsyZRF26xLbNmzmxeDGZqalcvniRjNRUhx54XZLizwjk3fDc1a/T7UtHXDVnzhzuuecePv30U1enIkSxKaWYNm0ar776KsuWLStSK7WEhISbPh8bG8u3336Ln58fLVq0uOl7Tp8+TZcuXQgNDSUmJsau3IUQwlW6du3K6NdfZ01sLLu+/po9/fuT9cgj5Pbpw4m33nLY3cCSrp6sr5S647qv/a782kQplXbjm7XW9vVmqoSu9vWV1m6ivFJKMWPGjCK/f/r06axbt46ePXtSv359lFIcPXqUJUuWkJWVxeLFi/H09PzDdWazmbvuuguLxUJeXh4BAQGO/BhCCFFqlFL069ePn7//nq+3bqVatWrcHRCAydubauvWcf7CBTxmz8bLx8euOCUt/qZcedzoxgO+FIUt3xzfCLSCu1r81a9f37WJCFFKevXqRVxcHF999RWJiYlYLBZq1qxJr169eOmll255+GlERATJyckopVi3bp0Uf0JUAufOneN///sfU6ZMwWBw2JHFZUJSUhLmmBh8zGYMeXkYfX0hKAjl5kbNXbuI2bqVZj162BWjJMXfM3ZFFH/KZrNdW9gud/5EZREZGUlkZGSxrnn99dfZunUrULjL+O6773ZGakKIMsRqtRIeHs7p06epXbs2zz//vKtTcqjY2FiSUlMJ9/EhUCky09KoCpCbS35+PnmHD0NpF39a68V2RRR/6tKlS+Tn5xMcHIyvr6+r0xGiTFq3bh1vv/02AD169GDcuHEuzkgIURqMRiPTp0/nySefZOzYsfTo0aNC3Sjx8fEhxWjkhNmMf14eOimJOunpuFutJOfnY3XA7EbFuldaQch6P1EZLFiwgGPHjpXo2qSkJB5++GEAQkNDWbNmjSNTE0KUcU888QRPPPEE2dnZPPvss9hsFeegkaZNm9K4TRsOAjttNn7Ny2NjRgYbc3LYHhRE6HUdkEqq4hyXXYFcnfKV9X6iovruu+/47LPPSE9Pp0+fPsW+XmuN0WjEaDSya9euCrfmRwjx5+bOncumTZvYuHEjH3zwASNHjnR1Sg7h7e3Nm2+9xegXXuDQyZOcBwKVomq1avR/4w1qO+DGkBR/ZVBQUBAPPfSQrF8SFdYDDzxA9+7d+de//sWRI0dKPM64ceOoVauWAzMTQpQXISEhfPDBB/Tt25dx48bx4IMP0rBhQ1en5RCtW7emZ4cOhJ46xdE2baj3+OPcFx5Oo0aNHDK+FH9lUI8ePehh52JOIcqyq0e2hISEEBYWVuJx6tat66iUhBDlUJ8+fXj66adZtmwZf//731m/fj1KFavTWZmUk5NDym+/0clkot6wYXR5/HGHji/FnxDCZUaOHFlhpmqEEK4xZ84cLly4wOTJkytE4Qdw+uRJ/C9cIDsggHYdOzp8fCn+hBBCCFFuBQUFXTvyqaLYs3UroUB2s2aE1Kzp8PFllbQQQgghRBlhsVg4vmkTVTw88O3aFTc3x9+nk+JPCCGEEKKMuHjxIraTJ7F4e1Ova1enxJDiTwghhBCijDi4ezfVzGayQkOp26SJU2KU++JPKdVRKTVHKRWtlMpWSp1TSi1XSjV1dW5CCCGEEEWltWb3hg0EeHjgfscd+Pj4OCVOuS/+gFeBPsB64CXgI+BeYK9SqpUrEyuJTZs2sXTp0msHPQshhBCi+MxmM1FRUa5Oo1gyMjJIPHAAo4cH9bp0cVqcilD8zQTqaa1f1Fp/rLWeCtxD4U7m8a5NrfgWLFjAoEGD2LBhg6tTEUIIIcql7OxsOnXqRHh4OMePH3d1OkV27MgRvDMyyPP3p0m7dk6LU+6LP631dq11wQ3PnQSigRauyarkpK+vEEIIYR8fHx/atm1LXl4ezzzzDFar1dUpFcmOLVsIdHfH0KQJgcHBTotT7ou/m1GFpzxWB5JdnUtxmM1mLl26RPPmzfH3969QjaqFEEKI0jRr1ixCQ0OJioriP//5j6vT+VMFBQUc37EDTy8vQjt3xmg0Oi1WhSz+gP5ALeDL271JKVVNKdXy+gfgmMZ5xZSbm8vixYuZOXMm27dvp0mTJpw+fVoKQCGEEKIE/P39mT9/PgCvv/46R48edXFGt3fu3DkKLl7E6uVF806dnBqrwhV/SqnmwFwgClj8J28fARy+4fGdUxO8hfXr1xMTc4YGDRrg7++Pj48PtWrVIjm5XN28FEIIIcqMnj178swzz5Cfn8+QIUOwWCyuTumWftuxg6pGI6pmTWrXq+fUWBWq+FNK1QDWAOlAX631n03yzwNa3fB4pCSx09Iy2L07hl9/Pc3Zs8l/ur7AYrGQnJzC3r3R/PBDFFlZVejUKYLExERycnKuvS8zM7Mk6QghhBACmDlzJrVq1WLXrl289957rk7npmw2G7t//RUfHx9C2rXD09PTqfEqTG9fpZQf8APgD9yjtY7/s2u01olA4g3jFCuu1Wrl22+3MHduMnFxrYAaVKlio0ePE4wZU4vAwKrX3peWlsHZs8mcOpVBaqoRkymQwMB6+PhoAgLSiI+P5siRfWitCQ0NJSUlhRo1ahQrHyGEEEL8f/7+/nz88cc89thjTi+qSiolJYVLp07R0MuL5h07Oj1ehSj+lFKewPdAUyBSa32ktGKvWrWeCRMOYTb/lapVG1C1qgdaw8qVAVy+fIYhQ5I5ejSRmBgjmZnBeHrWwMcnFDe3DPLyznPkyBas1guEhnrz44/fk5+fy7lzZwkJCaFOnTp0LIU/BEIIIURF1qNHD86cOVNmb6gcPHAAo8WCzd+fJs2bOz1euS/+lFJGCjd2/AV4RGtdaic6ZmRkMG/eN+TmtsPLywd/fxOhoYV3Dg0GWLeuJocPZxAY2AZPz2zgIlbrXjw8zlCrFrRr15D27TtSr97jeHl50b9/X7755hsuXbpE69at6dmzJx4eHqX1cYQQQogKqywWfomJiezdu5d1P/2ET0AAAQ0b4ufv7/S45b74A94DelN45y9QKTXg+he11p86K/DZs2eJjY0DmqFUFmBBawMGQ2HxBwYSE49iMBykUaNs7ryzHp06taBJk0iqVKmCwfD7JZehoaGMGjUKrXWxp5+FEEIIUX78+uuvfPzxxyilCAoKIqB5czK15uTJkzRr1sypsStC8dfuyq8PX3ncyGnFX0FBATZbChbLWTIz6+LhEYzZ7EVubiYWS3Ws1ky6d8/grbf6ERQUWOQze6TwE0IIISquS5cu8e6772Iymahfvz6NGjXCx8eHhIQEVq5cyahRo/D19XVa/HK/21dr3U1rrW71cGbs2rVrExysyMuLpqAgk+TkA5w48R7nz+8kKWk7Vushunb1o1q1EKce1iiEEEKI8mP79u0kJCRgMBjw8vIiMDCQGjVqEBgYSFpaGidOnHBq/HJf/LlStWrV6Nv3Edzc3PDwsGGzHcNqvYCnpxmD4RQtWhzmgQfauDpNIYQQQtxCbm5uqcdMSUkhNzeX3NxcMjIySEpKIiEhgfT0dHJycn535JszSPFnB6UUjz32GAEBIXh6FmAwHMZksuLhkUfLlkm8884DBAcHujpNIYQQQtwgKyuL4cOH07VrV8xmc6nGbt68OWazmbS0NOLi4jh69CgHDhwgJiaGrKwsatas6dT4UvzZKT7+IjabCU/PXDp29MfT04SnZx7Dh3ejXbvWrk5PCCGEEDehtebnn39m7969zJgxo1Rj33nnnbRt247ExETi4uI4ffo0J0+e5OzZs7Rt25b69es7Nb4Uf3Y6fvwkbm5GfHxyaNmyDh4eRry9c6lZs3h3/JYtW8aUKVOIjo52UqZCCCGEuKpKlSosXLgQgMmTJ3PgwIFSi20ymTCZfPDxqUVycgbp6QUUFFjo0aMHw4YNc/o+ASn+7KC15tixU3h5QdWqWWRmpuDpWVj8BQUVb5fOl19+yRtvvCHFnxBCCFFK7rvvPkaMGIHFYmHIkCEUFBSUStz09HTOn08lMLAZ3t61qV69DVOnTmHEiBF4e3s7Pb4UfyWktebQoUPExZ2lceMs6tZNJicnhaZN3QkIyCMgoEqxxjtz5gwADRo0cEa6QgghhLiJd955hwYNGrB//36mTZtWKjFPnjxFbq4HWVk5eHoG4ePjS6NGjUolNkjxV2IbNmxg8uTJGAyamjWDqV27JvXr1yUsrBoNG8KFCxeKPJbWWoo/IYQQwgV8fX2vTf++/fbb7Nu3z+kx9+7dj8EQRFZWFh4ewdSuHUSVKs471+9GUvyVQFxcHLNnz6agoAAfHx+CgoKoU6cODRs2pE6dOvj5+bJ69fekpaUVabzU1FQyMjLw9fUlKCjIuckLIYQQ4ne6devGCy+8gMViYdmyZU6NZbPZ2LPnGAaDH/n5ZgwGP8LC6v2h65czSfFXAj///DOJiYkYDAbc3d2vFW3Vq1cnJCSEqlWrkpGRwcGDB4s0XmxsLAD169eX7h5CCCGEC0yfPp0VK1Y4fedvZmYmJ0+mYrEYAA8MBi9atWrs1Jg3kuKvmLTWHD58GKvVitVqxWKxYLFYMJvNWCwWbDYbWmv0lf58RSFTvkIIIYRr+fj40LdvX6ffhImJOUN6uhfZ2Tm4uwfj6+tOgwZ1nBrzRhWht2+ps9lsWHJzccvNJSc7m+TkZAxa4+fvT4HFQlpaGjk5OVgsliKNJ8WfEEIIUTns3XsQm60aOTkXqVIllNq1q+DnV7VUc5Dir5iUUjRt2JDTBgP1gJTUVE7Ex5NhMOAbEAC1apGdl0dmZib16tUr0pgRERH8+9//5o477nBu8kIIIYRwGa01u3Ydw2CoSX5+DH5+gbRuXb1U1/uBFH/FlpGRwakdO2hetSpYLFxOTCTAaCQgIICkhAQyLRasBgMBAQF07NixSGO2b9+e9u3bOzlzIYQQQrhSdnY2R46kYbHUQWt3DAYv2rWrX+p5yJq/YrBarXzx/vu4HTtGVSAhNRXP3FwaenpCfj4X8/JISksjLy+PIUOGyM5dIYQQQlxz9uw5kpKqkJubjZtbCL6+Rho3Lt31fiDFX7Fs/vFHTn31FTWMRrLz8sgtKKCljw8+wMW8PLJMJsLCwnj77bfp0qWLq9MVQgghhB201ixevJi3337bIePt23eYgoJQcnNTcHMLpW5dEwEBfg4Zuzhk2reIzsXE8OOMGdSzWimw2Yi3WKivNdUNBpK0pt8bb9C2c2eCg4Nxc5PfViGEEKK8O3LkCEOHDgUgMjKSzp07l3gsrTU7dhwHmlBQcBofnxDatfNxeh/fm5E7f0WQnZ3Np5MmUS8hAR+rlfMFBfjm5tLYaESbzYTefz+RvXpRo0YNKfyEEEKICqJly5aMGTMGm83GkCFDyMvLK/FYubm5HDyYgdXqjtZGDAYfOnSo68Bsi06Kvz9hs9n4+sMP8f71V6oBiQUFZOXn0xrwtlrJrl2bx0aOdEnlLoQQQgjnmjx5Ms2bN+fYsWNMmjSpxONcuBDH2bNVKCjIxmCohq8vNGtWy4GZFp0Uf39ix8aNxH/4IQ21Js9s5qzVSouCAmooRYbBQNfRowmpXt3VaQohhBDCCby8vFi0aBEGg4F3332XHTt2FHuMs2fPM2PGGgoKWmGxuGM01ickRFO1aun1872eFH83UODmpRR7P/2UdWPHcmL8eFplZ2O0WDhus1EtJ4emgMVsxrdXLzpHRNgVb+bMmTz77LPs2bPHMR9ACCGEEA7VuXNnxo4de236Nzc3t8jXpqSk8OKLH/Lzz7Xx8upEYGBvqlS5j5MnW7FixXEnZn1rUvxdJ9RkCn0sIGDSv6tVo8nSpTRetoyeCQncaTBQR2tCzGbusFrxtlpJqlePB0aPtnuN39q1a1m4cCGJiYkO+hRCCCGEcLQ333yTsLAwjh8/znvvvVfk69au/Zm9e62YTKEEBITQoIEn9epVx8urAYsWuZORkeXErG9OdidcUdPNLbhLYODiHm5u4a0MBhqZTAS4u2NUCqxWgs1mQiwWzhkMnHNzo/2rr1I9NNTuuLGxsQDUr1/f7rGEEEII4Ryenp4sWrSIxYsXM3r06CJfd/jwEbQ2YDSCr6+BkBCwWBQZGZrERC8uX84q9elfKf4AN4NBtQwIGB3m5hZeQylV3cODAF9fjL6+KDc3KCjAmJlJtawssrQm/tFH6fDAA3bHtVqtnDt3DpDiTwghhCjrOnbsWOTuXVf5+1cBjmG1XiI3N420tCAsFsjLK8DL6xI+Pk2dk+xtSPEHKDe36iEeHv39lFI+bm7K19MTo58fKjAQPD0hNxdlMGAwmwm2WqndsCEmk8nuuHFxcZjNZmrUqIGXl5cDPokQQgghypIePbqzZMkvZGYeJCXFi5ycRmjtQU5OAj17JhEc3KnUc5I1f4CXh0dzTwgF0AYDuLmBhwf4+ICvb+HDywtMJowGA/kHDmC1Wu2Oe+bMGQAaNGhg91hCCCGEKHvatGnD+PEDCAzcQ37+D6Snf0de3kq6ddvNmDF/wWAo/VJM7vwBBoOhagEYc0HlaE221gRarRgLCsBoBLMZbbWitSYXyM3JQWttd9yrxZ9M+QohhBAVk8FgYODAAdx1V2e2bdtJTk4+LVo0JyTEj3nz5rBu3TpOnz5NZmYm9erVIzIykgkTJlCzZk2n5STFH2C1WtNSwXIZDMk2m0rIz6dqVhb+BgMqOxsKCtCZmeQUFJBss5EdFOSQSv3qZg+58yeEEEJUXEopmjZtStOm/3993/jx45kzZw4PP/wwTz75JF5eXuzYsYN58+bx6aefsn37dpo3b+6cfBxxB6u8c3d3r1Y7OHh7B2jYzmBQTdzcaODpSainJ75GI8piITc3l+T8fGK0xnvaNCIGDLA7bkxMDPv27aNJkya0adPGAZ9ECCGEEKUtOjqa7OxsOnUq+vq93bt307hxY/z9/X/3/EcffcTw4cN54oknWL58eVGHU0UOjBR/ACilVEBAwP/V8/R8qw0YmhkMqo7RSIjRSBWlMNhs5FksJGvNpbAwBnzxBQFBQa5OWwghhBAutnXrViIjI6lduzYHDx7Ex8fHrvEyMjLw8/OjWbNmHDt2rKiXFav4kw0fgNZaZ2RkzIk3m1ce1Np2wGrlkMXCwfx89ufns89sZo/WHKhRgy5vvimFnxBCCCGAwu4fzZs3JyYmhvHjx9s9XlxcHADVndg6Voq/KywWS/rl1NTnYrKzP9lptRKlFNuBX4FtXl4k3HMPT86fzx133+3qVIUQQghRRri7u7N48WLc3NyYM2cOmzZtsmu8iRMnAvDMM884ILubk2nfGyilWgGHZs2ahclopErVqoS1bEnLli3x9PR0dXpCCCGEKIPeeust3nzzTerXr8+hQ4fw9S1+145p06bx2muv8eijj/LNN9+gVJFnc2XNnz2UUi2Bw4cPH6Zly5auTkcIIYQQ5YDZbKZTp07s37+fESNGMHfu3GJdP3v2bEaPHk23bt1Ys2YN3t7exblc1vwJIYQQQpQmk8nEokWLMJlMzJs3jw0bNhT52pkzZzJ69GgiIiJKUvgVmxR/QgghhBAO0LZtWyZOnEiHDh2KvGHjnXfe4ZVXXqFHjx6sXr3a6YUfSPHnMmPGjCEiIoKoqChXpyKEEEIIBxk/fjxRUVFFWjo2bdo0xo8fT69evfj2229LbW+BdPhwke3bt7Nz504sFourUxFCCCGEg5hMpiK9b+7cubz22mtUr16dxx9/nBUrVvzudV9fXx599FEnZCjFn8tc7esrrd2EEEKIyue3334DICEhgaFDh/7h9Xr16jmt+JPdvjcojd2+OTk5+Pj4YDKZyM3NxWg0OiWOEEIIISoF2e1b1sXGxgKFVb0UfkIIIYQoTVL8uYBM+QohhBCVS1ZWFpmZma5OA5A1fy4hxZ8QQghReezatYv//Oc/hIWFsWzZshKNMWLECEaOHOmQfKT4c4AZM2awb98+9u7dy+nTpzEYDLfdxXu1+Ktfv34pZSiEEEIIV6lXrx5Lly5l6tSpHDlypERjJCUlOSwfKf4cYMKECfj7+9O+fXuysrL+9H/QxIkT6devH9WqVSulDIUQQgjhKlcPfA4JCSEsLKxEY4SEhDgsH9nte4OS7PY9ffo0jRo1AqBbt278+uuvcn6fEEIIIUqL7PYtbVcLPyGEEEKIsk6KPyGEEEKISkSKPyGEEEKISkSKPyGEEEKISkSKPyGEEEKISkSKv1Imu6uFEEII4UpS/JWyPn360KhRI3799VdXpyKEEEKISkiKv1J24sQJYmJi8PX1dXUqQgghhKiEKkSHD6WUBzAZGAgEAAeB17XW60oj/tKlSzl79iwAZ8+eRWvN1KlTr73++uuvA4VTvtLXVwghhBCuVCE6fCilvgD6ArOAk8AQoCNwn9a6WPOrJenw0a1bNzZv3nzL16/+HicmJlK9enUCAgJISUkpTlpCCCGEELdSrA4f5f7On1KqE/A0MFZr/e6V55YAh4F/AXc7O4dNmzYV6X2xsbEA1K9f32m5CCGEEELcTkVY89cXsAIfXX1Ca50HLAD+opSq46rEbiRTvkIIIYRwtYpQ/LUHTmitM254fteVX9uVbjq3JsWfEEIIIVyt3E/7AjWBizd5/upzobe6UClVDQi54elGtws2d+5c5s2bV6wEryV0sTAlKf6EEEII4SoVofjzAvJv8nzeda/fyghgUnGCJSUlceTIkeJc8jtjx45lwIABJb5eCCGEEMIeFaH4ywU8bvK853Wv38o8YMUNzzUCvrvVBSEhIYSFhRUrwevVq1cPPz+/El8vhBBCCGGPcn/Ui1JqHVBLax12w/MRwC9Ab63198UYr9hHvQghhBBCuFCxjnqpCBs+9gNNlVJVb3i+83WvCyGEEEIIKkbx9xVgBJ67+sSVjh/PADu11uddlZgQQgghRFlT7tf8aa13KqVWANOv7N49BQwG6gPPujI3IYQQQoiyptwXf1cMAqbw+96+vbTWW1yalRBCCCFEGVMhir8rHT3GXnkIIYQQQohbqAhr/oQQQgghRBFJ8SeEEEIIUYlI8SeEEEIIUYlI8SeEEEIIUYlI8SeEEEIIUYmU+/Zujna1vRvQSmsd7ep8hBBCCCEcSYq/GyilPIFGwOkrR8gIIYQQQlQYUvwJIYQQQlQisuZPCCGEEKISkeJPCCGEEKISkeJPCCGEEKISkeJPCCGEEKISkeJPCCGEEKISkeJPCCGEEKISkeJPCCGEEKISkeJPCCGEEKISkeJPCCGEEKIS+X9GxV7J+/q9uwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape,\n", + " ax=ax,\n", + " layout=\"bfs\",\n", + " show_molecules=True,\n", + " molecule_plot_backend=\"view\",\n", + ")\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "md-plotmol-landscape", + "metadata": {}, + "source": [ + "## Show molecules with the `plot_molecule` backend\n", + "\n", + "This cell displays the same landscape but uses `molecule_plot_backend=\"plot_molecule\"`. This backend is convenient when you want simple ASE-style options such as explicit `rotation` strings, so it is often the most direct choice for angle-based orientation control.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c61987ac-3a37-4db0-8895-5a21be218fa6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape,\n", + " ax=ax,\n", + " layout=\"bfs\",\n", + " show_molecules=True,\n", + " molecule_plot_backend=\"plot_molecule\",\n", + ")\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "md-focus-section", + "metadata": {}, + "source": [ + "## Focus on a subset of states\n", + "\n", + "In many practical situations the number of states in an energy landscape can become quite large, making the full network difficult to inspect in detail. Two useful ways to simplify the analysis are illustrated below.\n", + "\n", + "The first option is `select_states`, which keeps only a user-defined list of states together with the corresponding links between them. This is useful when you already know which minima and transition states you want to compare.\n", + "\n", + "The second option is `accessible_states`, which keeps the states that are accessible from a chosen starting state within a given energy window. This can be useful, for example, when you want to study which rearrangements are reachable from one intermediate under a limited thermal or activation-energy budget.\n", + "\n", + "In both cases, `keep_original_ids=True` is very convenient because it preserves the state numbering from the original, more complicated landscape. This makes it easier to trace the selected states back to the full network and can also help when choosing a specific state as the starting point for a new AMS calculation aimed at exploring a different energy or configuration region of the system.\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-view-abc", + "metadata": {}, + "source": [ + "### `view` with a custom orientation in the `abc` basis\n", + "\n", + "This cell selects a few states and applies a custom `ViewConfig` with an explicit normal and `normal_basis=\"abc\"`. Even though we are visualizing molecules rather than periodic systems, this is still a valid basis choice here because it simply refers to the first, second, and third axes of the coordinate frame used by `view`. It shows how to control the `view` backend with a configuration object shared by all selected states.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "single-layout", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True)\n", + "\n", + "conf = ViewConfig(\n", + " normal=(0.0, 1.0, 0.0),\n", + " normal_basis=\"abc\",\n", + " fixed_atom_size=False,\n", + ")\n", + "\n", + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape_filtered,\n", + " ax=ax,\n", + " show_molecules=True,\n", + " molecule_scale=0.25,\n", + " molecule_plot_backend=\"view\",\n", + " molecule_plot_kwargs={\"config\": conf},\n", + ")\n", + "ax.set_title(\"HCNO energy landscape with a custom ViewConfig\")\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "md-view-ase", + "metadata": {}, + "source": [ + "### `view` with the ASE rendering backend\n", + "\n", + "This cell keeps the `view` interface but switches its internal rendering backend to `ase_plot`. The point of this example is to show that the `view` backend can also be used in this way: it becomes closer in spirit to `molecule_plot_backend=\"plot_molecule\"`, although it still relies on `ViewConfig` and therefore requires `view`-specific options instead of direct rotation strings.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3f17680d-ceb9-43b7-ab1f-0d88a61f0d22", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True)\n", + "\n", + "conf = ViewConfig(\n", + " normal=(0.0, 1.0, 0.0),\n", + " normal_basis=\"xyz\",\n", + " fixed_atom_size=False,\n", + ")\n", + "\n", + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape_filtered,\n", + " ax=ax,\n", + " show_molecules=True,\n", + " molecule_scale=0.25,\n", + " molecule_plot_backend=\"view\",\n", + " molecule_plot_kwargs={\"backend\": \"ase_plot\", \"config\": conf},\n", + ")\n", + "ax.set_title(\"HCNO energy landscape with the ASE view backend\")\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "md-accessible", + "metadata": {}, + "source": [ + "### Accessible states around a reference state\n", + "\n", + "This cell uses `accessible_states` to build a reduced landscape containing only the states that can be reached from state 3 within a chosen energy window. This type of analysis is useful when you want to focus on the locally reachable part of the network instead of the full landscape.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "15500937-48e0-4e5e-8c94-b07bef4c2312", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy_landscape_filtered = energy_landscape.accessible_states(3, 3.5, unit=\"eV\", keep_original_ids=True)\n", + "\n", + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape_filtered,\n", + " ax=ax,\n", + " show_molecules=True,\n", + " molecule_scale=0.25,\n", + " molecule_plot_backend=\"view\",\n", + " molecule_plot_kwargs={\"backend\": \"ase_plot\", \"config\": conf},\n", + ")\n", + "ax.set_title(\"HCNO energy landscape with the ASE view backend\")\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "md-plotmol-rot-section", + "metadata": {}, + "source": [ + "## Orient molecules with `plot_molecule`\n", + "\n", + "The next two cells show how to use explicit `rotation` strings with `molecule_plot_backend=\"plot_molecule\"`. This is the most direct option when you want camera-like rotations such as `\"0x,0y,45z\"`, because the orientation is controlled directly through Euler-like angles.\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-plotmol-allrot", + "metadata": {}, + "source": [ + "### One rotation for all selected states\n", + "\n", + "This cell applies the same `plot_molecule` rotation to every molecule in the selected part of the landscape.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "plotmolecule-rotation-all", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True)\n", + "\n", + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape_filtered,\n", + " ax=ax,\n", + " show_molecules=True,\n", + " molecule_scale=0.30,\n", + " molecule_plot_backend=\"plot_molecule\",\n", + " molecule_plot_kwargs={\"rotation\": \"0x,0y,45z\"},\n", + ")\n", + "ax.set_title(\"Same rotation for all molecules\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-plotmol-bystate", + "metadata": {}, + "source": [ + "### Override the rotation for a few states\n", + "\n", + "This cell keeps a common base rotation for all molecules and then overrides it for selected states through `molecule_plot_kwargs_by_state`. This is useful when only a few structures need a different viewpoint.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "plotmolecule-rotation-by-state", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True)\n", + "\n", + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape_filtered,\n", + " ax=ax,\n", + " show_molecules=True,\n", + " molecule_scale=0.30,\n", + " molecule_plot_backend=\"plot_molecule\",\n", + " molecule_plot_kwargs={\"rotation\": \"0x,0y,45z\"},\n", + " molecule_plot_kwargs_by_state={\n", + " 6: {\"rotation\": \"90x,0y,0z\"},\n", + " 5: {\"rotation\": \"0x,0y,90z\"},\n", + " },\n", + ")\n", + "ax.set_title(\"Custom rotations for selected states\")\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "md-view-rot-section", + "metadata": {}, + "source": [ + "## Orient molecules with `view`\n", + "\n", + "The last two plotting examples show the same idea as above, but now using `molecule_plot_backend=\"view\"`. In this case the orientation is controlled through `ViewConfig` objects rather than rotation strings: instead of specifying Euler-like angles, you specify the viewing direction or the normal to the view plane. This can be more natural when you want to reason in terms of views rather than manual rotations.\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-view-all", + "metadata": {}, + "source": [ + "### One `ViewConfig` for all selected states\n", + "\n", + "This cell passes a single `ViewConfig` to all selected states, giving every molecule the same `view` orientation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "view-rotation-all", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True)\n", + "\n", + "conf_all = ViewConfig(\n", + " normal=(0.0, 0.0, 1.0),\n", + " normal_basis=\"xyz\",\n", + ")\n", + "\n", + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape_filtered,\n", + " ax=ax,\n", + " show_molecules=True,\n", + " molecule_scale=0.30,\n", + " molecule_plot_backend=\"view\",\n", + " molecule_plot_kwargs={\"config\": conf_all},\n", + ")\n", + "ax.set_title(\"Same view orientation for all molecules\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-view-bystate", + "metadata": {}, + "source": [ + "### Override the `ViewConfig` for selected states\n", + "\n", + "This cell starts from one common `ViewConfig` and then replaces it for a few states through `molecule_plot_kwargs_by_state`. This is the `view` analogue of the per-state rotation example shown above for `plot_molecule`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "view-rotation-by-state", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True)\n", + "\n", + "conf_all = ViewConfig(\n", + " normal=(0.0, 0.0, 1.0),\n", + " normal_basis=\"xyz\",\n", + ")\n", + "conf_state_6 = ViewConfig(\n", + " normal=(0.0, 1.0, 0.0),\n", + " normal_basis=\"xyz\",\n", + ")\n", + "conf_state_5 = ViewConfig(\n", + " normal=(0.0, 0.0, 1.0),\n", + " normal_basis=\"xyz\",\n", + ")\n", + "\n", + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape_filtered,\n", + " ax=ax,\n", + " show_molecules=True,\n", + " molecule_scale=0.30,\n", + " molecule_plot_backend=\"view\",\n", + " molecule_plot_kwargs={\"config\": conf_all},\n", + " molecule_plot_kwargs_by_state={\n", + " 6: {\"config\": conf_state_6},\n", + " 5: {\"config\": conf_state_5},\n", + " },\n", + ")\n", + "ax.set_title(\"Custom view orientations for selected states\")\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "highlight-section", + "metadata": {}, + "source": [ + "## Highlight selected states and links\n", + "\n", + "This final example shows how to highlight a chosen subset of states directly in the energy landscape. The highlighting is drawn as a thick underlay behind the usual state and link lines, so the original appearance of the figure is preserved while the selected region is emphasized. When two highlighted states are connected, the corresponding link is highlighted as well. This can be useful for emphasizing a reaction path or a region of special interest inside a larger network.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "highlight-example", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True)\n", + "\n", + "fig, ax = plt.subplots(dpi=120)\n", + "plot_energy_landscape(\n", + " energy_landscape_filtered,\n", + " ax=ax,\n", + " show_molecules=True,\n", + " molecule_plot_backend=\"plot_molecule\",\n", + " molecule_plot_kwargs={\"rotation\": \"0x,0y,45z\"},\n", + " highlight_states=[3, 5, 7],\n", + " highlight_color=\"gold\",\n", + " highlight_linewidth=8.0,\n", + ")\n", + "ax.set_title(\"Highlight underlay for selected states and links\")\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "id": "summary-rewritten", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook shows several complementary ways to inspect an AMS energy landscape: loading the landscape, comparing layouts, selecting subsets of states, visualizing molecules on top of the plot with either the `view` backend or the `plot_molecule` backend, and controlling the orientation globally or state by state.\n", + "\n", + "The two molecule-plotting backends expose different controls: `plot_molecule` uses explicit Euler-like rotation strings, while `view` uses a view-oriented description through `ViewConfig`. In addition, the `view` backend can also use `ase_plot` internally, which provides a route closer to `plot_molecule` while still using `view`-style options.\n", + "\n", + "Finally, the highlighting option can be used to emphasize selected states and the links between them without changing the overall style of the figure, which is useful for drawing attention to a particular reaction path or region of interest in a larger landscape.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/EnergyLandscape/EnergyLandscape.py b/examples/EnergyLandscape/EnergyLandscape.py new file mode 100644 index 000000000..c1edf238a --- /dev/null +++ b/examples/EnergyLandscape/EnergyLandscape.py @@ -0,0 +1,363 @@ +#!/usr/bin/env python +# coding: utf-8 + +# Here we introduce several options for plotting and analyzing an energy landscape obtained from an AMS calculation. +# +# In particular, `plot_energy_landscape()` can display molecular structures on top of the landscape by using two different molecule-plotting backends: `view` and `plot_molecule`. In this notebook we show how to use both approaches, how to compare layout options, and how to control the orientation of all molecules or only selected states. +# +# A useful practical difference between the two backends is that `plot_molecule` uses explicit Euler-like rotation strings such as `"90x,0y,0z"`, while `view` specifies the viewing direction through `ViewConfig`, for example with `direction`, `normal`, and `normal_basis`. Depending on whether you want direct angle-based control or a view-oriented description, one backend may be more convenient than the other. +# + +# ## Imports +# +# This cell imports PLAMS, the energy-landscape plotting tools, the `ViewConfig` helper used by the `view` backend, and Matplotlib for figure creation. +# + +import scm.plams as plams +from scm.plams.tools.plot import plot_molecule, plot_energy_landscape +from scm.plams.tools.view import ViewConfig +import matplotlib.pyplot as plt + + +# ## Build the molecular system +# +# This cell defines the small HCNO molecule used throughout the example and guesses its bonds so that the different visualization backends can draw it properly. +# + +molecule = plams.Molecule() +molecule.add_atom(plams.Atom(symbol="H", coords=(0.26799604, 1.56164318, 0.80172174))) +molecule.add_atom(plams.Atom(symbol="O", coords=(0.70317302, 1.15034441, 0.02980438))) +molecule.add_atom(plams.Atom(symbol="N", coords=(0.07385403, -1.26509181, -0.02723174))) +molecule.add_atom(plams.Atom(symbol="C", coords=(0.33895691, -0.13354579, 0.04083563))) + +molecule.guess_bonds() + + +# ## Preview the molecule with `view` +# +# This cell displays the molecule with the `view` interface. This is the first of the two backends that can also be used later inside the energy-landscape plot. +# + +plams.view(molecule) + + +# ## Preview the molecule with `plot_molecule` +# +# This cell displays the same molecule with `plot_molecule`, which is the second backend supported in the landscape examples below. +# + +plot_molecule(molecule) + + +# ## PES exploration settings +# +# This cell prepares the AMS settings used to generate the energy landscape. The setup follows the same HCNO PES exploration used in the related plotting tests. +# + +settings = plams.Settings() +settings.input.ams.UseSymmetry = "No" +settings.input.ams.Task = "PESExploration" +settings.input.ams.PESExploration.RandomSeed = 1 +settings.input.ams.PESExploration.Job = "ProcessSearch" +settings.input.ams.PESExploration.NumExpeditions = 500 +settings.input.ams.PESExploration.NumExplorers = 4 +settings.input.ams.PESExploration.SaddleSearch.MaxEnergy = 6.0 +settings.input.ams.PESExploration.SaddleSearch.MinEnergyBarrier = 0.1 +settings.input.ams.PESExploration.StructureComparison.UseCovalent = "Yes" +settings.input.MOPAC.Model = "AM1" + + +# ## Run/Load a precomputed landscape +# +# This cell presents two options: rerunning the AMS job or reusing previously generated results. If the `job.run()` line is active, the notebook will perform the calculation. However, by commenting out `job.run()` and uncommenting the subsequent line, the notebook will load an existing RKF file and extract the energy landscape object. + +job = plams.AMSJob(name="HCNO", molecule=molecule, settings=settings) + +# job.run() +job = plams.AMSJob.load_external("plams_workdir/HCNO/ams.rkf") + +energy_landscape = job.results.get_energy_landscape() +print(energy_landscape) + + +# ## Default energy-landscape plot +# +# This cell produces the basic landscape plot with the default layout and without molecule thumbnails, which is a good starting point for understanding the connectivity between states. +# + +fig, ax = plt.subplots(dpi=120) +ax = plot_energy_landscape(energy_landscape, ax=ax) +ax + + +# ## Change the layout +# +# This cell redraws the same landscape with the `bfs` layout so you can compare how the ordering of states changes while keeping the same data. +# + +fig, ax = plt.subplots(dpi=120) +ax = plot_energy_landscape(energy_landscape, ax=ax, layout="bfs") +ax + + +# ## Show molecules with the `view` backend +# +# This cell adds molecule thumbnails above the states while using `molecule_plot_backend="view"`. This backend is convenient when you want to control the visualization through `ViewConfig` or other `view()` options, that is, by specifying the viewing direction rather than explicit Euler angles. +# + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape, + ax=ax, + layout="bfs", + show_molecules=True, + molecule_plot_backend="view", +) +ax + + +# ## Show molecules with the `plot_molecule` backend +# +# This cell displays the same landscape but uses `molecule_plot_backend="plot_molecule"`. This backend is convenient when you want simple ASE-style options such as explicit `rotation` strings, so it is often the most direct choice for angle-based orientation control. +# + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape, + ax=ax, + layout="bfs", + show_molecules=True, + molecule_plot_backend="plot_molecule", +) +ax + + +# ## Focus on a subset of states +# +# In many practical situations the number of states in an energy landscape can become quite large, making the full network difficult to inspect in detail. Two useful ways to simplify the analysis are illustrated below. +# +# The first option is `select_states`, which keeps only a user-defined list of states together with the corresponding links between them. This is useful when you already know which minima and transition states you want to compare. +# +# The second option is `accessible_states`, which keeps the states that are accessible from a chosen starting state within a given energy window. This can be useful, for example, when you want to study which rearrangements are reachable from one intermediate under a limited thermal or activation-energy budget. +# +# In both cases, `keep_original_ids=True` is very convenient because it preserves the state numbering from the original, more complicated landscape. This makes it easier to trace the selected states back to the full network and can also help when choosing a specific state as the starting point for a new AMS calculation aimed at exploring a different energy or configuration region of the system. +# + +# ### `view` with a custom orientation in the `abc` basis +# +# This cell selects a few states and applies a custom `ViewConfig` with an explicit normal and `normal_basis="abc"`. Even though we are visualizing molecules rather than periodic systems, this is still a valid basis choice here because it simply refers to the first, second, and third axes of the coordinate frame used by `view`. It shows how to control the `view` backend with a configuration object shared by all selected states. +# + +energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True) + +conf = ViewConfig( + normal=(0.0, 1.0, 0.0), + normal_basis="abc", + fixed_atom_size=False, +) + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape_filtered, + ax=ax, + show_molecules=True, + molecule_scale=0.25, + molecule_plot_backend="view", + molecule_plot_kwargs={"config": conf}, +) +ax.set_title("HCNO energy landscape with a custom ViewConfig") +ax + + +# ### `view` with the ASE rendering backend +# +# This cell keeps the `view` interface but switches its internal rendering backend to `ase_plot`. The point of this example is to show that the `view` backend can also be used in this way: it becomes closer in spirit to `molecule_plot_backend="plot_molecule"`, although it still relies on `ViewConfig` and therefore requires `view`-specific options instead of direct rotation strings. +# + +energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True) + +conf = ViewConfig( + normal=(0.0, 1.0, 0.0), + normal_basis="xyz", + fixed_atom_size=False, +) + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape_filtered, + ax=ax, + show_molecules=True, + molecule_scale=0.25, + molecule_plot_backend="view", + molecule_plot_kwargs={"backend": "ase_plot", "config": conf}, +) +ax.set_title("HCNO energy landscape with the ASE view backend") +ax + + +# ### Accessible states around a reference state +# +# This cell uses `accessible_states` to build a reduced landscape containing only the states that can be reached from state 3 within a chosen energy window. This type of analysis is useful when you want to focus on the locally reachable part of the network instead of the full landscape. +# + +energy_landscape_filtered = energy_landscape.accessible_states(3, 3.5, unit="eV", keep_original_ids=True) + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape_filtered, + ax=ax, + show_molecules=True, + molecule_scale=0.25, + molecule_plot_backend="view", + molecule_plot_kwargs={"backend": "ase_plot", "config": conf}, +) +ax.set_title("HCNO energy landscape with the ASE view backend") +ax + + +# ## Orient molecules with `plot_molecule` +# +# The next two cells show how to use explicit `rotation` strings with `molecule_plot_backend="plot_molecule"`. This is the most direct option when you want camera-like rotations such as `"0x,0y,45z"`, because the orientation is controlled directly through Euler-like angles. +# + +# ### One rotation for all selected states +# +# This cell applies the same `plot_molecule` rotation to every molecule in the selected part of the landscape. +# + +energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True) + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape_filtered, + ax=ax, + show_molecules=True, + molecule_scale=0.30, + molecule_plot_backend="plot_molecule", + molecule_plot_kwargs={"rotation": "0x,0y,45z"}, +) +ax.set_title("Same rotation for all molecules") +plt.show() + + +# ### Override the rotation for a few states +# +# This cell keeps a common base rotation for all molecules and then overrides it for selected states through `molecule_plot_kwargs_by_state`. This is useful when only a few structures need a different viewpoint. +# + +energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True) + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape_filtered, + ax=ax, + show_molecules=True, + molecule_scale=0.30, + molecule_plot_backend="plot_molecule", + molecule_plot_kwargs={"rotation": "0x,0y,45z"}, + molecule_plot_kwargs_by_state={ + 6: {"rotation": "90x,0y,0z"}, + 5: {"rotation": "0x,0y,90z"}, + }, +) +ax.set_title("Custom rotations for selected states") +ax + + +# ## Orient molecules with `view` +# +# The last two plotting examples show the same idea as above, but now using `molecule_plot_backend="view"`. In this case the orientation is controlled through `ViewConfig` objects rather than rotation strings: instead of specifying Euler-like angles, you specify the viewing direction or the normal to the view plane. This can be more natural when you want to reason in terms of views rather than manual rotations. +# + +# ### One `ViewConfig` for all selected states +# +# This cell passes a single `ViewConfig` to all selected states, giving every molecule the same `view` orientation. +# + +energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True) + +conf_all = ViewConfig( + normal=(0.0, 0.0, 1.0), + normal_basis="xyz", +) + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape_filtered, + ax=ax, + show_molecules=True, + molecule_scale=0.30, + molecule_plot_backend="view", + molecule_plot_kwargs={"config": conf_all}, +) +ax.set_title("Same view orientation for all molecules") +plt.show() + + +# ### Override the `ViewConfig` for selected states +# +# This cell starts from one common `ViewConfig` and then replaces it for a few states through `molecule_plot_kwargs_by_state`. This is the `view` analogue of the per-state rotation example shown above for `plot_molecule`. +# + +energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True) + +conf_all = ViewConfig( + normal=(0.0, 0.0, 1.0), + normal_basis="xyz", +) +conf_state_6 = ViewConfig( + normal=(0.0, 1.0, 0.0), + normal_basis="xyz", +) +conf_state_5 = ViewConfig( + normal=(0.0, 0.0, 1.0), + normal_basis="xyz", +) + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape_filtered, + ax=ax, + show_molecules=True, + molecule_scale=0.30, + molecule_plot_backend="view", + molecule_plot_kwargs={"config": conf_all}, + molecule_plot_kwargs_by_state={ + 6: {"config": conf_state_6}, + 5: {"config": conf_state_5}, + }, +) +ax.set_title("Custom view orientations for selected states") +ax + + +# ## Highlight selected states and links +# +# This final example shows how to highlight a chosen subset of states directly in the energy landscape. The highlighting is drawn as a thick underlay behind the usual state and link lines, so the original appearance of the figure is preserved while the selected region is emphasized. When two highlighted states are connected, the corresponding link is highlighted as well. This can be useful for emphasizing a reaction path or a region of special interest inside a larger network. +# + +energy_landscape_filtered = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True) + +fig, ax = plt.subplots(dpi=120) +plot_energy_landscape( + energy_landscape_filtered, + ax=ax, + show_molecules=True, + molecule_plot_backend="plot_molecule", + molecule_plot_kwargs={"rotation": "0x,0y,45z"}, + highlight_states=[3, 5, 7], + highlight_color="gold", + highlight_linewidth=8.0, +) +ax.set_title("Highlight underlay for selected states and links") +ax + + +# ## Summary +# +# This notebook shows several complementary ways to inspect an AMS energy landscape: loading the landscape, comparing layouts, selecting subsets of states, visualizing molecules on top of the plot with either the `view` backend or the `plot_molecule` backend, and controlling the orientation globally or state by state. +# +# The two molecule-plotting backends expose different controls: `plot_molecule` uses explicit Euler-like rotation strings, while `view` uses a view-oriented description through `ViewConfig`. In addition, the `view` backend can also use `ase_plot` internally, which provides a route closer to `plot_molecule` while still using `view`-style options. +# +# Finally, the highlighting option can be used to emphasize selected states and the links between them without changing the overall style of the figure, which is useful for drawing attention to a particular reaction path or region of interest in a larger landscape. +# diff --git a/src/scm/plams/interfaces/adfsuite/ams.py b/src/scm/plams/interfaces/adfsuite/ams.py index 08586982e..399eb1504 100644 --- a/src/scm/plams/interfaces/adfsuite/ams.py +++ b/src/scm/plams/interfaces/adfsuite/ams.py @@ -2521,6 +2521,7 @@ def __init__( productsID: Optional[int] = None, prefactorsFromReactant: Optional[float] = None, prefactorsFromProduct: Optional[float] = None, + originalID: Optional[int] = None, ): self._landscape = landscape self.engfile = engfile @@ -2532,11 +2533,16 @@ def __init__( self.productsID = productsID self.prefactorsFromReactant = prefactorsFromReactant self.prefactorsFromProduct = prefactorsFromProduct + self.originalID = originalID @property def id(self) -> int: return self._landscape._states.index(self) + 1 + @property + def display_id(self) -> int: + return self.originalID if self.originalID is not None else self.id + @property def reactants(self) -> Optional["AMSResults.EnergyLandscape.State"]: return self._landscape._states[self.reactantsID - 1] if self.reactantsID is not None else None @@ -2548,7 +2554,7 @@ def products(self) -> Optional["AMSResults.EnergyLandscape.State"]: def __str__(self) -> str: if self.isTS: lines = [ - f"State {self.id}: {self.molecule.get_formula(False)} transition state @ {self.energy:.8f} Hartree (found {self.count} times" + f"State {self.display_id}: {self.molecule.get_formula(False)} transition state @ {self.energy:.8f} Hartree (found {self.count} times" + (f", results on {self.engfile})" if self.engfile is not None else ")") ] if self.reactantsID is not None: @@ -2568,7 +2574,7 @@ def __str__(self) -> str: lines += [f" Prefactors: {self.prefactorsFromReactant:.3E}:?"] else: lines = [ - f"State {self.id}: {self.molecule.get_formula(False)} local minimum @ {self.energy:.8f} Hartree (found {self.count} times" + f"State {self.display_id}: {self.molecule.get_formula(False)} local minimum @ {self.energy:.8f} Hartree (found {self.count} times" + (f", results on {self.engfile})" if self.engfile is not None else ")") ] return "\n".join(lines) @@ -2639,7 +2645,7 @@ def __str__(self) -> str: ] return "\n".join(lines) - def __init__(self, results: "AMSResults"): + def __init__(self, results: Optional["AMSResults"]): self._states: List["AMSResults.EnergyLandscape.State"] = [] self._fragments: List["AMSResults.EnergyLandscape.Fragment"] = [] self._fstates: List["AMSResults.EnergyLandscape.FragmentedState"] = [] @@ -2775,6 +2781,99 @@ def __iter__(self) -> Iterator["AMSResults.EnergyLandscape.State"]: def __len__(self) -> int: return len(self._states) + def select_states( + self, state_ids: Sequence[int], keep_original_ids: bool = False + ) -> "AMSResults.EnergyLandscape": + """Return a new energy landscape containing only the selected states. + + The returned landscape contains copies of the selected stationary + points in the order given by ``state_ids``. Reactant/product links + are kept only when the linked state is also part of the selected + set. If ``keep_original_ids`` is ``True``, the copied states display + their original IDs in string representations. Fragment and + fragmented-state information is not copied. + """ + unique_state_ids = list(dict.fromkeys(state_ids)) + invalid_ids = [state_id for state_id in unique_state_ids if state_id < 1 or state_id > len(self._states)] + if invalid_ids: + raise ValueError(f"Invalid state ids requested: {invalid_ids}") + + selected_landscape = AMSResults.EnergyLandscape(None) + id_map = {old_id: new_id for new_id, old_id in enumerate(unique_state_ids, start=1)} + + for old_id in unique_state_ids: + state = self._states[old_id - 1] + reactants_id = id_map.get(state.reactantsID) if state.reactantsID is not None else None + products_id = id_map.get(state.productsID) if state.productsID is not None else None + selected_landscape._states.append( + AMSResults.EnergyLandscape.State( + selected_landscape, + state.engfile, + state.energy, + state.molecule.copy(), + state.count, + state.isTS, + reactants_id, + products_id, + state.prefactorsFromReactant, + state.prefactorsFromProduct, + old_id if keep_original_ids else None, + ) + ) + + return selected_landscape + + def accessible_states( + self, start_state_id: int, energy_window: float, unit: str = "eV", keep_original_ids: bool = False + ) -> "AMSResults.EnergyLandscape": + """Return the states reachable from ``start_state_id`` within an energy window. + + A state is considered accessible if there exists a connected path from + the starting state such that the maximum energy encountered along the + path does not exceed the starting-state energy plus ``energy_window``. + The returned value is a new ``EnergyLandscape`` containing only the + accessible states. If ``keep_original_ids`` is ``True``, the copied + states display their original IDs in string representations. + """ + if start_state_id < 1 or start_state_id > len(self._states): + raise ValueError(f"Invalid start_state_id: {start_state_id}") + if energy_window < 0.0: + raise ValueError(f"energy_window must be non-negative, got {energy_window}") + + window_hartree = Units.convert(energy_window, unit, "hartree") + start_state = self._states[start_state_id - 1] + energy_limit = start_state.energy + window_hartree + + adjacency: Dict[int, Set[int]] = {state.id: set() for state in self._states} + for state in self._states: + if state.reactantsID is not None: + adjacency[state.id].add(state.reactantsID) + adjacency[state.reactantsID].add(state.id) + if state.productsID is not None: + adjacency[state.id].add(state.productsID) + adjacency[state.productsID].add(state.id) + + required_energy: Dict[int, float] = {start_state_id: start_state.energy} + pending_ids: Set[int] = {start_state_id} + + while pending_ids: + current_id = min(pending_ids, key=lambda state_id: required_energy[state_id]) + pending_ids.remove(current_id) + current_required = required_energy[current_id] + + for neighbor_id in adjacency[current_id]: + neighbor_energy = self._states[neighbor_id - 1].energy + path_required = max(current_required, neighbor_energy) + if path_required > energy_limit: + continue + previous_required = required_energy.get(neighbor_id) + if previous_required is None or path_required < previous_required: + required_energy[neighbor_id] = path_required + pending_ids.add(neighbor_id) + + accessible_ids = [state.id for state in self._states if state.id in required_energy] + return self.select_states(accessible_ids, keep_original_ids=keep_original_ids) + def get_energy_landscape(self) -> "AMSResults.EnergyLandscape": """Returns the energy landscape obtained from a PESExploration job run by the AMS driver. The energy landscape is a set of stationary PES points (local minima and transition states). diff --git a/src/scm/plams/interfaces/adfsuite/crs.py b/src/scm/plams/interfaces/adfsuite/crs.py index 5cd6351ad..a5134da3c 100644 --- a/src/scm/plams/interfaces/adfsuite/crs.py +++ b/src/scm/plams/interfaces/adfsuite/crs.py @@ -353,7 +353,7 @@ def get_x_axis(array: np.ndarray, x_axis: Optional[Union[str, np.ndarray]]) -> n import matplotlib if plot_fig: - if terminal == "jupyter": + if terminal == "jupyter" and ipython is not None: ipython.run_line_magic("matplotlib", "inline") else: matplotlib.use("TkAgg") diff --git a/src/scm/plams/tools/plot.py b/src/scm/plams/tools/plot.py index 88ea8c345..c0d8cdfa3 100644 --- a/src/scm/plams/tools/plot.py +++ b/src/scm/plams/tools/plot.py @@ -9,6 +9,9 @@ Literal, cast, Sequence, + Set, + Callable, + Iterable, ) import numpy as np @@ -16,6 +19,7 @@ from scm.plams.core.functions import requires_optional_package from scm.plams.interfaces.adfsuite.ams import AMSJob from scm.plams.mol.molecule import Molecule +from scm.plams.tools.units import Units try: from scm.base import ChemicalSystem @@ -29,6 +33,7 @@ import ase from os import PathLike from PIL import Image as PilImage + from scm.plams.interfaces.adfsuite.ams import AMSResults from scm.plams.recipes.md.trajectoryanalysis import AMSMSDJob __all__ = [ @@ -37,6 +42,7 @@ "plot_phonons_band_structure", "plot_phonons_dos", "plot_phonons_thermodynamic_properties", + "plot_energy_landscape", "plot_molecule", "plot_image_grid", "plot_correlation", @@ -961,3 +967,516 @@ def plot_work_function( ) return ax + + +@requires_optional_package("matplotlib") +def plot_energy_landscape( + energy_landscape: "AMSResults.EnergyLandscape", + ax: Optional["plt.Axes"] = None, + unit: str = "eV", + landscape_width: float = 0.45, + spacing: float = 1.0, + ts_color: str = "red", + min_color: str = "black", + connector_color: str = "black", + connector_linestyle: Any = (0, (4, 4)), + label_states: bool = True, + layout: Literal["auto", "dfs", "bfs", "longest_path", "force", "crossings"] = "auto", + force_iterations: int = 200, + show_molecules: bool = False, + molecule_y_offset: float = 0.06, + molecule_scale: float = 0.25, + molecule_plot_backend: Literal["view", "plot_molecule"] = "view", + molecule_plot_kwargs: Optional[Dict[str, Any]] = None, + molecule_plot_kwargs_by_state: Optional[Dict[int, Dict[str, Any]]] = None, + highlight_states: Optional[Sequence[int]] = None, + highlight_color: str = "gold", + highlight_linewidth: float = 8.0, + highlight_connector_color: Optional[str] = None, +) -> "plt.Axes": + """Plot an energy landscape returned by ``AMSResults.get_energy_landscape()``. + + State energies are shown relative to the global minimum in the requested + unit. Local minima are plotted in ``min_color`` and transition states in + ``ts_color``. Dashed connectors are drawn between connected states. + + Parameters + ---------- + energy_landscape + Energy landscape object returned by ``AMSResults.get_energy_landscape()``. + ax + Matplotlib axis to draw on. If ``None``, a new figure and axis are created. + unit + Energy unit used for the y-axis and for converting relative energies from Hartree. + landscape_width + Width of the horizontal line segment drawn for each state. + spacing + Horizontal spacing between consecutive plotted states within a component. + ts_color + Color used for transition-state level segments and their labels. + min_color + Color used for local-minimum level segments and their labels. + connector_color + Color used for the dashed lines connecting related states. + connector_linestyle + Matplotlib linestyle specification for the state-connection lines. + label_states + If ``True``, annotate each state with its integer state ID. + layout + Layout strategy used to order the states along the horizontal axis. + Supported values are ``"auto"``, ``"dfs"``, ``"bfs"``, + ``"longest_path"``, ``"force"``, and ``"crossings"``. + Use ``"auto"`` to compare the available strategies and pick the one + with the cleanest connector pattern. Use ``"dfs"`` to follow one branch + deeply before backtracking, which can resemble a reaction-path view. + Use ``"bfs"`` to expand level by level from one endpoint, keeping + nearby states grouped together. Use ``"longest_path"`` to place the + main backbone of the network first and then attach side branches around + it. Use ``"force"`` to apply a simple force-based relaxation that spreads + states while reducing visual crowding. Use ``"crossings"`` to minimize + connector crossings directly, which can help for dense networks. + force_iterations + Number of relaxation iterations used by the ``"force"`` layout. + show_molecules + If ``True``, draw a molecule sketch above each energy level. + molecule_y_offset + Vertical offset of the molecule sketches above the energy level, expressed as + a fraction of the visible energy span. + molecule_scale + Height of each molecule sketch, expressed as a fraction of the visible energy span. + molecule_plot_backend + Backend used to render molecule insets. Use ``"view"`` to forward + ``molecule_plot_kwargs`` to :func:`scm.plams.view`, or ``"plot_molecule"`` + to forward them to :func:`plot_molecule`. + molecule_plot_kwargs + Optional keyword arguments forwarded to the function selected by + ``molecule_plot_backend`` for all molecule insets. + molecule_plot_kwargs_by_state + Optional mapping from displayed state ID to per-state keyword arguments. These + overrides are merged on top of ``molecule_plot_kwargs`` for the matching states. + highlight_states + Optional sequence of state IDs to highlight. Highlighted states and links are + emphasized by drawing a thicker colored underlay behind the regular plot. + highlight_color + Color used for the highlight underlay behind state segments and, by default, + behind highlighted links. + highlight_linewidth + Line width used for the highlight underlay behind state segments and links. + highlight_connector_color + Optional color used specifically for the highlighted-link underlay. If ``None``, + ``highlight_color`` is used. + + Returns + ------- + matplotlib.axes.Axes + The axis containing the plotted energy landscape. + """ + import matplotlib.pyplot as plt + from collections import deque + import itertools + import numpy as np + from scm.plams.tools.view import view + + states = list(energy_landscape) + molecule_plot_kwargs = dict(molecule_plot_kwargs or {}) + highlight_state_ids = set(highlight_states or []) + highlight_connector_color = highlight_connector_color or highlight_color + highlight_connector_linewidth = 0.7 * highlight_linewidth + if molecule_plot_backend not in {"view", "plot_molecule"}: + raise ValueError("molecule_plot_backend must be either 'view' or 'plot_molecule'") + molecule_plot_kwargs_by_state = dict(molecule_plot_kwargs_by_state or {}) + + if ax is None: + _, ax = plt.subplots() + + if len(states) == 0: + ax.set_ylabel(f"Relative Energy ({unit})") + ax.set_xticks([]) + return ax + + state_map = {state.id: state for state in states} + # Build a graph representation of the landscape from the TS reactant/product links. + adjacency: Dict[int, Set[int]] = {state.id: set() for state in states} + edge_pairs: Set[Tuple[int, int]] = set() + for state in states: + if state.reactants is not None: + adjacency[state.id].add(state.reactants.id) + adjacency[state.reactants.id].add(state.id) + left_id, right_id = sorted((state.id, state.reactants.id)) + edge_pairs.add((left_id, right_id)) + if state.products is not None: + adjacency[state.id].add(state.products.id) + adjacency[state.products.id].add(state.id) + left_id, right_id = sorted((state.id, state.products.id)) + edge_pairs.add((left_id, right_id)) + + def _state_sort_key(state_id: int) -> Tuple[bool, float, int]: + state = state_map[state_id] + return (state.isTS, state.energy, state.id) + + def _component(start_id: int, remaining_ids: Set[int]) -> Set[int]: + component: Set[int] = set() + stack = [start_id] + while stack: + node = stack.pop() + if node in component: + continue + component.add(node) + stack.extend(adjacency[node] & remaining_ids) + return component + + def _component_start(component: Set[int]) -> int: + endpoints = [node for node in component if len(adjacency[node] & component) <= 1] + candidates = endpoints if endpoints else list(component) + return min(candidates, key=_state_sort_key) + + def _connected_components() -> List[Set[int]]: + # Layout disconnected reaction networks independently before placing them side by side. + remaining_ids = {state.id for state in states} + components: List[Set[int]] = [] + while remaining_ids: + start_id = min(remaining_ids, key=_state_sort_key) + component = _component(start_id, remaining_ids) + components.append(component) + remaining_ids -= component + components.sort(key=lambda component: _state_sort_key(_component_start(component))) + return components + + def _ordered_components(orderer: Callable[[Set[int]], List[int]]) -> List[int]: + ordered_ids: List[int] = [] + for component in _connected_components(): + ordered_ids.extend(orderer(component)) + return ordered_ids + + def _dfs_order(component: Set[int]) -> List[int]: + # Follow one branch deeply before backtracking, which often matches a reaction path view. + start_id = _component_start(component) + visited: Set[int] = set() + ordered_ids: List[int] = [] + + def _visit(node: int) -> None: + visited.add(node) + ordered_ids.append(node) + neighbors = sorted(adjacency[node] & component, key=_state_sort_key) + for neighbor in neighbors: + if neighbor not in visited: + _visit(neighbor) + + _visit(start_id) + return ordered_ids + + def _bfs_order(component: Set[int]) -> List[int]: + # Expand level by level from one endpoint to keep nearby states grouped together. + start_id = _component_start(component) + queue = deque([start_id]) + visited: Set[int] = {start_id} + ordered_ids: List[int] = [] + while queue: + node = queue.popleft() + ordered_ids.append(node) + neighbors = sorted(adjacency[node] & component, key=_state_sort_key) + for neighbor in neighbors: + if neighbor not in visited: + visited.add(neighbor) + queue.append(neighbor) + return ordered_ids + + # ``view()`` returns a raster image with its own canvas and margins. Cropping away + # empty borders makes ``molecule_scale`` control the apparent molecule size instead + # of mostly scaling surrounding whitespace inside the inset. + def _crop_view_image(image: Any) -> Any: + image_array = np.asarray(image) + if image_array.ndim < 2: + return image + + if image_array.ndim == 3 and image_array.shape[2] == 4: + mask = image_array[:, :, 3] > 0 + elif image_array.ndim == 3: + mask = np.any(image_array[:, :, :3] < 250, axis=2) + else: + mask = image_array < 250 + + nonempty = np.argwhere(mask) + if nonempty.size == 0: + return image + + y0, x0 = nonempty.min(axis=0) + y1, x1 = nonempty.max(axis=0) + 1 + if x0 == 0 and y0 == 0 and y1 == image_array.shape[0] and x1 == image_array.shape[1]: + return image + + return image.crop((int(x0), int(y0), int(x1), int(y1))) + + def _farthest(start_id: int, component: Set[int]) -> Tuple[int, Dict[int, int], Dict[int, Optional[int]]]: + distances: Dict[int, int] = {start_id: 0} + parents: Dict[int, Optional[int]] = {start_id: None} + queue = deque([start_id]) + while queue: + node = queue.popleft() + for neighbor in sorted(adjacency[node] & component, key=_state_sort_key): + if neighbor not in distances: + distances[neighbor] = distances[node] + 1 + parents[neighbor] = node + queue.append(neighbor) + farthest_id = max( + distances, key=lambda node: (distances[node], -int(state_map[node].isTS), -state_map[node].energy, -node) + ) + return farthest_id, distances, parents + + def _longest_path_order(component: Set[int]) -> List[int]: + # Use the graph backbone first, then attach side branches around that main path. + start_id = _component_start(component) + end_a, _, _ = _farthest(start_id, component) + end_b, _, parents = _farthest(end_a, component) + + backbone: List[int] = [] + node: Optional[int] = end_b + while node is not None: + backbone.append(node) + node = parents[node] + backbone.reverse() + + ordered_ids: List[int] = [] + placed: Set[int] = set() + + def _add_side_branch(root_id: int, blocked: Set[int]) -> None: + neighbors = sorted((adjacency[root_id] & component) - blocked - placed, key=_state_sort_key) + for neighbor in neighbors: + ordered_ids.append(neighbor) + placed.add(neighbor) + _add_side_branch(neighbor, blocked | {root_id}) + + for node in backbone: + if node not in placed: + ordered_ids.append(node) + placed.add(node) + _add_side_branch(node, set(backbone)) + + leftovers = sorted(component - placed, key=_state_sort_key) + ordered_ids.extend(leftovers) + return ordered_ids + + def _crossings_for_order(order: Iterable[int]) -> Tuple[int, int, Tuple[int, ...]]: + # Score a 1D ordering by counting connector crossings and total connector length. + positions = {state_id: idx for idx, state_id in enumerate(order)} + edge_list = sorted(edge_pairs) + crossings = 0 + edge_length = 0 + for idx, (a1, b1) in enumerate(edge_list): + x1, x2 = sorted((positions[a1], positions[b1])) + edge_length += x2 - x1 + for a2, b2 in edge_list[idx + 1 :]: + if len({a1, b1, a2, b2}) < 4: + continue + y1, y2 = sorted((positions[a2], positions[b2])) + if (x1 < y1 < x2 < y2) or (y1 < x1 < y2 < x2): + crossings += 1 + return crossings, edge_length, tuple(order) + + def _crossings_order(component: Set[int]) -> List[int]: + # For small graphs try all permutations; otherwise improve a good initial guess locally. + component_list = sorted(component, key=_state_sort_key) + if len(component_list) <= 8: + return list(min(itertools.permutations(component_list), key=lambda perm: _crossings_for_order(perm))) + + best = min( + (_dfs_order(component), _bfs_order(component), _longest_path_order(component)), + key=_crossings_for_order, + ) + improved = list(best) + improved_flag = True + while improved_flag: + improved_flag = False + best_score = _crossings_for_order(improved) + for i in range(len(improved) - 1): + candidate = improved.copy() + candidate[i], candidate[i + 1] = candidate[i + 1], candidate[i] + candidate_score = _crossings_for_order(candidate) + if candidate_score < best_score: + improved = candidate + best_score = candidate_score + improved_flag = True + return improved + + def _force_order(component: Set[int]) -> List[int]: + # Relax 1D positions with attractive edges and repulsive nodes, then sort by the relaxed positions. + component_ids = sorted(component, key=_state_sort_key) + initial = _longest_path_order(component) + x_pos: Dict[int, float] = {state_id: float(index) for index, state_id in enumerate(initial)} + ideal_gap = max(spacing, 1.0) + for _ in range(max(force_iterations, 1)): + delta: Dict[int, float] = {state_id: 0.0 for state_id in component_ids} + for i, left in enumerate(component_ids): + for right in component_ids[i + 1 :]: + distance = x_pos[right] - x_pos[left] + if abs(distance) < 1e-8: + distance = 1e-8 + repulsion = 0.02 / abs(distance) + delta[left] -= repulsion + delta[right] += repulsion + for left, right in edge_pairs: + if left in component and right in component: + distance = x_pos[right] - x_pos[left] + attraction = 0.08 * (distance - ideal_gap) + delta[left] += attraction + delta[right] -= attraction + for state_id in component_ids: + state = state_map[state_id] + delta[state_id] += 0.03 * Units.convert(state.energy - min(s.energy for s in states), "hartree", unit) + for state_id in component_ids: + x_pos[state_id] += delta[state_id] + centered = float(np.mean([x_pos[state_id] for state_id in component_ids])) + for state_id in component_ids: + x_pos[state_id] -= centered + return sorted(component_ids, key=lambda state_id: (x_pos[state_id],) + _state_sort_key(state_id)) + + layout_orderers: Dict[str, Callable[[], List[int]]] = { + "dfs": lambda: _ordered_components(_dfs_order), + "bfs": lambda: _ordered_components(_bfs_order), + "longest_path": lambda: _ordered_components(_longest_path_order), + "force": lambda: _ordered_components(_force_order), + "crossings": lambda: _ordered_components(_crossings_order), + } + + if layout == "auto": + # Compare all available strategies and keep the one with the cleanest connector pattern. + candidate_orders: Dict[str, List[int]] = {name: orderer() for name, orderer in layout_orderers.items()} + ordered_ids: List[int] = min( + candidate_orders.values(), + key=lambda order: _crossings_for_order(order) + + (sum(abs(i - order.index(state.id)) for i, state in enumerate(states)),), + ) + else: + if layout not in layout_orderers: + raise ValueError( + "Unsupported layout '{}'. Choose from 'auto', 'dfs', 'bfs', 'longest_path', 'force', or 'crossings'.".format( + layout + ) + ) + ordered_ids = layout_orderers[layout]() + + ordered_states = [state_map[state_id] for state_id in ordered_ids] + # Convert the chosen ordering into actual x-coordinates, leaving a gap between components. + component_gap = max(1.5 * spacing, spacing + landscape_width) + x_map = {} + current_x = 0.0 + for component in _connected_components(): + component_order = [state_id for state_id in ordered_ids if state_id in component] + for offset, state_id in enumerate(component_order): + x_map[state_id] = current_x + offset * spacing + current_x = x_map[component_order[-1]] + component_gap + + reference_energy = min(state.energy for state in states) + relative_energies = {state.id: Units.convert(state.energy - reference_energy, "hartree", unit) for state in states} + half_width = landscape_width / 2.0 + + for state in ordered_states: + x_pos = x_map[state.id] + y_pos = relative_energies[state.id] + state_label = getattr(state, "display_id", state.id) + state_highlight_id = state_label if hasattr(state, "display_id") else state.id + is_highlighted = state_highlight_id in highlight_state_ids + base_color = ts_color if state.isTS else min_color + if is_highlighted: + ax.hlines( + y=y_pos, + xmin=x_pos - half_width, + xmax=x_pos + half_width, + colors=highlight_color, + linewidth=highlight_linewidth, + zorder=2.5, + ) + ax.hlines(y=y_pos, xmin=x_pos - half_width, xmax=x_pos + half_width, colors=base_color, linewidth=1.6, zorder=3) + if label_states: + ax.text(x_pos, y_pos, str(state_label), ha="center", va="bottom", color=base_color, fontsize=11, zorder=4) + + plotted_pairs = set() + # Draw each connection only once, even though TS links are visible from both endpoints. + for state in ordered_states: + x_pos = x_map[state.id] + y_pos = relative_energies[state.id] + state_label = getattr(state, "display_id", state.id) + state_highlight_id = state_label if hasattr(state, "display_id") else state.id + state_highlighted = state_highlight_id in highlight_state_ids + for other in (state.reactants, state.products): + if other is None: + continue + pair = tuple(sorted((state.id, other.id))) + if pair in plotted_pairs: + continue + other_label = getattr(other, "display_id", other.id) + other_highlight_id = other_label if hasattr(other, "display_id") else other.id + other_highlighted = other_highlight_id in highlight_state_ids + other_x = x_map[other.id] + other_y = relative_energies[other.id] + if other_x < x_pos: + x1, x2 = other_x + half_width, x_pos - half_width + y1, y2 = other_y, y_pos + else: + x1, x2 = x_pos + half_width, other_x - half_width + y1, y2 = y_pos, other_y + link_highlighted = state_highlighted and other_highlighted + if link_highlighted: + ax.plot( + [x1, x2], + [y1, y2], + color=highlight_connector_color, + linestyle="solid", + linewidth=highlight_connector_linewidth, + zorder=1.5, + ) + ax.plot([x1, x2], [y1, y2], color=connector_color, linestyle=connector_linestyle, linewidth=1.2, zorder=2) + plotted_pairs.add(pair) + + ax.set_ylabel(f"Relative Energy ({unit})") + ax.set_xticks([]) + x_values = [x_map[state.id] for state in ordered_states] + ax.set_xlim(min(x_values) - half_width - 0.2, max(x_values) + half_width + 0.2) + + y_min = min(relative_energies.values()) + y_max = max(relative_energies.values()) + y_span = max(y_max - y_min, 1.0) + bottom_padding = 0.05 * y_span + top_padding = 0.05 * y_span + molecule_height = 0.0 + if show_molecules: + molecule_height = molecule_scale * y_span + top_padding = max(top_padding, (molecule_y_offset + molecule_scale + 0.04) * y_span) + ax.set_ylim(y_min - bottom_padding, y_max + top_padding) + + if show_molecules: + base_molecule_scale = 0.16 + scale_factor = molecule_scale / base_molecule_scale if base_molecule_scale > 0 else 1.0 + molecule_width = max(landscape_width * 1.6, 0.72 * spacing) * scale_factor + # Place a compact molecule sketch above each state without changing the energy layout itself. + for state in ordered_states: + x_pos = x_map[state.id] + y_pos = relative_energies[state.id] + inset_bounds = ( + x_pos - molecule_width / 2.0, + y_pos + molecule_y_offset * y_span, + molecule_width, + molecule_height, + ) + inset_ax = ax.inset_axes(inset_bounds, transform=ax.transData) + inset_ax.set_facecolor("none") + inset_ax.patch.set_alpha(0.0) + state_label = getattr(state, "display_id", state.id) + state_plot_kwargs = dict(molecule_plot_kwargs) + state_plot_kwargs.update(molecule_plot_kwargs_by_state.get(state_label, {})) + if molecule_plot_backend == "view": + state_plot_kwargs.setdefault("guess_bonds", True) + image = view(state.molecule, **state_plot_kwargs) + image = _crop_view_image(image) + inset_ax.imshow(image) + inset_ax.set_axis_off() + else: + plot_molecule(state.molecule, ax=inset_ax, keep_axis=False, **state_plot_kwargs) + inset_ax.set_zorder(5) + + ax.spines["top"].set_visible(False) + 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test_plot_work_function(run_calculations, rkf_tools_plot): ax.set_title("Electrostatic Potential Profile", fontsize=14) ax.set_xlabel("Length (Angstroms)", fontsize=13) ax.set_ylabel("Energy (eV)", fontsize=13) + + +def force_remove_text(ax): + # Workaround: Matplotlib's @image_comparison misses some text in subplot grids. + # We manually hide the remaining text here to ensure visual regression tests + # pass consistently across different operating systems. + for axis in ax.flat: + axis.set_xlabel("") + axis.set_ylabel("") + for txt in axis.texts: + txt.set_visible(False) + + +# ---------------------------------------------------------- +# Testing plot_energy_landscape layouts for molecules +# ---------------------------------------------------------- +@image_comparison( + baseline_images=["plot_energy_landscape_molecules_layouts"], + remove_text=True, + extensions=["png"], + style="mpl20", + tol=20, +) +def test_plot_energy_landscape_molecules_layouts(run_calculations, rkf_tools_plot, xyz_folder): + plt.close("all") + + mol = Molecule() + mol.add_atom(Atom(symbol="H", coords=(0.26799604, 1.56164318, 0.80172174))) + mol.add_atom(Atom(symbol="O", coords=(0.70317302, 1.15034441, 0.02980438))) + mol.add_atom(Atom(symbol="N", coords=(0.07385403, -1.26509181, -0.02723174))) + mol.add_atom(Atom(symbol="C", coords=(0.33895691, -0.13354579, 0.04083563))) + + sett = Settings() + sett.input.ams.UseSymmetry = "No" + sett.input.ams.Task = "PESExploration" + sett.input.ams.PESExploration.RandomSeed = 1 + sett.input.ams.PESExploration.Job = "ProcessSearch" + sett.input.ams.PESExploration.NumExpeditions = 500 + sett.input.ams.PESExploration.NumExplorers = 4 + sett.input.ams.PESExploration.SaddleSearch.MaxEnergy = 6.0 + sett.input.ams.PESExploration.SaddleSearch.MinEnergyBarrier = 0.1 + sett.input.ams.PESExploration.StructureComparison.UseCovalent = "Yes" + sett.input.ams.UseSymmetry = "No" + sett.input.MOPAC.Model = "AM1" + + if run_calculations: + job = AMSJob(name="HCNO", molecule=mol, settings=sett) + job.run() + else: + job = AMSJob.load_external(rkf_tools_plot / "HCNO") + + energy_landscape = job.results.get_energy_landscape() + + _, ax = plt.subplots(3, 2, figsize=(20, 10)) + plot_energy_landscape( + energy_landscape, ax=ax[0, 0], layout="auto", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[0, 1], layout="dfs", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[1, 0], layout="bfs", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[1, 1], layout="longest_path", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[2, 0], layout="force", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[2, 1], layout="crossings", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + + force_remove_text(ax) + + +# ---------------------------------------------------------- +# Testing plot_energy_landscape layouts for surfaces +# ---------------------------------------------------------- +@image_comparison( + baseline_images=["plot_energy_landscape_surfaces_layouts"], + remove_text=True, + extensions=["png"], + style="mpl20", + tol=20, +) +def test_plot_energy_landscape_surfaces_layouts(run_calculations, rkf_tools_plot, xyz_folder): + plt.close("all") + + mol = Molecule(xyz_folder / "MetOH_Cu111.xyz") + + sett = Settings() + sett.runscript.preamble_lines = ["export OMP_NUM_THREADS=1"] + sett.input.ams.Task = "PESExploration" + sett.input.ams.PESExploration.RandomSeed = 10 + sett.input.ams.PESExploration.Job = "ProcessSearch" + sett.input.ams.PESExploration.NumExpeditions = 50 + sett.input.ams.PESExploration.NumExplorers = 4 + sett.input.ams.PESExploration.SaddleSearch.MaxEnergy = 3.0 + sett.input.ams.PESExploration.SaddleSearch.MinEnergyBarrier = 0.1 + sett.input.ams.PESExploration.SaddleSearch.DisplaceAlongNormalModesWeight = 0.7 + sett.input.ams.PESExploration.StructureComparison.DistanceDifference = 0.5 + sett.input.ams.PESExploration.StructureComparison.EnergyDifference = 0.5 + sett.input.ReaxFF.ForceField = "CuCHO.ff" + sett.input.ReaxFF.Charges.Solver = "Direct" + sett.input.ams.Constraints.FixedRegion = "surface" + + if run_calculations: + job = AMSJob(name="MetOH_Cu111", molecule=mol, settings=sett) + job.run() + else: + job = AMSJob.load_external(rkf_tools_plot / "MetOH_Cu111") + + energy_landscape = job.results.get_energy_landscape() + + _, ax = plt.subplots(3, 2, figsize=(40, 20)) + plot_energy_landscape( + energy_landscape, ax=ax[0, 0], layout="auto", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[0, 1], layout="dfs", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[1, 0], layout="bfs", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[1, 1], layout="longest_path", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[2, 0], layout="force", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + plot_energy_landscape( + energy_landscape, ax=ax[2, 1], layout="crossings", show_molecules=True, molecule_plot_backend="plot_molecule" + ) + + force_remove_text(ax) + + +# ---------------------------------------------------------- +# Testing plot_energy_landscape functions +# ---------------------------------------------------------- +@image_comparison( + baseline_images=["plot_energy_landscape_functions"], + remove_text=True, + extensions=["png"], + style="mpl20", + tol=20, +) +def test_plot_energy_landscape_functions(run_calculations, rkf_tools_plot, xyz_folder): + plt.close("all") + + mol = Molecule() + mol.add_atom(Atom(symbol="H", coords=(0.26799604, 1.56164318, 0.80172174))) + mol.add_atom(Atom(symbol="O", coords=(0.70317302, 1.15034441, 0.02980438))) + mol.add_atom(Atom(symbol="N", coords=(0.07385403, -1.26509181, -0.02723174))) + mol.add_atom(Atom(symbol="C", coords=(0.33895691, -0.13354579, 0.04083563))) + + sett = Settings() + sett.input.ams.UseSymmetry = "No" + sett.input.ams.Task = "PESExploration" + sett.input.ams.PESExploration.RandomSeed = 1 + sett.input.ams.PESExploration.Job = "ProcessSearch" + sett.input.ams.PESExploration.NumExpeditions = 500 + sett.input.ams.PESExploration.NumExplorers = 4 + sett.input.ams.PESExploration.SaddleSearch.MaxEnergy = 6.0 + sett.input.ams.PESExploration.SaddleSearch.MinEnergyBarrier = 0.1 + sett.input.ams.PESExploration.StructureComparison.UseCovalent = "Yes" + sett.input.ams.UseSymmetry = "No" + sett.input.MOPAC.Model = "AM1" + + if run_calculations: + job = AMSJob(name="HCNO", molecule=mol, settings=sett) + job.run() + else: + job = AMSJob.load_external(rkf_tools_plot / "HCNO") + + energy_landscape = job.results.get_energy_landscape() + energy_landscape_select_states = energy_landscape.select_states([1, 6, 5, 7, 3], keep_original_ids=True) + energy_landscape_accessible_states = energy_landscape.accessible_states(3, 3.5, unit="eV", keep_original_ids=True) + + _, ax = plt.subplots(2, 1, figsize=(10, 5)) + plot_energy_landscape( + energy_landscape_select_states, + ax=ax[0], + layout="auto", + show_molecules=True, + molecule_y_offset=0.2, + molecule_scale=0.4, + molecule_plot_backend="plot_molecule", + molecule_plot_kwargs={"rotation": "0x,0y,0z"}, + molecule_plot_kwargs_by_state={ + 7: {"rotation": "90x,0y,0z"}, + 3: {"rotation": "0x,90y,0z"}, + }, + ) + + plot_energy_landscape( + energy_landscape_accessible_states, + ax=ax[1], + layout="auto", + show_molecules=True, + molecule_y_offset=0.2, + molecule_scale=0.3, + molecule_plot_backend="plot_molecule", + molecule_plot_kwargs={"rotation": "0x,0y,0z"}, + molecule_plot_kwargs_by_state={ + 7: {"rotation": "90x,0y,0z"}, + 3: {"rotation": "0x,90y,0z"}, + }, + ) + + force_remove_text(ax) diff --git a/unit_tests/xyz/MetOH_Cu111.xyz b/unit_tests/xyz/MetOH_Cu111.xyz new file mode 100644 index 000000000..016e52e80 --- /dev/null +++ b/unit_tests/xyz/MetOH_Cu111.xyz @@ -0,0 +1,29 @@ +24 + +C 5.48594596 1.20467584 4.93993340 region=adsorbate +H 4.68076815 1.96186981 4.85638220 region=adsorbate +H 5.03829043 0.20849484 4.66253719 region=adsorbate +H 5.75491656 1.13903851 6.00775189 region=adsorbate +H 7.35022696 0.76765200 4.26230892 region=adsorbate +O 6.69058408 1.55330884 4.06048127 region=adsorbate +Cu 2.6217042200 1.5380433300 0.0577350300 region=surface +Cu 0.0577350300 0.0577350300 2.1445737000 region=surface +Cu 3.9036888100 3.7585057800 0.0577350300 region=surface +Cu 1.3397196200 2.2781974800 2.1445737000 region=surface +Cu 5.1856734100 5.9789682300 0.0577350300 region=surface +Cu 2.6217042200 4.4986599300 2.1445737000 region=surface +Cu 5.1856734100 1.5380433300 0.0577350300 region=surface +Cu 2.6217042200 0.0577350300 2.1445737000 region=surface +Cu 6.4676580000 3.7585057800 0.0577350300 region=surface +Cu 3.9036888100 2.2781974800 2.1445737000 region=surface +Cu 7.7496426000 5.9789682300 0.0577350300 region=surface +Cu 5.1856734100 4.4986599300 2.1445737000 region=surface +Cu 7.7496426000 1.5380433300 0.0577350300 region=surface +Cu 5.1856734100 0.0577350300 2.1445737000 region=surface +Cu 9.0316271900 3.7585057800 0.0577350300 region=surface +Cu 6.4676580000 2.2781974800 2.1445737000 region=surface +Cu 10.3136117900 5.9789682300 0.0577350300 region=surface +Cu 7.7496426000 4.4986599300 2.1445737000 region=surface +VEC1 7.6919075700 0.0000000000 0.0000000000 +VEC2 3.8459537900 6.6613873600 0.0000000000 +VEC3 0.0000000000 0.0000000000 12.0000000000