From bd7982f505c986b46b5abbed8beb078fc5b008ff Mon Sep 17 00:00:00 2001 From: alexrad71 <140546242+alexrad71@users.noreply.github.com> Date: Thu, 5 Dec 2024 14:00:28 -0500 Subject: [PATCH] Delete TEMPO/L2_validation_codes/TEMPO_tropNO2_validation_with_Pandora_02.ipynb deleting obsolete version based on TEMPO V01 and V02. --- ...O_tropNO2_validation_with_Pandora_02.ipynb | 3405 ----------------- 1 file changed, 3405 deletions(-) delete mode 100644 TEMPO/L2_validation_codes/TEMPO_tropNO2_validation_with_Pandora_02.ipynb diff --git a/TEMPO/L2_validation_codes/TEMPO_tropNO2_validation_with_Pandora_02.ipynb b/TEMPO/L2_validation_codes/TEMPO_tropNO2_validation_with_Pandora_02.ipynb deleted file mode 100644 index 4b2c66b..0000000 --- a/TEMPO/L2_validation_codes/TEMPO_tropNO2_validation_with_Pandora_02.ipynb +++ /dev/null @@ -1,3405 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "81a3dde3d96d436ba9d568932081d1e8": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_716eef3312884d01bf605d834e16e966", - "IPY_MODEL_47483e59bc7e4b55879037a9e20d8b75", - "IPY_MODEL_0154eba9abfc4ce9b67599ec542581ab" - ], - "layout": "IPY_MODEL_2f82bebe7f824fea90c653fb0007cde6" - } - }, - "716eef3312884d01bf605d834e16e966": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3668af5205244cc481c26d20bbcffe34", - "placeholder": "​", - "style": "IPY_MODEL_884729e65ae74e23a71728eb15555e99", - "value": "QUEUEING TASKS | : 100%" - } - }, - "47483e59bc7e4b55879037a9e20d8b75": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4e5dc7e46a74417da29a75a85c8e0757", - "max": 12, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_f2a29f4bea8d45ca977d928c98cec0ba", - "value": 12 - } - }, - "0154eba9abfc4ce9b67599ec542581ab": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_8feaff5762ed4e3fabd2f257b65a30fc", - "placeholder": "​", - "style": "IPY_MODEL_436212e52ff349739ac61acaca466e47", - "value": " 12/12 [00:00<00:00, 250.52it/s]" - } - }, - "2f82bebe7f824fea90c653fb0007cde6": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "3668af5205244cc481c26d20bbcffe34": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "884729e65ae74e23a71728eb15555e99": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "4e5dc7e46a74417da29a75a85c8e0757": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f2a29f4bea8d45ca977d928c98cec0ba": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "8feaff5762ed4e3fabd2f257b65a30fc": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "436212e52ff349739ac61acaca466e47": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "d5739de67d2a4964a080710b66c0d42d": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8ac37128147a42b1adb0cc5311ef7685", - "IPY_MODEL_8e855327fe0546658ff5f705a3890d4b", - "IPY_MODEL_2c5de8de95884e4c8b829aea66b5b90c" - ], - "layout": "IPY_MODEL_dd8f79285fe2403a9edfb6ac23ac3c87" - } - }, - "8ac37128147a42b1adb0cc5311ef7685": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_ec91094eb8004fc888d455c60ecb91b4", - "placeholder": "​", - "style": "IPY_MODEL_b1eb4aa51b294eed965638218b0c10b6", - "value": "PROCESSING TASKS | : 100%" - } - }, - "8e855327fe0546658ff5f705a3890d4b": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6dfee462007944458f6f2608ad2492aa", - "max": 12, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_a4991eee48624c818134b8c05608300e", - "value": 12 - } - }, - "2c5de8de95884e4c8b829aea66b5b90c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_afe04c8caefa4720a8b220682bc05512", - "placeholder": "​", - "style": "IPY_MODEL_eccd441db95a4056953df3b4f9149004", - "value": " 12/12 [00:18<00:00,  1.07s/it]" - } - }, - "dd8f79285fe2403a9edfb6ac23ac3c87": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "ec91094eb8004fc888d455c60ecb91b4": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "b1eb4aa51b294eed965638218b0c10b6": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "6dfee462007944458f6f2608ad2492aa": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "a4991eee48624c818134b8c05608300e": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "afe04c8caefa4720a8b220682bc05512": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "eccd441db95a4056953df3b4f9149004": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "fefeeaaeebc7438c813eb74eb15413c1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_a8480fa660224b64895cce2c9b916859", - "IPY_MODEL_b4ad09eded994284b5c5a581f8473dd7", - "IPY_MODEL_8e9cfdaa72c64a28a19085e8bf217268" - ], - "layout": "IPY_MODEL_06fae94c546d4cafbda21cb99bfa2e1c" - } - }, - "a8480fa660224b64895cce2c9b916859": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_7fb6b8d5cb3d48eb91014bc2fc1ed6b4", - "placeholder": "​", - "style": "IPY_MODEL_c773904f1f90425f94ab9c7c5a6d8300", - "value": "COLLECTING RESULTS | : 100%" - } - }, - "b4ad09eded994284b5c5a581f8473dd7": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_dbe9fa4070644cdf8968b0acf759bafb", - "max": 12, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_c8454424afbf47249aa3accca871bfb0", - "value": 12 - } - }, - "8e9cfdaa72c64a28a19085e8bf217268": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_28bfe5e502d4404ea73b0e7a33aa3ec4", - "placeholder": "​", - "style": "IPY_MODEL_83bb042eb04342e994accd7173b3ec29", - "value": " 12/12 [00:00<00:00, 371.64it/s]" - } - }, - "06fae94c546d4cafbda21cb99bfa2e1c": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "7fb6b8d5cb3d48eb91014bc2fc1ed6b4": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "c773904f1f90425f94ab9c7c5a6d8300": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "dbe9fa4070644cdf8968b0acf759bafb": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "c8454424afbf47249aa3accca871bfb0": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "28bfe5e502d4404ea73b0e7a33aa3ec4": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "83bb042eb04342e994accd7173b3ec29": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - } - } - } - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "### **TEMPO NO2 validation**\n", - "\n", - "This notebook illustrates comparison of nitrogen dioxide tropospheric column retrievals by TEMPO and Pandora ground stations.\n", - "\n", - "It allows a user to choose Pandora station of interest. Since TEMPO spatial coverage is regional and limited to North America, it is user's responsibilty to select the station within TEMPO's field of regard (FOR). If the selected station is outside FOR, no TEMPO time series will be generated.\n", - "\n", - "The user is allowed to choose the time period of interest by providing start and end dates in the form YYYYMMDD. Please be aware, that if the selecte period of interest is outside of available time span of one of the sensors, corresponding time series will not be generated.\n", - "\n", - "Data files for both sensors are downloaded on-the-fly. TEMPO data are downloaded with earthaccess library that needs to be installed first.\n", - "\n", - "TEMPO data files are read by means of netCDF library that needs to be installed first.\n", - "\n", - "Pandora data files are ASCII files with header and space separated columns. Custome made function is included to read nitrogen dioxide total column along with its total uncertainty.\n", - "\n", - "This code takes into account quality flags (QFs) from both TEMPO and Pandora. This is implemented as follow. On the TEMPO side, data set \"/product/main_data_quality_flag\" is read, all pixels with non-zero QFs are discarded. However, negative values of tropospheric NO2 column are NOT discarded and used for averaging/interpolationg to the point of interest. For the purpose of physical sanity, another way is also implemented, i.e., negative retrievals are not used in averaging. Therefore, TWO values are returned, trop_NO2_col, and trop_NO2_col_noneg. On Pandora side negative columns also occur despite high quality flags, though they are rare. So, two Pandora time series are considered - with and without negative columns.\n", - "\n", - "The resulting time series are plotted with and without uncertainty of both measurement in the end of the notebook." - ], - "metadata": { - "id": "kjVKCytfEnRt" - } - }, - { - "cell_type": "markdown", - "source": [ - "# 1 Installing and importing necessary libraries" - ], - "metadata": { - "id": "luJG0oPIPGjC" - } - }, - { - "cell_type": "markdown", - "source": [ - "## 1.1 Installing netCDF" - ], - "metadata": { - "id": "m5ru-FMpPXoE" - } - }, - { - "cell_type": "code", - "source": [ - "! pip3 install netCDF4" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5NWX4mCVQJt_", - "outputId": "1aabf824-17d4-4698-f231-7c5845aa4121" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting netCDF4\n", - " Downloading netCDF4-1.6.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.5 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting cftime (from netCDF4)\n", - " Downloading cftime-1.6.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m18.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from netCDF4) (2024.2.2)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from netCDF4) (1.25.2)\n", - "Installing collected packages: cftime, netCDF4\n", - "Successfully installed cftime-1.6.3 netCDF4-1.6.5\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## 1.2 Installing earthaccess" - ], - "metadata": { - "id": "cQJCMByjPp9i" - } - }, - { - "cell_type": "code", - "source": [ - "! pip3 install earthaccess" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "N7Gm15VaYKW9", - "outputId": "2ca3f0a0-60c6-40f0-a1df-89d27c8e241d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting earthaccess\n", - " Downloading earthaccess-0.9.0-py3-none-any.whl (57 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/57.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m51.2/57.1 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.1/57.1 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: fsspec>=2022.11 in /usr/local/lib/python3.10/dist-packages (from earthaccess) (2023.6.0)\n", - "Collecting multimethod>=1.8 (from earthaccess)\n", - " Downloading multimethod-1.11.2-py3-none-any.whl (10 kB)\n", - "Collecting pqdm>=0.1 (from earthaccess)\n", - " Downloading pqdm-0.2.0-py2.py3-none-any.whl (6.8 kB)\n", - "Collecting python-cmr>=0.9.0 (from earthaccess)\n", - " Downloading python_cmr-0.9.0-py3-none-any.whl (11 kB)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from earthaccess) (2.8.2)\n", - "Requirement already satisfied: requests>=2.26 in /usr/local/lib/python3.10/dist-packages (from earthaccess) (2.31.0)\n", - "Collecting s3fs>=2022.11 (from earthaccess)\n", - " Downloading s3fs-2024.3.1-py3-none-any.whl (29 kB)\n", - "Collecting tinynetrc<2.0.0,>=1.3.1 (from earthaccess)\n", - " Downloading tinynetrc-1.3.1-py2.py3-none-any.whl (3.9 kB)\n", - "Collecting bounded-pool-executor (from pqdm>=0.1->earthaccess)\n", - " Downloading bounded_pool_executor-0.0.3-py3-none-any.whl (3.4 kB)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from pqdm>=0.1->earthaccess) (4.66.2)\n", - "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from pqdm>=0.1->earthaccess) (4.10.0)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->earthaccess) (1.16.0)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->earthaccess) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->earthaccess) (3.6)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->earthaccess) (2.0.7)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->earthaccess) (2024.2.2)\n", - "Collecting aiobotocore<3.0.0,>=2.5.4 (from s3fs>=2022.11->earthaccess)\n", - " Downloading aiobotocore-2.12.2-py3-none-any.whl (76 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.5/76.5 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting fsspec>=2022.11 (from earthaccess)\n", - " Downloading fsspec-2024.3.1-py3-none-any.whl (171 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m172.0/172.0 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.10/dist-packages (from s3fs>=2022.11->earthaccess) (3.9.3)\n", - "Collecting botocore<1.34.52,>=1.34.41 (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2022.11->earthaccess)\n", - " Downloading botocore-1.34.51-py3-none-any.whl (12.0 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.0/12.0 MB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: wrapt<2.0.0,>=1.10.10 in /usr/local/lib/python3.10/dist-packages (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2022.11->earthaccess) (1.14.1)\n", - "Collecting aioitertools<1.0.0,>=0.5.1 (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2022.11->earthaccess)\n", - " Downloading aioitertools-0.11.0-py3-none-any.whl (23 kB)\n", - "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs>=2022.11->earthaccess) (1.3.1)\n", - "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs>=2022.11->earthaccess) (23.2.0)\n", - "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs>=2022.11->earthaccess) (1.4.1)\n", - "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs>=2022.11->earthaccess) (6.0.5)\n", - "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs>=2022.11->earthaccess) (1.9.4)\n", - "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs>=2022.11->earthaccess) (4.0.3)\n", - "Collecting jmespath<2.0.0,>=0.7.1 (from botocore<1.34.52,>=1.34.41->aiobotocore<3.0.0,>=2.5.4->s3fs>=2022.11->earthaccess)\n", - " Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n", - "Installing collected packages: tinynetrc, bounded-pool-executor, pqdm, multimethod, jmespath, fsspec, aioitertools, python-cmr, botocore, aiobotocore, s3fs, earthaccess\n", - " Attempting uninstall: fsspec\n", - " Found existing installation: fsspec 2023.6.0\n", - " Uninstalling fsspec-2023.6.0:\n", - " Successfully uninstalled fsspec-2023.6.0\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "torch 2.2.1+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-nccl-cu12==2.19.3; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "torch 2.2.1+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", - "gcsfs 2023.6.0 requires fsspec==2023.6.0, but you have fsspec 2024.3.1 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed aiobotocore-2.12.2 aioitertools-0.11.0 botocore-1.34.51 bounded-pool-executor-0.0.3 earthaccess-0.9.0 fsspec-2024.3.1 jmespath-1.0.1 multimethod-1.11.2 pqdm-0.2.0 python-cmr-0.9.0 s3fs-2024.3.1 tinynetrc-1.3.1\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## 1.3 Importing necessary libraries" - ], - "metadata": { - "id": "TxfhRi7ySyFY" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "IAAuhYMcEkvP" - }, - "outputs": [], - "source": [ - "import earthaccess # needed to discover and download TEMPO data\n", - "import netCDF4 as nc # needed to read TEMPO data\n", - "\n", - "import os\n", - "import sys\n", - "\n", - "import platform\n", - "from subprocess import Popen\n", - "import shutil\n", - "\n", - "from shapely.geometry import Point, Polygon # needed to search a point within a polygon\n", - "from scipy.interpolate import griddata # needed to interpolate TEMPO data to the point of interest\n", - "from scipy import stats # needed for linear regression analysis\n", - "\n", - "import requests # needed to search for and download Pandora data\n", - "import codecs # needed to read Pandora data\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt # needed to plot the resulting time series\n", - "from urllib.request import urlopen, Request # needed to search for and download Pandora data\n", - "from pathlib import Path # needed to check whether a needed data file is already downloaded\n", - "from datetime import datetime, timedelta # needed to work with time in plotting time series" - ] - }, - { - "cell_type": "markdown", - "source": [ - "#2 Defining functions to work with Pandora and TEMPO data" - ], - "metadata": { - "id": "ANsfYumeXjKm" - } - }, - { - "cell_type": "markdown", - "source": [ - "#2.1 functions to work with Pandora" - ], - "metadata": { - "id": "OQGUPNbgXyKN" - } - }, - { - "cell_type": "markdown", - "source": [ - "###2.1.1 function creating the list of available Pandora sites" - ], - "metadata": { - "id": "RB9IWsMvX6Kd" - } - }, - { - "cell_type": "code", - "source": [ - "# function read_pandora_web returns the list of available Pandora sites\n", - "def read_pandora_web():\n", - " url = 'https://data.pandonia-global-network.org/'\n", - " page = urlopen(url)\n", - " html_bytes = page.read()\n", - " html = html_bytes.decode(\"utf-8\")\n", - " html_len = len(html)\n", - "\n", - " pos1 = 0\n", - "\n", - " big_line = str(html)\n", - " lines = big_line.split('\\n')\n", - "\n", - " ref_lines = [i for i in lines if 'href' in i]\n", - " refs = []\n", - " for line in ref_lines:\n", - " pos1 = line.find('\"')\n", - " pos2 = line.rfind('\"')\n", - " if pos1 > 0 and pos2 > pos1 and line[pos2-1] =='/' and line[pos1+1] == '.':\n", - " refs.append(line[pos1+3 : pos2-1])\n", - "\n", - " return refs\n" - ], - "metadata": { - "id": "YEwbJTAFsGT1" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "###2.1.2 functions allowing user to choose a Pandora site of interest" - ], - "metadata": { - "id": "Ht9LHL28YUmR" - } - }, - { - "cell_type": "code", - "source": [ - "# function check_site checks whether user entered site is in the list of available Pandora sites\n", - "def check_site(site_name, refs):\n", - " site_list = []\n", - " for line in refs:\n", - " if site_name in line:\n", - " site_list.append(line)\n", - "\n", - " return site_list\n", - "\n", - "\n", - "# function take_pandora_sites takes user input and checks whether the site is in the list of available Pandora sites\n", - "def take_pandora_sites(refs):\n", - " print('please select a Pandora site name from the list')\n", - " for ref in refs:\n", - " print(ref)\n", - "\n", - " answer = 'y'\n", - " while answer == 'y':\n", - " site_name = input('Enter a name of a Pandora site: ')\n", - " print(site_name)\n", - " site_list = check_site(site_name, refs)\n", - " site_num = len(site_list)\n", - " if site_num == 0:\n", - " print('site ', site_name, 'was not found')\n", - " continue\n", - "\n", - " if site_num > 1:\n", - " print('there are ', site_num, ' site names, select one from')\n", - " for site in site_list: print(site)\n", - "\n", - " site_name = input('Enter a name of a Pandora site: ')\n", - " if site_list.count(site_name) != 1:\n", - " print('Entered name is not the exact match of one of the following sites')\n", - " for site in site_list: print(site)\n", - " print('program terminated')\n", - " sys.exit()\n", - "\n", - " for site in site_list:\n", - " if site == site_name:\n", - " pandora_site = site_name\n", - " print('site ', site_name, 'was found and added to the list of sites ')\n", - " break\n", - "\n", - " if site_num == 1:\n", - " pandora_site = site_list[0]\n", - " print('site ', site_list[0], 'was found and added to the list of sites ')\n", - "\n", - " answer = 'n'\n", - "\n", - " return pandora_site" - ], - "metadata": { - "id": "YUOuYq-8sclr" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "###2.1.3 function creating the list of links to tropospheric NO2 data files at the selected Pandora sites and downloading the data files" - ], - "metadata": { - "id": "F5sX6MEaZGFI" - } - }, - { - "cell_type": "code", - "source": [ - "# Pandora site may have several instruments. In this case each instrument has its own directory.\n", - "# However, the most recent version of the troposperic NO2 data, rnvh3p1-8, is available only in one of these directories.\n", - "# The function creates all possible links, but some of them may be non-existing. This is checked and cleared later.\n", - "def instrument_path(site):\n", - "# function instrument_path returns links to possible Pandora NO2 retrievals files\n", - " url = 'https://data.pandonia-global-network.org/' + site + '/'\n", - " page = urlopen(url)\n", - " html_bytes = page.read()\n", - " html = html_bytes.decode(\"utf-8\")\n", - " html_len = len(html)\n", - "\n", - " pos1 = 0\n", - " big_line = str(html)\n", - " lines = big_line.split('\\n')\n", - "\n", - " ref_lines = [i for i in lines if 'href' in i]\n", - " links = []\n", - " for line in ref_lines:\n", - "\n", - " pos1 = line.find('\"')\n", - " pos2 = line.rfind('\"')\n", - " if pos1 > 0 and pos2 > pos1 and line[pos2-1] =='/' and\\\n", - " line[pos1+3 : pos1 + 10] == 'Pandora':\n", - " link = url + line[pos1+3 : pos2] + 'L2/' + line[pos1+3 : pos2-1] + '_' + site + '_L2_rnvh3p1-8.txt'\n", - " print(link)\n", - " links.append(link)\n", - "\n", - " return links\n", - "\n", - "\n", - "# function downloading Pandora data file with given url\n", - "def download(url):\n", - " response = requests.get(url)\n", - " response_code = response.status_code\n", - "\n", - " file_name = url.split('/')[-1]\n", - "\n", - " if response_code == 200:\n", - " content = response.content\n", - " data_path = Path(file_name)\n", - " data_path.write_bytes(content)\n", - "\n", - " return file_name, response_code" - ], - "metadata": { - "id": "AViZrctjsVWN" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "###2.1.4 function reading Pandora NO2 data files rnvh3p1-8" - ], - "metadata": { - "id": "HLnx6gUAaMg6" - } - }, - { - "cell_type": "code", - "source": [ - "# function converting Pandora timestamp into a set of year, month, day, hour, minute, and second\n", - "# function read_timestamp converts Pandora timestamp of the format\n", - "# 'yyyymmddThhmmssZ' into a set of 6 numbers:\n", - "# integer year, month, day, hour, minute, and real second.\n", - "def read_timestamp(timestamp):\n", - "\n", - " yyyy = int(timestamp[0:4])\n", - " mm = int(timestamp[4:6])\n", - " dd = int(timestamp[6:8])\n", - " hh = int(timestamp[9:11])\n", - " mn = int(timestamp[11:13])\n", - " ss = float(timestamp[13:17])\n", - "\n", - " return yyyy, mm, dd, hh, mn, ss\n", - "\n", - "\n", - "# function reading Pandora NO2 data file rnvh3p1-8\n", - "#\n", - "# Below is the second version of function read_Pandora_NO2_rnvs3p1_8. It is to be used for the future validation efforts.\n", - "# The difference with the original version is that instead of discriminating negative values of the total NO2 column,\n", - "# it uses quality flags. It was previously found that QF == 0 does not occure often enough,\n", - "# so we will have to use QF == 10 (not-assured high quality).\n", - "#\n", - "# function read_Pandora_NO2_rnvh3p1_8 reads Pandora total NO2 column data files ending with rnvh3p1-8.\n", - "# Arguments:\n", - "# fname - name file to be read, string;\n", - "# start_date - beginning of the time interval of interest,\n", - "# integer of the form YYYYMMDD;\n", - "# end_date - end of the time interval of interest,\n", - "# integer of the form YYYYMMDD.\n", - "#\n", - "# if start_date is greater than end_date, the function returns a numpy array\n", - "# with shape (0, 8), otherwise it returns an 8-column numpy array\n", - "# with with columns being year, month, day, hour, minute, second of observation\n", - "# and retrieved total NO2 column along with its total uncertainty.\n", - "#\n", - "# NO2 column is in mol/m^2, so conversion to molecules/cm^2 is performed by\n", - "# multiplication by Avogadro constant, NA = 6.02214076E+23, and division by 1.E+4\n", - "def read_Pandora_NO2_rnvh3p1_8(fname, start_date, end_date):\n", - "\n", - " conversion_coeff = 6.02214076E+19 # Avogadro constant divided by 10000\n", - "\n", - " data = np.empty([0, 8])\n", - " if start_date > end_date: return -999., -999., data\n", - "\n", - " with codecs.open(fname, 'r', encoding='utf-8', errors='ignore') as f:\n", - "\n", - " while True:\n", - "# Get next line from file\n", - " line = f.readline()\n", - "\n", - " if line.find('Short location name:') >= 0:\n", - " loc_name = line.split()[-1] # location name, to be used in the output file name\n", - " print('location name ', loc_name)\n", - "\n", - " if line.find('Location latitude [deg]:') >= 0:\n", - " lat = float(line.split()[-1]) # location latitude\n", - " print('location latitude ', lat)\n", - "\n", - " if line.find('Location longitude [deg]:') >= 0:\n", - " lon = float(line.split()[-1]) # location longitude\n", - " print('location longitude ', lon)\n", - "\n", - " if line.find('--------') >= 0: break\n", - "\n", - " while True:\n", - "# Get next line from file\n", - " line = f.readline()\n", - "\n", - " if line.find('--------') >= 0: break\n", - "\n", - " while True:\n", - "# now reading line with data\n", - " line = f.readline()\n", - "\n", - " if not line: break\n", - "\n", - " line_split = line.split()\n", - "\n", - " yyyy, mm, dd, hh, mn, ss = read_timestamp(line_split[0])\n", - " date_stamp = yyyy*10000 + mm*100 + dd\n", - " if date_stamp < start_date or date_stamp > end_date: continue\n", - "\n", - " QF = int(line_split[52]) # total column uncertainty\n", - "\n", - " if QF == 0 or QF == 10:\n", - " column = float(line_split[61]) # Nitrogen dioxide tropospheric vertical column amount [moles per square meter]\n", - " column_unc = float(line_split[62]) # Independent uncertainty of nitrogen dioxide tropospheric vertical column amount [moles per square meter]\n", - " data = np.append(data, [[yyyy, mm, dd, hh, mn, ss\\\n", - " , column*conversion_coeff\\\n", - " , column_unc*conversion_coeff]], axis = 0)\n", - "\n", - " return lat, lon, loc_name, data" - ], - "metadata": { - "id": "1NYdVqlBuKBT" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "##2.2 functions to work with TEMPO" - ], - "metadata": { - "id": "Ep0Fl-Kzas5x" - } - }, - { - "cell_type": "markdown", - "source": [ - "###2.2.1 function reading TEMPO NO2 data file" - ], - "metadata": { - "id": "iQS-0PYfbRYy" - } - }, - { - "cell_type": "code", - "source": [ - "# function reading TEMPO NO2 data file for tropospheric column\n", - "def read_TEMPO_tropNO2_L2(fn):\n", - " '''\n", - " function read_TEMPO_tropNO2_L2 reads the following arrays from the\n", - " TEMPO L2 NO2 product TEMPO_NO2_L2_V01:\n", - " vertical_column_troposphere;\n", - " vertical_column_troposphere_uncertainty;\n", - " and returns respective fields along with coordinates of the pixels.\n", - "\n", - " If one requested variables cannot be read, all returned variables are zeroed\n", - " '''\n", - " var_name = 'vertical_column_troposphere'\n", - " var_unc_name = 'vertical_column_troposphere_uncertainty'\n", - " var_QF_name = 'main_data_quality_flag'\n", - "\n", - " try:\n", - " ds = nc.Dataset(fn)\n", - "\n", - " prod = ds.groups['product'] # this opens group product, /product, as prod\n", - "\n", - " var = prod.variables[var_name] # this reads variable column_amount_o3 from prod (group product, /product)\n", - " trop_NO2_column = np.array(var)\n", - " fv_prod = var.getncattr('_FillValue')\n", - " prod_unit = var.getncattr('units')\n", - "\n", - " var_unc = prod.variables[var_unc_name] # this reads variable column_amount_o3 from prod (group product, /product)\n", - " trop_NO2_column_unc = np.array(var_unc)\n", - "\n", - " var_QF = prod.variables[var_QF_name] # this reads variable column_amount_o3 from prod (group product, /product)\n", - " trop_NO2_column_QF = np.array(var_QF)\n", - " fv_QF = var_QF.getncattr('_FillValue')\n", - "\n", - " geo = ds.groups['geolocation'] # this opens group geolocation, /geolocation, as geo\n", - "\n", - " lat = np.array(geo.variables['latitude']) # this reads variable latitude from geo (geolocation group, /geolocation) into a numpy array\n", - " lon = np.array(geo.variables['longitude']) # this reads variable longitude from geo (geolocation group, /geolocation) into a numpy array\n", - " fv_geo = geo.variables['latitude'].getncattr('_FillValue')\n", - " time = np.array(geo.variables['time'] )# this reads variable longitude from geo (geolocation group, /geolocation) into a numpy array\n", - "\n", - " ds.close()\n", - "\n", - " except:\n", - " print('variable '+var_name+' cannot be read in file '+fn)\n", - " lat = 0.\n", - " lon = 0.\n", - " time = 0.\n", - " fv_geo = 0.\n", - " trop_NO2_column = 0.\n", - " trop_NO2_column_unc = 0.\n", - " trop_NO2_column_QF = 0.\n", - " fv_prod = 0.\n", - " fv_QF = -999\n", - " prod_unit = ''\n", - "\n", - " return lat, lon, fv_geo, time, trop_NO2_column, trop_NO2_column_unc\\\n", - ", trop_NO2_column_QF, fv_prod, fv_QF, prod_unit" - ], - "metadata": { - "id": "GedTZnASz9o_" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "###2.2.2 function creating TEMPO NO2 granule polygon" - ], - "metadata": { - "id": "Q5p9Tzafb5dz" - } - }, - { - "cell_type": "code", - "source": [ - "# function creating TEMPO NO2 granule polygon\n", - "def TEMPO_L2_polygon(lat, lon, fv_geo):\n", - " nx = lon.shape[0]\n", - " ny = lon.shape[1]\n", - " print('granule has %3d scanlines by %4d pixels' %(nx, ny))\n", - "\n", - " dpos = np.empty([0,2])\n", - "\n", - " x_ind = np.empty([nx, ny], dtype = int) # creating array in x indices\n", - " for ix in range(nx): x_ind[ix, :] = ix # populating array in x indices\n", - " y_ind = np.empty([nx, ny], dtype = int)\n", - " for iy in range(ny): y_ind[:, iy] = iy # populating array in x indices\n", - "\n", - " mask = (lon[ix, iy] != fv_geo)&(lat[ix, iy] != fv_geo)\n", - " if len(lon[mask]) == 0:\n", - " print('the granule is empty - no meaningful positions')\n", - " return dpos\n", - "\n", - "# right boundary\n", - " r_m = min(x_ind[mask].flatten())\n", - " local_mask = (lon[r_m, :] != fv_geo)&(lat[r_m, :] != fv_geo)\n", - " r_b = np.stack((lon[r_m, local_mask], lat[r_m, local_mask])).T\n", - "\n", - "# left boundary\n", - " l_m = max(x_ind[mask].flatten())\n", - " local_mask = (lon[l_m, :] != fv_geo)&(lat[l_m, :] != fv_geo)\n", - " l_b = np.stack((lon[l_m, local_mask], lat[l_m, local_mask])).T\n", - "\n", - "#top and bottom boundaries\n", - " t_b = np.empty([0,2])\n", - " b_b = np.empty([0,2])\n", - " for ix in range(r_m + 1, l_m):\n", - " local_mask = (lon[ix, :] != fv_geo)&(lat[ix, :] != fv_geo)\n", - " local_y_ind = y_ind[ix, local_mask]\n", - " y_ind_top = min(local_y_ind)\n", - " y_ind_bottom = max(local_y_ind)\n", - " t_b = np.append(t_b, [[lon[ix, y_ind_top], lat[ix, y_ind_top]]], axis=0)\n", - " b_b = np.append(b_b, [[lon[ix, y_ind_bottom], lat[ix, y_ind_bottom]]], axis=0)\n", - "\n", - "# combining right, top, left, and bottom boundaries together, going along the combined boundary counterclockwise\n", - " dpos = np.append(dpos, r_b[ : :-1, :], axis=0) # this adds right boundary, counterclockwise\n", - " dpos = np.append(dpos, t_b, axis=0) # this adds top boundary, counterclockwise\n", - " dpos = np.append(dpos, l_b, axis=0) # this adds left boundary, counterclockwise\n", - " dpos = np.append(dpos, b_b[ : :-1, :], axis=0) # this adds bottom boundary, counterclockwise\n", - "\n", - " print('polygon shape: ',dpos.shape)\n", - "\n", - " return dpos" - ], - "metadata": { - "id": "5OozR4ra0jK9" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "##2.3 auxiliary functions to handle data" - ], - "metadata": { - "id": "OkRA1M7PcIYx" - } - }, - { - "cell_type": "markdown", - "source": [ - "###2.3.1 function smoothing Pandora retievals and interpolating them onto TEMPO times of observations" - ], - "metadata": { - "id": "AKzFo9EjcTzx" - } - }, - { - "cell_type": "code", - "source": [ - "# Pandora timeseries has significantly more data points then TEMPO. It is also very noisy.\n", - "# To make comparison easier, Pandora timeseries is interpolated to the moments of TEMPO observations.\n", - "#\n", - "# Interpolation is performed by the function defined below with the help of Gaussian smooting as follow:\n", - "# x_int(t) = SUM(x_p(t_i)*wt(t_i, t)),\n", - "#\n", - "# wt(t_i, t) = exp(-(t - t_i)^2/(2 * sigma^2))/SUM(exp(-(t - t_i)^2/(2 * sigma^2))),\n", - "#\n", - "# where sums are taken over times t_i falling into time interval (t-dt_max, t+dt_max).\n", - "#\n", - "# Parameters dt_max and sigma can be chosen by the user.\n", - "def gauss_interpolation(timeseries, new_times):\n", - "#\n", - "# function gauss_interpolation takes 2D array timeseries with function\n", - "# to be interpolated and 1D array new_times containing times to which\n", - "# the function is to be interpolated\n", - "# arguments:\n", - "# timeseries - array with at least 2 columns,\n", - "# 1st column - times, 2nd (3rd, ...) column(s) - function to be interpolated\n", - "# new_times - 1D array of times to which the function(s) to be interpolated\n", - "#\n", - "# parameters\n", - "# dt_max = 0.0041666667 - 6 minutes expressed in days\n", - "# sigma = 0.00041666667 - 36 seconds expressed in days\n", - "\n", - " dt_max = 0.0041666667 # 6 minutes expressed in days\n", - " sigma = 0.00041666667 # 36 seconds expressed in days\n", - "\n", - " nnt = len(new_times)\n", - " (nt, nfun) = timeseries.shape\n", - "\n", - " timeseries_smooth = np.empty([0, nfun])\n", - "\n", - " for new_time in new_times:\n", - " llim = new_time - dt_max\n", - " ulim = new_time + dt_max\n", - "\n", - " timeseries_subset = timeseries[((timeseries[:, 0] < ulim)\\\n", - " & (timeseries[:, 0] > llim))]\n", - " if len(timeseries_subset) < 1: continue\n", - " t_delta = timeseries_subset[:, 0] - new_time\n", - " wt = np.exp(-t_delta**2/(2.*sigma**2))\n", - " wt = wt/np.sum(wt)\n", - "\n", - " timeseries_smooth_loc = np.array([new_time])\n", - " for ifun in range(1, nfun):\n", - " timeseries_smooth_loc = np.append(timeseries_smooth_loc,\\\n", - " np.sum(timeseries_subset[:, ifun]*wt))\n", - "\n", - " timeseries_smooth = np.append(timeseries_smooth,\\\n", - " np.array([timeseries_smooth_loc]), axis = 0)\n", - "\n", - " return timeseries_smooth" - ], - "metadata": { - "id": "XLSYQezN28Bq" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "###2.3.2 function computing linear regression with zero intercept" - ], - "metadata": { - "id": "nNoGDd0MdP9Y" - } - }, - { - "cell_type": "code", - "source": [ - "# custom made function regress_0intercept takes vectors x and y\n", - "# representing coordinates and function values at these coordinates\n", - "# and returns slope of regression fit y = a*x\n", - "# along with coefficient of determination\n", - "def regress_0intercept(x, y):\n", - " success = False\n", - "\n", - " if len(x) != len(y):\n", - " a = 0.\n", - " R2 = 0.\n", - "\n", - " elif len(x) == 1:\n", - " if x[0] != 0.:\n", - " a = y[0]/x[0]\n", - " R2 = 1.\n", - " success = True\n", - " else:\n", - " if y[0] != 0.:\n", - " a = np.inf\n", - " R2 = 1.\n", - " success = True\n", - " else:\n", - " a = np.inf\n", - " R2 = 0.\n", - "\n", - " else:\n", - " xy_sum = np.dot(x, y)\n", - " x2_sum = np.dot(x, x)\n", - " a = xy_sum/x2_sum\n", - "\n", - " res_y = y - a*x\n", - " res_sum_2 = np.dot(res_y, res_y)\n", - " y2_sum = np.dot(y, y)\n", - " sum_tot_2 = y2_sum - len(y)*np.mean(y)**2\n", - " R2 = 1. - res_sum_2/sum_tot_2\n", - "\n", - " success = True\n", - "\n", - " return success, a, R2" - ], - "metadata": { - "id": "WjLz_BRH3MO7" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#Main code begins here" - ], - "metadata": { - "id": "0aDnIkZVdu93" - } - }, - { - "cell_type": "markdown", - "source": [ - "#3 Establishing access to EarthData" - ], - "metadata": { - "id": "vXy2ArJ93d9e" - } - }, - { - "cell_type": "markdown", - "source": [ - "##3.1 Logging in\n", - "\n", - "User needs to create an account at https://www.earthdata.nasa.gov/\n", - "\n", - "Function earthaccess.login prompts for EarthData login and password." - ], - "metadata": { - "id": "8BnoNI0k3mi1" - } - }, - { - "cell_type": "code", - "source": [ - "auth = earthaccess.login(strategy=\"interactive\", persist=True)" - ], - "metadata": { - "id": "aHZmh8-xYZFe", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "be033ca5-d430-4630-f195-7b2fde01ce55" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Enter your Earthdata Login username: alexrad71\n", - "Enter your Earthdata password: ··········\n", - "Persisting credentials to .netrc\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "##3.2 Creating local directory" - ], - "metadata": { - "id": "AR7UGlswVGHj" - } - }, - { - "cell_type": "code", - "source": [ - "homeDir = os.path.expanduser(\"~\") + os.sep\n", - "\n", - "with open(homeDir + '.dodsrc', 'w') as file:\n", - " file.write('HTTP.COOKIEJAR={}.urs_cookies\\n'.format(homeDir))\n", - " file.write('HTTP.NETRC={}.netrc'.format(homeDir))\n", - " file.close()\n", - "\n", - "print('Saved .dodsrc to:', homeDir)\n", - "\n", - "# Set appropriate permissions for Linux/macOS\n", - "if platform.system() != \"Windows\":\n", - " Popen('chmod og-rw ~/.netrc', shell=True)\n", - "else:\n", - " # Copy dodsrc to working directory in Windows\n", - " shutil.copy2(homeDir + '.dodsrc', os.getcwd())\n", - " print('Copied .dodsrc to:', os.getcwd())" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tizBt7IvY0lx", - "outputId": "a12ab611-9048-46e7-f3ab-95af7f5edd51" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved .dodsrc to: /root/\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#4 Working with Pandora data" - ], - "metadata": { - "id": "5QaStYVXVmdN" - } - }, - { - "cell_type": "markdown", - "source": [ - "##4.1 Discovering existing Pandora stations and selecting one of them" - ], - "metadata": { - "id": "NN7_YcCackvI" - } - }, - { - "cell_type": "code", - "source": [ - "# Discovering existing Pandora stations and selecting one of them\n", - "# Discovering available Pandora site.\n", - "# Please bear in mind that some sites do not have tropospheric NO2 data files\n", - "print('gathering Pandora sites information')\n", - "refs = read_pandora_web()\n", - "\n", - "pandora_site = take_pandora_sites(refs) # create list of Pandora sites of interest\n", - "print('the following sites were selected')\n", - "print(pandora_site)\n", - "print('from the list of existing Pandora sites')\n", - "\n", - "# create a list of !AVAILABLE! Pandora files for the Pandora site\n", - "pandora_files = []\n", - "\n", - "links = instrument_path(pandora_site)\n", - "\n", - "npfiles = 0\n", - "\n", - "for link in links:\n", - " pandora_fname = link.split('/')[-1]\n", - "\n", - "# check if file exists in the local directory, if not download from Pandora site\n", - " if not os.path.exists(pandora_fname):\n", - " print(pandora_fname,' does not exit in local directory, downloading from the web')\n", - " print(link)\n", - "\n", - " pandora_fname, response_code = download(link)\n", - "\n", - " if response_code == 200:\n", - " print('Pandora L2 file ', pandora_fname, ' has been downloaded')\n", - " npfiles = npfiles + 1\n", - " pandora_files.append(pandora_fname)\n", - " else:\n", - " print('Pandora L2 file ', link, ' does not exist')\n", - "\n", - " else:\n", - " print(pandora_fname,' exits in local directory')\n", - " npfiles = npfiles + 1\n", - " pandora_files.append(pandora_fname)\n", - "\n", - "if npfiles == 0: # no files were found, STOP here\n", - " print('no files were found for Pandora site ', pandora_site, 'program terminated')\n", - " sys.exit()\n", - "if npfiles > 1: # normally there should be only one file per site. if there are more - STOP\n", - " print('there are too many files for site ', pandora_site, '- STOP and investigate file names below. Program terminated')\n", - " for pandora_fname in pandora_files:\n", - " print(pandora_fname)\n", - " sys.exit()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9bAQvpKS4ZUZ", - "outputId": "397ff070-4826-4d24-9f31-8ca22ce55688" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "gathering Pandora sites information\n", - "please select a Pandora site name from the list\n", - "Agam\n", - "AldineTX\n", - "AliceSprings\n", - "Altzomoni\n", - "ArlingtonTX\n", - "Athens-NOA\n", - "AtlantaGA-SouthDeKalb\n", - "AtlantaGA\n", - "AustinTX\n", - "Bandung\n", - "Bangkok\n", - "Banting\n", - "BayonneNJ\n", - "Beijing-RADI\n", - "BeltsvilleMD\n", - "Berlin\n", - "BlueHillMA\n", - "BostonMA\n", - "BoulderCO-NCAR\n", - "BoulderCO\n", - "Bremen\n", - "BristolPA\n", - "BronxNY\n", - "Brussels-Uccle\n", - "Bucharest\n", - "BuenosAires\n", - "BuffaloNY\n", - "Busan\n", - "Cabauw\n", - "calibrationfiles\n", - "CambridgeMA\n", - "CameronLA\n", - "CapeElizabethME\n", - "ChapelHillNC\n", - "CharlesCityVA\n", - "ChiangMai\n", - "ChicagoIL\n", - "Cologne\n", - "ComodoroRivadavia\n", - "Cordoba\n", - "CornwallCT\n", - "Dalanzadgad\n", - "Davos\n", - "DearbornMI\n", - "Dhaka\n", - "Downsview\n", - "EastProvidenceRI\n", - "EdwardsCA\n", - "Egbert\n", - "Eureka-PEARL\n", - "FairbanksAK\n", - "Fajardo\n", - "FortMcKay\n", - "FortYatesND\n", - "Fukuoka\n", - "Gongju-KNU\n", - "Granada\n", - "GrandForksND\n", - "GreenbeltMD\n", - "HamptonVA-HU\n", - "HamptonVA\n", - "Helsinki\n", - "HoustonTX-SanJacinto\n", - "HoustonTX\n", - "HuntsvilleAL\n", - "Incheon-ESC\n", - "Innsbruck\n", - "IowaCityIA-WHS\n", - "Islamabad-NUST\n", - "Izana\n", - "Juelich\n", - "KenoshaWI\n", - "Kobe\n", - "Kosetice\n", - "LaPorteTX\n", - "LapwaiID\n", - "LibertyTX\n", - "Lindenberg\n", - "LondonderryNH\n", - "LynnMA\n", - "MadisonCT\n", - "ManhattanKS\n", - "ManhattanNY-CCNY\n", - "MaunaLoaHI\n", - "MexicoCity-UNAM\n", - "MexicoCity-Vallejo\n", - "MiamiFL-FIU\n", - "MountainViewCA\n", - "Nagoya\n", - "Nainital-ARIES\n", - "NewBrunswickNJ\n", - "NewHavenCT\n", - "NewLondonCT\n", - "NyAlesund\n", - "OldFieldNY\n", - "operationfiles\n", - "Palau\n", - "PhiladelphiaPA\n", - "PittsburghPA\n", - "Pontianak\n", - "QueensNY\n", - "RichmondCA\n", - "Rome-IIA\n", - "Rome-ISAC\n", - "Rome-SAP\n", - "SaltLakeCityUT-Hawthorne\n", - "SaltLakeCityUT\n", - "SanJoseCA\n", - "Sapporo\n", - "Seosan\n", - "Seoul-SNU\n", - "Seoul\n", - "Singapore-NUS\n", - "Songkhla\n", - "SouthJordanUT\n", - "StGeorge\n", - "StonyPlain\n", - "Suwon-USW\n", - "SWDetroitMI\n", - "Tel-Aviv\n", - "Thessaloniki\n", - "Tokyo-Sophia\n", - "Tokyo-TMU\n", - "Toronto-Scarborough\n", - "Toronto-West\n", - "Trollhaugen\n", - "Tsukuba-NIES-West\n", - "Tsukuba-NIES\n", - "Tsukuba\n", - "TucsonAZ\n", - "TurlockCA\n", - "Ulaanbaatar\n", - "Ulsan\n", - "Vientiane\n", - "VirginiaBeachVA-CBBT\n", - "WacoTX\n", - "Wakkerstroom\n", - "WallopsIslandVA\n", - "Warsaw-UW\n", - "WashingtonDC\n", - "WestportCT\n", - "WhittierCA\n", - "Windsor-West\n", - "WrightwoodCA\n", - "Yokosuka\n", - "Yongin\n", - "Enter a name of a Pandora site: BoulderCO\n", - "BoulderCO\n", - "there are 2 site names, select one from\n", - "BoulderCO-NCAR\n", - "BoulderCO\n", - "Enter a name of a Pandora site: BoulderCO\n", - "site BoulderCO was found and added to the list of sites \n", - "the following sites were selected\n", - "BoulderCO\n", - "from the list of existing Pandora sites\n", - "https://data.pandonia-global-network.org/BoulderCO/Pandora57s1/L2/Pandora57s1_BoulderCO_L2_rnvh3p1-8.txt\n", - "Pandora57s1_BoulderCO_L2_rnvh3p1-8.txt does not exit in local directory, downloading from the web\n", - "https://data.pandonia-global-network.org/BoulderCO/Pandora57s1/L2/Pandora57s1_BoulderCO_L2_rnvh3p1-8.txt\n", - "Pandora L2 file Pandora57s1_BoulderCO_L2_rnvh3p1-8.txt has been downloaded\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "##4.2 Selecting timeframe of interest\n", - "\n", - "It is going to be common for both instruments" - ], - "metadata": { - "id": "4vYb5skDdNvg" - } - }, - { - "cell_type": "code", - "source": [ - "print('enter period of interest, start and end dates, in the form YYYYMMDD')\n", - "datestamp_ini = input('enter start date of interest ')\n", - "datestamp_fin = input('enter end date of interest ')\n", - "\n", - "start_date = int(datestamp_ini)\n", - "end_date = int(datestamp_fin)\n", - "\n", - "yyyy_ini = start_date//10000\n", - "mm_ini = (start_date//100 - yyyy_ini*100)\n", - "dd_ini = (start_date - yyyy_ini*10000 - mm_ini*100)\n", - "\n", - "yyyy_fin = end_date//10000\n", - "mm_fin = (end_date//100 - yyyy_fin*100)\n", - "dd_fin = (end_date - yyyy_fin*10000 - mm_fin*100)\n", - "print(yyyy_ini, mm_ini, dd_ini, yyyy_fin, mm_fin, dd_fin)\n", - "\n", - "date_start = str('%4.4i-%2.2i-%2.2i 00:00:00' %(yyyy_ini, mm_ini, dd_ini))\n", - "date_end = str('%4.4i-%2.2i-%2.2i 23:59:59' %(yyyy_fin, mm_fin, dd_fin))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JEqqz73v5OIo", - "outputId": "a761e1cf-4f2f-4b48-d78f-73d7df6c033a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "enter period of interest, start and end dates, in the form YYYYMMDD\n", - "enter start date of interest 20230802\n", - "enter end date of interest 20230802\n", - "2023 8 2 2023 8 2\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "##4.3 Reading Pandora file within selected timeframe and creating point of interest" - ], - "metadata": { - "id": "mTFV2Fkadj8e" - } - }, - { - "cell_type": "code", - "source": [ - "pandora_file = pandora_files[0]\n", - "lat, lon, POI_name, Pandora_data = read_Pandora_NO2_rnvh3p1_8(pandora_file, start_date, end_date)\n", - "\n", - "if lat == -999.:\n", - " print('error reading pandora file ', pandora_file, 'program terminated')\n", - " sys.exit()\n", - "\n", - "POI = np.array([lat, lon])\n", - "\n", - "# print # of points in Pandora timeseries\n", - "n_Pandora_data = len(Pandora_data)\n", - "print(n_Pandora_data,\\\n", - "' Pandora measurements found within period of interes between',\\\n", - "date_start, 'and', date_end)\n", - "if n_Pandora_data == 0:\n", - " print('program terminated')\n", - " sys.exit()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "elO1qjkY5cyd", - "outputId": "851c7890-c7d6-4ffb-803a-6cf0ba4a3ac3" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "location name BoulderCO\n", - "location latitude 39.99\n", - "location longitude -105.26\n", - "34 Pandora measurements found within period of interes between 2023-08-02 00:00:00 and 2023-08-02 23:59:59\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "##4.4 Setting TEMPO name constants and writing Pandora timeseries to a file" - ], - "metadata": { - "id": "3wvhtsq5kXgZ" - } - }, - { - "cell_type": "code", - "source": [ - "# Setting TEMPO name constants\n", - "short_name = 'TEMPO_NO2_L2' # collection name to search for in the EarthData\n", - "out_Q = 'trop_NO2_col' # name of the output quantity with unit\n", - "out_Q_unit = out_Q+',_molecules/cm^2' # name of the output quantity with unit\n", - "\n", - "# write Pandora timeseries to a file\n", - "POI_name_ = POI_name.replace(' ','_')\n", - "Pandora_out = open(out_Q+'_Pandora_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", - "for line in Pandora_data:\n", - " Pandora_out.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %12.4e %12.4e\\n'\\\n", - " %(line[0], line[1], line[2], line[3], line[4], line[5], line[6], line[7])))\n", - "Pandora_out.close()" - ], - "metadata": { - "id": "uEaqIobZ7F81" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#5 Working with TEMPO data" - ], - "metadata": { - "id": "VGfpNlrWei36" - } - }, - { - "cell_type": "markdown", - "source": [ - "##5.1 Searching TEMPO data files within 0.5 degree range around the POI (position of the Pandora station)" - ], - "metadata": { - "id": "qO9NEF61jlcy" - } - }, - { - "cell_type": "code", - "source": [ - "POI_lat = POI[0]\n", - "POI_lon = POI[1]\n", - "bbox = (POI_lon - 0.5, POI_lat - 0.5, POI_lon + 0.5, POI_lat + 0.5)\n", - "\n", - "for version in ['V02', 'V01']:\n", - " POI_results = earthaccess.search_data(short_name = short_name\\\n", - " , version = version\\\n", - " , temporal = (date_start, date_end)\\\n", - " , bounding_box = bbox)\n", - " n_gr = len(POI_results)\n", - " if n_gr == 0: continue\n", - " print('total number of TEMPO version ', version,' granules found for POI', POI_name, \\\n", - " '\\nwithin period of interes between', date_start, 'and', date_end, ' is', n_gr)\n", - " break\n", - "\n", - "if n_gr == 0:\n", - " print('program terminated')\n", - " sys.exit()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iysK7xDc7eV4", - "outputId": "100262f1-cf51-4092-aab2-99958711413b" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Granules found: 12\n", - "total number of TEMPO version V02 granules found for POI BoulderCO \n", - "within period of interes between 2023-08-02 00:00:00 and 2023-08-02 23:59:59 is 12\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "##5.2 Printing explicit links to the granules and downloading the files" - ], - "metadata": { - "id": "OvC5eZRhk7Xz" - } - }, - { - "cell_type": "code", - "source": [ - "granule_links = []\n", - "for result in POI_results: granule_links.append(result['umm']['RelatedUrls'][0]['URL'])\n", - "for granule_link in granule_links: print(granule_link)\n", - "\n", - "# Downloading TEMPO data files\n", - "downloaded_files = earthaccess.download(\n", - " POI_results,\n", - " local_path='.')\n", - "\n", - "# Checking whether all TEMPO data files have been downloaded\n", - "for granule_link in granule_links:\n", - " TEMPO_fname = granule_link.split('/')[-1]\n", - "# check if file exists in the local directory\n", - " if not os.path.exists(TEMPO_fname):\n", - " print(TEMPO_fname, 'does not exist in local directory')\n", - "# repeat attempt to download\n", - " downloaded_files = earthaccess.download(granule_link,\n", - " local_path='.')\n", - "# if file still does not exist in the directory, remove its link from the list of links\n", - " if not os.path.exists(TEMPO_fname): granule_links.remove(granule_link)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 359, - "referenced_widgets": [ - "81a3dde3d96d436ba9d568932081d1e8", - "716eef3312884d01bf605d834e16e966", - "47483e59bc7e4b55879037a9e20d8b75", - "0154eba9abfc4ce9b67599ec542581ab", - "2f82bebe7f824fea90c653fb0007cde6", - "3668af5205244cc481c26d20bbcffe34", - "884729e65ae74e23a71728eb15555e99", - "4e5dc7e46a74417da29a75a85c8e0757", - "f2a29f4bea8d45ca977d928c98cec0ba", - "8feaff5762ed4e3fabd2f257b65a30fc", - "436212e52ff349739ac61acaca466e47", - "d5739de67d2a4964a080710b66c0d42d", - "8ac37128147a42b1adb0cc5311ef7685", - "8e855327fe0546658ff5f705a3890d4b", - "2c5de8de95884e4c8b829aea66b5b90c", - "dd8f79285fe2403a9edfb6ac23ac3c87", - "ec91094eb8004fc888d455c60ecb91b4", - "b1eb4aa51b294eed965638218b0c10b6", - "6dfee462007944458f6f2608ad2492aa", - "a4991eee48624c818134b8c05608300e", - "afe04c8caefa4720a8b220682bc05512", - "eccd441db95a4056953df3b4f9149004", - "fefeeaaeebc7438c813eb74eb15413c1", - "a8480fa660224b64895cce2c9b916859", - "b4ad09eded994284b5c5a581f8473dd7", - "8e9cfdaa72c64a28a19085e8bf217268", - "06fae94c546d4cafbda21cb99bfa2e1c", - "7fb6b8d5cb3d48eb91014bc2fc1ed6b4", - "c773904f1f90425f94ab9c7c5a6d8300", - "dbe9fa4070644cdf8968b0acf759bafb", - "c8454424afbf47249aa3accca871bfb0", - "28bfe5e502d4404ea73b0e7a33aa3ec4", - "83bb042eb04342e994accd7173b3ec29" - ] - }, - "id": "_lPBPwOG7pUX", - "outputId": "51510073-377c-477c-a74d-0369c7e428d1" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T155007Z_S001G07.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T155620Z_S001G08.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T165238Z_S002G07.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T165851Z_S002G08.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T175509Z_S003G07.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T180122Z_S003G08.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T185740Z_S004G07.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T190353Z_S004G08.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T200011Z_S005G07.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T200624Z_S005G08.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T210242Z_S006G07.nc\n", - "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V02/2023.08.02/TEMPO_NO2_L2_V02_20230802T210855Z_S006G08.nc\n", - " Getting 12 granules, approx download size: 0.0 GB\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/12 [00:00 0.)&\\\n", - " (trop_NO2_column_unc_loc != fv_prod)&\\\n", - " (trop_NO2_column_QF_loc == 0)\n", - "\n", - " points_noFV = np.column_stack((lon_loc[mask_noFV], lat_loc[mask_noFV]))\n", - " points_noneg = np.column_stack((lon_loc[mask_noneg], lat_loc[mask_noneg]))\n", - " ff_noFV = trop_NO2_column_loc[mask_noFV]\n", - " ff_noneg = trop_NO2_column_loc[mask_noneg]\n", - " ff_unc_noFV = trop_NO2_column_unc_loc[mask_noFV]\n", - " ff_unc_noneg = trop_NO2_column_unc_loc[mask_noneg]\n", - "\n", - " if ff_noFV.shape[0] == 0:\n", - " continue\n", - " elif ff_noFV.shape[0] < 4:\n", - " trop_NO2_column_noFV = np.mean(ff_noFV)\n", - " trop_NO2_column_unc_noFV = np.mean(ff_unc_noFV)\n", - " elif ff_noFV.shape[0] == 4:\n", - " trop_NO2_column_noFV = griddata(points_noFV, ff_noFV, pp,\\\n", - "method='linear', fill_value=-1., rescale=False)[0]\n", - " trop_NO2_column_unc_noFV = griddata(points_noFV, ff_unc_noFV, pp,\\\n", - "method='linear', fill_value=-1., rescale=False)[0]\n", - "\n", - " if ff_noneg.shape[0] == 0:\n", - " continue\n", - " elif ff_noneg.shape[0] < 4:\n", - " trop_NO2_column_noneg = np.mean(ff_noneg)\n", - " trop_NO2_column_unc_noneg = np.mean(ff_unc_noneg)\n", - " elif ff_noneg.shape[0] == 4:\n", - " trop_NO2_column_noneg = griddata(points_noneg, ff_noneg, pp,\\\n", - "method='linear', fill_value=-1., rescale=False)[0]\n", - " trop_NO2_column_unc_noneg = griddata(points_noneg, ff_unc_noneg, pp,\\\n", - "method='linear', fill_value=-1., rescale=False)[0]\n", - "\n", - "# handling time first:\n", - " delta_t = (time[ix+1] + time[ix])*0.5 - time[0]\n", - " ss = ss + delta_t\n", - " if ss >= 60.:\n", - " delta_mn = int(ss/60.)\n", - " ss = ss - 60.*delta_mn\n", - " mn = mn + delta_mn\n", - " if mn >= 60:\n", - " mn = mn - 60\n", - " hh = hh + 1\n", - " if hh == 24:\n", - " hh = hh - 24\n", - " dd = dd + 1\n", - " day_month = days[mm]\n", - " if (yyyy//4)*4 == yyyy and mm == 2: day_month = day_month + 1\n", - " if dd > day_month:\n", - " dd = 1\n", - " mm = mm + 1\n", - " if mm > 12:\n", - " mm = 1\n", - " yyyy = yyyy + 1\n", - "\n", - " fout_noFV.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %2.2i %10.3e %10.3e '\\\n", - " %(yyyy, mm, dd, hh, mn, ss, trop_NO2_column_noFV, trop_NO2_column_unc_noFV)))\n", - " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", - " %(lat[ix, iy], -lon[ix, iy],\\\n", - "trop_NO2_column[ix, iy], trop_NO2_column_unc[ix, iy])))\n", - " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", - " %(lat[ix, iy+1], -lon[ix, iy+1],\\\n", - "trop_NO2_column[ix, iy+1], trop_NO2_column_unc[ix, iy+1])))\n", - " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", - " %(lat[ix+1, iy+1], -lon[ix+1, iy+1],\\\n", - "trop_NO2_column[ix+1, iy+1], trop_NO2_column_unc[ix+1, iy+1])))\n", - " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e\\n'\\\n", - " %(lat[ix+1, iy], -lon[ix+1, iy],\\\n", - "trop_NO2_column[ix+1, iy], trop_NO2_column_unc[ix+1, iy])))\n", - "\n", - " fout_noneg.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %2.2i %10.3e %10.3e '\\\n", - " %(yyyy, mm, dd, hh, mn, ss, trop_NO2_column_noneg, trop_NO2_column_unc_noneg)))\n", - " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", - " %(lat[ix, iy], -lon[ix, iy],\\\n", - "trop_NO2_column[ix, iy], trop_NO2_column_unc[ix, iy])))\n", - " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", - " %(lat[ix, iy+1], -lon[ix, iy+1],\\\n", - "trop_NO2_column[ix, iy+1], trop_NO2_column_unc[ix, iy+1])))\n", - " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", - " %(lat[ix+1, iy+1], -lon[ix+1, iy+1],\\\n", - "trop_NO2_column[ix+1, iy+1], trop_NO2_column_unc[ix+1, iy+1])))\n", - " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e\\n'\\\n", - " %(lat[ix+1, iy], -lon[ix+1, iy],\\\n", - "trop_NO2_column[ix+1, iy], trop_NO2_column_unc[ix+1, iy])))\n", - "\n", - " break\n", - "\n", - " if POI_found: break\n", - "\n", - "fout_noFV.close()\n", - "fout_noneg.close()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "VjGkJjYH8Ssu", - "outputId": "3e7f218a-79bb-494b-e32f-90e87a12a0af" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - " TEMPO_NO2_L2_V02_20230802T155007Z_S001G07.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is not within the granule polygon\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T155620Z_S001G08.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is within the granule polygon\n", - "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", - " 2 695 39.991142 -105.234749 5.9021e+15 3.7556e+15 0\n", - " 2 696 39.970364 -105.229591 2.6769e+15 1.4540e+15 0\n", - " 3 695 39.990238 -105.289040 2.6365e+15 2.3711e+15 0\n", - " 3 696 39.968800 -105.283524 3.1257e+15 3.6272e+15 0\n", - "POI BoulderCO at 39.99 -105.26 found\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T165238Z_S002G07.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is not within the granule polygon\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T165851Z_S002G08.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is within the granule polygon\n", - "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", - " 2 695 39.997566 -105.226707 8.5543e+14 4.2644e+15 0\n", - " 2 696 39.976799 -105.221550 9.3540e+14 3.5998e+15 0\n", - " 3 695 39.998348 -105.281776 2.9108e+15 2.8606e+15 0\n", - " 3 696 39.976727 -105.276169 5.7809e+14 3.3634e+15 0\n", - "POI BoulderCO at 39.99 -105.26 found\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T175509Z_S003G07.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is not within the granule polygon\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T180122Z_S003G08.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is within the granule polygon\n", - "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", - " 2 695 40.001518 -105.223190 -1.0890e+14 1.7783e+15 0\n", - " 2 696 39.980724 -105.218018 -1.1254e+15 8.1882e+14 0\n", - " 3 695 40.004494 -105.278778 -1.0000e+30 -1.0000e+30 2\n", - " 3 696 39.982601 -105.273018 -1.0000e+30 -1.0000e+30 2\n", - "POI BoulderCO at 39.99 -105.26 found\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T185740Z_S004G07.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is not within the granule polygon\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T190353Z_S004G08.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is within the granule polygon\n", - "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", - " 3 695 40.000908 -105.257240 1.0861e+15 4.7961e+15 0\n", - " 3 696 39.980011 -105.252014 1.9938e+15 8.4946e+15 0\n", - " 4 695 39.999943 -105.315063 -1.3093e+14 8.0084e+15 0\n", - " 4 696 39.975975 -105.308220 -4.3900e+15 9.4965e+15 0\n", - "POI BoulderCO at 39.99 -105.26 found\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T200011Z_S005G07.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is not within the granule polygon\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T200624Z_S005G08.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is within the granule polygon\n", - "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", - " 3 696 39.993324 -105.236404 -9.9572e+13 8.9118e+14 0\n", - " 3 697 39.972549 -105.231239 2.6722e+15 7.7697e+15 0\n", - " 4 696 39.991688 -105.294189 4.7744e+14 1.1164e+16 0\n", - " 4 697 39.969360 -105.288193 2.7939e+15 9.4285e+15 0\n", - "POI BoulderCO at 39.99 -105.26 found\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T210242Z_S006G07.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is not within the granule polygon\n", - "\n", - " TEMPO_NO2_L2_V02_20230802T210855Z_S006G08.nc\n", - "granule has 123 scanlines by 2048 pixels\n", - "polygon shape: (4338, 2)\n", - "point 39.99 -105.26 is within the granule polygon\n", - "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", - " 3 696 39.998035 -105.227577 3.1528e+14 2.7886e+15 0\n", - " 3 697 39.977280 -105.222420 -3.2442e+13 5.2558e+15 0\n", - " 4 696 39.999744 -105.287323 -7.9185e+15 6.3629e+15 0\n", - " 4 697 39.977474 -105.281372 4.8854e+15 1.4978e+16 0\n", - "POI BoulderCO at 39.99 -105.26 found\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#6 Plotting the results" - ], - "metadata": { - "id": "xaRNZa_4bl11" - } - }, - { - "cell_type": "markdown", - "source": [ - "##6.1 Reading created data files for TEMPO, create timeseries" - ], - "metadata": { - "id": "XglvHW3AbfFl" - } - }, - { - "cell_type": "code", - "source": [ - "# reading TEMPO file that was created at the previous step\n", - "# only read POI information from the header and first 8 columns of data:\n", - "# yyyy, mm, dd, hh, mn, ss, NO2 column, and its incertainty\n", - "fout = open(out_Q+'_noneg_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", - "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", - "\n", - "header1 = fout.readline()\n", - "header2 = fout.readline()\n", - "data_lines_noneg = fout.readlines()\n", - "\n", - "fout.close()\n", - "\n", - "yyyy = yyyy_ini\n", - "mm = mm_ini\n", - "dd = dd_ini\n", - "hh = 0\n", - "mn = 0\n", - "ss = 0\n", - "dt0 = datetime(yyyy, mm, dd, hh, mn, ss)\n", - "\n", - "yyyy = yyyy_fin\n", - "mm = mm_fin\n", - "dd = dd_fin\n", - "hh = 23\n", - "mn = 59\n", - "ss = 59\n", - "dt_fin = datetime(yyyy, mm, dd, hh, mn, ss) # this is time 1 second before the end of the timeframe of interest\n", - "\n", - "time_series_TEMPO_noneg = np.empty([0, 3])\n", - "\n", - "for line in data_lines_noneg:\n", - " split = line.split()\n", - " yyyy = int(split[0])\n", - " mm = int(split[1])\n", - " dd = int(split[2])\n", - " hh = int(split[3])\n", - " mn = int(split[4])\n", - " ss = int(split[5])\n", - "# dt below is time since the beginning of the period of interest in hours\n", - " dt = (datetime(yyyy, mm, dd, hh, mn, ss) - dt0).total_seconds()/86400.\n", - " time_series_TEMPO_noneg = np.append(time_series_TEMPO_noneg,\\\n", - " [[dt, float(split[6]), float(split[7])]], axis = 0)\n", - "\n", - "fout = open(out_Q+'_noFV_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", - "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", - "\n", - "header1 = fout.readline()\n", - "header2 = fout.readline()\n", - "data_lines = fout.readlines()\n", - "\n", - "fout.close()\n", - "\n", - "time_series_TEMPO = np.empty([0, 3])\n", - "\n", - "for line in data_lines:\n", - " split = line.split()\n", - " yyyy = int(split[0])\n", - " mm = int(split[1])\n", - " dd = int(split[2])\n", - " hh = int(split[3])\n", - " mn = int(split[4])\n", - " ss = int(split[5])\n", - "# dt below is time since the beginning of the period of interest in hours\n", - " dt = (datetime(yyyy, mm, dd, hh, mn, ss) - dt0).total_seconds()/86400.\n", - " time_series_TEMPO = np.append(time_series_TEMPO,\\\n", - " [[dt, float(split[6]), float(split[7])]], axis = 0)\n", - "\n", - "if len(time_series_TEMPO) == len(time_series_TEMPO_noneg):\n", - " print('\\nTEMPO standard and \"no negative\" are of equal length')\n", - " nt = len(time_series_TEMPO)\n", - " equal = True\n", - " for i in range(nt):\n", - " if time_series_TEMPO[i,1] != time_series_TEMPO_noneg[i,1]:\n", - " equal = False\n", - " break\n", - "else: equal = False\n", - "\n", - "if equal: print('TEMPO standard and \"no negative\" are the same')\n", - "else: print('TEMPO standard and \"no negative\" are different')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yAg1TWrF80yk", - "outputId": "654b81cc-660a-43f5-bc8c-8e7835da28a6" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "TEMPO standard and \"no negative\" are of equal length\n", - "TEMPO standard and \"no negative\" are different\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "##6.2 creating Pandora timeseries" - ], - "metadata": { - "id": "O8JvXRRcwT9O" - } - }, - { - "cell_type": "code", - "source": [ - "time_series_Pandora = np.empty([0, 3])\n", - "time_series_Pandora_noneg = np.empty([0, 3])\n", - "\n", - "for line in Pandora_data:\n", - " yyyy = int(line[0])\n", - " mm = int(line[1])\n", - " dd = int(line[2])\n", - " hh = int(line[3])\n", - " mn = int(line[4])\n", - " ss = int(line[5])\n", - "# dt below is time since the beginning of the period of interest in hours\n", - " dt = (datetime(yyyy, mm, dd, hh, mn, ss) - dt0).total_seconds()/86400.\n", - " time_series_Pandora = np.append(time_series_Pandora,\\\n", - "[[dt, line[6], line[7]]], axis = 0)\n", - " if line[6] > 0:\n", - " time_series_Pandora_noneg = np.append(time_series_Pandora_noneg,\\\n", - "[[dt, line[6], line[7]]], axis = 0)" - ], - "metadata": { - "id": "6cHK-fIs9KCs" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "##6.3 Plotting timeseries" - ], - "metadata": { - "id": "NGXMMgcNcMb4" - } - }, - { - "cell_type": "markdown", - "source": [ - "###6.3.1 No error bars" - ], - "metadata": { - "id": "WEkwDqvOccOK" - } - }, - { - "cell_type": "code", - "source": [ - "plot_title = out_Q+'_'+datestamp_ini+'_'+datestamp_fin+'\\n'+POI_name\n", - "img_name = out_Q+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", - "\n", - "plt.plot(time_series_Pandora[:, 0], time_series_Pandora[:, 1],\\\n", - " label = \"Pandora\", c = 'r')\n", - "plt.plot(time_series_TEMPO[:, 0], time_series_TEMPO[:, 1],\n", - " label = \"TEMPO\", c = 'b')\n", - "\n", - "# Set the range of x-axis\n", - "l_lim = 0.\n", - "u_lim = ((dt_fin - dt0).total_seconds() + 1.)/86400.\n", - "plt.xlim(l_lim, u_lim)\n", - "\n", - "# some research is required to set the vertical range\n", - "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", - "plt.ylabel(out_Q_unit.replace('_',' '), fontsize=12)\n", - "\n", - "plt.legend(loc='lower left')\n", - "\n", - "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", - "plt.savefig(img_name, format='jpg', dpi=300)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 497 - }, - "id": "up8Rj77x9bMs", - "outputId": "6d9820d3-ae88-49d6-95f8-d74eb80f5752" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAHgCAYAAABAcKICAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACn/ElEQVR4nOzdd1hTZxsG8DussBGQqThx4MA9wIHWvUfr3qNax1dt3aPuuq1V6x61tY6qdbTWat1b66677gniAgSU+X5/HBOIIZCEBEK4f9eVK8k5b855kgPk4Z0yIYQAEREREcEiuwMgIiIiMhVMjIiIiIg+YGJERERE9AETIyIiIqIPmBgRERERfcDEiIiIiOgDJkZEREREHzAxIiIiIvqAiRERERHRB0yMiEhvPXv2RKFChbI7DCIig2FiRCbr5MmTmDRpEiIiIrI7FI3Wrl0LmUwGW1tbPH36VG1/nTp1UKZMGbXtCQkJWLhwIapUqQInJyc4OjqiSpUqWLhwIRISElTKxsbGYvHixWjYsCF8fHzg5OSEChUqYOnSpUhKSjLaezOWbdu2oUOHDihSpAjs7e1RokQJDBs2TON1/v3331GxYkXY2tqiQIECmDhxIhITE1XKHDhwAL1790bx4sVhb2+PIkWKoG/fvggNDVU73vTp01G9enV4eHjA1tYWxYoVw9ChQ/HixQu1ssnJyZg9ezYKFy4MW1tbBAYGYuPGjWnGuXnzZlSvXh158uSBu7s7QkJC8Oeff+p1zOTkZKxduxYtW7aEn58fHBwcUKZMGUybNg3v37/X9NGmSZ9jrV69GgEBAcrPZ9GiRWpldLmOX331FSpWrAg3NzfY29sjICAAkyZNQnR0tFrZuLg4jBo1Cr6+vrCzs0O1atWwb9++NN/XsmXLUL58eTg6OsLLywtNmjTByZMn9Tqmuf2eUSYIIhM1Z84cAUDcv38/u0PR6McffxQABAAxePBgtf0hISGidOnSKtuio6NFSEiIACCaN28ufvjhB7FkyRLRsmVLAUCEhISI6OhoZfkrV64ImUwm6tevL2bPni2WLVsm2rRpIwCI7t27G/09pqdHjx6iYMGCOr3G3d1dlC1bVnzzzTdi5cqV4ssvvxQ2NjaiZMmSIjY2VqXs7t27hUwmE3Xr1hUrVqwQ//vf/4SFhYX44osvVMpVqlRJFC5cWIwcOVKsXLlSjBkzRjg5OQkvLy8RGhqqUrZt27aif//+Yv78+WLVqlVi2LBhwtnZWfj7+6t87kIIMXr0aAFAfP7552LFihWiWbNmAoDYuHGjSrmFCxcKAKJZs2Zi6dKlYv78+aJcuXICgPjtt990Pubbt28FAFG9enUxbdo0sWLFCtGrVy9hYWEh6tSpI5KTk7X+vHU91rJlywQA8emnn4oVK1aIbt26CQBi5syZKuV0uY41atQQX375pVi4cKFYsWKFGDBggJDL5aJGjRoiKSlJpWzHjh2FlZWVGD58uFi+fLkICgoSVlZW4tixYyrlvv76awFAdO3aVSxfvlzMmjVLFClSRFhZWYkzZ87ofExT/j2jrMXEiEyWtolRUlKSePfuXdYE9RFFYlS+fHkhl8vF06dPVfanlRj169dPABCLFi1SO94PP/wgAKh88b948UJcvXpVrWyvXr0EAHH79m0DvRvd6ZMYHTp0SG3bTz/9JACIlStXqmwvVaqUKFeunEhISFBuGzdunJDJZOLGjRvKbUeOHFH7gj1y5IgAIMaNG5dhTFu3blVLTp48eSKsra3FoEGDlNuSk5NFrVq1RP78+UViYqJye7FixUSVKlVUkozIyEjh6OgoWrZsqfMx4+LixIkTJ9TinDx5sgAg9u3bl+F7UtDlWLGxscLd3V00a9ZMpWyXLl2Eg4ODeP36tXKbLtcxLXPnzhUAxKlTp5Tbzpw5IwCIOXPmKLe9e/dOFC1aVAQFBSm3JSQkCDs7O/HZZ5+pHPPevXsCgPjyyy91PqYp/55R1mJTGpmkSZMmYcSIEQCAwoULQyaTQSaT4cGDB5DJZBg8eDDWr1+P0qVLQy6XY8+ePQCAixcvokmTJnB2doajoyPq1auH06dPqxxb0fx19OhR9O/fH+7u7nB2dkb37t3x5s0bveIdO3YskpKSMHPmzHTLPXnyBKtXr8Ynn3yCwYMHq+0fNGgQ6tati1WrVuHJkycAgLx586J06dJqZdu0aQMAuHHjhs7x/vLLL6hatSrs7e3h6uqK2rVr4++//1Yps2TJEuXn6+vri0GDBhmkWbNOnTpq29J6L9evX8f169fRr18/WFlZKbcPHDgQQghs3bpVua127dqwsFD9c1a7dm24ublp9fko+kmlfn87d+5EQkICBg4cqNwmk8kwYMAAPHnyBKdOnVJuj4qKgqenJ2QymXKb4mfQzs5O52Pa2NggODhYLU59rrkuxzp06BBevXqlEh8g/VzGxMSoNA1qex01Sesz37p1KywtLdGvXz/lNltbW/Tp0wenTp3C48ePAUhN0e/evYOXl5fKMT09PWFhYaHymWt7TGP8nlHOxMSITFLbtm3RqVMnAMD8+fOxbt06rFu3Dh4eHgCAgwcP4quvvkKHDh2wYMECFCpUCNeuXUOtWrVw+fJljBw5Et988w3u37+POnXq4MyZM2rnGDx4MG7cuIFJkyahe/fuWL9+PVq3bg0hhM7xFi5cGN27d8fKlSvx7NkzjeX++usvJCUloXv37hrLdO/eHYmJicpkT5OwsDAA0h90XUyePBndunWDtbU1pkyZgsmTJ8PPzw8HDx5Ulpk0aRIGDRoEX19fzJs3D59++imWL1+Ohg0bqvWBMoS03svFixcBAJUrV1Yp6+vri/z58yv3axIdHY3o6Og0Px8hBF6+fImwsDAcO3YMX375JSwtLVW+7C9evAgHBwcEBASovLZq1aoq8QFSkrBnzx4sWrQIDx48wM2bNzFo0CBERkZiyJAheh0zLfpec22Ppekzr1SpEiwsLDIVX2JiIl6+fIlnz57h77//xvjx4+Hk5KR874rzFy9eHM7OziqvVZS5dOkSACj7Ca1duxbr16/Ho0eP8O+//6Jnz55wdXVVSYK0PaY+74nMVDbXWBFppKkpDYCwsLAQ165dU9neunVrYWNjI+7evavc9uzZM+Hk5CRq166t3KZo/qpUqZKIj49Xbp89e7YAIHbu3Kl1jIpjnT17Vty9e1dYWVmpVON/3JQ2dOhQAUBcvHhR4zEvXLggAIivv/5aY5m4uDhRqlQpUbhwYZVmpozcvn1bWFhYiDZt2qg1PSmagcLDw4WNjY1o2LChShlFM9+aNWuU2/RpSktLnz59hKWlpfjvv/+U2xTX/9GjR2rlq1SpIqpXr57uMadOnSoAiAMHDqjtCw0NVfYNAyDy588vfv31V5UyzZo1E0WKFFF7bUxMjAAgRo8erdz2/PlzUa9ePZVj5s2bV5w8eVLvY6alfv36wtnZWbx58ybdctpI61iDBg0SlpaWaZb38PAQHTt2TPeYaV1HhVOnTql8PiVKlFBrjitdurT45JNP1F577do1AUAsW7ZMue327duiYsWKKscsUqSIuHnzpt7H/Ji+v2eUs7HGiHKkkJAQlCpVSvk8KSkJf//9N1q3bo0iRYoot/v4+KBz5844fvw4oqKiVI7Rr18/WFtbK58PGDAAVlZW2L17t14xFSlSBN26dcOKFSvSHA0FAG/fvgUAODk5aTyOYt/H8aY2ePBgXL9+HT/88INKM1NGduzYgeTkZEyYMEGt6UnRDLR//37Ex8dj6NChKmU+//xzODs7pznSKjM2bNiA1atXY9iwYShWrJhy+7t37wAAcrlc7TW2trbK/Wk5evQoJk+ejPbt2+OTTz5R2+/m5oZ9+/bhjz/+wJQpU5A3b161EVLv3r3TeO7U8QFQjsrq0aMHtmzZgjVr1sDHxwdt27bFnTt39Drmx6ZPn479+/dj5syZyJMnj8Zy2tB0rHfv3sHGxibN12T0mWu6jgqlSpXCvn37sGPHDowcORIODg6Z+sydnJxQunRpDBo0CNu2bcOSJUuQmJiI1q1b4+XLl3od82P6/p5RzsYrTTlS4cKFVZ6/ePECsbGxKFGihFrZgIAAJCcn4/Hjxyp9CD7+4+3o6AgfHx88ePBA77jGjx+PdevWYebMmViwYIHafkXSo0iQ0pJR8jRnzhysXLkSU6dORdOmTXWK7+7du7CwsFBJKj/28OFDAFD7LG1sbFCkSBHlfkM4duwY+vTpg0aNGuHbb79V2afoJxIXF6f2uvfv36v0I0nt5s2baNOmDcqUKYNVq1alWcbGxgb169cHADRv3hz16tVDjRo14OnpiebNmyvPr+ncqeMDgHbt2sHKygp//PGHclurVq1QrFgxjBs3Dr/++qvOx0zt119/xfjx49GnTx8MGDAgzTLaSu9YdnZ2iI+PT/N16X3m6V1HBWdnZ+Vn3qpVK2zYsAGtWrXChQsXUK5cOeX5tfl8EhMTUb9+fdSpU0dlKoH69eujdOnSmDNnDmbNmqXTMT+Wmd8zytlYY0Q5kqY/ZtmtSJEi6Nq1q8ZaI0Xfkn///VfjMRT70kpe1q5di1GjRuGLL77A+PHjDRR19rh8+TJatmyJMmXKYOvWrWr/kfv4+ABAmp9jaGgofH191bY/fvwYDRs2hIuLC3bv3p1uzVxqwcHB8PHxwfr161XOHxYWptbnTBGP4vz37t3Dnj170LJlS5Vybm5uqFmzJk6cOKHzMVPbt28funfvjmbNmmHZsmVavR9NMjqWj48PkpKSEB4errI9Pj4er169SjO+jK6jJm3btgUAbNq0SeX8mq43kPL5HD16FFevXlX7zIsVK4aAgAC1z1ybY6ZmTr9npDsmRmSyUo/wyYiHhwfs7e1x69YttX03b96EhYUF/Pz8VLbfvn1b5Xl0dDRCQ0MzPZPz+PHjkZiYqPyPNbUmTZrA0tIS69at0/j6n3/+GVZWVmjcuLHK9p07d6Jv375o27YtFi9erFdsRYsWRXJyMq5fv66xTMGCBQFA7bOMj4/H/fv3lfsz4+7du2jcuDE8PT2xe/duODo6qpUpX748AODcuXMq2589e4YnT54o9yu8evUKDRs2RFxcHPbu3atMrLT1/v17REZGqpw/NjZWbTSSoiO/4vzPnz8HgDQnAUxISFCZjFLbY6be3qZNG1SuXBmbN2/OVHOONsfS9JmfO3cOycnJavFpcx01iYuLQ3Jystpn/t9//6k1I2f2M9fmmAqG+D2jHC67OzkRabJ06dI0OyoDUJkHRqF169ZCLperdNYOCwsTzs7OOnW+3rFjh9Yxpu58nVrPnj2Fra2tKFGihNo8Rn379hUAxJIlSzS+5/79+6tsP3LkiLC1tRV169YV79+/1zq+j+nS+bpx48Yq8/IsWbLEIJ2vQ0NDRZEiRYSvr2+Gc1SVLFlSlCtXTmXOoPHjxwuZTCauX7+u3BYdHS2qVq0qnJycxLlz5zQeLzo6WsTExKhtV8xj9M033yi3PX78WOOcQ/ny5VPGFB4enuZkiY8fPxaOjo6icePGOh9TCCGuX78u3N3dRenSpVXmD9KHtseKjY0Vbm5uonnz5irbu3btKuzt7cWrV6+U27S9jm/evFH5PVNQzGO0evVq5bbTp0+rzTn0/v174e/vL6pVq6bcdu7cOQFA9OjRQ+WY58+fV5sAVNtjCmG43zPK2djHiExWpUqVAADjxo1Dx44dYW1tjRYtWmgsP23aNOzbtw81a9bEwIEDYWVlheXLlyMuLg6zZ89WKx8fH4969eqhffv2uHXrFpYsWYKaNWuqVc/rY9y4cVi3bh1u3bqlNjfK/PnzcfPmTQwcOBB79uxR1gzt3bsXO3fuREhICObNm6cs//DhQ7Rs2RIymQyfffYZtmzZonK8wMBABAYGahWXv78/xo0bh6lTp6JWrVpo27Yt5HI5zp49C19fX8yYMQMeHh4YM2YMJk+ejMaNG6Nly5bKz6dKlSro2rVrpj6bxo0b4969exg5ciSOHz+O48ePK/d5eXmhQYMGyudz5sxBy5Yt0bBhQ3Ts2BFXr17FDz/8gL59+6oMee/SpQv++ecf9O7dGzdu3FCpkXF0dETr1q0BSLWE9evXR4cOHVCyZElYWFjg3Llz+OWXX1CoUCGVofX58+fH0KFDMWfOHCQkJKBKlSrYsWMHjh07hvXr18PS0hKAVFvZu3dvrFq1CvXq1UPbtm3x9u1bLFmyBO/evcOYMWN0Pubbt2/RqFEjvHnzBiNGjFDr8F60aFEEBQVp9Xnrciw7OztMnToVgwYNQrt27dCoUSMcO3YMv/zyC7799lu4ubnpfB0PHz6ML7/8Ep999hmKFSuG+Ph4HDt2DNu2bUPlypVVfp6qVauGdu3aYcyYMQgPD4e/vz9++uknPHjwAKtXr1aWq1SpEho0aICffvoJUVFRaNiwIUJDQ7Fo0SLY2dlh6NChOh/TkL9nlMNld2ZGlJ6pU6eKfPnyCQsLC+XQfWioMRJCGureqFEj4ejoKOzt7UXdunXVhkwranmOHDki+vXrJ1xdXYWjo6Po0qWLyn/E2tBUYySEVJsCQK3GSAhpGPD8+fNFpUqVhIODg7C3txcVK1YU33//vdp/14cOHVIZkvzxbeLEiTrFLIQQa9asERUqVBByuVy4urqKkJAQtdmUf/jhB1GyZElhbW0tvLy8xIABA9SGietTY5TeewkJCVErv337duXM4vnz5xfjx49X+4wKFiyo8Zip43vx4oXo16+fKFmypHBwcBA2NjaiWLFiYujQoeLFixdq505KShLTp08XBQsWFDY2NqJ06dLil19+USuXkJAgFi1aJMqXLy8cHR2Fo6OjqFu3rjh48KBex1T8nGu6fVxTkh59jrVixQpRokQJYWNjI4oWLSrmz5+vtnSIttfxzp07onv37qJIkSLCzs5O2NraitKlS4uJEyeqLcEihDQr9fDhw4W3t7eQy+WiSpUqYs+ePWrlYmNjxZQpU0SpUqWEnZ2dcHFxEc2bN09zKgxtjmmM3zPKmWRC6DGbHVEOtnbtWvTq1Qtnz55Vm8iOiIhyN3a+JiIiIvqAfYyI0qBYTiI9Hh4eyj4hpkCxdIEmdnZ2cHFxMdr5X79+rXEOHACwtLRULulChvHixYs0R2Yp2NjYqPQLIqKMMTEiSsPcuXMxefLkdMvcv38/00P7DSmj4ek9evTA2rVrjXb+tm3b4siRIxr3FyxYMFOTZ5K6KlWqpDvhZkhICA4fPpx1ARGZAfYxIkrDvXv3cO/evXTL1KxZU7msgCnYv39/uvt9fX3TnfE6s86fP483b95o3G9nZ4caNWoY7fy50YkTJ9Jd0sLV1VU5upOItMPEiIiIiOgDdr4mIiIi+oCJEVEG1q5dC5lMplX/mEKFCqFnz55Gj4mIiIyDiRGZPEVikvrm6emJunXr4q+//sru8LJMVFQUJk+ejHLlysHR0RF2dnYoU6YMRo0ahWfPnqmV37VrFxo3bgx3d3fY2tqiePHiGD58OF69epXpWH7//XdUrFgRtra2KFCgACZOnKiyPhUgLfTZsmVL+Pn5wdbWFt7e3mjcuLHKAp8KCQkJmDx5MooUKQK5XI4iRYpg2rRpasfU5Pnz5+jVqxc8PT1hZ2eHihUrqs1crLBp0yZl7B4eHujTpw9evnyZZtnVq1cjICAAtra2KFasmMpK7qk9ffoU7du3R548eeDs7IxWrVqp9VF78OCB8uf3t99+UzvGpEmTIJPJNMaiEB0djYkTJ6Jx48Zwc3ODTCZLt1P9jRs30LhxYzg6OsLNzQ3dunXDixcvNMb28S31Iq+aHDhwAL1790bx4sVhb2+PIkWKoG/fvmku3gpIs85Pnz4dJUuWhK2tLby8vNCsWTM8efJEpVxcXBxGjRoFX19f2NnZoVq1ati3b1+axzx58iRq1qwJe3t7eHt748svv1QZWbp582bIZDJs375d7bXlypWDTCbDoUOH1PYVKFAAwcHBGX4GZEayc3ZJIm0oZpeeMmWKWLdunfj555/FnDlzROnSpQUA8ccff2TJ+TNa10sIaQZmXWYl1tbdu3dF4cKFhaWlpejYsaP44YcfxIoVK8TgwYOFu7u7KFasmEr5YcOGCQCiXLlyYtasWWLlypViwIABQi6Xi3z58ombN2/qHcvu3buFTCYTdevWFStWrBD/+9//1NanEkKIlStXilatWolp06aJVatWiTlz5ohy5coJCwsL8ddff6mUbd++vZDJZKJPnz5i6dKlylnDP//88wzjiYyMFP7+/sLJyUmMHz9e/PDDD6J27doCgFi/fr1KWcV6b/Xq1ROLFy8WY8aMEfb29iIwMFC8e/dOpeyyZcsEAPHpp5+KFStWiG7dugkAYubMmSrl3r59K4oVKyY8PT3FrFmzxHfffSf8/PxE/vz5xcuXL5XlUs9AHRgYqDaT9MSJEwWANGfgTk1xnAIFCog6deoIAOLHH39Ms+zjx49F3rx5RdGiRcWCBQvEt99+K1xdXUW5cuVEXFyc2jE7deok1q1bp3J78OBBuvEIIUSlSpVE4cKFxciRI8XKlSvFmDFjhJOTk/Dy8hKhoaEqZePj40X9+vWFvb29GDJkiFi9erWYO3euaNeunbh69apK2Y4dOworKysxfPhwsXz5chEUFCSsrKzEsWPHVMpdvHhR2NraigoVKoilS5eKcePGCblcrrJO3dOnTwUA8fXXX6u8NjIyUlhYWAgrKysxdepUlX2PHj0SAMSIESMy/AzIfDAxIpOnadmN169fC2tra9G5c+csOX9WJ0YJCQkiLi5OJCQkiHLlygl7e3u1LwQhpD/sY8eOVT7fsGGDACA6dOigsiipEEKcOXNG2Nvbi7Jly4qEhAS94ipVqpQoV66cyuvHjRsnZDKZuHHjRrqvjYmJEV5eXqJRo0bKbf/884/aAq5CSMmdTCYTly9fTveYisV/Dxw4oNyWlJQkqlSpIry9vZUJQFxcnMiTJ4+oXbu2SlLyxx9/CABi4cKFym2xsbHC3d1dNGvWTOVcXbp0EQ4ODioLsc6aNUsAEP/8849y240bN4SlpaUYM2aMcpsi+ShfvrwAIH777TeVY2ubGL1//16ZbJw9ezbdxGjAgAHCzs5OPHz4ULlt3759AoBYvny5WmypF1rVxZEjR9QWJT5y5IgAIMaNG6eyfdasWcLa2lqcOXMm3WOeOXNGLaZ3796JokWLiqCgIJWyTZo0ET4+PiIyMlK5beXKlQKA2Lt3r3Jb4cKFRdWqVVVeu2fPHiGTyUSnTp1Ufi6FSPld2rlzZ7qxknlhUxrlWHny5IGdnR2srFSn44qJicGwYcPg5+cHuVyOEiVKYO7cuRCpBmAqmg7SaoKQyWSYNGlSuucWQmDatGnInz8/7O3tUbduXVy7di3NshERERg6dKgyHn9/f8yaNQvJyclq8cydOxfff/89ihYtCrlcjuvXr+O3337D5cuXMW7cONSsWVPt+M7Ozvj222+VzydPngxXV1esWLFCbQLKqlWrYtSoUbhy5Qq2bt2a7ntMy/Xr13H9+nX069dP5XMfOHAghBAZHtPe3h4eHh6IiIhQbjt27BgAoGPHjiplO3bsCCEEfv3113SPeezYMXh4eOCTTz5RbrOwsED79u0RFhamnFvp6tWriIiIQIcOHSCTyZRlmzdvDkdHR5Umo0OHDuHVq1cYOHCgyrkGDRqEmJgYlYVYt27diipVqqBKlSrKbSVLlkS9evWwefNmtXg7duyI4sWLY8qUKSo/k9qSy+Xw9vbWquxvv/2G5s2bo0CBAspt9evXR/HixdOMDZB+f9KbqDMttWvXhoWFhdo2Nzc3lQV9k5OTsWDBArRp0wZVq1ZFYmIiYmNj0zzm1q1bYWlpiX79+im32draok+fPjh16hQeP34MQGpi3rdvH7p27QpnZ2dl2e7du8PR0VHlfdasWRMXL15UmeLgxIkTKF26NJo0aYLTp0+r/F6eOHECMpmM00zkMkyMKMeIjIzEy5cv8eLFC1y7dg0DBgxAdHS0yurcQgi0bNkS8+fPR+PGjfHdd9+hRIkSGDFiBL7++muDxTJhwgR88803KFeuHObMmYMiRYqgYcOGiImJUSkXGxuLkJAQ/PLLL+jevTsWLlyIGjVqYMyYMWnG8+OPP2LRokXo168f5s2bBzc3N/z+++8AgG7dumUY1+3bt3Hr1i20atVK5Usite7duwOQ+iDp6uLFiwCgtsacr68v8ufPr9yfWlRUFF6+fImbN29i7NixuHr1KurVq6fcHxcXB0Ca5yg1e3t7ANL8SOmJi4tTe21ar9d0HsW2ixcvKr8UNb3PSpUqwcLCQrk/OTkZ//77b5pr7lWtWhV3797F27dvVbZbWlpi/PjxuHz5cpr9XQzl6dOnCA8P1xhbWtdq8uTJcHR0hK2tLapUqYK///5b7/MrZo/Pmzevctv169fx7NkzBAYGol+/fnBwcICDgwMCAwPV+vdcvHgRxYsXV/s5rlq1KgDg0qVLAIArV64gMTFR7X3a2NigfPnyKu+zZs2aSEhIwJkzZ5TbTpw4geDgYAQHByMyMhJXr15V2VeyZEm4u7vr/TlQzsOZrynHqF+/vspzuVyONWvWoEGDBsptv//+Ow4ePIhp06Zh3LhxAKT/8tu1a4cFCxZg8ODBKFq0aKbiePHiBWbPno1mzZrhjz/+UNY+jBs3DtOnT1cp+9133+Hu3bu4ePEiihUrBgDo378/fH19MWfOHGXNlsKTJ09w584dlaUzbty4ARcXF5Vymly/fh2A1JlUk0KFCsHZ2VnlP3ltKTrTpjXLto+PT5qdwNu3b4+9e/cCkL6s+vfvj2+++Ua5v0SJEgCkL6HChQsrtytqkp4+fZpuTCVKlMD+/fvx8OFDFCxYUOPrixUrBplMhhMnTqBXr17Kcrdu3VJ2Rn7z5g3c3d0RGhoKS0tLeHp6qpzLxsYG7u7uyvf5+vVrxMXFafw8AODZs2fK96jQuXNnTJ06FVOmTEGbNm1UarAMJaNrpYhdLpfDwsICDRs2RJs2bZAvXz7cu3cP3333HZo0aYLff/8dzZo10/n833//PeLj49GhQwflttu3bwMA5s+fDzc3NyxfvhwAMH36dDRu3Bhnz55FYGCgMv6MPldt3qfi5wCAssb1+PHjqFOnDhITE3HmzBn06NEDRYsWhZeXF44fP47AwEC8ffsWV65cQe/evXV+75SzscaIcozFixdj37592LdvH3755RfUrVsXffv2xbZt25Rldu/eDUtLS3z55Zcqrx02bBiEEAYZxbZ//37Ex8fjf//7n8oX2tChQ9XKbtmyBbVq1YKrqytevnypvNWvXx9JSUk4evSoSvlPP/1UbT2xqKgoODk5aRWbonYio/JOTk6IiorS6pipKZog5HK52j5bW9s0Z2GeOXMm/v77b6xevRrVq1dHfHy8ymizpk2bomDBghg+fDi2bduGhw8fYvPmzRg3bhysrKzSndkZAPr27QtLS0u0b98eJ0+exN27dzFjxgxlbYzi9Xnz5kX79u3x008/Yd68ebh37x6OHTuGDh06wNraWqXsu3fvYGNjk+b5Ur/PjD6P1GVSS11rtGPHjnTfn750ia1AgQLYu3cvvvjiC7Ro0QJDhgzBxYsX4eHhgWHDhul87qNHj2Ly5Mlo3769ShOnYpTY27dvceDAAfTs2RM9e/bE/v37IYTA7NmzVeLXJnZdfiYDAgLg7u6O48ePAwAuX76MmJgY5aiz4OBg5ajJU6dOISkpKc3mazJvTIxSOXr0KFq0aAFfX1/IZDKd/2C9f/8ePXv2RNmyZWFlZYXWrVurlTl8+HCaQ2IzWgCUpCr0+vXro379+ujSpQv+/PNPlCpVCoMHD1b2iXj48CF8fX3VEoOAgADl/sxSHENRA6Tg4eEBV1dXlW23b9/Gnj174OHhoXJT1H6Fh4erlE9dY6Lg7Oys1hyjieJ9Z1T+7du3WidbqSmaoRTNUqm9f/8+zWaq8uXLo0GDBujduzf27duHf/75R2WuJ1tbW/z5559wd3fHp59+ikKFCqF79+6YMGEC3Nzc4OjomG5MgYGB2LBhA+7evYsaNWrA398fCxcuxPfffw8AKq9fvnw5mjZtiuHDh6No0aKoXbs2ypYtixYtWqiUtbOz09jPJvX7zOjzSF3mY126dIG/v7/efY0ykpnYAMDNzQ29evXCrVu31IbRp+fmzZto06YNypQpg1WrVqUZU40aNVRqQAsUKICaNWvi5MmTKmW1iV2Xn0mZTIbg4GBlX6ITJ07A09MT/v7+AFQTI8U9E6Pch4lRKjExMShXrhwWL16s1+uTkpJgZ2eHL7/8Uq3Z52O3bt1CaGio8vZxlT1lzMLCAnXr1kVoaKiyil5bmpou0lupXB/Jyclo0KCBsqbr49unn36qUj6tL6qSJUsiMjJS2dk0PYoE8N9//9VY5uHDh4iKitJr3TRFc0Va89OEhobC19c33dfb2NigZcuW2LZtm8p/8qVLl8bVq1dx9epVHDt2DM+ePcPnn3+Oly9fonjx4hnG9dlnn+HZs2f4559/cOrUKTx8+BBFihQBAJXXu7i4YOfOnXj48CGOHDmCBw8eYN26dQgNDYWHhwfy5MmjfJ9JSUlqiWt8fDxevXqlfJ9ubm6Qy+UaPw8AGj8TRa3RpUuXsHPnzgzfo64yulaK2NOjSF5ev36t1TkfP36Mhg0bwsXFBbt371ZLvhWfhZeXl9prPT09Vdba8/Hx0epz1fVnsmbNmoiMjMSVK1eU/YsUgoOD8fDhQzx9+hTHjx+Hr6+v8ueIcg8mRqk0adIE06ZNQ5s2bdLcHxcXh+HDhyNfvnxwcHBAtWrVVFaudnBwwNKlS/H5559nOGrE09MT3t7eytvHIzpIO4omGUUVfcGCBfHs2TO1GpObN28q9wNQ1uykHh0FaFejpDjGx8nYixcv1BZRLVq0KKKjo5U1XR/fUo8W0kRRm/HLL79kWLZ48eIoXrw4duzYobHW6OeffwYgjcbSVfny5QEA586dU9n+7NkzPHnyRLk/Pe/evYMQQi0+mUyG0qVLo2bNmnBzc8OhQ4eQnJyc4T8ZCjY2NqhSpQqqV68OGxsb5aK6ab2+QIECqF27NgoWLIiIiAicP39epZym93nu3DkkJycr91tYWKBs2bJq5QDgzJkzKFKkSLo1c127doW/vz8mT55s8FqjfPnywcPDI83Y/vnnH62ulWKSyo+bd9Py6tUrNGzYEHFxcdi7d2+afX7Kli0La2vrNPuNPXv2TOU85cuXx3///afW5KvoOK2Iv0yZMrCyslJ7n/Hx8bh06ZLa+0zdz+jEiRMqI84qVaoEuVyOw4cP48yZMxyNlltl0zQBJg+A2L59u8q2vn37iuDgYHH06FFx584dMWfOHCGXy8V///2n9voePXqIVq1aqW0/dOiQACAKFiwovL29Rf369cXx48eN9C7Mg6Z5jOLj40WxYsWEjY2Ncv6SHTt2CABi+vTpKmU7dOggZDKZuHPnjnJb3rx5RZs2bVTKKSZGnDhxotr5FfMYhYeHC2tra9GsWTOV+XDGjh0rAKjMYzRp0iQBQOzZs0ftfb1580Y5F1B688jEx8eLsmXLCgcHB3Hy5Em1/VFRUSrzGP3yyy8CgOjcubPaPEbnzp0TDg4OokyZMiI+Pl7tWNooWbKkKFeunMqxx48fL2Qymbh+/bpy2/Pnz9Ve++bNG+Hn5yf8/PzSPUdsbKyoWLGi8PHxEVFRUcrtMTEx4saNGxnO9fPff/8JJycn0bx58wzfzxdffCEsLCxU5iGKjY0Vbm5uaq/v2rWrsLe3F69evVJumzlzptrP582bN4WlpaUYNWqUcpuma7x27VqV+Y0yem+pZTSP0RdffCHs7OzEo0ePlNv2798vAIilS5cqt4WHh6u99smTJ8LV1VUEBgaqbH/27Jm4ceOGys9PdHS0qFq1qnBychLnzp1LN+ZWrVoJS0tLlTmvrl+/LiwtLcXAgQOV206fPq32eb1//174+/uLatWqqRyzcePGaj8rq1atEgDUJhONi4sTtra2IigoSAAQJ06cUNkfFBSk3LdgwYJ03wuZJyZGGnycGD18+FBYWlqKp0+fqpSrV6+eyiRuCpoSo5s3b4ply5aJc+fOiRMnTohevXoJKysrcf78eUO/BbPx8czX69atE/PmzROVKlUSAMTo0aOVZZOSkkTdunWFTCYT/fr1E4sXLxatWrUSAMTQoUNVjjt69GgBQDnbcqdOnZTHTC8xEkKIMWPGCACiadOm4ocffhB9+vQRvr6+Im/evCqJUUxMjKhYsaKwsrISffv2FUuXLhVz584VPXr0EA4ODsovwYwm2Lt9+7YoWLCgsLKyEp07dxaLFy8WK1asEEOGDBEeHh6iePHiKuWHDBmi/LKdM2eOWLVqlRg4cKCwtbVNc+ZrRcKe+n1r8scffwiZTCY++eQTsWLFCvHll18KCwsLtVmqK1asKFq2bCm+/fZbsXLlSvHNN9+I/PnzCwsLC7FlyxaVsu3atRNDhgwRy5cvF3PmzBEBAQFCLpeL/fv3axVnQECAmDBhgli1apUYN26ccHNzEwULFhRPnjxRKTdjxgzRpUsXsXDhQrFkyRLRsGFDAUBMmzZN7X0uXrxYABCfffaZWLlypejevbsAIL799luVclFRUaJo0aLC09NTzJ49W8yfP1/4+fkJX19flYRD0zVOSEgQRYsWVc6KrU1itGjRIjF16lQxYMAAAUC0bdtWTJ06VUydOlVEREQoyz169Ei4u7uLokWLioULF4rp06cLV1dXUbZsWfH+/XtluZ49e4patWqJSZMmiRUrVoixY8cKd3d3YWNjIw4dOqRybsWs5Kl/HxS/Y71791abOfvjfzCvXbsmHB0dhY+Pj5gxY4aYMWOG8PHxER4eHmrXq127dsLKykqMGDFCLF++XAQHBwsrKytx5MgRlXLnz58XcrlcZeZrW1tb0bBhwzQ/v1q1agkAQi6Xq3wOQqT8cwSAf5dzKSZGGnycGO3atUsAEA4ODio3Kysr0b59e7XXa0qM0lK7dm3RtWtXA0VufhSJSeqbra2tKF++vFi6dKna0gpv374VX331lfD19RXW1taiWLFiYs6cOWrlYmNjRZ8+fYSLi4twcnIS7du3F+Hh4VolRklJSWLy5MnCx8dH2NnZiTp16oirV6+mOfP127dvxZgxY4S/v7+wsbERefPmFcHBwWLu3LnK/7q1mXn4zZs3YsKECaJs2bLC3t5e2NraijJlyogxY8aoLbsghFR71qBBA+Hq6irkcrnw9/cXw4YNS/OLVzH787JlyzSeP7Xt27eL8uXLC7lcLvLnzy/Gjx+vVgP1ww8/iJo1a4q8efMKKysr4eHhIVq0aCGOHj2qdrxZs2aJkiVLCltbW+Hq6ipatmwpLl68qFZOU2LUsWNH4efnJ2xsbISvr6/44osv0qyx2rVrl7Jmw97eXlSvXl1s3rxZ4/tcsWKFKFGihLCxsRFFixYV8+fPV/s5EkJaeuOzzz4Tzs7OwtHRUTRv3lzcvn1bpUx61zj1z7g2iVHBggXVficUt49naL969apo2LChsLe3F3ny5BFdunQRYWFhKmU2bNggateuLTw8PISVlZWyNjWtxCCtxCi9eAoWLKh2jPPnz4v69esLBwcH4eTkJFq1apVmzfu7d+/E8OHDhbe3t5DL5aJKlSpp1r4KIcSxY8dEcHCwsLW1FR4eHmLQoEEqNUipKf6xCQ4OVtu3bds2AUA4OTmp1bhS7iATwgjDIcyAYrFBxciyX3/9FV26dMG1a9fUZhN2dHRU61PUs2dPREREaDWybcSIETh+/DhOnTplqPCJdDJy5Ehs3LgRd+7cybBDLhGROeMEj1qqUKGCcpRKrVq1DHrsS5cupdlRkSirHDp0CN988w2TIiLK9ZgYpRIdHY07d+4on9+/fx+XLl2Cm5sbihcvji5duqB79+6YN28eKlSogBcvXuDAgQMIDAxUzgx7/fp1xMfH4/Xr13j79q1y2nrFyIjvv/8ehQsXRunSpfH+/XusWrUKBw8ezNTU+0SZdfbs2ewOgYjIJLApLZXDhw+jbt26att79OiBtWvXIiEhAdOmTcPPP/+Mp0+fIm/evKhevTomT56MsmXLApCWW0hryLfiY549ezZWrFiBp0+fwt7eHoGBgZgwYUKa5yUiIqKsxcSIiIiI6APOKkhERET0ARMjIiIiog9yfefr5ORkPHv2DE5OThrXzyIiIiLTIj4sLeTr62vQZbVyfWL07NkzlVWeiYiIKOd4/Pgx8ufPb7Dj5frESLHA4+PHj+Hs7JzN0RAREZE2oqKi4Ofnl+5CzfrI9YmRovnM2dmZiREREVEOY+huMCbb+XrmzJmQyWQYOnSoxjJr166FTCZTudna2mZdkERERGRWTLLG6OzZs1i+fDkCAwMzLOvs7Ixbt24pn7MDNREREenL5GqMoqOj0aVLF6xcuRKurq4ZlpfJZPD29lbevLy8siBKIiIiMkcmlxgNGjQIzZo1Q/369bUqHx0djYIFC8LPzw+tWrXCtWvXjBwhERERmSuTakrbtGkTLly4oPWCliVKlMCaNWsQGBiIyMhIzJ07F8HBwbh27ZrGoXtxcXGIi4tTPo+KijJI7ERERJTzmUyN0ePHjzFkyBCsX79e6w7UQUFB6N69O8qXL4+QkBBs27YNHh4eWL58ucbXzJgxAy4uLsob5zAiIiIiBZNZRHbHjh1o06YNLC0tlduSkpIgk8lgYWGBuLg4lX2atGvXDlZWVti4cWOa+9OqMfLz80NkZCSH6xMREeUQUVFRcHFxMfj3t8k0pdWrVw9XrlxR2darVy+ULFkSo0aN0iopSkpKwpUrV9C0aVONZeRyOeRyeabjJSIiIvNjMomRk5MTypQpo7LNwcEB7u7uyu3du3dHvnz5MGPGDADAlClTUL16dfj7+yMiIgJz5szBw4cP0bdv3yyPn4iIiHI+k0mMtPHo0SOVheLevHmDzz//HGFhYXB1dUWlSpVw8uRJlCpVKhujJCIiopzKZPoYZRdjtVESERGR8Rjr+9tkRqURERERZbcc1ZRGRERZKz4eCA8HkpKAggWzOxoi42ONERERafTrr4CfH9CnT3ZHQpQ1mBgREZFGHh7S/YsX2RsHUVZhYkRERBp5ekr3TIwot2BiREREGilqjF6+BHL3GGbKLZgYERGRRorEKCEBiIzM3liIsgITIyIi0sjWFnB0lB6zOY1yAyZGRESULkWtUXh49sZBlBWYGBERUbo4Mo1yEyZGRESULiZGlJswMSIionRxyD7lJkyMiIgoXawxotyEiREREaWLiRHlJkyMiIgoXUyMKDdhYkREROnicH3KTZgYERFRulhjRLkJEyMiIkpX6sSI66WRuWNiRERE6VIkRvHxwNu32RsLkbExMSIionQ5OAD29tJjNqeRuWNiREREGWI/I8otmBgREVGGmBhRbsHEiIiIMsQh+5RbMDEiIqIMscaIcgsmRkRElCEmRpRbMDEiIqIMeXpK90yMyNwxMSIiogyxxohyCyZGRESUISZGlFswMSIiogwxMaLcgokRERFliOulUW7BxIiIiDKkSIzevQNiYrI3FiJjYmJEREQZcnQE5HLpMZvTyJyZbGI0c+ZMyGQyDB06NN1yW7ZsQcmSJWFra4uyZcti9+7dWRMgEVEuIpNxyD7lDiaZGJ09exbLly9HYGBguuVOnjyJTp06oU+fPrh48SJat26N1q1b4+rVq1kUKRFR7sEO2JQbmFxiFB0djS5dumDlypVwdXVNt+yCBQvQuHFjjBgxAgEBAZg6dSoqVqyIH374IYuiJSLKPZgYUW5gconRoEGD0KxZM9SvXz/DsqdOnVIr16hRI5w6dUrja+Li4hAVFaVyIyKijDExotzAKrsDSG3Tpk24cOECzp49q1X5sLAweHl5qWzz8vJCWFiYxtfMmDEDkydPzlScRES5ERMjyg1Mpsbo8ePHGDJkCNavXw9bW1ujnWfMmDGIjIxU3h4/fmy0cxERmRNFYhQenr1xEBmTydQYnT9/HuHh4ahYsaJyW1JSEo4ePYoffvgBcXFxsLS0VHmNt7c3nj9/rrLt+fPn8Pb21ngeuVwOuWLMKRERaY01RpQbmEyNUb169XDlyhVcunRJeatcuTK6dOmCS5cuqSVFABAUFIQDBw6obNu3bx+CgoKyKmwiolyDw/UpNzCZGiMnJyeUKVNGZZuDgwPc3d2V27t37458+fJhxowZAIAhQ4YgJCQE8+bNQ7NmzbBp0yacO3cOK1asyPL4iYjMHWuMKDcwmRojbTx69AihoaHK58HBwdiwYQNWrFiBcuXKYevWrdixY4dagkVERJnHxIhyA5kQuXs5wKioKLi4uCAyMhLOzs7ZHQ4RkcmKjATy5JEex8YCdnbZGg7lcsb6/s5RNUZERJR9nJ0Ba2vpMWuNyFwxMSIiIq3IZByyT+aPiREREWmN/YzI3BklMXr37h0ePXpkjEMTEVE24pB9Mnc6JUYHDx5E7dq14eHhgYCAAEyePBmxsbFq5bZt24bChQsbLEgiIjINrDEic6d1YnT+/Hk0atQIt27dQkhICNzd3TFlyhSUL18eN27cMGaMRERkIpgYkbnTOjGaOHEiChcujBs3bmDr1q04fvw4Dh8+jHfv3qFGjRo4fvy4MeMkIiITwMSIzJ3WidGFCxfQv39/uLm5KbfVqlULFy5cgL+/Pxo2bIidO3caJUgiIjINTIzI3GmdGEVHR8PFxUVtu4eHBw4fPoxatWqhXbt2WL16tUEDJCIi08Hh+mTutE6MihYtin/++SfNffb29ti1axfatm2Lfv36YenSpQYLkIiITAdrjMjcaZ0YNWjQAL/99luao9AAwNraGhs3bsQXX3yBkydPGixAIiIyHRyuT+bOStuCffr0gRACt27dQoUKFdIsI5PJsHjxYhQrVgyXL182WJBERGQaFDVGb98CcXGAXJ698RAZGheR5SKyRERaEwKwsQESE4HHj4H8+bM7IsqtuIgsERFlO5kMyJtXeszmNDJHeidGz58/N2QcRESUQ7ADNpkzvRKjGzduoHr16oaOhYiIcgAmRmTOdE6Mjh8/jlq1aqFcuXLGiIeIiEwc5zIic6ZTYrRlyxY0bNgQwcHB2Lp1q7FiIiIiE8Yh+2TOtE6M5s2bh44dO6JevXrYtm0brKy0HulPRERmhE1pZM60ToxGjBiB2rVrMykiIsrlmBiROdM6MfL29salS5dw6dIlI4ZDRESmjokRmTOtE6PTp0/D19cXDRs2xPnz540ZExERmTAmRmTOtE6MChQogBMnTiAwMBANGjTAhQsXjBkXERGZKCZGZM50GpWWJ08e7Nu3Dw0bNkSDBg2MFRMREZkwRWIUEQHEx2drKEQGp/M8RjY2Nti0aRP69OljjHiIiMjEubkBFh++PV6+zN5YiAxN7yVBZs+ebcg4iIgoh7Cw4HppZL64iCwREemM/YzIXDExIiIinTExInOl90yNx48fx5o1a3Dv3j28efMGQgiV/TKZDJcvX850gEREZHqYGJG50isx+u677zBixAjY2tqiRIkScHNzM3RcRERkwpgYkbnSKzGaM2cOatSogT/++AMuLi6GjomIiEycIjEKD8/eOIgMTa8+RrGxsejSpYvBk6KlS5ciMDAQzs7OcHZ2RlBQEP766y+N5deuXQuZTKZys7W1NWhMRESkjjVGZK70qjGqW7curly5YuhYkD9/fsycORPFihWDEAI//fQTWrVqhYsXL6J06dJpvsbZ2Rm3bt1SPpfJZAaPi4iIVHl6SvdMjMjc6FVjtGjRIhw4cABz587F69evDRZMixYt0LRpUxQrVgzFixfHt99+C0dHR5w+fVrja2QyGby9vZU3Ly8vg8VDRERpY40RmSu9EiM/Pz/0798fo0ePhoeHBxwcHJTNX4pbZpvZkpKSsGnTJsTExCAoKEhjuejoaBQsWBB+fn5o1aoVrl27lu5x4+LiEBUVpXIjIiLdMDEic6VXU9qECRPw7bffIl++fKhcubJB+xpduXIFQUFBeP/+PRwdHbF9+3aUKlUqzbIlSpTAmjVrEBgYiMjISMydOxfBwcG4du0a8ufPn+ZrZsyYgcmTJxssXiKi3EiRGL1+DSQmAlZ6T/5CZFpk4uMJiLTg6emJ6tWrY8eOHbCwMOwckfHx8Xj06BEiIyOxdetWrFq1CkeOHNGYHKWWkJCAgIAAdOrUCVOnTk2zTFxcHOLi4pTPo6Ki4Ofnh8jISDg7OxvsfRARmbOkJMDaGhACCAsD2IuBslpUVBRcXFwM/v2tV44fHx+PZs2aGTwpAqRFav39/QEAlSpVwtmzZ7FgwQIsX748w9daW1ujQoUKuHPnjsYycrkccrncYPESEeVGlpbSYrKvXknNaUyMyFzoldk0b94cx44dM3QsaUpOTlap4UlPUlISrly5Ah8fHyNHRUREnMuIzJFeNUYTJ05Ehw4dMHDgQPTp0wcFChSApaWlWjldZ8QeM2YMmjRpggIFCuDt27fYsGEDDh8+jL179wIAunfvjnz58mHGjBkAgClTpqB69erw9/dHREQE5syZg4cPH6Jv3776vC0iItKBpydw8yY7YJN50SsxKlGiBADg0qVL6TZxJSUl6XTc8PBwdO/eHaGhoXBxcUFgYCD27t2LBg0aAAAePXqk0nz35s0bfP755wgLC4OrqysqVaqEkydPatUfiYiIMocj08gc6T0qzRgTKa5evTrd/YcPH1Z5Pn/+fMyfP9/gcRARUcaYGJE50isxmjRpkoHDICKinIaJEZkjww8rIyKiXIGJEZkjvRKj8ePHo3z58hr3V6hQgZMoEhGZOSZGZI70Soy2bt2KJk2aaNzftGlT/Prrr3oHRUREpo/D9ckc6ZUYPXr0CEWLFtW4v3Dhwnj48KHeQRERkenz9JTuWWNE5kSvxMjR0THdxOf+/fuwtbXVOygiIjJ9ihqjV6+kJUKIzIFeiVGdOnWwfPlyPH36VG3f48ePsWLFCtStWzfTwRERkelyd5fuhZAWkyUyB3otInvr1i1UrVoVMpkMffr0QenSpQEAV69exZo1ayCEwOnTpxEQEGDwgA3NWIvQERHlBm5uwJs3wLVrAOfWpaxkUovIlihRAseOHcP//vc/tQkWa9eujYULF+aIpIiIiDLHw0NKjNjPiMyFXokRAAQGBuLIkSN4+fIl7t27BwAoUqQI8ubNa7DgiIjItHl4AP/9x8SIzIfWiVHz5s3RvHlzNG3aFAUKFFBuz5s3L5MhIqJcikP2ydxo3flaLpdj1KhRKFy4MMqWLYtRo0bh6NGjSE5ONmZ8RERkwjhkn8yN1onRb7/9hlevXmHfvn1o1KgR/vjjD9SpUwd58+ZFx44dsW7dOrzgbwYRUa7C2a/J3Og0XN/KygqffPIJ5s6di+vXr+PevXuYMmUKoqKi0L9/f/j4+KB69eqYOnUqzp8/b6yYiYjIRDAxInOTqUVkCxUqhMGDB2P37t149eoVduzYgUqVKmHNmjWoUqUKfH19uTQIEZEZY2JE5kbvUWkfs7OzU3bQBoBr165h9+7dsLS0NNQpiIjIxDAxInNjsMRICIFDhw4hLi4ONWvWROnSpZUTPxIRkXliYkTmRq+mtHHjxqks+SGEQMOGDdGgQQM0a9YMZcuWxd27dw0WJBERmSZFYvTyJcBBymQO9EqMfvvtN1StWlX5fOvWrThw4ACmTZuGXbt2ISkpCZMmTTJUjEREZKIUiVFSkjQDNlFOp1dT2tOnT+Hv7698vm3bNpQqVQpjxowBAAwYMABLly41TIRERGSybGwAFxcgMlJqTlMsLEuUU+lVY2RlZYW4uDgAUjPagQMH0LhxY+V+Ly8vvHz50jAREhGRSWM/IzIneiVGZcqUwS+//II3b97gxx9/xKtXr9CsWTPl/ocPH3KZECKiXIKJEZkTvZrSJkyYgBYtWiiTnxo1aqh0xv7zzz9RpUoVw0RIREQmjYkRmRO9EqMGDRrgwoUL2LdvH/LkyYMOHToo97158wa1a9dGq1atDBYkERGZLiZGZE70nseoVKlSKFWqlNp2V1dXzJ8/P1NBERFRzsHEiMxJpiZ4PH36NA4dOoTw8HAMHDgQxYoVQ2xsLG7evInixYvD0dHRUHESEZGJ8vSU7sPDszcOIkPQq/N1fHw82rZtixo1amDcuHFYuHAhHj9+LB3QwgINGzbEggULDBooERGZJtYYkTnRKzH65ptvsGvXLixduhS3bt2CEEK5z9bWFu3atcPOnTsNFiQREZkuJkZkTvRKjDZu3IgBAwagX79+cHNzU9sfEBCAe/fuZTo4IiIyfUyMyJzolRiFh4ejbNmyGvdbWloiNjZW76CIiCjnSL1eWqoGBKIcSa/EyM/PDzdv3tS4/8SJEypLhhARkflSJEYJCdLSIEQ5mV6JUefOnbF8+XKcOnVKuU0mkwEAVq5cic2bN6N79+46H3fp0qUIDAyEs7MznJ2dERQUhL/++ivd12zZsgUlS5aEra0typYti927d+t8XiIi0p+tLaAYhMzmNMrp9EqMxo0bh+DgYNSuXRt169aFTCbDV199hQIFCqB///5o3LgxvvrqK52Pmz9/fsycORPnz5/HuXPn8Mknn6BVq1a4du1amuVPnjyJTp06oU+fPrh48SJat26N1q1b4+rVq/q8LSIi0hOH7JO5kAmhX4uwEALr16/H1q1bcfv2bSQnJ6No0aJo3749unXrpqxByiw3NzfMmTMHffr0UdvXoUMHxMTEYNeuXcpt1atXR/ny5bFs2TKtjh8VFQUXFxdERkbC2dnZIDETEeU21asDZ84A27cDrVtndzSUGxjr+1vvCR5lMhm6du2Krl27GiyY1JKSkrBlyxbExMQgKCgozTKnTp3C119/rbKtUaNG2LFjh8bjxsXFIS4uTvk8KirKIPESEeVmHJlG5kKvpjRjunLlChwdHSGXy/HFF19g+/btaS49AgBhYWHw8vJS2ebl5YWwsDCNx58xYwZcXFyUNz8/P4PGT0SUGzExInOhVY3RJ598ovOBZTIZDhw4oPPrSpQogUuXLiEyMhJbt25Fjx49cOTIEY3Jka7GjBmjUssUFRXF5IiIKJOYGJG50CoxSk5O1rnPkJ5dl2BjY6Mc6l+pUiWcPXsWCxYswPLly9XKent74/nz5yrbnj9/Dm9vb43Hl8vlkMvlesVGRERpY2JE5kKrxOjw4cNGDkOz5ORklT5BqQUFBeHAgQMYOnSoctu+ffs09kkiIiLjYGJE5kLvztfGMGbMGDRp0gQFChTA27dvsWHDBhw+fBh79+4FAHTv3h358uXDjBkzAABDhgxBSEgI5s2bh2bNmmHTpk04d+4cVqxYkZ1vg4go1+FwfTIXeiVGjx490qpcgQIFdDpueHg4unfvjtDQULi4uCAwMBB79+5FgwYNlOe1sEjpLx4cHIwNGzZg/PjxGDt2LIoVK4YdO3agTJkyOp2XiIgyhzVGZC70msfIwsJCqz5HSUlJegWVlTiPERFR5j18CBQqBNjYAO/fAwaayo5II5Oax2jNmjVqiVFSUhIePHiAn3/+GZ6enhg0aJBBAiQiItOnqDGKjwfevgX4fyblVHolRj179tS4b9SoUahWrRoiuZIgEVGuYW8v3WJjpeY0JkaUUxl8gkcHBwf06tUL8+fPN/ShiYjIhLGfEZkDo8x8nZycnO7s00REZH6YGJE5MOhw/aioKBw9ehRz5sxBhQoVDHloIiIycYoh+0yMKCfTKzFKb1SaEAIFChTAkiVLMhUYERHlLIoaI85lRDmZXonRhAkT1BIjmUwGV1dXFC1aFA0bNoSVlUnNHUlEREbGpjQyB3plL5MmTTJwGERElNMxMSJzoFfn68TERERFRWncHxUVhcTERL2DIiKinIeJEZkDvRKjL7/8EsHBwRr316hRA8OGDdM7KCIiynmYGJE50Csx2rNnDz777DON+z/77DPs3r1b76CIiCjnYWJE5kCvxOjZs2fIly+fxv2+vr54+vSp3kEREVHOk3q4vu6rcBKZBr0SI3d3d9y6dUvj/hs3bnBBViKiXEZRY/TuHRATk72xEOlLr8SocePGWL58OS5evKi278KFC1ixYgWaNGmS6eCIiCjncHAAbG2lx2xOo5xKJoTuFZ7Pnj1DlSpVEB4ejpYtW6J06dIAgKtXr+KPP/6Ap6cnzpw5g/z58xs8YEOLioqCi4sLIiMjWctFRJRJBQoAjx8DZ84AVatmdzRkzoz1/a3XPEa+vr44d+4cRo8ejZ07d2L79u0AAGdnZ3Tp0gXTp0+Hr6+vwYIkIqKcwcNDSoxYY0Q5ld7TU/v4+OCnn36CEAIvPvwGeHh4aFwqhIiIzB9HplFOl+l1O2QyGeRyORwdHZkUERHlckyMKKfTq/M1AJw7dw6NGzeGvb093N3dceTIEQDAy5cv0apVKxw+fNhQMRIRUQ6Resg+UU6kV2J08uRJ1KxZE7dv30bXrl2RnJys3Jc3b15ERkZi+fLlBguSiIhyBkWNUXh49sZBpC+9EqOxY8ciICAA169fx/Tp09X2161bF2fOnMl0cERElLOwKY1yOr0So7Nnz6JXr16Qy+Vp9ivKly8fwsLCMh0cERHlLEyMKKfTKzGytrZWaT772NOnT+Ho6Kh3UERElDMxMaKcTq/EqHr16ti6dWua+2JiYvDjjz8iJCQkU4EREVHOw8SIcjq9EqPJkyfj3LlzaNasGf766y8AwOXLl7Fq1SpUqlQJL168wDfffGPQQImIyPQpEqOYGGnNNKKcRq8lQQDg4MGDGDBgAG7fvq2yvWjRoli1alWOqTHikiBERIYjhLReWnw88PChtEQIkTGY1JIgAPDJJ5/g1q1buHTpEm7fvo3k5GQULVoUlSpV4kSPRES5lEwm1Ro9fSoN2WdiRDlNpme+Ll++PMqXL2+AUIiIyBwoEiP2M6KcSKvE6OjRo3odvHbt2nq9joiIci52wKacTKvEqE6dOjo1jwkhIJPJkJSUpHdgRESUMzExopxMq8To0KFDxo6DiIjMBBMjysm0SoyyaoTZjBkzsG3bNty8eRN2dnYIDg7GrFmzUKJECY2vWbt2LXr16qWyTS6X4/3798YOl4iI0sDEiHIyveYxSi06Oho3btzAjRs3EB0dnaljHTlyBIMGDcLp06exb98+JCQkoGHDhoiJiUn3dc7OzggNDVXeHj58mKk4iIhIf56e0j0TI8qJ9B6VdvbsWYwcORLHjx9XLg9iYWGBWrVqYfbs2ahcubLOx9yzZ4/K87Vr18LT0xPnz59PtyO3TCaDt7e3zucjIiLDY40R5WR6JUZnzpxBnTp1YGNjg759+yIgIAAAcOPGDWzcuBG1a9fG4cOHUbVq1UwFFxkZCQBwc3NLt1x0dDQKFiyI5ORkVKxYEdOnT0fp0qUzdW4iItKPIjEKD8/eOIj0odfM1/Xr18eDBw9w/PhxtZqa58+fo0aNGihcuDD27dund2DJyclo2bIlIiIicPz4cY3lTp06hdu3byMwMBCRkZGYO3cujh49imvXriF//vxq5ePi4hAXF6d8HhUVBT8/P858TURkIP/9B5QoATg5AVFR2R2NGRk3DnBzA4YNy+5ITIKxZr7WKzFycnLChAkTMGLEiDT3z549G1OnTsXbt2/1DmzAgAH466+/cPz48TQTHE0SEhIQEBCATp06YerUqWr7J02ahMmTJ6ttZ2JERGQYb95I398A8P49IJdnbzxmITQU8PUFLC2BhARpivFczliJkV6dry0sLJCYmKhxf1JSEiws9O/XPXjwYOzatQuHDh3SKSkCAGtra1SoUAF37txJc/+YMWMQGRmpvD1+/FjvOImISF2ePIDVh44a7GdkIK9fS/dJSdJCdGQ0emUvwcHBWLx4cZqjvx49eoQlS5agRo0aOh9XCIHBgwdj+/btOHjwIAoXLqzzMZKSknDlyhX4+PikuV8ul8PZ2VnlRkREhiOTAXnzSo+ZGBlIRETKY05HY1R6db6ePn06ateujZIlS6JNmzYoXrw4AODWrVvYuXMnrKysMGPGDJ2PO2jQIGzYsAE7d+6Ek5MTwsLCAAAuLi6ws7MDAHTv3h358uVTHn/KlCmoXr06/P39ERERgTlz5uDhw4fo27evPm+NiIgMwMMDCAtjYmQwHwYjAZASIxeX7IvFzOmVGFWoUAFnzpzBuHHj8PvvvyM2NhYAYG9vj8aNG2PatGkoVaqUzsddunQpAGkJktR+/PFH9OzZE4BUI5W6me7Nmzf4/PPPERYWBldXV1SqVAknT57U6/xERGQYnMvIwD5OjMho9J7HqFSpUti+fTuSk5Px4sNPvoeHR6b6FmnTD/zw4cMqz+fPn4/58+frfU4iIjI8Dtk3MCZGWUbvxEjBwsICXl5ehoiFiIjMBCd5NDD2McoyeidGMTEx+O2333Dv3j28efNGrbZHJpNhwYIFmQ6QiIhynixPjEJDgaZNgT59gMGDs+ikWYg1RllGr8TowIEDaNeuHSJSZ7AfYWJERJR7ZXlidPQocOkSsGwZEyPKFL06BA0aNAgODg7Yu3cvIiIikJycrHZLSkoydKxERJRDZHlipFhs/O5d4MP6nWaFTWlZRq/E6NGjRxg5ciQaNGjAeYCIiEhNtiVG798DT55k0UmzEGuMsoxeiZFiXTIiIqK0ZPlwfUViBAC3b2fRSbMQE6Mso1diNGvWLCxZsgTnzp0zdDxERGQGFDVGERFZtIKFuSdGbErLMnp1vg4JCcH333+PoKAgBAQEwM/PD5aWliplZDIZdu7caZAgiYgoZ3F1ldY7TUoCXr6U1j81KnNPjFhjlGX0Sox+++03dO3aFUlJSXjy5Anevn2rVkbGlX+JiHItCwvA3V2a4PHFiyxIjD6swACAiRFlil6J0ejRo1GiRAn89ttvynXSiIiIUvPwSEmMjM6ca4ySkoDUFRBMjIxKrz5Gz549w4ABA5gUERGRRlk6Mi11YnTvnpRMmIuoKNXnTIyMSq/EqEqVKnj06JGhYyEiIjOSbYlRfDxgTt9RH48CZ2JkVHolRosWLcKmTZuwefNmQ8dDRERmIkuH7KdOjADzak5jYpSl9Opj1KVLFyQmJqJTp074/PPPkT9//jRHpV2+fNkgQRIRUc6TpTVGis7XdnbAu3fAnTtAw4ZZcOIs8PHyW0yMjEqvxMjNzQ3u7u4oVqyYoeMhIiIzoUiMwsOz4GSKGqOyZYF//mGNEelNr8To8OHDBg6DiIjMTbb0MSpfnokRZYpefYyIiIgykm2JEQDcupUFJ80iHydGcXHZE0cuwcSIiIiMIlsSo6AgQCaT+hiZy2Kyij5G1tbSPWuMjIqJERERGYUiMXr9GkhMNOKJEhKkGwAUKABUqyY93r3biCfNQooaI8UwPyZGRsXEiIiIjMLdXaq8AYBXr4x4otTLgTg4AM2aSY///NOIJ81CisTI21u6Z2JkVEyMiIjIKCwtpeQIMHJzmqIZzdISsLFJSYz27zePJELRlOblJd2bw3syYUyMiIjIaLJkyL4iMbK3l6qoypeXVq2NjQWOHDHiibOIosaIiVGWYGJERERGkyUdsBWJkYODdC+TAU2bSo81NaclJxsxIANjU1qWMkpiVKRIEdSsWRN79uwxxuGJiCiHyJLESNHHSJEYAar9jIRQLb97t1T7MnSoEYMyIDalZSmjJEbJycn477//0LRpU9SsWdMYpyAiohwgW2qMAKB+fam/0b17qnMarVwJtGwJvHwJ7NhhxKAMiE1pWcooidGDBw8QHh6Oq1evonPnzsY4BRER5QDZlhg5OgIhIdLj3bulWqNvvgH69QOSkqTtz57ljCY1NqVlKaP2MSpVqhQGDhxozFMQEZEJU0y9kyWJkb296nZFc9qOHUDPnsC0adLzsWOlfkgJCVk0+2QmxMdLi+ICqjVGHzcPksGw8zURERlNttUYASmJ0bFjwM8/S8P5V60Cvv02JWN7+tSIgRlA6uVAFDELkTKhZUYePgRKlgQWLzZ8bGZKq0Vke/furfOBZTIZVq9erfPriIjIfGTJcP20Ol8DgL8/ULw48N9/0r4tW4AmTaR9+fMDz59LiVHFikYMLpMUiZGjo3RTeP9e6kOVkT//lPpYjRgBfPppSnMcaaRVYnTw4EHIFNOXaknX8kREZH6ytcYIAKZMAZYvB2bPBipXTtmeLx9w/rzpr6emSIxcXFQToffvAWfnjF+v+ODfvQNmzgS+/97gIZobrRKjBw8eGDkMIiIyR4rE6NUrqc+zpaURTpJeYtShg3T7WP780n1mm9IePgSiooCyZTN3HE0UQ/VdXKR+Uba2UlKkbQfs1BnpsmXA8OEp753SxD5GRERkNIolQYSQFpM1ivQSI03y5ZPuM5MYCQHUrg1UqWK8KjFFjVGePNK9ra10r2tiJJMBcXHAjBkGDc8cZSoxun//PpYsWYJRo0Zh1KhRWLJkCe7fv6/38WbMmIEqVarAyckJnp6eaN26NW6lnn9Cgy1btqBkyZKwtbVF2bJlsdtcVlQmIsrhrK0BV1fpsdGa0zSNSkuPIjHKTFPaq1fAo0dSwnH1qv7HSU/qpjRA/8Sob1/pfuVKqZaLNNI7MRo2bBiKFSuGwYMHY86cOZgzZw4GDx6MYsWKYfjw4Xod88iRIxg0aBBOnz6Nffv2ISEhAQ0bNkSM4oc+DSdPnkSnTp3Qp08fXLx4Ea1bt0br1q1x1Vg/pEREpBOjD9nX1Pk6PYZoSrtzJ+Xxf//pf5z0GCox+uwz4JNPpNFs335r2BjNjF6J0bx58zB//ny0bdsWp06dQkREBCIiInDq1Cl89tlnmD9/PubPn6/zcffs2YOePXuidOnSKFeuHNauXYtHjx7h/PnzGl+zYMECNG7cGCNGjEBAQACmTp2KihUr4ocfftDnrRERkYEZvQN2ZprSMlNjlDoxun1b/+OkR9HHKLNNaR4eUkd0AFizBujfH7hyxVBRmhW9EqOVK1eiZcuW2Lx5M6pVqwZnZ2c4OzujWrVq2LRpE1q0aIHly5dnOrjID5mym5ubxjKnTp1C/fr1VbY1atQIp06dSrN8XFwcoqKiVG5ERGQ8akP2//1XmlcnMdEwJ8hMYvT2rXTTh6nXGCUnS0ufANJFqFED6NZN6gW/YgUQGAjUrQtwgJUKvRKjBw8eoFGjRhr3N2rUKNMj2ZKTkzF06FDUqFEDZcqU0VguLCwMXorZQD/w8vJCWFhYmuVnzJgBFxcX5c3Pzy9TcRIRUfpUaoz27QOCgoDBgwFDLTSuT2Lk5JQy3F3f5rS7d1MeG6vG6OPESC6X7rVJjCIiUpY/UVyEn34CDh+WmtYsLaXH//ufAQPO+fRKjDw9PXH58mWN+y9fvgwPxUXQ06BBg3D16lVs2rQpU8f52JgxYxAZGam8PX782KDHJyIiVcrE6J97QPPmKX2CDFVToU/nayCln5G+zWmpa4zu3k1JQgwpM01pimY0J6eUhEomk9aQ27IFOHtW2rZ7t+nPAJ6F9EqM2rVrh1WrVmHmzJkqHaNjYmIwa9YsrFq1Ch3SmjdCS4MHD8auXbtw6NAh5M9gvgVvb288f/5cZdvz58/hrWF2T7lcrmz6U9yIiMh4lInRX+ektb8UX+4f/e3Wmz6dr4HMD9lPnRglJBhntFdmmtJS9y9KS4UKQM2aUpPbTz9lLk4zoldiNHXqVISEhGDs2LFwdXVFoUKFUKhQIbi6umLMmDEICQnBFEUnLx0IITB48GBs374dBw8eROHChTN8TVBQEA4cOKCybd++fQgKCtL5/EREZHgeV6S/0S9EXqmPi2LksqESI32a0oDMdcCOjEzpv1OggHRvjOY0TYlRXFzGr1UkRophgWnp00e6X71aSpBIv8TI3t4eBw4cwPbt29G7d28EBAQgICAAvXv3xo4dO7B//37Y61qlCan57JdffsGGDRvg5OSEsLAwhIWF4Z1iZWEA3bt3x5gxY5TPhwwZgj179mDevHm4efMmJk2ahHPnzmHw4MH6vDUiIjKkefPguVqaVPCFWwlg7dqUhCS7E6PMDNlX9C/y8kpZa80YHbAN0ZSWXteWdu2kprZ794AjR/SN0qxotSSIJq1atUKrVq0MFQuWLl0KAKhTp47K9h9//BE9e/YEADx69AgWFin5XHBwMDZs2IDx48dj7NixKFasGHbs2JFuh20iIjIyIYDJk4HJk+GBQADAC2tfwEImJROAYRIjITJfY6RPYqRoRvP3B4oVkx5nZY2RoRIjBwegc2dpPblVq6RRaulJTgY2bQICAqSmODOkV2J0//59XL16FS1atEhz/x9//IGyZcuiUKFCOh1XCJFhmcOHD6tta9euHdq1a6fTuYiIyEiEkJrLvvsOAOAxsjcwG3j5UobkZMDCkIlRXFxKE1BWdr5WJEZFiwLFi0uPDVVjdOqUVFPUuLHxEyNAak5bvhz47Tfghx9Spir/WEwM0L07sG2blAwaa4qCbKZXU9rw4cOxcOFCjfsXL16M0aNH6x0UERHlUElJ0uSBH5IifP898k4dotz15g1Ua4y0+Ic4XYqO14B51Bg9fw7UqQM0bSqtw5aQIG03ZmJUubI0p1FcHLB+fdplnj6V4tm2TXp++3ZKTZ2Z0SsxOnXqFBo0aKBxf7169XDs2DG9gyIiohwoIUGqUVi5UhoWvno1MGQIbGxSvtdfvEBKYvTuHRAdnblzKr6cbWwAKx0bQRQ1Rs+fS6PldKHoY+Tvn1Jj9OCB7sf52IYNKcc4fly6t7AAHB2lx8ZIjGSylE7Yq1apJ6tRUdLcUxcuAHnzpsTCGqMUb968gZOTk8b9jo6OePXqld5BERFRDvP+vdSRd8MGKUHZuBHo3Vu5W2WSR0fHlGavzDan6du/CJC+5G1spMehobq9NnWNkbe39J6Sk6VOzJmhGDY/bhzQpo30uFAhKTkCjJMYAUDXrtJcR5cvSwlQan//DTx+LNWwnTkDlC8vbb95M+Pj5kB6JUYFChTAiRMnNO4/duxYhvMPERGRmYiJAVq0AHbulL5ct28HPprLTm29NEP1M8pMYiSTAb6+0mNdmtNiYoBnz6THRYtKxzFEc9rly9LNxgb4+mup2ersWWD//pQyxkqM3NyAtm2lx6tWqe47elS6b9MGKFIEKFlSen7rVsbHzYH0Sow6deqEjRs3YuHChUhONe9BUlISFixYgF9//RWdO3c2WJBERGSiIiKAhg2lL28HB2kW5ebN1YopptJRS4yUC6jpKTOJEaBfB2xFrZCrq5RQACmJUWaal37+Wbpv0SLluJUrA6nn9NM2MRJCt8QISGlO27BBte+WIjGqXVu6L1FCujfTGiO9RqWNGTMGx48fx9ChQ/Htt9+ixIcP6datW3jx4gXq1KmDcePGGTRQIiIyMS9eAI0aARcvSvPs/PUXUL16mkWNVmOk+ALXY+48APp1wE7djKaQ2RqjxMSUjs89emgup21i9PZtSl8lbROjunWlJOz+fWDrVqm/2Js30qK/AFCrlnTPGiN1crkcf//9N1avXo2qVavi5cuXePnyJapWrYo1a9Zg//79kCvWZSEiIvPz7Jm05tbFi9IX7+HDGpMiwESb0gD9aoxSd7xWyOyQ/b//lj4LDw9pmL4m2iZGig/a3l77pNHCQnUmbAA4cUKqfSpeXOpLBaTUGN26ZZazZes9waOFhQV69eqFXr16GTIeIiIydffvA/XrS01K+fNLzWiKL0sNFImRsuXMVBKjzNQYFS2asi2zNUaKTtedOwPW1prL6ZoY6bqge8+ewIQJUvPZf/+pN6MBUq2StbU0qvDxY6BgQd3OYeL0qjEiIqJc6uZNqUnl3j2pI+6xYxkmRYAeNUZRUcA//2QcT2YTIz8/6f7EiQ+TLGkhraY0RY3Rkyeq/XO08eaN1HEdSL8ZDTB+YpQvH9CkifR49eq0EyMrq5T3bobNaUyMiIhIO5cuSV+QT58CpUpJSZGWKxzonBj16wdUqwYcOpT+gTObGDVsKL2HJ0+kPjXaNA2llRi5u6fMGK3Yr60tW6TJFcuWTRkKr4mxEyMgpTntxx+B8+elx6kTIyCln5EZdsBmYkRERBk7dUrqnPvihbRo6pEjKUPdtaBzYqToq5PGMlAqFBNE6psYOTtLS2HI5cCuXcD06emXj4uTmo8A1cQI0L85TdGM1r27NPQ/PVmRGDVvLg0jfPFC6hReoIB6c5kZd8BmYkREROk7eBBo0EAaml+jhvQ8b16dDqEYrv/y5YeJlTNKjF6/lu4vXkz/wG/fSvfOzjrFo6JiRWDJEunxhAlSR2hNHjyQapUcHVPelII+HbDv3AFOnpQ6PnfpknH5rEiMrK2lvkYKH9cWAWY9ZJ+JERERabZrl7RuV0yMlBzt3ZuyvocOFN/PCQkf1kVVJEZv30qdeD+m6O+jbWKUzmoMWundG/j8cylr+/przeVSd7z+uHZH0Rn7/n3tz6uYu6hRI8DHJ+PyWZEYASqzlqeZGLEpjYiIcp1ff5VmO46LA1q3Bv74Q+8mK7k8JXd58QJSDY9iWpePa42SkqTO14DU9+flS80HNlRiBADTpkn316+nHPdjafUvUlDUoilquzKSnJySGHXvrt1rFJ+ZsROjEiWk2cvd3YFmzdLeD0jTNmj6rHIonROjd+/e4dq1a3iezhDL+/fv42fFxSYiopxn9WqgUyepj0mXLsDmzSlfynpSGbIvk2luTouIUH1+6ZLmgxoyMfL0lPpNCZEyqeHH0prDSEExW7W2o9uOHQMePpRq4Fq10u41WVVjBEgzYL94kXZfsjx5Uq6fmfUz0ikxmjx5Mtzd3REYGAhfX1/UrVsXV69eVSt38uRJzm9ERJRTLVgA9O0rJQj9+0u1GunNraMlrTtgf1zjkl5zmiETIwCoUEG615SMpVdjpBiVpm1ipOh03b49YGen3WtSJ0ZCaC5niMTIwiL9zuCpJ3o0I1onRps3b8bkyZMRHByMBQsWYPTo0bhx4waqVKmCDRs2GDNGIiLKCkJIzUlDh0rPhw0Dli5NWdk9k7ROjD5OLLIyMVIMl9d0zrQmd1RQJEbaNKW9fy8N0we0b0YDUhKj5GSpNk8TQyRGGTHTfkZaz3w9f/58fPLJJ9ifapXf4cOHo3PnzujWrRuePXuG4cOHGyVIIiIyMiGA0aOB2bOl55MnA998k/HwcR3kiMQovRqjxMSUjtWZbUq7elWaasDDQxrppy1FYgRIyVVcnDSVQkgIYGMjbY+NTZlkkomRzrT+N+DmzZv49NNPVba5urpi9+7d+OKLLzBq1CgmRkREOVFyMjBoUEpS9N130rB1AyZFQMrodq0To4AA6f7WrZSJHD9mrBqjK1ekIXSpPX4sJUdyecpSIqkpaoyiotKvzQGkxAiQJnXU5XNO3c/r/Xtp9FjDhkClSsCZM9J2xQdsY2O4zyUtub0pzdraGolpXGiZTIbFixdjwoQJ+O6779CjRw8kfPzDREREpikxUZqzZulS6Qt6xQrgq6+Mciqd+xiVLCktXJpeZ2hDJ0aFC0sj5uLj1WtCUjejpdW8mCdPyuOPO5B/7No16b50ad3is7BIqRl69AjYtk16fPUqEBQkTTXw8KG0zcPD4MmtCkWN0X//SSMJzYTWiVFAQAAOpTM1+8SJE7Fo0SKsX78eo0ePNkhwRERkRHFx0pDsdesAS0tg/XppLh8jUUuMFFVImmqM3NxSmrbSak5LSkppMjJUYmRhAZQrl/Y50+tfBEgd1BVxZNScpm9iBKQ0p61eLX0GFSoA3bpJCeT8+dK8U4Bxm9EAaTZsuVz6OXr0yLjnykJaJ0bNmzfH7t278VgxFXoaBg0ahPXr1+ONtj3yiYgoe8TGSkPEt22TaiC2bZOG5xuRzn2MXF3TT4wUy4EAhm0y0tTPKL0RaQradsA2RGK0bp1036+fNHLwr7+k5TsUzY7GTowsLVOWQTGjfkZaJ0Z9+vTBb7/9huQMFtjr0KEDjh8/jjVr1mQ6OCIiMoKoKKBxY2kWa3t74M8/gZYtjX5alXmMgMwnRopmNGvrTM+xpELTyDRtEiNtOmC/fZtSw5KZxCg6WnrfHTtKzxs3lprU/vc/qearTh3dj60rM+yArfWoNDc3NzRLa/bLNFSpUgVVqlTROygiIjKS9++BevWAc+ekiQV37waCg7Pk1KlrjIQAZIrEKCJCao5RJDeK2pbUiZGiM3Tq+ZQM3b9IIXWNkRAp/XTSm9xRQZu5jK5fl+59fFLK6yL1yLQ2bVT7Njk5AQsXArNmaT83UmaYYQdsrROj1OLi4nDixAncuHEDUVFRcHJyQqlSpVCjRg3IDZm1ExGRYe3eLSVFbm7Avn3SAqpZRJEYxcdLOY2zqytgZSV1AA8PB/z8pAKp+xgpOkNHRQE3bgCBgSkHVCwbYujEqFQpKQGLiJA6MhcqJI3c0yUxSq8pTTEirUwZ/eJLnRilXuw1taxIioDcXWMEAEIIzJ07F7NmzcKbN28gUs26KZPJ4Orqqhy2LzNmT3giItKPojmoceMsTYoAqdXOwUHqAvPiBeDsbCF1wH72TGpO+zgxcnWVmoTKlweOHpWatlInRsaqMbKxkZq4Ll2SboUKSTG+fy8lcgUKaH6tNk1pmelfBKQkRvnyAfXr63cMQzHDGiOdpjPt0qULRo0aBXd3d0yYMAHbtm3Dvn37sG3bNkyYMAHu7u4YPXo0unbtaqx4iYgoMxQTFBYpki2n16oDdurECNDcz8hYiVFa51QklIUKScmRJtrUGGU2MVLUBnXvLnWAzk6KxCgsLOMpCnIIrWuM1q1bh02bNmH48OGYMWMGLD+6GK1bt8Y333yDsWPHYs6cOWjSpAkTJCIiU3PvnnRfuHC2nN7DA3jwIIPEKHUfIyB7EiNFB+ydO4EBA7RrRgOypsbof/+T3vOQIfq93pCcnaW+UqGhUq1RtWrZHVGmaV1jtHLlSoSEhGD27NlqSZHyYBYWmDlzJkJCQrBixQqDBUlERAZi6jVGCQkpw80VSUbqztCpR0YbMzFq2lRqsrp8WUpg1q6VtmeUGGXU+ToiAnj6VHpcqpR+sX36KfDHHymfXXZT9DMyk+Y0rROjf//9V21JEE3atm2LfzXNUkpERFknKUmamVjx+MED6XE21hgB6QzZT51QuLhI9wEB0oi1qKiUxA4wbmLk7w/884+UlL1+DRw/Lm3XNLmjQkZNaYraovz5U95fTmdmHbC1TowSEhJgm7onfDrkcnmay4cQEVEWevJEqnUpX14aCvbsmVQjY2UlfTFngwxrjBSJkYtLSv8Za+uUEVypm9OMmRgB0jpmZ85IC+oq+hWVLZv+azJqSlMkRvqOSDNFZtYBW+vEyN/fH0ePHtWq7LFjx1BEj2rao0ePokWLFvD19YVMJsOOHTvSLX/48GHIZDK1W1hYmM7nJiIyO/nySTUt794BZ8+m1LYULJhtnXYzTIw+7l+kkFY/I2MnRoCUlE2YIDXjbdgAfPJJ+uUzakrLbP8iU5Rba4w+++wzbNy4EX/++We65f78809s3LgR7dq10zmYmJgYlCtXDosXL9bpdbdu3UJoaKjy5qlYf4eIKDeTyYCQEOnx4cPZ3vEaSFkeLcMaI0XNi0J2JUYKpUtLS6ZkNBWNtk1p5pQYKWqMbt+W5qTK4bROjIYNG4YSJUqgdevW6NevH44dO4aoqCgIIRAVFYXjx4+jX79+aN26NUqUKIFhw4bpHEyTJk0wbdo0tGnTRqfXeXp6wtvbW3mzSGvVYyKi3EixLMSRIyk1RtmYGGndlGYqNUa6UiR0795J8x59zBwTowIFpI7qCQkpfdhyMK0zCHt7exw8eBD16tXDqlWrUKdOHbi6usLKygqurq4ICQnBqlWrULduXezfvx/29vbGjFtF+fLl4ePjgwYNGuDEiRPplo2Li0NUVJTKjYjIbClqjE6cSOmEnU0j0oB0EqNXr6TaBk2JUWCgVFsTFibdANNMjJydU2qVPm5Oe/UqJXZ9R6SZIgsLoHhx6bEZNKfpNPO1p6cn9uzZgzNnzuCPP/7A9evX8fbtWzg5OSEgIADNmzdHUFCQsWJV4+Pjg2XLlqFy5cqIi4tTJmxnzpxBRQ0zus6YMQOTJ0/OshiJiLJVqVJA3rzAy5fSYrGAydQYCQHI3N2lL9bkZGmjpj5GDg5Sk83Nm1KtUZMmppkYWVhIsb9+LSVGPj4p+xS1RQULAo6O2ROfsZQsCfz7r9QBu3nz7I4mU/RaK61atWqoZgKTOJUoUQIlFG2bAIKDg3H37l3Mnz8f69atS/M1Y8aMwddff618HhUVBT/FNPRERObGwgKoXRvYti0lkTCBxOjdO2m6IkdHS2nj8+fSTVMfI0BqTjP1xAhQTYxSM8cRaQpm1AHb7DrjVK1aFXcUU7enQS6Xw9nZWeVGRGTWFP2MFLKxKc3BIWWprzT7GWlqSgPU+xkpEiNT+zuuqQO2OfYvUlBUUphBYqR1jVHLli11OrBMJsPOnTt1DiizLl26BJ/UVZdERLmdop8RIDXhuLtnWygymVRB9PixlBgVLozMJ0amVmOkaS4jc06MzGj2a60To3///ReyjIYpAnj//j2eP3+uVdmPRUdHq9T23L9/H5cuXYKbmxsKFCiAMWPG4OnTp/j5558BAN9//z0KFy6M0qVL4/3791i1ahUOHjyIv//+W+dzExGZrTJlpC/r16+lTESPv8+G5OmZkhgBUE2MNPUxAlISo7t3gchI002MNM1lZM6JkaLztaKfWFpNoTmE1onRgwyG4L1//x7Lly/HrFmzAAC1a9fWOZhz586hbt26yueKvkA9evTA2rVrERoaikePHin3x8fHY9iwYXj69Cns7e0RGBiI/fv3qxyDiCjXU/Qz2rEjW5vRFNIdsp9eHyN3d8DPT8qqzp6VhocDppcYKWJP3ZQWHi69YZlMWuLE3Dg6SrOpP3ki1Rpl4UAsQ9Or83VqcXFxWLZsGWbPno2wsDCEhIRg48aNCElddaulOnXqQAihcf9axSJ+H4wcORIjR47U+TxERLlOly5SYlS/fnZHol1ilFaNESDVGj1+LM3LpGBqI7zSqjFS1BYVLgxk4XQ2WapECSkxunkzRydGene+jouLw4IFC1CkSBF89dVXKFGiBA4dOoSDBw/qlRQREZERffaZlIkMHJjdkaSfGKXXlAakNKcplqiyt8+25U00SqvztTk3oymYST8jnWuM4uLisHTpUmUNUd26dbFp0ybUqlXLGPEREZGh5M2b3REASEmMwsM/bFAkRg8fpswWnVFidOaMdG9qzWhA2p2vzXmovoKZDNnXOjF6//49li5dijlz5iAsLAyffPIJfv31VyZERESkE401RnfvSvcWFpqH4CsSo7g46d4UE6P0mtLMucbITIbsa50YFS5cGOHh4ShfvjyWLl2qTIhea1ooD4BbDu6VTkRExqExMUpKku7z5JGSo7T4+aWMsANMOzFSxChE7kiMFDVGd+5Iy88oRqrlMFonRs8/LPB38eJFtG3bVqvXJCl+yImIiD7w9JTulYmRIlNS0NSMBkijuipUAA4ckJ6bYmL0cVOaou+UhUVK8mCO8ueXOvfv3w907Sqtz2dtnd1R6UzrxGjixInGjIOIiHIJtRojKytpKP6rV9Lz9BIjwPQTo9RNaUIAV69Kz4sWTZn22xzJZMCaNdKCv2fPAtOmATlwbVImRkRElKUUiVFMjLRmmp0dpOY0RWKUUTcMRT8jwDQTI0X8CQnSm8wNzWgKfn7A0qVAp07At98CjRvnuKH7ZrdWGhERmTYnJ8DGRnqs1s8I0K7GKPXBTI29fUoT0ps3uWNEWmodOwKdO0t9xrp1A6KjszsinTAxIiKiLKVYLw3QMzEqXjxlkkRTTIxkMtUO2Lmpxkhh8WKp9ujuXeCrr7I7Gp0wMSIioizn7S3dT578YfCWLomRpaXUjwUwzcQIUO2AnRsTozx5gJ9/lpLEVauAbFhUXl9MjIiIKMuNHSs1p/3xh9QydvJ9xZSd2kz1oljaxFRHeSmSuytXpAVvLS1z7PB1vdWpAwwfLj3u2xcIC8vWcLTFxIiIiLJc27bAyZOAvz/w6BFQe2VXzMBoJEOWcY0RAEycCNy4AXToYPxg9aFI7o4fl+6LFQPk8uyLJ7tMnQqUKwe8fAn8+mt2R6MVJkZERJQtKlUCLlyQ1rdNSrbAWMxAI+xFGLwzfrGVlVRbJJMZP1B9KJK7Y8ek+9zUjJaaXA788ouUFA0Zkt3RaEXntdJS27VrF3bv3o0HDx4AAAoVKoSmTZuiefPmhoiNiIjMnJMTsG4dUK/IfQye6on9aIByI+Kxzg9o2DC7o8sERWIUGird55YRaWkpUyZHvX+9aowiIiJQt25dtGrVCqtWrcK1a9dw7do1rFq1Cq1atUKdOnUQERFh4FCJiMgcyWRAr8+tcA6VURb/IvyNDRo1AsaMkaYCypE+7ieVW2uMciC9EqMhQ4bg2LFjmDVrFt68eYOHDx/i4cOHePPmDWbOnInjx49jSA6pMiMiIhPg6YkA3MQZVMOAbtK8NzNnAiEhwMOH2RybPj7uJ8XEKMfQqyltx44dGDhwIIYrept/4ODggBEjRuDRo0f4+eefDRIgERHlAnI5MHo07MLDsWSVAz5pKQ1kOnUKKF8eWL1a6rCdY6ROjKytpc7XlCPoVWNkbW2NEiVKaNxfsmRJWOfAheOIiCgbzZghZUAyGT77DLh0CahWDYiIAD79FBg0CHj/PruD1FLqprTixXPkYqq5lV6J0aeffootW7YgKSlJbV9iYiI2b96Mdu3aZTo4IiLKvQoVkgZ1jRolPV+yREqUbt7M1rC0k7rGiM1oOYpeTWldu3bF4MGDERwcjH79+sHf3x8AcPv2baxYsQLx8fHo0qULLly4oPK6ihUrpnU4IiKiNFlbS32N6taVlt36919pmP+SJUCPHtkdXTpS1xjloBFZBMiEEELXF1lYqFY0yT7MI5H6ULJUc0sIISCTydKsYcpuUVFRcHFxQWRkJJydnbM7HCIi0iA0FOjaFTh4UHrerZu0JJdJrgoSFgb4+EiPf/sth3WQyhmM9f2tV43Rjz/+aLAAiIiItOHjA/z9t1SDNHGiNP/R6dPS3IEVKmR3dB9hU1qOpVeNkTlhjRERUc5z/DjQuTPw+LG05trcucDgwSY2EfaQIdIisj/9ZGKBmQdjfX9nOjGKjo7G48ePAQB+fn5wdHQ0SGBZhYkREVHO9Po10Lt3ysLtrVoBa9ZotwYt5XzG+v7We620s2fPom7dunB1dUWZMmVQpkwZuLq64pNPPsG5c+cMFiAREVFa3NyA7duBhQulWqOdO6U5j06cyO7IKCfTq8bozJkzqFOnDmxsbNC5c2cEBAQAAG7cuIGNGzciPj4ehw8fRtWqVQ0esKGxxoiIKOe7cAHo2BG4fRuwtAQmTwZGj5Yek3kyqaa0+vXr48GDBzh+/Di8vVVXQX7+/Dlq1KiBwoULY9++fQYL1FiYGBERmYe3b4GBA6XF3AGgXj2pg7ZicBiZF5NqSjtz5gz69++vlhQBgJeXF/r164fTp09nOjgiIiJtOTkBP/8MrF0L2NsDBw5ITWt792Z3ZJST6JUYWVhYIDExUeP+pKQktbmOiIiIjE0mkyZ+PH8eCAwEwsOBxo2l2bMTErI7OsoJ9MpegoODsXjxYjxMY8njR48eYcmSJahRo0amgyMiItJHyZLSHEcDB0rPZ88GatcGHjzI1rAoB9Crj9HFixdRq1YtJCUloU2bNihevDgA4NatW9i5cyesrKxw7NgxlCtXzuABGxr7GBERmbdt24A+faTFaF1cpHVqP/00u6OizDKpPkYVKlTAP//8g8aNG+P333/HlClTMGXKFPzxxx9o3LgxTp8+rVdSdPToUbRo0QK+vr6QyWTYsWNHhq85fPgwKlasCLlcDn9/f6xdu1b3N0RERGarbVvg4kWgenUgMhL47DOpJundu+yOjEyRzolRXFwcfv/9dyQmJmL79u2IiopCaGgoQkNDERUVhW3btqFUqVJ6BRMTE4Ny5cph8eLFWpW/f/8+mjVrhrp16+LSpUsYOnQo+vbti73saUdERKkUKgQcPSoN4QeApUulROnGjWwNi0yQzk1pQgjY2tpiwYIF+OKLL4wVF2QyGbZv347WrVtrLDNq1Cj8+eefuHr1qnJbx44dERERgT179mh1HjalERHlLn//LS1AGx4ujV774QegZ0+u2pHTmExTmkwmQ7FixfDy5UuDBaGvU6dOoX79+irbGjVqhFOnTmVTREREZOoaNgQuXwbq1wdiY6VlRbp1k+ZBItKrj9HYsWPxww8/4NatW4aORydhYWHw8vJS2ebl5YWoqCi809B4HBcXh6ioKJUbERHlLt7e0vxG334rzY69fj1QsaI0gzblblb6vOj06dNwd3dHmTJlUKdOHRQqVAh2dnYqZWQyGRYsWGCQIA1pxowZmDx5cnaHQURE2czCAhg7FggJATp1Au7cAYKCgDlzgP/9j01ruZVew/W1mbxRJpMhKSlJr6AUr8+oj1Ht2rVRsWJFfP/998ptP/74I4YOHYrIyMg0XxMXF4e4uDjl86ioKPj5+bGPERFRLvb6tTSkXzEYumVLYM0awN09W8OidJhMHyMASE5OzvCWmaRIW0FBQThw4IDKtn379iEoKEjja+RyOZydnVVuRESUu7m5SfMdLVoE2NgAv/8uLSdy7Fh2R0ZZTa/E6NGjRxr78ADAu3fv8OjRI52PGx0djUuXLuHSpUsApOH4ly5dUh5rzJgx6N69u7L8F198gXv37mHkyJG4efMmlixZgs2bN+Orr77S+dxERJS7yWTA4MHAmTNA8eLAkydAnTrAtGlAFvyvTyZCr8SocOHC2L59u8b9v//+OwoXLqzzcc+dO4cKFSqgQoUKAICvv/4aFSpUwIQJEwAAoaGhKglX4cKF8eeff2Lfvn0oV64c5s2bh1WrVqFRo0Y6n5uIiAiQaorOn5dGqiUnA998I41k41id3EGvztcZdUtKSEjQaxHZOnXqpHvstGa1rlOnDi5evKjzuYiIiDRxdAR+/lka0j9wIHDwoDSCbdas7I6MjE3rxCgqKgoRERHK569evUqzuSwiIgKbNm2Cj4+PQQIkIiLKLt27A66uUmfsxYuBESOAvHmzOyoyJq1HpU2ePBlTpkzR6qBCCEybNg1jx47NVHBZgTNfExFReoQAqlSRmtdGjwZmzMjuiAgw3ve31onRqVOncPLkSQghMHLkSHTq1AkVK1ZUPZhMBgcHB1SqVAmVK1c2WJDGxMSIiIgy8vvvQKtWUhPbgwccxm8KjPX9rXVTWlBQkHIYfExMDD799FOUKVPGYIEQERGZqhYtgAoVgIsXgfnzpZFqZJ70muDRnLDGiIiItLFjB9CmDeDkJNUaublld0S5m0lN8EhERJTbtGwJBAZKi82mWnCBzAxrjLTMOJOSkpCQkJCFkZG2rK2tYWlpmd1hEFEu8NtvwGefAc7OwMOHQJ482R1R7pXtfYxyKyEEwsLCVKYqINOTJ08eeHt7Q8ZVH4nIiNq0AcqUAa5eBRYsACZOzO6IyNBYY5RBxhkaGoqIiAh4enrC3t6eX7wmRgiB2NhYhIeHI0+ePJw/i4iMbvNmoEMHqbbowQPAxSW7I8qdWGOUDZKSkpRJkTvHZposOzs7AEB4eDg8PT3ZrEZERvXZZ0CpUsD168DChdKSIWQ+2Pk6HYo+Rfb29tkcCWVEcY3YD4yIjM3CIiUZmj+fa6iZGyZGWmDzmenjNSKirNSuHVCyJPDmDfDDD9kdDRkSEyMiIiIdWVoC48dLj+fNk4bwk3lgYkQG07NnT7Ru3Tq7wyAiyhIdOwLFiwOvX0sLzJJ5YGJkpnr27AmZTAaZTAYbGxv4+/tjypQpSExMzO7QiIjMQupao7lzgejo7I2HDIOJkRlr3LgxQkNDcfv2bQwbNgyTJk3CnDlzsjssjYQQTNyIKEfp1Anw9wdevQKWLs3uaMgQmBjpSgggJiZ7bjpOOSWXy+Ht7Y2CBQtiwIABqF+/Pn7//Xd89913KFu2LBwcHODn54eBAwciOtW/OmvXrkWePHmwd+9eBAQEwNHRUZlkKSQlJeHrr79Gnjx54O7ujpEjR+LjKbHi4uLw5ZdfwtPTE7a2tqhZsybOnj2r3H/48GHIZDL89ddfqFSpEuRyOY4fP467d++iVatW8PLygqOjI6pUqYL9+/frecGIiIzHygoYN056PGeO9Kc6u/H/y8xhYqSr2FjA0TF7brGxmQrdzs4O8fHxsLCwwMKFC3Ht2jX89NNPOHjwIEaOHPnR24zF3LlzsW7dOhw9ehSPHj3C8OHDlfvnzZuHtWvXYs2aNTh+/Dhev36N7du3qxxj5MiR+O233/DTTz/hwoUL8Pf3R6NGjfD69WuVcqNHj8bMmTNx48YNBAYGIjo6Gk2bNsWBAwdw8eJFNG7cGC1atMCjR48y9f6JiIyha1egSBHgxQtg2bLsjeXAAWlm7lu3sjeOHE3kcpGRkQKAiIyMVNv37t07cf36dfHu3buUjdHRQkh1N1l/i47W+n316NFDtGrVSgghRHJysti3b5+Qy+Vi+PDhamW3bNki3N3dlc9//PFHAUDcuXNHuW3x4sXCy8tL+dzHx0fMnj1b+TwhIUHkz59fec7o6GhhbW0t1q9frywTHx8vfH19la87dOiQACB27NiR4fspXbq0WLRokcb9aV4rIqIssnq19Gfa01OImJjsieHff4Vwdpbi6N8/e2LISul9f2cGZ77Wlb199vWw03GiyV27dsHR0REJCQlITk5G586dMWnSJOzfvx8zZszAzZs3ERUVhcTERLx//x6xsbHKiRLt7e1RtGhR5bF8fHwQHh4OAIiMjERoaCiqVaum3G9lZYXKlSsrm9Pu3r2LhIQE1KhRQ1nG2toaVatWxY0bN1TirFy5ssrz6OhoTJo0CX/++SdCQ0ORmJiId+/escaIiExWt27A1KnSEiErVgBDh2bt+Z88AZo2lSabrF1bWseN9MPESFcyGeDgkN1RaKVu3bpYunQpbGxs4OvrCysrKzx48ADNmzfHgAED8O2338LNzQ3Hjx9Hnz59EB8fr0yMrK2tVY4lk8nU+hAZisNHn+fw4cOxb98+zJ07F/7+/rCzs8Nnn32G+Ph4o5yfiCizrK2BsWOBfv2AWbOA/v2BD6sVGV1UFNCsmZQclSwJbN8OyOVZc25zxD5GZszBwQH+/v4oUKAArKykHPj8+fNITk7GvHnzUL16dRQvXhzPnj3T6bguLi7w8fHBmTNnlNsSExNx/vx55fOiRYvCxsYGJ06cUG5LSEjA2bNnUapUqXSPf+LECfTs2RNt2rRB2bJl4e3tjQcPHugUIxFRVuvRAyhQAAgLA1auzJpzJiRIa7f9+y/g5QX89Rfg5pY15zZXTIxyGX9/fyQkJGDRokW4d+8e1q1bh2V69BYcMmQIZs6ciR07duDmzZsYOHAgIiIilPsdHBwwYMAAjBgxAnv27MH169fx+eefIzY2Fn369En32MWKFcO2bdtw6dIlXL58GZ07d0ZycrLOMRIRZSUbG6nWCJBqjd6/N+75hJBqpvbtk3pa/PknUKiQcc+ZGzAxymXKlSuH7777DrNmzUKZMmWwfv16zJgxQ+fjDBs2DN26dUOPHj0QFBQEJycntGnTRqXMzJkz8emnn6Jbt26oWLEi7ty5g71798LV1TXdY3/33XdwdXVFcHAwWrRogUaNGqFixYo6x0hElNV69gTy5weePQNWrzbuuaZMAX78UVrUdvNmoFIl454vt5AJY3UcySGioqLg4uKCyMhIODs7q+x7//497t+/j8KFC8PW1jabIiRt8FoRkalYsgQYNEhKkO7cMU5/n7VrgV69pMfLl0t9m3Kb9L6/M4M1RkRERAbUpw+QL5/UGXrNGsMff98+4PPPpcdjxuTOpMiYmBgREREZkFwOjB4tPZ4xA4iLM9yxL18GPv1Umt26c2dg2jTDHZskTIyIiIgMrG9fwMcHePwY+OknwxzzyRNpWP7bt0CdOlJtlAW/xQ2OHykREZGB2doCo0ZJj6dPBzI7DVtkpDSB49OnQKlSwLZtnKvIWJgYERERGUG/foC3N/DwIfDzz/ofJz5emqvoyhXpeLt3AxkM7qVMYGJERERkBHZ2gGJ97m+/lSZj1JUQUkfr/fulRRd27wYKFjRsnKSKiREREZGR9O8PeHpKa6j98ovur580SaptsrQEtmwBKlQwdIT0MZNMjBYvXoxChQrB1tYW1apVwz///KOx7Nq1ayGTyVRunMeGiIhMgb09MGKE9Pjbb6XRZNpas0aaxBEAli4FmjQxfHykzuQSo19//RVff/01Jk6ciAsXLqBcuXJo1KiRcmX3tDg7OyM0NFR5e/jwYRZGTEREpNmAAUDevMDdu8D69dq9Zu/elPmJxo1LmbeIjM/kEqPvvvsOn3/+OXr16oVSpUph2bJlsLe3x5p0ZsmSyWTw9vZW3ry8vLIwYtPzcQ3ax7dJkybhwYMHGvefPn0aQEptXEBAgNo5tmzZAplMhkKpFuZJXXtnYWGB/Pnzo1evXmpJ7a5duxASEgInJyfY29ujSpUqWLt2rTE/EiKibOPgAAwfLj2eNi3jWqNLl6TO1klJQLduwNSpRg+RUjGpxCg+Ph7nz59H/fr1ldssLCxQv359nDp1SuProqOjUbBgQfj5+aFVq1a4du2axrJxcXGIiopSuZmb1LVn33//vVqN2nDFbyiA/fv3q+wLDQ1FpVQL7jg4OCA8PFzt81+9ejUKFCigdm7FuZ48eYKVK1fir7/+Qrdu3ZT7Fy1ahFatWqFGjRo4c+YM/v33X3Ts2BFffPGFSlxEROZk0CDA3V1aImTTJs3lHj2ShuVHRwN16wKrVgEyWdbFSSaWGL18+RJJSUlqNT5eXl4ICwtL8zUlSpTAmjVrsHPnTvzyyy9ITk5GcHAwnjx5kmb5GTNmwMXFRXnz8/Mz+PvIbqlrz1xcXNRq1BwdHZVl3d3dVfZ5e3vD2tpaud/KygqdO3dWqbF78uQJDh8+jM6dO6udW3EuX19fNGnSBF9++SX279+Pd+/e4fHjxxg2bBiGDh2K6dOno1SpUvD398ewYcMwZ84czJs3D2fOnDHuh0NElA0cHYFhw6TH06ZJtUEfi4iQkqLQUKB0aWmuIhubLA2TYGKJkT6CgoLQvXt3lC9fHiEhIdi2bRs8PDywfPnyNMuPGTMGkZGRytvjx491Op8QQExM9tyya7nf3r17Y/PmzYiNjQUgNZk1btxYqyZLOzs7JCcnIzExEVu3bkVCQkKaNUP9+/eHo6MjNm7caPD4iYhMweDBgJsbcOsW8Ouvqvvi44G2bYFr1wBfX+Cvv4A8ebIlzFzPKrsDSC1v3rywtLTE8+fPVbY/f/4c3t7eWh3D2toaFSpUwJ07d9LcL5fLIc/EdKGxsVLmnx2io6W2akMKDg6GxUdzykdHR6s8r1ChAooUKYKtW7eiW7duWLt2Lb777jvcu3cv3WPfvn0by5YtQ+XKleHk5IT//vsPLi4u8PHxUStrY2ODIkWK4L///sv8myIiMkFOTsBXXwHffCP1G+rQQRqGL4S08OyhQ9L3y59/AmbYmJFjmFSNkY2NDSpVqoQDBw4otyUnJ+PAgQMICgrS6hhJSUm4cuVKml++pO7XX3/FpUuXVG5p6d27N3788UccOXIEMTExaNq0aZrlIiMj4ejoCHt7e5QoUQJeXl5Yr+0wDCIiM/e//0k1QTdvAlu3StsmTJDmOLK0lLaVL5+dEZJJ1RgBwNdff40ePXqgcuXKqFq1Kr7//nvExMSgV69eAIDu3bsjX758mDFjBgBgypQpqF69Ovz9/REREYE5c+bg4cOH6Nu3r1His7eXam6yg7294Y/p5+cHf3//DMt16dIFI0eOxKRJk9CtWzdYWaX9o+Pk5IQLFy7AwsICPj4+sLOzU+4rXrw4IiMj8ezZM/j6+qq8Lj4+Hnfv3kXdunUz94aIiEyYi4tUazRxolRrFBkp9TkCgBUrgEaNsjc+MsHEqEOHDnjx4gUmTJiAsLAwlC9fHnv27FH2Z3n06JFK08+bN2/w+eefIywsDK6urqhUqRJOnjyJUqVKGSU+mczwzVk5gZubG1q2bInNmzdj2bJlGstZWFhoTLQ+/fRTjBo1CvPmzcO8efNU9i1btgwxMTHo1KmTQeMmIjI1X34JfPed1J+of39p24QJQO/e2RsXSUwuMQKAwYMHY/DgwWnuO3z4sMrz+fPnY/78+VkQlXl69eqV2oi/PHnypDl7+Nq1a7FkyRK4u7vrda4CBQpg9uzZGDZsGGxtbdGtWzdYW1tj586dGDt2LIYNG4Zq1arpdWwiopwiTx5gyJCUWa179JCW/iDTYJKJEWWd1HNGKWzcuBEdO3ZU225nZ6fSNKaPoUOHokiRIpg7dy4WLFiApKQklC5dGkuXLlU2lxIRmbuvvpIWhC1USGpC41xFpkMmRHYNAjcNUVFRcHFxQWRkJJydnVX2vX//Hvfv30fhwoW5/pqJ47UiIspd0vv+zgyTGpVGRERElJ2YGBERERF9wMSIiIiI6AMmRkREREQfMDEiIiIi+oCJkRZy+cC9HIHXiIiIDIGJUTqsra0BQLmqPJkuxTVSXDMiIiJ9cILHdFhaWiJPnjwIDw8HANjb20PGWbhMihACsbGxCA8PR548eWBpaZndIRERUQ7GxCgD3t7eAKBMjsg05cmTR3mtiIiI9MXEKAMymQw+Pj7w9PREQkJCdodDabC2tmZNERERGQQTIy1ZWlryy5eIiMjMsfM1ERER0QdMjIiIiIg+YGJERERE9EGu72OkmBgwKioqmyMhIiIibSm+tw09wW+uT4xevXoFAPDz88vmSIiIiEhXr169gouLi8GOl+sTIzc3NwDAo0ePDPrBku6ioqLg5+eHx48fw9nZObvDyfV4PUwHr4Xp4LUwHZGRkShQoIDye9xQcn1iZGEhdbNycXHhD7mJcHZ25rUwIbwepoPXwnTwWpgOxfe4wY5n0KMRERER5WBMjIiIiIg+yPWJkVwux8SJEyGXy7M7lFyP18K08HqYDl4L08FrYTqMdS1kwtDj3IiIiIhyqFxfY0RERESkwMSIiIiI6AMmRkREREQfMDEiIiIi+iBXJEaLFy9GoUKFYGtri2rVquGff/5Jt/yWLVtQsmRJ2NraomzZsti9e3cWRWr+dLkWK1euRK1ateDq6gpXV1fUr18/w2tHutH1d0Nh06ZNkMlkaN26tXEDzEV0vRYREREYNGgQfHx8IJfLUbx4cf6tMhBdr8X333+PEiVKwM7ODn5+fvjqq6/w/v37LIrWfB09ehQtWrSAr68vZDIZduzYkeFrDh8+jIoVK0Iul8Pf3x9r167V/cTCzG3atEnY2NiINWvWiGvXronPP/9c5MmTRzx//jzN8idOnBCWlpZi9uzZ4vr162L8+PHC2tpaXLlyJYsjNz+6XovOnTuLxYsXi4sXL4obN26Inj17ChcXF/HkyZMsjtw86Xo9FO7fvy/y5csnatWqJVq1apU1wZo5Xa9FXFycqFy5smjatKk4fvy4uH//vjh8+LC4dOlSFkdufnS9FuvXrxdyuVysX79e3L9/X+zdu1f4+PiIr776KosjNz+7d+8W48aNE9u2bRMAxPbt29Mtf+/ePWFvby++/vprcf36dbFo0SJhaWkp9uzZo9N5zT4xqlq1qhg0aJDyeVJSkvD19RUzZsxIs3z79u1Fs2bNVLZVq1ZN9O/f36hx5ga6XouPJSYmCicnJ/HTTz8ZK8RcRZ/rkZiYKIKDg8WqVatEjx49mBgZiK7XYunSpaJIkSIiPj4+q0LMNXS9FoMGDRKffPKJyravv/5a1KhRw6hx5jbaJEYjR44UpUuXVtnWoUMH0ahRI53OZdZNafHx8Th//jzq16+v3GZhYYH69evj1KlTab7m1KlTKuUBoFGjRhrLk3b0uRYfi42NRUJCgsEXDMyN9L0eU6ZMgaenJ/r06ZMVYeYK+lyL33//HUFBQRg0aBC8vLxQpkwZTJ8+HUlJSVkVtlnS51oEBwfj/Pnzyua2e/fuYffu3WjatGmWxEwpDPX9bdaLyL58+RJJSUnw8vJS2e7l5YWbN2+m+ZqwsLA0y4eFhRktztxAn2vxsVGjRsHX11ftB590p8/1OH78OFavXo1Lly5lQYS5hz7X4t69ezh48CC6dOmC3bt3486dOxg4cCASEhIwceLErAjbLOlzLTp37oyXL1+iZs2aEEIgMTERX3zxBcaOHZsVIVMqmr6/o6Ki8O7dO9jZ2Wl1HLOuMSLzMXPmTGzatAnbt2+Hra1tdoeT67x9+xbdunXDypUrkTdv3uwOJ9dLTk6Gp6cnVqxYgUqVKqFDhw4YN24cli1blt2h5TqHDx/G9OnTsWTJEly4cAHbtm3Dn3/+ialTp2Z3aKQns64xyps3LywtLfH8+XOV7c+fP4e3t3ear/H29tapPGlHn2uhMHfuXMycORP79+9HYGCgMcPMNXS9Hnfv3sWDBw/QokUL5bbk5GQAgJWVFW7duoWiRYsaN2gzpc/vho+PD6ytrWFpaancFhAQgLCwMMTHx8PGxsaoMZsrfa7FN998g27duqFv374AgLJlyyImJgb9+vXDuHHjYGHB+oesoun729nZWevaIsDMa4xsbGxQqVIlHDhwQLktOTkZBw4cQFBQUJqvCQoKUikPAPv27dNYnrSjz7UAgNmzZ2Pq1KnYs2cPKleunBWh5gq6Xo+SJUviypUruHTpkvLWsmVL1K1bF5cuXYKfn19Whm9W9PndqFGjBu7cuaNMTgHgv//+g4+PD5OiTNDnWsTGxqolP4qEVXAp0ixlsO9v3fqF5zybNm0ScrlcrF27Vly/fl3069dP5MmTR4SFhQkhhOjWrZsYPXq0svyJEyeElZWVmDt3rrhx44aYOHEih+sbiK7XYubMmcLGxkZs3bpVhIaGKm9v377NrrdgVnS9Hh/jqDTD0fVaPHr0SDg5OYnBgweLW7duiV27dglPT08xbdq07HoLZkPXazFx4kTh5OQkNm7cKO7duyf+/vtvUbRoUdG+ffvsegtm4+3bt+LixYvi4sWLAoD47rvvxMWLF8XDhw+FEEKMHj1adOvWTVleMVx/xIgR4saNG2Lx4sUcrq/JokWLRIECBYSNjY2oWrWqOH36tHJfSEiI6NGjh0r5zZs3i+LFiwsbGxtRunRp8eeff2ZxxOZLl2tRsGBBAUDtNnHixKwP3Ezp+ruRGhMjw9L1Wpw8eVJUq1ZNyOVyUaRIEfHtt9+KxMTELI7aPOlyLRISEsSkSZNE0aJFha2trfDz8xMDBw4Ub968yfrAzcyhQ4fS/A5QfP49evQQISEhaq8pX768sLGxEUWKFBE//vijzueVCcG6PiIiIiLAzPsYEREREemCiRERERHRB0yMiIiIiD5gYkRERET0ARMjIiIiog+YGBERERF9wMSIiIiI6AMmRkSZsHbtWshkMjx48CDLz71nzx6UL18etra2kMlkiIiIyPIYtNWzZ084Ojpmy7kPHz4MmUyGw4cPG/U8PXv2RKFChYx6DkNYt24dSpYsCWtra+TJkye7wyEyOUyMKEvdv38fgwcPRvHixWFvbw97e3uUKlUKgwYNwr///qtSdtKkSZDJZLCwsMDjx4/VjhUVFQU7OzvIZDIMHjwYAFCnTh3IZLIMb5MmTcqKt2s0r169Qvv27WFnZ4fFixdj3bp1cHBwyO6wyMTdvHkTPXv2RNGiRbFy5UqsWLFCY9kDBw6gd+/eyt/VIkWKoG/fvggNDU2z/MmTJ1GzZk3Y29vD29sbX375JaKjo1XKnD17FoMHD0bp0qXh4OCAAgUKoH379vjvv//Ujrdy5UqEhITAy8sLcrkchQsXRq9evTT+E7J69WoEBATA1tYWxYoVw6JFi9Ist3//ftStWxd58+ZFnjx5ULVqVaxbt07vY27btg0dOnRAkSJFYG9vjxIlSmDYsGEm/Y8Kpc8quwOg3GPXrl3o0KEDrKys/t/euUc1fWRx/AuSF0QQJYIRRRupWILiWh/oqahgAKmPSgEFClLOgi4qtnpg1VoUtFZad9Gm0sJSrTxchaNbRdTTrmCRsyoWYdkqIsLaasvDAOGhEgKzf2h+h18SEFAE1/mc8zuSO3fu3Bl/SSZz584P/v7+mDJlCgwNDVFaWorjx48jISEBlZWVsLGxYdXj8Xg4cuQIIiMjWfLjx4/rtLF161bmKdfA4w/i/fv3Y8uWLZg0aRIjnzx58nPu3YuloKAATU1NiI2Nhaur60C7M6iZO3cuHj582O8PV01KSmI91HUwkpubi46ODuzbtw8TJkzoVjcqKgp1dXXw9vaGra0tKioqIJfLkZWVhaKiItbT5ouKiuDi4oJJkybhL3/5C+7evYvPP/8ct27dwpkzZxi9PXv2ID8/H97e3pg8eTKqqqogl8vxhz/8AZcuXYJUKmV0r127hvHjx2PJkiUwNzdHZWUlkpKSkJWVheLiYojFYkb366+/xurVq+Hl5YUPP/wQeXl5WL9+PR48eICoqChG7+TJk1i2bBmcnJyYH17Hjh1DYGAg7t+/jw8++KDXNkNDQyEWixEQEICxY8eipKQEcrkc2dnZKCws7NVT3SmDhGd9lgmF0hPKy8uJiYkJmTRpEvntt990ytva2si+ffvIL7/8wsiio6MJALJ8+XLi6OioU2fhwoXEy8uLACDh4eF6283IyCAASE5OznPrS2cOHjxIAJDKysp+sd8V3377LQFACgoKnqrb0tLyAjzqnqCgIGJiYjLQbrzy7NixgwAgtbW1T9W9cOECaW9v15EBIFu3bmXJPTw8yKhRo4hSqWRkSUlJBAA5d+4cI8vPzyetra2sumVlZYTH4xF/f/+n+nT16lUCgOzevZuRPXjwgIwYMYJ4enqydP39/YmJiQmpq6tjZAsXLiRisZg8evSIkbW1tRGJREImT57cJ5v6Pls078+kpKSn9oky+KChNMoLIS4uDi0tLTh48CBGjRqlU25kZIT169djzJgxOmV+fn4oKipCaWkpI6uqqsL58+fh5+fXr3535ueff8aCBQsgEAhgbW2NnTt36l0h+O677+Dp6QmxWAwejweJRILY2Fi0t7czOtHR0eBwOKitrdWpHxoaimHDhuHRo0d6/Zg3bx6CgoIAANOnT4eBgQFWrVrFlEmlUvz000+YO3cujI2NsWXLFgBATU0NQkJCYGlpCT6fjylTpuDbb79l2f7vf/8LAwMDfP755/jyyy+Z8IBMJsOvv/4KQghiY2NhbW0NgUCApUuXoq6ursdjWFFRATc3N5iYmEAsFiMmJgZE63GNHR0diI+Ph729Pfh8PiwtLREWFob6+nodve3bt0MsFsPY2Bjz58/H9evXMW7cOGY8AP17jDTjdP36dcyfPx/GxsYYPXo04uLiWG1o6h47dgy7du2CtbU1+Hw+XFxcUF5eztLV3mPUeSwTExMhkUjA4/Ewffp0FBQU6IxNRkYG3njjDfD5fEilUpw4caJX+5YOHDgAe3t78Hg8iMVihIeHs8I548aNQ3R0NABAJBI9NaQ8d+5cGBoa6siGDx+OGzduMLLGxkZ8//33CAgIgKmpKSMPDAyEUCjEsWPHGNns2bN1Vu5sbW1hb2/PstkVmrHo3K+cnBwoFAr86U9/YumGh4ejpaUFp0+fZvlqbm4OHo/HyIyMjGBhYcFa2emNzXnz5un4+c477wBAj/pEGXzQUBrlhZCVlYUJEyZg5syZva47d+5cWFtbIz09HTExMQCAo0ePQigUwtPT83m7qpeqqirMnz8farUaf/7zn2FiYoLExES9y+SHDh2CUCjEhx9+CKFQiPPnz+Pjjz9GY2MjPvvsMwDAe++9h5iYGBw9epTZHwUAKpUKmZmZ8PLyAp/P1+vL1q1bMXHiRCQmJiImJgbjx4+HRCJhyhUKBTw8PLBixQoEBATA0tISDx8+xLx581BeXo61a9di/PjxyMjIwKpVq9DQ0ICIiAhWG2lpaVCpVFi3bh3q6uoQFxcHHx8fLFiwALm5uYiKikJ5eTm++OILbNq0Cd98881Tx7C9vR3u7u6YNWsW4uLicPbsWURHR0OtVjP/rwAQFhaGQ4cOITg4GOvXr0dlZSXkcjmuXbuG/Px8cDgcAMDmzZsRFxeHxYsXw83NDcXFxXBzc+tyQqlNfX093N3dsXz5cvj4+CAzMxNRUVFwcHCAh4cHS/fTTz+FoaEhNm3aBKVSibi4OPj7++Py5ctPbSc9PR1NTU0ICwuDgYEB4uLisHz5clRUVDB9OX36NHx9feHg4IDdu3ejvr4eISEhGD16dI/6sn37duzYsQOurq5Ys2YNbt68iYSEBBQUFDBjFh8fj8OHD+PEiRNISEiAUCjsdUi5ubkZzc3NsLCwYGQlJSVQq9V48803WbpcLheOjo64du1atzYJIaiuroa9vb3ecoVCgfb2dvzyyy/MfeLi4sKUa+xrtz9t2jQYGhri2rVrCAgIAPB4ErNnzx5s27YNQUFBMDAwQHp6Oq5evcqawPXGpj6qqqoAgDVOlJeIAV6xorwCKJVKAoAsW7ZMp6y+vp7U1tYy14MHD5gyTSittraWbNq0iUyYMIEpmz59OgkODiaEkBcSStuwYQMBQC5fvszIampqiJmZmU4orXMfNISFhRFjY2PWEr6TkxOZOXMmS+/48eM98lcTwtMOpTk7OxMA5KuvvmLJ4+PjCQCSmprKyFQqFXFyciJCoZA0NjYSQgiprKwkAIhIJCINDQ2M7ubNmwkAMmXKFNLW1sbIV65cSbhcLqtf+ggKCiIAyLp16xhZR0cH8fT0JFwulwnt5OXlEQAkLS2NVf/s2bMseVVVFTEyMtK5p7Zv304AkKCgIEaWk5OjM6aacTp8+DAja21tJVZWVsTLy0un7qRJk1ghoH379hEApKSkhNVHGxsb5rVmLEeMGMEKvXz33XcEADl16hQjc3BwINbW1qSpqYmR5ebmEgAsm/qoqakhXC6XyGQyVuhLLpcTAOSbb75hZJ3fU30hNjaWACD//Oc/GZnmPfbjjz/q6Ht7exMrK6tubaakpBAAJDk5WW85j8cjAJix3L9/P6s8PDycDBkyRG9dkUhEVqxYwbxubm4mPj4+xMDAgLFpbGxM/vGPf/TZpj5CQkLIkCFDSFlZWbd6lMEJDaVR+p3GxkYA0JuuPW/ePIhEIub68ssv9drw8/NDeXk5CgoKmH9fZBgtOzsbs2bNwowZMxiZSCSCv7+/jm7nVaSmpibcv38fb731Fh48eMAKBwYGBuLy5cu4ffs2I0tLS8OYMWPg7OzcZ195PB6Cg4N1/LeyssLKlSsZGYfDYTKHLly4wNL39vaGmZkZ81qz0hcQEAAjIyOWXKVS4d69ez3yrfPqmCabUKVS4YcffgDwOJxkZmaGhQsX4v79+8w1bdo0CIVC5OTkAHicMaVWq3VCHevWreuRH8Dj+7Hzr34ul4sZM2agoqJCRzc4OJgVAnrrrbcAQK+uNr6+vjA3N++y7m+//YaSkhIm9KTB2dkZDg4OT7X/ww8/QKVSYcOGDazQ1x//+EeYmpqywj7Pwo8//ogdO3YwK4caHj58CACs8JQGPp/PlOujtLQU4eHhcHJyYsLD2pw5cwbZ2dnYu3cvxo4di5aWFlZ5dxvrtdvn8Xh4/fXX8e677+LIkSNITU3Fm2++iYCAAFy6dKlPNrVJT09HcnIyNm7cCFtb2y71KIMXGkqj9DtDhw4FAJ3UXeBx5kdTUxOqq6u7XZqeOnUq7OzskJ6ejmHDhsHKyor14dzf3LlzR28YcOLEiTqyn3/+GR999BHOnz/PTAo1KJVK5m9fX19s2LABaWlp+Pjjj6FUKpGVlYUPPvgABgYGffZ19OjROh/qd+7cga2trc6eEU2m3p07d1jysWPHsl5rJknae8A0cu39P/owNDTEa6+9xpK9/vrrAMCkYN+6dQtKpRIjR47Ua6Ompoblr3Zm1fDhw1mTkO6wtrbWGWdzc3OdYyMA3fHQtNGTfj+tbld90cgKCwu7ta+pr30vcrlcvPbaazr/t32htLQU77zzDqRSKf72t7+xyjQ/BFpbW3XqPXr0qMusrKqqKnh6esLMzAyZmZkYMmSIXr358+cDADw8PLB06VJIpVIIhUJmki0QCKBSqfTW1W5/7dq1uHTpEgoLC5n3go+PD+zt7REREcGERntjszN5eXkICQmBm5sbdu3apVeHMvihEyNKv2NmZoZRo0bhP//5j06ZZrLRkwMS/fz8kJCQgKFDh8LX11fnS34w0NDQAGdnZ5iamiImJgYSiQR8Ph+FhYWIiopibdY2NzfH22+/zUyMMjMz0dra2u0EsSc8j/Tgrr6kupITrQ3UfaWjowMjR45EWlqa3nKRSPRc2gF615dn6Xd/j1l/8+uvv0Imk8HMzAzZ2dnMDx0NmmQKfecb/f7776y0eg1KpRIeHh5oaGhAXl6eXh19SCQSTJ06FWlpaczEaNSoUWhvb0dNTQ1rQq1SqaBQKBjbKpUKycnJiIyMZH12cDgceHh4QC6XQ6VSgcvl9thmZ4qLi7FkyRJIpVJkZmayVlYpLxeD75uF8n+Jp6cnysvLceXKlT7b8PPzw++//46ysrIXGkYDABsbG9y6dUtHfvPmTdbr3NxcKBQKHDp0CBEREXj77bfh6ura5SpGYGAgysrKUFBQgLS0NEydOrXLTajPw3/tLDpNaE/77Kj+oKOjQyf0pDnYT5NtJJFIoFAoMGfOHLi6uupcU6ZMYfmrnRmmUCh6tIozmOiqL13JuqqvfS+qVCq954L1BoVCAZlMhtbWVpw7d05vRqlUKoWRkRGuXr2q035RUREcHR1Z8kePHmHx4sUoKytDVlYW3njjjV759PDhQ9bKq8a+dvtXr15FR0cHU65QKKBWq1nZoRra2trQ0dHBlPXUpobbt2/D3d0dI0eORHZ29oCd8k55PtCJEeWFEBkZCWNjY7z//vuorq7WKe/Jr2eJRIL4+Hjs3r2btdfnRbBo0SJcunSJNbGrra3VWdnQrA507o9KpcKBAwf02vXw8ICFhQX27NmDCxcuPPNqUVcsWrQIVVVVOHr0KCNTq9X44osvIBQKn2lPU2+Qy+XM34QQyOVycDgcJsvIx8cH7e3tiI2N1amrVquZNG0XFxcYGRkhISGhS/svC2KxGFKpFIcPH2aFmy9cuICSkpKn1nd1dQWXy8X+/ftZ911ycjKUSmWfMzdbWlqwaNEi3Lt3D9nZ2V3ulzEzM4OrqytSU1PR1NTEyFNSUtDc3Axvb29G1t7eDl9fX/zrX/9CRkYGnJyc9NpUq9V6J7hXrlxBSUkJK1tswYIFGD58uM69kJCQAGNjY6b/I0eOxLBhw3DixAlWmKy5uRmnTp2CnZ0ds9raU5vA45CgTCaDoaEhzp0791xXNSkDA13ro7wQbG1tkZ6ejpUrV2LixInMydeEEFRWViI9PR2Ghoawtrbu1o52WvmzoklzzsnJ0XseiYbIyEikpKTA3d0dERERTLq+jY0Na0/K7NmzYW5ujqCgIKxfvx4GBgZISUnpcuLH4XCwYsUKyOVyDBkyhLU5+nkSGhqKr7/+GqtWrcJPP/2EcePGITMzE/n5+YiPj9cJj/QHfD4fZ8+eRVBQEGbOnIkzZ87g9OnT2LJlC/Nl4uzsjLCwMOzevRtFRUWQyWTgcDi4desWMjIysG/fPrz77ruwtLREREQE9u7diyVLlsDd3R3FxcU4c+YMLCwsnmmP1kDwySefYOnSpZgzZw6Cg4NRX18PuVwOqVSqd29eZ0QiETZv3owdO3bA3d0dS5Yswc2bN3HgwAFMnz69z5Ntf39/XLlyBe+//z5u3LjBOpNHKBRi2bJlzOtdu3Zh9uzZcHZ2RmhoKO7evYu9e/dCJpPB3d2d0du4cSNOnjyJxYsXo66uDqmpqaw2Nb42NzdjzJgx8PX1ZR4fUlJSgoMHD8LMzAzbtm1j6ggEAsTGxiI8PBze3t5wc3NDXl4eUlNTsWvXLgwfPhzA4x8tmzZtwkcffYRZs2YhMDAQ7e3tSE5Oxt27d1m+9NQmALi7u6OiogKRkZG4ePEiLl68yJRZWlpi4cKFfRp/ygAyUOlwlFeT8vJysmbNGjJhwgTC5/OJQCAgdnZ2ZPXq1aSoqIil29PUYjxDuv7GjRuJgYEBuXHjxlN9//e//02cnZ0Jn88no0ePJrGxsSQ5OVknXT8/P5/MmjWLCAQCIhaLSWRkJDl37lyXfly5coUAIDKZ7Kk+aOguXd/e3l5vnerqahIcHEwsLCwIl8slDg4O5ODBgywdTYr5Z599xpJr0tYzMjJ65Ic2mpOvb9++TWQyGTE2NiaWlpYkOjpa53RlQghJTEwk06ZNIwKBgAwdOpQ4ODiQyMhI1qnparWabNu2jVhZWRGBQEAWLFhAbty4QUaMGEFWr16t47t2ur6+cdJOue+q35px6jx+XaXra48lIY/v2ejoaJbs73//O7GzsyM8Ho9IpVJy8uRJ4uXlRezs7HTq60MulxM7OzvC4XCIpaUlWbNmDamvr2fp9CZd38bGhklp1770HSGQl5dHZs+eTfh8PhGJRCQ8PJw5BkKD5piEri4Nra2tJCIigkyePJmYmpoSDodDbGxsSEhISJenzCcmJpKJEycSLpdLJBIJ+etf/0o6Ojp09NLS0siMGTPIsGHDiEAgIDNnziSZmZl9ttldf5ydnbsfZMqgxICQl2QHIIXSD8yYMQM2NjbIyMgYMB+Ki4vh6OiIw4cP47333hswP/4faGhogLm5OXbu3ImtW7cOtDvPjKOjI0QiEb7//vuBdoVCeWWge4woryyNjY0oLi5mnbo8ECQlJUEoFGL58uUD6sfLhr6zZOLj4wHof0zDYKatrQ1qtZoly83NRXFx8UvXFwrlZYfuMaK8spiamuo9e+VFcerUKVy/fh2JiYlYu3YtTExMBsyXl5GjR4/i0KFDWLRoEYRCIS5evIgjR45AJpNhzpw5A+1er7h37x5cXV0REBAAsViM0tJSfPXVV7CyssLq1asH2j0K5ZWChtIolAFi3LhxqK6uhpubG1JSUl7IBuj/JwoLCxEZGYmioiI0NjbC0tISXl5e2Llz50uXLq1UKhEaGor8/HzU1tbCxMQELi4u+PTTT1nPwaNQKP0PnRhRKBQKhUKhPIHuMaJQKBQKhUJ5Ap0YUSgUCoVCoTyBTowoFAqFQqFQnkAnRhQKhUKhUChPoBMjCoVCoVAolCfQiRGFQqFQKBTKE+jEiEKhUCgUCuUJdGJEoVAoFAqF8gQ6MaJQKBQKhUJ5wv8AX/ChZqs/Wd0AAAAASUVORK5CYII=\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "###6.3.2 Plotting TEMPO and smoothed Pandora retievals with error bars" - ], - "metadata": { - "id": "QyhmWrUMXU5z" - } - }, - { - "cell_type": "code", - "source": [ - "# Plotting smoothed Pandora retievals with error bars\n", - "timeseries_Pandora_TEMPO = gauss_interpolation(time_series_Pandora[:, 0:3]\\\n", - " , time_series_TEMPO[:, 0])\n", - "\n", - "timeseries_Pandora_TEMPO_noneg =\\\n", - " gauss_interpolation(time_series_Pandora_noneg[:, 0:3]\\\n", - " , time_series_TEMPO_noneg[:, 0])\n", - "\n", - "plot_title = out_Q+'_unc_'+datestamp_ini+'_'+datestamp_fin+'\\n'+POI_name\n", - "img_name = out_Q+'_unc_'+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", - "\n", - "fig = plt.figure()\n", - "\n", - "plt.errorbar(time_series_TEMPO[:, 0], time_series_TEMPO[:, 1],\\\n", - "yerr=time_series_TEMPO[:, 2], label = \"TEMPO\", c = 'b', ls = '', marker = \".\")\n", - "\n", - "plt.errorbar(timeseries_Pandora_TEMPO[:, 0],\\\n", - " timeseries_Pandora_TEMPO[:, 1],\\\n", - " yerr=timeseries_Pandora_TEMPO[:, 2],\\\n", - " label = \"Pandora smoothed at TEMPO times\",\\\n", - " c = 'r', ls = '', marker = \".\")\n", - "\n", - "# Set the range of x-axis\n", - "l_lim = 0.\n", - "u_lim = ((dt_fin - dt0).total_seconds() + 1.)/86400.\n", - "plt.xlim(l_lim, u_lim)\n", - "\n", - "# some research is required to set the vertical range\n", - "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", - "plt.ylabel(out_Q_unit.replace('_',' '), fontsize=12)\n", - "\n", - "#plt.legend(loc='lower left')\n", - "plt.legend(loc='upper left')\n", - "\n", - "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", - "plt.savefig(img_name, format='jpg', dpi=300)\n", - "\n", - "plot_title = out_Q+'_unc_positive_'+datestamp_ini+'_'+datestamp_fin+'\\n'+POI_name\n", - "img_name = out_Q+'_unc_positive'+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", - "\n", - "fig = plt.figure()\n", - "\n", - "plt.errorbar(time_series_TEMPO_noneg[:, 0], time_series_TEMPO_noneg[:, 1],\\\n", - "yerr=time_series_TEMPO_noneg[:, 2], label = \"TEMPO\", c = 'b', ls = '', marker = \".\")\n", - "\n", - "plt.errorbar(timeseries_Pandora_TEMPO_noneg[:, 0],\\\n", - " timeseries_Pandora_TEMPO_noneg[:, 1],\\\n", - " yerr=timeseries_Pandora_TEMPO_noneg[:, 2],\\\n", - " label = \"Pandora smoothed at TEMPO times\",\\\n", - " c = 'r', ls = '', marker = \".\")\n", - "\n", - "# Set the range of x-axis\n", - "l_lim = int(min(time_series_TEMPO_noneg[:, 0]))\n", - "u_lim = int(max(time_series_TEMPO_noneg[:, 0])) + 1\n", - "plt.xlim(l_lim, u_lim)\n", - "\n", - "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", - "plt.ylabel(out_Q_unit.replace('_',' '), fontsize=12)\n", - "\n", - "plt.legend(loc='lower left')\n", - "#plt.legend(loc='upper left')\n", - "\n", - "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", - "plt.savefig(img_name, format='jpg', dpi=300)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 977 - }, - "id": "4jIPncJ3_Vzb", - "outputId": "5fc260b6-5991-430e-bcad-bfee9d1397bf" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlEAAAHgCAYAAACFAjiTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACWQklEQVR4nOzdd1gUV9sG8HtpS5UuRREpKliwF1BRjAV719jArom9RY2xm9hbbLFFE2PsLZZo7F1jjwV7L9gFBQWB5/vDj31dF5BdF1nw/l3XXrJnzpx5ZmZlH86cOaMQEQERERERacUoswMgIiIiyoqYRBERERHpgEkUERERkQ6YRBERERHpgEkUERERkQ6YRBERERHpgEkUERERkQ6YRBERERHpgEkUERERkQ6YRBFRqtq2bYu8efNmdhhERAaJSRRlmkOHDmHEiBF48eJFZoeSqsWLF0OhUMDc3Bz37t3TWF65cmUULlxYo/zt27f4+eefUbp0adjY2MDa2hqlS5fGzz//jLdv36rVjY2NxaxZs1C9enW4ubnBxsYGxYsXx5w5c5CYmJhh+/aluXPnDkaOHIkyZcrA3t4eTk5OqFy5Mnbs2JFi/RcvXqBz585wdnaGlZUVQkJCcPLkSbU6T58+xcSJExEcHAxnZ2fY2dmhXLlyWLFihUZ758+fR9OmTeHt7Q1LS0s4OTkhODgYGzduTHH7ERERCA0NhbW1NRwcHNCmTRs8fvxYo96DBw/QuXNneHl5wcLCAj4+Pujbty+ePn2qU5sXL17Ed999h2LFisHGxgZubm6oXbs2jh8/nuqxTY22bd27dw/NmjWDnZ0dcuTIgfr16+P69etqdbQ5j/v27UO9evXg4eEBc3NzuLq6IjQ0FAcPHkxx+4cOHUKFChVgaWkJV1dX9OzZE69evdKod+XKFXz99dfInTs3LC0t4efnh1GjRiE2NlanNo8dO4bu3bujUKFCsLKyQp48edCsWTNcvnw51WNLBkKIMsnEiRMFgNy4cSOzQ0nVokWLBIAAkO7du2ssr1SpkhQqVEit7NWrV1KpUiUBIHXq1JGZM2fK7NmzpV69egJAKlWqJK9evVLVP3v2rCgUCqlatapMmDBBfvnlF2nYsKEAkLCwsAzfx7SEh4eLp6dnpsagLzNmzBALCwtp0aKFzJw5U6ZNmyYlSpQQAPLrr7+q1U1MTJSgoCCxsrKSESNGyMyZM6VgwYJiY2Mjly9fVtXbuHGjmJqaSv369WXatGkyc+ZMCQkJEQAybNgwtTY3b94sNWrUkBEjRsi8efNk2rRpUrFiRQEgc+fOVat7584dcXJyEh8fH5k+fbr8+OOPYm9vL0WLFpW4uDhVvZcvX4qnp6c4OTnJsGHDZP78+dK9e3cxNTWVYsWKSWJiotZt9uvXT+zs7KRDhw4yd+5cmTBhgvj4+IixsbFs375dq2OuTVsvX76UfPnySc6cOWX8+PEyZcoU8fDwkNy5c8uTJ090Oo/z58+X+vXry5gxY2TBggUyceJEKVq0qBgZGcnff/+tVvfUqVNibm4uxYsXlzlz5siQIUNEqVRKaGioWr3bt2+LnZ2deHp6ytixY2Xu3LnStm1bASD16tXTqc3GjRuLq6ur9OjRQ+bPny+jR48WFxcXsbKykrNnz2p1zOnzYhJFmSa9SVRiYqK8fv368wT1geQkqlixYqJUKuXevXtqy1NKojp37iwAZMaMGRrtzZw5UwBI165dVWWPHz+Wc+fOadRt166dAJArV67oaW+0l52SqHPnzsnjx4/Vyt68eSN+fn6SO3dutfIVK1YIAFm1apWq7NGjR2JnZyctWrRQlV2/fl1u3ryptm5SUpJUqVJFlEqlWrKckoSEBClatKgUKFBArfybb74RCwsLuXXrlqps+/btGgnX0qVLBYBs2rRJbf1hw4YJADl58qTWbR4/flxevnyp1t6TJ0/E2dlZypcvn+b+fEibtsaPHy8A5N9//1WVRUREiLGxsQwePFhVps15TElMTIy4uLhIjRo11Mpr1qwpbm5uEhUVpSqbP3++AJBt27apyn788UcBoPF/NiwsTADIs2fPtG7z4MGDaomsiMjly5dFqVRKq1atPrpPlHmYRFGmGD58uKqH5/3XjRs3BIB069ZN/vjjDylYsKCYmJjIunXrRETk5MmTEhoaKjY2NmJlZSVVqlSRw4cPq7WdnPjs3btXOnfuLA4ODmJjYyNt2rRR+wWXHsltrVy5UkxMTKRHjx5qyz9Mou7cuSPGxsZSpUqVVNsMCQkRExMTuXPnTprb/uuvvwSA/PXXX1rFLCKyZMkSKV26tFhYWIidnZ1UrFhR7Ze2iMisWbOkYMGCYmZmJm5ubvLtt9/K8+fP1erokkR5enpKeHi4RnmlSpWkUqVKqve7d+8WALJixQoZM2aM5MqVS5RKpVSpUiXFxPHIkSNSs2ZNsbOzE0tLSylSpIhMmzZNq9hS0rdvXwEg0dHRqrKmTZuKi4uLWk+OyLsE2dLSUt68eZNmmz///LMAkP/++++j269Tp464uLioleXMmVOaNm2qUTd//vzy1Vdfqd7PmTNHAMixY8fU6iWXR0REaN1maho1aiQODg4frZceKbVVunRpKV26tEbd6tWri4+Pz0fbTOk8pqZw4cJStmxZ1fuoqCgxMTGRAQMGqNWLi4sTa2tr6dChg6ps4MCBAkAjkRs4cKAYGRmpEmdt2kxNiRIlpESJEh+tR5mHY6IoUzRq1AgtWrQAAEydOhVLlizBkiVL4OzsDADYtWsX+vTpg+bNm2P69OnImzcvzp8/j4oVK+LMmTP47rvvMHToUNy4cQOVK1fG0aNHNbbRvXt3REREYMSIEQgLC8PSpUvRoEEDiIjW8Xp5eSEsLAzz58/H/fv3U633999/IzExEWFhYanWCQsLQ0JCArZu3ZrmNiMjIwEATk5OWsU6cuRItGnTBqamphg1ahRGjhwJDw8P7Nq1S1VnxIgR6NatG9zd3TF58mQ0btwYc+fORfXq1TXGbGW0cePGYd26dejfvz8GDx6MI0eOoFWrVmp1tm/fjuDgYFy4cAG9evXC5MmTERISgk2bNn3y9iMjI2FpaQlLS0tV2alTp1CiRAkYGan/iixTpgxiY2M/OlYlrXMXExODJ0+e4Nq1a5g6dSr+/vtvfPXVV6rl9+7dw6NHj1CqVCmNdcuUKYNTp06p3gcHB8PIyAi9evXCkSNHcPfuXWzZsgU//vgjGjRoAD8/P63bTGuftP0spretpKQk/Pfff6nGd+3aNbx8+fKjbX54HpNFR0fjyZMnuHjxIr7//nucO3dO7ZifPXsWCQkJGts3MzNDsWLF1I5P5cqVAQAdOnTA6dOncefOHaxYsQJz5sxBz549YWVlpXWbKRERPHz4UG/HnDJIZmdx9OVK7XIeADEyMpLz58+rlTdo0EDMzMzk2rVrqrL79++LjY2NBAcHq8qSe49Kliwp8fHxqvIJEyYIANmwYUO6Y0xu69ixY3Lt2jUxMTGRnj17qpZ/2BPVu3dvASCnTp1Ktc2TJ08KAOnbt2+qdeLi4qRgwYLi5eUlb9++TXe8V65cESMjI2nYsKFGL0pSUpKIvLssZWZmJtWrV1erk3yp8f1xJZ+jJ8rf31/tUsb06dMFgGosSEJCgnh5eYmnp6dGT1nyPunqypUrYm5uLm3atFErt7Kykvbt22vU37x5swCQrVu3ptrm06dPJWfOnFKxYsUUl3fp0kXV82pkZCRNmjRR6yE9duyYAJDff/9dY90BAwYIALWesAULFoidnZ1aj254eLja50bbNj+0b98+USgUMnTo0FTrpFdKbT1+/FgAyKhRozTqz5o1SwDIxYsXU20ztfOYrEaNGqpjY2ZmJl26dFEbIrBq1SoBIPv27dNYt2nTpuLq6qpWNnr0aLGwsFA75kOGDFGro22bH1qyZIkAkIULF6ZZjzIXe6LIIFWqVAkFCxZUvU9MTMQ///yDBg0awNvbW1Xu5uaGli1b4sCBA4iOjlZro3PnzjA1NVW9/+abb2BiYoItW7boFJO3tzfatGmDefPm4cGDBynWSf5r2cbGJtV2kpd9GO/7unfvjgsXLmDmzJkwMTFJd4zr169HUlIShg0bptGLolAoAAA7duxAfHw8evfurVanU6dOyJEjBzZv3pzu7elDu3btYGZmpnpfsWJFAFDdlXXq1CncuHEDvXv3hp2dndq6yfuki9jYWDRt2hQWFhYYN26c2rLXr19DqVRqrGNubq5anpKkpCS0atUKL168wIwZM1Ks07t3b2zfvh2//fYbatasicTERMTHx6ttG0C6t58rVy6UKVMG06ZNw7p169C3b18sXboUgwYN0rnN9z169AgtW7aEl5cXvvvuuxTrpFdqbX1KfGmdx2Tjxo3DP//8g4ULF6JcuXKIj49HQkJCurf/4bbz5s2L4OBgzJs3D2vWrEH79u3x008/YebMmTq3+b6LFy+iW7duCAwMRHh4eKr1KPOl/7cz0Wfk5eWl9v7x48eIjY1FgQIFNOr6+/sjKSkJd+7cQaFChVTl+fLlU6tnbW0NNzc33Lx5U+e4fvjhByxZsgTjxo3D9OnTNZYnJ0hpXXr4WKI1ceJEzJ8/H6NHj0atWrW0iu/atWswMjJSS0A/dOvWLQDQOJZmZmbw9vZWLf9c8uTJo/be3t4eAPD8+XMA7/YJQIpTSegqMTERX3/9NS5cuIC///4b7u7uasstLCwQFxensd6bN29Uy1PSo0cPbN26Fb///juKFi2aYh0/Pz/VZbawsDBUr14ddevWxdGjR6FQKFRtp2f7Bw8eRJ06dXDkyBHVZaMGDRogR44cGDlyJNq3b4+CBQtq1eb7YmJiUKdOHbx8+RIHDhyAtbV1ivuUHmm1pWt8HzuPyYoVK6b6uXXr1ihRogTatm2L1atXp2v77297+fLl6Ny5My5fvozcuXMDeDc8ISkpCQMHDkSLFi3g6OioVZvvi4yMRO3atWFra4vVq1fD2Ng4xXpkGNgTRQYptV8wmc3b2xutW7dOtTfK398fAPDff/+l2kbyspQSncWLF2PgwIHo2rUrfvjhBz1F/Xml1juU2pxXqX1JiA5j19KrU6dO2LRpExYvXowqVapoLHdzc0vx/CaXpfRlPXLkSMyePRvjxo1DmzZt0h1LkyZNcOzYMdU4Kzc3N7Vtfbh9BwcHVe/G3Llz4eLiojHupl69ehARHDp0SOs2k8XHx6NRo0b477//sGHDhk9KYj/WVvL2tT3mHzuPKTEzM0O9evWwdu1aVW/Qx47P+9uePXs2ihcvrkqgktWrVw+xsbGqsU7atJksKioKNWvWxIsXL7B169ZUk0IyHEyiKNNocynG2dkZlpaWuHTpksayixcvwsjICB4eHmrlV65cUXv/6tUrPHjw4JNn4P7hhx+QkJCA8ePHayyrWbMmjI2NsWTJklTX//3332FiYoLQ0FC18g0bNqBjx45o1KgRZs2apVNsPj4+SEpKwoULF1Kt4+npCQAaxzI+Ph43btxQLdeVvb19ihOo6trD5ePjAwA4d+7cp4SlMmDAACxatAhTp05V3dzwoWLFiuHkyZNISkpSKz969CgsLS2RP39+tfJZs2ZhxIgR6N27NwYOHKhVPMlf5FFRUQDeXZ5zdnZOcULKf//9V61X5eHDhykmp8k3ByRfstKmTeDdZcmwsDDs3LkTf/75JypVqqTVPmnblpGREYoUKZJifEePHoW3t7dGz216zmNqXr9+DRFR9QoXLlwYJiYmGtuPj4/H6dOndTrm2rQJvOudqlu3Li5fvoxNmzal2ZtMBiSTx2TRFyz5NuwPB2Hj/6c4+FCDBg1EqVSqDUSPjIyUHDlyaDWwfP369emO8f2B5e9r27atmJubS4ECBTTmierYsaMAkNmzZ6e6z126dFEr37t3r5ibm0tISMhHb59PizYDy0NDQ9UGZs+ePVsvA8ubNGkiLi4uaoPFN27cqJpoNFnywPL352ISEdU0F4sWLRKRd/OE6WtgefJn4Pvvv0+z3vLlyzVie/z4sdjZ2Unz5s016hoZGUmrVq3SjOfhw4caZfHx8VKiRAmxsLBQm0+pa9euYmFhIbdv31aV7dixQwDInDlzVGXdu3cXALJ79261dpNvcDhy5IjWbYqIfPvttylOAqqL9LY1btw4jf9rFy9eFGNjYxk4cKBa3fSex5SO+fPnz8XDw0M8PDzUykNDQ8XNzU1tioQFCxYIALWJOevUqSNmZmZy6dIltfUbNGggRkZGanPJpbfNhIQEqVevnpiYmMjmzZvT3CcyLAqRDOwzJ0rDsWPHUKZMGdSqVQtff/01TE1NUbduXVhbW6Nbt25qgzSBd4/NKFu2LOzs7PDtt9/CxMQEc+fOxb1797B3716ULVsWwLtLYu3atUORIkVgZ2eHZs2a4dKlS5g9ezaCgoKwb9++dPeCJbd17NgxtUsmV69ehZ+fHxITE1GoUCG1XpJXr16hZs2aOHDgAOrVq6fqcdq2bRs2bNiASpUqYfPmzapboW/duoWiRYsiPj4ekyZNQo4cOdRiCAgIQEBAQLqP67BhwzB69GgEBQWhUaNGUCqVOHbsGNzd3TF27FgA76Y4GDlyJKpXr4569eqpjk+JEiVw8OBB1YD8tm3bYs+ePVqNI9u2bRtCQ0MREhKCZs2a4dq1a/jjjz9gZWWF3LlzY8+ePQCAPXv2ICQkBKtWrUKTJk1U69+8eRNeXl5YtGgR2rZtq2qzbt26cHd3R7t27eDm5oaLFy/i/Pnz2LZtW7riWrduHRo1aoR8+fJh2LBhGsurVasGFxcXAO8uPVaoUAHnzp3DgAED4OTkhNmzZ+P27ds4duyYajzZv//+i4oVK8LW1hbjx49Xu5EBAIKCglQ3QjRs2BDR0dEIDg5Grly5EBkZiaVLl+LixYuYPHky+vbtq1rvzp07KF68OOzs7NCrVy+8evUKEydORO7cuXHs2DHVpbdLly6hZMmSUCgU6NGjBzw9PbF3714sW7YM1apVwz///KN1m9OmTUOfPn0QGBiIb7/9VuM4NWzYUPXZ/Rht2nr58iWKFy+Oly9fon///jA1NcWUKVOQmJiI06dPq6Y/0eY8lixZErlz50bZsmWRM2dO3L59G4sWLcL9+/exYsUKtc/dyZMnERQUhIIFC6Jz5864e/cuJk+ejODgYLXP2L59+1ClShU4Ojqie/fucHR0xKZNm/D333+jY8eOmD9/vtZt9u7dG9OnT0fdunXRrFkzjX1q3bp1uo43ZYLMzuLoyzZ69GjJlSuXGBkZaUy2mZKTJ09KjRo1xNraWiwtLSUkJEQOHTqkVufDyTbt7e3F2tpaWrVqJU+fPtUqvtR6okTe9dIA0OiJEnk3RcHUqVOlZMmSYmVlJZaWllKiRAmZNm2aWu+YyP96ZFJ7DR8+XKuYRUR+/fVXKV68uCiVSrG3t5dKlSppPGZj5syZ4ufnJ6ampuLi4iLffPONXibbFBGZPHmyavLM8uXLy/Hjx1Od4uBjPVHJDhw4INWqVVNNtBoQEJDirPCpSW2C1+TXh705z549kw4dOoijo6NYWlpKpUqVND4H7z8WKKXX+/uwbNkyqVq1qri4uIiJiYnY29tL1apVU51y49y5c1K9enWxtLQUOzs7adWqlURGRmrUu3jxojRp0kQ8PDzE1NRUPD09pX///hITE6NTm8mf69Re2jymSdu27ty5I02aNJEcOXKItbW11KlTR2PiVW3O48yZM6VChQri5OQkJiYm4uzsLHXr1k1x2gERkf3790tQUJCYm5uLs7OzdOvWLcXJO48ePSo1a9YUV1dXMTU1lfz588uPP/6Y4nQk6Wkz+TFRqb3IcLEnirKd1HqPiIiI9IkDy4mIiIh0wHmi6Iv06tUrvHr1Ks06zs7OBjVHS/KjRFJjYWEBW1vbDNv+s2fP1CaF/JCxsbFq3MrnFB8fj2fPnqVZx9bW1mCnzciKXr9+rbqbMDUODg5qk6gSZUdMouiLNGnSJIwcOTLNOjdu3Pjk6RD0KXnemdSEh4dj8eLFGbb9Ro0aYe/evaku9/T0/KSJTHV16NAhhISEpFnn/UHq9OlWrFiBdu3apVln9+7dqufMEWVXHBNFX6Tr16+rHiuSmgoVKqgeOWEIduzYkeZyd3f3DJ1b5sSJE6pZxFNiYWGB8uXLZ9j2U/P8+XOcOHEizTqFChX6aBJK6ffgwQOcP38+zTolS5ZUzT5PlF0xiSIiIiLSAQeWExEREemASRSRHi1evBgKhSJdY4Py5s3LcTpERFkYkyjKVpKTmPdfOXPmREhICP7+++/MDu+ziY6OxsiRI1G0aFFYW1vDwsIChQsXxsCBA3H//n2N+ps2bUJoaCgcHR1hbm6O/Pnzo3///nj69Oknx/LXX3+hRIkSMDc3R548eTB8+HDV88WS7du3D/Xq1YOHhwfMzc3h6uqK0NBQHDx4UKO9t2/fYuTIkfD29oZSqYS3tzfGjBmj0WZqHj58iHbt2iFnzpywsLBAiRIlsGrVqhTrLl++XBW7s7MzOnTogCdPnqRYd+HChfD394e5uTny5cuHGTNmpFjv3r17aNasGezs7JAjRw7Ur19fY3zezZs3VZ/fNWvWaLQxYsQIKBSKVGNJ9urVKwwfPhyhoaFwcHCAQqFI8+aDiIgIhIaGwtraGg4ODmjTpg0eP36camwfvpYvX55mPACwc+dOtG/fHvnz54elpSW8vb3RsWPHFB/UC7y7+/Knn36Cn58fzM3N4eLigtq1a+Pu3btq9eLi4jBw4EC4u7vDwsICZcuWxfbt21Ns89ChQ6hQoQIsLS3h6uqKnj17qt2tu3LlSigUCqxbt05j3aJFi0KhUGD37t0ay/LkyYOgoKCPHgPKRjJzpk8ifUueQXrUqFGyZMkS+f3332XixIlSqFAhASAbN278LNtPz6zOnp6eEh4ervcYrl27Jl5eXmJsbCxff/21zJw5U+bNmyfdu3cXR0dHyZcvn1r9fv36CQApWrSojB8/XubPny/ffPONKJVKyZUrl1y8eFHnWLZs2SIKhUJCQkJk3rx50qNHDzEyMpKuXbuq1Zs/f77Ur19fxowZIwsWLJCJEydK0aJFxcjISO0ZYyIizZo1E4VCIR06dJA5c+aoZsXu1KnTR+OJiooSX19fsbGxkR9++EFmzpwpwcHBAkCWLl2qVjf5WYJfffWVzJo1SwYPHiyWlpYSEBAgr1+/Vqv7yy+/CABp3LixzJs3T9q0aSMAZNy4cWr1Xr58Kfny5ZOcOXPK+PHjZcqUKeLh4SG5c+eWJ0+eqOolz9oOQAICAjSeyZc8a/fjx4/T3N/kdvLkySOVK1dOcSb4ZHfu3BEnJyfx8fGR6dOny48//ij29vZStGhRtecgJrfZokULWbJkidrr5s2bacYjIlKyZEnx8vKS7777TubPny+DBw8WGxsbcXFxkQcPHqjVjY+Pl6pVq4qlpaX06tVLFi5cKJMmTZKmTZvKuXPn1Op+/fXXYmJiIv3795e5c+dKYGCgmJiYyP79+9XqnTp1SszNzaV48eIyZ84cGTJkiCiVSgkNDVXVuXfvngCQvn37qq0bFRUlRkZGYmJiIqNHj1Zbdvv2bQEgAwYM+OgxoOyDSRRlK6k9puXZs2diamoqLVu2/Czb/9xJ1Nu3byUuLk7evn0rRYsWFUtLS40vD5F3XwLvP7T1zz//FADSvHlzSUhIUKt79OhRsbS0lCJFiqT4OIv0KFiwoBQtWlRt/SFDhohCoZCIiIg0142JiREXFxepUaOGquzff/8VADJ06FC1uv369ROFQiFnzpxJs83kB9fu3LlTVZaYmCilS5cWV1dXVbIQFxcndnZ2EhwcrJbAJD9I+eeff1aVxcbGiqOjo9SuXVttW61atRIrKyt59uyZqmz8+PECQP79919VWUREhBgbG8vgwYNVZcmJSrFixQSArFmzRq3t9CZRb968USUmx44dSzOJ+uabb8TCwkJu3bqlKtu+fbvGw4OTY5s4cWKa207N3r17NR6OvXfvXgEgQ4YMUSsfP368mJqaytGjR9Ns8+jRoxoxvX79Wnx8fCQwMFCtbs2aNcXNzU2ioqJUZfPnzxcAsm3bNlWZl5eXlClTRm3drVu3ikKhkBYtWqh9LkX+938ptcf4UPbEy3n0RbCzs4OFhQVMTNSnRouJiUG/fv3g4eEBpVKJAgUKYNKkSZD3blpNvnyR0mUQhUKBESNGpLltEcGYMWOQO3duWFpaIiQkJNXbw1+8eIHevXur4vH19cX48eORlJSkEc+kSZMwbdo0+Pj4QKlU4sKFC1izZg3OnDmDIUOGoEKFChrt58iRAz/++KPq/ciRI2Fvb4958+ZpTCxapkwZDBw4EGfPnsXq1avT3MeUXLhwARcuXEDnzp3Vjvu3334LEflom5aWlnB2dsaLFy9UZfv37wcAfP3112p1v/76a4gIVqxYkWab+/fvh7OzM6pUqaIqMzIyQrNmzRAZGamaB+vcuXN48eIFmjdvrvaw6jp16sDa2lrtstXu3bvx9OlTjQfsduvWDTExMdi8ebOqbPXq1ShdujRKly6tKvPz88NXX32FlStXasT79ddfI3/+/Bg1apTaZzK9lEolXF1d01V3zZo1qFOnDvLkyaMqq1q1KvLnz59ibMC7/z9pTcCakuDgYBgZGWmUOTg4ICIiQlWWlJSE6dOno2HDhihTpgwSEhIQGxubYpurV6+GsbExOnfurCozNzdHhw4dcPjwYdy5cwfAu8vc27dvR+vWrdUe9B0WFgZra2u1/axQoQJOnTqF169fq8oOHjyIQoUKoWbNmjhy5Ija/8uDBw9CoVBkyjQflHmYRFG2FBUVhSdPnuDx48c4f/48vvnmG7x69Urtaegignr16mHq1KkIDQ3FlClTUKBAAQwYMAB9+/bVWyzDhg3D0KFDUbRoUUycOBHe3t6oXr06YmJi1OrFxsaiUqVK+OOPPxAWFoaff/4Z5cuXx+DBg1OMZ9GiRZgxYwY6d+6MyZMnw8HBAX/99RcAoE2bNh+N68qVK7h06RLq16+v9oXyvrCwMADvxkxp69SpUwCg8fxCd3d35M6dW7X8fdHR0Xjy5AkuXryI77//HufOncNXX32lWh4XFwcAGrOPW1paAsBH54uKi4tLcebyD9dPbTvJZadOnVJ9gaa2nyVLloSRkZFqeVJSEv77778Un+dYpkwZXLt2DS9fvlQrNzY2xg8//IAzZ86kOD5HX+7du4dHjx6lGltK52rkyJGwtraGubk5SpcujX/++Ufn7Sc/QcDJyUlVduHCBdy/fx8BAQHo3LkzrKysYGVlhYCAAI3xSKdOnUL+/Pk1PsdlypQBAJw+fRoAcPbsWSQkJGjsp5mZGYoVK6a2nxUqVMDbt29x9OhRVdnBgwcRFBSEoKAgREVF4dy5c2rL/Pz84OjoqPNxoKyHM5ZTtlS1alW190qlEr/++iuqVaumKvvrr7+wa9cujBkzBkOGDAHwrvegadOmmD59Orp37w4fH59PiuPx48eYMGECateujY0bN6p6NYYMGYKffvpJre6UKVNw7do1nDp1Cvny5QMAdOnSBe7u7pg4caKqxyzZ3bt3cfXqVbVHrURERMDW1latXmouXLgA4N1A2dTkzZsXOXLkUOshSK/kgcIpTXLp5uaW4gD3Zs2aYdu2bQDefbF16dIFQ4cOVS0vUKAAgHdfWF5eXqry5B6qe/fupRlTgQIFsGPHDty6dQuenp6prp8vXz4oFAocPHhQbWbuS5cuqQZaP3/+HI6Ojnjw4AGMjY2RM2dOtW2ZmZnB0dFRtZ/Pnj1DXFxcqscDAO7fv6/ax2QtW7bE6NGjMWrUKDRs2FCtZ0xfPnaukmNXKpUwMjJC9erV0bBhQ+TKlQvXr1/HlClTULNmTfz111+oXbu21tufNm0a4uPj0bx5c1XZlStXAABTp06Fg4MD5s6dCwD46aefEBoaimPHjiEgIEAV/8eOa3r2M/lzAEDVk3vgwAFUrlwZCQkJOHr0KMLDw+Hj4wMXFxccOHAAAQEBePnyJc6ePYv27dtrve+UtbEnirKlWbNmYfv27di+fTv++OMPhISEoGPHjli7dq2qzpYtW2BsbIyePXuqrduvXz+IiF7u5tuxYwfi4+PRo0cPtS+/3r17a9RdtWoVKlasCHt7ezx58kT1qlq1KhITE7Fv3z61+o0bN9Z4Vl10dDRsbGzSFVtyr8fH6tvY2CA6Ojpdbb4v+TKIUqnUWGZubq52mSTZuHHj8M8//2DhwoUoV64c4uPj1e66q1WrFjw9PdG/f3+sXbsWt27dwsqVKzFkyBCYmJik2Ob7OnbsCGNjYzRr1gyHDh3CtWvXMHbsWFUvT/L6Tk5OaNasGX777TdMnjwZ169fx/79+9G8eXOYmpqq1X39+nWqz4h7fz8/djzer/O+93uj1q9fn+b+6Uqb2PLkyYNt27aha9euqFu3Lnr16oVTp07B2dkZ/fr103rb+/btw8iRI9GsWTO1y6zJd8u9fPkSO3fuRNu2bdG2bVvs2LEDIoIJEyaoxZ+e2LX5TPr7+8PR0REHDhwAAJw5cwYxMTGqu++CgoJUd48ePnwYiYmJKV5Cp+yNSZSO9u3bh7p168Ld3R0KhULrX25v3rxB27ZtUaRIEZiYmKBBgwYadfbs2ZPibcQfexAtvevGr1q1KqpWrYpWrVph8+bNKFiwILp3764aw3Hr1i24u7trJBH+/v6q5Z8quY3knqVkzs7OGo/EuHLlCrZu3QpnZ2e1V3Kv2qNHj9Tqv98TkyxHjhwal4RSk7zfH6v/8uXLdCdm70u+FJZ8aex9b968SfFSWbFixVCtWjW0b98e27dvx7///qs2l5a5uTk2b94MR0dHNG7cGHnz5kVYWBiGDRsGBwcHWFtbpxlTQEAA/vzzT1y7dg3ly5eHr68vfv75Z0ybNg0A1NafO3cuatWqhf79+8PHxwfBwcEoUqQI6tatq1bXwsIi1XFB7+/nx47H+3U+1KpVK/j6+uo8NupjPiU24N3Dhtu1a4dLly5pTD2QlosXL6Jhw4YoXLgwFixYkGJM5cuXV+tZzZMnDypUqIBDhw6p1U1P7Np8JhUKBYKCglRjnw4ePIicOXPC19cXgHoSlfwvk6gvD5MoHcXExKBo0aKYNWuWTusnJibCwsICPXv21Lj09KFLly7hwYMHqteHlw3o44yMjBASEoIHDx6oLhOkV2qXTxITE/URmkpSUhKqVaum6kH78NW4cWO1+il9qfn5+SEqKko1kDYtycnif//9l2qdW7duITo6Wqdn8iVfMklp/p8HDx7A3d09zfXNzMxQr149rF27Vq2HoFChQjh37hzOnTuH/fv34/79++jUqROePHmC/PnzfzSuJk2a4P79+/j3339x+PBh3Lp1C97e3gCgtr6trS02bNiAW7duYe/evbh58yaWLFmCBw8ewNnZGXZ2dqr9TExM1Ehy4+Pj8fTpU9V+Ojg4QKlUpno8AKR6TJJ7o06fPo0NGzZ8dB+19bFzlRx7WpITnWfPnqVrm3fu3EH16tVha2uLLVu2aCTqycfCxcVFY92cOXOqPcfRzc0tXcdV289khQoVEBUVhbNnz6rGQyULCgrCrVu3cO/ePRw4cADu7u6qzxF9OZhE6ahmzZoYM2YMGjZsmOLyuLg49O/fH7ly5YKVlRXKli2LPXv2qJZbWVlhzpw56NSp00fvnsmZMydcXV1Vrw/vbKH0Sb4slHyZwNPTE/fv39foibl48aJqOQBVj9H7d4kB6eupSm7jw8Tt8ePHGg/z9fHxwatXr1Q9aB++3r9rKjXJvSR//PHHR+vmz58f+fPnx/r161Ptjfr9998BvLsrTVvFihUDABw/flyt/P79+7h7965qeVpev34NEdGIT6FQoFChQqhQoQIcHBywe/duJCUlffQPkmRmZmYoXbo0ypUrBzMzM9XDnVNaP0+ePAgODoanpydevHiBEydOqNVLbT+PHz+OpKQk1XIjIyMUKVJEox4AHD16FN7e3mn2+LVu3Rq+vr4YOXKk3nujcuXKBWdn5xRj+/fff9N1rpInDP3wEnNKnj59iurVqyMuLg7btm1LcYxSkSJFYGpqmuI4t/v376ttp1ixYrh8+bLGZefkQeHJ8RcuXBgmJiYa+xkfH4/Tp09r7Of746IOHjyoduddyZIloVQqsWfPHhw9epR35X2pMmlqhWwFgKxbt06trGPHjhIUFCT79u2Tq1evysSJE0WpVMrly5c11g8PD5f69etrlO/evVsAiKenp7i6ukrVqlXlwIEDGbQX2UNq80TFx8dLvnz5xMzMTDU/zPr16wWA/PTTT2p1mzdvLgqFQq5evaoqc3JykoYNG6rVS56kcvjw4RrbT54n6tGjR2Jqaiq1a9dWm2/o+++/FwBq80SNGDFCAMjWrVs19uv58+equZbSmqcnPj5eihQpIlZWVnLo0CGN5dHR0WrzRP3xxx8CQFq2bKkxT9Tx48fFyspKChcuLPHx8RptpYefn58ULVpUre0ffvhBFAqFXLhwQVX28OFDjXWfP38uHh4e4uHhkeY2YmNjpUSJEuLm5ibR0dGq8piYGImIiPjoXEqXL18WGxsbqVOnzkf3p2vXrmJkZKQ2z1NsbKw4ODhorN+6dWuxtLSUp0+fqsrGjRun8fm8ePGiGBsby8CBA1VlqZ3jxYsXq80f9bF9e9/H5onq2rWrWFhYyO3bt1VlO3bsEAAyZ84cVdmjR4801r17967Y29tLQECAWvn9+/clIiJC7fPz6tUrKVOmjNjY2Mjx48fTjLl+/fpibGysNqfYhQsXxNjYWL799ltV2ZEjRzSO15s3b8TX11fKli2r1mZoaKjGZ2XBggUCQGNi17i4ODE3N5fAwEABIAcPHlRbHhgYqFo2ffr0NPeFsicmUXrwYRJ169YtMTY2lnv37qnV++qrr9Qm1EuWWhJ18eJF+eWXX+T48eNy8OBBadeunZiYmMiJEyf0vQvZxoczli9ZskQmT54sJUuWFAAyaNAgVd3ExEQJCQkRhUIhnTt3llmzZkn9+vUFgPTu3Vut3UGDBgkA1SzZLVq0ULWZVhIlIjJ48GABILVq1ZKZM2dKhw4dxN3dXZycnNSSqJiYGClRooSYmJhIx44dZc6cOTJp0iQJDw8XKysr1RfmxyY7vHLlinh6eoqJiYm0bNlSZs2aJfPmzZNevXqJs7Oz5M+fX61+r169VF/MEydOlAULFsi3334r5ubmKc5Ynpzcv7/fqdm4caMoFAqpUqWKzJs3T3r27ClGRkYas4uXKFFC6tWrJz/++KPMnz9fhg4dKrlz5xYjIyNZtWqVWt2mTZtKr169ZO7cuTJx4kTx9/cXpVIpO3bsSFec/v7+MmzYMFmwYIEMGTJEHBwcxNPTU+7evatWb+zYsdKqVSv5+eefZfbs2VK9enUBIGPGjNHYz1mzZgkAadKkicyfP1/CwsIEgPz4449q9aKjo8XHx0dy5swpEyZMkKlTp4qHh4e4u7urJSepneO3b9+Kj4+Pajbz9CRRM2bMkNGjR8s333wjAKRRo0YyevRoGT16tLx48UJV7/bt2+Lo6Cg+Pj7y888/y08//ST29vZSpEgRefPmjape27ZtpWLFijJixAiZN2+efP/99+Lo6ChmZmaye/dutW0nzyb//v+H5P9j7du315jx/MM/Rs+fPy/W1tbi5uYmY8eOlbFjx4qbm5s4OztrnK+mTZuKiYmJDBgwQObOnStBQUFiYmIie/fuVat34sQJUSqVajOWm5ubS/Xq1VM8fhUrVhQAolQq1Y6DyP/+kALA38tfKCZRevBhErVp0yYBIFZWVmovExMTadasmcb6qSVRKQkODpbWrVvrKfLsJzmJef9lbm4uxYoVkzlz5mg8PuPly5fSp08fcXd3F1NTU8mXL59MnDhRo15sbKx06NBBbG1txcbGRpo1ayaPHj1KVxKVmJgoI0eOFDc3N7GwsJDKlSvLuXPnUpyx/OXLlzJ48GDx9fUVMzMzcXJykqCgIJk0aZLqr/n0zBj9/PlzGTZsmBQpUkQsLS3F3NxcChcuLIMHD9Z4tIbIu165atWqib29vSiVSvH19ZV+/fql+CWdPGv3L7/8kur237du3TopVqyYKJVKyZ07t/zwww8aPVszZ86UChUqiJOTk5iYmIizs7PUrVtX9u3bp9He+PHjxc/PT8zNzcXe3l7q1asnp06d0qiXWhL19ddfi4eHh5iZmYm7u7t07do1xZ6wTZs2qXpMLC0tpVy5crJy5cpU93PevHlSoEABMTMzEx8fH5k6darG50jk3eNVmjRpIjly5BBra2upU6eOXLlyRa1OWuf4/c94epIoT09Pjf8Tya8PZ9Y/d+6cVK9eXSwtLcXOzk5atWolkZGRanX+/PNPCQ4OFmdnZzExMVH10qaURKSURKUVj6enp0YbJ06ckKpVq4qVlZXY2NhI/fr1U+zRf/36tfTv319cXV1FqVRK6dKlU+zVFRHZv3+/BAUFibm5uTg7O0u3bt3Ueqbel/xHUFBQkMaytWvXCgCxsbHR6MmlL4NCJANu9fjCJD+oMvkOuxUrVqBVq1Y4f/68xizQ1tbWGmOg2rZtixcvXqTrDr8BAwbgwIEDOHz4sL7CJ9LKd999h2XLluHq1asfHWxMRJSdcbLNDFC8eHHV3ToVK1bUa9unT59OcRAm0eeye/duDB06lAkUEX3xmETp6NWrV7h69arq/Y0bN3D69Gk4ODggf/78aNWqFcLCwjB58mQUL14cjx8/xs6dOxEQEKCa0ffChQuIj4/Hs2fP8PLlS9WjCZLvEJk2bRq8vLxQqFAhvHnzBgsWLMCuXbs+6fEKRJ/q2LFjmR0CEZFB4OU8He3ZswchISEa5eHh4Vi8eDHevn2LMWPG4Pfff8e9e/fg5OSEcuXKYeTIkShSpAiAd4/USOk2+eRTMmHCBMybNw/37t2DpaUlAgICMGzYsBS3S0RERJ8XkygiIiIiHXDWRiIiIiIdMIkiIiIi0gEHlmspKSkJ9+/fh42NTarPVCMiIiLDIv//CCl3d3e9PT6NSZSW7t+/r/ZEcSIiIso67ty5g9y5c+ulLSZRWkp+QOidO3eQI0eOTI6GiIiI0iM6OhoeHh5pPuhbW0yitJR8CS9HjhxMooiIiLIYfQ7F4cByIiIiIh0wiSIiIiLSAZMoIiIiIh1wTFQGSUxMxNu3bzM7DCL6wpmZmentdm4iUsckSs9EBJGRkXjx4kVmh0JEBCMjI3h5ecHMzCyzQyHKdphE6VlyApUzZ05YWlpyQk4iyjTJkwM/ePAAefLk4e8jIj1jEqVHiYmJqgTK0dExs8MhIoKzszPu37+PhIQEmJqaZnY4RNkKL5TrUfIYKEtLy0yOhIjoneTLeImJiZkcCVH2wyQqA7DLnIgMBX8fEWUcJlEGKCYGUCjevWJiMjsaIiIiSgmTKCIiIiIdMIn6wikUijRfI0aMwM2bN1NdfuTIEQDA4sWLoVAo4O/vr7GNVatWQaFQIG/evKqy5PoKhQJGRkbInTs32rVrh0ePHqmtu2nTJlSqVAk2NjawtLRE6dKlsXjx4ow8JEREROnCu/O+cA8ePFD9vGLFCgwbNgyXLl1SlVlbW+PJkycAgB07dqBQoUJq679/F6KVlRUePXqEw4cPIzAwUFW+cOFC5MmTR2PbOXLkwKVLl5CUlIQzZ86gXbt2uH//PrZt2wYAmDFjBnr37o2BAwdizpw5MDMzw4YNG9C1a1ecO3cOkyZN0s9BICIi0gGTKAN37x6QP3/Gte/q6qr62dbWFgqFQq0MgCqJcnR01Fj2PhMTE7Rs2RK//vqrKom6e/cu9uzZgz59+mDZsmVq9d/flru7O3r27ImhQ4fi9evXePLkCfr164fevXvjp59+Uq3Tr18/mJmZoWfPnmjatCnKli37aQeAiIhIR7ycZ4B+++1/P/v7AwsXZl4s2mrfvj1WrlyJ2NhYAO8u24WGhsLFxeWj61pYWCApKQkJCQlYvXo13r59i/79+2vU69KlC6ytrTWSMiIiSj/exPTpmEQZmLt3gR49/vc+KQno0uVdeWYLCgqCtbW12utDxYsXh7e3N1avXg0RweLFi9G+ffuPtn3lyhX88ssvKFWqFGxsbHD58mXY2trCzc1No66ZmRm8vb1x+fJlvewXERGRLng5z8BcufIucXpfYiJw9SqQO3fmxJRsxYoVKQ4c/1D79u2xaNEi5MmTBzExMahVqxZmzpypUS8qKgrW1tZISkrCmzdvUKFCBSxYsCAjQiciItI7JlEGJl8+wMhIPZEyNgZ8fTMvpmQeHh7wTUcgrVq1wnfffYcRI0agTZs2MDFJ+WNmY2ODkydPwsjICG5ubrCwsFAty58/P6KionD//n24u7urrRcfH49r164hJCTk03aIiIjoE/BynoHJnRuYMeN/742NgblzM78XShsODg6oV68e9u7dm+alPCMjI/j6+sLb21stgQKAxo0bw9TUFJMnT9ZY75dffkFMTAxatGih99iJiIjSiz1RBig8HOjW7d3PFy5k7N152nj69CkiIyPVyuzs7GBubq5Rd/HixZg9e7bOD2LOkycPJkyYgH79+sHc3Bxt2rSBqakpNmzYgO+//x79+vXjnXlERJSpmEQZuFy5MjuC/6latapG2bJly/D1119rlFtYWGj0Lmmrd+/e8Pb2xqRJkzB9+nQkJiaiUKFCmDNnDtq1a/dJbRMREX0qhYhIZgeRlURHR8PW1hZRUVHIkSOH2rI3b97gxo0b8PLySrF3Jr1iYoDkG99evQKsrD4lYiL6kunr9xJlP1/ad01a39+64pgoIiIiIh3wcp4BsrIC2D9IRERk2NgTRURERKQDJlFEREREOmASRURERKQDJlFEREREOmASRURERKQDJlFEREREOmASZYhiYgCF4t0rJiazoyEiIqIUMImiTNO2bVs0aNAgs8MweIsXL4adnZ3e27158yYUCgVOnz6t97a/ZAqFAuvXr8/sMIjoM2ASRWjbti0UCgUUCgXMzMzg6+uLUaNGISEhIbND++LkzZsX06ZNy+ww0m3Pnj1QKBR48eJFqnXe/3yl9MqbNy8AoHLlyiku79q1q6qt5LIjR46obSMuLg6Ojo5QKBTYs2ePRn2FQgFbW1uUL18eu3btUlv3zp07aN++Pdzd3WFmZgZPT0/06tULT58+TXPfR4wYgWLFimmUP3jwADVr1kxzXSLKHphEEQAgNDQUDx48wJUrV9CvXz+MGDECEydOzOyw0iQiTPSygOnTp+PBgweqFwAsWrRI9f7YsWOqup06dVKr++DBA0yYMEGtPQ8PDyxatEitbN26dbBOfgjYB5K3dfDgQTg5OaFOnTq4fv06AOD69esoVaoUrly5gmXLluHq1av45ZdfsHPnTgQGBuLZs2da76+rqyuUSqXW6xFR1sMkytDdu/dZNqNUKuHq6gpPT0988803qFq1Kv766y8AwJQpU1CkSBFYWVnBw8MD3377LV69eqVaN/ly07Zt2+Dv7w9ra2tVUpYsMTERffv2hZ2dHRwdHfHdd9/hw2dfx8XFoWfPnsiZMyfMzc1RoUIFtS/Y5F6Pv//+GyVLloRSqcSBAwdw7do11K9fHy4uLrC2tkbp0qWxY8eONPf3zJkzCAkJgY2NDXLkyIGSJUvi+PHjavuzadMmFChQAJaWlmjSpAliY2Px22+/IW/evLC3t0fPnj2RmJioavP58+cICwuDvb09LC0tUbNmTVy5ckVtu2vWrEGhQoWgVCqRN29eTJ48WbWscuXKuHXrFvr06aPqPXlfWscXABYsWAB/f3+Ym5vDz88Ps2fPVlv+77//onjx4jA3N0epUqVw6tSpNI8RACxZsgSlSpWCjY0NXF1d0bJlSzx69AjAu8uBISEhAAB7e3soFAq0bdtWow1bW1u4urqqXgBgZ2eneu/s7Kyqa2lpqVbX1dVV40Gh4eHhWL58OV6/fq0q+/XXXxEeHp7iPiRvq3DhwpgzZw5ev36N7du3AwC6desGMzMz/PPPP6hUqRLy5MmDmjVrYseOHbh37x6GDBmSYpuLFy/GyJEjcebMGdW5Wrx4MQD1y3nJl0xXrlyJihUrwsLCAqVLl8bly5dx7NgxlCpVCtbW1qhZsyYeP36sto20zmd8fDy6d+8ONzc3mJubw9PTE2PHjk0xViLKQEJaiYqKEgASFRWlsez169dy4cIFef369adtZNYskXePzxMxMhJZsODT2vuI8PBwqV+/vlpZvXr1pESJEiIiMnXqVNm1a5fcuHFDdu7cKQUKFJBvvvlGVXfRokViamoqVatWlWPHjsmJEyfE399fWrZsqaozfvx4sbe3lzVr1siFCxekQ4cOYmNjo7bdnj17iru7u2zZskXOnz8v4eHhYm9vL0+fPhURkd27dwsACQgIkH/++UeuXr0qT58+ldOnT8svv/wiZ8+elcuXL8sPP/wg5ubmcuvWrVT3uVChQtK6dWuJiIiQy5cvy8qVK+X06dNq+1OtWjU5efKk7N27VxwdHaV69erSrFkzOX/+vGzcuFHMzMxk+fLlasfM399f9u3bJ6dPn5YaNWqIr6+vxMfHi4jI8ePHxcjISEaNGiWXLl2SRYsWiYWFhSxatEhERJ4+fSq5c+eWUaNGyYMHD+TBgwfpPr5//PGHuLm5yZo1a+T69euyZs0acXBwkMWLF4uIyMuXL8XZ2Vlatmwp586dk40bN4q3t7cAkFOnTqV6nBYuXChbtmyRa9euyeHDhyUwMFBq1qwpIiIJCQmyZs0aASCXLl2SBw8eyIsXL1JtKxkAWbdunUZ5pUqVpFevXulaNyAgQJYsWSIiIrdu3RKlUimXL18WALJ79+5Ut/Xs2TMBID///LM8ffpUFAqF/PTTTyluq1OnTmJvby9JSUkay2JjY6Vfv35SqFAh1bmKjY3V2OaNGzcEgPj5+cnWrVvlwoULUq5cOSlZsqRUrlxZDhw4ICdPnhRfX1/p2rWrqv2Pnc+JEyeKh4eH7Nu3T27evCn79++XP//8M8X90NvvJcp2Xr3631fNq1eZHU3GS+v7W1dMorSU4UnUnTvvEqfkTzYgYmz8rjyDvJ9EJSUlyfbt20WpVEr//v1TrL9q1SpxdHRUvV+0aJEAkKtXr6rKZs2aJS4uLqr3bm5uMmHCBNX7t2/fSu7cuVXbffXqlZiamsrSpUtVdeLj48Xd3V21XnIStX79+o/uU6FChWTGjBmpLrexsVF9IX0opf3p0qWLWFpaysuXL1VlNWrUkC5duoiIqL7ADx48qFr+5MkTsbCwkJUrV4qISMuWLaVatWpq2xowYIAULFhQ9d7T01OmTp360Xg+PL4+Pj4aX6KjR4+WwMBAERGZO3euODo6qn0258yZ89Ek6kPHjh0TAKrjkHxOnj9/nu420kqiTE1NxcrKSu31xx9/aKw7bdo0CQkJERGRkSNHSsOGDeX58+dpJlExMTHy7bffirGxsZw5c0aOHDmSaiwiIlOmTBEA8vDhwxSXDx8+XIoWLZrm/iUnUQve+0No2bJlAkB27typKhs7dqwUKFBA9f5j57NHjx5SpUqVFBO8DzGJotQwifp0Jp+hs4u0ceUKkJSkXpaYCFy9CuTOnWGb3bRpE6ytrfH27VskJSWhZcuWGDFiBABgx44dGDt2LC5evIjo6GgkJCTgzZs3iI2NhaWlJYB3l2F8fHxU7bm5uaku+0RFReHBgwcoW7asarmJiQlKlSqluqR37do1vH37FuXLl1fVMTU1RZkyZRAREaEWa6lSpdTev3r1CiNGjMDmzZvx4MEDJCQk4PXr17h9+3aq+9u3b1907NgRS5YsQdWqVdG0aVO1+D/cHxcXF+TNm1dt3I2Li4tqHyMiImBiYqK2j46OjihQoIAq/oiICNSvX18tjvLly2PatGlITEyEsbFxqvGmdXxjYmJw7do1dOjQAZ06dVLVSUhIgK2trWrbAQEBMDc3Vy0PDAxMdXvJTpw4gREjRuDMmTN4/vw5kv7/s3n79m0ULFjwo+trq1WrVhqX0FxcXDTqtW7dGoMGDcL169exePFi/Pzzz6m22aJFCxgbG+P169dwdnbGwoULERAQgKNHjwKAxmXljBAQEKD6OXl/ihQpolamzfls27YtqlWrhgIFCiA0NBR16tRB9erVM3w/iEgdkyhDky8fYGSknkgZGwO+vhm62ZCQEMyZMwdmZmZwd3eHicm7j8bNmzdRp04dfPPNN/jxxx/h4OCAAwcOoEOHDoiPj1clUaampmrtKRSKDPtysrKyUnvfv39/bN++HZMmTYKvry8sLCzQpEkTxMfHp9rGiBEj0LJlS2zevBl///03hg8fjuXLl6Nhw4YAUt6flMqSPkx4M0haxzd5fNr8+fPVkjgAaSZmHxMTE4MaNWqgRo0aWLp0KZydnXH79m3UqFEjzWP7KWxtbeGbjs+6o6Mj6tSpgw4dOuDNmzeoWbMmXr58mWLdqVOnomrVqrC1tVUbf+Xr6wuFQoGIiAjVeX9fREQE7O3t1dbR1fvnL3ms24dlyZ+l9JzPEiVK4MaNG/j777+xY8cONGvWDFWrVsXq1as/OVYiSj8OLDc0uXMDM2b8772xMTB3bob2QgHvEhNfX1/kyZNHlUAB73oikpKSMHnyZJQrVw758+fH/fv3tWrb1tYWbm5uqr/8gXd/VZ84cUL13sfHB2ZmZjh48KCq7O3btzh27NhHezwOHjyItm3bomHDhihSpAhcXV1x8+bNj8aVP39+9OnTB//88w8aNWqkcceXNvz9/ZGQkKC2j0+fPsWlS5dU8fv7+6vtX3Ls+fPnV305mpmZqQ1WTw8XFxe4u7vj+vXr8PX1VXt5eXmptv3ff//hzZs3qvU+nCbgQxcvXsTTp08xbtw4VKxYEX5+fqrekmRmZmYAoHXM+tC+fXvs2bMHYWFhaSaLrq6u8PX11UiGHB0dUa1aNcyePVttkDoAREZGYunSpWjevLnGAP9kupyr9EjP+QSAHDlyoHnz5pg/fz5WrFiBNWvW6HQ3IRHpjj1Rhig8HOjW7d3PFy4A+fNnWii+vr54+/YtZsyYgbp16+LgwYP45ZdftG6nV69eGDduHPLlywc/Pz9MmTJFbW4hKysrfPPNNxgwYAAcHByQJ08eTJgwAbGxsejQoUOabefLlw9r165F3bp1oVAoMHTo0DR7iF6/fo0BAwagSZMm8PLywt27d3Hs2DE0btxY6/16P4b69eujU6dOmDt3LmxsbDBo0CDkypVLdQmvX79+KF26NEaPHo3mzZvj8OHDmDlzptpdV3nz5sW+ffvw9ddfQ6lUwsnJKV3bHzlyJHr27AlbW1uEhoYiLi4Ox48fx/Pnz9G3b1+0bNkSQ4YMQadOnTB48GDcvHkTkyZNSrPNPHnywMzMDDNmzEDXrl1x7tw5jB49Wq2Op6cnFAoFNm3ahFq1asHCwiLVqQbSIzY2FpGRkWplSqUS9vb2GnVDQ0Px+PFjjbv3tDFz5kwEBQWhRo0aGDNmDLy8vHD+/HkMGDAAuXLlwo8//pjqunnz5sWNGzdw+vRp5M6dGzY2Nnqb2uBj53PKlClwc3ND8eLFYWRkhFWrVsHV1TVDJmUlotRl2Z6ovHnzpjgxX7fk5OMDixcv1qj7/vgQg5UrV6ZuvmjRopgyZQrGjx+PwoULY+nSpTrdSt2vXz+0adMG4eHhCAwMhI2NjcYllHHjxqFx48Zo06YNSpQogatXr2Lbtm0pfoG+b8qUKbC3t0dQUBDq1q2LGjVqoESJEqnWNzY2xtOnTxEWFob8+fOjWbNmqFmzJkaOHKn1fr1v0aJFKFmyJOrUqYPAwECICLZs2aK6bFOiRAmsXLkSy5cvR+HChTFs2DCMGjVKbVqAUaNG4ebNm/Dx8dHqMlLHjh2xYMECLFq0CEWKFEGlSpWwePFiVc+FtbU1Nm7ciLNnz6J48eIYMmQIxo8fn2abzs7OWLx4MVatWoWCBQti3LhxGolXrly5MHLkSAwaNAguLi7o3r17umNOyfz58+Hm5qb2atGiRYp1FQoFnJycVL1husiXLx+OHz8Ob29vNGvWDD4+PujcuTNCQkJw+PBhODg4pLpu48aNERoaipCQEDg7O2PZsmU6x/Ghj51PGxsbTJgwAaVKlULp0qVx8+ZNbNmyBUZGWfZXOlGWpJDPMaoyAzx+/FitK/3cuXOoVq0adu/ejcqVK2vUX7x4MXr16oVLly6pyhQKRYqDVtMSHR0NW1tbREVFafwF/ObNG9y4cQNeXl6flqDFxADJf82/egV8MAaIiCi99PZ7idIlK/36zkqx6kNa39+6yrKX8z78K33cuHHw8fFBpUqVUl1HoVCoJvsjIiIi+hTZou83Pj4ef/zxB9q3b5/qIFDg3V0vnp6e8PDwQP369XH+/PnPGKUWrKz+N0tUdv/TgIiIKIvKFknU+vXr8eLFixQfOZGsQIEC+PXXX7Fhwwb88ccfSEpKQlBQEO7evZtm23FxcYiOjlZ7ERGRppgYQKF494qJyexoiDJetkiiFi5ciJo1a8Ld3T3VOoGBgQgLC0OxYsVQqVIlrF27Fs7Ozpg7d26abY8dOxa2traql4eHh77DJyIioiwoyydRt27dwo4dO9CxY0et1jM1NUXx4sVx9erVNOsNHjwYUVFRqtedO3c+2nYWHatPRNkQfx8RZZwsn0QtWrQIOXPmRO3atbVaLzExEWfPnoWbm1ua9ZRKJXLkyKH2Sk3yreyxsbFaxUJElFGSZ5f/lNnriShlWfbuPABISkrCokWLEB4erjbLNgCEhYUhV65cqjmNRo0ahXLlysHX1xcvXrzAxIkTcevWLa17sNJibGwMOzs71azOlpaWaQ50JyLKSElJSXj8+DEsLS01fkcS0afL0v+rduzYgdu3b6N9+/Yay27fvq028dzz58/RqVMnREZGwt7eHiVLlsShQ4f0/hDV5CkUPnw8BhFRZjAyMkKePHn4Bx1RBsiyk21mlvRO1pWYmIi3b99+xsiIiDSZmZl9tpnMv7TJG1OSlY5BVopVHzjZZhZibGzMMQhERETZWJYfWE5ERESUGZhEEREREemASRQRERGRDphEEREREekgQ5Ko169f4/bt2xnRNBEREZFB0CqJ2rVrF4KDg+Hs7Ax/f3+MHDkyxdm5165dCy8vL70FSURERGRo0p1EnThxAjVq1MClS5dQqVIlODo6YtSoUShWrBgiIiIyMkYiIiIig5PuJGr48OHw8vJCREQEVq9ejQMHDmDPnj14/fo1ypcvjwMHDmRknEREREQGJd1J1MmTJ9GlSxc4ODioyipWrIiTJ0/C19cX1atXx4YNGzIkSCIiIiJDk+4k6tWrV7C1tdUod3Z2xp49e1CxYkU0bdoUCxcu1GuARERERIYo3UmUj48P/v333xSXWVpaYtOmTWjUqBE6d+6MOXPm6C1AIiIiIkOU7iSqWrVqWLNmTYp34wGAqakpli1bhq5du+LQoUN6C5CIiIjIEKX7AcQdOnSAiODSpUsoXrx4inUUCgVmzZqFfPny4cyZM3oLkoiIiMjQKEREMjuIrCQ6Ohq2traIiopCjhw5MjscIiKDERMDWFu/+/nVK8DKKnPjyQxZ6RhkpVj1ISO+v/nYFyIiIiId6JxEPXz4UJ9xEBEREWUpOiVRERERKFeunL5jISIiIsoytE6iDhw4gIoVK6Jo0aIZEQ8RERFRlqBVErVq1SpUr14dQUFBWL16dUbFRERERGTw0p1ETZ48GV9//TW++uorrF27FiYm6Z4dgYiIiCjbSXcSNWDAAAQHBzOBIiIiIoIWSZSrqytOnz6N06dPZ2A4RERERFlDupOoI0eOwN3dHdWrV8eJEycyMiYiIiIig5fuJCpPnjw4ePAgAgICUK1aNZw8eTIj4yIiIiIyaFrdnWdnZ4ft27ejevXqqFatWkbFRERERGTwtJ4nyszMDMuXL0eHDh0yIh4iIiKiLEHnx75MmDBBn3EQERERZSl8ADERERGRDphEEREREelA51kzDxw4gF9//RXXr1/H8+fPISJqyxUKBc6cOfPJARIREREZIp2SqClTpmDAgAEwNzdHgQIF4ODgoO+4iIiIiAyaTknUxIkTUb58eWzcuBG2trb6jomIiIjI4Ok0Jio2NhatWrViAkVERERfLJ2SqJCQEJw9e1bfsRARERFlGTolUTNmzMDOnTsxadIkPHv2TN8xERER0f+LiQEUinevmJjMjobep1MS5eHhgS5dumDQoEFwdnaGlZUVcuTIofbipT4iIiLKznQaWD5s2DD8+OOPyJUrF0qVKsWEiYiIiL44OiVRv/zyC2rXro3169fDyIjzdRIREdGXR6cMKD4+HrVr187UBGrEiBFQKBRqLz8/vzTXWbVqFfz8/GBubo4iRYpgy5YtnylaIiIiym50yoLq1KmD/fv36zsWrRUqVAgPHjxQvQ4cOJBq3UOHDqFFixbo0KEDTp06hQYNGqBBgwY4d+7cZ4yYiIiIsgudkqjhw4fjwoUL+Pbbb3HixAk8fvwYz54903hlNBMTE7i6uqpeTk5OqdadPn06QkNDMWDAAPj7+2P06NEoUaIEZs6cmeFxEhERUfaj05ioAgUKAABOnz6NuXPnplovMTFRt6jS6cqVK3B3d4e5uTkCAwMxduxY5MmTJ8W6hw8fRt++fdXKatSogfXr12dojERERJQ96Xx3nkKh0HcsWilbtiwWL16MAgUK4MGDBxg5ciQqVqyIc+fOwcbGRqN+ZGQkXFxc1MpcXFwQGRmZ5nbi4uIQFxeneh8dHa2fHSAiIqIsTackasSIEXoOQ3s1a9ZU/RwQEICyZcvC09MTK1euRIcOHfS2nbFjx2LkyJF6a4+IiIiyh2wzP4GdnR3y58+Pq1evprjc1dUVDx8+VCt7+PAhXF1d02x38ODBiIqKUr3u3Lmjt5iJiIgo69Ipifrhhx9QrFixVJcXL178s/fevHr1CteuXYObm1uKywMDA7Fz5061su3btyMwMDDNdpVKpcZs7EREREQ6JVGrV69Wu5z2oVq1amHFihU6B5Ue/fv3x969e3Hz5k0cOnQIDRs2hLGxMVq0aAEACAsLw+DBg1X1e/Xqha1bt2Ly5Mm4ePEiRowYgePHj6N79+4ZGicREX0aPjuODJVOY6Ju374NHx+fVJd7eXnh1q1bOgeVHnfv3kWLFi3w9OlTODs7o0KFCjhy5AicnZ1VMb4/GWhQUBD+/PNP/PDDD/j++++RL18+rF+/HoULF87QOImIiCh70imJsra2TjNJunHjBszNzXUOKj2WL1+e5vI9e/ZolDVt2hRNmzbNoIiIiIjoS6LT5bzKlStj7ty5uHfvnsayO3fuYN68eQgJCfnk4IiIiIgMlU49UaNHj0aZMmVQqFAhdOjQAYUKFQIAnDt3Dr/++itEBKNHj9ZroERERESGROcZy/fv348ePXpg6tSpasuCg4Px888/w9/fXy8BEhERERkinZIo4N0El3v37sWTJ09w/fp1AIC3t3eaz68jIqIvw717QP78mR0FUcZK95ioOnXq4JdffsHt27fVyp2cnFCmTBmUKVOGCRQR0Rfst9/+97O/P7BwYebFQvQ5pDuJUiqVGDhwILy8vFCkSBEMHDgQ+/btQ1JSUkbGR0REWcDdu0CPHv97n5QEdOnyrpwou0p3ErVmzRo8ffoU27dvR40aNbBx40ZUrlwZTk5O+Prrr7FkyRI8fvw4I2MlIiIDdeXKu8TpfYmJQCpP4iLKFrSa4sDExARVqlTBpEmTcOHCBVy/fh2jRo1CdHQ0unTpAjc3N5QrVw6jR4/GiRMnMipmIiIyMPnyAUYffKMYGwO+vpkTD9Hn8EkPIM6bNy+6d++OLVu24OnTp1i/fj1KliyJX3/9FaVLl4a7u3uGP/6FiIgyX+7cwIwZ/3tvbAzMnfuunCi70vnuvA9ZWFigTp06qFOnDgDg/Pnz2LJlC4yNjfW1CSIiMmDh4UC3bu9+vnCBd+dR9qe3JEpEsHv3bsTFxaFChQooVKiQahJOIiL6suTKldkREGU8nS7nDRkyRO2xLiKC6tWro1q1aqhduzaKFCmCa9eu6S1IIiIiIkOjUxK1Zs0alClTRvV+9erV2LlzJ8aMGYNNmzYhMTERI0aM0FeMRERERAZHp8t59+7dg+97t1ysXbsWBQsWxODBgwEA33zzDebMmaOfCImIiIgMkE49USYmJoiLiwPw7lLezp07ERoaqlru4uKCJ0+e6CdCIiIiIgOkUxJVuHBh/PHHH3j+/DkWLVqEp0+fonbt2qrlt27d4iNgiIiIKFvT6XLesGHDULduXVWiVL58ebWB5ps3b0bp0qX1EyERERGRAdIpiapWrRpOnjyJ7du3w87ODs2bN1cte/78OYKDg1G/fn29BUlERERkaHSeJ6pgwYIoWLCgRrm9vT2mTp36SUERERERGbpPmmzzyJEj2L17Nx49eoRvv/0W+fLlQ2xsLC5evIj8+fPD2tpaX3ESERERGRSdBpbHx8ejUaNGKF++PIYMGYKff/4Zd+7cedegkRGqV6+O6dOn6zVQIiIiIkOiUxI1dOhQbNq0CXPmzMGlS5cgIqpl5ubmaNq0KTZs2KC3IImIiIgMjU5J1LJly/DNN9+gc+fOcHBw0Fju7++P69evf3JwRERERIZKpyTq0aNHKFKkSKrLjY2NERsbq3NQRERERIZOpyTKw8MDFy9eTHX5wYMH1R4LQ0RERJTd6JREtWzZEnPnzsXhw4dVZQqFAgAwf/58rFy5EmFhYfqJkIiIiMgA6TTFwZAhQ3DkyBEEBwfD398fCoUCffr0wbNnz3D37l3UqlULffr00XesRERERAZDp54oMzMzbN26FYsWLYK3tzf8/PwQFxeHgIAALF68GBs3boSxsbG+YyUiIiIyGDpPtqlQKNC6dWu0bt1an/EQERERZQk69UQRERERfenS1RNVpUoVrRtWKBTYuXOn1usRERERZQXpSqKSkpJUd9+l1/uzmBMRERFlN+lKovbs2ZPBYRARERFlLRwTRURERKQDne7Ou337drrq5cmTR5fmiYiIiAyeTklU3rx50zVGKjExUZfmiYiIiAyeTknUr7/+qpFEJSYm4ubNm/j999+RM2dOdOvWTS8BEhERERkinZKotm3bprps4MCBKFu2LKKionSNiYiIiMjg6X1guZWVFdq1a4epU6fqu2kiIiIig5Ehd+clJSUhMjIyI5omIiIiMgh6TaKio6OxadMmTJw4EcWLF9dn0xrGjh2L0qVLw8bGBjlz5kSDBg1w6dKlNNdZvHgxFAqF2svc3DxD4yQiIqLsSacxUUZGRqnenSciyJMnD2bPnv1JgX3M3r170a1bN5QuXRoJCQn4/vvvUb16dVy4cAFWVlaprpcjRw61ZEvbmdiJiIiIAB2TqGHDhmkkHwqFAvb29vDx8UH16tVhYqJT0+m2detWtfeLFy9Gzpw5ceLECQQHB6e6nkKhgKura4bGRkRERNmfTpnOiBEj9BzGp0u+G9DBwSHNeq9evYKnpyeSkpJQokQJ/PTTTyhUqFCq9ePi4hAXF6d6Hx0drZ+AiYiIKEvTaUxUQkJCmslEdHQ0EhISdA5KW0lJSejduzfKly+PwoULp1qvQIEC+PXXX7Fhwwb88ccfSEpKQlBQEO7evZvqOmPHjoWtra3q5eHhkRG7QERERFmMTklUz549ERQUlOry8uXLo1+/fjoHpa1u3brh3LlzWL58eZr1AgMDERYWhmLFiqFSpUpYu3YtnJ2dMXfu3FTXGTx4MKKiolSvO3fu6Dt8IiIiyoJ0SqK2bt2KJk2apLq8SZMm2LJli85BaaN79+7YtGkTdu/ejdy5c2u1rqmpKYoXL46rV6+mWkepVCJHjhxqLyIiIiKdkqj79+8jV65cqS53d3fHvXv3dA4qPUQE3bt3x7p167Br1y54eXlp3UZiYiLOnj0LNze3DIiQiIiIsjOdBpY7OjqmOSdTREREhvfYdOvWDX/++Sc2bNgAGxsb1eSetra2sLCwAACEhYUhV65cGDt2LABg1KhRKFeuHHx9ffHixQtMnDgRt27dQseOHTM0ViIiIsp+dOqJCg0Nxdy5c3Hq1CmNZSdPnsS8efNQs2bNTw4uLXPmzEFUVBQqV64MNzc31WvFihWqOrdv38aDBw9U758/f45OnTrB398ftWrVQnR0NA4dOoSCBQtmaKxERESU/ShERLRd6f79+yhdujQePXqEevXqqaYIOHfuHDZu3IicOXPi6NGjWo9Rygqio6Nha2uLqKgojo8iInpPTAxgbf3u51evgDTmPTaIdjNCRsTK46ofGfH9rdPlPHd3dxw/fhyDBg3Chg0bsG7dOgDvZgNv1aoVfvrpJ7i7u+slQCIioly4i3y4AsW9fED+7PcHOmVNOk8r7ubmht9++w0igsePHwMAnJ2d+RgVIiLSK5PfFuIWOsMYSRB/I2DePKBDh8wOi+jTH0CsUCigVCrh6OjIBIqIiPTr7l2Y9XiXQAGAIikJ6NIFSGOSZKLPReck6vjx4wgNDYWlpSUcHR2xd+9eAMCTJ09Qv3597NmzR18xEhHRl+rKlXeJ0/sSE4E05vcj+lx0SqIOHTqEChUq4MqVK2jdujWS3vuAOzk5ISoqKs1ZwImIiNIlXz6I0QdfVcbGgK9v5sRD9B6dkqjvv/8e/v7+uHDhAn766SeN5SEhITh69OgnB0dERF+43LkRP2MeEmAMABBjY2DuXCAb3v1NWY9OSdSxY8fQrl07KJXKFMdB5cqVSzX5JRER0adICO+AvLiJytiN1xdufpGDynPhLipjNxT3OBbMkOiURJmamqpdwvvQvXv3YJ08+QQREdEnuofc2IvKkFxfXg+UybzZuAsP7EYVWPh7AgsX6q1tJmefRqckqly5cli9enWKy2JiYrBo0SJUqlTpkwIjIiL64t29C7P+PVRv9Xl34rupIzwzJDn7UuiURI0cORLHjx9H7dq18ffffwMAzpw5gwULFqBkyZJ4/Pgxhg4dqtdAiYiIsgK99u5k1N2JnDpCL3RKosqWLYstW7bg6tWrCAsLAwD069cPnTt3RmJiIrZs2YKAgAC9BkpERGTo9N67k1F3J3LqCL3Q6dl57zt9+jSuXLmCpKQk+Pj4oGTJktl60k0+O4+IKGVf/DPe7t6FeHqqJyfGxsDNm590N2Hc7IUw7tYFJkiEGBtDMXfupw+uz6BYDZnBPDvvfcWKFUOxYsX0EAoREVEWllbvzickJgnhHeDTrQZ8cRVbLvjCUh/PDvz/qSM0krNsmkBllHQlUfv27dOp8eDgYJ3WIyIiynL+/9KbRu+OHiYGvYfcuIfckFyf3JRKhiRnX5h0JVGVK1fW6hKdiEChUCAxMVHnwIiIiD507x6QP39mR5GKLNi7kxHJ2ZckXUnU7t27MzoOIiKiFP322/9+9vcH5s0z3Pk22bvzZfnkgeVfGg4sJyJKWUYMAL97F/D0BLLK+OeMOAZf/IB9PTHIgeWvXr3CnTt3AAAeHh6cqZyIiPTmyhX1BArQy1htIr3QaZ4o4N3z80JCQmBvb4/ChQujcOHCsLe3R5UqVXD8+HF9xkhERFmAlRUg8u6lr16NfPmAjJgmiUgfdOqJOnr0KCpXrgwzMzN07NgR/v7+AICIiAgsW7YMwcHB2LNnD8qUKaPXYImI6MuSOzcwYwbQrdu798bGgIGP1aYviE5joqpWrYqbN2/iwIEDcHV1VVv28OFDlC9fHl5eXti+fbveAjUUHBNFRPR5vT9259IlA747DxwTZcgy4vtbp8t5R48eRZcuXTQSKABwcXFB586dceTIkU8OjoiI6H25stCt+PfuZXYElNF0SqKMjIyQkJCQ6vLExEQYfXgRm4iIKJv7cDqGT310Hhk2nTKdoKAgzJo1C7du3dJYdvv2bcyePRvly5f/5OCIiIiyirt3gR49/vc+KQno0uVdOWVPOg0s/+mnnxAcHAw/Pz80bNgQ+f//AvWlS5ewYcMGmJiYYOzYsXoNlIiIyJBxOoYvj05JVPHixXH06FEMGTIEf/31F2JjYwEAlpaWCA0NxZgxY1CwYEG9BkpERGTIkqdjyIBH55GB0nmyzYIFC2LdunVISkrC48ePAQDOzs4cC0VERF+krDwdg0E/k9CAfXLGY2RkBBcXF7i4uDCBIiKiL1p4+P9+vnDBcJ/xB3AQvD7o3BMVExODNWvW4Pr163j+/Dk+nG5KoVBg+vTpnxwgERFRVmTI0zGkNgi+Ro2s0XNmKHRKonbu3ImmTZvixYsXqdZhEkVERGSYOAheP3S6/tatWzdYWVlh27ZtePHiBZKSkjReiYmJ+o6ViIiI9IDPJNQPnZKo27dv47vvvkO1atX46BMiIqIsJnkQfLKsNAjekOiURAUEBCAqKkrfsRAREVEa9Pkomaw0CN5Q6ZREjR8/HrNnz8bx48f1HQ8RERG953PcRWfIg+ANmU4DyytVqoRp06YhMDAQ/v7+8PDwgLGxsVodhUKBDRs26CVIIiKiLxHvojNsOiVRa9asQevWrZGYmIi7d+/i5cuXGnUUCsUnB0dERPQl4110hk2nJGrQoEEoUKAA1qxZo3puHhEREekXHyVj2HQaE3X//n188803TKCIiIgyEO+iM2w6JVGlS5fG7du39R0LERERfYB30RkunZKoGTNmYPny5Vi5cqW+49HarFmzkDdvXpibm6Ns2bL4999/06y/atUq+Pn5wdzcHEWKFMGWLVs+U6RERESfhnfRGRadkqhWrVohISEBLVq0gK2tLQoVKoSAgAC1V9GiRfUdq4YVK1agb9++GD58OE6ePImiRYuiRo0aePToUYr1Dx06hBYtWqBDhw44deoUGjRogAYNGuDcuXMZHisRERFlLwr58MnB6VC5cuV03X23e/dunYJKr7Jly6J06dKYOXMmACApKQkeHh7o0aMHBg0apFG/efPmiImJwaZNm1Rl5cqVQ7FixfDLL7+ka5vR0dGwtbVFVFQUZ2snIvoMYmIAa+t3P796BVhZZW48acmIWDNq/7PScdWHjPj+1unuvD179uhl458iPj4eJ06cwODBg1VlRkZGqFq1Kg4fPpziOocPH0bfvn3VymrUqIH169enup24uDjExcWp3kdHR39a4ERERJQt6HQ5zxA8efIEiYmJcHFxUSt3cXFBZGRkiutERkZqVR8Axo4dC1tbW9XLw8Pj04MnIiKiLC/LJlGfy+DBgxEVFaV63blzJ7NDIiIiIgOg0+U8Q+Dk5ARjY2M8fPhQrfzhw4dwdXVNcR1XV1et6gOAUqmEUqn89ICJiIgoW8myPVFmZmYoWbIkdu7cqSpLSkrCzp07ERgYmOI6gYGBavUBYPv27anWJyIiIkpNlu2JAoC+ffsiPDwcpUqVQpkyZTBt2jTExMSgXbt2AICwsDDkypULY8eOBQD06tULlSpVwuTJk1G7dm0sX74cx48fx7x58zJzN4iIiCgLytJJVPPmzfH48WMMGzYMkZGRKFasGLZu3aoaPH779m0YGf2vsy0oKAh//vknfvjhB3z//ffIly8f1q9fj8KFC2fWLhAREVEWpdM8UV8yzhNFRPR5ZaX5jDhPlOHKiO/vDBkT5e3tjQoVKmDr1q0Z0TwRERFRpsuQJCopKQmXL19GrVq1UKFChYzYBBEREVGmypAxUTdv3gQAXLhwwSBmNyciIiLStwwdWF6wYEEULFgwIzdBRERElCmy7DxRRERERJkpXT1R7du317phhUKBhQsXar0eERERUVaQriRq165dUCgUWjWsbX0iIiKirCRdSVTyQHEiIiIieodjooiIiIh08El35924cQN///03bt26BQDw9PREzZo14eXlpZfgiIiIiAyVzklUv379MH36dCQlJamVGxkZoXfv3pg0adInB0dERERkqHS6nDd58mRMnToVjRo1wuHDh/HixQu8ePEChw8fRpMmTTB16lRMnTpV37ESERERGQydHkDs5+cHPz8/rF+/PsXlDRo0wMWLF3Hx4sVPjc/g8AHERESfV1Z6UC4fQGy4DOYBxDdv3kSNGjVSXV6jRg3e0UdERETZmk5JVM6cOXHmzJlUl585cwbOzs46B0VERERk6HRKopo2bYoFCxZg3LhxiImJUZXHxMRg/PjxWLBgAZo3b663IImIiIgMjU5jomJjY1G3bl3s3r0bJiYmcHd3BwDcv38fCQkJCAkJwcaNG2Fpaan3gDMbx0QREX1eWWnsDsdEGa6M+P7WaYoDS0tL7Ny5Exs2bFCbJyo0NBS1atVC3bp1+dgXIiIiytY+abLN+vXro379+vqKhYiIiCjL0GlM1I0bN7Bx48ZUl2/cuJF35xEREVG2plNPVP/+/REdHY26deumuHzWrFmws7PD8uXLPyk4IiIiIkOlU0/U4cOHUa1atVSXf/XVV9i/f7/OQREREREZOp2SqOfPn8PGxibV5dbW1nj69KnOQREREREZOp2SqDx58uDgwYOpLt+/fz9y586tc1BEREREhk6nJKpFixZYtmwZfv75ZyQlJanKExMTMX36dKxYsQItW7bUW5BEREREhkanyTbj4uJQu3Zt7Nq1C87OzihQoAAA4NKlS3j8+DEqV66Mv//+G0qlUu8BZzZOtklE9HllpUkhOdmm4TKYBxArlUr8888/WLhwIcqUKYMnT57gyZMnKFOmDH799Vfs2LEjWyZQRERERMl0nmzTyMgI7dq1Q7t27fQZDxEREVGWoFNPFBEREdGXjkkUERERkQ6YRBERERHpgEkUERERkQ6YRBERERHpgEkUERERkQ60TqJev36N8+fP4+HDh6nWuXHjBn7//fdPCoyIiIjIkGmVRI0cORKOjo4ICAiAu7s7QkJCcO7cOY16hw4d4vxRRERElK2lO4lauXIlRo4ciaCgIEyfPh2DBg1CREQESpcujT///DMjYyQiIiIyOOmesXzq1KmoUqUKduzYoSrr378/WrZsiTZt2uD+/fvo379/hgRJREREZGjS3RN18eJFNG7cWK3M3t4eW7ZsQdeuXTFw4EAmUUREpHdWVoDIu1d2f0guZS3pTqJMTU2RkJCgUa5QKDBr1iwMGzYMU6ZMQXh4ON6+favXID908+ZNdOjQAV5eXrCwsICPjw+GDx+O+Pj4NNerXLkyFAqF2qtr164ZGisRERFlT+m+nOfv74/du3ejR48eKS4fPnw4nJyc0KtXL2zbtk1vAabk4sWLSEpKwty5c+Hr64tz586hU6dOiImJwaRJk9Jct1OnThg1apTqvaWlZYbGSkRERNlTupOoOnXqYOjQobhz5w48PDxSrNOtWzc4OTkhLCxMbwGmJDQ0FKGhoar33t7euHTpEubMmfPRJMrS0hKurq4ZGh8RERFlf+lOojp06ICCBQsiKSkpzXrNmzeHt7c3Lly48MnBaSMqKgoODg4frbd06VL88ccfcHV1Rd26dTF06NA0e6Pi4uIQFxeneh8dHa2XeImIiChrS3cS5eDggNq1a6erbunSpVG6dGmdg9LW1atXMWPGjI/2QrVs2RKenp5wd3fHf//9h4EDB+LSpUtYu3ZtquuMHTsWI0eO1HfIRERElMUpRES0XSkuLg4HDx5EREQEoqOjYWNjg4IFC6J8+fJQKpU6BzNo0CCMHz8+zToRERHw8/NTvb937x4qVaqEypUrY8GCBVptb9euXfjqq69w9epV+Pj4pFgnpZ4oDw8PREVFIUeOHFptj4iIsreYGMDa+t3Pr17p527CjGgzI9s1VNHR0bC1tdXr93e6e6IAQEQwadIkjB8/Hs+fP8f7+ZdCoYC9vb1qqgOFQqF1MP369UPbtm3TrOPt7a36+f79+wgJCUFQUBDmzZun9fbKli0LAGkmUUql8pMSQyIiIsqetEqiWrVqheXLlyNfvnzo0aMHihYtChsbG7x8+RJnzpzBn3/+iUGDBuH06dNYunSp1sE4OzvD2dk5XXXv3buHkJAQlCxZEosWLYKRkfbPUj59+jQAwM3NTet1iYiI6MuW7st5S5YsQXh4OPr374+xY8fC2NhYo05SUhK+//57TJw4Eb/99htat26t94CBdwlU5cqV4enpid9++00tluQ77+7du4evvvoKv//+O8qUKYNr167hzz//RK1ateDo6Ij//vsPffr0Qe7cubF37950bzsjugOJiCh74OU8w5UR39/pTqKCg4NhbGyM3bt3f7RulSpVkJCQgH379n1ygClZvHhxqg84Tt6dmzdvwsvLC7t370blypVx584dtG7dGufOnUNMTAw8PDzQsGFD/PDDD1odTCZRRESUGiZRhitTx0T9999/GDNmTLrqNmrUCD/88IPOQX1M27ZtPzp2Km/evGpjtjw8PLTqcSIiIiJKS7oHEr19+xbm5ubpqqtUKlN8RAwRERFRdpHuJMrX1zfdl+f279+vdhcdERERUXaT7iSqSZMmWLZsGTZv3pxmvc2bN2PZsmVo2rTpJwdHREREZKjSnUT169cPBQoUQIMGDdC5c2fs378f0dHREBFER0fjwIED6Ny5Mxo0aIACBQqgX79+GRk3ERERUaZK98ByS0tL7Nq1C2FhYViwYAEWLlyoUUdEULVqVfz+++9pPo+OiIiIKKvTarLNnDlzYuvWrTh69Cg2btyICxcu4OXLl7CxsYG/vz/q1KmDwMDAjIqViIiIyGBolUQlK1u2rOqRKURERERfIu2flUJERERE6e+JqlevnlYNKxQKbNiwQeuAiIiIiLICrWYsVygUH6335s0bPHz4MF11iYiIiLKqdCdRN2/eTHP5mzdvMHfuXIwfPx7Au2ftEREREWVXnzwmKi4uDtOnT4ePjw/69u0LPz8/7N69O10PKiYiIiLKqnS6Ow94lzz98ssvmDBhAh48eIDKlStj2bJl7IEiIiKiL4LWSVRcXBzmzJmDCRMmIDIyEiEhIVi+fDkqVqyYEfERERERGaR0J1Fv3rzBnDlzMHHiRERGRqJKlSpYsWIFkyciIiL6IqU7ifLy8sKjR49QrFgxzJkzR5U8PXv2LNV1HBwcPj1CIiIiIgOU7iTq4cOHAIBTp06hUaNG6VonMTFRt6iIiIiIDFy6k6jhw4dnZBxEREREWQqTKCIiIiId8Nl5RERERDpgEkVERESkAyZRRERERDpgEkVERESkAyZRRERERDpgEkVERESkA50fQAwAmzZtwpYtW3Dz5k0AQN68eVGrVi3UqVNHH7ERERERGSydkqgXL16gYcOG2LdvH4yNjeHm5gYA2LFjB+bOnYuKFSti/fr1sLOz02esRERERAZDp8t5vXr1wv79+zF+/Hg8f/4ct27dwq1bt/D8+XOMGzcOBw4cQK9evfQdKxEREZHB0Kknav369fj222/Rv39/tXIrKysMGDAAt2/fxu+//66XAImIiIgMkU49UaampihQoECqy/38/GBqaqpzUERERESGTqckqnHjxli1ahUSExM1liUkJGDlypVo2rTpJwdHREREZKh0upzXunVrdO/eHUFBQejcuTN8fX0BAFeuXMG8efMQHx+PVq1a4eTJk2rrlShR4tMjJiIiIjIAOiVRlSpVUv187NgxKBQKAICIpFhHRKBQKFLsuSIiIiLKinRKohYtWqTvOIiIiIiyFJ2SqPDwcH3HQURERJSlfNKM5QDw6tUr3LlzBwDg4eEBa2vrTw6KiIiI3rGyAt4bLUMGROdn5x07dgwhISGwt7dH4cKFUbhwYdjb26NKlSo4fvy4PmMkIiIiMjg69UQdPXoUlStXhpmZGTp27Ah/f38AQEREBJYtW4bg4GDs2bMHZcqU0WuwRERERIZCIaJ9J2HVqlVx8+ZNHDhwAK6urmrLHj58iPLly8PLywvbt2/XW6CGIjo6Gra2toiKikKOHDkyOxwiIjIgMTFA8qiWV6/eXYozVFkpVn3IiO9vnS7nHT16FF26dNFIoADAxcUFnTt3xpEjRz45uLTkzZsXCoVC7TVu3Lg013nz5g26desGR0dHWFtbo3Hjxnj48GGGxklERETZk05JlJGRERISElJdnpiYCCMjnYdbpduoUaPw4MED1atHjx5p1u/Tpw82btyIVatWYe/evbh//z4aNWqU4XESERFR9qPTmKigoCDMmjULLVu2hKenp9qy27dvY/bs2ShfvrxeAkyLjY1Nir1hKYmKisLChQvx559/okqVKgDezXfl7++PI0eOoFy5chkZKhEREWUzOo2JOnXqFCpWrIjExEQ0bNgQ+fPnBwBcunQJGzZsgImJCfbv34+iRYvqPeBkefPmxZs3b/D27VvkyZMHLVu2RJ8+fWBiknJeuGvXLnz11Vd4/vw57OzsVOWenp7o3bs3+vTpk67tckwUERGlJiuNM8pKsepDRnx/69QTVbx4cfz7778YMmQI/vrrL8TGxgIALC0tERoaijFjxqBgwYJ6CTA1PXv2RIkSJeDg4IBDhw5h8ODBePDgAaZMmZJi/cjISJiZmaklUMC7MVyRkZGpbicuLg5xcXGq99HR0XqJn4iIiLI2rZOouLg4bNu2DXnz5sW6deuQlJSEx48fAwCcnZ0/aSzUoEGDMH78+DTrREREwM/PD3379lWVBQQEwMzMDF26dMHYsWOhVCp1juFDY8eOxciRI/XWHhEREWUPWidRZmZmaNq0KaZPn46AgAAYGRnBxcVFL8H069cPbdu2TbOOt7d3iuVly5ZFQkICbt68iQIFCmgsd3V1RXx8PF68eKHWG/Xw4cM0x1UNHjxYLWGLjo6Gh4dH2jtCRERE2Z7WSZRCoUC+fPnw5MkTvQfj7OwMZ2dnndY9ffo0jIyMkDNnzhSXlyxZEqampti5cycaN24M4N0Yrtu3byMwMDDVdpVKpV57toiIiCh70Ona2/fff4+ZM2fi0qVL+o4nXQ4fPoxp06bhzJkzuH79OpYuXYo+ffqgdevWsLe3BwDcu3cPfn5++PfffwEAtra26NChA/r27Yvdu3fjxIkTaNeuHQIDA3lnHhEREWlNp4HlR44cgaOjIwoXLozKlSsjb968sLCwUKujUCgwffp0vQT5IaVSieXLl2PEiBGIi4uDl5cX+vTpo3bZ7e3bt7h06ZJq0DsATJ06FUZGRmjcuDHi4uJQo0YNzJ49O0NiJCIiouxNpykO0jN4XKFQIDExUaegDBmnOCAiotRkpWkDslKs+mAwUxwkJSXpZeNEREREWZVOY6Ju376N169fp7r89evXuH37ts5BERERERk6nZIoLy8vrFu3LtXlf/31F7y8vHQOioiIiMjQ6ZREfWwY1du3bz/LA4iJiIiIMku6x0RFR0fjxYsXqvdPnz5N8ZLdixcvsHz5cri5ueklQCIiIiJDlO4kaurUqRg1ahSAd3fe9e7dG717906xrohgzJgxegmQiIiIyBClO4mqXr06rK2tISL47rvv0KJFC5QoUUKtjkKhgJWVFUqWLIlSpUrpPVgiIiIiQ5HuJCowMFD1eJSYmBg0btwYhQsXzrDAiIiIiAyZTvNEDR8+XN9xEBEREWUpvIWOiIiISAc69UQRERGRJisrQPuHqVFWxZ4oIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh1kySRqz549UCgUKb6OHTuW6nqVK1fWqN+1a9fPGDkRERFlFyaZHYAugoKC8ODBA7WyoUOHYufOnShVqlSa63bq1AmjRo1Svbe0tMyQGImIiCh7y5JJlJmZGVxdXVXv3759iw0bNqBHjx5QKBRprmtpaam2LhEREZEusuTlvA/99ddfePr0Kdq1a/fRukuXLoWTkxMKFy6MwYMHIzY2Ns36cXFxiI6OVnsRERERZcmeqA8tXLgQNWrUQO7cudOs17JlS3h6esLd3R3//fcfBg4ciEuXLmHt2rWprjN27FiMHDlS3yETERFRFqcQEcnsIJINGjQI48ePT7NOREQE/Pz8VO/v3r0LT09PrFy5Eo0bN9Zqe7t27cJXX32Fq1evwsfHJ8U6cXFxiIuLU72Pjo6Gh4cHoqKikCNHDq22R0REZChiYgBr63c/v3oFWFllbjwZLTo6Gra2tnr9/jaonqh+/fqhbdu2adbx9vZWe79o0SI4OjqiXr16Wm+vbNmyAJBmEqVUKqFUKrVum4iIiLI3g0qinJ2d4ezsnO76IoJFixYhLCwMpqamWm/v9OnTAAA3Nzet1yUiIsrKrKwAw7kWlTVl6YHlu3btwo0bN9CxY0eNZffu3YOfnx/+/fdfAMC1a9cwevRonDhxAjdv3sRff/2FsLAwBAcHIyAg4HOHTkRERFmcQfVEaWvhwoUICgpSGyOV7O3bt7h06ZLq7jszMzPs2LED06ZNQ0xMDDw8PNC4cWP88MMPnztsIiIiygYMamB5VpARA9OIiIgoY2XE93eWvpxHRERElFmYRBERERHpgEkUERERkQ6YRBERERHpgEkUERERkQ6YRBERERHpgEkUERERkQ6YRBERERHpgEkUERERkQ6YRBERERHpgEkUERERkQ6YRBERERHpwCSzA8hqkp/XHB0dncmREBERUXolf28nf4/rA5MoLT19+hQA4OHhkcmREBERkbaePn0KW1tbvbTFJEpLDg4OAIDbt2/r7SSQbqKjo+Hh4YE7d+4gR44cmR3OF43nwnDwXBgWng/DERUVhTx58qi+x/WBSZSWjIzeDSOztbXlfwgDkSNHDp4LA8FzYTh4LgwLz4fhSP4e10tbemuJiIiI6AvCJIqIiIhIB0yitKRUKjF8+HAolcrMDuWLx3NhOHguDAfPhWHh+TAcGXEuFKLPe/2IiIiIvhDsiSIiIiLSAZMoIiIiIh0wiSIiIiLSAZMoIiIiIh0wiUrBrFmzkDdvXpibm6Ns2bL4999/06y/atUq+Pn5wdzcHEWKFMGWLVs+U6TZnzbnYv78+ahYsSLs7e1hb2+PqlWrfvTcUfpp+/8i2fLly6FQKNCgQYOMDfALou25ePHiBbp16wY3NzcolUrkz5+fv6f0RNtzMW3aNBQoUAAWFhbw8PBAnz598ObNm88Ubfa1b98+1K1bF+7u7lAoFFi/fv1H19mzZw9KlCgBpVIJX19fLF68WPsNC6lZvny5mJmZya+//irnz5+XTp06iZ2dnTx8+DDF+gcPHhRjY2OZMGGCXLhwQX744QcxNTWVs2fPfubIsx9tz0XLli1l1qxZcurUKYmIiJC2bduKra2t3L179zNHnv1oey6S3bhxQ3LlyiUVK1aU+vXrf55gszltz0VcXJyUKlVKatWqJQcOHJAbN27Inj175PTp05858uxH23OxdOlSUSqVsnTpUrlx44Zs27ZN3NzcpE+fPp858uxny5YtMmTIEFm7dq0AkHXr1qVZ//r162JpaSl9+/aVCxcuyIwZM8TY2Fi2bt2q1XaZRH2gTJky0q1bN9X7xMREcXd3l7Fjx6ZYv1mzZlK7dm21srJly0qXLl0yNM4vgbbn4kMJCQliY2Mjv/32W0aF+MXQ5VwkJCRIUFCQLFiwQMLDw5lE6Ym252LOnDni7e0t8fHxnyvEL4a256Jbt25SpUoVtbK+fftK+fLlMzTOL016kqjvvvtOChUqpFbWvHlzqVGjhlbb4uW898THx+PEiROoWrWqqszIyAhVq1bF4cOHU1zn8OHDavUBoEaNGqnWp/TR5Vx8KDY2Fm/fvtXrwya/RLqei1GjRiFnzpzo0KHD5wjzi6DLufjrr78QGBiIbt26wcXFBYULF8ZPP/2ExMTEzxV2tqTLuQgKCsKJEydUl/yuX7+OLVu2oFatWp8lZvoffX138wHE73ny5AkSExPh4uKiVu7i4oKLFy+muE5kZGSK9SMjIzMszi+BLufiQwMHDoS7u7vGfxTSji7n4sCBA1i4cCFOnz79GSL8cuhyLq5fv45du3ahVatW2LJlC65evYpvv/0Wb9++xfDhwz9H2NmSLueiZcuWePLkCSpUqAARQUJCArp27Yrvv//+c4RM70ntuzs6OhqvX7+GhYVFutphTxRlS+PGjcPy5cuxbt06mJubZ3Y4X5SXL1+iTZs2mD9/PpycnDI7nC9eUlIScubMiXnz5qFkyZJo3rw5hgwZgl9++SWzQ/vi7NmzBz/99BNmz56NkydPYu3atdi8eTNGjx6d2aGRjtgT9R4nJycYGxvj4cOHauUPHz6Eq6triuu4urpqVZ/SR5dzkWzSpEkYN24cduzYgYCAgIwM84ug7bm4du0abt68ibp166rKkpKSAAAmJia4dOkSfHx8MjbobEqX/xdubm4wNTWFsbGxqszf3x+RkZGIj4+HmZlZhsacXelyLoYOHYo2bdqgY8eOAIAiRYogJiYGnTt3xpAhQ2BkxH6NzyW17+4cOXKkuxcKYE+UGjMzM5QsWRI7d+5UlSUlJWHnzp0IDAxMcZ3AwEC1+gCwffv2VOtT+uhyLgBgwoQJGD16NLZu3YpSpUp9jlCzPW3PhZ+fH86ePYvTp0+rXvXq1UNISAhOnz4NDw+Pzxl+tqLL/4vy5cvj6tWrqkQWAC5fvgw3NzcmUJ9Al3MRGxurkSglJ7fCx9h+Vnr77tZuzHv2t3z5clEqlbJ48WK5cOGCdO7cWezs7CQyMlJERNq0aSODBg1S1T948KCYmJjIpEmTJCIiQoYPH84pDvRE23Mxbtw4MTMzk9WrV8uDBw9Ur5cvX2bWLmQb2p6LD/HuPP3R9lzcvn1bbGxspHv37nLp0iXZtGmT5MyZU8aMGZNZu5BtaHsuhg8fLjY2NrJs2TK5fv26/PPPP+Lj4yPNmjXLrF3INl6+fCmnTp2SU6dOCQCZMmWKnDp1Sm7duiUiIoMGDZI2bdqo6idPcTBgwACJiIiQWbNmcYoDfZkxY4bkyZNHzMzMpEyZMnLkyBHVskqVKkl4eLha/ZUrV0r+/PnFzMxMChUqJJs3b/7MEWdf2pwLT09PAaDxGj58+OcPPBvS9v/F+5hE6Ze25+LQoUNStmxZUSqV4u3tLT/++KMkJCR85qizJ23Oxdu3b2XEiBHi4+Mj5ubm4uHhId9++608f/788weezezevTvF3//Jxz88PFwqVaqksU6xYsXEzMxMvL29ZdGiRVpvVyHCPkQiIiIibXFMFBEREZEOmEQRERER6YBJFBEREZEOmEQRERER6YBJFBEREZEOmEQRERER6YBJFBEREZEOmEQRfSaLFy+GQqHAzZs3P/u2t27dimLFisHc3BwKhQIvXrz47DGkV9u2bWFtbZ0p296zZw8UCgX27NmTodtp27Yt8ubNm6Hb0IclS5bAz88PpqamsLOzy+xwiAwOkygyWDdu3ED37t2RP39+WFpawtLSEgULFkS3bt3w33//qdUdMWIEFAoFjIyMcOfOHY22oqOjYWFhAYVCge7duwMAKleuDIVC8dHXiBEjPsfuZpinT5+iWbNmsLCwwKxZs7BkyRJYWVlldlhk4C5evIi2bdvCx8cH8+fPx7x581Ktu3PnTrRv3171f9Xb2xsdO3bEgwcPUqx/6NAhVKhQAZaWlnB1dUXPnj3x6tUrtTrHjh1D9+7dUahQIVhZWSFPnjxo1qwZLl++rNHe/PnzUalSJbi4uECpVMLLywvt2rVL9Q+WhQsXwt/fH+bm5siXLx9mzJiRYr0dO3YgJCQETk5OsLOzQ5kyZbBkyRKd21y7di2aN28Ob29vWFpaokCBAujXr59B/1FDaTPJ7ACIUrJp0yY0b94cJiYmaNWqFYoWLQojIyNcvHgRa9euxZw5c3Djxg14enqqradUKrFs2TJ89913auVr167V2MaQIUNUT1MH3v3S/vnnn/H999/D399fVR4QEKDnvfu8jh07hpcvX2L06NGoWrVqZodj0IKDg/H69esMfzDv/Pnz1R4IbIj27NmDpKQkTJ8+Hb6+vmnWHThwIJ49e4amTZsiX758uH79OmbOnIlNmzbh9OnTcHV1VdU9ffo0vvrqK/j7+2PKlCm4e/cuJk2ahCtXruDvv/9W1Rs/fjwOHjyIpk2bIiAgAJGRkZg5cyZKlCiBI0eOoHDhwqq6p06dgpeXF+rVqwd7e3vcuHED8+fPx6ZNm3DmzBm4u7ur6s6dOxddu3ZF48aN0bdvX+zfvx89e/ZEbGwsBg4cqKr3119/oUGDBggMDFT9kbZy5UqEhYXhyZMn6NOnj9Ztdu7cGe7u7mjdujXy5MmDs2fPYubMmdiyZQtOnjwJCwsL3U4WZZ5PfV4Nkb5dvXpVrKysxN/fX+7fv6+x/O3btzJ9+nS5ffu2qmz48OECQBo1aiTFihXTWKdatWrSuHFjASDdunVLcburVq0SALJ792697cv7Fi1aJADkxo0bGdJ+an777TcBIMeOHfto3ZiYmM8QUdrCw8PFysoqs8P44o0cOVIAyOPHjz9ad+/evZKYmKhRBkCGDBmiVl6zZk1xc3OTqKgoVdn8+fMFgGzbtk1VdvDgQYmLi1Nb9/Lly6JUKqVVq1Yfjen48eMCQMaOHasqi42NFUdHR6ldu7Za3VatWomVlZU8e/ZMVVatWjVxd3eXN2/eqMrevn0rPj4+EhAQoFObKf1uSf7/OX/+/I/uExkeXs4jgzNhwgTExMRg0aJFcHNz01huYmKCnj17wsPDQ2NZy5Ytcfr0aVy8eFFVFhkZiV27dqFly5YZGvf7zp8/jypVqsDCwgK5c+fGmDFjUux52LBhA2rXrg13d3colUr4+Phg9OjRSExMVNUZPnw4TE1N8fjxY431O3fuDDs7O7x58ybFOCpXrozw8HAAQOnSpaFQKNC2bVvVssKFC+PEiRMIDg6GpaUlvv/+ewDAo0eP0KFDB7i4uMDc3BxFixbFb7/9ptb2zZs3oVAoMGnSJMyaNUt1iaJ69eq4c+cORASjR49G7ty5YWFhgfr16+PZs2fpPobXr19HjRo1YGVlBXd3d4waNQrywaM+k5KSMG3aNBQqVAjm5uZwcXFBly5d8Pz5c416I0aMgLu7OywtLRESEoILFy4gb968quMBpDwmKvk4XbhwASEhIbC0tESuXLkwYcIEtW0kr7ty5Ur8+OOPyJ07N8zNzfHVV1/h6tWranU/HBP1/rGcN28efHx8oFQqUbp0aRw7dkzj2KxatQoFCxaEubk5ChcujHXr1mk1zmr27NkoVKgQlEol3N3d0a1bN7VLSnnz5sXw4cMBAM7Ozh+9rB0cHAwjIyONMgcHB0RERKjKoqOjsX37drRu3Ro5cuRQlYeFhcHa2horV65UlQUFBWn0CObLlw+FChVSazM1ycfi/f3avXs3nj59im+//Vatbrdu3RATE4PNmzerxWpvbw+lUqkqMzExgZOTk1qPkTZtVq5cWSPOhg0bAkC69okMDy/nkcHZtGkTfH19UbZsWa3XDQ4ORu7cufHnn39i1KhRAIAVK1bA2toatWvX1neoKYqMjERISAgSEhIwaNAgWFlZYd68eSl21S9evBjW1tbo27cvrK2tsWvXLgwbNgzR0dGYOHEiAKBNmzYYNWoUVqxYoRrPBQDx8fFYvXo1GjduDHNz8xRjGTJkCAoUKIB58+Zh1KhR8PLygo+Pj2r506dPUbNmTXz99ddo3bo1XFxc8Pr1a1SuXBlXr15F9+7d4eXlhVWrVqFt27Z48eIFevXqpbaNpUuXIj4+Hj169MCzZ88wYcIENGvWDFWqVMGePXswcOBAXL16FTNmzED//v3x66+/fvQYJiYmIjQ0FOXKlcOECROwdetWDB8+HAkJCarzCgBdunTB4sWL0a5dO/Ts2RM3btzAzJkzcerUKRw8eBCmpqYAgMGDB2PChAmoW7cuatSogTNnzqBGjRqpJp8fev78OUJDQ9GoUSM0a9YMq1evxsCBA1GkSBHUrFlTre64ceNgZGSE/v37IyoqChMmTECrVq1w9OjRj27nzz//xMuXL9GlSxcoFApMmDABjRo1wvXr11X7snnzZjRv3hxFihTB2LFj8fz5c3To0AG5cuVK176MGDECI0eORNWqVfHNN9/g0qVLmDNnDo4dO6Y6ZtOmTcPvv/+OdevWYc6cObC2ttb6svarV6/w6tUrODk5qcrOnj2LhIQElCpVSq2umZkZihUrhlOnTqXZpojg4cOHKFSoUIrLnz59isTERNy+fVv1Ofnqq69Uy5Pb/3D7JUuWhJGREU6dOoXWrVsDeJfwjB8/HkOHDkV4eDgUCgX+/PNPHD9+XC3Z06bNlERGRgKA2nGiLCSTe8KI1ERFRQkAadCggcay58+fy+PHj1Wv2NhY1bLky3mPHz+W/v37i6+vr2pZ6dKlpV27diIin+VyXu/evQWAHD16VFX26NEjsbW11bic9/4+JOvSpYtYWlqqXUYIDAyUsmXLqtVbu3ZtuuJNvoz44eW8SpUqCQD55Zdf1MqnTZsmAOSPP/5QlcXHx0tgYKBYW1tLdHS0iIjcuHFDAIizs7O8ePFCVXfw4MECQIoWLSpv375Vlbdo0ULMzMzU9isl4eHhAkB69OihKktKSpLatWuLmZmZ6vLS/v37BYAsXbpUbf2tW7eqlUdGRoqJiYnGZ2rEiBECQMLDw1Vlu3fv1jimycfp99//r727D4qqeuMA/uVll11cWEAWVkRxJGTLpWAsRBhnS3F5UbFCQJEwZCIcLGxotqk0x5CsaZjQNilq0xEWc2CmEoOYLEFgIjQGogJ5sWGSFHSTBUxZF87vD+fe4e4L4qoBv85nZkf23HvPPfe6sGfPOc+zR9my0dFRIpVKSXx8vNmxDz/8MGca6sCBAwQAaWtr41yjn58f+5y5l3PnzuVM/3z99dcEAKmoqGDLgoKCiK+vLxkeHmbLampqCABOnZYMDAwQPp9PlEolZ/pNrVYTAOTzzz9nyyb+TtkiNzeXACDff/89W8b8jp05c8Zs/4SEBCKVSiets7i4mAAgGo3G4nYnJycCgL2XBw8e5GzPysoiDg4OFo+VSCRk06ZN7PORkRGSmJhI7Ozs2DqdnZ3JV199ZXOdlqSnpxMHBwfS2dk56X7UzESn86gZZWhoCAAshrg/+eSTkEgk7OOjjz6yWEdycjK6u7tx9uxZ9t9/cyqvsrISYWFhCA0NZcskEgm2bNlitu/E0anh4WFcvXoVK1euxD///MOZkkxNTcVPP/2Enp4etkyr1WLBggVQKBQ2t9XJyQlpaWlm7ZdKpdi8eTNbxuPx2Aiq2tpazv4JCQkQi8Xsc2YEMSUlBY6Ojpxyg8GAvr6+KbVt4qgbE1VpMBhw6tQpALentMRiMdasWYOrV6+yj2XLlkEkEuH06dMAbkeOGY1Gs+mWl156aUrtAG6/HieOJvD5fISGhuLChQtm+6alpXGmoVauXAkAFvc1lZSUBHd3d6vH/vXXX2hra2OnvxgKhQJBQUF3rP/UqVMwGAzYuXMnZ/rthRdegKurK2fq6V6cOXMGe/fuZUckGTdu3AAAzhQZQyAQsNst6ejoQFZWFlasWMFOUZuqqqpCZWUl8vPzsXDhQly/fp2zfbKgAdPzOzk5YcmSJdi4cSOOHTuGkpISPP7440hJSUFjY6NNdZoqLS2FRqNBTk4OAgICrO5HzVx0Oo+aUVxcXADALNwZuB0BMzw8jP7+/kmHx0NCQiCTyVBaWgo3NzdIpVLOH/IHrbe31+JUZGBgoFnZb7/9hl27duGHH35gO5AMvV7P/pyUlISdO3dCq9Xirbfegl6vx8mTJ/HKK6/Azs7O5rbOnz/f7A2gt7cXAQEBZmtcmIjF3t5eTvnChQs5z5kOlemaNabcdL2SJfb29li8eDGnbMmSJQDAhq13dXVBr9fDy8vLYh0DAwOc9ppGmHl4eHA6LJPx9fU1u8/u7u5mqTYA8/vBnGMq132nY61dC1PW3Nw8af3M8aavRT6fj8WLF5v939qio6MDzzzzDORyOT777DPONuZDw+joqNlxN2/etBqddvnyZaxduxZisRjl5eVwcHCwuN9TTz0FAIiJicGGDRsgl8shEonYDrlQKITBYLB4rOn5d+zYgcbGRjQ3N7O/C4mJiVi6dCmys7PZ6dm7qXOiuro6pKenIyoqCnl5eRb3oWY+2omiZhSxWIx58+bh119/NdvGdEymkqwyOTkZhYWFcHFxQVJSklmHYCYYHByEQqGAq6sr3n77bfj7+0MgEKC5uRmvvfYaZyG6u7s71q1bx3aiysvLMTo6OmlnciruR0i1tTc0a+XEZHG4rcbHx+Hl5QWtVmtxu0QiuS/nAe7uWu7luh/0PXvQ/vzzTyiVSojFYlRWVrIfihhMoIil/FGXLl3ipCJg6PV6xMTEYHBwEHV1dRb3scTf3x8hISHQarVsJ2revHkYGxvDwMAAp/NtMBig0+nYug0GAzQaDVQqFedvB4/HQ0xMDNRqNQwGA/h8/pTrnKi1tRVxcXGQy+UoLy/njNhSs8vMe2eh/vPWrl2L7u5uNDU12VxHcnIyLl26hM7Ozn91Kg8A/Pz80NXVZVZ+/vx5zvOamhrodDocOXIE2dnZWLduHSIjI62OjqSmpqKzsxNnz56FVqtFSEiI1QW296P9ptGEzPSiaW6uB2F8fNxs+otJsshEXfn7+0On0yEiIgKRkZFmj8cee4zTXtMIOZ1ON6XRoZnE2rVYK7N2vOlr0WAwWMy7djd0Oh2USiVGR0dRXV1tMbJWLpfD0dER586dMzt/S0sLgoODOeU3b97E+vXr0dnZiZMnT+KRRx65qzbduHGDM6LL1G96/nPnzmF8fJzdrtPpYDQaOVGyjFu3bmF8fJzdNtU6GT09PYiOjoaXlxcqKyunLTs/dX/QThQ146hUKjg7O2Pbtm3o7+832z6VT+X+/v4oKCjA/v37OWuT/g2xsbFobGzkdAKvXLliNmLCjDpMvB6DwYBDhw5ZrDcmJgaenp547733UFtbe8+jUNbExsbi8uXLOH78OFtmNBrx4YcfQiQS3dMarLuhVqvZnwkhUKvV4PF4bLRVYmIixsbGkJuba3as0WhkQ9tXr14NR0dHFBYWWq1/tvDx8YFcLsfRo0c5U961tbVoa2u74/GRkZHg8/k4ePAg53Wn0Wig1+ttjmC9fv06YmNj0dfXh8rKSqvre8RiMSIjI1FSUoLh4WG2vLi4GCMjI0hISGDLxsbGkJSUhB9//BFlZWVYsWKFxTqNRqPFznBTUxPa2to4UXOrVq2Ch4eH2WuhsLAQzs7O7PV7eXnBzc0NX375JWeqbmRkBBUVFZDJZOwo7lTrBG5PSyqVStjb26O6uvq+jpZS04OOIVIzTkBAAEpLS7F582YEBgayGcsJIfjjjz9QWloKe3t7+Pr6TlqPaSj+vWJCw0+fPm0x3wtDpVKhuLgY0dHRyM7OZlMc+Pn5cdbQhIeHw93dHVu3bsXLL78MOzs7FBcXW+0k8ng8bNq0CWq1Gg4ODpyF3/dTRkYGPvnkEzz//PP4+eefsWjRIpSXl6OhoQEFBQVmUzQPgkAgwLfffoutW7di+fLlqKqqwjfffIM33niDfeNRKBR48cUXsX//frS0tECpVILH46GrqwtlZWU4cOAANm7cCG9vb2RnZyM/Px9xcXGIjo5Ga2srqqqq4OnpeU9ryqbDO++8gw0bNiAiIgJpaWm4du0a1Go15HK5xbWEE0kkErz++uvYu3cvoqOjERcXh/Pnz+PQoUN44oknbO6Yb9myBU1NTdi2bRva29s5OY9EIhGefvpp9nleXh7Cw8OhUCiQkZGBixcvIj8/H0qlEtHR0ex+OTk5OHHiBNavX4+///4bJSUlnHMybR0ZGcGCBQuQlJTEfkVMW1sbDh8+DLFYjN27d7PHCIVC5ObmIisrCwkJCYiKikJdXR1KSkqQl5cHDw8PALc/4Lz66qvYtWsXwsLCkJqairGxMWg0Gly8eJHTlqnWCQDR0dG4cOECVCoV6uvrUV9fz27z9vbGmjVrbLr/1DSarrBAirqT7u5usn37dvLQQw8RgUBAhEIhkclkJDMzk7S0tHD2nWo4Nu4hxUFOTg6xs7Mj7e3td2z7L7/8QhQKBREIBGT+/PkkNzeXaDQasxQHDQ0NJCwsjAiFQuLj40NUKhWprq622o6mpiYCgCiVyju2gTFZioOlS5daPKa/v5+kpaURT09PwufzSVBQEDl8+DBnHyYs//333+eUM6H+ZWVlU2qHKSZjeU9PD1EqlcTZ2Zl4e3uTPXv2mGXFJoSQoqIismzZMiIUComLiwsJCgoiKpWKk+3eaDSS3bt3E6lUSoRCIVm1ahVpb28nc+fOJZmZmWZtN01xYOk+maYpsHbdzH2aeP+spTgwvZeE3H7N7tmzh1P2xRdfEJlMRpycnIhcLicnTpwg8fHxRCaTmR1viVqtJjKZjPB4POLt7U22b99Orl27xtnnblIc+Pn5sWkATB+W0i7U1dWR8PBwIhAIiEQiIVlZWWzqDAaTWsLagzE6Okqys7PJo48+SlxdXQmPxyN+fn4kPT3d6rcDFBUVkcDAQMLn84m/vz/54IMPyPj4uNl+Wq2WhIaGEjc3NyIUCsny5ctJeXm5zXVOdj0KhWLym0zNSHaEzJIVixQ1zUJDQ+Hn54eysrJpa0NrayuCg4Nx9OhRPPfcc9PWjv8Hg4ODcHd3x759+/Dmm29Od3PuWXBwMCQSCb777rvpbgpF/WfQNVEUNQVDQ0NobW3lZMueDp9++ilEIhGeffbZaW3HbGMpV09BQQEAy1/FMZPdunULRqORU1ZTU4PW1tZZdy0UNdvRNVEUNQWurq4Wc9v8WyoqKvD777+jqKgIO3bswJw5c6atLbPR8ePHceTIEcTGxkIkEqG+vh7Hjh2DUqlERETEdDfvrvT19SEyMhIpKSnw8fFBR0cHPv74Y0ilUmRmZk538yjqP4VO51HULLBo0SL09/cjKioKxcXF/8ri7v8nzc3NUKlUaGlpwdDQELy9vREfH499+/bNuhBzvV6PjIwMNDQ04MqVK5gzZw5Wr16Nd999l/O9iBRFPXi0E0VRFEVRFGUDuiaKoiiKoijKBrQTRVEURVEUZQPaiaIoiqIoirIB7URRFEVRFEXZgHaiKIqiKIqibEA7URRFURRFUTagnSiKoiiKoigb0E4URVEURVGUDWgniqIoiqIoygb/AwI9EiMwG2GmAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "##6.4 Plotting scatter plots along with regressions" - ], - "metadata": { - "id": "E9-sFIpHzb-3" - } - }, - { - "cell_type": "code", - "source": [ - "# Plotting scatter plot for all retrievals\n", - "# (negative values have not been discarded in processing)\n", - "TEMPO_Pandora_scatter = np.empty([0, 4])\n", - "for td in time_series_TEMPO:\n", - " for pd in timeseries_Pandora_TEMPO:\n", - " if td[0] == pd[0]:\n", - " TEMPO_Pandora_scatter = np.append(TEMPO_Pandora_scatter,[[td[1], pd[1], td[2], pd[2]]], axis = 0)\n", - " break\n", - "\n", - "regress = stats.linregress(TEMPO_Pandora_scatter[:, 0], TEMPO_Pandora_scatter[:, 1])\n", - "slope = regress.slope\n", - "intercept = regress.intercept\n", - "r2 = regress.rvalue**2\n", - "stderr = regress.stderr\n", - "intercept_stderr = regress.intercept_stderr\n", - "\n", - "success, slope_0intercept, r2_0intercept =\\\n", - " regress_0intercept(TEMPO_Pandora_scatter[:, 0]\\\n", - " , TEMPO_Pandora_scatter[:, 1])\n", - "\n", - "plot_title = out_Q+'_unc_'+datestamp_ini+'_'+datestamp_fin+'\\n'+POI_name\n", - "img_name = 'scatter_'+out_Q+'_unc_'+datestamp_ini+'_'\\\n", - "+datestamp_fin+'_'+POI_name+'.jpg'\n", - "\n", - "fig = plt.figure()\n", - "\n", - "plt.errorbar(TEMPO_Pandora_scatter[:, 0], TEMPO_Pandora_scatter[:, 1],\\\n", - "xerr=TEMPO_Pandora_scatter[:, 2], yerr=TEMPO_Pandora_scatter[:, 3],\\\n", - "c = 'b', ls = '', marker = \".\")\n", - "\n", - "plt.xlabel(r'TEMPO NO2 column, molecules/cm^2', fontsize=12)\n", - "plt.ylabel(r'Pandora NO2 column, molecules/cm^2', fontsize=12)\n", - "\n", - "fig.text(0.15, 0.75,\\\n", - "str('# of points: %3i\\nslope:%6.3f ±%6.3f\\nintercept: %8.2e ± %8.2e\\nR2 = %6.3f'\\\n", - " %(len(TEMPO_Pandora_scatter), slope, stderr, intercept, intercept_stderr, r2)))\n", - "\n", - "# Set the range of x-axis\n", - "l_lim = min(0., min(TEMPO_Pandora_scatter[:, [0,1]].flatten()))*1.05\n", - "u_lim = max(TEMPO_Pandora_scatter[:, [0,1]].flatten())*1.05\n", - "plt.xlim(l_lim, u_lim)\n", - "plt.ylim(l_lim, u_lim)\n", - "\n", - "plt.plot([l_lim, u_lim], [l_lim, u_lim],\\\n", - "c = 'g', ls = '--')\n", - "\n", - "plt.plot([l_lim, u_lim], [l_lim*slope+intercept, u_lim*slope+intercept],\\\n", - "c = 'r', ls = '--')\n", - "\n", - "if success:\n", - " plt.plot([l_lim, u_lim], [l_lim*slope_0intercept, u_lim*slope_0intercept], c = 'r', ls = '-.')\n", - " fig.text(0.6, 0.15, str('\"no-intercept\" regression:\\nslope:%6.3f R2 = %6.3f'\\\n", - " %(slope_0intercept, r2_0intercept)))\n", - "\n", - "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", - "plt.savefig(img_name, format='jpg', dpi=300)\n", - "\n", - "# Plotting scatter plot for positive retrievals\n", - "TEMPO_Pandora_scatter_noneg = np.empty([0, 4])\n", - "for td in time_series_TEMPO_noneg:\n", - " for pd in timeseries_Pandora_TEMPO_noneg:\n", - " if td[0] == pd[0]:\n", - " TEMPO_Pandora_scatter_noneg = np.append(TEMPO_Pandora_scatter_noneg,[[td[1], pd[1], td[2], pd[2]]], axis = 0)\n", - " break\n", - "\n", - "regress = stats.linregress(TEMPO_Pandora_scatter_noneg[:, 0], TEMPO_Pandora_scatter_noneg[:, 1])\n", - "slope = regress.slope\n", - "intercept = regress.intercept\n", - "r2 = regress.rvalue**2\n", - "stderr = regress.stderr\n", - "intercept_stderr = regress.intercept_stderr\n", - "\n", - "success, slope_0intercept, r2_0intercept =\\\n", - " regress_0intercept(TEMPO_Pandora_scatter_noneg[:, 0]\\\n", - " , TEMPO_Pandora_scatter_noneg[:, 1])\n", - "\n", - "plot_title = out_Q+' unc noneg '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", - "img_name = 'scatter_'+out_Q+'_unc_noneg_'+datestamp_ini+'_'\\\n", - "+datestamp_fin+'_'+POI_name+'.jpg'\n", - "\n", - "fig = plt.figure()\n", - "\n", - "plt.errorbar(TEMPO_Pandora_scatter_noneg[:, 0], TEMPO_Pandora_scatter_noneg[:, 1],\\\n", - "xerr=TEMPO_Pandora_scatter_noneg[:, 2], yerr=TEMPO_Pandora_scatter_noneg[:, 3],\\\n", - "c = 'b', ls = '', marker = \".\")\n", - "\n", - "plt.xlabel(r'TEMPO NO2 column, molecules/cm^2', fontsize=12)\n", - "plt.ylabel(r'Pandora NO2 column, molecules/cm^2', fontsize=12)\n", - "\n", - "fig.text(0.15, 0.75,\\\n", - "str('# of points: %3i\\nslope:%6.3f ±%6.3f\\nintercept: %8.2e ± %8.2e\\nR2 = %6.3f'\\\n", - " %(len(TEMPO_Pandora_scatter_noneg), slope, stderr, intercept, intercept_stderr, r2)))\n", - "\n", - "# Set the range of x-axis\n", - "l_lim = 0.\n", - "u_lim = max(TEMPO_Pandora_scatter_noneg[:, [0,1]].flatten())*1.05\n", - "plt.xlim(l_lim, u_lim)\n", - "plt.ylim(l_lim, u_lim)\n", - "\n", - "plt.plot([l_lim, u_lim], [l_lim, u_lim],\\\n", - "c = 'g', ls = '--')\n", - "\n", - "plt.plot([l_lim, u_lim], [l_lim*slope+intercept, u_lim*slope+intercept],\\\n", - "c = 'r', ls = '--')\n", - "\n", - "if success:\n", - " plt.plot([l_lim, u_lim], [l_lim*slope_0intercept, u_lim*slope_0intercept], c = 'r', ls = '-.')\n", - " fig.text(0.6, 0.15, str('\"no-intercept\" regression:\\nslope:%6.3f R2 = %6.3f'\\\n", - " %(slope_0intercept, r2_0intercept)))\n", - "\n", - "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", - "plt.savefig(img_name, format='jpg', dpi=300)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "-itPCr4RBpr-", - "outputId": "143e7b79-3cd1-470c-fb15-598922a7dc83" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[7.07615741e-01 1.30600000e+15 3.55000000e+15] [7.07615741e-01 1.99387058e+15 4.82361431e+13]\n", - "1306000000000000.0 1993870584228399.8 3550000000000000.0 48236143059448.0\n", - "[7.94479167e-01 9.30800000e+14 6.75400000e+15] [7.94479167e-01 1.55750626e+15 5.85502635e+13]\n", - "930800000000000.0 1557506264758800.0 6754000000000000.0 58550263539100.01\n", - "[8.37893519e-01 4.16600000e+14 5.00700000e+15] [8.37893519e-01 1.71600901e+15 7.81312542e+13]\n", - "416600000000000.0 1716009009562000.0 5007000000000000.0 78131254220240.0\n", - "[8.8130787e-01 7.0710000e+14 8.3660000e+15] [8.81307870e-01 1.65626937e+15 6.43887290e+13]\n", - "707100000000000.0 1656269373222800.0 8366000000000000.0 64388729005920.01\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAHgCAYAAABEhXI/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd1gTWReHfwkBQu9VERELVlAXUCyIFdfeu9h7W+vaxd57xYJl7bv2uuraEcQCiogFO6BgoSMt5/tjvgzE0IJAEO77PPOQuffMnTOThDk599xzBEREYDAYDAaDwSjFCJWtAIPBYDAYDIayYQYRg8FgMBiMUg8ziBgMBoPBYJR6mEHEYDAYDAaj1MMMIgaDwWAwGKUeZhAxGAwGg8Eo9TCDiMFgMBgMRqmHGUQMBoPBYDBKPcwgYjAYDAaDUephBhGDwZBhwIABKF++vLLVYDAYjCKFGUSMIsXHxwfz5s1DdHS0slXJlt27d0MgEEAsFiMsLEyuv0mTJqhRo4Zce2pqKtavXw9HR0fo6OhAW1sbjo6OWL9+PVJTU2VkExMTsWnTJrRs2RIWFhbQ0dFB7dq1sWXLFqSnpxfatZU23r9/D09PTzg5OcHAwADGxsZo0qQJLl++nKV8dHQ0hg0bBhMTE2hpacHNzQ0PHjyQkfny5QtWrFiBxo0bw8TEBPr6+qhXrx4OHz4sN96TJ0/QrVs3VKhQAZqamjA2Nkbjxo1x+vTpLM//9OlTuLu7Q1tbG4aGhujXrx+ioqLk5CIiIjBs2DDY2NhAQ0MDtra2mDhxIr58+ZKvMUNCQjB16lQ4ODhAR0cHFhYWaNOmDe7du5ftvc0ORccKCwtD9+7doa+vD11dXXTo0AGvXr2SkVHkfbxx4wbat28PKysriMVimJubw93dHbdv387y/D4+PmjYsCE0NTVhbm6OcePGIT4+Xk7uxYsX6NmzJ8qWLQtNTU3Y2dlh/vz5SExMzNeY/v7+GDNmDKpXrw4tLS2UK1cO3bt3x/Pnz7O9t4xChhiMImTFihUEgF6/fq1sVbLF29ubABAAGjNmjFy/q6srVa9eXaYtPj6eXF1dCQC1bduWNm7cSJs3b6b27dsTAHJ1daX4+Hhe/vHjxyQQCKh58+a0fPly2rp1K3Xq1IkAUP/+/Qv9GnPCw8ODrK2tlapDQbFhwwbS0NCgXr160caNG2nt2rVUp04dAkC7du2SkU1PTycXFxfS0tKiefPm0caNG6latWqko6NDz58/5+VOnz5Nqqqq1KFDB1q7di1t3LiR3NzcCADNmTNHZsyzZ89Sq1ataN68eeTl5UVr166lRo0aEQDatm2bjOz79+/J2NiYbG1tad26dbRo0SIyMDAge3t7Sk5O5uXi4uLI2tqajI2Nac6cObR9+3YaM2YMqaqqkoODA6Wnpys85qRJk0hfX58GDx5M27Zto+XLl5OtrS2pqKjQpUuXFLrniowVFxdHlSpVIlNTU1q2bBmtXr2arKysqGzZsvT58+d8vY/bt2+nDh060MKFC2nHjh20YsUKsre3J6FQSOfPn5eRffjwIYnFYqpduzZt2bKFZs6cSerq6uTu7i4j9+7dO9LX1ydra2tasmQJbdu2jQYMGEAAqH379vkas0uXLmRubk5jx46l7du304IFC8jMzIy0tLTo8ePHCt1zRsHADCJGkZJXgyg9PZ2SkpKKRqkfkBpEDg4OpK6uTmFhYTL9WRlEw4YNIwC0YcMGufE2btxIAGjEiBF8W1RUFAUFBcnJDhw4kADQixcvCuhqFKckGURBQUEUFRUl0/b9+3eys7OjsmXLyrQfPnyYANDRo0f5tsjISNLX16devXrxba9evaI3b97IHCuRSKhp06akrq4uY/hmRVpaGtnb21OVKlVk2keOHEkaGhr09u1bvu3SpUtyxtP+/fsJAJ05c0bm+Dlz5hAAevDggcJj3rt3j+Li4mTG+/z5M5mYmFCDBg1yvJ4fUWSsZcuWEQC6e/cu3/b06VNSUVGh6dOn822KvI9ZkZCQQGZmZtSqVSuZ9tatW5OFhQXFxMTwbdu3bycAdPHiRb5t0aJFBEDuO9u/f38CQF+/flV4zNu3b8sYpUREz58/J3V1derTp0+u18QoeJhBxCgy5s6dy3teMm+vX78mADR69Gj666+/qFq1aiQSiej48eNERPTgwQNyd3cnHR0d0tLSoqZNm9KdO3dkxpYaMdevX6dhw4aRoaEh6ejoUL9+/WT+WeUF6VhHjhwhkUhEY8eOlen/0SB6//49qaioUNOmTbMd083NjUQiEb1//z7Hc586dYoA0KlTpxTSmYho37595OjoSBoaGqSvr0+NGjWS+QdMRLRp0yaqVq0aqampkYWFBY0aNYq+ffsmI5Mfg8ja2po8PDzk2l1dXcnV1ZXfv3r1KgGgw4cP08KFC6lMmTKkrq5OTZs2zdII9PX1pdatW5O+vj5pampSzZo1ae3atQrplhUTJ04kABQbG8u3devWjczMzGQ8LEScsaupqUnfv3/Pccz169cTAHr06FGu52/bti2ZmZnJtJmamlK3bt3kZCtXrkzNmjXj97ds2UIAyN/fX0ZO2v706VOFx8yOzp07k6GhYa5yeSGrsRwdHcnR0VFOtmXLlmRra5vrmFm9j9lRo0YNcnZ25vdjYmJIJBLRlClTZOSSk5NJW1ubBg8ezLdNmzaNAMgZZdOmTSOhUMgbwYqMmR116tShOnXq5CrHKHhYDBGjyOjcuTN69eoFAFizZg327duHffv2wcTEBADw33//4Y8//kCPHj2wbt06lC9fHk+ePEGjRo0QGBiIqVOnYvbs2Xj9+jWaNGkCPz8/uXOMGTMGT58+xbx589C/f3/s378fHTt2BBEprK+NjQ369++P7du3Izw8PFu58+fPIz09Hf37989Wpn///khLS8OFCxdyPOfHjx8BAMbGxgrp6unpiX79+kFVVRXz58+Hp6cnrKys8N9///Ey8+bNw+jRo2FpaYlVq1ahS5cu2LZtG1q2bCkX41TYLF26FMePH8fkyZMxffp0+Pr6ok+fPjIyly5dQuPGjREcHIzx48dj1apVcHNzw5kzZ376/B8/foSmpiY0NTX5tocPH6JOnToQCmX/LTo5OSExMTHX2I6c3ruEhAR8/vwZoaGhWLNmDc6fP49mzZrx/WFhYYiMjMRvv/0md6yTkxMePnzI7zdu3BhCoRDjx4+Hr68vPnz4gHPnzmHRokXo2LEj7OzsFB4zp2tS9LOY17EkEgkePXqUrX6hoaGIi4vLdcwf30cpsbGx+Pz5M0JCQjBjxgwEBQXJ3PPHjx8jLS1N7vxqampwcHCQuT9NmjQBAAwePBgBAQF4//49Dh8+jC1btmDcuHHQ0tJSeMysICJ8+vSpwO45Q0GUbZExShfZTZkBIKFQSE+ePJFp79ixI6mpqVFoaCjfFh4eTjo6OtS4cWO+TerVqVu3LqWkpPDty5cvJwB08uTJPOsoHcvf359CQ0NJJBLRuHHj+P4fPUQTJkwgAPTw4cNsx3zw4AEBoIkTJ2Yrk5ycTNWqVSMbGxtKTU3Ns74vXrwgoVBInTp1kvNuSCQSIuKmftTU1Khly5YyMtLpvMxxGEXhIapatarMdMG6desIAB87kZaWRjY2NmRtbS3nwZJeU3558eIFicVi6tevn0y7lpYWDRo0SE7+7NmzBIAuXLiQ7ZhfvnwhU1NTatSoUZb9w4cP5z2iQqGQunbtKuO59Pf3JwC0d+9euWOnTJlCAGQ8VDt27CB9fX0ZT6uHh4fM50bRMX/kxo0bJBAIaPbs2dnK5JWsxoqKiiIANH/+fDn5TZs2EQAKCQnJdszs3kcprVq14u+NmpoaDR8+XGYa/ujRowSAbty4IXdst27dyNzcXKZtwYIFpKGhIXPPZ86cKSOj6Jg/sm/fPgJAO3fuzFGOUTgwDxGj2ODq6opq1arx++np6fj333/RsWNHVKhQgW+3sLBA7969cevWLcTGxsqMMWzYMKiqqvL7I0eOhEgkwrlz5/KlU4UKFdCvXz94eXkhIiIiSxnpr1gdHZ1sx5H2/ahvZsaMGYPg4GBs3LgRIpEozzqeOHECEokEc+bMkfNuCAQCAMDly5eRkpKCCRMmyMgMHToUurq6OHv2bJ7PVxAMHDgQampq/H6jRo0AgF9d9PDhQ7x+/RoTJkyAvr6+zLHSa8oPiYmJ6NatGzQ0NLB06VKZvqSkJKirq8sdIxaL+f6skEgk6NOnD6Kjo7Fhw4YsZSZMmIBLly5hz549aN26NdLT05GSkiJzbgB5Pn+ZMmXg5OSEtWvX4vjx45g4cSL279+PP//8M99jZiYyMhK9e/eGjY0Npk6dmqVMXslurJ/RL6f3UcrSpUvx77//YufOnahXrx5SUlKQlpaW5/P/eO7y5cujcePG8PLywj///INBgwZh8eLF2LhxY77HzExISAhGjx6N+vXrw8PDI1s5RuGR9/+6DEYhY2NjI7MfFRWFxMREVKlSRU62atWqkEgkeP/+PapXr863V6pUSUZOW1sbFhYWePPmTb71mjVrFvbt24elS5di3bp1cv1SYycn935uRtOKFSuwfft2LFiwAL///rtC+oWGhkIoFMoYkz/y9u1bAJC7l2pqaqhQoQLfX1SUK1dOZt/AwAAA8O3bNwDcNQHIMr1BfklPT0fPnj0RHByM8+fPw9LSUqZfQ0MDycnJcsd9//6d78+KsWPH4sKFC9i7dy/s7e2zlLGzs+Onsvr374+WLVuiXbt28PPzg0Ag4MfOy/lv376Ntm3bwtfXl5+a6dixI3R1deHp6YlBgwahWrVqCo2ZmYSEBLRt2xZxcXG4desWtLW1s7ymvJDTWPnVL7f3UYqDgwP/um/fvqhTpw4GDBiAv//+O0/nz3zuQ4cOYdiwYXj+/DnKli0LgAsBkEgkmDZtGnr16gUjIyOFxszMx48f0aZNG+jp6eHvv/+GiopKlnKMwoV5iBjFhuz+WSibChUqoG/fvtl6iapWrQoAePToUbZjSPuyMlp2796NadOmYcSIEZg1a1YBaV20ZOe1yS6nUnb/8CkfsV55ZejQoThz5gx2796Npk2byvVbWFhk+f5K27J68Hp6emLz5s1YunQp+vXrl2ddunbtCn9/fz4uycLCQuZcP57f0NCQ9zps27YNZmZmcnEq7du3BxHBx8dH4TGlpKSkoHPnznj06BFOnjz5UwZpbmNJz6/oPc/tfcwKNTU1tG/fHseOHeO9NLndn8zn3rx5M2rXrs0bQ1Lat2+PxMREPjZIkTGlxMTEoHXr1oiOjsaFCxeyNfAYhQ8ziBhFiiLTHSYmJtDU1MSzZ8/k+kJCQiAUCmFlZSXT/uLFC5n9+Ph4RERE/HTm5VmzZiEtLQ3Lli2T62vdujVUVFSwb9++bI/fu3cvRCIR3N3dZdpPnjyJIUOGoHPnzti0aVO+dLO1tYVEIkFwcHC2MtbW1gAgdy9TUlLw+vVrvj+/GBgYZJlsM7+eJ1tbWwBAUFDQz6jFM2XKFHh7e2PNmjV8YP+PODg44MGDB5BIJDLtfn5+0NTUROXKlWXaN23ahHnz5mHChAmYNm2aQvpIH8oxMTEAuCkwExOTLJMX3r17V8bb8enTpywNTWlgvHRaSJExAW7qr3///rhy5QoOHDgAV1dXha5J0bGEQiFq1qyZpX5+fn6oUKGCnEc1L+9jdiQlJYGIeG9tjRo1IBKJ5M6fkpKCgICAfN1zRcYEOK9Ru3bt8Pz5c5w5cyZHLy+jCFByDBOjlCFdGvxjADL+v+z+Rzp27Ejq6uoyQdgfP34kXV1dhYKqT5w4kWcdMwdVZ2bAgAEkFoupSpUqcnmIhgwZQgBo8+bN2V7z8OHDZdqvX79OYrGY3Nzccl3SnROKBFW7u7vLBCVv3ry5QIKqu3btSmZmZjKB0qdPn+aTUkqRBlVnzvVDRHzqBW9vbyLi8lAVVFC19DMwY8aMHOUOHTokp1tUVBTp6+tTjx495GSFQiH16dMnR30+ffok15aSkkJ16tQhDQ0NmXw9I0aMIA0NDXr37h3fdvnyZQJAW7Zs4dvGjBlDAOjq1asy40qD+319fRUek4ho1KhRWSaMzA95HWvp0qVy37WQkBBSUVGhadOmycjm9X3M6p5/+/aNrKysyMrKSqbd3d2dLCwsZJbt79ixgwDIJHFs27Ytqamp0bNnz2SO79ixIwmFQplcZXkdMy0tjdq3b08ikYjOnj2b4zUxigYBUSH6qBmMH/D394eTkxN+//139OzZE6qqqmjXrh20tbUxevRomQBFgCt94OzsDH19fYwaNQoikQjbtm1DWFgYrl+/DmdnZwDctNPAgQNRs2ZN6Ovro3v37nj27Bk2b94MFxcX3LhxI8/eKelY/v7+MtMSL1++hJ2dHdLT01G9enUZ70V8fDxat26NW7duoX379rwn6OLFizh58iRcXV1x9uxZfnnu27dvYW9vj5SUFKxcuRK6uroyOtSqVQu1atXK832dM2cOFixYABcXF3Tu3Bnq6urw9/eHpaUllixZAoBbdu/p6YmWLVuiffv2/P2pU6cObt++zQejDxgwANeuXVMo7urixYtwd3eHm5sbunfvjtDQUPz111/Q0tJC2bJlce3aNQDAtWvX4ObmhqNHj6Jr16788W/evIGNjQ28vb0xYMAAfsx27drB0tISAwcOhIWFBUJCQvDkyRNcvHgxT3odP34cnTt3RqVKlTBnzhy5/hYtWsDMzAwAN73XsGFDBAUFYcqUKTA2NsbmzZvx7t07+Pv78/FXd+/eRaNGjaCnp4dly5bJBPEDgIuLC78IoFOnToiNjUXjxo1RpkwZfPz4Efv370dISAhWrVqFiRMn8se9f/8etWvXhr6+PsaPH4/4+HisWLECZcuWhb+/Pz+99ezZM9StWxcCgQBjx46FtbU1rl+/joMHD6JFixb4999/FR5z7dq1+OOPP1C/fn2MGjVK7j516tSJ/+zmhiJjxcXFoXbt2oiLi8PkyZOhqqqK1atXIz09HQEBAXxKDkXex7p166Js2bJwdnaGqakp3r17B29vb4SHh+Pw4cMyn7sHDx7AxcUF1apVw7Bhw/DhwwesWrUKjRs3lvmM3bhxA02bNoWRkRHGjBkDIyMjnDlzBufPn8eQIUOwfft2hcecMGEC1q1bh3bt2qF79+5y19S3b9883W9GAaJsi4xR+liwYAGVKVOGhEKhXGLGrHjw4AG1atWKtLW1SVNTk9zc3MjHx0dG5sfEjAYGBqStrU19+vShL1++KKRfdh4iIs57AkDOQ0TELZtfs2YN1a1bl7S0tEhTU5Pq1KlDa9eulfFaEWV4SrLb5s6dq5DORES7du2i2rVrk7q6OhkYGJCrq6tcqYSNGzeSnZ0dqaqqkpmZGY0cObJAEjMSEa1atYpPtNigQQO6d+9etsvuc/MQSbl16xa1aNGCT8pZq1atLLOBZ0d2yUCl249elq9fv9LgwYPJyMiINDU1ydXVVe5zkLm0S1Zb5ms4ePAgNW/enMzMzEgkEpGBgQE1b9482zQQQUFB1LJlS9LU1CR9fX3q06cPffz4UU4uJCSEunbtSlZWVqSqqkrW1tY0efJkSkhIyNeY0s91dpsipXYUHev9+/fUtWtX0tXVJW1tbWrbtq1ckk5F3seNGzdSw4YNydjYmEQiEZmYmFC7du2yXApPRHTz5k1ycXEhsVhMJiYmNHr06CwTPfr5+VHr1q3J3NycVFVVqXLlyrRo0aIsU2TkZUxpqZ/sNkbRwzxEjBJBdl4dBoPBYDDyAguqZjAYDAaDUepheYgYpYb4+HjEx8fnKGNiYlKscoBIy0Fkh4aGBvT09Art/F+/fpVJIPgjKioqfJxHUZKSkoKvX7/mKKOnp1dsUzn8iiQlJfGr4rLD0NBQJuEmg/ErwQwiRqlh5cqV8PT0zFHm9evXP71EvyCR5jXJDg8PD+zevbvQzt+5c2dcv349235ra+ufSnqZX3x8fODm5pajTOYAbcbPc/jwYQwcODBHmatXr/J1vxiMXw0WQ8QoNbx69YovDZEdDRs25MsGFAcuX76cY7+lpWWh5i65f/8+nz06KzQ0NNCgQYNCO392fPv2Dffv389Rpnr16rkalIy8ExERgSdPnuQoU7duXT7rOIPxq8EMIgaDwWAwGKUeFlTNYDAYDAaj1MMMIgbjJ9m9ezcEAkGeYmnKly/P4loYDAajGMIMIsYvj9QgybyZmprCzc0N58+fV7Z6RUZsbCw8PT1hb28PbW1taGhooEaNGpg2bRrCw8Pl5M+cOQN3d3cYGRlBLBajcuXKmDx5Mr58+fLTupw6dQp16tSBWCxGuXLlMHfuXL7ek5QbN26gffv2sLKyglgshrm5Odzd3XH79m258VJTU+Hp6YkKFSpAXV0dFSpUwMKFC+XGzI5Pnz5h4MCBMDU1hYaGBurUqYOjR49mKXvo0CFedxMTEwwePBifP3/OUnbnzp2oWrUqxGIxKlWqhA0bNmQpFxYWhu7du0NfXx+6urro0KGDXDzbmzdv+M/vP//8IzfGvHnzIBAIstVFSnx8PObOnQt3d3cYGhpCIBDkGHj/9OlTuLu7Q1tbG4aGhujXrx+ioqKy1e3H7dChQznqAwBXrlzBoEGDULlyZWhqaqJChQoYMmRIlkVQAW4V4eLFi2FnZwexWAwzMzO0adMGHz58kJFLTk7GtGnTYGlpCQ0NDTg7O+PSpUtZjunj44OGDRtCU1MT5ubmGDdunMyq0yNHjkAgEOD48eNyx9rb20MgEODq1atyfeXKlYOLi0uu94DxC6DMrJAMRkEgzRw8f/582rdvH+3du5dWrFhB1atXJwB0+vTpIjl/XrL5Wltbk4eHR4HrEBoaSjY2NqSiokI9e/akjRs3kpeXF40ZM4aMjIyoUqVKMvKTJk0iAGRvb0/Lli2j7du308iRI0ldXZ3KlClDISEh+dbl3LlzJBAIyM3Njby8vGjs2LEkFAppxIgRMnLbt2+nDh060MKFC2nHjh20YsUKsre3J6FQKFPziYioe/fuJBAIaPDgwbRlyxY+G/LQoUNz1ScmJoYqVqxIOjo6NGvWLNq4cSM1btyYAND+/ftlZKW13Zo1a0abNm2i6dOnk6amJtWqVYuSkpJkZLdu3UoAqEuXLuTl5UX9+vUjALR06VIZubi4OKpUqRKZmprSsmXLaPXq1WRlZUVly5alz58/83LSbN0AqFatWnI10qTZmqOionK8Xuk45cqVoyZNmmSZAVzK+/fvydjYmGxtbWndunW0aNEiMjAwIHt7e5m6dNIxe/XqRfv27ZPZ3rx5k6M+RER169YlGxsbmjp1Km3fvp2mT59OOjo6ZGZmRhERETKyKSkp1Lx5c9LU1KTx48fTzp07aeXKldStWzcKCgqSke3ZsyeJRCKaPHkybdu2jerXr08ikYhu3rwpI/fw4UMSi8VUu3Zt2rJlC82cOZPU1dXJ3d2dlwkLCyMANHHiRJljY2JiSCgUkkgkogULFsj0vXv3jgDQlClTcr0HjOIPM4gYvzzZldr4+vUrqaqqUu/evYvk/EVtEKWmplJycjKlpqaSvb09aWpqyj0IiLh/6JkLYh44cIAAUI8ePSgtLU1G1s/PjzQ1NalmzZpZliTIC9WqVSN7e3uZ42fOnEkCgYCePn2a47EJCQlkZmZGrVq14tvu3r1LAGj27NkyspMmTSKBQECBgYE5jiktCnrlyhW+LT09nRwdHcnc3Jx/8CcnJ5O+vj41btxYxhiRFqldv34935aYmEhGRkbUpk0bmXP16dOHtLS06OvXr3zbsmXLCADdvXuXb3v69CmpqKjQ9OnT+Tap0eHg4EAA6J9//pEZO68G0ffv33kjw9/fP0eDaOTIkaShoUFv377l2y5duiRXmFWq24oVK3I8d3Zcv35drvDw9evXCQDNnDlTpn3ZsmWkqqpKfn5+OY7p5+cnp1NSUhLZ2tpS/fr1ZWRbt25NFhYWFBMTw7dt376dANDFixf5NhsbG3JycpI59sKFCyQQCKhXr14yn0uijO9SdqVYGL8WbMqMUWLR19eHhoYGRCLZdFsJCQmYNGkSrKysoK6ujipVqmDlypWgTAsupVMEWU01CAQCzJs3L8dzExEWLlyIsmXLQlNTE25ubtkuWY6OjsaECRN4fSpWrIhly5ZBIpHI6bNy5UqsXbsWtra2UFdXR3BwMP755x8EBgZi5syZaNiwodz4urq6WLRoEb/v6ekJAwMDeHl5ySWhdHJywrRp0/D48WP8/fffOV5jVgQHByM4OBjDhg2Tue+jRo0CEeU6pqamJkxMTBAdHc233bx5EwDQs2dPGdmePXuCiHD48OEcx7x58yZMTEzQtGlTvk0oFKJ79+74+PEjn2cpKCgI0dHR6NGjh0wh4LZt20JbW1tmaujq1av48uWLXPHS0aNHIyEhAWfPnuXb/v77bzg6OsLR0ZFvs7OzQ7NmzXDkyBE5fXv27InKlStj/vz5Mp/JvKKurg5zc/M8yf7zzz9o27YtypUrx7c1b94clStXzlI3gPv+5JSsMysaN24MoVAo12ZoaIinT5/ybRKJBOvWrUOnTp3g5OSEtLQ0JCYmZjnm33//DRUVFQwbNoxvE4vFGDx4MO7cuYP3798D4KaSL126hL59+8oUUe7fvz+0tbVlrrNhw4Z4+PAhkpKS+Lbbt2+jevXqaN26NXx9fWW+l7dv34ZAIFBK6glGwcMMIkaJISYmBp8/f0ZUVBSePHmCkSNHIj4+XqZqNBGhffv2WLNmDdzd3bF69WpUqVIFU6ZMkak8/rPMmTMHs2fPhr29PVasWIEKFSqgZcuWSEhIkJFLTEyEq6sr/vrrL/Tv3x/r169HgwYNMH369Cz18fb2xoYNGzBs2DCsWrUKhoaGOHXqFACgX79+uer14sULPHv2DB06dJB5OGSmf//+ALgYI0V5+PAhAMjVk7O0tETZsmX5/szExsbi8+fPCAkJwYwZMxAUFIRmzZrx/cnJyQAgl3VaU1MTAHLNR5ScnJxlxuofj8/uPNK2hw8f8g/D7K6zbt26EAqFfL9EIsGjR4+yrK/n5OSE0NBQxMXFybSrqKhg1qxZCAwMzDKepaAICwtDZGRktrpl9V55enpCW1sbYrEYjo6O+Pfff/N9fmnmeGNjY74tODgY4eHhqFWrFoYNGwYtLS1oaWmhVq1acvE7Dx8+ROXKleU+x05OTgCAgIAAAMDjx4+RlpYmd51qampwcHCQuc6GDRsiNTUVfn5+fNvt27fh4uICFxcXxMTEICgoSKbPzs4ORkZG+b4PjOIDy1TNKDE0b95cZl9dXR27du1CixYt+LZTp07hv//+w8KFCzFz5kwA3K/6bt26Yd26dRgzZgxsbW1/So+oqCgsX74cbdq0wenTp3lvw8yZM7F48WIZ2dWrVyM0NBQPHz5EpUqVAADDhw+HpaUlVqxYwXuypHz48AEvX76UKZfx9OlT6OnpychlR3BwMAAuSDQ7ypcvD11dXZlf7nlFGiSbVUJECwuLLIO7u3fvjosXLwLgHlLDhw/H7Nmz+f4qVaoA4B4+NjY2fLvUcxQWFpajTlWqVMHly5fx9u1bWFtbZ3t8pUqVIBAIcPv2bZmMzM+ePeODjL99+wYjIyNERERARUUFpqamMudSU1ODkZERf51fv35FcnJytvcDAMLDw/lrlNK7d28sWLAA8+fPR6dOnWQ8VgVFbu+VVHd1dXUIhUK0bNkSnTp1QpkyZfDq1SusXr0arVu3xqlTp9CmTRuFz7927VqkpKSgR48efNuLFy8AAGvWrIGhoSG2bdsGAFi8eDHc3d3h7++PWrVq8frndl/zcp3SzwEA3sN669YtNGnSBGlpafDz84OHhwdsbW1hZmaGW7duoVatWoiLi8Pjx48xaNAgha+dUTxhHiJGiWHTpk24dOkSLl26hL/++gtubm4YMmQIjh07xsucO3cOKioqGDdunMyxkyZNAhEVyKq0y5cvIyUlBWPHjpV5kE2YMEFO9ujRo2jUqBEMDAzw+fNnfmvevDnS09Nx48YNGfkuXbrI1Q6LjY2Fjo5OnnSTeiNyk9fR0UFsbGyexsyMdKpBXV1drk8sFstMRUhZunQp/v33X+zcuRP16tVDSkqKzOqx33//HdbW1pg8eTKOHTuGt2/f4siRI5g5cyZEIlGWY2ZmyJAhUFFRQffu3eHj44PQ0FAsWbKE975Ijzc2Nkb37t2xZ88erFq1Cq9evcLNmzfRo0cPqKqqysgmJSVlW7Mr83Xmdj8yy2Qms5foxIkTOV5fflFEt3LlyuHixYsYMWIE2rVrh/Hjx+Phw4cwMTHBpEmTFD73jRs34Onpie7du8tMZUpXfcXFxeHKlSsYMGAABgwYgMuXL4OIsHz5chn986K7Ip/JqlWrwsjICLdu3QIABAYGIiEhgV9F5uLiwq+CvHPnDtLT07Ocpmb8mjCDSEFu3LiBdu3awdLSEgKBQOF/Vt+/f8eAAQNQs2ZNiEQidOzYUU7m2rVrWS5vza3QZ2nHyckJzZs3R/PmzdGnTx+cPXsW1apVw5gxY/iYh7dv38LS0lLOIKhatSrf/7NIx5B6fKSYmJjIlTV48eIFLly4ABMTE5lN6u2KjIyUkc/sIZGiq6srN+2SHdLrzk0+Li4uz0ZWZqTTTdLpp8x8//49y+koBwcHtGjRAoMGDcKlS5dw9+5dmVxNYrEYZ8+ehZGREbp06YLy5cujf//+mDNnDgwNDaGtrZ2jTrVq1cKBAwcQGhqKBg0aoGLFili/fj3Wrl0LADLHb9u2Db///jsmT54MW1tbNG7cGDVr1kS7du1kZDU0NLKNo8l8nbndj8wyP9KnTx9UrFgx37FEufEzugFcIdeBAwfi2bNncsvhcyIkJASdOnVCjRo1sGPHjix1atCggYzHs1y5cmjYsCF8fHxkZPOiuyKfSYFAABcXFz5W6Pbt2zA1NUXFihUByBpE0r/MICo5MINIQRISEmBvb49Nmzbl6/j09HRoaGhg3LhxclM8P/Ls2TNERETw24/ueUbOCIVCuLm5ISIignfF55XspijS09MLQjUeiUSCFi1a8J6tH7cuXbrIyGf1gLKzs0NMTAwfRJoTUsPv0aNH2cq8ffsWsbGx+aqRJp2WyCq/TEREBCwtLXM8Xk1NDe3bt8exY8dkfrlXr14dQUFBCAoKws2bNxEeHo6hQ4fi8+fPqFy5cq56de3aFeHh4bh79y7u3LmDt2/fokKFCgAgc7yenh5OnjyJt2/f4vr163jz5g327duHiIgImJiYQF9fn7/O9PR0OYM1JSUFX7584a/T0NAQ6urq2d4PANneE6mXKCAgACdPnsz1GhUlt/dKqntOSI2Wr1+/5umc79+/R8uWLaGnp4dz587JGd3Se2FmZiZ3rKmpqUxdPQsLizzdV0U/kw0bNkRMTAweP37Mxw9JcXFxwdu3bxEWFoZbt27B0tKS/xwxfn2YQaQgrVu3xsKFC9GpU6cs+5OTkzF58mSUKVMGWlpacHZ2xrVr1/h+LS0tbNmyBUOHDs11JYipqSnMzc357cdVGozckU69SF3x1tbWCA8Pl/OQhISE8P0AeE9O5tVOQN48SNIxfjTCoqKi5Aql2traIj4+nvds/bhlXv2THVLvxV9//ZWrbOXKlVG5cmWcOHEiWy/R3r17AXCrqxTFwcEBAHDv3j2Z9vDwcHz48IHvz4mkpCQQkZx+AoEA1atXR8OGDWFoaIirV69CIpHk+sNCipqaGhwdHVGvXj2oqanxhXOzOr5cuXJo3LgxrK2tER0djfv378vIZXed9+7dg0Qi4fuFQiFq1qwpJwcAfn5+qFChQo6euL59+6JixYrw9PQscC9RmTJlYGJikqVud+/ezdN7JU0u+eM0blZ8+fIFLVu2RHJyMi5evJhlTE/NmjWhqqqaZVxYeHi4zHkcHBzw/PlzualdaUC0VP8aNWpAJBLJXWdKSgoCAgLkrjNzHNHt27dlVpDVrVsX6urquHbtGvz8/NjqspKGkpb7lwgA0PHjx2XahgwZQi4uLnTjxg16+fIlrVixgtTV1en58+dyx3t4eFCHDh3k2q9evUoAyNramszNzal58+Z069atQrqKX5/s8hClpKRQpUqVSE1Njc8/cuLECQJAixcvlpHt0aMHCQQCevnyJd9mbGxMnTp1kpGTJjScO3eu3PmleYgiIyNJVVWV2rRpI5PPZsaMGQRAJg/RvHnzCABduHBB7rq+ffvG5/LJKQ9MSkoK1axZk7S0tMjHx0euPzY2ViYP0V9//UUAqHfv3nJ5iO7du0daWlpUo0YNSklJkRsrL9jZ2ZG9vb3M2LNmzSKBQEDBwcF826dPn+SO/fbtG1lZWZGVlVWO50hMTKQ6deqQhYUFxcbG8u0JCQn09OnTXHP1PH/+nHR0dKht27a5Xs+IESNIKBTK5BFKTEwkQ0NDueP79u1Lmpqa9OXLF75t6dKlcp/PkJAQUlFRoWnTpvFt2b3Hu3fvlslPlNu1ZSa3PEQjRowgDQ0NevfuHd92+fJlAkBbtmzh2yIjI+WO/fDhAxkYGFCtWrVk2sPDw+np06cyn5/4+HhycnIiHR0dunfvXo46d+jQgVRUVGRyVgUHB5OKigqNGjWKb/P19ZW7X9+/f6eKFSuSs7OzzJju7u5yn5UdO3YQALkkoMnJySQWi6l+/foEgG7fvi3TX79+fb5v3bp1OV4L49eCGUQ/wY8G0du3b0lFRYXCwsJk5Jo1ayaTgE1KdgZRSEgIbd26le7du0e3b9+mgQMHkkgkovv37xf0JZQIfsxUvW/fPlq1ahXVrVuXANCff/7Jy6anp5ObmxsJBAIaNmwYbdq0iTp06EAAaMKECTLj/vnnnwSAz47cq1cvfsycDCIiounTpxMA+v3332njxo00ePBgsrS0JGNjYxmDKCEhgerUqUMikYiGDBlCW7ZsoZUrV5KHhwdpaWnxD7/cEuO9ePGCrK2tSSQSUe/evWnTpk3k5eVF48ePJxMTE6pcubKM/Pjx4/mH7IoVK2jHjh00atQoEovFWWaqlhrpma87O06fPk0CgYCaNm1KXl5eNG7cOBIKhXJZpevUqUPt27enRYsW0fbt22n27NlUtmxZEgqFdPToURnZbt260fjx42nbtm20YsUKqlq1Kqmrq9Ply5fzpGfVqlVpzpw5tGPHDpo5cyYZGhqStbU1ffjwQUZuyZIl1KdPH1q/fj1t3ryZWrZsSQBo4cKFcte5adMmAkBdu3al7du3U//+/QkALVq0SEYuNjaWbG1tydTUlJYvX05r1qwhKysrsrS0lDE0snuPU1NTydbWls9inReDaMOGDbRgwQIaOXIkAaDOnTvTggULaMGCBRQdHc3LvXv3joyMjMjW1pbWr19PixcvJgMDA6pZsyZ9//6dlxswYAA1atSI5s2bR15eXjRjxgwyMjIiNTU1unr1qsy5pVnEM38fpN+xQYMGyWW6/vFH5ZMnT0hbW5ssLCxoyZIltGTJErKwsCATExO596tbt24kEoloypQptG3bNnJxcSGRSETXr1+Xkbt//z6pq6vLZKoWi8XUsmXLLO9fo0aNCACpq6vL3AeijB9FANj/5BIGM4h+gh8NojNnzhAA0tLSktlEIhF1795d7vjsDKKsaNy4MfXt27eANC9ZSA2SzJtYLCYHBwfasmWLXAmEuLg4+uOPP8jS0pJUVVWpUqVKtGLFCjm5xMREGjx4MOnp6ZGOjg51796dIiMj82QQpaenk6enJ1lYWJCGhgY1adKEgoKCssxUHRcXR9OnT6eKFSuSmpoaGRsbk4uLC61cuZL/lZ2XTMHfvn2jOXPmUM2aNUlTU5PEYjHVqFGDpk+fLlcegYjzlrVo0YIMDAxIXV2dKlasSJMmTcrygSvN1rx169Zsz5+Z48ePk4ODA6mrq1PZsmVp1qxZch6njRs3UsOGDcnY2JhEIhGZmJhQu3bt6MaNG3LjLVu2jOzs7EgsFpOBgQG1b9+eHj58KCeXnUHUs2dPsrKyIjU1NbK0tKQRI0Zk6aE6c+YM78nQ1NSkevXq0ZEjR7K9Ti8vL6pSpQqpqamRra0trVmzRu5zRMSVyOjatSvp6uqStrY2tW3bll68eCEjk9N7nPkznheDyNraWu47Id1+zKgeFBRELVu2JE1NTdLX16c+ffrQx48fZWQOHDhAjRs3JhMTExKJRLz3NCuDICuDKCd9rK2t5ca4f/8+NW/enLS0tEhHR4c6dOiQpZc9KSmJJk+eTObm5qSurk6Ojo5ZeluJiG7evEkuLi4kFovJxMSERo8eLeMxyoz0B42Li4tc37FjxwgA6ejoyHlYGb82AqJCWL5QSpAWApSuFDt8+DD69OmDJ0+eyGUA1tbWlosZGjBgAKKjo/O0Um3KlCm4desW7ty5U1DqMxh5ZurUqTh48CBevnyZa6Atg8Fg/IqwxIwFSO3atfmVJ40aNSrQsQMCArIMQmQwioKrV69i9uzZzBhiMBglFmYQKUh8fDxevnzJ779+/RoBAQEwNDRE5cqV0adPH/Tv3x+rVq1C7dq1ERUVhStXrqBWrVp8Ntfg4GCkpKTg69eviIuL41PMS1c7rF27FjY2NqhevTq+f/+OHTt24L///vupNPkMxs/g7++vbBUYDAajUGFTZgpy7do1uLm5ybV7eHhg9+7dSE1NxcKFC7F3716EhYXB2NgY9erVg6enJ2rWrAmAK42Q1fJt6VuxfPlyeHl5ISwsDJqamqhVqxbmzJmT5XkZDAaDwWD8PMwgYjAYDAaDUephmf4YDAaDwWCUephBxGAwGAwGo9TDgqrzgEQiQXh4OHR0dLKtccVgMBgMBqN4Qf8vA2RpaZlr+StmEOWB8PBwmcrLDAaDwWAwfh3ev3+PsmXL5ijDDKI8IC2++P79e+jq6ipZGwaDwWAwShFJSUBgIFCtGiB9Bi9eDCxbJiunrQ1J3Tp4Vl4XVacsBaysEBsbCysrqxyLKEthBlEekE6T6erqMoOIwWDkzKdPwKJF3JaHf8IMBiMTRMDLl4CvL+Dnx/0NDATS0oBTp4B27Tg5Nzfg3DmgXj1uc3bGazN19DjeG/7hN/BXek/00a3OD5uXcBdmEJUwQkJCMGDAAAQEBMDOzo5P+ljQ7N69GxMmTEB0dHShjM9g/JJIJICHB3DxIvD+PXD8uLI1YjCKN0SA1Fi5fBno0QP4+lVezswMyPy8adWK2/7PsafHMGjHIMQkx0BfrA9ddcWdF2yVmZKIioqCmpoaEhISkJqaCi0tLbx79+6nx507dy60tLTw7NkzXLlypQA0zZoePXrg+fPnCh3TpEkTTJgwoXAUyiMvX76Ejo4O9PX1laoHo4SyejVnDGloAAsXKlsbBqN4kZYGPHwIbNkCDBgA2NkB69dn9FtZccaQujrg4gL88Qdw+DDw5g0QEQH06yc3ZHJaMsaeG4suR7ogJjkG9crWQ8DwALSr0k5h9ZiHSEncuXMH9vb20NLSgp+fHwwNDVGuXLmfHjc0NBRt2rSBtbV1AWiZPRoaGtDQ0CjUcxQ0qamp6NWrFxo1agQfHx9lq8Moady9C0yfzr1etw6oXj1neQajNPD1K7B0KTf1df8+kJgo23/nDjB+PPe6UiXA3x+oVQtQU8t16NCvoejxdw/cj7gPAJjiMgWLmi6CqopqvlRlHiIl4ePjgwYNGgAAbt26xb/OCYlEgvnz56Ns2bJQV1eHg4MDLly4wPcLBALcv38f8+fPh0AgwLx587Icp0mTJhgzZgzGjBkDPT09GBsbY/bs2cictPzbt2/o378/DAwMoKmpidatW+PFixd8/+7du2W8LPPmzYODgwP27duH8uXLQ09PDz179kRcXBwAYMCAAbh+/TrWrVsHgUAAgUCAN2/e4Nu3b+jTpw9MTEygoaGBSpUqwdvbW5FbmWdmzZoFOzs7dO/evVDGZ5RiYmKAnj25X8DdugFDhihbIwajaElMBG7eBFauBHbtymgXiznP6c2bnIyeHtCyJTB7NnD2LLBxY4asUAj89luejCEAeP7lOe5H3IeRhhHO9DqD5S2W59sYAgAQI1diYmIIAMXExPzUOG/fviU9PT3S09MjVVVVEovFpKenR2pqaqSurk56eno0cuTIbI9fvXo16erq0sGDBykkJISmTp1Kqqqq9Pz5cyIiioiIoOrVq9OkSZMoIiKC4uLishzH1dWVtLW1afz48RQSEkJ//fUXaWpqkpeXFy/Tvn17qlq1Kt24cYMCAgKoVatWVLFiRUpJSSEiIm9vb9LT0+Pl586dS9ra2tS5c2d6/Pgx3bhxg8zNzWnGjBlERBQdHU3169enoUOHUkREBEVERFBaWhqNHj2aHBwcyN/fn16/fk2XLl2iU6dOyejq4eGR31vOc+XKFbKxsaGYmBg53RmMn0IiIerRgwggKl+e6Ns3ZWvEYBQ+ISFEu3cTjRxJVLs2kYoK9x0AiBwdZWUXLCDy9iYKDiZKTy9QNbzuedH7mPfZ9ivy/GYGUR4oKIMoNTWVXr9+TYGBgaSqqkqBgYH08uVL0tbWpuvXr9Pr168pKioq2+MtLS1p0aJFMm2Ojo40atQoft/e3p7mzp2box6urq5UtWpVkkgkfNu0adOoatWqRET0/PlzAkC3b9/m+z9//kwaGhp05MgRIsraINLU1KTY2Fi+bcqUKeTs7Cxz3vHjx8vo0q5dOxo4cGC2uvbr14/+/PPPHK8nNz5//kxWVlZ0/fr1LHVnMH6KHTu4h4BIRHTnjrK1YTAKns+fifz9ZdusrDIMIOlmYUHUqRPR6tWFokZIVAi5ervS62+v83yMIs9vFkNUhIhEIpQvXx5HjhyBo6MjatWqhdu3b8PMzAyNGzfO8djY2FiEh4fLTa01aNAAgYGBCutSr149mWWI9evXx6pVq5Ceno6nT59CJBLB2dmZ7zcyMkKVKlXw9OnTbMcsX768TK4HCwsLREZG5qjHyJEj0aVLFzx48AAtW7ZEx44d4eLiwvfv3btX4Wv7kaFDh6J379653mMGQ2GCg4GxY7nXCxdyy3+LOQkJgLY29zo+HtDSUq4+jGJGairw6JHssvcXLwBDQ+Dz54wVYW5uQGgov+Qd9eoBZctm9Bcw+x/tx/Azw5GQmoBx58fhVK9TBX4OZhAVIdWrV8fbt2+RmpoKiUQCbW1tpKWlIS0tDdra2rC2tsaTJ0+UrWa+UVWVnbsVCASQSCQ5HtO6dWu8ffsW586dw6VLl9CsWTOMHj0aK1euLDC9/vvvP5w6dYofk4ggkUggEong5eWFQYMGFdi5GKWIpCQubigpCWjRApgyRdkaMRg/x/jxgJcX8P27fJ+REWcQmZhw+3v2FIlKiamJGHd+HHY+3AkAcCvvhm1ttxXKuZhBVIScO3cOqampaNasGZYvX466deuiZ8+eGDBgANzd3eUMiszo6urC0tISt2/fhqurK99++/ZtODk5KayLn5+fzL6vry8qVaoEFRUVVK1aFWlpafDz8+O9NV++fMGzZ89QrVo1hc8lRU1NDenp6XLtJiYm8PDwgIeHBxo1aoQpU6YUqEF0584dmfOePHkSy5Ytg4+PD8qUKVNg52GUMiZNAh4/5vKj7NvHBYQyGMWZhATg3r0Mz8/du5w3yNCQ69fU5Iwhff0Mr4+zM+DkxBlERUxwVDC6H+2OJ1FPIIAAc1znYHbj2VARqhTOCfM/m1fwLF68mH777TfS1tYmExMT6tChA4WEhOR63JEjR6hKlSqkrq5ONWrUoLNnz8r0SyQSmj17Npmbm5NYLKZmzZrxgch5oaBiiIi4wGd1dXVKSkqi79+/k1gspvDw8Dwdu2bNGtLV1aVDhw5RSEgITZs2TSaomijvMUTa2tr0xx9/UEhICB04cIC0tLRo69atvEyHDh2oWrVqdPPmTQoICCB3d/dcg6rt7e3l9LW2tub3hw4dSo6OjnysVHp6Os2ePZtOnDhBL168oKCgIGrbti05OTnxxxREDNGPsBgixk9z7FhG3MTFi8rWRiHi4zNUj49XtjaMQufWLaLhw4kcHGQDn6Xb+fMZsm/fcsHSBRz4nB983vmQ5iJNwjyQ+UpzuvLqSr7G+WVjiK5fv47Ro0fD0dERaWlpmDFjBlq2bIng4GBoZTPR7ePjg169emHJkiVo27YtDhw4gI4dO+LBgweoUaMGAGD58uVYv3499uzZAxsbG8yePRutWrVCcHAwxGJxUV4irl27BkdHR4jFYty8eRNly5aFhYVFno4dN24cYmJiMGnSJERGRqJatWo4deoUKlWqpLAe/fv3R1JSEpycnKCiooLx48dj2LBhfL+3tzfGjx+Ptm3bIiUlBY0bN8a5c+dy9GLlxuTJk+Hh4YFq1aohKSkJr1+/hpqaGqZPn443b95AQ0MDjRo1wqFDh/hj3r17l2uFYgajyPntN6BRIy55XMuWytaGweCms+7e5Tw/ffoAVapw7cHBwLZMU0xly8p6f377LaOvAHLhFRS1LWqjslFlmGiaYF+nfTDTNiv0cwqIMiWfKWZERUXB1NQU169fzzYgtkePHkhISMCZM2f4tnr16sHBwQFbt24FEcHS0hKTJk3C5MmTAQAxMTEwMzPD7t270bNnz1z1iI2NhZ6eHmJiYkpELbMmTZrAwcEBa9euVbYqDMavS1oa9xv7J34kKAMWVF0CSEnh6ntJp778/Lj6X1I2bQJGjeJev3gBbN/OGT/OzpxBVEx5/uU5bA1s+SmxqIQoGGkaQSjI/49iRZ7fxfqnd0xMDADAUDq/mQV37txB8+bNZdpatWqFO3fuAABev36Njx8/ysjo6enB2dmZl2EwGIw8kblcjUj0yxlDPxIWpmwNGLlCBLx9y9XGk3LrFhfXM3YssH9/hjFUpQpXS8/OLkO2UiVg+XKgS5diawwREbzue8F+qz3mX5/Pt5tomfyUMaQoxWrKLDMSiQQTJkxAgwYN+KmvrPj48SPMzGRdaWZmZvj48SPfL23LTuZHkpOTkZyczO/HxsYC4H5ZqRRSLFdRkp7OraxMSFC2JgzGr4Pw6hWIO7RE2vAxSFm25pcNovbyynhdtSqwYQP3DGUUE+LiIHxwDyr3/CD094Xwri+EkZ+QOnYiUpas4mSq/gZNYxOk1/kNEsd6kDjVQ3odR8DAIGOcX+T/e2xyLMZdGo6/Q7hQibsf7iMuXlJghpAiz7liaxCNHj0aQUFBuHXrVpGfe8mSJfD09JRrt7QsclUKiWu4dQvYvFnZejAYvw4zcQcLIcGuLd8xYsuvaQz9iEQCjB7NbQzloodoXIcraiAIKpBNV5IKEfZsiMHQDdIWXQCfgH8FwL9FrWkBYv4Q6NYdMHoJSFSAK4txwWcydPsr5/tVLA2iMWPG4MyZM7hx4wbK5uLiMzc3x6dPn2TaPn36BHNzc75f2pY5ePnTp09wcHDIcszp06dj4sSJ/H5sbCysrKzycykMBqOEsAiz4It68IFL7sIMRhaYIBLO8IMz/FAPvghDGQwAl88nBnowwyeoQIK3KAc/OMMX9eCLeniI2viOH4tpF04CxKKBgN+2Au5/AKJkIMYK+PsQ8F65361iZRAREcaOHYvjx4/j2rVrsLGxyfWY+vXr48qVK5gwYQLfdunSJdSvXx8AYGNjA3Nzc1y5coU3gGJjY+Hn54eRI0dmOaa6ujrU1dXl2sPDgcKKqR4+fABiYqJx6NCJwjkBg8HIH0SZsu82z1G0uBMWxk2TZc6XqqLCLURiKbkKB9HObVC5dZ2b/nrzWqZPYmaOri+lny8BhHdPINGqHIwtLNEGQBulaFz4vIt5h7rek5CUlozfbdthi7s3jBYUTp6j2FgFZnfytbC/kBg5ciTp6enRtWvX+AKgERERlJiYyMv8mJfm9u3bJBKJaOXKlfT06VOaO3cuqaqq0uPHj3mZpUuXkr6+Pp08eZIePXpEHTp0IBsbG0pKSsqTXgWZhyg7PDw8qEOHDoU2/s+QnzxOeckp9fLlS+rYsSMZGxuTjo4OdevWjT5+/Cgjs3DhQqpfvz5paGgUWe6gwrreiIgI6tu3L5mZmZGmpibVrl2b/v77b7mxzpw5Q05OTiQWi0lfX7/Yfi5KBX5+RE2acPlZSgibNmWkoFFR4UqxMX4SiYTo1SuiAweIFi+W7atfXzbvT9WqRAMHEm3dShQQwB1bCvF+6E2rfVbL1NQsDH7Z4q4Asty8vb15mayqnx85coQqV65MampqVL169WwTM5qZmZG6ujo1a9aMnj17lme9SrtBtHTpUtLT06MTJ05QYGAgtW/fPleDslWrVuTt7U1BQUEUEBBAv//+O5UrV47i/58JLj4+nipUqECdOnWiR48e8Yaqo6MjpWdKCjZnzhxavXo1TZw4Md8GkbW1NV29elWp10tE1KJFC3J0dCQ/Pz8KDQ2lBQsWkFAopAcPHvAyf//9NxkYGNCWLVvo2bNn9OTJEzp8+HC+rpvxk0RHE9nYcA+xQYOUrU2BkTkxowL/BhmZiYkhunyZaNEionbtiExMMm6qUEiUqcg17dhBNH8+0b//En37pjSVlYlEIqG1d9bS7Xe3cxcuYH5Zg6i4UlAG0dGjR6lGjRokFovJ0NCQmjVrxj8wfzSIvn//TmPHjiUTExNSV1enBg0a0N27d/n+q1evEgA6c+YM1axZk9TV1cnZ2VnGM0ZEdPPmTWrYsCGJxWIqW7YsjR07VuYhnRsSiYTMzc1pxYoVfFt0dDSpq6vTwYMH8zxOZGQkAeArzl+8eJGEQqHMPY2OjiaBQECXLl2SO/5nsksrYhAV1vUSEWlpadHevXtl5AwNDWn79u1ERJSamkplypShHewnu/KRSIh69OAecOXLl6gHGctUrSBpaUSPHnF/pfTtK5/xWVWVyNGRaMwYok+flKdvMeNL4hfqcLADYR7IarUVxXwvPMdCVijy/C4ZSyV+ASIiItCrVy8MGjQIT58+xbVr19C5c2dQNnkxp06din/++Qd79uzBgwcPULFiRbRq1Qpfv36VkZsyZQpWrVoFf39/mJiYoF27dkhNTQUAhIaGwt3dHV26dMGjR49w+PBh3Lp1C2PGjOGPnzdvHsqXL5+t3gWVx+nHnFLJyckQCAQysVpisRhCoVApKwulFNb1AoCLiwsOHz6Mr1+/QiKR4NChQ/j+/TuaNGkCAHjw4AHCwsIgFApRu3ZtWFhYoHXr1ggKCiqYi2PknV27gMOHuVxDBw9ytZ0YpYNPn4BTp4AZM4CmTbn3vlYt4OnTDJl69YDy5YEePYA1awAfHy5Y5e5dLo+BqamytC9W+H7wRe1ttXHy2UmoqahhaoOp0FHTUbZa2aOotZWYmEhBQUFysR6ZefXqFe3Zs0fRoYstBeEhun//PgGgN2/eZNmf2UMUHx9PqqqqtH//fr4/JSWFLC0tafny5USU4SE6dOgQL/PlyxfS0NDgp1gGDx5Mw4YNkznPzZs3SSgU8tM/GzZsoKZNm2ar9+3btwmAXL21bt26Uffu3fN07enp6dSmTRtq0KAB3xYZGUm6uro0fvx4SkhIoPj4eBozZgwBkNOZSDEP0fDhw0lLS4vfBAIBicVimbaivl4iom/fvlHLli0JAIlEItLV1aWLmepgHTx4kABQuXLl6O+//6Z79+5Rr169yMjIiL58+ZKnczMKgCdPiDQ0uF/9S5cqW5sCh3mIsuHIEc4b+KPnByDS1iY6fTpDthjU+irOpEvSafmt5SSaLyLMA9mus6X74feVokuheYg8PT1hZGSEWrVqwdLSEm5ubln+evXx8cHAgQN/1lYrUdjb26NZs2aoWbMmunXrhu3bt+Pbt29ZyoaGhiI1NRUNGjTg21RVVeHk5ISnmX+lAPxqOoDzRlSpUoWXCQwMxO7du6Gtrc1vrVq1gkQiwevX3GqHMWPG4MqVKwV9uTJIc0plrlFmYmKCo0eP4vTp09DW1oaenh6io6NRp06dn65dNn/+fAQEBPCbpaUlduzYIdNWmGR1vQAwe/ZsREdH4/Lly7h37x4mTpyI7t274/HjxwC4ZKQAMHPmTHTp0gV169aFt7c3BAIBjh49Wqg6M/5PUhL3qz8pCWjRApgyRdkaMQoKIiA0FDhwABg3jitjcfZsRr+ODvDmDbfiq3p1YPBgLovlo0dAdDTQtm2G7C+alLMoSExNRPuD7TH18lSkSdLQo3oPPBj+AHUs6ihbtVzJ87L7I0eOwNPTE02bNkXHjh0RERGBnTt3wtHRETt37kTv3r0LU89fHhUVFVy6dAk+Pj74999/sWHDBsycORN+fn55Si+QH+Lj4zF8+HCMGzdOrq9cHov45SePU2ZyyinVsmVLhIaG4vPnzxCJRNDX14e5uTkqVKiQJ92yw9TUFKaZXNYikQhlypRBxYoVcz22sK43NDQUGzduRFBQEKpXrw6AM5Jv3ryJTZs2YevWrfz5qlWrxh+nrq6OChUq4N27d7mem1EATJoEBAUBZmbAvn0l8sGnpcXZBqWC8HBu+lNa7+vzZ9n+W7eANv9f3N6gAXD5MuDoWHj5VUoBGiINqKqoQl1FHevc12FY3WEQCH6NnEl5NojWrFmDpk2b4vLly3zb5MmT0bt3b/Tr1w/h4eF88VRG1ggEAjRo0AANGjTAnDlzYG1tjePHj8skgQQAW1tbqKmp4fbt27C2tgYApKamwt/fXybfEgD4+vryxs23b9/w/PlzVK1aFQBQp04dBAcH58kQyI785HECFMspZWxsDAD477//EBkZifbt2+db35+lsK43MTERAOS8XyoqKrxnqG7dulBXV8ezZ8/QsGFDANz7/ubNG/5zwChE/vkH2LKFe713L2cUMX4N0tKAJ084w8fGBmjZkmuPjwdmz86QU1MDatfOqPTeqFFGn44O0KxZ0epdQpCQBMlpydBQ1YBAIMCu9rvwLuYd7M3tla2aYuR1Hk5fX582b94s1y6RSGjUqFEkFApp0qRJRET0119/kVAozPMcX3GnIGKIfH19adGiReTv709v376lI0eOkJqaGp07d46I5FeZjR8/niwtLen8+fP05MkT8vDwIAMDA/r69SsRZcQQVa9enS5fvkyPHz+m9u3bU7ly5Sg5OZmIiAIDA0lDQ4NGjx5NDx8+pOfPn9OJEydo9OjR/HlyiyEiylsep6ZNm9KGDRv4/bzklNq1axfduXOHXr58Sfv27SNDQ0OaOHGizLnfvn1LDx8+JE9PT9LW1qaHDx/Sw4cPKS4uLlt9o6OjZc6Z1VbU15uSkkIVK1akRo0akZ+fH718+ZJWrlxJAoFAJk3E+PHjqUyZMnTx4kUKCQmhwYMHk6mpKf++MwqJN2+I9PW5eJFp05StDSM3wsOJjh/n3itXVyItrYx4n8yxfhIJlzJh3ToiX1+i79+VpXGJ5VP8J2q5ryX1/LtnoecUyg+FsuzexMSE1q9fn23/vHnzSCAQUP/+/cnb25sZRD8QHBxMrVq14pfRV65cWeaB+qNBlJSURGPHjiVjY+Mcl92fPn2aqlevTmpqauTk5ESBgYEy57179y61aNGCtLW1SUtLi2rVqkWLFi3i++fOnUvW1tY56p6XPE7W1tY0d+5cfh95yCk1bdo0MjMzI1VVVapUqRKtWrVK7gvl4eGR5Tg5LaPP7pjMmzKu9/nz59S5c2cyNTUlTU1NqlWrltwy/JSUFJo0aRKZmpqSjo4ONW/enIKCgnLUl/GTpKRkJM9zdub2GcWHxESi168z9pOSiEQi+cBnHR2iZs2I1qxRlqaljquvr5LFSgvCPJDGQg0KiQrJ/aAiRpHnt4Aob7PJrq6uMDIywrFjx7KV2bRpE8aPHw9jY2NERUUhPT09766qYkxsbCz09PQQExMD3WIyt3zt2jW4ubnh27dv0GdLghmM/LNqFTB5Mhc3EhDATbkwlAMR8PJlRsyPnx/3ntjbA/fuZcg5O3OB7/XqZUx/2dlxdUgYhU66JB2Lbi6C53VPSEiCqsZVcaTbEdQwraFs1eRQ5Pmd5xiitm3bYvbs2Xj//n22hU5Hjx4NY2Nj9O/fXzGNGQwGQ1kMH84FUrduzYwhZTJkCHD8OPBDrjUAXG6gtDQuLxQA3L6d8ZpRpHyM/4g+x/rgv9f/AQAGOgzEhtYboKWmpWTNfp48f6IGDx6MatWq8QGg2dGjRw9UqFABwcHBP60cg8FgFDra2oC3t7K1KPmkpXGGp9T78/QpcOdORuHcuDjOGFJXB+rUyfD81KsHlCuXqcAumDGkJIgIv+//HQ8/PoSWqha2tNmCfvb9lK1WgZHnKbPSTHGcMmMwGD+BRMKtKuvSpUQurS823LwJnD7NGUD37gH/X23J8+wZULky9zogAEhN5abH1NSKXFVG3rj25hr+uPgHDnY5CDtjO2WrkyuFMmXGYDAYJYbVq7mki126AEePynofGIqTmAg8eMB5fwYPBgwMuPZz54AVKzLkdHU5r4/U81OmTEZfHvJ8MYqesNgwPIl6gpa2XCqDJuWb4P6w+xAKSt4PiXwbRNevX4erq2tB6sJgMBhFg54eIBZz+WqYMaQYRMCLF5zxI53+CgwEpItoatQA3N25161aAd++ZRhAVaowj9wvxIWXF9DveD98T/uO+8Puo7IR580ricYQkE+D6NChQxg4cCCSkpIKWp8STZMmTeDg4IC1a9cqWxUGo3QzdChXmoMlvMydr1+51Vt6etz+7t3AoEHychYWnNGjrZ3R1qQJtzF+KVLTUzH76mwsu70MAOBg7lBijaDMKHyFy5cvR9++fTF37tzC0KdEc+zYMSxYsCDP8m/evIFAICj02lsFyYABA9CxY8d8Hbto0SK4uLhAU1MzX6kERowYAYFAIGdwPnjwAC1atIC+vj6MjIwwbNgwxMfH50vHvJKXaxEIBHLbj/XPCpI3b95g8ODBsLGxgYaGBmxtbTF37lykpKTkeFyTJk3k9BwxYoSMjL+/P5o1awZ9fX0YGBigVatWCAwMLLRrAYBx48bx2b1zK6vy8uVL6OjoyL4X5csz79CPpKYC9+8DmzcD/ftzHh0jI+CvvzJkHB0571qDBlypkyNHgHfvgLAw4Ngx4P9Z1hm/Ju9i3qHJnia8MTTacTTuDL6Diob5r3jwq5Bng4iIMGbMGEyfPh3z58/Hn3/+WZh6lUgMDQ2ho6OjlHOnpqYq5byKkJKSgm7duuVYIiM7jh8/Dl9fX1haWsq0h4eHo3nz5qhYsSL8/Pxw4cIFPHnyBAMGDPgpXZs0aYLdu3dn25/Xa/H29kZERAS/KWJMSg3mvBISEgKJRIJt27bhyZMnWLNmDbZu3YoZM2bkeuzQoUNl9Fy+fDnfFx8fD3d3d5QrVw5+fn64desWdHR00KpVq5/63JUvXx7Xrl3LUWbQoEHo0aNHjjKpqano1asXGpUvD8TGctM8DFleveIMGV1d4LffgNGjuVpuz59n9EupVo27j7duAStXAt26AVZWzLgsAZx+dhq1t9WGz3sf6Krr4mi3o9j4+0aIRWJlq1Y05DXbY4cOHUgoFNLChQvzmS/y16UgMlUTEbm6utL48eP5fWtra1q0aBENHDiQtLW1ycrKirZt28b344esx66urnzf9u3byc7OjtTV1alKlSq0adMmvu/169cEgA4dOkSNGzcmdXV1PmPyzp07qVq1aqSmpkbm5uYyZTy+fftGgwcPJmNjY9LR0SE3NzcKCAjg++fOnUv29va0detWKlu2LGloaFC3bt0oOjqa7/9R55wySmeHt7c36enp5Vn+w4cPVKZMGQoKCiJra2takylT7bZt28jU1JTS09P5tkePHhEAevHiBd/2+PFjcnd3Jy0tLTI1NaW+fftSVFRUtud0dXWVyUKdn2sBQMePH891jOyQvs8/w/Lly8nGxiZHmR8/tz/i7+9PAOjdu3d8W1b3+ObNm9SwYUMSi8VUtmxZGjt2LMXHx2c7rrW1dZ4+P9LPZXZMnTqV+rZrR96qqqQHEC1dmuuYJZL4eKJr14iWLSPq1Iko8//y2FgioZDL+KyvT9SqFdHcuUTnzhF9/qw0lRlFy5R/pxDmgX7z+o1Cv4YqW50CQZHnd549RKdOnUKbNm0wc+bMgrTHSj2rVq3Cb7/9hocPH2LUqFEYOXIknj17BgC4e/cuAODy5cuIiIjgs4Tv378fc+bMwaJFi/D06VMsXrwYs2fPxp49e2TG/vPPPzF+/Hg8ffoUrVq1wpYtWzB69GgMGzYMjx8/xqlTp2QKv3br1g2RkZE4f/487t+/jzp16qBZs2b4milR2suXL3HkyBGcPn0aFy5c4PUGuGK/3bt3h7u7O+9JcHFxAcB5VH7WK5MVEokE/fr1w5QpU/gq8plJTk6GmpqaTFFVDQ0NAMCtW7cAANHR0WjatClq166Ne/fu4cKFC/j06RO6d+9e4Pr+iDSZqZOTE3bt2gUq4iwYMTExMDQ0zFVu//79MDY2Ro0aNTB9+nS+WC0AVKlSBUZGRti5cydSUlKQlJSEnTt3omrVqihfvjwAIDQ0FO7u7ujSpQsePXqEw4cP49atWxgzZkxhXRoArmDw0SNHsCk0lJsOEom41WWlgfR0Lt5nxAiuoKmeHhfPM20alwDx3LkMWR0d4O+/gZAQ4MsX4MIFYN48LlmlkZGSLoBR1CxqugirW67GrYG3UMGggrLVKXryamW5ubmRUCikrVu3/oyx9ktSmB6ivn378vsSiYRMTU1py5YtRJThAXj48KHMOLa2tnTgwAGZtgULFlD9+vVljlu7dq2MjKWlJc2cOTNL3W7evEm6urr0/Yfih7a2trzXau7cuaSiokIfPnzg+8+fP09CoZAvmPpjTTYp/fr1oz///DPLc/+IIh6ixYsXU4sWLfgaaD96iIKCgkgkEtHy5cspOTmZvn79Sl26dCEAtHjxYiLi7l3Lli1lxn3//j0BkKthJqUgPETz58+nW7du0YMHD2jp0qWkrq5O69aty3G8atWqkZaWFmlpaZGmpiYB4Pe1tLTI3d09V52kvHjxgnR1dcnLyytHuW3bttGFCxfo0aNH9Ndff1GZMmWoU6dOMjKPHz8mW1tbEgqFJBQKqUqVKvTmzRu+f/DgwTRs2DCZY27evElCoVCmaG5mftZD9PnzZ7KysqLrHToQAeStq0t6urq5jvdL8vkz0dmzRH/9ldEmkRCZm8vW+ypThqhzZ6Lly4lu31aevoxiwbHgY9T6r9aUklZy6/cp8vzO8yqzixcvYsCAARg9ejSEQiGGDh1aKAZaaaNWrVr8a4FAAHNzc0RGRmYrn5CQgNDQUAwePFjmPUhLS4OedBXI//ntt9/415GRkQgPD0ezZs2yHDcwMBDx8fEw+uHXYFJSEkJDQ/n9cuXKoUym3CH169eHRCLBs2fPYG5unq3ee/fuzbYvv9y/fx/r1q3DgwcPso2lqV69Ovbs2YOJEydi+vTpUFFRwbhx42BmZsZ7jQIDA3H16lVoZ14d839CQ0NRuXJlLF68GIsXL+bbk5KS4OvrK+PhCA4ORrly5fKs/+zZs/nXtWvXRkJCAlasWIFx48Zle8y5c+f4uJywsDA0adJEJuhe6v3KjbCwMLi7u6Nbt265fpeHDRvGv65ZsyYsLCzQrFkzhIaGwtbWFklJSRg8eDAaNGiAgwcPIj09HStXrkSbNm3g7+8PDQ0NBAYG4tGjR9i/fz8/FhFBIpHg9evXqFq1KkaMGIG/MgXvJiYmonXr1lDJVJ9KkWD4oUOHonfdumh84oS0AdixI8/HF1tSUoBHjzKWvPv6cvW/AMDMDOjdm4vnEQi4nEDJyRlZn8uWVa7ujGJBcloyplyagg13NwAANvtvxvh645WslfLJs0GkqqqK/fv3w8rKCiNHjoRQKMTgwYMLU7dSgaqqqsy+QCDIsTyK9IGwfft2ODs7y/Sp/FDYUEsro7ZMbg/K+Ph4WFhYZBnEWlyLx968eRORkZEyRkh6ejomTZqEtWvX4s2bNwCA3r17o3fv3vj06RO0tLQgEAiwevVqVKjAuYTj4+PRrl07LFu2TO4cFhYWALgVbJmn0Pr06YMuXbqgc+fOfNuPAd2K4uzsjAULFiA5ORnq6upZylhnWiYu+n/5gszTnnkhPDwcbm5ucHFxgZeXV770BLjpU1tbWxw4cABv3rzBnTt3eCPzwIEDMDAwwMmTJ9GzZ0/Ex8dj+PDhWRp70vdv/vz5mDx5Mt/epEkTLFu2TO5znlf+u3wZp+LisBIABALQmjWQSCQQiUTw8vLCoKyWjhc3iICPH7kl7VJatQKyCjavUoUzfL5/B6Tf94ULi0RNxq9D6NdQ9Pi7B+5H3AcATK4/GaMcRylZq+KBwnmIli5dinLlymHUqFHMICpk1P6fvj5dmvAMgJmZGSwtLfHq1Sv06dMnz2Pp6OigfPnyuHLlCtzc3OT669Spg48fP0IkEvFxH1nx7t07hIeH8w9/X19fCIVCVKlShdc5s76FSb9+/dC8eXOZtlatWqFfv34YOHCgnLyZmRkAYNeuXRCLxWjRogUA7tr/+ecflC9fnjcyfsTQ0FAm1kZDQwOmpqYKGyM5ERAQAAMDg2yNoYIgLCwMbm5uqFu3Lry9vWViq/KK1CMlNRYTExMhFAplvHTSfalxX6dOHQQHB+d4v0xNTWFqasrvi0QilClTJn/3ODUVdypUQHpgIFCzJrBnD06eO4dly5bBx8dHxstZrIiP50pcZPb+fPoEREdzK8AArs5XYKBsrS8np4zs0AxGNhx5cgRDTg1BXEocjDSMsKfjHrSp3EbZahUb8pWYcdSoUdlWvGcUHKamptDQ0MCFCxdQtmxZiMVi6OnpwdPTE+PGjYOenh7c3d2RnJyMe/fu4du3b5g4cWK2482bNw8jRoyAqakpWrdujbi4ONy+fRtjx45F8+bNUb9+fXTs2BHLly9H5cqVER4ejrNnz6JTp0789JtYLIaHhwdWrlyJ2NhYjBs3Dt27d+eny8qXL4+LFy/i2bNnMDIygp6eHlRVVdG/f3+UKVMGS5YsyVa/d+/e4evXr3j37h3S09P5B2/FihX56Sw7OzssWbIEnTp1gpGRkdwUn6qqKszNzXkDDQA2btwIFxcXaGtr49KlS5gyZQqWLl3Ke75Gjx6N7du3o1evXpg6dSoMDQ3x8uVLHDp0CDt27JDzvOWF3K7l9OnT+PTpE+rVqwexWIxLly5h8eLFMh6SrIiKiuINTrFYjIiICHz8+JHvV1NTyzZIWjrFZm1tjZUrVyIqKorvk75/YWFhaNasGfbu3QsnJyeEhobiwIED+P3332FkZIRHjx7hjz/+QOPGjfnp3hYtWmDKlCkYPXo0xo4dC4lEgqVLl0IkEvHG97Rp01CvXj2MGTMGQ4YMgZaWFoKDg3Hp0iVs3LhR4fsLcB6q+Ph4fPz4EUlJSfw9rlatGtQ8PVE1MJAzIk6eBGxscC8wEEKhEDVq1MjX+QoVb29g7Vqu+OmPHmKRiCuEKvWULVjALXdny9wZCrDi9gpMvTwVANDAqgEOdT2EsrpsClWGwg9p+vUpzKDqzAHARET29vY0d+5cfn/79u1kZWVFQqFQZtn9/v37ycHBgdTU1MjAwIAaN25Mx44dI6Lsg7GJiLZu3UpVqlQhVVVVsrCwoLFjx/J9sbGxNHbsWLK0tCRVVVWysrKiPn368MuppcGrmzdvJktLSxKLxdS1a1f6+vUrP0ZkZCS1aNGCtLW1ZZbdu7q6koeHR473x8PDQ27ZPn5Yug8gx2DmrO5pv379yNDQkNTU1KhWrVq0d+9eueOeP39OnTp1In19fdLQ0CA7OzuaMGECH6z9I7kFVed2LefPnycHBwfS1tYmLS0tPp1B5vQA2V1fVuNKt8yfkR/x9vbO9jgp0s+OVM93795R48aNydDQkNTV1alixYo0ZcoUue/Cv//+Sw0aNCA9PT0yMDCgpk2b0p07d2Rk7t69y382tLS0qFatWrRo0aIcrzWnoGpXV9csr+X1X38RCQRcEPGRIzLXr0g6hwInMpLo9GmimTOJmjcnevIko2/TpozAZysrom7diFauJLp1iygxUXk6M0oMzz8/J90lujT98nRKTU9VtjpFhiLPb1btPg+wavcc8+bNw4kTJ36pzNmMUkZkJFct/eNHYNgwYNs25eny/j23vF069ZU5uSEAeHlxgd4Al+n5/n3OC/STsWgMhpSgyCDUMM3wiEYlRMFEy0SJGhU9RVrtXiKRICYmJsv8KXnJb8JgMBgFxsaNnDFUvTqwZk3RnJMIePuWM3qqVQOkK0efPAHG/7Byp2rVjNif/8ewAQDKleM2BqMASExNxLjz4+Ad4I3L/S7DzYabui5txpCi5MsgSk1NxbJly7Br1y68f/8+21VRRRVcy2AwGAC4ZIKamkDbttzfwiAuDvD3lw18lqbK+PPPDIPIyQn4/XfOAKpXj6sBVkxXbDJKDsFRweh+tDueRD2BAAIEfAzgDSJGzuRrymzQoEHYs2cP6tWrB2dnZ7n8N1JKSgFYNmXGYJRS0tO5ul3SFVxv3wI2NpxXKDMiEZcNun9/oJCzbzMY2bE7YDdGnxuNxNREmGmZYX/n/WhWIevcc6WFQp8yO3r0KPr165djcUsGg8EoEmJigBUrgJkzM/Lv5JdPnzK8Pr6+nCeodWuuojvATWsZGHClLqRL3uvV44whcSkpgMkodsSnxGP0udHYG8glwG1m0wx/df4L5trZJ8tlyJMvg0hTUxP16tUraF0YDAZDcUaNAg4c4HLznD6t+PFEgIcHcPMm8P9knjI8fpzxWiAAQkPZ1BejWHH62WnsDdwLoUAIzyaemN5wOlSEiqcLKe0onpUNQK9evXDmzJmC1qXEM2DAAAgEAggEAqiqqsLGxgZTp07F9+/feZk3b95g8ODBsLGxgYaGBmxtbTF37lykpKQUub5Hjx6FnZ0dxGIxatasiXOZi0Fmwa1bt9CgQQMYGRlBQ0MDdnZ2WJNDYOvSpUshEAgwYcIEmXYvLy80adIEurq6EAgEiI6OBgAkJGRUJEhI+NmrY5QYBg7kSlLkVHiaiFvldeAAMG4ckLnQsEDA5fl584Z7Xb06MGgQtwosMJDLDZQZZgwxihk9a/TEH/X+wH/9/8OsxrOYMZRP8uUhWr58OQYNGoS2bdti0KBBsLKyyjJ5XZ06dX5awZKGu7s7vL29kZqaivv378PDwwMCgYAvGxESEgKJRIJt27ahYsWKCAoKwtChQ5GQkICVK1cWmZ4+Pj7o1asXlixZgrZt2+LAgQPo2LEjHjx4kG1iOy0tLYwZMwa1atWClpYWbt26heHDh0NLS0umHhYA+Pv7Y9u2bTK13KQkJibC3d0d7u7umD59eqFcH6ME0bw5V8vrxwzfPj7A1asZ01+fP2f0iUTA5s0ZgdcLF3Jtjo4ZGaEZjGJKXHIcZl+djTmuc2CoYciVI2q1Wtlq/frkJ9FRbGws9erVi69s/eMmEAhIKBTmZ+hiSUElZsyqEnznzp2pdu3aOR63fPlysrGx+alzK0r37t2pTZs2Mm3Ozs40fPhwhcbp1KkT9e3bV6YtLi6OKlWqRJcuXZJLVpmZq1evEgD69u0bERHFx2fkrouPV0gNRkkjMZHo1SvudVoaUWAg0Y4dRJkTW/boIVvpXU2NyNmZaNw4ogMHWMJDxi/Jw4iHVGl9JcI8UJfDXZStTrGnUKrdZ2bQoEE4fvw4evbsmeMqM0bOBAUFwcfHR6ZgZ1bExMTkmtOpdevWuHnzZrb91tbWePLkSZ51u3PnjlwZkFatWuGEtHJ4Hnj48CF8fHyw8IcCk6NHj0abNm3QvHlzuT4GI1ciIrgprf/+AypV4qa6pHOoDRtyRU4BLhhaIMgIfHZwkPciMRi/CESELfe2YOLFiUhOT4aVrhUm1s++VBNDcfJlEF28eBFjx47NMT4kP9y4cQMrVqzA/fv3ERERgePHj6Njx47Zyg8YMAB79uyRa69WrRr/8J83bx48PT1l+qtUqYKQkJAC1T2vnDlzBtra2khLS0NycjKEQmGOtZxevnyJDRs25DpdtmPHDiQlJWXbr6qqqpCeHz9+5IuhSjEzM5Opm5UdZcuWRVRUFNLS0jBv3jwMGTKE7zt06BAePHgAf39/hfRhlFKSkgAVFeD/hY6xZAkwY0ZGv9TI19Hh8v4kJmb0eXhwG4PxixPzPQZDTg/B38F/AwDaVW4H7w7eMNI0yuVIhiLkyyDS1dUt0CrfUhISEmBvb49Bgwahc+fOucqvW7cOS5cu5ffT0tJgb2+Pbt26ychVr14dly9f5vezq2heFLi5uWHLli1ISEjAmjVrIBKJ0KVLlyxlw8LC4O7ujm7dumGoNMV/NhSn6t03b95EfHw8fH198eeff6JixYro1asX3r9/j/Hjx+PSpUsQsyXKjB8h4lZwZU54GBDAFWf9/XdOxtQ0Q97eHhg7lvP+2NlxhhODUcJ4GvUUbQ+2xatvryASirCs+TL8Ue8PCFhx3wInX5bB0KFDcfDgQYwYMSJflcCzo3Xr1mjdunWe5fX09GSm606cOIFv375h4MCBMnIikYiv5q1stLS0eGNy165dsLe3x86dOzF48GAZufDwcLi5ucHFxQVeXl65jlvQU2bm5ub49OmTTNunT5/ydB9tbGwAADVr1sSnT58wb9489OrVC/fv30dkZKRMsH16ejpu3LiBjRs3Ijk5uUA/T4xfhIAAboWYnx/w5UvW/b//DqSmAtu3c23OztwyeQU9nwzGr4aZthnSJGmw1rPG4a6H4VzWWdkqlVjyZRBVq1YNJ0+eRJ06deDh4ZHtKrO8eHkKkp07d6J58+ZyMTkvXryApaUlxGIx6tevjyVLlqBcDnWDkpOTkZyczO/HxsYWir5CoRAzZszAxIkT0bt3b2j8P6lcWFgY3NzcULduXXh7e0MozD07QkFPmdWvXx9XrlyRWRJ/6dIl1K9fX6FxJBIJfy+bNWuGx5lzugAYOHAg7OzsMG3aNGYMlWTS0rjl61LvT6tWQM+eXJ9IBEhTOqirA3XqZNT7qlcvo8bXvHncsbq6wMGDzBhilFjiU+KhpaoFgUAAQw1DnO19FmV0ysBAw0DZqpVs8hO1LRAIct1+dpUZADp+/Hie5cPCwkhFRYUOHz4s037u3Dk6cuQIBQYG0oULF6h+/fpUrlw5io2NzXasuXPnEgC5rTBWmaWmplKZMmVoxYoVRET04cMHqlixIjVr1ow+fPhAERER/FaU3L59m0QiEa1cuZKePn1Kc+fOJVVVVXr8+DEv8+eff1K/fv34/Y0bN9KpU6fo+fPn9Pz5c9qxYwfp6OjQzJkzsz1PVqvMIiIi6OHDh7R9+3YCQDdu3KCHDx/Su3df2CqzX4WkJKJ//iGaMoWocWMiTU3ZFV99+mTIpqURbdhAdPcuUXJy1uNdvkwkEHDHHjlSNNfAYCgB3/e+ZL3Gmrbf365sVUoEhb7K7OrVqwVljxUYe/bsgb6+vlwQduYpuFq1asHZ2RnW1tY4cuSI3DSVlOnTp8ussIqNjYWVlVWh6C0SiTBmzBgsX74cI0eOxKVLl/Dy5Uu8fPkSZcuWlZElxcvO5RsXFxccOHAAs2bNwowZM1CpUiWcOHFCJgdRREQE3r17x+9LJBJMnz4dr1+/hkgkgq2tLZYtW4bhw4crdO6tW7fKBMI3btz4/+3eAAb81HUxCoHERODBA25Ky+3/RSTT0oBu3YDMhZ91dbnA53r1gGaZ6iupqORc/ysyEujblzOlhg7lxmUwShgSkmDNnTX488qfSJOkYZ3fOgxwGACRUHkxr6WNfBV3LQoEAkGuq8ykEBEqV66Mtm3b5mnlm6OjI5o3b44lS5bkSRdW3LV4kJAAaGtzr+PjAS0t5epTKiECXrzISHbo58dlc05PB+rX55IhSunenav7JZ3+srMD8jD9K4NEArRpA1y4AFSrxtUWK6wq9gyGkviS+AUeJzxw9sVZAEC3at2wvd126IlZSpufpdCLu379+hUfPnzIMsswADx+/Bhly5aFgUHRzHdev34dL1++zNbjk5n4+HiEhoaiX79+RaAZo7AICwMqV1a2FqWAxERZA6RmzYyl7pkxNwesrTmDSbr6RVoQ9WdYvZozhsRi4PBhZgwxShy3391Gz3964kPsB6irqGOt+1oMrzucrSJTAvkyiP744w88e/YMvr6+WfYPHz4cVatWxc6dOxUaNz4+Hi9fvuT3X79+jYCAABgaGqJcuXKYPn06wsLCsHfvXpnjdu7cCWdn5yxLSkyePBnt2rWDtbU1wsPDMXfuXKioqKBXr14K6QZwHgoW96s8Mi+2q1oV2LCBpZkpUFJTIXzyGMK7vhDe84OKvy8QE4Ok0AjeyFGvUBkqoaGQ1K4LyW/OSHesB4mjM6isFSeTmMs5FEB43x/i6dMhAJC8bC3SbGoArIYdowQRHheGpnubIiU9BZUMKmNvuyOoaWovk06L8XMoVPcyP0FKZcuWpUWLFmXbv3jxYipXrpzC40pLNfy4eXh4EBEXlOzq6ipzTHR0NGloaJCXl1eWY/bo0YMsLCxITU2NypQpQz169KCXL18qpJc0KAuIkYkLZRvbSsI2HFvoBhpSAjSyFLDCW37XBJ9IhJQi0WsW5hMBdARdCZAo/T6xjW2FsjWZS+jch6AWq3xdSuRWyEHVUVFRMDY2zrbfyMgIkZGRCo/bpEkTEFG2/bt375Zr09PTQ2IO5vShQ4cU1oPBKGloIgF1cR/O8EM9+GIgvBEHbj69Il6iEW4BAL5BH35whh+c4Yt6uAsnfEVGNtwomGY5fmGwELPxCLVwHa4A2PQBo4RgfR2IswS+VuL2r88BSAD2GVc++TKILCws8PDhw2z779+/DxMTk3wrVVwJD2eFsJVFWBg3TZZ50ZKKChAcDBSjJN1FTkICIK2w8ulTRqC54MN7qFy7AqG/H4T+vhA+eQxBejp/3O+nR0Hixq30Ej7sheTgmkj/zRlqlSqjkVCIRkV8HdnTQdkKMBgFQrokHSv8FmOxzzzUMKmF/3rfgVgkBqDgQgOGQsTGApaWeZPNl0HUsWNHbNq0Ca1bt0b79u1l+k6ePAlvb2+MHDkyP0MXa7S02MomZVG5MhczNHo0t6+iAmzbxgKrAcAQX+AMP2jH2EPT9P/W4X9ngFGjZAUtLflCpxo1bAHpZ7lhXW4rDjx9Ckybxr25FhbK1obBKBA+xn9E3+N9ceX1FQBAHUsHaGhKoMlyixY6mX4H5kq+lt3HxMSgYcOGCA4Ohr29PR/MHBQUhMDAQFStWhW3bt2Cvr6+okMXS9iy++JB5mX3z56VUmMoJQV49Ihf8i654wthKLcQIXn9NqiPHcbJPXrE5faRZnt2dgZ+yGtV7CACGjQA7tzhslgfPKhsjRiMn+bKqyvoc6wPPiV8gqaqJra02YL+9v2VrVapodCX3evp6cHX1xfLly/HsWPH8PffXAVeW1tbzJ49G1OmTIEWc6UwCpFSMU1GxCU7lFZ69/MDmjQBvn/nRaTO9hBUgU3mJZC1agE3bhSZqgWCQADs2gWMHw/kIZ8Yg1GcSZOkYf71+Vh4YyEIhBqmNXCk6xFUNamqbNUY2ZBnD1F6enqprTXFPETFgxKfmDE+Hrh3T7bae//+wLJlXH90NJfoMFOyw+8O9WDR0QnRMCiZ94TB+EVJTU9FI+9G8Avzw9A6Q7HOfR00VDWUrVapo1A8REZGRmjVqhXatm0Ld3f3Ehk0zWAUOYmJwIQJnAEUFCQbNQ4Ad+9mvNbXB169AsqX5/MCpScA0UWkaqHx9i3w7h3QqPiEcjMY+YWIIBAIoKqiisNdD8PnvQ961VQ87x2j6Mmzh2jDhg04d+4crl+/jpSUFNStWxdt2rRBmzZtULduMQnILCSYh6h48Et7iKKiMrw+YjEwaxbXTgSYmABfvnD7VlYZcT/16nGV3zWy/1X5y8dVpaUBrq7cfdm5ExgwQNkaMRj5Ik2Shtn/zQaBsLT5UmWrw/g/ijy/FQ6qTkpKwuXLl3H+/HmcO3cO7969g7m5Odzd3dG2bVu0aNECOjo6P3UBxQ3pDQ0PZwaRMsluiXlxRPjgHoS+PlC59/9l769f8X2SMmWR9Ow9vy/auwukp89lfLZULDjKywuQ1iEWCn+97N2qnrOgtmIRSFcXST4BoPI2ylaJwVCYD7HvMfBsL9wJuw0A8PN4jOom8pUTGEVPbGwsLC0LySD6kaCgIJw9exbnz5+Hj48PBAIBGjZsiN9//x1t2rSBnZ3dzwxfLJAaREAMAGYQMTJDsMZbVMVTXEBrvtUXznDGXRnJp7CDL+rBD87YjqGQoHTG5Elpiiu4hBYQgtANR/A3WBV7xi9I5TNARw9A8yvwXRc4vR140l3ZWjF4YgEUkUGUmZiYGFy8eBHnzp3DhQsXEBUVhcWLF2PatGkFdQqlwAwihhRtxMER/nzG53rwhRkikQYV6CIWSeCKj87DXPyGe7wBdBdOiIG+cpUvRpggEoGwhwU+wgtDMRxeuR/EYBQnVFKAZjMAl1Xcfnhd4Ohh4JutcvVi/ICSDKIf8ff3BwA4OjoW1imKBDZlVjwo8imz9HRuHur/Acyqs6ZCdd1KCH74ypBIBIl9bSTvPgSyqVDISmXwy2bvlkig3qUNRJcuQGJXDUk3/FkVe8YvBRGh/d8tcfXtZQDAyDrjsLDxcqiL1JWsGeNHFJkyy1ceori4OERHR8PKyopvCw8Px9atW5GcnIyuXbvC0dHxlzeEfoRlqi4+FMp78ekTF/gsDX729+dWeUmnfcuX4YKgra1lAp8FtWtDRSxGUT/Sf9ns3avWAJcuAGIxhEcPQ8uEGUOMXw0BhtQdiIef7sG7gzc62nVUtkKMbCj0TNW9evXC69ev4evrC4CzwGrUqIEPHz5AKBRCJBLhwoULaNKkiaJDF0vYKrPiQaGsMvPx4awKX1/gzRv5fm/vjJVPX78CycnFqqTEL7fKzN8fcHHhVpdt3QoMH65sjRiMPJGclozX0a9hZ5wRF/sl8QuMNI1yOIqhbBR5fuerqtytW7fQtm1bfv+vv/5CeHg4fHx88O3bN9SqVQsLFy7Mz9AMRrZoaXEOGiIFjSEiLn/PgQNcFuTbtzP6vnwBDh3ijCGBAKheHRg0iFu+FRgI9OuXIWtoWKyMoR8p1tNkABATw5XkSEsDunYFhg1TtkYMRp4I/RqKBrsaoOmepohMiOTbmTFUssjXlNnnz59RJtN/31OnTqFhw4aoV68eAKB///7w9PQsGA0ZDEVJSuKMHunUl58flwdIip4eVzMLAOrXBxYu5Ka/HB0B5gEsHIiAESM4w9TaGti+nY/NYjCKM0efHMWQ00MQmxwLQw1DvPjyAqZapspWi1EI5Msg0tfXx8ePHwFweYlu3ryJmTNnZgwqEiExMbFgNGQwciI9HXjyhAugqV6da3v9GmjRQlZOVZVLcujsDLi5ZbQbGwOZPruMQsLbm/PEqahwRVtLSOFnRsnle9p3TLw4EVvubQEANLBqgINdDsJKzyqXIxm/KvkyiFxcXLB582bY2dnhwoUL+P79Ozp06MD3P3/+XMaDxGAUGBER8oHPCQlA797A/v2cjJ0dV9y0WrWMSu8ODlyGaIZySErijNL58zmvXA780hnJGSWC51+eo/vR7gj8FAgA+LPBn5jvNh+qKqpK1oxRmOTLIFq2bBlatmyJLl26AAAmTZqE6v//dZ6eno6jR4/C3d294LRklE4kEm7ZO8DFnVSpwk25/IiODvewlSIUcvE/jOLD6NGcZ64EJGpllHyW3VqGwE+BMNY0xr5O++BekT3PSgP5MogqVqyIZ8+eITg4GHp6eihfvjzfl5iYiI0bN8Le3r6gdGSUBoiA0FDZSu8aGsCNG1y/SMQZPtLAZ2mtL2dnLhmPSunO+lxsSUkB1NS419WqKVcXBiOPrHFfg3RKx6Kmi1BGl812lBYKNTFjSYEtuy9ENm4Ezp/njCBpgVMpampAbCyg/v9kZ8+ecau82HvAU6ynl44dA2bM4GKHHBzyfFixviZGieRp1FN4B3hjWfNlELBg/xJFoS+7l55k6dKlaNWqFWrXro27d7m6TV+/fsXq1avx8uXL/A7NKGmkpQEBAVzWwNGjOW+QlKtXgXPnOGNITY3z+kyYwAXePnuW4V0AuCkzZgz9GkgkgKcn9x4ePqxsbRiMbNkTsAe/bf8NK3xWYNv9bcpWh6FE8jVl9uHDB7i6uuL9+/eoVKkSQkJCEB8fDwAwNDTEtm3b8PbtW6xbt65AlWX8Inz6xCU89PXltnv3gMyrDidPBmz+X9V84ECgSRNu6svePsMbxMgT0txMxQ6hEPjvP2DFCi6QmsEoZiSkJGD0udHYE7gHANDMphnLOF3KyZdBNGXKFMTFxSEgIACmpqYwNZXNydCxY0ecOXOmQBRkFHOSkoAHD7gpEencxsqV3JYZXV3O6HF2lvX6ZErwyShhGBkBS5cqWwsGQ46gyCB0O9oNIZ9DIBQIMc91HmY0mgEVIYtFLM3kyyD6999/8ccff6BatWr48mPcB4AKFSrg/fv3P60co5hBBLx4IRv4/OgRNyV26RLQvDkn16AB8O+/soHPdnYZK8YYJZf//gPevQM8PFjiRUax5FDQIQw8ORDf077DUscSBzofgGt5V2WrxSgG5MsgSkpKgomJSbb9cXFx+VaIUYwgynionT0L9O/P1fP6EXNz2faOHbmNUbqIjAT69AE+fuRiiAYN+ukhw8J+gfpsjF+KcnrlkJqeila2rbCv0z6YaGX/LGOULvJlEFWrVg03btzA8GwKM544cQK1a9f+KcWKIwkJJXh1d2oqhE8eQ+jvB6G/L1T8fZE6dhLSBnH1poSGZaDx9StILIakdl1IfnNGumM9SBydQWWtOMMpQcnXwFAeEgnU+3pA9PEjJHbVkNSuZ74/D15eGa+rVuVq73p4FIyajNJJXEocdNR0AAD2hi643Ps26po7QgghEtj/rRKNQu8v5YN9+/aRUCikpUuXUmhoKAkEArp06RK9ePGC+vbtS0KhkI4fP56foYslMTExBICAGMooL/rrbyb4RMsxmW6gISVAQ05gFwbwuypIpTq4RyKkKF1vthW/bSJWEgGUCDFVx2Ol68M2tnGbhPDbZsI0A4JJUDHQh21Fv3HP75iYmFyf9fnOQ7Ro0SLMmzcPRASJRAKhUAgiglAoxMKFCzFt2rT8DFsskeYxAGIA/HrLvjWRgLq4D2f4IQIW2I++AAA9RCMaBrzcN+jDD87wgzN8UQ934YSvYNWcGTnzG/zhAxeoIg3DsRVeyNpzzGAUKeoxQLthQI0j3L7veODCWqWqxFAGsQDylofopxIzvnv3Dv/88w9evnwJiUQCW1tbdO7cGRUqVMjvkMUSqUEUHv4LJGYkguD5M6j4+/LTX8InjyFITwcApDdojO8Xr/PiqgvngmwqIP03Z1ClyizwmaEYMTHQaFgHwtevkNaxK5L3HfmpYOqwMG6aTCLJaFNRAYKDAVYekZFXHn68j/6nu+N1zCuIhCLMb7QUY377A0IB+/9W2oiNjYWlZREYRKWFYp2p+ssX4O1brpI7wHkITU2Bz59l5SwtuRVfTZoAY8cWuZqMEggR0KsXl3jR2ppLvlkAVew3b+bydwKcMbRtGzB48E8PyygFEBE23t2IyZcmIyU9BdZ61jjU9RDqla2nbNUYSkKR53e+gqoZSiIlhVvmnnnZ+8uX3E/nDx84GYGAK6IZEZGx5L1ePaBsWeXqzih57NrFGUMqKlxm8QIwhgAugFpqEAUHs1VmjLxzKOgQxl0YBwDoaNcRu9rvgoGGQS5HMRgceTKIhEKhwvVdBAIB0tLS8qUUIwtGjAD27AG+f5fv09ICYmIAPT1u/8iRotWNUfp4+jTD07hwIVC/fqGchk2TMRShe/Xu2B24G20qtcFYp7GsLhlDIfJkEM2ZM4d9sAqb+Hjg/v2Mchf373MPHWn2Z7GYM4YMDDK8Ps7OgJMTYGioXN0ZpYukJKBHD+5v8+bA1KnK1ohRSiEi7Ancg541ekIsEkNFqIILfS6w5xUjfxTaWvV8cP36dWrbti1ZWFgQgFyX7l+9epUAyG0REREychs3biRra2tSV1cnJycn8vPzU0gv6bL7vCzbU4hr14iGDSOytycSCuXXC16/niH76hXRs2dEEknB6sBgKMrIkdzn09SU6IfvWkEQH5/xFYiPL/DhGSWEzwmfqe2BtoR5oDFnxyhbHUYxRZHnd7EKuU9ISIC9vT02bdqk0HHPnj1DREQEv2WurXb48GFMnDgRc+fOxYMHD2Bvb49WrVohMjKyoNXPns+fuUzPs2cDr19ntAcGclnoAgO5ZTVWVkDXrlwdsJs3Oe+PFBsbLpiC/fJhKJvWrQFjY2DvXi5LOYNRxPi890HtbbVx5vkZqKuoo5pJNWWrxCgB5Cuoeu/evXmS69+/v0Ljtm7dGq1bt1ZYH1NTU+hnE9C5evVqDB06FAMHDgQAbN26FWfPnsWuXbvw559/KnyuXElJ4VbbSIOe/fyA0NCMflvbjErvzZpx0w3SoqcsYILxK9CuHWfYa2srWxNGKUNCEqy4vQIz/5uJdEpHJcNKONLtCBzMHZStGqMEkC+DaMCAAdn2ZZ67VdQgyi8ODg5ITk5GjRo1MG/ePDRo0AAAkJKSgvv372P69Om8rFAoRPPmzXHnzp1sx0tOTkZycjK/Hxsbm7UgEbfkXU2NW9YOcEVOs6rgbmfHxf1kztFUvTqwbFneL5TBUBZpaVytMunnnBlDjCImKiEK/U/0x4WXFwAAvWv2xtY2W6GjrqNkzRglhXwZRK8zT/v8n/T0dLx58wabN2/Gu3fvsGfPnp9WLjcsLCywdetW/Pbbb0hOTsaOHTvQpEkT+Pn5oU6dOvj8+TPS09NhZmYmc5yZmRlCQkKyHXfJkiXw9PSU74iLA+7dy/D++PpyD4lZs4AFCzgZJyduOsHJSTbwuYCWJDMYSmHePC5B0N69WRv8BYiWFvdbg8HITHxKPO68vwOxSIwNrTdgcO3BLHiaUaDkyyCytrbOsr1ChQpo2rQp2rRpg40bNyocC6QoVapUQZUqVfh9FxcXhIaGYs2aNdi3b1++x50+fTomTpzI78fGxsLKyirrXD4iEfDtW8a+iQlnJLEvKqOkkJoKXL7Mfc4TE5WtDaMUQUS80WNjYIPDXQ/DUscSNc1qKlkzRkmkUIKq27Zti8OHDxfG0Lni5OSEly9fAgCMjY2hoqKCT58+ych8+vQJ5jkEg6qrq0NXV1dm47G2Brp3B1avBnx8OK/Rxo2yAzBjiFGSUFUFbtwAjh7lPvsMRhHwKf4TWv3VCudfnOfbWlVsxYwhRqFRKJmqQ0NDZWJwipKAgABYWFgAANTU1FC3bl1cuXIFHTt2BABIJBJcuXIFY8aMUXzwZ89Y2lxG6URNjVsByWAUAVdeXUGfY33wKeETnn95jhdjX0BVRVXZajFKOPkyiG7cuJFle3R0NG7cuIH169fzBogixMfH894dgItVCggIgKGhIcqVK4fp06cjLCyMX+W2du1a2NjYoHr16vj+/Tt27NiB//77D//++y8/xsSJE+Hh4YHffvsNTk5OWLt2LRISEvhVZwrBlhgzShNr1gBRUYCnJ+clYjAKmXRJOuZfn48FNxaAQKhuUh1Huh1hxhCjSMiXQdSkSZMsg9mICCoqKujWrRs2bNig8Lj37t2Dm5sbvy+N4/Hw8MDu3bsRERGBd+/e8f0pKSmYNGkSwsLCoKmpiVq1auHy5csyY/To0QNRUVGYM2cOPn78CAcHB1y4cEEu0JrBYGTC359LCZGWxhUOZt4hRiETHheOPsf64NqbawCAIbWHYF3rddBU1VSuYoxSQ76q3V+/fl1+IIEABgYGsLa2Ln4V4X+SYl3tnsEoaGJjgdq1gVevOEPoyBEWF8coVD7Gf0StLbUQlRgFbTVtbGu7Db1r9la2WowSQKFXu3d1dc2XYgwGo5hDBAwfzhlD1tbA9u3MGGIUOuba5mhXuR3uR9zHkW5HUNmIxWoyip58rTJ7/fo1Tp8+nW3/6dOn8ebNm/zqxGAwlIW3N3DoEKCiAhw8yPJnMQqN9zHvEZUQxe9v+H0DfIf4MmOIoTTyZRBNnjwZ69evz7Z/06ZNhVMWg8FgFB5PnwLS1ZcLFwL16ytXH0aJ5ezzs3DY5oB+x/tBQhIAgKaqJsQisZI1Y5Rm8mUQ3blzBy1atMi2v1mzZrh582a+lWIwGEVMUhLQowf3t3lzLqCawShgUtNTMeXfKWh7sC2+Jn1FVGIUviV9y/1ABqMIyFcM0bdv36Cjk339GG1tbXz58iXfSjEYjCJm0iTg8WPA1BTYtw8QFkrOVkYp5m30W/T4uwf8wvwAAGOdxmJFixVQF6krWTMGgyNf//XKlSuH27dvZ9t/8+ZNlM2qzAWDwSh+HDsGbNnCvd67l+XbYhQ4J0JOwGGbA/zC/KCnrod/uv+D9a3XM2OIUazIl0HUq1cvHDx4EOvXr4dEIuHb09PTsW7dOhw+fBi9e7MlkwxGseftW2DwYO711KlAq1bK1YdR4khJT8GUS1MQ/T0aTmWc8HD4Q3Su2lnZajEYcuQrD1FycjLatGmD//77DyYmJnyB1WfPniEqKgpNmjTB+fPnoa5eMqx/loeIUWJp1gz47z/AyQm4dYtlpGYUCg8iHuDg44NY1GwR1FTUlK0OoxShyPM7XwYRwNUE27NnD44dO4bQ0FAAgK2tLbp06YL+/ftDWIJiEJhBxCixPHoEDBsGHDgAVKigbG0YJYS/g//Gt6RvGFp3qLJVYZRyisQgKk0wg4hRoiFiyRcZBcL3tO+YdHESNt/bDDUVNdwfdh81TGsoWy1GKabQM1VLSU5OxoMHDxAZGYkGDRrA2Nj4Z4ZjMBhFQWQkFzvk6MjtM2OIUQC8+PIC3f/ujoCPAQCAifUmoopRFeUqxWAoQL7ntdavXw8LCws0aNAAnTt3xqNHjwAAnz9/hrGxMXbt2lVgSjIYjAJCIgEGDABcXIA9e5StDaOEcPDxQdTxqoOAjwEw1jTG+T7nsaT5ElalnvFLkS+DyNvbGxMmTIC7uzt27dqFzLNuxsbGaNq0KQ4dOlRgSjIYjALi+3dAWxsQibgq9gzGT0BEGHV2FHof6434lHg0tm6MgOEBcK/ormzVGAyFyZdBtGrVKnTo0AEHDhxAu3bt5Prr1q2LJ0+e/LRyDAajgNHUBA4fBh48AGrWVLY2jF8cgUCAcnrlIIAAsxrNwpX+V1BGt4yy1WIw8kW+YohevnyJcePGZdtvaGjIMlUzGMWJ5GRATY2LFxIIgKpVla0R4xcm5nsM9MR6AICpDaaimU0zOJZxVLJWDMbPkS8Pkb6+Pj5//pxtf3BwMMxZtlsGo3hAxMUNdesGfGN1oxj5JyElAQNPDkSDXQ2QmJoIABAKhMwYYpQI8mUQ/f777/Dy8kJ0dLRc35MnT7B9+3a0b9/+Z3VjMBgFgbc3cOgQcOIE8Py5srVh/KI8iXwCpx1O2B2wG08/P8WVV1eUrRKDUaDkKw9ReHg4nJ2dQURo164dvLy80LdvX6Snp+Off/6BhYUF7t69W2KW4bM8RIxflqdPgd9+AxITgSVLgD//VLZGjF8MIsKuh7sw9vxYJKUlwULbAge6HECT8k2UrRqDkStFkpgxMjISM2bMwLFjx3hPkY6ODrp06YKlS5fC1NQ0P8MWS5hBxPglSUoC6tXjslE3bw5cvMiq2DMUIi45DiPPjsT+x/sBAC1tW2Jfp30w1So5/98ZJZsiz1QdFRUFiUQCExOTElWyQwoziBi/JKNHA5s3A6amQGAgq2LPUJhBJwfBO8AbKgIVLHBbgGkNp0EoKHn/4xkllyLLVC3FxMSkIIZhMBgFxbFjnDEEAHv3MmOIkS8WuC1AwMcArG+9Hg3LNVS2OgxGoZIng2j+/PkKDywQCDB79myFj2MwGD/J27fA4MHc6ylTgFatlKsP45chNjkWJ0JOoL99fwBAGd0yuD/sPgSsvAujFJCnKbP8TIMJBAKkp6fnS6niBpsyY/wypKUBrq6Ajw/g5ATcvMnlH2IwcuFBxAN0P9odod9Ccaz7MXSq2knZKjEYP02BT5lJJJICUYzBYBQy8+ZxxpCuLnDwIDOGGLlCRNjkvwmT/p2ElPQUlNMrBwsdC2WrxWAUOQUSQ8RgMIoBV64Aixdzr728gAoVlKsPo9gT/T0ag08NxrGnxwAAHap0gHcHbxhoGChZMwaj6PkpgyghIQHXr1/H27dvAQDW1tZwdXWFlpZWgSjHYDDyCBEweTL3d8gQoEcPZWvEKObcDbuLHn/3wJvoN1AVqmJFixUY5zyOxQsxSi35Nog2bNiAWbNmIT4+XqbavY6ODhYtWoQxY8YUiIIMBiMPCATA+fOApyewapWytWH8AnyI/YA30W9go2+Dw10Ps/IbjFJPvvIQ7d27FwMGDED9+vUxbtw4VP1/ocinT59iw4YNuHPnDnbv3o1+/foVuMLKgAVVMxiMkgARyXiAdgfsRke7jtAX6ytPKQajECn0xIwODg7Q19fHlStXoKKiItOXnp6OZs2aITo6GgEBAYoOXSxhBhGj2HLvHvDqFdC9u7I1YRRzfN77YNz5cTjV6xQsdSyVrQ6DUSQo8vzOV8rRZ8+eoVu3bnLGEACoqKigW7duePbsWX6GZjAYeSU2losV6tED2L5d2dowiikSkmD57eVo7N0Y9yPuY8aVGcpWicEoluTLINLT08ObN2+y7X/z5g3zpDAYhY2mJtCrF2BjA3TtqmxtGMWQqIQotD3QFtMuT0M6paNnjZ5Y33q9stViMIol+TKI2rRpgw0bNuDQoUNyfYcPH8bGjRvRrl27n1aOwWDkgEgELFwIPH4MGLBl0gxZbry9AYdtDjj/8jzEIjG82nrhQOcD0FVnP1YZjKzIVwxRVFQUXF1d8ezZM5ibm6NSpUoAgBcvXuDjx4+ws7PD9evXYWxsXOAKKwMWQ8QoVoSFASYmLOkiI1vOPj+L9ofaQ0IS2Bnb4UjXI6hpVlPZajEYRU6hxxCZmJjgwYMHWL16NWrWrIlPnz7h06dPqFmzJtasWYP79+/nyxi6ceMG2rVrB0tLSwgEApw4cSJH+WPHjqFFixYwMTGBrq4u6tevj4sXL8rIzJs3DwKBQGazs7NTWDcGo1iQlAT8/jvg4gK8fq1sbRjFlCblm6CKURX0t+8P/6H+zBhiMPJAvvMQicVijB8/HuPHjy8wZRISEmBvb49Bgwahc+fOucrfuHEDLVq0wOLFi6Gvrw9vb2+0a9cOfn5+qF27Ni9XvXp1XL58md8XiViCbsYvyuTJwKNHgKkpoKGhbG0YxYgHEQ/gYO4AoUAILTUt+Az2YcvpGQwFKFaWQevWrdG6des8y69du1Zmf/HixTh58iROnz4tYxCJRCKYm5sXlJoMhnI4fhzYvJl7vXcvwD7TDADpknTMvz4fC24swNLmSzG1wVQAYMYQg6Eg+TaIbt26hV27duHVq1f49u0bfgxFEggECAwM/GkFFUEikSAuLg6GhoYy7S9evIClpSXEYjHq16+PJUuWoFy5ctmOk5ycjOTkZH4/Nja20HRmMPLE27fAoEHc66lTgVatlKsPo1gQHheOPsf64NqbawCAV99eySVfZDAYeSNfBtHq1asxZcoUiMViVKlSRc4AURYrV65EfHw8umdKUufs7Izdu3ejSpUqiIiIgKenJxo1aoSgoCDo6OhkOc6SJUvg6elZVGozGDmTlgb07g1ERwNOTtzKMkap59/Qf9H3WF9EJUZBS1ULW9tuRd9afZWtFoPxy5KvVWYWFhaoVKkSTp8+DT09vcLQCwKBAMePH0fHjh3zJH/gwAEMHToUJ0+eRPPmzbOVi46OhrW1NVavXo3BgwdnKZOVh8jKyoqtMmMoh1mzgEWLAF1d4OFDVsW+lJMmScPcq3Ox5NYSEAi1zGrhSNcjqGJcRdmqMRjFDkVWmeXLQ5SYmIg+ffr8r737Dovi3OIA/FsQFlCqVBURUVFUxBII9oJiR2MUrNhLLLGXm0RssfeKHU1U7JrYFcWKHcSCRBRsAbFQBKm75/6xl7mutGUpSznv8+zj7jffzJzZ2XUPM18ptGQor3x9fTF8+HAcPHgwx2QIAAwMDFCrVi2EhYVlW0csFkMsFhd0mIzl3cWLwMKFsudbtnAyxPD0w1Msu7EMBMLoxqOx0nUltDW4gT1j+aVUQtSmTRs8fPiwoGNRyr59+zB06FD4+vqiS5cuudZPSEjA8+fPS83Es6wUi44G+vcHiIARI2RTdLAyr55pPazvvB76Yn241+PPBGMFRalxiNatWwc/Pz8sX74cnz59KrBgEhISEBQUJEwKGx4ejqCgILx69QoAMGvWLAwaNEiov3fvXgwaNAgrVqyAk5MToqKiEBUVhbi4OKHO1KlTcfnyZURERODGjRvo2bMn1NXV0bdv3wKLm7ECJ5UCgwcDUVGAnR3wTY9KVnakSdIw68Is3I+8L5SNbDySkyHGChopadWqVaSurk5qamqko6NDurq6cg89Pb08b/PSpUsEINPD09OTiIg8PT2pVatWQv1WrVrlWJ+IyN3dnSwsLEhTU5MqV65M7u7uFBYWlqe44uLiCADFxcXl+ZgYU8ry5UQAkZYWUXCwqqNhKvIy9iU5b3MmzAHVWFuDktOSVR0SYyVKXn6/lWpUPXv2bPz++++oXLkymjRpkm1bop07dyqVpBU3PHUHK1Lp6cD33wP37gHe3sCoUaqOiKnAX6F/YfCxwYhJjoG+WB873Hbghzq5D1jLGPu/vPx+K5UQmZqa4vvvv8exY8egpqbUXbcShRMiVuSSkoA9e4BhwwAeU6ZMSZWkYsb5GVh9azUA4LtK32H/j/thbWit2sAYK4EKvZdZamoqunTpUiaSIcZUQlsbGD5c1VGwIvbxy0d02tMJd/69AwCY/P1kLHJZBE11nsiXscKmVEbTtWtXXL16taBjYaxs8/GRDbookag6EqYihtqGsoeWIf7y+AsrXFdwMsRYEVHqltmzZ8/g7u6O77//HsOGDUPVqlWhrq6eqV5xGcE6v/iWGSt0b98CtWoBX77I5injYSHKjOT0ZBCRMJZQdGI0ktOTUVU/++mFGGOKKfQ2RF/fKstpzhxJKflLlxMiViR275ZN4Hr4MMC3o8uEZx+fwf2QOxpZNMK27ttUHQ5jpU6htyGaPXs2Tx7IWEEbNEh2ZYi/W2WC7yNfjPx7JD6nfsbr+NeY/3k+LHQtVB0WY2WWUleIyhq+QsQKzfXrgK0tYGys6khYEUlKS8LEMxOx5f4WAEBLq5bY+8NeVNarrOLIGCt9Cv0KEWOsALx8CXTtCujoAJcuydoQsVLt6Yen6HOwDx5GP4QIIvzS4hd4tfZCOTX+r5gxVeNvIWOqkJ4um6csNlaWCFWrpuqIWCFLk6Sh055OiIiNgGl5U/zZ80+0t2mv6rAYY//DLTcZU4W5c2W3y/T0gH37AE3uWl3aaahrYGPnjWhr3RZBo4I4GWKsmOE2RArgNkSsQF28CLi4yGax9/XlWexLscfRj/Hv53/lkh8i4k4pjBWRvPx+8xUixopSdLTsVhmRbCRqToZKJSLCzsCd+G7rd+hzqA8iYiOEZZwMMVY8cRsixoqKVAoMHgxERQF16gBr1qg6IlYIElITMObkGPwZ/CcAoIVVC+ho6Kg4KsZYbgolIXr16hUMDAz49hJjX1u1Cjh9GtDSAvbvl/UuY6VK8Ltg9DnYB6EfQ6EuUsf8NvMxo/kMqIn4YjxjxV2hfEurVauGSpUqYebMmfj06VNh7IKxkuXOHWDWLNnz1auB+vVVGg4reFvubYHjVkeEfgxFZd3K8B/sj1ktZnEyxFgJUSjf1EGDBqFTp07w8fFB9erVC2MXjJUc8fGAhweQlgb06gWMHKnqiFgheBD1ACmSFHSu2RlBo4PQvGpzVYfEGMuDQrll5uPjIzx/8uRJYeyCsZKBCBg1CnjxArCyArZu5ak5SpGve4ytcF2BRhaNMKThEL4qxFgJVOjfWjs7u8LeBWPFV0oKkJAAqKvLxhsyNFR1RKwAEBHW316Pzns7QyKVTWKtVU4LwxoN42SIsRKKe5kxVpi0tIC//gLu3gW++07V0bACEJsci+F/DcfhkMMAZJO09rfvr+KoGGP5pdSfMkSEzZs3w9HREcbGxlBXV8/0KFeOcy1Whkkk/38uEnEyVErceXsHjTY3wuGQw9BQ08Bq19XoV7+fqsNijBUApbKW6dOnY+XKlXBwcMCAAQNgyLcBGJP388+yxtQbNgC6uqqOhuUTEWHNrTWYfn460qRpsDawxv4f9+O7ypzoMlZaKDV1h6mpKVq3bo0DBw4URkzFDk/dwfLk2TPZwIsSCXDhAtCunaojYvk0+exkrLq5CgDQq04vbOu+DQZaBqoNijGWq0KfuiMpKQkuLi5KBcdYqVezJnDpErB0KSdDpcQQhyEw0DLA+k7rcbD3QU6GGCuFlLpC1KNHD5iYmGDr1q2FEVOxw1eIGCtbpCTFvX/vyd0Si0uOg76WvgqjYozlVaFfIdq4cSNu3ryJhQsX4uPHj0oFyVip4+0NhISoOgqWTx++fEC3fd3gvN0ZAa8DhHJOhhgr3ZS6QqSrqwupVIrk5GQAgJaWFtTV1eU3LBIhLi6uYKJUMb5CxHJ18SLg4gJoawNPnsgGYWQlztWXV9H3cF+8/fwWWuW0sKP7DvSt31fVYTHGlJSX32+lepn16tVLGJ2VsTLv/XtgwADZqNT9+nEyVAJJSYrF1xZj9qXZkJAEthVtcaD3Adib2as6NMZYEVEqIfp6ag7GyjSpFPD0BCIjZT3L1qxRdUQsj94lvMPAowNx/sV5AMBA+4HY2GUjKmhWUHFkjLGixKMnMpYfq1cDp0/LRqTevx/Q0VF1RCyPjj49ivMvzkO7nDY2dtmIwQ6DVR0SY0wF8pUQXblyBS9evEBMTAy+bYokEokwadKkfAXHWLF25w4wc6bs+apVQP36qo2HKWVU41F4/uk5hjQcAjsTnnuRsbJKqUbVQUFBcHd3R1hYWKZESNiwSATJ19MXlGDcqJplEh8PNGwom8W+Vy/g4EGexb6EiPwciV8v/orVHVdDV8yjiDNWmhV6o+rhw4cjOjoa3t7ecHJygr4+d0dlZQgRMHq0LBmysgK2buVkqIQ4//w8BhwdgOjEaBAIO9x2qDokxlgxoVRC9PjxY8ybNw8jRowo6HgYK/58fIB9+wB1ddm/PJdfsZcuTccc/zlYeHUhCAR7M3vMaDZD1WExxooRpQZmrFmzZqF0u79y5Qq6deuGSpUqQSQS4dixY7mu4+/vj0aNGkEsFqNGjRpZ9oDbsGEDqlWrBi0tLTg5OeH27dsFHjsrI0JCgHHjZM/nzwecnVUbD8vVm/g3aLurLX6/+jsIhFGNR+HmsJuwNbZVdWiMsWJEqYRozpw52LBhA96+fVugwSQmJqJBgwbYsGGDQvXDw8PRpUsXtGnTBkFBQZg4cSKGDx+Os2fPCnX279+PyZMnw8vLC/fv30eDBg3g6uqK6OjoAo2dlQFJSYCHB/Dli2wQxhl8haG4C3gdAAdvB1x9dRW6mrrw7eUL767e0NbQVnVojLFiRqlG1QCwd+9ejBw5Eu3atUOVKlWyHKl6TT7GZBGJRDh69Ch69OiRbZ0ZM2bg5MmTePTokVDm4eGB2NhYnDlzBgDg5OSE7777DuvXrwcASKVSWFpaYvz48ZiZ0UMoF9yomgEAUlNlvcr27gWCggBzc1VHxHLxLuEdHDY7oJJuJez/cT9qGNVQdUiMsSJU6I2qL1++jDFjxuDLly/4+++/s6yT34RIEQEBAXBxcZErc3V1xcSJEwEAqampuHfvHmbNmiUsV1NTg4uLCwICApCdlJQUpKSkCK/j4+MLNnBWMmlqAitXAr/+ChgZqToalo1PSZ9gpC07P2YVzHBx0EVUN6wOcTmxiiNjjBVnSt0yGz9+PPT09HD27FnExsZCKpVmehRFl/uoqCiYmZnJlZmZmSE+Ph5JSUn48OEDJBJJlnWioqKy3e6iRYugr68vPCwtLQslflZCfPwIpKf//zUnQ8XWX6F/ocbaGtj3cJ9QVsekDidDjLFcKZUQhYWFYdq0aWjfvn2pvIU0a9YsxMXFCY/Xr1+rOiSmKunpgJsb0Lo1wJ+DYitVkorJZyfDzdcNMckx2B64Pdsx0hhjLCtK3TKrW7dusZjJ3tzcHO/evZMre/fuHfT09KCtrQ11dXWoq6tnWcc8h/YfYrEYYjH/Rckgm7n+4UPZ87Q01cbCshQeEw73Q+648+8dAMCk7ydhsctinoCaMZYnSl0hWr58OTZv3qzy7uvOzs7w8/OTKzt//jyc/9cVWlNTE40bN5arI5VK4efnJ9RhLEf29kBgoGwk6urVVR0N+8aRkCNouLkh7vx7B4ZahjjucRwrXVdCU11T1aExxkoYpa4QrVixArq6unB2doadnR2qVq2aZS+z48eP52m7CQkJCAsLE16Hh4cjKCgIRkZGqFq1KmbNmoW3b99i9+7dAIDRo0dj/fr1mD59OoYOHYqLFy/iwIEDOHnypLCNyZMnw9PTE02aNIGjoyNWr16NxMREDBkyRJlDZ2VR9eqcDBVDT94/Qa8DvQAAzlWc4fujL6rqV1VxVIyxkkqphCg4OBgikQhVq1ZFQkICnjx5kqmOMper7969izZt2givJ0+eDADw9PSEj48PIiMj8erVK2G5tbU1Tp48iUmTJmHNmjWoUqUKtm3bBldXV6GOu7s73r9/j9mzZyMqKgoODg44c+ZMpobWjAmkUmDYMKBvX6BDB1VHw7JhZ2KHGc1mgIiwoO0CaKhrqDokxlgJpvQ4RGUJj0NUxqxcCUyZAmhrAxERgKmpqiNi/3Pw8UE4VnaElYEVAICIuK0QYyxbefn9VqoNEWOl1t27ssEXAVlixMlQsZCUloTRJ0ajz6E+8DjsgTSJrIE7J0OMsYKi1C2zr29b5aRqVb6fz0qQ+HjZ1BxpaUCvXsCoUaqOiAEI/RCKPof6IPhdMEQQwcXahRMhxliBUyohqlatmkL/IRXF4IyMFQgiYPRo4PlzwMoK2LoV4B9dlfsz+E+MPjEaiWmJMC1vij97/on2Nu1VHRZjrBRSKiHasWNHpoRIIpEgIiICu3fvhqmpKcaOHVsgATJWJHx8gH37AHV12b+GhqqOqEz7kvYF40+Nx46gHQCANtXaYM8Pe2Cha6HiyBhjpZVSCdHgwYOzXTZjxgw4OTkVi4EbGVNISAgwbpzs+fz5AI9RpXJqIjXci7wHEUTwauWFX1v+CnU19dxXZIwxJRVKL7MVK1Zg/fr1CA8PL+hNqwT3MivFkpMBJycgOBhwcQHOngXUuK+BKmT8V5Rx9fmfj//gbfxbtLFuk9NqjDGWLZX3MpNKpTlOnspYsTF1qiwZMjUF/viDkyEVSUhNgOcxT8y7PE8oq1WxFidDjLEio9Qts+zEx8fjypUrWLZsGRo2bFiQm2as4B09CmzYIHu+ezeQw/x2rPAEvwuG+yF3PP3wFBpqGhjWaBiq6FVRdViMsTJGqYRITU0t215mRISqVati48aN+QqMsUIVHQ0MHSp7Pm0a8NXo5qxoEBG23t+Kn8/8jOT0ZFTWrYx9vfZxMsQYUwmlEqLZs2dnSohEIhEMDQ1hY2ODDh06oFy5Ar34xFjBMjEBFi0CfH2BBQtUHU2ZE58Sj1EnRsH3kS8AoFONTtjdczeMdYxVHBljrKziqTsUwI2qSzGplNsNFTGJVIJGWxoh+F0w1EXqWNRuEaY0nQI1EZ8HxljBUnmjasaKraAgIDb2/685GSpy6mrqGPfdOFTVr4qrQ65iWrNpnAwxxlROoStEQzPaWuRlwyIRtm/frlRQxQ1fISol3r8HGjQAxGLgzBnA1lbVEZUZcclxePv5LexM7ADI2g8lpCZAV6yr4sgYY6VZXn6/FWroc/HixTzPHcRzDbFi5907QEtL9rC0VHU0Zcadt3fgfsgdUpIicFQgDLUNIRKJOBlijBUrCiVEERERhRwGY0WgXj0gMBD48AHQ0VF1NKUeEWHtrbWYdn4a0qRpsDawRmRCJAy1eVoUxljxw13BWOmXng5k9HrU15c9WKH6lPQJQ48PxfHQ4wCAXnV6YVv3bTDQMlBtYIwxlo18JUTh4eE4ffo0Xr58CQCwsrJCp06dYG1tXSDBMZZv8fFAs2bAmDGyB9/KLXQ339yE+yF3vIp7BU11TazssBI/ffcT30ZnjBVrSidEU6ZMwZo1ayCVSuXK1dTUMHHiRCxfvjzfwTGWL0SyJOjRI2DJEmDgQECX260UtuU3luNV3CvYGNrgQO8DaGTRSNUhMcZYrpTq67pixQqsWrUKP/zwAwICAhAbG4vY2FgEBATgxx9/xKpVq7Bq1aqCjpWxvNm1C9i7F1BXB/bt42SoiGzptgXjHcfj/qj7nAwxxkoMpQZmrF27NmrXro1jx45lubxHjx54+vQpnj59mt/4igXudl8CPX0KNG4MfPkCLFwIzJql6ohKrWuvruFoyFEs77Ccb4sxxoqVQh+YMSIiAq45zP3k6urKPdOY6iQnA+7usmSoXTtgxgxVR1QqSUmKRVcXobVPa6y8uRL7Hu1TdUiMMaY0pdoQmZqa4sGDB9kuf/DgAUxMTJQOirF8mToVCA6WzVf2xx88GnUhiE6MxsCjA3Hu+TkAwAD7Aehu213FUTHGmPKU+qXo3bs3tm3bhsWLFyMxMVEoT0xMxJIlS7Bt2za4u7sXWJCMKezoUWDDBtnz3bsBCwvVxlMK+Uf4w8HbAeeen4N2OW3s6L4Du3vsRgXNCqoOjTHGlKZUG6IvX76gW7duuHTpEsqVK4dKlSoBAP7991+kp6ejTZs2+Pvvv6FTSga/4zZEJcSrV4CDAxATA0ybBixdquqISp21t9Zi0tlJkJIUdiZ2OPDjAdQ1ravqsBhjLEsFPnXHt3R0dODn54fjx4/LjUPUsWNHdO7cGd26dePGlaxopacD/frJkqHvvgMWLFB1RKVSPdN6ICIMdRiKdZ3XQUejdPzRwxhjSl0hKmv4ClEJ8NtvsiRIT082PUf16qqOqNT4+OUjKupUFF4/fPcQ9c3qqzAixhhTTKH3MgsPD8fff/+d7fK///6be5mxohMRASxaJHu+ZQsnQwUkXZqOXy/+Cpu1Nnj28ZlQzskQY6w0UuqW2dSpUxEfH49u3bpluXzDhg0wMDCAr69vvoJjTCHVqgFnzgAXLsi627N8exv/Fv2O9MOVl1cAAEefHsX0ZtNVHBVjjBUepa4QBQQEoH379tkub9euHa5evap0UIzlmYsLsHixqqMoFc6EnYHDZgdceXkFupq62NdrHydDjLFST6mEKCYmBro5TINQoUIFfPz4UemgGFOIry/w/Lmqoyg10iRpmHlhJjrt6YQPXz6goXlD3Bt5Dx71PFQdGmOMFTqlEqKqVavi+vXr2S6/evUqqlSponRQjOXq7l1g0CCgYUMgLEzV0ZQK3ne9seT6EgDA2O/G4sawG6hZsaaKo2KMsaKhVELUt29f7Nu3D2vXrpWb7V4ikWDNmjXYv38/+vXrV2BBMpaJmRng5AR06ADY2Kg6mlJhdJPR6FijIw71PoT1nddDq5yWqkNijLEio1S3+5SUFHTp0gUXL16EiYkJbG1tAQChoaF4//49WrdujdOnT0MsFhd4wKrA3e6LqfR02bxlFXiEZGWkSlLhfdcbY5qMgYa6BgCAiHgMMcZYqVHo3e7FYjHOnTuH7du3w9HRER8+fMCHDx/g6OiIHTt24MKFC/lKhjZs2IBq1apBS0sLTk5OuH37drZ1W7duDZFIlOnRpUsXoc7gwYMzLe/YsaPS8TEVioz8//Ny5TgZUlJEbARa7GyBn8/8jF8v/iqUczLEGCurlOp2DwBqamoYMmQIhgwZUpDxYP/+/Zg8eTK8vb3h5OSE1atXw9XVFaGhoTA1Nc1U/8iRI0hNTRVef/z4EQ0aNEDv3r3l6nXs2BE7d+4UXpeWq1dlytOnQJMmwPDhsmk5NDVVHVGJdDTkKIb+NRSxybEw0DJAU8umqg6JMcZUrthNA75y5UqMGDECQ4YMgZ2dHby9vaGjo4MdO3ZkWd/IyAjm5ubC4/z589DR0cmUEInFYrl6hoaGRXE4rKAkJ8vGGEpMBB49AtTVVR1RiZOSnoIJpyfghwM/IDY5Ft9X+R5Bo4LgVttN1aExxpjKFauEKDU1Fffu3YOLi4tQpqamBhcXFwQEBCi0je3bt8PDwwPly5eXK/f394epqSlsbW0xZsyYHIcFSElJQXx8vNyDqdi0aUBwMGBiAvzxBydEefQi5gWa7WiGdbfXAQCmN52OK4OvwMrASsWRMcZY8VCsEqIPHz5AIpHAzMxMrtzMzAxRUVG5rn/79m08evQIw4cPlyvv2LEjdu/eDT8/PyxZsgSXL19Gp06dIJFIstzOokWLoK+vLzwsLS2VPyiWf8eOAevXy57v3g1YWKg0nJIoTZKGpx+eoqJ2RZzsdxJL2i8RGlIzxhjLRxui4mj79u2oX78+HB0d5co9PP4/sFz9+vVhb28PGxsb+Pv7o127dpm2M2vWLEyePFl4HR8fz0mRqrx6BQwdKns+dSrAjeEVJiUp1ESyv3lsjW1xuM9h1DWtiyp6PEYYY4x9q1hdITI2Noa6ujrevXsnV/7u3TuYm5vnuG5iYiJ8fX0xbNiwXPdTvXp1GBsbIyybAf3EYjH09PTkHkwF0tOBfv2AmBjgu++A339XdUQlRuiHUDTZ0gT+Ef5CmWsNV06GGGMsG8UqIdLU1ETjxo3h5+cnlEmlUvj5+cHZ2TnHdQ8ePIiUlBQMGDAg1/28efMGHz9+hAXfeine5s4Frl8H9PRk03RwrzKF7Aneg8ZbGiMwKhCTzk6CEkONMcZYmZPnhCgpKQmPHz/OdBXna+Hh4di9e7dSAU2ePBlbt27Frl27EBISgjFjxiAxMVHo3j9o0CDMmjUr03rbt29Hjx49ULFiRbnyhIQETJs2DTdv3kRERAT8/Pzg5uaGGjVqwNXVVakYWRG4dOn/V4S2bAGqV1dtPCXAl7QvGP7XcAw4OgCJaYloXa01TvU7xWMLMcaYAvKUEM2dOxcVK1aEvb09KlWqhDZt2uDRo0eZ6t24cUPp8Ync3d2xfPlyzJ49Gw4ODggKCsKZM2eEhtavXr1C5NeD80E2Qva1a9eyvF2mrq6O4OBgdO/eHbVq1cKwYcPQuHFjXL16lcciKq7evwf69weIgGHDZN3tWY6evH8Cx62O2B64HSKI4NXKCxcGXoCFLl8FZYwxRSg8dceBAwfg4eGBtm3bokePHoiMjMT27dsRFxeH7du3y81dtmfPHgwaNCjbXlwlDU/dUYSkUqBrV+D0aaBOHeDOHeCbIRSYvH8+/oOGmxviS9oXmFcwx54f9qCtdVtVh8UYYyqXl99vhXuZrVq1Cm3btsWFCxeEsqlTp6Jfv34YOHAg/v33X0ydOlX5qBkDgJAQ4PJlQCwG9u/nZEgBNY1qorttd3z48gF/9vwTZhXMcl+JMcaYHIUToqdPn2LhwoVyZYaGhjh16hTGjRuHGTNmICoqCsuXLy/wIFkZUrcucO8e8PAhUL++qqMpth6+e4gqelVgqG0IkUiEHd13QFxOLHSzZ4wxljcKJ0QaGhpIT0/PVC4SibBhwwaYmppi7ty5eP/+Pdq0aVOgQbIypnZt2YNlQkTYdn8bJpyZgE41OuFwn8MQiUTQ1tBWdWiMMVaiKfznZJ06dXDp0qVsl3t5eWHdunXYs2cPZs6cWSDBsTKCCJgyBbhyRdWRFGvxKfHod6QfRp4YieT0ZCSnJyMpPUnVYTHGWKmgcELUtWtXnDp1Cq9fv862ztixY7Fnzx7ExMQUSHCsjDhwAFi5EmjfHvj3X1VHUywFRgai8ZbG8H3kC3WROpa4LMGJfiego6Gj6tAYY6xUUPiW2bBhw2BnZwepVJpjPXd3d1SvXh1PnjzJd3CsjOjSBfD0BGxtgUqVVB1NsUJE2HR3EyafnYwUSQos9Szh+6Mvmlo2VXVojDFWqijc7b4s4273RYQI4EEE5cSnxKPexnp4Hf8a3Wp1g08PHxhpG6k6LMYYKxEKpdv911JSUnD9+nWEhIQgPj4eurq6sLOzQ7NmzXiwQ6a4ixeB1q0Btf/dueVkKBM9sR58f/TFrTe3MPH7iTzqNGOMFZI8JUREhOXLl2PJkiWIiYmRmyNJJBLB0NAQM2bMwNSpU/k/bpazY8eAnj1lt8uOHwfU1VUdUbFARFh7ay30tfQx2GEwAKCpZVO+RcYYY4UsTwlR//794evri5o1a2L8+PFo0KABdHV18fnzZzx48AB79+7FzJkzERQUhD179hRWzKyke/0aGDpU9rxOHU6G/icmKQZD/xqKY0+PQbucNlpXa41qBtVUHRZjjJUJCrch+uOPP+Dp6YmpU6di0aJFUM/iR0wqleI///kPli1bhl27dik083xJwG2IClB6OtCmDXDtGvDdd7J/eRZ73HxzEx6HPPAy7iU01TWxosMKjP1uLF9pZYyxfMjL77fCCVHLli2hrq6e41hEGdq2bYv09HRcKSXjynBCVIBmzwbmzwf09IDAwDI/i72UpFgZsBKz/GYhXZoOG0Mb7P9xPxpXaqzq0BhjrMTLy++3wuMQBQcHo1evXgrV/eGHHxAcHKzopllZcekSsGCB7PnmzZwMkRQ99/fEtPPTkC5Nh3tdd9wfdZ+TIcYYUwGFE6K0tDRoaWkpVFcsFmc5zQcrw96/B/r3l3WtHzYM8PBQdUQqpyZSQwOzBhCri7G562bs67UPemK+AskYY6qgcEJUo0YNhW+BXb16FdXL+F//7CtSKTB4MBAZKWtEvWaNqiNSGSlJ8eHLB+G1VysvBI0OwsjGI7m9EGOMqZDCCdGPP/6Iffv24eTJkznWO3nyJPbt24fevXvnOzhWSqxZA5w6BYjFwP79QPnyqo5IJaITo9FpTye4/umKlPQUAIC6mjpqG/NEtowxpmoKJ0RTpkyBra0tevTogZEjR+Lq1auIj48HESE+Ph7Xrl3DyJEj0aNHD9ja2mLKlCmFGTcrKe7eBWbMkD1ftQqoX1+18aiIf4Q/HLwdcO75OYS8D8G9yHuqDokxxthX8jR1R3R0NAYNGoRz585leXmfiODi4oLdu3fD3Ny8QANVJe5lpqT4eKBRI+D5c+CHH4BDh8rcaNQSqQQLrizAvCvzICUp6hjXwcHeB1HXtK6qQ2OMsVKv0KbuMDU1xZkzZ3Dr1i38/fffePLkCT5//gxdXV3UqVMHXbt2hbOzc76CZ6XIy5dAcjJQtSqwbVuZS4aiEqLQ/0h/XAy/CAAY4jAE6zqtQ3nNsnnLkDHGijOe3FUBfIUoHz5+BN6+BeztVR1Jkeu+rzv+/udv6GjowLuLNwY2GKjqkBhjrEwp9MldGcvR17PWV6woe5RBazquQVxKHDZ33cwNpxljrJhTOCHq3r17njYsEolw/PjxPAfESrjkZKBdO2DECMDTs0zdJnsb/xZnws5gWKNhAABrQ2tcHnxZxVExxhhThMIJUXBwsELjpCQnJ+Pdu3c8pkpZtXUrcOMG8OwZ4OYGGBqqOqIicSbsDAYeHYgPXz6gkm4ldKrZSdUhMcYYywOFE6KIiIgclycnJ2Pz5s1YsmQJANncZ6wM+ukn4PNnWe+yMpAMpUnS8Nul37Dkuuxz72DugBpGNVQcFWOMsbzKdxuilJQUeHt7Y+nSpYiKikKrVq2wb98+tGrVqiDiYyWNujrwn/+oOooi8TruNTwOe+DG6xsAgJ+a/IQVriugVU6xKW4YY4wVHwoPzPitlJQUrFmzBtWrV8ekSZNga2uLS5cu4eLFi5wMlTXp6cDy5UBSkqojKTIn/zkJh80OuPH6BvTEejjY+yA2dNnAyRBjjJVQeU6IUlJSsHr1alhbW2PSpEmoXbs2Ll++jIsXL/JtsrJq3jxg2jTAxUXWw6wMiEmOwaekT2hSqQkCRwXiR7sfVR0SY4yxfFD4lllycjI2bdqEZcuWISoqCm3btsX+/fvRokWLwoyPFXeXLgELFsiejx9fqnuVSUkKNZHsb4gB9gNQTq0cetbuCXE5sYojY4wxll8KJ0TW1taIjo6Gg4MDNm3aJCRCnz59ynYdIyOj/EfIiq/374EBA2RXhYYNAzw8VB1RoTkScgS/XfoN/p7+MClvAgDwqFd6j5cxxsoahUeqVlP7/901RbvUSyQS5aIqZnik6iwQAV27ymaxr1MHuHOnVM5in5KegqnnpmL9nfUAgKnOU7GswzIVR8UYY0wRhTJStZeXV74DY6XI6tWyZEgsBvbvL5XJUNinMLgfcsf9yPsAZMnQwnYLVRwVY4yxwsBzmSmArxB94+5doGlTIC0N2LgRGDNG1REVuAOPD2D4X8PxOfUzKmpXxK4eu9ClVhdVh8UYYywPeC4zVnji42VthdLSgB9+AEaPVnVEBW5n4E4M/WsoAKCZZTP4/uiLKnpVVBwVY4yxwqT0OESFacOGDahWrRq0tLTg5OSE27dvZ1vXx8cHIpFI7qGlJT8WDBFh9uzZsLCwgLa2NlxcXPDs2bPCPozSh0h2Nej5c6BqVWDbtlLZq6yXXS/UNKqJWc1nwX+wPydDjDFWBhS7hGj//v2YPHkyvLy8cP/+fTRo0ACurq6Ijo7Odh09PT1ERkYKj5cvX8otX7p0KdauXQtvb2/cunUL5cuXh6urK5KTkwv7cEqXXbuAvXtlo1Hv21eqpubwj/BHxt1jPbEegkYHYWG7hSinxhdRGWOsLCh2CdHKlSsxYsQIDBkyBHZ2dvD29oaOjg527NiR7ToikQjm5ubCw8zMTFhGRFi9ejV+/fVXuLm5wd7eHrt378a///6LY8eOFcERlRJPnwJjx8qez5sna0NUCnxJ+4Lhfw1Hm11tsOHOBqFcR0NHhVExxhgrasUqIUpNTcW9e/fg4uIilKmpqcHFxQUBAQHZrpeQkAArKytYWlrCzc0Njx8/FpaFh4cjKipKbpv6+vpwcnLKdpspKSmIj4+Xe5R5mpqAnR3Qrh0wY4aqoykQT94/geNWR2wP3A4RRIhLjlN1SIwxxlSkWCVEHz58gEQikbvCAwBmZmaIiorKch1bW1vs2LEDx48fx59//gmpVIqmTZvizZs3ACCsl5dtLlq0CPr6+sLD0tIyv4dW8lWvDly/Dhw4ILtlVsL5BPngu63f4fH7xzArb4YLgy7gl5a/qDosxhhjKpLvBhKfP39GXFwcpFJppmVVq1bN7+Zz5ezsDGdnZ+F106ZNUadOHWzevBnz589XapuzZs3C5MmThdfx8fFlNymKifl/WyFNTaCEjz6ekJqAsafGYveD3QAAl+ou+LPnnzCrYJbLmowxxkozpROiTZs2YeXKlXjx4kW2dfI6UrWxsTHU1dXx7t07ufJ3797B3NxcoW1oaGigYcOGCAsLAwBhvXfv3sHCwkJumw4ODlluQywWQyzm+anw+jXQsCEwYoSs3ZCGhqojyrfH0Y+xJ3gP1ERqmNt6LmY1nwV1tZJ/xYsxxlj+KHXLzNvbG2PHjkWNGjWwYMECEBEmTpyImTNnwtzcHA0aNMD27dvzvF1NTU00btwYfn5+QplUKoWfn5/cVaCcSCQSPHz4UEh+rK2tYW5uLrfN+Ph43Lp1S+FtllmHDwMfPwJ+fqVmFnunKk5Y12kdLg66iF9b/srJEGOMMRlSgp2dHXXs2JGIiD58+EAikYj8/PyIiCg2NpZq1KhBy5cvV2bT5OvrS2KxmHx8fOjJkyc0cuRIMjAwoKioKCIiGjhwIM2cOVOoP3fuXDp79iw9f/6c7t27Rx4eHqSlpUWPHz8W6ixevJgMDAzo+PHjFBwcTG5ubmRtbU1JSUkKxRQXF0cAKC4uTqljKtEOHCB6/lzVUSgtPjmehhwbQo+jH+demTHGWKmSl99vpW6ZPX/+HGP/1wVb43+3UVJTUwHIenANHz4cGzduxJQpU/K8bXd3d7x//x6zZ89GVFQUHBwccObMGaFR9KtXr+Qmmo2JicGIESMQFRUFQ0NDNG7cGDdu3ICdnZ1QZ/r06UhMTMTIkSMRGxuL5s2b48yZM5kGcGRZ6N1b1REoLSgqCH0O9sGzT89wP/I+7o+6DzVRsepHwBhjrJhQKiHS19dHeno6ANmgiDo6Onj9+rWwXFdXN9seXIoYN24cxo0bl+Uyf39/uderVq3CqlWrctyeSCTCvHnzMG/ePKVjKjPevwcmTQKWLQO+anNVkhARvO96Y9LZSUiRpKCKXhVs6LyBkyHGGGPZUiohqlevHh48eCC8/v7777Fp0yZ07twZUqkUmzdvRq1atQosSFZEiIAhQ4CTJ4E3b4Bvks+SIC45DiP+HoGDTw4CALrW6gofNx9U1Kmo4sgYY4wVZ0olRAMGDIC3tzdSUlIgFosxd+5cuLi4CN3sNTQ0cPjw4QINlBWBNWtkyZBYDKxdq+po8uxV3Cu02dUGL2JeoJxaOSxxWYJJ30+CqBTOt8YYY6xgiYgKpvvQixcv8Pfff0NdXR0dOnQoVVeI4uPjoa+vj7i4OOjp6ak6nMJx7x7g7CybxX7jRtkkriVMujQd7Xa3w6u4V9j/4344VnZUdUiMMcZUKC+/33lOiJKTk7FlyxY4ODigZcuW+Qq0pCj1CVF8PNCokWwW+549Zd3tS8hVlZikGOho6EBcTjZuVFRCFLTKacFAy0C1gTHGGFO5vPx+57mVqZaWFmbMmIHQ0FClA2TFCJHsatDz50DVqsD27SUmGbr15hYabm6I6eenC2XmFcw5GWKMMZZnSnW7qVevHiIiIgo4FKYSu3YBe/fK5ifbu/f/03QUY1KSYsWNFWi+szlexr3EyWcnEZ/CE/AyxhhTnlIJ0e+//47NmzfjwoULBR0PK0qhocD/xpPCvHlAs2aqjUcBH798RPd93TH1/FSkS9PRp24f3Bt5D3riUngrkzHGWJFRqpfZ+vXrYWRkBFdXV1hbW8Pa2hra2tpydUQiEY4fP14gQbJCkJwMuLsDX74AbdsCM2aoOqJcXX91HR6HPfAm/g3E6mKs7rgaoxqP4l5kjDHG8k2phCg4OBgikQhVq1aFRCIRJlL9Gv9IFXPTpgEPHgAmJsAff8humRVjiamJcPN1w8ekj6hpVBMHeh+Ag7mDqsNijDFWSiiVEHH7oRLu5Elg/XrZ8127gEqVVBuPAsprlseWbltwOOQwvLt4Q1esq+qQGGOMlSIFNg5RaVbqut3HxgIjRwJWVrIpOoqpyxGXZWMLVW+n6lAYY4yVQHn5/VbqClGGy5cv4+TJk3j58iUAwMrKCl26dEGrVq3ys1lW2AwMgP37AalU1ZFkSSKVYOHVhZhzeQ6MtI3wYPQDVNIt/lexGGOMlVxKJUSpqano27cvjh07BiKCgYEBACA2NhYrVqxAz549sW/fPmhoaBRkrCy/7t4FGjeWjTMkEhXLdkNRCVEYcGQA/ML9AABdanaBvlhfxVExxhgr7ZTqdj937lwcPXoUU6ZMQWRkJD59+oRPnz4hKioKU6dOxZEjR3hm+eLm0iXA0RHo3Vs2PUcx5PfCDw7eDvAL94OOhg583Hzg08MH5TXLqzo0xhhjpZxSCdHevXvh6emJpUuXwszMTCg3NTXFkiVLMGjQIPzxxx8FFiQrAGFhQLlygL4+UMyu3BERZl+ajfZ/tMe7xHeoZ1oPd0fchaeDp6pDY4wxVkYolRBFRkbCyckp2+VOTk6IiopSOihWCEaMAG7eLJaz2ItEIryJfwMCYUSjEbg9/DbqmNRRdViMMcbKEKUSoipVqsDf3z/b5ZcvX0aVKlWUjYkVpK8bTjdqBJQvPrefJFKJ8Hxdp3U40ucItnTbAm0N7RzWYowxxgqeUgmRp6cnDhw4gNGjRyM0NBQSiQRSqRShoaEYM2YMDh48iMGDBxdwqCzP7t0DHByAoCBVRyInTZKGWRdmwc3XDVKSJWzlNcujZ52eKo6MMcZYWaXUOEQSiQTDhg3D7t27IRKJoKYmy6ukUimICJ6enti+fbtQXtKVyHGIPn+WXREKC5NN0eHrq+qIAACv417D47AHbry+AQA4O+AsOth0UHFUjDHGSqO8/H7na2DG4OBgnDp1Sm4cos6dO8Pe3l7ZTRZLJS4hIgIGDgT27AGqVpVdISoGs9if+OcEPI954lPSJ+iJ9bCt2zb0rttb1WExxhgrpYpsYEZ7e/tSl/yUCrt3y5IhdXVg716VJ0OpklTMujALK2+uBAA0tmiM/T/uh42RjUrjYowxxjLkKyFixVBoKPDTT7Lnc+cCzZqpNh4Ag48Nxr5H+wAAExwnYGn7pRCXE6s4KsYYY+z/FEqI1NTUlJq9XiKR5F6JFZzkZFl7oS9fgLZtgZkzVR0RAGCy82RcDL8I767e6FG7h6rDYYwxxjJRKCGaPXt2poTo6NGjePz4MVxdXWFrawsAePr0Kc6dO4d69eqhR48eBR4sy8W0acCDB4CJCfDHHyqbmiMlPQW3395GC6sWAIAmlZog/Odw7k7PGGOs2FIoIZozZ47c6y1btiA6OhqPHj0SkqEMISEhaNu2LSpV4sk4i9Tx48D69bLnu3YBKnr/n396DvdD7ngY/RA3h91EQ4uGAMDJEGOMsWJNqX7xy5Ytw7hx4zIlQwBQp04djBs3DkuXLs13cExBr18DQ4bInk+ZAnTqpJIwDj4+iEZbGuFe5D1U0KyAj0kfVRIHY4wxlldKNap+8+ZNjjPZa2ho4M2bN0oHxfIgPR3o1w+IiQGaNAEWLizyEJLTkzH57GRsursJANDMshn29doHS33LIo+FMcYYU4ZSV4jq1auHjRs34u3bt5mWvXnzBhs3bkT9+vXzHRxTwPbtwLVrgK6ubPBFTc0i3f0/H//B99u+F5KhWc1n4ZLnJU6GGGOMlShKXSFatWoVXF1dUatWLfTs2RM1atQAADx79gzHjh0DEeHPP/8s0EBZNoYNk90yq1sXsCn6cX2OhhzFg3cPYKxjjD97/gnXGq5FHgNjjDGWX0qPVP3o0SP89ttvOHfuHJKSkgAA2tracHV1xdy5c0vVFaISN1J1EZKSFL9e/BXjHMehki43pGeMMVZ8FNnUHYBs/rL3798DAExMTErN/GVfK3YJERGwbRswaBAgLtoBDkPeh2DO5TnY6bYTOho6RbpvxhhjLC/y8vud7+xFTU0NZmZmMDMzK5XJULG0ejUwciTQrh0glRbZbncF7UKTrU1w4PEB/Hrx1yLbL2OMMVbYlJ66IyYmBvv27cOLFy8QExODby80iUQibN++Pd8BsizUqAEYGcl6lxVBEpqYmoixp8Zi14NdAIB21u0wvdn0Qt8vY4wxVlSUSojOnj2LH3/8EYmJidDT04NhFpOHKjPVR4YNGzZg2bJliIqKQoMGDbBu3To4OjpmWXfr1q3YvXs3Hj16BABo3LgxFi5cKFd/8ODB2LVrl9x6rq6uOHPmjNIxqlS3bsDTp4CxcaHv6lH0I/Q52AchH0KgJlLDnFZz8J8W/4G6mmpGwWaMMcYKg1IJ0ZQpU2Bubo4jR44UeOPp/fv3Y/LkyfD29oaTkxNWr14NV1dXhIaGwtTUNFN9f39/9O3bF02bNoWWlhaWLFmCDh064PHjx6hcubJQr2PHjti5c6fwWlzEbW/yjQj49AmoWFH22sSk0Hd54p8T6H2wN5LTk2FRwQL7eu1Dq2qtCn2/jDHGWFFT6n5LWFgYJkyYUCg9yVauXIkRI0ZgyJAhsLOzg7e3N3R0dLBjx44s6+/Zswc//fQTHBwcULt2bWzbtg1SqRR+fn5y9cRiMczNzYVHVle1irXdu4FatWRTdBQRB3MHlNcoD1cbVwSNDuJkiDHGWKmlVEJUs2ZNfP78uaBjQWpqKu7duwcXFxehTE1NDS4uLggICFBoG1++fEFaWhqMjIzkyv39/WFqagpbW1uMGTMGHz+WoGklQkOBsWNlV4j+d2uwsER+jhSeV9GrgpvDb+JU/1MwLZ/56hxjjOWkWrVqWL16tarDYEXE398fIpEIsbGxqg5FKUolRAsWLMDGjRsRERFRoMF8+PABEokEZmZmcuVmZmaIiopSaBszZsxApUqV5JKqjh07Yvfu3fDz88OSJUtw+fJldOrUCRKJJMttpKSkID4+Xu6hMsnJgIcHkJgItG0LzJxZKLshInjf9Ub1tdVx7OkxobyGUQ2oibj3IGMl3eDBg4WJukUiUYH//52VO3fuYOTIkQrXL4k/qK1bt8bEiRPlyiIiIoR2tHPmzMHgwYOLPjAVaNq0KSIjI6Gvr6/qUJSiVBsiPz8/mJiYoE6dOmjfvj0sLS2hri7fyFYkEmHNmjUFEqSiFi9eDF9fX/j7+0NLS0so9/DwEJ7Xr18f9vb2sLGxgb+/P9q1a5dpO4sWLcLcuXOLJOZcTZ8OBAXJ2gz98QegXvCNmeOS4zDyxEgceHwAAHA45DB61O5R4PthjJUtJkXQ1jErRASJRIJy5ZTuSK0yEokEIpGoUIaxSU1NhWYhTu+kqakJc3PzQtt+oSMliESiXB9qamp53m5KSgqpq6vT0aNH5coHDRpE3bt3z3HdZcuWkb6+Pt25c0ehfRkbG5O3t3eWy5KTkykuLk54vH79mgBQXFycQtsuMMeOEcmaUxOdOlUou7j79i5VX1OdMAdUbl45Wn59OUml0kLZF2NMdTw9PcnLy4uIiABQeHg4ERFdunSJANCFCxeocePGpK2tTc7OzvT06VO59Tdu3EjVq1cnDQ0NqlWrFu3evTvXfVpZWdGqVauE1wBo69at1KNHD9LW1qYaNWrQ8ePHiYgoPDycAMg9PD09iYhIIpHQwoULqVq1aqSlpUX29vZ08OBBYbsZx3Dq1Clq1KgRaWho0KVLl0gikdCSJUvIxsaGNDU1ydLSkhYsWCCs9+rVK+rduzfp6+uToaEhde/eXXhfMt4zNzc3mjNnDhkbG5Ouri6NGjWKUlJShOXfxhweHi4cCxGRl5eXcBxZ2blzJ+nr69Px48epTp06pK6uTuHh4ZScnExTpkyhSpUqkY6ODjk6OtKlS5fk1t2yZQtVqVKFtLW1qUePHrRixQrS19cXlnt5eVGDBg1o69atVK1aNRKJREREFBMTQ8OGDROOqU2bNhQUFCSsFxQURK1bt6YKFSqQrq4uNWrUSPhtjYiIoK5du5KBgQHp6OiQnZ0dnTx5Uu48xMTECNs6dOgQ2dnZkaamJllZWdHy5cszfUZ+//13GjJkCFWoUIEsLS1p8+bN2b5feRUXF6fw77dSCVFhcnR0pHHjxgmvJRIJVa5cmRYtWpTtOkuWLCE9PT0KCAhQaB+vX78mkUgkfBFzk5c3tMC8ekVkaChLhqZMKfDNS6VSWntzLWnO1yTMAVmtsqKA14q9f4yxkie3hMjJyYn8/f3p8ePH1KJFC2ratKmw7pEjR0hDQ4M2bNhAoaGhtGLFClJXV6eLFy/muM+sEqIqVarQ3r176dmzZzRhwgSqUKECffz4kdLT0+nw4cMEgEJDQykyMpJiY2OJiGjBggVUu3ZtOnPmDD1//px27txJYrGY/P395Y7B3t6ezp07R2FhYfTx40eaPn06GRoako+PD4WFhdHVq1dp69atRESUmppKderUoaFDh1JwcDA9efKE+vXrR7a2tnIJT4UKFcjd3Z0ePXpEJ06cIBMTE/rPf/5DRESxsbHk7OxMI0aMoMjISIqMjKT09PQ8J0QaGhrUtGlTun79Oj19+pQSExNp+PDh1LRpU7py5QqFhYXRsmXLSCwW0z///ENERNeuXSM1NTVatmwZhYaG0oYNG8jIyChTQlS+fHnq2LEj3b9/nx48eEBERC4uLtStWze6c+cO/fPPPzRlyhSqWLEiffz4kYiI6tatSwMGDKCQkBD6559/6MCBA0LC1KVLF2rfvj0FBwfT8+fP6e+//6bLly/LnYeMhOju3bukpqZG8+bNo9DQUNq5cydpa2vTzp075T4jRkZGtGHDBnr27BktWrSI1NTU5BLyVq1a5fge5qREJ0S+vr4kFovJx8eHnjx5QiNHjiQDAwOKiooiIqKBAwfSzJkzhfqLFy8mTU1NOnTokPCBjIyMpM+fPxMR0efPn2nq1KkUEBBA4eHhdOHCBWrUqBHVrFmTkpOTFYqpyBOitDSi5s1lyVCTJkT/+3IWpKsvrxLmgDAH1MO3B3368qnA98EYK/6+vkKU4eTJkwSAkpKSiIioadOmNGLECLn1evfuTZ07d85x21klRL/++qvwOiEhgQDQ6dOn5WL5+gpDcnIy6ejo0I0bN+S2PWzYMOrbt6/ceseOHROWx8fHk1gsFhKgb/3xxx9ka2srd0U8JSWFtLW16ezZs0QkS4iMjIwoMTFRqLNp0yaqUKECSSQSIpL9WP/88885vg852blzJwGQu0Lz8uVLUldXp7dv38rVbdeuHc2aNYuIiNzd3alLly5yy/v3758pIdLQ0KDo6Gih7OrVq6Snp5fp98/Gxka4MqOrq0s+Pj5Zxlu/fn2aM2dOlsu+PX/9+vWj9u3by9WZNm0a2dnZCa+trKxowIABwmupVEqmpqa0adMmoezb3/28yMvvd7G7weru7o73799j9uzZiIqKgoODA86cOSM0tH716pXcvdVNmzYhNTUVP/74o9x2vLy8MGfOHKirqyM4OBi7du1CbGwsKlWqhA4dOmD+/PnFdyyi+fOBa9cAXV3A1xcohHu+zas2x6TvJ6GaQTWMdxyfr4E0GWMln729vfDcwsICABAdHY2qVasiJCQkU+PoZs2aCe1E9+zZg1GjRgnLTp8+jRYtWuS6n/Lly0NPTw/R0dHZxhUWFoYvX76gffv2cuWpqalo2LChXFmTJk2E5yEhIUhJScmynSgAPHjwAGFhYdDV1ZUrT05OxvPnz4XXDRo0gI7O/+dtdHZ2RkJCAl6/fg0rK6ts484LTU1Nuffl4cOHkEgkqFWrlly9lJQUVPzfWHShoaHo2bOn3HJHR0ecOHFCrszKykquLdeDBw+QkJAgbCdDUlKScNyTJ0/G8OHD8ccff8DFxQW9e/eGjY0NAGDChAkYM2YMzp07BxcXF/Tq1Usu9q+FhITAzc1NrqxZs2ZYvXo1JBKJ0Pb46/VFIhHMzc3lPhO7d+/OcvsFTemE6PTp01i5ciXu37+PuLi4TFN3AMi2F1duxo0bh3HjxmW5zN/fX+51bj0ltLW1cfbsWaXiUIlLl2QJEQBs3gz870OYX0SEDXc24Ic6Pwiz0q90XVkg22aMlXwaGhrC84w/kKQKzpXYvXt3ODk5Ca+/HhQ3p/1k7Cun/SQkJAAATp48mWm73/5RW758eeG5trZ2jjEnJCSgcePG2LNnT6ZlRd0YXFtbW+6P0oSEBKirq+PevXuZOixVqFAhT9v++j3J2LaFhUWm31IAMDAwACDrGdevXz+cPHkSp0+fhpeXF3x9fdGzZ08MHz4crq6uOHnyJM6dO4dFixZhxYoVGD9+fJ7i+lpePxOFRamE6PDhw+jTpw/q1q0LDw8PbNq0Cf369QMR4fjx46hZsyZ69OhRwKGWAe/fAwMGyJpRDx0K9O1bIJv9+OUjBh8fjBP/nMDhkMO4MPACT73BGFNYnTp1cP36dXh6egpl169fh52dHQBAV1c305UWZWT0gPr6j2k7OzuIxWK8evUKrVopPjhszZo1oa2tDT8/PwwfPjzT8kaNGmH//v0wNTXNcRb0Bw8eICkpSUiwbt68iQoVKsDS0lKIWdk//rPTsGFDSCQSREdHZ3ulzdbWFnfu3JEr+/Z1Vho1aoSoqCiUK1cO1apVy7ZerVq1UKtWLUyaNAl9+/bFzp07hStSlpaWGD16NEaPHo1Zs2Zh69atWSZEGZ+br12/fh21atXKlOgVB0r161u0aBEcHR0RGBgodE8fOnQo9uzZg0ePHiEyMhLW1tYFGmiZsHcv8O+/QO3awNq1BbLJG69voOHmhjjxzwmI1cXoY9eHxxVijOXJtGnT4OPjg02bNuHZs2dYuXIljhw5gqlTpxbofqysrCASiXDixAm8f/8eCQkJ0NXVxdSpUzFp0iTs2rULz58/x/3797Fu3bpMc1R+TUtLCzNmzMD06dOxe/duPH/+HDdv3hQmHe/fvz+MjY3h5uaGq1evIjw8HP7+/pgwYQLevHkjbCc1NRXDhg3DkydPcOrUKXh5eWHcuHFC041q1arh1q1biIiIwIcPHwrkykatWrXQv39/DBo0CEeOHEF4eDhu376NRYsW4eTJkwCA8ePH49SpU1i5ciWePXuGzZs34/Tp07k2f3BxcYGzszN69OiBc+fOISIiAjdu3MAvv/yCu3fvIikpCePGjYO/vz9evnyJ69ev486dO6hTpw4AYOLEiTh79izCw8Nx//59XLp0SVj2rSlTpsDPzw/z58/HP//8g127dmH9+vV5/twMGjQIs2bNytM6SlGmkZK2tjatXr2aiGTd90QiEZ05c0ZYPnfuXLlGUyVdkTaq3rWL6H89AfJDIpXQ4quLSX2uOmEOqObamhQYGZj/+BhjpUpWDZkDAwPleqIRFVy3+2+HVdHX15frdTRv3jwyNzcnkUgk9CySSqW0evVqsrW1JQ0NDTIxMSFXV9dsezdlkEgktGDBArKysiINDQ2qWrUqLVy4UFgeGRlJgwYNImNjYxKLxVS9enUaMWKE8H99Rrf72bNnU8WKFalChQo0YsQIuQbJoaGh9P3335O2tnam90wRGd3uv5WamkqzZ8+matWqkYaGBllYWFDPnj0pODhYqLNlyxaqXLmy0O1+wYIFZG5uLizP6Hb/rfj4eBo/fjxVqlSJNDQ0yNLSkvr370+vXr2ilJQU8vDwIEtLS9LU1KRKlSrRuHHjhAb248aNIxsbGxKLxWRiYkIDBw6kDx8+ZHseMrrdZ7z/y5Ytk4vl288IEVGDBg2EHpFERdfLTESUReOfXBgbG2P+/PkYM2YMANn9z02bNgmjcW7evBmTJk3Cly9fCiRpU7X4+Hjo6+sjLi4ux0urxcWnpE/of6Q/zoSdAQD0q98P3l28oSvO/yVtxhgrKwYPHozY2FgcO3ZM1aEoZMSIEXj69CmuXr2q6lCKjbz8fit178TW1hZPnjwRXjs4OOCPP/5Aeno6kpOTsXfvXlStWlWZTZc9nz8DY8YAHz4U2CbF6mJExEZAq5wWtnbbij97/snJEGOMlTLLly8Xestl3EL8up0XyxulGlX37NkTa9euxfLlyyEWi/HLL7/Azc0NBgYGEIlESExMzHZ2evaNn38Gdu4EHjwArl8HlOz+LpH+b7h3kRrKa5bHod6HICUp6pvVL+CAGWOMFQe3b9/G0qVL8fnzZ1SvXh1r167NsgE5U4xSt8yycvXqVRw5cgTq6uro0qUL2rRpUxCbLRYK9ZbZgwdA//6yLvbNmim1iaiEKAw4MgAda3TE1KYF28iRMcYYK6ny8vudp4QoOTkZx48fR3h4OIyNjdGlSxdhAK/SrNDbEEkkSk/a6vfCD/2P9Me7xHfQF+sjYmIEDLQMCjY+xhhjrATKy++3wrfMoqOj0bRpU4SHhwuDMOro6ODYsWNwcXHJX8RlTXIyEBICZIyyqkQyJJFKMO/yPMy/Mh8EQl2TujjQ+wAnQ4yxPCtpjYcZKwwKN6qeP38+IiIiMGnSJJw4cQKrV6+Gtra23HDtTEHTpwOOjoC3t1Kr//v5X7Tb3Q7zrswDgTC84XDcHnEbdiZ2BRwoY4yp1pEjR9ChQwdUrFgRIpEIQUFBCq23evVq2NraQltbG5aWlpg0aRKSk5OF5Zs2bYK9vT309PSgp6cHZ2dnnD59Wm4bycnJGDt2LCpWrIgKFSqgV69eePfuXY77bd26NUQiEUQiEbS0tFCrVi0sWrRIbjaHBw8eoG/fvrC0tIS2tjbq1KkjTINS1CZMmIDGjRtDLBbDwcEhyzrBwcFo0aIFtLS0YGlpiaVLlyq0bR8fH9jb20NLSwumpqYYO3Zsnrb79Xv59aNLly5KHWtuFL5CdO7cOQwaNAjLly8XyszMzNCvXz+EhobC1ta2UAIsdf76C1i3TvZciZ54SWlJcNzqiLef36KCZgVs7roZ/er3K+AgGWOseEhMTETz5s3Rp08fjBgxQqF19u7di5kzZ2LHjh1o2rQp/vnnHwwePBgikQgrV8qmLKpSpQoWL16MmjVrgoiwa9cuuLm5ITAwEHXr1gUATJo0CSdPnsTBgwehr6+PcePG4Ycffsg0+vK3RowYgXnz5iElJQUXL17EyJEjYWBgIAxVc+/ePZiamuLPP/+EpaUlbty4gZEjR0JdXT3baasK09ChQ3Hr1i0EBwdnWhYfH48OHTrAxcUF3t7eePjwIYYOHQoDA4NM89t9beXKlVixYgWWLVsGJycnJCYmyk21pch2jxw5gtTUVGGdjx8/okGDBujdu3fBHfzXFB3cSEtLi7Zv3y5X9ubNGxKJRMLgWKVVgQ3M+OoVkZGRbBb7KVOU3szqgNXUYFMDCv0Qmr94GGNlxsGDB6levXqkpaVFRkZG1K5dO0pISCCi/w9AmCE5OZnGjx9PJiYmJBaLqVmzZnT79m1hecYAfCdOnKD69euTWCwmJycnevjwodw+r169Ss2bNyctLS2qUqUKjR8/XthnXoWHhxMACgwMzLXu2LFjqW3btnJlkydPpmbNmuW4nqGhIW3bto2IiGJjY0lDQ4MOHjwoLA8JCSEAFBAQkO02WrVqRT///LNcWaNGjahnz5457vunn36iNm3a5FinMGU3iOPGjRvJ0NCQUlJShLIZM2aQra1tttv69OkTaWtr04ULF7Kto8x2V61aRbq6unn6DOXl91vhW2YpKSnQ0tKSK8t4nZ6eXmAJWqmVni7rTfbpE9CkCbBwocKrvol/g8fRj4XXE5wm4NbwW6hVsVYOazHGmExkZCT69u2LoUOHIiQkBP7+/vjhhx+ynJQbAKZPn47Dhw9j165duH//PmrUqAFXV1d8+vRJrt60adOwYsUK3LlzByYmJujWrRvS0tIAAM+fP0fHjh3Rq1cvBAcHY//+/bh27ZrcFZA5c+bkOJ+Wspo2bYp79+7h9u3bAIAXL17g1KlT6Ny5c5b1JRIJfH19kZiYCGdnZwCyqzhpaWlybWRr166NqlWrIiAgQKE4iAhXr17F06dPhXnashMXFwcjI6Mc69StWxcVKlTI9tGpUyeF4sqLgIAAtGzZUi5+V1dXhIaGIiYmJst1zp8/D6lUirdv36JOnTqoUqUK+vTpg9evX+dru9u3b4eHh0emCWsLSp7GIYqIiMD9+/eF13FxcQCAZ8+eCbPkfq1Ro0b5i640mT8fuHoV0NUFfH2BXL4cGU7+cxKexzxhpG2EeyPvQVesC5FIBHE5ce4rM8YYZAlReno6fvjhB1hZWQEA6tfPeoyyxMREbNq0CT4+PsIP7NatW3H+/Hls374d06ZNE+p6eXmhffv2AIBdu3ahSpUqOHr0KPr06YNFixahf//+mDhxIgDZZKtr165Fq1atsGnTJmhpacHY2Bg2NjYFfrz9+vXDhw8f0Lx5cxAR0tPTMXr0aPznP/+Rq/fw4UM4OzsjOTkZFSpUwNGjR4UJa6OioqCpqZnpt83MzAxRUVE57n/jxo3Ytm0bUlNTkZaWBi0tLUyYMCHb+jdu3MD+/fuFecqyc+rUKSHhzErGBLQFKSoqKtPcpGZmZsIyQ0PDTOu8ePECUqkUCxcuxJo1a6Cvr49ff/0V7du3R3BwMDQ1NfO83du3b+PRo0fCXHSFIU8J0W+//YbffvstU/lPP/0k95qIIBKJCnwG4BLL31+WEAGy8YYU+A8gTZKG//j9B8sDZG22rAysEJscyyNOM8byrEGDBmjXrh3q168PV1dXdOjQAT/++GOWP2bPnz9HWloamn01LpqGhgYcHR0REhIiVzfjagoAGBkZwdbWVqjz4MEDBAcHY8+ePUIdIoJUKkV4eDjq1KmDcePGFUqbGX9/fyxcuBAbN26Ek5MTwsLC8PPPP2P+/Plyv2G2trYICgpCXFwcDh06BE9PT1y+fFlIipTVv39//PLLL4iJiYGXlxeaNm2Kpk2bZln30aNHcHNzg5eXFzp06JDjdjOSWWV06tRJmNLDysoKjx8/zmUN5UmlUqSlpWHt2rXCMe3btw/m5ua4dOkSXF1d87zN7du3o379+nB0dCzocAUKJ0Q7d+4stCBKtQ8fZLfKiIChQ4G+fXNd5WXsS3gc9sDNNzcBAOMdx2NZ+2V8VYgxphR1dXWcP38eN27cwLlz57Bu3Tr88ssvuHXrVqa/0gtKQkICRo0aleWVkcKe2um3337DwIEDhVGb69evj8TERIwcORK//PKLMFO9pqYmatSoAQBo3Lgx7ty5gzVr1mDz5s0wNzdHamoqYmNj5a4SvXv3Dubm5jnuX19fX9jugQMHUKNGDXz//feZhqh58uQJ2rVrh5EjR+LXX3/N9bjq1q2Lly9fZru8RYsWmXrKZdi2bRuSkpIAyBJcRZmbm2fqWZfxOrv3IWN8wq8TSxMTExgbG+PVq1d53m5iYiJ8fX0xb948heNWhsIJEc+PogQiYPBg4N9/gdq1gbVrc13l+NPjGHx8MGKTY2GgZYAd3XegZ52ehR8rY6xUE4lEaNasGZo1a4bZs2fDysoKR48exeTJk+Xq2djYQFNTE9evXxeuSKSlpeHOnTvC7a8MN2/eFJKbmJgY/PPPP6hTpw4AWZOJJ0+eCIlBUfry5YuQ9GRQ/994b9m1mwJkVzZSUlIAyBIkDQ0N+Pn5oVevXgCA0NBQvHr1Su7KWG4qVKiAn3/+GVOnTkVgYCBE/5ue6fHjx2jbti08PT3x+++/K7St/Nwyq1y5ssIxf83Z2Rm//PIL0tLShETq/PnzsLW1zfIKIwDh6mJoaCiqVKkCAPj06RM+fPggfKbyst2DBw8iJSUFAwYMUOoYFKZwU+0yTOleZqtWyXqUicVEDx7kWl0qlVKHPzoQ5oActzpSeEy4UvEyxtjXbt68Sb///jvduXOHXr58SQcOHCBNTU06deoUEWXuZfbzzz9TpUqV6PTp0/T48WPy9PQkQ0ND+vTpExH9v5dZ3bp16cKFC/Tw4UPq3r07Va1aVeg19ODBA9LW1qaxY8dSYGAg/fPPP3Ts2DEaO3assJ9169Zl6g32rY8fP1JgYCCdPHmSAJCvry8FBgZSZGSkUGfgwIE0c+ZM4bWXlxfp6urSvn376MWLF3Tu3DmysbGhPn36CHVmzpxJly9fpvDwcAoODqaZM2eSSCSic+fOCXVGjx5NVatWpYsXL9Ldu3fJ2dmZnJ2dc4w3q15mHz9+JG1tbaHH2sOHD8nExIQGDBhAkZGRwiM6OjrHbReGZ8+eUWBgII0aNYpq1apFgYGBFBgYKJzH2NhYMjMzo4EDB9KjR4/I19eXdHR0aPPmzcI2jhw5kql3mJubG9WtW5euX79ODx8+pK5du5KdnR2lpqYqvN0MzZs3J3d3d6WOLy+/35wQKUCphOjuXSINDVlCtGGDwqtFfY6i2RdnU0p6Su6VGWNMAU+ePCFXV1ehG32tWrVo3bp1wvJvE6KkpCQaP348GRsb59jt/u+//6a6deuSpqYmOTo60oNv/vC7ffs2tW/fnipUqEDly5cne3t7+v3334XlXl5eZGVllWPsO3fuJACZHl5eXkKdVq1akaenp/A6LS2N5syZQzY2NqSlpUWWlpb0008/UUxMjFBn6NChZGVlRZqammRiYkLt2rWTS4Yy3oeffvqJDA0NSUdHh3r27CmXiGUlq4SIiGjUqFFUt25dkkgk5OXlleUx5fZeFIZWrVplGUt4eLhQ58GDB9S8eXMSi8VUuXJlWrx4sdw2Ms7R1+Li4mjo0KFkYGBARkZG1LNnT3r16pVcndy2S0T09OlTApDp3CgqL7/fBTa5a2mm1FxmmzcDP/0EuLkBhw9nO4v9oSeHEPA6ACtcVxRgxIwxVnj8/f3Rpk0bxMTEZNnDmLHiolDmMmN5NGoUUK8eUKdOlslQcnoyppydgo13NwIA2lVvh841sx4jgzHGGGOFixOigkb0/wToq26rX3v28Rn6HOqDoKggAMCMZjPQvnr7IgqQMcYYY99SeKRqpoDQUFkS9M1YHV/b93AfGm1phKCoIBjrGON0/9NY7LIYGuqKd4NkjDFVat26NYiIb5exUoUTooI0YQIQEAB8NZLr12acn4F+R/ohITUBLa1aImhUEDrW6FjEQTLGGGPsW5wQFaRduwB3d2DLliwXt7RqCTWRGn5t8Sv8Bvmhsp5y40IwxhhjrGBxLzMFKNXL7H/exr+VS3zCPoWhhlHRD1TGGGOMlTV5+f3mK0T59fo1cOhQpuLE1EQMOT4E9TfVx8vY/w+1zskQY4wxVvxwQpQf6emyecp69waWLROKH0c/huM2R/gE+SAuJQ7+Ef6qi5ExxhhjueJu9/kxfz5w9Sqgqwv07Akiwo7AHRh/ejyS0pNgUcECe3vtRetqrVUdKWOMMcZywAmRsvz9ZQkRAGzejM+WZhhzdCD2PNwDAOhg0wF/9PwDpuVNVRcjY4wxxhTCt8yU8eGD7FYZETBkCNC3L1YErMCeh3ugLlLHwrYLcbr/aU6GGGOMsRKCrxDlFREweDDw77+ArS2wbh0AYGbzmbgXeQ8zms1A86rNVRsjY4wxxvKEE6K8WrMGOHkSJBZj23QXDNXWgjoArXJa+Lvv36qOjjHGGGNKKJa3zDZs2IBq1apBS0sLTk5OuH37do71Dx48iNq1a0NLSwv169fHqVOn5JYTEWbPng0LCwtoa2vDxcUFz549y3tggYHA9OkAgDnddDHy9Qb8fvX3vG+HMcYYY8VKsUuI9u/fj8mTJ8PLywv3799HgwYN4Orqiujo6Czr37hxA3379sWwYcMQGBiIHj16oEePHnj06JFQZ+nSpVi7di28vb1x69YtlC9fHq6urkhOTs5TbDR0KJCWhmN1RJhX9wOq6ldFB5sO+TpexhhjjKlesRup2snJCd999x3Wr18PAJBKpbC0tMT48eMxc+bMTPXd3d2RmJiIEydOCGXff/89HBwc4O3tDSJCpUqVMGXKFEydOhUAEBcXBzMzM/j4+MDDwyPXmISRLgHE6gEOo4GWDd2ww20HjLSNCubAGWOMMVagSuxI1ampqbh37x5cXFyEMjU1Nbi4uCAgICDLdQICAuTqA4Crq6tQPzw8HFFRUXJ19PX14eTklO02s5MOYGBvdXj1XI2j7kc5GWKMMcZKiWLVqPrDhw+QSCQwMzOTKzczM8PTp0+zXCcqKirL+lFRUcLyjLLs6nwrJSUFKSkpwuu4uDgAwKo2epgz9RgaV2qMz58/5+HIGGOMMVbU4uPjAcjaEuemWCVExcWiRYswd+7cTOULLsVjQZ22KoiIMcYYY8r6/Pkz9PX1c6xTrBIiY2NjqKur4927d3Ll7969g7m5eZbrmJub51g/4993797BwsJCro6Dg0OW25w1axYmT54svI6NjYWVlRVevXqV6xta0sXHx8PS0hKvX7/O9X5rSVeWjhUoW8fLx1o6laVjBcrW8RbWsRIRPn/+jEqVKuVat1glRJqammjcuDH8/PzQo0cPALJG1X5+fhg3blyW6zg7O8PPzw8TJ04Uys6fPw9nZ2cAgLW1NczNzeHn5yckQPHx8bh16xbGjBmT5TbFYjHEYnGmcn19/VL/ocygp6fHx1pKlaXj5WMtncrSsQJl63gL41gVvZBRrBIiAJg8eTI8PT3RpEkTODo6YvXq1UhMTMSQIUMAAIMGDULlypWxaNEiAMDPP/+MVq1aYcWKFejSpQt8fX1x9+5dbNmyBQAgEokwceJELFiwADVr1oS1tTV+++03VKpUSUi6GGOMMVa2FbuEyN3dHe/fv8fs2bMRFRUFBwcHnDlzRmgU/erVK6ip/b9zXNOmTbF37178+uuv+M9//oOaNWvi2LFjqFevnlBn+vTpSExMxMiRIxEbG4vmzZvjzJkz0NLSKvLjY4wxxljxU+wSIgAYN25ctrfI/P39M5X17t0bvXv3znZ7IpEI8+bNw7x585SKRywWw8vLK8vbaKUNH2vpVZaOl4+1dCpLxwqUreMtDsda7AZmZIwxxhgrasVqYEbGGGOMMVXghIgxxhhjZR4nRIwxxhgr8zghYowxxliZV2YTog0bNqBatWrQ0tKCk5MTbt++nWP9gwcPonbt2tDS0kL9+vVx6tQpueVEhNmzZ8PCwgLa2tpwcXHBs2fPCvMQFJaXY926dStatGgBQ0NDGBoawsXFJVP9wYMHQyQSyT06duxY2IehkLwcq4+PT6bj+HYohtJyXlu3bp3pWEUiEbp06SLUKa7n9cqVK+jWrRsqVaoEkUiEY8eO5bqOv78/GjVqBLFYjBo1asDHxydTnbz+H1AU8nqsR44cQfv27WFiYgI9PT04Ozvj7NmzcnXmzJmT6bzWrl27EI9CcXk9Xn9//yw/x9/OS1kazm1W30eRSIS6desKdYrruV20aBG+++476OrqwtTUFD169EBoaGiu66n6d7ZMJkT79+/H5MmT4eXlhfv376NBgwZwdXVFdHR0lvVv3LiBvn37YtiwYQgMDESPHj3Qo0cPPHr0SKizdOlSrF27Ft7e3rh16xbKly8PV1dXJCcnF9VhZSmvx+rv74++ffvi0qVLCAgIgKWlJTp06IC3b9/K1evYsSMiIyOFx759+4ricHKU12MFZKOifn0cL1++lFteWs7rkSNH5I7z0aNHUFdXzzRcRXE8r4mJiWjQoAE2bNigUP3w8HB06dIFbdq0QVBQECZOnIjhw4fLJQrKfFaKQl6P9cqVK2jfvj1OnTqFe/fuoU2bNujWrRsCAwPl6tWtW1fuvF67dq0wws+zvB5vhtDQULnjMTU1FZaVlnO7Zs0auWN8/fo1jIyMMn1ni+O5vXz5MsaOHYubN2/i/PnzSEtLQ4cOHZCYmJjtOsXid5bKIEdHRxo7dqzwWiKRUKVKlWjRokVZ1u/Tpw916dJFrszJyYlGjRpFRERSqZTMzc1p2bJlwvLY2FgSi8W0b9++QjgCxeX1WL+Vnp5Ourq6tGvXLqHM09OT3NzcCjrUfMvrse7cuZP09fWz3V5pPq+rVq0iXV1dSkhIEMqK63n9GgA6evRojnWmT59OdevWlStzd3cnV1dX4XV+37+ioMixZsXOzo7mzp0rvPby8qIGDRoUXGCFRJHjvXTpEgGgmJiYbOuU1nN79OhREolEFBERIZSVlHMbHR1NAOjy5cvZ1ikOv7Nl7gpRamoq7t27BxcXF6FMTU0NLi4uCAgIyHKdgIAAufoA4OrqKtQPDw9HVFSUXB19fX04OTllu82ioMyxfuvLly9IS0uDkZGRXLm/vz9MTU1ha2uLMWPG4OPHjwUae14pe6wJCQmwsrKCpaUl3Nzc8PjxY2FZaT6v27dvh4eHB8qXLy9XXtzOqzJy+74WxPtXXEmlUnz+/DnT9/XZs2eoVKkSqlevjv79++PVq1cqirBgODg4wMLCAu3bt8f169eF8tJ8brdv3w4XFxdYWVnJlZeEcxsXFwcAmT6XXysOv7NlLiH68OEDJBKJMBVIBjMzs0z3oTNERUXlWD/j37xssygoc6zfmjFjBipVqiT3IezYsSN2794NPz8/LFmyBJcvX0anTp0gkUgKNP68UOZYbW1tsWPHDhw/fhx//vknpFIpmjZtijdv3gAovef19u3bePToEYYPHy5XXhzPqzKy+77Gx8cjKSmpQL4XxdXy5cuRkJCAPn36CGVOTk7w8fHBmTNnsGnTJoSHh6NFixb4/PmzCiNVjoWFBby9vXH48GEcPnwYlpaWaN26Ne7fvw+gYP7PK47+/fdfnD59OtN3tiScW6lUiokTJ6JZs2ZyU2p9qzj8zhbLqTtY8bB48WL4+vrC399frrGxh4eH8Lx+/fqwt7eHjY0N/P390a5dO1WEqhRnZ2c4OzsLr5s2bYo6depg8+bNmD9/vgojK1zbt29H/fr14ejoKFdeWs5rWbV3717MnTsXx48fl2tT06lTJ+G5vb09nJycYGVlhQMHDmDYsGGqCFVptra2sLW1FV43bdoUz58/x6pVq/DHH3+oMLLCtWvXLhgYGGSakLwknNuxY8fi0aNHxaJtU27K3BUiY2NjqKur4927d3Ll7969g7m5eZbrmJub51g/49+8bLMoKHOsGZYvX47Fixfj3LlzsLe3z7Fu9erVYWxsjLCwsHzHrKz8HGsGDQ0NNGzYUDiO0nheExMT4evrq9B/lsXhvCoju++rnp4etLW1C+SzUtz4+vpi+PDhOHDgQKbbDt8yMDBArVq1Stx5zY6jo6NwLKXx3BIRduzYgYEDB0JTUzPHusXt3I4bNw4nTpzApUuXUKVKlRzrFoff2TKXEGlqaqJx48bw8/MTyqRSKfz8/OSuFnzN2dlZrj4AnD9/XqhvbW0Nc3NzuTrx8fG4detWttssCsocKyBryT9//nycOXMGTZo0yXU/b968wcePH2FhYVEgcStD2WP9mkQiwcOHD4XjKG3nFZB1a01JScGAAQNy3U9xOK/KyO37WhCfleJk3759GDJkCPbt2yc3jEJ2EhIS8Pz58xJ3XrMTFBQkHEtpO7eArMdWWFiYQn/EFJdzS0QYN24cjh49iosXL8La2jrXdYrF72yBNM0uYXx9fUksFpOPjw89efKERo4cSQYGBhQVFUVERAMHDqSZM2cK9a9fv07lypWj5cuXU0hICHl5eZGGhgY9fPhQqLN48WIyMDCg48ePU3BwMLm5uZG1tTUlJSUV+fF9La/HunjxYtLU1KRDhw5RZGSk8Pj8+TMREX3+/JmmTp1KAQEBFB4eThcuXKBGjRpRzZo1KTk5WSXHmCGvxzp37lw6e/YsPX/+nO7du0ceHh6kpaVFjx8/FuqUlvOaoXnz5uTu7p6pvDif18+fP1NgYCAFBgYSAFq5ciUFBgbSy5cviYho5syZNHDgQKH+ixcvSEdHh6ZNm0YhISG0YcMGUldXpzNnzgh1cnv/VCWvx7pnzx4qV64cbdiwQe77GhsbK9SZMmUK+fv7U3h4OF2/fp1cXFzI2NiYoqOji/z4vpXX4121ahUdO3aMnj17Rg8fPqSff/6Z1NTU6MKFC0Kd0nJuMwwYMICcnJyy3GZxPbdjxowhfX198vf3l/tcfvnyRahTHH9ny2RCRES0bt06qlq1KmlqapKjoyPdvHlTWNaqVSvy9PSUq3/gwAGqVasWaWpqUt26denkyZNyy6VSKf32229kZmZGYrGY2rVrR6GhoUVxKLnKy7FaWVkRgEwPLy8vIiL68uULdejQgUxMTEhDQ4OsrKxoxIgRKv/PJkNejnXixIlCXTMzM+rcuTPdv39fbnul5bwSET19+pQA0Llz5zJtqzif14yu1t8+Mo7P09OTWrVqlWkdBwcH0tTUpOrVq9POnTszbTen909V8nqsrVq1yrE+kWzIAQsLC9LU1KTKlSuTu7s7hYWFFe2BZSOvx7tkyRKysbEhLS0tMjIyotatW9PFixczbbc0nFsiWbdybW1t2rJlS5bbLK7nNqvjBCD3PSyOv7Oi/wXPGGOMMVZmlbk2RIwxxhhj3+KEiDHGGGNlHidEjDHGGCvzOCFijDHGWJnHCRFjjDHGyjxOiBhjjDFW5nFCxBhjjLEyjxMixphK+fj4QCQSISIiQtWhlDhz5syBSCRSdRgAAJFIhDlz5qg6DFbMXLlyBd26dUOlSpUgEolw7NixPK2fnJyMwYMHo379+ihXrlymCW4BwN/fHyKRKNMjKioqT/vihIgVK1l9qLN6+Pv7IyIiIsc6ixcvFrbbunVriEQi1KxZM8v9nj9/Xljv0KFDQnnGj3XGQ0tLC7Vq1cK4ceMyTTIIAK9evcLo0aNRrVo1iMVimJqaokePHrh+/Xqe34MVK1ZkWpYRz927dzMtu379Onr27AkzMzOIxWJUq1YNo0aNwqtXrzLV9fPzw9ChQ1GrVi3o6OigevXqGD58OCIjIxWOk7GiJpVKYWJigqVLl6oshtOnT0NDQwPa2to5zuDO3zGZxMRENGjQABs2bFBqfYlEAm1tbUyYMCHXiYtDQ0MRGRkpPExNTfO0r3JKRchYIfnjjz/kXu/evRvnz5/PVF6nTh0kJSUBAPr27YvOnTtn2lbDhg3lXmtpaSEsLAy3b9+Go6Oj3LI9e/ZAS0sLycnJWcY1b948WFtbIzk5GdeuXcOmTZtw6tQpPHr0CDo6OgBkCUlGHMOHD4ednR2ioqLg4+ODFi1aYM2aNRg/frzC78WyZcswZswYYfs5WbduHX7++WdUr14d48ePh4WFBUJCQrBt2zbs378fp06dQtOmTYX6M2bMwKdPn9C7d2/UrFkTL168wPr163HixAkEBQWV2JnBWel2+/ZtfPjwQaFJbAvDvXv30KdPH9ja2iIpKQlubm64fv06ateunakuf8dkOnXqhE6dOmW7PCUlBb/88gv27duH2NhY1KtXD0uWLEHr1q0BAOXLl8emTZsAyP6PjY2NzXZbpqamMDAwUD7YApsEhLFCMHbsWMruYxoeHk4AaNmyZblup1WrVlS3bl2ytbWliRMnyi1LSkoiPT096tWrFwGggwcPCst27txJAOjOnTty60yePJkA0N69e4mI6NOnT2Rubk5mZmaZ5hL68uULtWjRgtTU1Oj69eu5xgqAHBwcCACtWLFCbllW8Vy7do3U1NSoRYsWlJiYKFc/LCyMzMzMyMLCgj59+iSUX758mSQSiVzdy5cvEwD65Zdfco2xIGUcU3h4eJHutzTw8vLK9vtR1PDVnIeF5bfffiMrK6tC3Ud2wsPDydzcnOrVq0fR0dH08uVLql69OlWrVi3LOf+K03esuABAR48elSsbPnw4NW3alK5cuUJhYWG0bNkyEovF9M8//2Ra39PTk9zc3DKVZ8wTZ2VlRebm5uTi4kLXrl3Lc3x8y4yVKX379sX+/fshlUqFsr///htfvnxBnz59FN5O27ZtAQDh4eEAgM2bNyMqKgrLli2DjY2NXF1tbW3s2rULIpEI8+bNU2j7zZo1Q9u2bbF06VLhSlh25s+fD5FIhF27dmW6mmRjY4OlS5ciMjISmzdvFspbtmwJNTX5r3/Lli1hZGSEkJAQhWK8desWOnfuDENDQ5QvXx729vZYs2aNXJ2LFy+iRYsWKF++PAwMDODm5qbQ9rNrj1KtWjUMHjxYeJ1xC/HatWuYMGECTExMYGBggFGjRiE1NRWxsbEYNGgQDA0NYWhoiOnTp4O+mr4x47br8uXLsWXLFtjY2EAsFuO7777DnTt3FHofsot/3LhxOHjwIOzs7KCtrQ1nZ2c8fPgQgOzzUqNGDWhpaaF169ZZtp86ePAgGjduDG1tbRgbG2PAgAF4+/atQvv/888/hXWNjIzg4eGB169fZ6qX2zls3bq18Jf61wYPHoxq1arlGsfbt28xdOhQ4TZu3bp1sWPHjkz11q1bh7p160JHRweGhoZo0qQJ9u7dm6neyZMnM10dOn36NFq1agVdXV3o6enhu+++k1u3devWqFevHoKDg9GqVSvo6OigRo0awq3xy5cvw8nJCdra2rC1tcWFCxcy7ffTp0/o1KkTTExMcPHiRZiYmKBq1arw9/eHmpoaunTpgsTERLl1CuI7Vtq9evUKO3fuxMGDB9GiRQvY2Nhg6tSpaN68OXbu3KnwdiwsLODt7Y3Dhw/j8OHDsLS0ROvWrXH//v08xcMJESvxvnz5gg8fPmR6pKenZ6rbr18/REZGwt/fXyjbu3cv2rVrl6f7zc+fPwcAVKxYEYAsqdLS0so2qbK2tkbz5s1x8eLFXBOcDHPmzMG7d++Ey8VZ+fLlC/z8/NCiRQtYW1tnWcfd3R1isRgnTpzIcX8JCQlISEiAsbFxrrGdP38eLVu2xJMnT/Dzzz9jxYoVaNOmjdw+Lly4AFdXV0RHR2POnDmYPHkybty4gWbNmhV4A+rx48fj2bNnmDt3Lrp3744tW7bgt99+Q7du3SCRSLBw4UI0b94cy5Yty3T7FZB9BpYtW4ZRo0ZhwYIFiIiIwA8//IC0tDSlY7p69SqmTJkCT09PzJkzByEhIejatSs2bNiAtWvX4qeffsK0adMQEBCAoUOHyq3r4+ODPn36QF1dHYsWLcKIESNw5MgRNG/ePMdbBgDw+++/Y9CgQahZsyZWrlyJiRMnws/PDy1btpRbV5FzmB/v3r3D999/jwsXLmDcuHFYs2YNatSogWHDhmH16tVCva1bt2LChAmws7PD6tWrMXfuXDg4OODWrVty24uKikJgYKDc7XEfHx906dIFnz59wqxZs7B48WI4ODjgzJkzcuvGxMSga9eucHJywtKlSyEWi+Hh4YH9+/fDw8MDnTt3xuLFi5GYmIgff/wRnz9/FtZNSUmBm5sbNDU1hWQog6WlJfz9/REbG4vevXtn+X/O1/LyHSsLHj58CIlEglq1aqFChQrC4/Lly8L/sYqwtbXFqFGj0LhxYzRt2hQ7duxA06ZNsWrVqrwFlOdrSowVIUVumWX3CAgIEOpm3DIjImrSpAkNGzaMiIhiYmJIU1OTdu3aJVx2zeqW2YULF+j9+/f0+vVr8vX1pYoVK5K2tja9efOGiIgMDAyoQYMGOR7LhAkTCAAFBwfnWA8AjR07loiI2rRpQ+bm5vTlyxe5eDJumQUFBREA+vnnn3Pcpr29PRkZGeVYZ/78+QSA/Pz8cqyXnp5O1tbWZGVlRTExMXLLpFKp8NzBwYFMTU3p48ePQtmDBw9ITU2NBg0aJJRldcsM2dx+sbKyIk9Pz0zrurq6yu3b2dmZRCIRjR49Wi7uKlWqUKtWrYSyjM9QxYoV5W4pHj9+nADQ33//neN7kR0AJBaL5Y5p8+bNBIDMzc0pPj5eKJ81a5bc8aemppKpqSnVq1ePkpKShHonTpwgADR79myh7NtbZhEREaSurk6///67XDwPHz6kcuXKCeWKnsNWrVrJvV8ZPD09M926+vacDRs2jCwsLOjDhw9y9Tw8PEhfX1/4TLu5uQnfzZxs376dtLW1hfViY2NJV1eXnJyc5N6nrI4BX93eJiJ6+vQpASA1NTW6efOmUH727FkCQDt37sw1HmUo+h0rrfDNLTNfX19SV1enp0+f0rNnz+QekZGRmdbP7pZZVqZOnUrff/99nuLjK0SsxBs5ciTOnz+f6WFnZ5dl/X79+uHIkSNITU3FoUOHoK6ujp49e+a4DxcXF5iYmMDS0hIeHh6oUKECjh49isqVKwMAPn/+DF1d3Ry3kbE8Pj5e4WObM2cOoqKi4O3tneXyjL9kFdl3Tvu9cuUK5s6diz59+gi3A7MTGBiI8PBwTJw4MVMDxowu4JGRkQgKCsLgwYNhZGQkLLe3t0f79u1x6tSpHPeRV8OGDZPrfu7k5AQiwrBhw4QydXV1NGnSBC9evMi0vru7OwwNDYXXLVq0AIAs6yqqXbt2creVnJycAAC9evWSO18Z5Rn7unv3LqKjo/HTTz9BS0tLqNelSxfUrl0bJ0+ezHafR44cgVQqRZ8+feSulpqbm6NmzZq4dOkSAMXOYX4QEQ4fPoxu3bqBiORicXV1RVxcnHA7w8DAAG/evMn1FuWpU6fQpk0baGtrA5Bd4fr8+TNmzpwp9z5ldQwVKlSAh4eH8NrW1hYGBgaoU6eO8P4Dmc9FQcrLd6ysaNiwISQSCaKjo1GjRg25R34bnQcFBcHCwiJP63AvM1bi1axZM9fumF/z8PDA1KlTcfr0aezZswddu3bNNaHYsGEDatWqhXLlysHMzAy2trZy7QN0dXXlLrNnRdHk5WstW7ZEmzZtsHTpUowePTrT8oxtKbLv7Pb79OlT9OzZE/Xq1cO2bdtyjSnjUna9evWyrfPy5UsAsh+eb9WpUwdnz55FYmIiypcvn+v+FFG1alW51/r6+gBktzS+LY+Jicl1/YzkKKu6hRHT1/vK6b2rXbt2jl29nz17BiLKdngJDQ0NAIqdw/x4//49YmNjsWXLFmzZsiXLOtHR0QBkvbEuXLgAR0dH1KhRAx06dEC/fv3QrFkzoW5aWhrOnz+PRYsWCWV5OYYqVapkSpL09fVzPRcFJa/fsdIkISEBYWFhwuvw8HAEBQXByMgItWrVQv/+/TFo0CCsWLECDRs2xPv37+Hn5wd7e3uhvdiTJ0+QmpqKT58+4fPnzwgKCgIAODg4AABWr14Na2tr1K1bF8nJydi2bRsuXryIc+fO5SlWTohYmWNhYYHWrVtjxYoVuH79Og4fPpzrOo6OjmjSpEm2y+vUqYPAwECkpKRALBZnWSc4OBgaGhrZ/lhlx8vLC61bt8bmzZsz/TVfo0YNlCtXDsHBwdmun5KSgtDQ0Czjf/36NTp06AB9fX2cOnUqT8maKkgkkizL1dXVFS6nrxpV57Z+VnUVlZeY8ruvDFKpFCKRCKdPn85yPxUqVMjT9kQiUZZxZXcevo4DAAYMGABPT88s69jb2wOQfXdCQ0Nx4sQJnDlzBocPH8bGjRsxe/ZszJ07FwBw7do1xMfHZzm8hiJUcS4ylLTvWEG7e/cu2rRpI7yePHkyAMDT0xM+Pj7YuXMnFixYgClTpuDt27cwNjbG999/j65duwrrdO7cWfhDAfj/kCoZ5yk1NVVYX0dHB/b29rhw4YLcfhXBCRErk/r164fhw4fDwMBA6f9kv9a1a1cEBATg4MGDGDBgQKblERERuHr1KlxcXIRL/opq1aoVWrdujSVLlmD27Nlyy8qXL482bdrg4sWLePnyJaysrDKtf+DAAaSkpMj9BwMAHz9+RIcOHZCSkgI/Pz+FLy9n9KJ79OhRtlfmMuIIDQ3NtOzp06cwNjbO8eqQoaFhpsbDqampZWJQu6/fu29vrYSGhmZ5jjPY2NiAiGBtbY1atWrlWA/I+RwCsvOQ1e2jr3+csmJiYgJdXV1IJBKFrt6WL18e7u7ucHd3R2pqKn744Qf8/vvvmDVrFrS0tHDy5EnY2dnJ3YL8+hhq1KiR6z5UQdnvWGnSunXrHBNMDQ0NzJ07V0h+s5JbJ4zp06dj+vTpyoYo4DZErEz68ccf4eXlhY0bN0JTUzPf2xs1ahRMTU0xbdq0TD8gycnJGDJkCIgoU0KjqIy2RFndfvj1119BRBg8eHCmHmzh4eGYPn06LCwsMGrUKKE8MTERnTt3xtu3b3Hq1Kk8XbVq1KgRrK2tsXr16kxJS8Z/fBYWFnBwcMCuXbvk6jx69Ajnzp3LNQm1sbHBlStX5Mq2bNmS65WJ0qBJkyYwNTWFt7c3UlJShPLTp08jJCQkx0EJf/jhB6irq2Pu3LmZfoSICB8/fgSg2DkEZOfh6dOneP/+vVD2kk7ymAAABE5JREFU4MGDXEdeV1dXR69evXD48GE8evQo0/Kvt5cRUwZNTU3Y2dmBiIRefqdOncp03B06dICuri4WLVqUaUDVgrzCo6z8fMeYavAVIlbi3b9/H3/++WemchsbGzg7O2e5jr6+foHOu1SxYkUcOnQIXbp0QaNGjTKNVB0WFoY1a9bIjRadF61atUKrVq1w+fLlTMtatmyJ5cuXY/LkybC3t8fgwYNhYWGBp0+fYuvWrZBKpTh16pRco+H+/fvj9u3bGDp0KEJCQuTGRalQoUKW8wVlUFNTw6ZNm9CtWzc4ODhgyJAhwv4eP36Ms2fPApCNtN2pUyc4Oztj2LBhSEpKwrp16xR674cPH47Ro0ejV69eaN++PR48eICzZ8+qtLtyREQErK2thUv9hUVDQwNLlizBkCFD0KpVK/Tt2xfv3r3DmjVrUK1aNUyaNCnbdW1sbLBgwQLMmjULERER6NGjB3R1dREeHo6jR49i5MiRmDp1qsLncOjQoVi5ciVcXV0xbNgwREdHw9vbG3Xr1s21c8DixYtx6dIlODk5YcSIEbCzs8OnT59w//59XLhwAZ8+fQIgS2zMzc3RrFkzmJmZISQkBOvXr0eXLl2E2ENCQjINP6Gnp4dVq1Zh+PDh+O6779CvXz8YGhriwYMH+PLlC3bt2pXPM5E/+fmOMRXJU580xopYfrrdf909++tu99nJqdv9tyNVZyc8PJxGjBhBVatWJQ0NDTI2Nqbu3bvT1atXFVqfSL7bfVbxZRfPlStXyM3NjYyNjUlDQ4OqVq1KI0aMoIiIiEx1rayssn3fFB0J+Nq1a9S+fXvS1dWl8uXLk729Pa1bt06uzoULF6hZs2akra1Nenp61K1bN3ry5Ilcnay63UskEpoxYwYZGxuTjo4Oubq6UlhYWLbd7r99PzK6o79//16u3NPTk8qXLy+8zmm0c3zTjfzhw4cEgGbOnJnre5PVOcxuX1l97oiI9u/fTw0bNiSxWExGRkbUv39/YZiHb4/zW4cPH6bmzZtT+fLlqXz58lS7dm0aO3YshYaGytVT5Bz++eefVL16ddLU1CQHBwc6e/asQt3uiYjevXtHY8eOJUtLS9LQ0CBzc3Nq164dbdmyRaizefNmatmyJVWsWJHEYjHZ2NjQtGnTKC4ujoiI1q9fT/r6+pSWlpbpOImI/vrrL2ratKnwGXN0dKR9+/YJy7P77ltZWVGXLl0ylWf3/curgviOsaIlIioG1xYZY6yY27hxI6ZPn47nz5/DzMxM1eGUGZ07d0aFChVw4MABVYfCSjm+ZcYYYwq4dOkSJkyYwMlQEWvdurUwLhRjhYmvEDHGGGOszONeZowxxhgr8zghYowxxliZxwkRY4wxxso8TogYY4wxVuZxQsQYY4yxMo8TIsYYY4yVeZwQMcYYY6zM44SIMcYYY2UeJ0SMMcYYK/M4IWKMMcZYmccJEWOMMcbKvP8CZtfYR9QKS/YAAAAASUVORK5CYII=\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHgCAYAAABDx6wqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAD3vElEQVR4nOydd1hTSReHfwkhhN4REERFBaxYULEhVuwdO9h7W+u6dl17Xbuga3dta1vXsth7BxVF7IiAikrvkPP9cb9cCAkt0p33ee4Dd2buzLmXhJycOUVARAQGg8FgMBiMUoqwqAVgMBgMBoPBKEiYssNgMBgMBqNUw5QdBoPBYDAYpRqm7DAYDAaDwSjVMGWHwWAwGAxGqYYpOwwGg8FgMEo1TNlhMBgMBoNRqmHKDoPBYDAYjFINU3YYDAaDwWCUapiyw2AUMwYNGoTy5csXtRgMBoNRamDKzk/ErVu3MH/+fERGRha1KNmya9cuCAQCSCQShISEKPQ3b94c1atXV2hPSUnB+vXr4eTkBF1dXejo6MDJyQnr169HSkqK3Nj4+Hhs2rQJbdq0gYWFBXR1dVG7dm1s2bIFaWlpBXZvjJLLixcvMH36dDg6OkJXVxcWFhbo0KEDHjx4oHR8SEgI3N3dYWBgAD09PXTp0gVv376VGxMcHIwFCxagfv36MDQ0hImJCZo3b44LFy4ozHft2jV07twZ1tbWkEgkMDc3h5ubG27evKl0/Vu3bqFJkybQ0tKCubk5JkyYgNjYWIVxr169Qp8+fWBlZQUtLS3Y29tj4cKFiI+PV2nO+/fvY9y4cahWrRq0tbVRrlw5uLu74+XLl1k+24xcvHgRQ4YMQZUqVaClpYWKFSti2LBhCAsLU/k+8yKTt7c3XFxcUKZMGWhoaKBChQoYPHgw3r9/r3T9HTt2wMHBARKJBJUrV8aGDRuUjrtw4QJcXV1hYmICAwMD1K9fH3v37lV5zmPHjqF3796oWLEitLS0YGdnhylTphT7/+9FBjF+GlauXEkA6N27d0UtSrbs3LmTABAAGjdunEK/i4sLVatWTa4tNjaWXFxcCAB17NiRNm7cSJs3b6bOnTsTAHJxcaHY2Fh+/NOnT0kgEFCrVq1oxYoVtHXrVurWrRsBIA8PjwK/x+zw9PQkGxubIpWBociUKVPIwMCAhg4dStu2baMVK1aQra0tqampkY+Pj9zYmJgYqly5MpmZmdHy5ctpzZo1ZG1tTVZWVvT161d+3IYNG0hTU5P69u1LGzdupHXr1lGdOnUIAP35559yc3p7e1OXLl3o999/p+3bt9PKlSupVq1aJBQK6ezZs3JjfX19SSKRUO3atWnLli00a9Ys0tDQIDc3N7lxHz58IAMDA7KxsaGlS5fStm3baNCgQQSAOnfurNKcPXr0IHNzcxo/fjx5e3vTokWLqEyZMqStrU1Pnz7N8TnXrVuXKlSoQNOnTydvb2+aOXMm6erqUpkyZSgsLKzAZRo9ejR5enrSqlWraMeOHTR79mwqU6YMmZiYUEhIiNzYrVu3EgDq0aMHeXl50cCBAwkALVu2TG7cyZMnSSAQUKNGjWjDhg20ceNGatasGQGgNWvWqDSnsbEx1ahRg+bMmUPe3t40YcIEEovFZG9vT/Hx8Tk+558Npuz8RORF2UlLS6OEhISCF0oJMmXH0dGRNDQ0FP7BKFN2RowYQQBow4YNCvNt3LiRANCoUaP4tvDwcPL391cYO3jwYAJAr169yqe7yTtM2SmePHjwgGJiYuTavn79SqamptS4cWO59uXLlxMAunfvHt8WEBBAampqNHPmTL7N39+fwsPD5a5NTEwke3t7srKyylGmuLg4KlOmDLVt21auvV27dmRhYUFRUVF8m7e3NwGg8+fP822LFy8mAArvBQ8PDwJA379/z/OcN2/epKSkJLn5Xr58SRoaGtS/f/8c7+nq1auUlpam0AaAZs2apdJ9/qhMDx48IAC0dOlSvi0+Pp6MjY2pQ4cOcmP79+9P2tracs+udevWZGlpSYmJiXxbSkoK2draUs2aNVWa8/Llywpy7t69mwCQt7d3jvf0s8GUnZ+EefPm8daSjIdM8QFAY8eOpX379lHVqlVJJBLR8ePHiYjo0aNH5ObmRrq6uqStrU0tWrSg27dvy80vU1CuXr1KI0aMICMjI9LV1aWBAwfKvUFzg2yuw4cPk0gkovHjx8v1Z1Z2goODSU1NjVq0aJHlnK6uriQSiSg4ODjbtU+dOkUA6NSpU3mSmYho79695OTkRJqammRgYEBNmzaV+4dLRLRp0yaqWrUqicVisrCwoDFjxlBERITcmNwqOwBo3rx5Cu02Njbk6enJn8ue540bN+iXX34hExMT0tLSoq5du9KXL18Urj9z5gw1a9aMdHR0SFdXl+rVq0f79+/PVhbZ6+vVq1fk6elJ+vr6pKenR4MGDaK4uDi5sSkpKbRw4UKqWLEiicVisrGxoZkzZ8p9EMjuo0OHDnT9+nVycnIiDQ0NqlChAu3evVth/YiICJo4cSJZWVmRWCwmW1tbWrZsmcKH5tevX2nAgAGkq6tL+vr65OHhQX5+fgSAdu7cme09ZkX37t3JyMhIrs3JyYmcnJwUxrZp04ZsbW1znHPy5MkEgKKjo3McW716dWrQoAF/HhUVRSKRiKZNmyY3LikpiXR0dGjo0KF824wZMwiAgsI1Y8YMEgqFvDU0L3NmRZ06dahOnTo5jssKIyMj6t69O39emDJ9/fqVANCMGTP4tn///ZcA0L///is39tatWwSA9u7dy7c1aNBA4QuarD3j3y4vcyojOjqaANDkyZNzvKefDeaz85PQvXt39O3bFwCwdu1a7N27F3v37oWpqSk/5tKlS/jll1/Qu3dv/PHHHyhfvjyePXuGpk2b4vHjx5g+fTrmzJmDd+/eoXnz5rh7967COuPGjUNAQADmz58PDw8P7N+/H127dgUR5VnmChUqwMPDA97e3ggNDc1y3NmzZ5GWlgYPD48sx3h4eCA1NRXnzp3Lds1Pnz4BAExMTPIk64IFCzBw4ECoq6tj4cKFWLBgAaytrXHp0iV+zPz58zF27FhYWlpi9erV6NGjB7Zt24Y2bdoo+BQVBOPHj8fjx48xb948jB49Gv/88w/GjRsnN2bXrl3o0KEDvn//jpkzZ2LZsmVwdHTM8bnJcHd3R0xMDJYuXQp3d3fs2rULCxYskBszbNgwzJ07F3Xq1MHatWvh4uKCpUuXok+fPgrzvX79Gj179kTr1q2xevVqGBoaYtCgQXj27Bk/Jj4+Hi4uLti3bx88PDywfv16NG7cGDNnzsTkyZP5cVKpFJ06dcJff/0FT09PLF68GGFhYfD09MzLY1Tg06dPcq8XqVSKJ0+eoF69egpj69evjzdv3iAmJibHObW0tKClpaXQFx0dja9fv+LFixf47bff4O/vj5YtW/L9T58+RWpqqsL6YrEYjo6O8PX15duaN28OABg6dCj8/PwQHByMQ4cOYcuWLZgwYQK0tbXzPKcyiAifP3/O8/tKRmxsLGJjY+WuL2iZvn37hi9fvuDBgwcYPHgwAMg9Z9n8mdevW7cuhEKhwnN+9uwZ5syZg9evX+PNmzdYtGgRHjx4gOnTp6s0pzJU/f/1U1DEyhajEMluGwsACYVCevbsmVx7165dSSwW05s3b/i20NBQ0tXVpWbNmvFtMutB3bp1KTk5mW9fsWIFAaCTJ0/mWk7ZXPfv36c3b96QSCSiCRMm8P2ZLTuTJk0iAOTr65vlnI8ePcrxG09SUhJVrVqVKlSoQCkpKbmW99WrVyQUCqlbt24KlgSpVEpERF++fCGxWExt2rSRGyPbYsvon1FQlp1WrVrx8hAR/fLLL6SmpkaRkZFERBQZGUm6urrUoEEDhS3MjNcpQ2bZGTJkiFx7t27dyNjYmD+XWVGGDRsmN27q1KkEgC5duiR3HwDo2rVrfNuXL19IQ0ODpkyZwrctWrSItLW16eXLl3Jz/vrrr6SmpkYfPnwgIqK///6bANC6dev4MWlpadSiRQuVLTvXrl0jgUBAc+bM4dvCw8MJAC1cuFBh/KZNmwgAvXjxIss5X716RRKJhAYOHKi0v23btrxlViwW08iRI+X+XkeOHFF4bjJ69epF5ubmcm2LFi0iTU1NOYtv5u2ivM6Zmb179xIA2rFjR7bjsmLRokUEgC5evFhoMmloaPDPw9jYmNavXy/XP3bsWFJTU1N6rampKfXp04c/j42NJXd3dxIIBPycWlpadOLECZXnVMbQoUNJTU1N4b3AYJYdRgZcXFxQtWpV/jwtLQ3//fcfunbtiooVK/LtFhYW6NevH27cuIHo6Gi5OUaMGAF1dXX+fPTo0RCJRDhz5oxKMlWsWBEDBw6El5dXltEYsm/Jurq6Wc4j68ssb0bGjRuH58+fY+PGjRCJRLmW8cSJE5BKpZg7dy6EQvm3lEAgAMBFYiQnJ2PSpElyY4YPHw49PT38+++/uV5PVUaMGMHLAwBNmzZFWloagoKCAAA+Pj6IiYnBr7/+ColEovQ+cmLUqFFy502bNsW3b9/45y57HWS0uADAlClTAEDhOVStWhVNmzblz01NTWFnZycX1XTkyBE0bdoUhoaG+Pr1K3+0atUKaWlpuHbtGgDg3LlzUFdXx/Dhw/lrhUIhxo4dm6t7y8yXL1/Qr18/VKhQQe7beUJCAgBAQ0ND4RrZc5WNyUx8fDx69eoFTU1NLFu2TOmYZcuW4b///sOOHTvQsGFDJCcnIzU1NdfrZ167fPnyaNasGby8vPD3339jyJAhWLJkCTZu3KjynBl58eIFxo4dC2dnZ5WsaNeuXcOCBQvg7u6OFi1aFJpMZ8+exZkzZ7B69WqUK1cOcXFxcv0JCQkQi8VKr828voaGBqpUqYKePXvir7/+wr59+1CvXj0MGDAAd+7cUWnOzBw4cAA7duzAlClTULly5SzH/azk/j86o9RToUIFufPw8HDEx8fDzs5OYayDgwOkUimCg4NRrVo1vj3zm0xHRwcWFhZZhm3mhtmzZ2Pv3r1YtmwZ/vjjD4V+mSKT3dZATgrRypUr4e3tjUWLFqF9+/Z5ku/NmzcQCoVyimJmZApF5mcpFotRsWJFvr8gKVeunNy5oaEhACAiIgIAdx8AlIb158caenp6CAoKglAoRKVKleTGmZubw8DAQOE5ZJ5PNqdMZoALnX7y5InclmxGvnz5AoD7G1hYWChsDWWWJTfExcWhY8eOiImJwY0bN6Cjo8P3aWpqAgCSkpIUrktMTJQbk5G0tDT06dMHz58/x9mzZ2Fpaal0bUdHR/73AQMGoE6dOhg0aBCOHj2aq/Uzrn3w4EGMGDECL1++hJWVFQBuy1sqlWLGjBno27cvjI2N8zRnRj59+oQOHTpAX18fR48ehZqamtJxWfHixQt069YN1atXx/bt2+X6ClomV1dXAEC7du3QpUsXVK9eHTo6OvzWr6amJpKTk5Vem3n9cePG4c6dO3j06BH/Zcfd3R3VqlXDxIkTeZeAvMyZkevXr2Po0KFo27YtFi9erHTMzw6z7DB4snojFTUVK1bEgAEDsrTuODg4AACePHmS5RyyPmUKya5duzBjxgyMGjUKs2fPziepi46s8gRl9U+dVPCnyorcrpFbS1Fu5pNKpWjdujV8fHyUHj169Mil9LkjOTkZ3bt3x5MnT3Dy5EkF5dDIyAgaGhpKX6uyNmWKzPDhw3H69Gns2rVLzoKRHWKxGJ07d8axY8f4b/0WFhZya2VeP+PamzdvRu3atXlFR0bnzp0RHx/P+4jkZU4ZUVFRaNeuHSIjI3Hu3LkslbesCA4ORps2baCvr48zZ84ofFEpTJlsbW1Ru3Zt7N+/X279tLQ0XpmWkZycjG/fvvFzJycnY8eOHejQoYOcVVddXR3t2rXDgwcPeAUnt3Nm5PHjx+jcuTOqV6+Oo0eP5skq/TPBlJ2fiNx+wMgwNTWFlpYWAgMDFfpevHgBoVAIa2trufZXr17JncfGxiIsLOyHMwLPnj0bqampWL58uUJfu3btoKamlmWCLgDYs2cPRCIR3Nzc5NpPnjyJYcOGoXv37ti0aZNKstna2kIqleL58+dZjrGxsQEAhWeZnJyMd+/e8f15wdDQUCGBWHJycpbbfTlha2sLAPD391fp+txgY2MDqVSq8Dr5/PkzIiMjVXoOtra2iI2NRatWrZQeMuuQjY0NwsLCFJLlvX79OtdrSaVSeHh44OLFizhw4ABcXFwUxgiFQtSoUUNpssG7d++iYsWKCh/c06ZNw86dO7F27Vo+kCC3JCQkgIh462X16tUhEokU1k9OToafn5+cZejz589KlWOZw7xseywvcwKcFaJTp054+fIlTp8+na3VUxnfvn1DmzZtkJSUhPPnz/OKTUYKW6aEhARERUXx57L5M6//4MEDSKVSvv/bt29ITU3N8jlLpVK+L7dzynjz5g3c3NxgZmaGM2fOyFkYGZkoWpchRmGyZcuWLB158f/Q88x07dqVNDQ05JyaP336RHp6enlyUM7siJcdGR2UMzJo0CCSSCRkZ2enEMY5bNgwAkCbN29WmE923yNHjpRrv3r1KkkkEnJ1dVUIe84LeXFQdnNzk3P23bx5s8oOyvXq1aPatWvLtW3YsIEAKHVQzvw8L1++TAD4fB1RUVGkq6tL9evXV9lBOXMIs2xt2etH5qA8YsQIuXHTp09X6qCcOd8IEeeg7uLiwp/Pnz+fANC5c+cUxkZERPDO5kePHv1hB+UxY8YQANq2bVu245YtW6bwzF+8eEFqampy4ctE6e+R3377Lds5P3/+rNAWERFB1tbWZG1tLdfu5uZGFhYWcqHr27dvJwByCQg7duxIYrGYAgMD5a7v2rUrCYVCuRxXuZ0zNTWVOnfuTCKRSCGEOjfExsZS/fr1SVdXlx48eJDt2PyWKSUlRWmqjLt375Kampqc03h8fDwZGRlRx44d5cYOGDCAtLS06Nu3b/zaBgYGVKVKFblcPzExMWRlZUX29vZ5npOIKCwsjCpWrEiWlpbFPlFscYDZu34i6tatCwCYNWsW+vTpA3V1dXTq1IkPL1XG77//Dh8fHzRp0gRjxoyBSCTCtm3bkJSUhBUrViiMT05ORsuWLeHu7o7AwEBs3rwZTZo0QefOnX9Y/lmzZmHv3r0IDAyU8xMCuHD6Fy9eYMyYMTh37hxvwTl//jxOnjwJFxcXrF69mh8fFBSEzp07QyAQoGfPnjhy5IjcfDVr1kTNmjVzJVelSpUwa9YsLFq0CE2bNkX37t2hoaGB+/fvw9LSEkuXLoWpqSlmzpyJBQsWwM3NDZ07d+afj5OTEwYMGJDn5zFs2DCMGjUKPXr0QOvWrfH48WOcP39e5bBTPT09rF27FsOGDYOTkxP69esHQ0NDPH78GPHx8di9e7dK82akVq1a8PT0hJeXFyIjI+Hi4oJ79+5h9+7d6Nq1K+8nkRemTZuGU6dOoWPHjhg0aBDq1q2LuLg4PH36FEePHsX79+9hYmKCrl27on79+pgyZQpev34Ne3t7nDp1Ct+/fweQs+Vz3bp12Lx5M5ydnaGlpYV9+/bJ9Xfr1o1/L40ZMwbe3t7o0KEDpk6dCnV1daxZswZlypThnbEB4Pjx45g+fToqV64MBwcHhTlbt26NMmXKAOAsmFZWVmjQoAHMzMzw4cMH7Ny5E6GhoTh06JDcdYsXL0ajRo3g4uKCESNG4OPHj1i9ejXatGkjZ92cNm0azp49i6ZNm2LcuHEwNjbG6dOncfbsWQwbNkxu2yS3c06ZMgWnTp1Cp06d8P37d4V7yum13r9/f9y7dw9DhgxBQEAAAgIC+D4dHR107dq1wGSKjY2FtbU1evfuzZeWePr0KXbu3Al9fX3MmTOHv0ZTUxOLFi3C2LFj0atXL7Rt2xbXr1/Hvn37sHjxYhgZGQHgtmKnTp2K2bNno2HDhvDw8EBaWhp27NiBjx8/ysmS2zkBwM3NDW/fvsX06dNx48YN3Lhxg+8rU6YMWrdune1z/ukoam2LUbgsWrSIypYtS0KhUGlSQWU8evSI2rZtSzo6OqSlpUWurq5069YtuTGZkwoaGhqSjo4O9e/fX+7bSG7IyhJBxFk9AChN0JWUlERr166lunXrkra2NmlpaVGdOnVo3bp1ctYmonSrRlaHspDunPjzzz+pdu3apKGhQYaGhuTi4qJQRmDjxo1kb29P6urqVKZMGRo9erTKSQXT0tJoxowZfJLAtm3b0uvXr7MMPc/JsiPj1KlT1KhRI9LU1CQ9PT2qX78+/fXXX9nKklvLDhH37XnBggVUoUIFUldXJ2tr62yTCmYms2WHiPuWPHPmTKpUqRKJxWIyMTGhRo0a0apVq+T+9uHh4dSvXz8+qeCgQYPo5s2bBIAOHjyY7T3KXntZHZm/XQcHB1PPnj1JT0+PdHR0qGPHjgqZubNK9ik7Mv5tNm7cSE2aNCETExMSiURkampKnTp1Uhp6TUR0/fp1atSoEUkkEjI1NaWxY8cqTVJ49+5dateuHZmbm5O6ujpVqVKFFi9erDT9Qm7mlJVtyerICVnKAWWHsvdFfsqUlJREEydOpJo1a5Kenh6pq6uTjY0NDR06NEvriZeXF9nZ2fHJLNeuXavUErp//36qX78+GRgYkKamJjVo0ICOHj2q8pzZ3U/m9weDSECUj96JjJ+WXbt2YfDgwbh//77SZGoMRnHlxIkT6NatG27cuIHGjRsXtTgMBqMAYA7KDAbjpyFznpK0tDRs2LABenp6qFOnThFJxWAwChrms8MoNGQp37PD1NQ0z7k4ChJZ+vWs0NTUhL6+fiFJw/hRxo8fj4SEBDg7OyMpKQnHjh3DrVu3sGTJkmKbeoHBYPw4TNlhFBqrVq1SqJOUmXfv3v1wmHp+oizkNSOenp7YtWtX4QjD+GFatGiB1atX4/Tp00hMTESlSpWwYcMGhRphDAajdMF8dhiFxtu3b+XS/CujSZMmCqUKipILFy5k229paZnnfB0MBoPBKFyYssNgMBgMBqNUwxyUGQwGg8FglGqYssNg5MCuXbsgEAhyVcy0fPnyGDRoUIHLxGAwGIzcw5QdRrFHpmxkPMzMzODq6oqzZ88WtXiFRnR0NBYsWIBatWpBR0cHmpqaqF69OmbMmIHQ0FCF8adPn4abmxuMjY0hkUhQpUoVTJ06Fd++ffthWU6dOoU6depAIpGgXLlymDdvHl9HSca1a9fQuXNnWFtbQyKRwNzcHG5ubrh586bCfCkpKViwYAEqVqwIDQ0NVKxYEb///rvCnFnx+fNnDB48GGZmZtDU1ESdOnUUsmLLOHjwIC+7qakphg4diq9fvyodu2PHDjg4OEAikaBy5crYsGGD0nEhISFwd3eHgYEB9PT00KVLFwX/tPfv3/Ov37///lthjvnz50MgEGQpi4zY2FjMmzcPbm5uMDIygkAgyNZJPiAgAG5ubtDR0YGRkREGDhyI8PDwLGXLfBw8eDBbeQDg4sWLGDJkCKpUqQItLS1UrFgRw4YNy7JOW3JyMpYsWQJ7e3tIJBKUKVMGHTp0wMePH+XGJSUlYcaMGbC0tISmpiYaNGgAHx8fpXPeunULTZo0gZaWFszNzTFhwgS56M/Dhw9DIBDg+PHjCtfWqlULAoEAly9fVugrV64cGjVqlOMzYBRzijKjIYORG2RZeBcuXEh79+6lPXv20MqVK6latWoEgP75559CWT839WcyZy/OL968eUMVKlQgNTU16tOnD23cuJG8vLxo3LhxZGxsTJUrV5YbP2XKFAJAtWrVouXLl5O3tzeNHj2aNDQ0qGzZsvTixQuVZTlz5gwJBAJydXUlLy8vGj9+PAmFQho1apTcOG9vb+rSpQv9/vvvtH37dlq5ciXVqlWLhEKhXN0iIiJ3d3cSCAQ0dOhQ2rJlC5+tePjw4TnKExUVRZUqVSJdXV2aPXs2bdy4kZo1a0YAaP/+/XJjZbXIWrZsSZs2baKZM2eSlpYW1axZU6Ee2NatWwkA9ejRg7y8vGjgwIEEgJYtWyY3LiYmhipXrkxmZma0fPlyWrNmDVlbW5OVlRV9/fqVH/fu3Ts+w23NmjUVMuJmlYU6M7J5ypUrR82bN8+2rldwcDCZmJiQra0t/fHHH7R48WIyNDSkWrVqydVpks3Zt29f2rt3r9zx/v37bOUhIqpbty5VqFCBpk+fTt7e3jRz5kzS1dWlMmXKUFhYmNzY5ORkatWqFWlpadHEiRNpx44dtGrVKurVqxf5+/vLje3Tpw+JRCKaOnUqbdu2jZydnUkkEtH169flxvn6+pJEIqHatWvTli1baNasWaShoUFubm78mJCQEAJAkydPlrs2KiqKhEIhiUQiWrRokVzfhw8fCABNmzYtx2fAKN4wZYdR7Mmq3MH3799JXV2d+vXrVyjrF7ayk5KSQklJSZSSkkK1atUiLS0thX/yRNw/64xFJA8cOEAAqHfv3pSamio39u7du6SlpUU1atRQWg4gN1StWpVq1aold/2sWbNIIBBQQEBAttfGxcVRmTJlqG3btnzbvXv3CADNmTNHbuyUKVNIIBDQ48ePs51TVkjz4sWLfFtaWho5OTmRubk5/6GelJREBgYG1KxZMzlF459//iEAtH79er4tPj6ejI2NFcpV9O/fn7S1teWKRS5fvpwA0L179/i2gIAAUlNTo5kzZ/JtMoXC0dGRANDff/8tN3dulZ3ExERegbh//362ys7o0aNJU1OTgoKC+DYfHx+FYqYy2VauXJnt2llx9epVhSK4V69eJQA0a9Ysufbly5eTuro63b17N9s57969qyBTQkIC2drakrOzs9zYdu3akYWFBUVFRfFt3t7eBIDOnz/Pt1WoUIHq168vd+25c+dIIBBQ37595V6XROnvpZMnT2YrK6P4w7axGCUWAwMDaGpqQiSSTxcVFxeHKVOmwNraGhoaGrCzs8OqVatAGQIPZWZ7ZeZ/gUCA+fPnZ7s2EeH333+HlZUVtLS04OrqimfPnikdGxkZiUmTJvHyVKpUCcuXL4dUKlWQZ9WqVVi3bh1sbW2hoaGB58+f4++//8bjx48xa9YsNGnSRGF+PT09LF68mD9fsGABDA0N4eXlpZCgsX79+pgxYwZfJDOvPH/+HM+fP8eIESPknvuYMWNARDnOqaWlBVNTU0RGRvJt169fBwD06dNHbmyfPn1ARApFLjNz/fp1mJqaokWLFnybUCiEu7s7Pn36hKtXrwIA/P39ERkZid69e8sV/ezYsSN0dHTktmsuX76Mb9++YcyYMXJrjR07FnFxcfj333/5tqNHj8LJyQlOTk58m729PVq2bInDhw8ryNunTx9UqVIFCxculHtN5hYNDQ2Ym5vnauzff/+Njh07oly5cnxbq1atUKVKFaWyAdz7Jzk5OU8yNWvWDEKhUKHNyMhIrpCnVCrFH3/8gW7duqF+/fpITU1FfHy80jmPHj0KNTU1jBgxgm+TSCQYOnQobt++jeDgYADc9q6Pjw8GDBgAPT09fqyHhwd0dHTk7rNJkybw9fWVy6R98+ZNVKtWDe3atcOdO3fk3pc3b96EQCBgZURKAUzZYZQYoqKi8PXrV4SHh+PZs2cYPXo0YmNj5aooExE6d+6MtWvXws3NDWvWrIGdnR2mTZuGyZMn55ssc+fOxZw5c1CrVi2sXLkSFStWRJs2bRAXFyc3Lj4+Hi4uLti3bx88PDywfv16NG7cGDNnzlQqz86dO7FhwwaMGDECq1evhpGREU6dOgUAGDhwYI5yvXr1CoGBgejSpYvcP/6MeHh4AOB8evKKr68vACjUP7O0tISVlRXfn5Ho6Gh8/foVL168wG+//QZ/f3+0bNmS709KSgIAhQzGWlpaAICHDx9mK1NSUpLS7MeZr89qHVmbr68v/0GX1X3WrVsXQqGQ75dKpXjy5InSenD169fHmzdvEBMTI9eupqaG2bNn4/Hjx0r9R/KLkJAQfPnyJUvZlP2tFixYAB0dHUgkEjg5OeG///5TeX1ZxnQTExO+7fnz5wgNDUXNmjUxYsQIaGtrQ1tbGzVr1lTwl/H19UWVKlUUXsf169cHAPj5+QEAnj59itTUVIX7FIvFcHR0lLvPJk2aICUlBXfv3uXbbt68iUaNGqFRo0aIioqCv7+/XJ+9vT2MjY1Vfg6M4gHLoMwoMbRq1UruXENDA3/++Sdat27Nt506dQqXLl3C77//jlmzZgHgvo336tULf/zxB8aNGwdbW9sfkiM8PBwrVqxAhw4d8M8///BWglmzZmHJkiVyY9esWYM3b97A19cXlStXBgCMHDkSlpaWWLlyJW+BkvHx40e8fv0apqamfFtAQAD09fXlxmXF8+fPAXAOl1lRvnx56OnpyX3jzi0yh1NlmaUtLCyUOkq7u7vj/PnzALgPoJEjR2LOnDl8v52dHQDug6VChQp8u8ziExISkq1MdnZ2uHDhAoKCgmBjY5Pl9ZUrV4ZAIMDNmzcxePBgflxgYCDvsBsREQFjY2OEhYVBTU0NZmZmcmuJxWIYGxvz9/n9+3ckJSVl+TwAIDQ0lL9HGf369cOiRYuwcOFCdOvWTc7SlF/k9LeSya6hoQGhUIg2bdqgW7duKFu2LN6+fYs1a9agXbt2OHXqFDp06JDn9detW4fk5GT07t2bb3v16hUAYO3atTAyMsK2bdsAAEuWLIGbmxvu37+PmjVr8vLn9Fxzc5+y1wEA3jJ648YNNG/eHKmpqbh79y48PT1ha2uLMmXK4MaNG6hZsyZiYmLw9OlTDBkyJM/3zih+MMsOo8SwadMm+Pj4wMfHB/v27YOrqyuGDRuGY8eO8WPOnDkDNTU1TJgwQe7aKVOmgIjyJXrrwoULSE5Oxvjx4+U+pCZNmqQw9siRI2jatCkMDQ3x9etX/mjVqhXS0tJw7do1ufE9evSQU3QAzjKiq6ubK9lkVoScxuvq6iI6OjpXc2ZEZv7X0NBQ6JNIJAqFNgFg2bJl+O+//7Bjxw40bNgQycnJclFW7du3h42NDaZOnYpjx44hKCgIhw8fxqxZsyASiZTOmZFhw4ZBTU0N7u7uuHXrFt68eYOlS5fyVhPZ9SYmJnB3d8fu3buxevVqvH37FtevX0fv3r2hrq4uNzYhIQFisVjpehnvM6fnkXFMRjJad06cOJHt/alKXmQrV64czp8/j1GjRqFTp06YOHEifH19YWpqiilTpuR57WvXrmHBggVwd3eX216URUfFxMTg4sWLGDRoEAYNGoQLFy6AiLBixQo5+XMje15ekw4ODjA2NsaNGzcAAI8fP0ZcXBwfbdWoUSM+WvD27dtIS0tTunXMKHkwZScD165dQ6dOnWBpaQmBQJDnf0KJiYkYNGgQatSoAZFIhK5duyqMuXLlitLwzpwKTjI483WrVq3QqlUr9O/fH//++y+qVq2KcePG8T4GQUFBsLS0VPiwd3Bw4Pt/FNkcMkuNDFNTUxgaGsq1vXr1CufOnYOpqancIbNSffnyRW58RsuGDD09PYWtkKyQ3XdO42NiYnKtQGVEtgUk2xLKSGJiotItIkdHR7Ru3RpDhgyBj48P7t27J5eLSCKR4N9//4WxsTF69OiB8uXLw8PDA3PnzoWRkRF0dHSylalmzZo4cOAA3rx5g8aNG6NSpUpYv3491q1bBwBy12/btg3t27fH1KlTYWtri2bNmqFGjRro1KmT3FhNTc0s/VYy3mdOzyPjmMz0798flSpVUtl3Jyd+RDYAMDIywuDBgxEYGKgQEp4dL168QLdu3VC9enVs375dqUyNGzeWs1SWK1cOTZo0wa1bt+TG5kb2vLwmBQIBGjVqxPvm3Lx5E2ZmZqhUqRIAeWVH9pMpO6UDpuxkIC4uDrVq1cKmTZtUuj4tLQ2ampqYMGGCwpZLZgIDAxEWFsYfmc3ljJwRCoVwdXVFWFgYbx7PLVltG6SlpeWHaDxSqRStW7fmLVKZjx49esiNV/bhY29vj6ioKN4hMztkSt2TJ0+yHBMUFITo6GiVanrJtgqU5U8JCwuDpaVltteLxWJ07twZx44dk/vGXa1aNfj7+8Pf3x/Xr19HaGgohg8fjq9fv6JKlSo5ytWzZ0+Ehobi3r17uH37NoKCglCxYkUAkLteX18fJ0+eRFBQEK5evYr3799j7969CAsLg6mpKQwMDPj7TEtLU1BGk5OT8e3bN/4+jYyMoKGhkeXzAJDlM5FZd/z8/HDy5Mkc7zGv5PS3ksmeHTKF5Pv377laMzg4GG3atIG+vj7OnDmjoFDLnkWZMmUUrjUzM0NERISc/Ll5rnl9TTZp0gRRUVF4+vQp768jo1GjRggKCkJISAhu3LgBS0tL/nXEKNkwZScD7dq1w++//45u3bop7U9KSsLUqVNRtmxZaGtro0GDBrhy5Qrfr62tjS1btmD48OE5RkuYmZnB3NycPzJHMjByh2w7RGYet7GxQWhoqIJl48WLF3w/AN4CkzEqCMid5Uc2R2YFKzw8XO6fNQDY2toiNjaWt0hlPjJGyWSFzOqwb9++HMdWqVIFVapUwYkTJ7K07uzZswcAF4WUVxwdHQEADx48kGsPDQ3Fx48f+f7sSEhIABEpyCcQCFCtWjU0adIERkZGuHz5MqRSaY5fHGSIxWI4OTmhYcOGEIvFfBFXZdeXK1cOzZo1g42NDSIjI/Hw4UO5cVnd54MHDyCVSvl+oVCIGjVqKIwDgLt376JixYrZWtAGDBiASpUqYcGCBflu3SlbtixMTU2Vynbv3r1c/a1kiREzb60q49u3b2jTpg2SkpJw/vx5pT40NWrUgLq6ulI/rNDQULl1HB0d8fLlS4XtVplzsUz+6tWrQyQSKdxncnIy/Pz8FO4zo9/OzZs35SKt6tatCw0NDVy5cgV3795lUViliSIKeS/2AKDjx4/LtQ0bNowaNWpE165do9evX9PKlStJQ0ODXr58qXC9p6cndenSRaH98uXLBIBsbGzI3NycWrVqRTdu3CiguygdZJVnJzk5mSpXrkxisZjPr3HixAkCQEuWLJEb27t3bxIIBPT69Wu+zcTEhLp16yY3TpaMb968eQrry/LsfPnyhdTV1alDhw5y+Vp+++03AiCXZ2f+/PkEgM6dO6dwXxEREXyumuzynCQnJ1ONGjVIW1ubbt26pdAfHR0tl2dn3759BID69eunkGfnwYMHpK2tTdWrV6fk5GSFuXKDvb091apVS27u2bNnk0AgoOfPn/Ntnz9/Vrg2IiKCrK2tydraOts14uPjqU6dOmRhYUHR0dF8e1xcHAUEBOSYi+bly5ekq6tLHTt2zPF+Ro0aRUKhUC5PTnx8PBkZGSlcP2DAANLS0qJv377xbcuWLVN4fb548YLU1NRoxowZfFtWf+Ndu3bJ5d/J6d4yklOenVGjRpGmpiZ9+PCBb7tw4QIBoC1btvBtX758Ubj248ePZGhoSDVr1pRrDw0NpYCAALnXT2xsLNWvX590dXXpwYMH2crcpUsXUlNTk8vJ9Pz5c1JTU6MxY8bwbXfu3FF4XomJiVSpUiVq0KCB3Jxubm4Kr5Xt27cTAIUElklJSSSRSMjZ2ZkA0M2bN+X6nZ2d+b4//vgj23thlByYspMFmZWdoKAgUlNTo5CQELlxLVu2lEscJiMrZefFixe0detWevDgAd28eZMGDx5MIpGIHj58mN+3UGrInEF57969tHr1aqpbty4BoF9//ZUfm5aWRq6uriQQCGjEiBG0adMm6tKlCwGgSZMmyc3766+/EgA+a2/fvn35ObNTdoiIZs6cSQCoffv2tHHjRho6dChZWlqSiYmJnLITFxdHderUIZFIRMOGDaMtW7bQqlWryNPTk7S1tfkPtpySur169YpsbGxIJBJRv379aNOmTeTl5UUTJ04kU1NTqlKlitz4iRMn8h+gK1eupO3bt9OYMWNIIpEozaAsU8Iz3ndW/PPPPyQQCKhFixbk5eVFEyZMIKFQqJDtuE6dOtS5c2davHgxeXt705w5c8jKyoqEQiEdOXJEbmyvXr1o4sSJtG3bNlq5ciU5ODiQhoYGXbhwIVdyOjg40Ny5c2n79u00a9YsMjIyIhsbG/r48aPcuKVLl1L//v1p/fr1tHnzZmrTpg0BoN9//13hPjdt2kQAqGfPnuTt7U0eHh4EgBYvXiw3Ljo6mmxtbcnMzIxWrFhBa9euJWtra7K0tJRTIrL6G6ekpJCtrS2fXTk3ys6GDRto0aJFNHr0aAJA3bt3p0WLFtGiRYsoMjKSH/fhwwcyNjYmW1tbWr9+PS1ZsoQMDQ2pRo0alJiYyI8bNGgQNW3alObPn09eXl7022+/kbGxMYnFYrp8+bLc2rLs1hnfD7L32JAhQxQyMGf+0vjs2TPS0dEhCwsLWrp0KS1dupQsLCzI1NRU4e/Vq1cvEolENG3aNNq2bRs1atSIRCIRXb16VW7cw4cPSUNDQy6DskQioTZt2ih9fk2bNiUApKGhIfcciNK/8ABg/5dLEUzZyYLMys7p06cJAGlra8sdIpGI3N3dFa7PStlRRrNmzWjAgAH5JHnpQ6ZsZDwkEgk5OjrSli1bFNLux8TE0C+//EKWlpakrq5OlStXppUrVyqMi4+Pp6FDh5K+vj7p6uqSu7s7ffnyJVfKTlpaGi1YsIAsLCxIU1OTmjdvTv7+/kozKMfExNDMmTOpUqVKJBaLycTEhBo1akSrVq3ivx3nJoNtREQEzZ07l2rUqEFaWlokkUioevXqNHPmTIWU/ESclat169ZkaGhIGhoaVKlSJZoyZYrSD1NZFuGtW7dmuX5Gjh8/To6OjqShoUFWVlY0e/ZsBUvRxo0bqUmTJmRiYkIikYhMTU2pU6dOdO3aNYX5li9fTvb29iSRSMjQ0JA6d+5Mvr6+CuOyUnb69OlD1tbWJBaLydLSkkaNGqXUsnT69GneAqGlpUUNGzakw4cPZ3mfXl5eZGdnR2KxmGxtbWnt2rUKryMirixDz549SU9Pj3R0dKhjx4706tUruTHZ/Y0zvsZzo+zY2NgovCdkR+ZM3/7+/tSmTRvS0tIiAwMD6t+/P3369EluzIEDB6hZs2ZkampKIpGIt3oq+7BXpuxkJ4+NjY3CHA8fPqRWrVqRtrY26erqUpcuXZRayBMSEmjq1Klkbm5OGhoa5OTkpNRKSkR0/fp1atSoEUkkEjI1NaWxY8fKWXoyIvuy0qhRI4W+Y8eOEQDS1dVVsIwySi4CogIIAygFyArGySKqDh06hP79++PZs2cKWWl1dHQUfHQGDRqEyMjIXEV0TZs2DTdu3MDt27fzS3wGI09Mnz4df/31F16/fp2j0yqDwWCUNFhSwVxSu3ZtPjqjadOm+Tq3n5+fUmc+BqOwuHz5MubMmcMUHQaDUSphyk4GYmNj8fr1a/783bt38PPzg5GREapUqYL+/fvDw8MDq1evRu3atREeHo6LFy+iZs2afIbR58+fIzk5Gd+/f0dMTAyf0lwWEbBu3TpUqFAB1apVQ2JiIrZv345Lly79UFp2BuNHuX//flGLwGAwGAUG28bKwJUrV+Dq6qrQ7unpiV27diElJQW///479uzZg5CQEJiYmKBhw4ZYsGABatSoAYBLxa8sfFn2mFesWAEvLy+EhIRAS0sLNWvWxNy5c5Wuy2AwGAwG48dhyg6DwWAwGIxSDctkx2AwGAwGo1TDlB0Gg8FgMBilmp/eQVkqlSI0NBS6urpZ1ktiMBgMBoNRvKD/l52xtLTMseTST6/shIaGylXfZTAYDAaDUXIIDg6GlZVVtmN+emVHVqQvODgYenp6RSwNg8FgMBiMrDj54iRa27aGlroWoqOjYW1tnW2xXRk/vbIj27rS09Njyg6DwWAwGMWQuOQ4jD0zFrsf78aw2sPg3dmb78uNCwpzUC5BvHjxAg0bNoREIuGTFBYEu3btgoGBQYHNz2AwGAxGbvH/4g8nbyfsfrwbQoEQ5fTLIa9Zc5iyUwCEh4dDLBYjLi4OKSkp0NbWxocPH3543nnz5kFbWxuBgYG4ePFiPkiqnN69e+Ply5d5uqZ58+aYNGlSwQiUS16/fg1dXV2mqDEYDEYpgIiw49EO1Peuj4CvAbDQscBFj4uY4zInzwFFTNkpAG7fvo1atWpBW1sbjx49gpGREcqVK/fD87558wZNmjSBjY0NjI2N80FS5WhqasLMzKzA5i8IUlJS0Ldv33yvW8ZgMBiMwic2ORYDjw/EsH+GISE1AW1s28BvlB+al2+u0nxM2SkAbt26hcaNGwMAbty4wf+eHVKpFAsXLoSVlRU0NDTg6OiIc+fO8f0CgQAPHz7EwoULIRAIMH/+fKXzNG/eHOPGjcO4ceOgr68PExMTzJkzR87kFxERAQ8PDxgaGkJLSwvt2rXDq1ev+P7M21jz58+Ho6Mj9u7di/Lly0NfXx99+vRBTEwMAK7C+9WrV/HHH39AIBBAIBDg/fv3iIiIQP/+/WFqagpNTU1UrlwZO3fuzMujzDWzZ8+Gvb093N3dC2R+BoPBYBQekYmROPf6HNQEaljacinO9j8LM+0f+BJOPzlRUVEEgKKion5onqCgINLX1yd9fX1SV1cniURC+vr6JBaLSUNDg/T19Wn06NFZXr9mzRrS09Ojv/76i168eEHTp08ndXV1evnyJRERhYWFUbVq1WjKlCkUFhZGMTExSudxcXEhHR0dmjhxIr148YL27dtHWlpa5OXlxY/p3LkzOTg40LVr18jPz4/atm1LlSpVouTkZCIi2rlzJ+nr6/Pj582bRzo6OtS9e3d6+vQpXbt2jczNzem3334jIqLIyEhydnam4cOHU1hYGIWFhVFqaiqNHTuWHB0d6f79+/Tu3Tvy8fGhU6dOycnq6emp6iPnuXjxIlWoUIGioqIUZGcwGAxGyeS/1//R9aDrWfbn5fP7p4/Gyi8sLS3h5+eH6Oho1KtXD3fv3oW2tjYcHR3x77//oly5ctDR0cny+lWrVmHGjBno06cPAGD58uW4fPky1q1bh02bNsHc3BwikQg6OjowNzfPVhZra2usXbsWAoEAdnZ2ePr0KdauXYvhw4fj1atXOHXqFG7evIlGjRoBAPbv3w9ra2ucOHECvXr1UjqnVCrFrl27+BC/gQMH4uLFi1i8eDH09fUhFouhpaUlJ9uHDx9Qu3Zt1KtXDwBXJDUj5cqVg4WFRfYPNge+ffuGQYMGYd++fSyajsFgMEoo0UnRGPHPCPSp3gdd7bsCAFrbts63+dk2Vj4hEolQvnx5vHjxAk5OTqhZsyY+ffqEMmXKoFmzZihfvjxMTEyUXhsdHY3Q0FCF7a7GjRsjICAgz7I0bNhQznnL2dkZr169QlpaGgICAiASidCgQQO+39jYGHZ2dtmuVb58eblcBhYWFvjy5Uu2cowePRoHDx6Eo6Mjpk+fjlu3bsn179mzB0uXLs3r7ckxfPhw9OvXD82aNfuheRgMBoNRNDwMfYg62+rg0LNDGHl6JOJT4vN9DWbZySeqVauGoKAgpKSkQCqVQkdHB6mpqUhNTYWOjg5sbGzw7NmzohZTZdTV1eXOBQIBpFJptte0a9cOQUFBOHPmDHx8fNCyZUuMHTsWq1atyje5Ll26hFOnTvFzEhGkUilEIhG8vLwwZMiQfFuLwWAwGPkHEWHjvY2Y6jMVyWnJsNG3wcGeB6GlrpXvazHLTj5x5swZ+Pn5wdzcHPv27YOfnx+qV6+OdevWwc/PD2fOnMnyWj09PVhaWuLmzZty7Tdv3kTVqlXzLMvdu3flzu/cuYPKlStDTU0NDg4OSE1NlRvz7ds3BAYGqrSWDLFYjLS0NIV2U1NTeHp6Yt++fVi3bh28vLxUXkMZt2/fhp+fH38sXLgQurq68PPzQ7du3fJ1LQaDwWDkD5GJkeh5pCcmnJuA5LRkdLXvCt+Rvmho1bBA1mOWnXzCxsYGnz59wufPn9GlSxcIBAI8e/YMPXr0yJVfyrRp0zBv3jzY2trC0dERO3fuhJ+fH/bv359nWT58+IDJkydj5MiRePToETZs2IDVq1cDACpXrowuXbpg+PDh2LZtG3R1dfHrr7+ibNmy6NKlS57XklG+fHncvXsX79+/h46ODoyMjDB//nzUrVsX1apVQ1JSEk6fPg0HBwf+Gg8PD5QtW/aHtrIyzgcADx48gFAoRPXq1VWek8FgMBgFR2RiJGpvq433ke+hLlTHqjarML7++AItxs2UnXzkypUrcHJygkQiwfXr12FlZZVrB9wJEyYgKioKU6ZMwZcvX1C1alWcOnUKlStXzrMcHh4eSEhIQP369aGmpoaJEydixIgRfP/OnTsxceJEdOzYEcnJyWjWrBnOnDmjsFWVF6ZOnQpPT09UrVoVCQkJePfuHcRiMWbOnIn3799DU1MTTZs2xcGDB/lrPnz4kGOlWgaDwWCULgwkBmhXqR3OvzmPQz0PoZ5lvQJfU0CUx5zLpYzo6Gjo6+sjKiqqVETzNG/eHI6Ojli3bl1Ri8JgMBgMBgDgW/w3pEpTUUanDAAgMTURSalJ0JfoqzxnXj6/2ddqBoPBYDAYBcat4Fuova02+v7dF2lSzrdTIpL8kKKTV5iyw2AwGAwGI9+RkhTLbyxHs53NEBwdjODoYITFhhWJLMxnp5Rx5cqVohaBwWAwGD854XHh8DzhibOvzwIA+lbvi20dt0FXQzeHKwsGpuwwGAwGg8HIN64HXUefv/sgNCYUEpEE693WY1idYQUabZUTTNkpBAYNGoTIyEicOHGiqEVhMBgMBqPASJOmYcyZMQiNCYW9iT0O9zyMGmVqFLVYzGeHATx58gRNmzaFRCKBtbU1VqxYkeM1EyZMQN26dfkK7Zl5//49XwE943Hnzh1+jLe3N5o2bQpDQ0MYGhqiVatWuHfvXn7emlKuXLmCOnXqQENDA5UqVcKuXbtyHN+lSxdYWFjw9c6U5T86cuQI7O3tIZFIUKNGDaWJJAMCAtC5c2fo6+tDW1sbTk5O+PDhQ37dGoPBYBQpakI1/NXjLwyrPQz3h98vFooOwJSdn57o6Gi0adMGNjY2ePjwIVauXIn58+fnKtPxkCFD0Lt372zHXLhwAWFhYfxRt25dvu/KlSvo27cvLl++jNu3b8Pa2hpt2rRBSEhIruWfP38+Bg0alOvx7969Q4cOHeDq6go/Pz9MmjQJw4YNw/nz57O85tatW6hZsyb+/vtvPHnyBIMHD4aHhwdOnz4tN6Zv374YOnQofH190bVrV3Tt2hX+/v78mDdv3qBJkyawt7fHlStX8OTJE8yZMwcSiSTX8jMYDEZx49K7S9j2YBt/Xt2sOrw7e0NHnHXx60Inv0qxl1TyUiI+O44cOULVq1cniURCRkZG1LJlS4qNjSUiIk9PT+rSpQs/NjExkcaPH0+mpqakoaFBjRs3pnv37vH9ly9fJgB0+vRpqlGjBmloaFCDBg3o6dOncmtev36dmjRpQhKJhKysrGj8+PH8mrll8+bNZGhoSElJSXzbjBkzyM7OLlfXz5s3j2rVqqXQ/u7dOwJAvr6+uZYlNTWVdHV1affu3bm+Zt68eeTp6Znr8dOnT6dq1arJtfXu3Zvatm2b6zmIiNq3b0+DBw/mz93d3alDhw5yYxo0aEAjR46UW2fAgAF5WofBYDCKK6lpqTT30lwSzBeQaKGI7ofcL9T18/L5zSw7+UBYWBj69u2LIUOGICAgAFeuXEH37t1BWeRrnD59Ov7++2/s3r0bjx49QqVKldC2bVt8//5dbty0adOwevVq3L9/H6ampujUqRNSUlIAcFYCNzc39OjRA0+ePMGhQ4dw48YNjBs3jr9+/vz5KF++fLay3759G82aNYNYLObb2rZti8DAQERERKj4RNLp3LkzzMzM0KRJE5w6dSrbsfHx8UhJSYGRkdEPr5sVt2/fRqtWreTa2rZti9u3b+dpnqioKDk5c5pXKpXi33//RZUqVdC2bVuYmZmhQYMGzI+LwWCUSEJjQtFqbyssvLYQBMKgWoNQ1VT1+ooFTl41qfj4ePL396dPnz5lOebt27d5+nZelOSHZefhw4cEgN6/f6+0P6NlJzY2ltTV1Wn//v18f3JyMllaWtKKFSuIKN2yc/DgQX7Mt2/fSFNTkw4dOkREREOHDqURI0bIrXP9+nUSCoWUkJBAREQbNmygFi1aZCt769atFeZ59uwZAaDnz5/neO9ZWXbCw8Np9erVdOfOHbp37x7NmDGDBAIBnTx5Msu5Ro8eTRUrVuTlV8a1a9dIW1ubP9TV1UkkEsm17du3L8vrK1euTEuWLJFr+/fffwkAxcfH53i/RESHDh0isVhM/v7+fJu6ujodOHBAbtymTZvIzMyMiIjCwsIIAGlpadGaNWvI19eXli5dSgKBgK5cuZKrdRkMBqM4cO7VOTJdYUqYD9JZokP7n+zP+aICIC+f33mKxlqwYAGWL1+OpKQkAECzZs2wYcMGhaKLt27d4v0afgZq1aqFli1bokaNGmjbti3atGmDnj17wtDQUGHsmzdvkJKSgsaNG/Nt6urqqF+/PgICAuTGOjs7878bGRnBzs6OH/P48WM8efJEzlGWiCCVSvHu3Ts4ODhg3LhxcpaeatWqISgoCADQtGlTnD17Nn8egBJMTEwwefJk/tzJyQmhoaFYuXIlOnfurDB+2bJlOHjwIK5cuZKtD0u9evXg5+fHn69fvx4hISFYvnw531amTJn8uQklXL58GYMHD4a3tzeqVauW6+ukUikAoEuXLvjll18AAI6Ojrh16xa2bt0KFxeXApGXwWAw8pP5V+ZjwdUFAIBaZWrhcK/DqGJcpYilyplcKzuHDx/GggUL0KJFC3Tt2hVhYWHYsWMHnJycsGPHDvTr168g5SzWqKmpwcfHB7du3cJ///2HDRs2YNasWbh79y4qVKhQIGvGxsZi5MiRmDBhgkJfuXLllF5z5swZfhtMU1MTAGBubo7Pnz/LjZOdm5ub56fIaNCgAXx8fBTaV61ahWXLluHChQuoWbNmtnNoamqiUqVK/LmRkRGio6Pl2rIjq/vV09Pjn0lWXL16FZ06dcLatWsVFPms5pU9QxMTE4hEIlStKm/mdXBwwI0bN3IlO4PBYBQ1BhIDAMCouqOw1m0tJKKSEWCRa5+dtWvXokWLFrhw4QLGjRuHxYsXIyAgAM2bN8fAgQOxatWqHxZm6dKlcHJygq6uLszMzNC1a1cEBgZme82uXbsUwpuLIrpFIBCgcePGWLBgAXx9fSEWi3H8+HGFcba2thCLxbh58ybflpKSgvv37yt8EGYM046IiMDLly/h4OAAAKhTpw6eP3+OSpUqKRwZ/W8yYmNjw48pW7YsAM56dO3aNV4JAgAfHx/Y2dkptUz9CH5+fgpV4FesWIFFixbh3LlzqFev4CvfOjs74+LFi3JtPj4+clY0ZVy5cgUdOnTA8uXL5SrI53ZesVgMJycnhdfzy5cvYWNjo8qtMBgMRqEQlxzH/z6xwURc8byCLR23lBhFB0DufXYMDAxo8+bNCu1SqZTGjBlDQqGQpkyZQkRE+/btI6FQmPuNt//Ttm1b2rlzJ/n7+5Ofnx+1b9+eypUrl22E0c6dO0lPT4/CwsL4Izt/oszkh8/OnTt3aPHixXT//n0KCgqiw4cPk1gspjNnzhCRYjTWxIkTydLSks6ePUvPnj0jT09PMjQ0pO/fvxNRus9OtWrV6MKFC/T06VPq3LkzlStXjo+aevz4MWlqatLYsWPJ19eXXr58SSdOnKCxY8fy6+TGZycyMpLKlClDAwcOJH9/fzp48CBpaWnRtm3b+DHHjh1TiM569eoV+fr60siRI6lKlSrk6+tLvr6+vHy7du2iAwcOUEBAAAUEBNDixYtJKBTSn3/+yc+xbNkyEovFdPToUbm/X0xMTJbyJiUlyY1VdmTne/P27VvS0tKiadOmUUBAAG3atInU1NTo3LlzWT63S5cukZaWFs2cOVNunW/fvvFjbt68SSKRiFatWkUBAQE0b948UldXl4ugO3bsGKmrq5OXlxe9evWKNmzYQGpqanT9+vXs/kQMBoNRJCSnJtPU81OpyoYqFJ0YXdTiKJCXz+9cKzumpqa0fv36LPvnz59PAoGAPDw8aOfOnSopO5n58uULAaCrV69mOWbnzp2kr6+v8hr5oew8f/6c2rZty4eSV6lShTZs2MD3Z1Z2EhISaPz48WRiYpJt6Pk///xD1apVI7FYTPXr16fHjx/LrXvv3j1q3bo16ejokLa2NtWsWZMWL17M98+bN49sbGxylP/x48fUpEkT0tDQoLJly9KyZcvk+nfu3EmZ9WIXFxcCoHC8e/eOiDhlx8HBgbS0tEhPT4/q169PR44ckZvDxsZG6Rzz5s3LUlbZs8nu2LlzZ7b3e/nyZXJ0dCSxWEwVK1ZUGJ/5uXl6eipdx8XFRe66w4cPU5UqVUgsFlO1atXo33//VVh7x44dVKlSJZJIJFSrVi06ceJEtrIyGAxGUfA+4j013N6QMB+E+aA9fnuKWiQF8vL5LSDKIj46Ey4uLjA2NsaxY8eyHLNp0yZMnDgRJiYmCA8PR1paWu5NTEp4/fo1KleujKdPnyo4QcvYtWsXhg0bhrJly0IqlaJOnTpYsmRJls6jSUlJvIM1wCXVs7a2RlRUFPT09H5I3vziypUrcHV1RUREBAwMDIpaHAaDwWD8RJx8cRKDTg5CZGIk9DX08WeXP9HdoXtRi6VAdHQ09PX1c/X5nWufnY4dO+LMmTMIDg7OcszYsWOxf//+fMnPIpVKMWnSJDRu3DhLRQcA7Ozs8Oeff+LkyZPYt28fpFIpGjVqhI8fPyodv3TpUujr6/OHtbX1D8vKYDAYDEZJJzktGZPOTULXQ10RmRiJ+mXrw3ekb7FUdPJKri07379/x+3bt1G9evUcHSrv37+P58+fw9PTU2XBRo8ejbNnz+LGjRuwsrLK9XUpKSlwcHBA3759sWjRIoV+ZtlhMBgMBkORX879gnV31wEApjhPwZKWSyBWUx7wUhzIi2Un18pOYTJu3DicPHkS165dUyl0u1evXhCJRPjrr79yHJuXh8VgMBgMRmnlc+xntNzTEktbLkUnu05FLU6OFMg2VmFARBg3bhyOHz+OS5cuqaTopKWl4enTpwohzgwGg8FgMNJJTE3EX0/TjQJldMrgyegnJULRySsqKztXr17NTzkAcD4/+/btw4EDB6Crq4tPnz7h06dPSEhI4Md4eHhg5syZ/PnChQvx33//4e3bt3j06BEGDBiAoKAgDBs2LN/lYzAYDAajNPDq2ys473BGv2P95BQeoaBY2UDyDZXu6uDBg3Bzc8tvWbBlyxZERUWhefPmsLCw4I9Dhw7xYz58+ICwsDD+PCIiAsOHD4eDgwPat2+P6Oho3Lp1SyFBX0HTvHlzTJo0qVDXZDAYDAYjr/z19C/U8aoDv09+MNEygZFmwRVfLjbkNa59+fLlpKamRkuXLs3rpcWS/MizQ8QV6oyOzn3SpXfv3hEA8vX1/aF1C5PM+YJyy7t372jIkCFUvnx5kkgkVLFiRZo7dy6fgDArEhISaMyYMWRkZETa2trUvXt3hYSR48ePpzp16pBYLFZakLQgyGlN2d8283H79u0Ck+nr16/Utm1bsrCwILFYTFZWVjR27NgcX9cPHz6kVq1akb6+PhkZGdHw4cOVJnXcuXMn1ahRgzQ0NMjU1JTGjBlTULdCRHn7u7569Yp0dHR+KN8Wg/EzEJ8cT8NPDedz5zTb2Yw+Rn0sarFUJi+f37m27ND//WlmzpyJhQsX4tdffy0I3avEYmRkBF1d3SJZO2Oph+LIixcvIJVKsW3bNjx79gxr167F1q1b8dtvv2V73S+//IJ//vkHR44cwdWrVxEaGoru3RVDIIcMGYLevXvnm7zly5fHlStXsh2TmzUvXLiAsLAw/qhbt26uZbhy5QrKly+f6/FCoRBdunTBqVOn8PLlS+zatQsXLlzAqFGjsrwmNDQUrVq1QqVKlXD37l2cO3cOz549w6BBg+TGrVmzBrNmzcKvv/6KZ8+e4cKFC2jbtm2uZVNGfj3jlJQU9O3bF02bNv0heRiM0s6Lry/QYHsDeD/yhgACzG46Gxc9LqKsXtmiFq1wyK0G1aVLFxIKhfT777//iCJW7Mgvy46LiwtNnDiRP7exsaHFixfT4MGDSUdHh6ytreVKMCCbbLze3t5kb29PGhoaZGdnR5s2beL7ZFaDgwcPUrNmzUhDQ4PPALxjxw6qWrUqicViMjc3lysdERERQUOHDiUTExPS1dUlV1dX8vPz4/vnzZtHtWrVoq1bt5KVlRVpampSr169KDIyku/PLPPly5dVfl4rVqygChUqZNkfGRlJ6urqclmXAwICsrSQyORXxvXr16lJkyYkkUjIysqKxo8fn20JEhsbm1zdW1Zr5ofV7vLly7nKfp0df/zxB1lZWWXZv23bNjIzM6O0tDS+7cmTJwSAXr16RURE379/J01NTbpw4UK2axX2M5Yxffp0GjBgwA9nUmcwSjunA08T5oPMVpqRzxufohYnXygQy86pU6fQoUMHzJo1K3+1rVLM6tWrUa9ePfj6+mLMmDEYPXo0Xwjy3r17ANK//csyU+/fvx9z587lC60uWbIEc+bMwe7du+Xm/vXXXzFx4kQEBASgbdu22LJlC8aOHYsRI0bg6dOnOHXqlFwl8F69euHLly84e/YsHj58iDp16qBly5b4/v07P+b169c4fPgw/vnnH5w7d46XGwCmTp0Kd3d3uLm58ZaKRo0aAeD8lTJbA3IiKioKRkZZ7xM/fPgQKSkpaNWqFd9mb2+PcuXK4fbt27le582bN3Bzc0OPHj3w5MkTHDp0CDdu3MC4cePyJK8qdO7cGWZmZmjSpAlOnTpV4OtlJDQ0FMeOHYOLi0uWY5KSkiAWiyEUpv8bkFV+l1Vi9/HxgVQqRUhICBwcHGBlZQV3d3e55KJF9YwvXbqEI0eOYNOmTQW6DoNRGuhQpQO8O3nDb6QfWlVslfMFpY3calCurq4kFApp69atP6SJFTcK0rIzYMAA/lwqlZKZmRlt2bKFiLL+9m9ra0sHDhyQa1u0aBE5OzvLXbdu3Tq5MZaWljRr1iylsl2/fp309PQoMTFRYS2ZtWnevHmkpqZGHz+m79+ePXuWhEIhhYWFEVHWPjsDBw6kX3/9Venaynj16hXp6emRl5dXlmP2799PYrFYod3JyYmmT5+u0J6VBWDo0KE0YsQIubbr16+TUCikhIQEpWv/qNUhPDycVq9eTXfu3KF79+7RjBkzSCAQ0MmTJ7OdT1tbmz8kEgkJBAK5tpEjR+YoU58+fUhTU5MAUKdOnbK8RyIif39/EolEtGLFCkpKSqLv379Tjx49CAAtWbKEiIiWLl1K6urqZGdnR+fOnaPbt29Ty5Ytyc7Ojve5Kopn/PXrV7K2tubr5jHLDoMhj/9nf2ryZxN6H/G+qEUpMPLy+S3KrVJ0/vx5DBo0CGPHjoVQKMTw4cMLRvsqRdSsWZP/XSAQwNzcHF++fMlyfFxcHN68eYOhQ4fKPd/U1FTo6+vLja1Xrx7/+5cvXxAaGoqWLVsqnffx48eIjY2FsbGxXHtCQgLevHnDn5crVw5ly6bv3zo7O0MqlSIwMBDm5uZZyr1nz54s+zITEhICNzc39OrVq1BeQ48fP8aTJ0+wf/9+vo2IIJVK8e7dOzg4OGDUqFHYt28f3x8fH4927dpBTU2Nb4uNjc31miYmJpg8eTJ/7uTkhNDQUKxcuRKdO3fO8jo/Pz/+97t372LGjBlyfi25SXq5du1azJs3Dy9fvsTMmTMxefJkbN68WenYatWqYffu3Zg8eTJmzpwJNTU1TJgwAWXKlOGtPVKpFCkpKVi/fj3atGkDAPjrr79gbm6Oy5cvo23btkXyjIcPH45+/fqhWbNmub6GwfgZICLs9NuJcWfGISE1AZPOT8Lx3seLWqwiJ9fKjrq6Ovbv3w9ra2uMHj0aQqEQQ4cOLUjZSjzq6upy5wKBAFKpNMvxsn/23t7eaNCggVxfxg8FANDW1uZ/l209ZDevhYWFUofQwixHERoaCldXVzRq1AheXl7ZjjU3N0dycjIiIyPlZPz8+XO2ildmYmNjMXLkSEyYMEGhr1y5cgC4XE1Tp07l25s3b47ly5cr/A1+hAYNGsDHxyfbMRm3HT9+/AiRSCTXlhvMzc1hbm4Oe3t7GBkZoWnTppgzZ06WSTb79euHfv364fPnz9DW1oZAIMCaNWtQsWJFAOCvy5jKwdTUFCYmJvjw4QOAonnGly5dwqlTp7Bq1SoA6cqVSCSCl5cXhgwZotK8DEZJJjY5FqNOj8L+p9wXjza2bbCt47Yilqp4kGtlR8ayZctQrlw5jBkzhik7P4BYzNUbyVgZvkyZMrC0tMTbt2/Rv3//XM+lq6uL8uXL4+LFi3B1dVXor1OnDj59+gSRSJRthM+HDx8QGhoKS0tLAMCdO3cgFAphZ2fHy6xqJfuQkBC4urqibt262Llzp5yfiDLq1q0LdXV1XLx4ET169AAABAYG4sOHD3B2ds71unXq1MHz58+zVRrMzMxgZmbGn4tEIpQtWzbPikZ2+Pn5FXpWb5linbEWXFaUKVMGAPDnn39CIpGgdevWAIDGjRsD4J69rEbd9+/f8fXrV75GXlE849u3b8u9Fk+ePInly5fj1q1bctZJBuNn4fGnx3A/6o6X315CTaCGRa6LMKPJjFKbJDCv5FnZAYAxY8awauE/iJmZGTQ1NXHu3DlYWVlBIpFAX18fCxYswIQJE6Cvrw83NzckJSXhwYMHiIiIkNsaycz8+fMxatQomJmZoV27doiJicHNmzcxfvx4tGrVCs7OzujatStWrFiBKlWqIDQ0FP/++y+6devGb4lJJBJ4enpi1apViI6OxoQJE+Du7s5bUsqXL4/z588jMDAQxsbG0NfXh7q6Ojw8PFC2bFksXbpUqWwhISFo3rw5bGxssGrVKoSHh/N9srlDQkLQsmVL7NmzB/Xr14e+vj6GDh2KyZMnw8jICHp6ehg/fjycnZ3RsGFD/vrXr18jNjaWz7Qt2wqqWrUqxGIxZsyYgYYNG2LcuHEYNmwYtLW18fz5c/j4+GDjxo0q/e1yWnP37t0Qi8WoXbs2AODYsWP4888/sX379mzn/fTpE/+7vb097ty5I9emqampsJ0p48yZM/j8+TOcnJygo6ODZ8+eYdq0aWjcuDGv4N67dw8eHh64ePEirxBs3LgRjRo1go6ODnx8fDBt2jQsW7aMt6ZVqVIFXbp0wcSJE+Hl5QU9PT3MnDkT9vb2vGJdFM/YwcFBbvyDBw8gFApRvXp1ldZjMEoy14Ouo/Xe1khKS0JZ3bI42PMgmpRrUtRiFS8K2H+o2FOQDspr166VG1OrVi2aN28ef+7t7U3W1tYkFArlQs/3799Pjo6OJBaLydDQkJo1a0bHjh0jouzDmrdu3Up2dnakrq5OFhYWNH78eL4vOjqaxo8fT5aWlqSurk7W1tbUv39/+vDhAxGlO4Ju3ryZLC0tSSKRUM+ePen79+/8HF++fKHWrVuTjo6OXOi5i4sLeXp6Zvlsdu7cqTTJXsaXn+y+MjqtypIKGhoakpaWFnXr1o13ls743JXN++7dO37MvXv3eLm1tbWpZs2atHjx4izlzcl5Nqc1d+3aRQ4ODqSlpUV6enpUv359uRD6rMjqGcmO7J7xpUuXyNnZmfT19UkikVDlypVpxowZFBERwY+5fPmywrMZOHAgGRkZkVgsppo1a9KePXsU5o6KiqIhQ4aQgYEBGRkZUbdu3fjXjYzCfsaZYQ7KjJ+ZhJQEqrWlFrXf357C48KLWpxCIy+f38Wy6nlhwqqec8yfPx8nTpyQc5JlMBgMRvEkIDwAVYyrQE3I+XN+jf8KI02jn2rbqlCrnkulUkREROD79+8KB4PBYDAYjPyDiLDx3kY4bnPE4uuL+XYTLZOfStHJKyr57KSkpGD58uX4888/ERwcnGWEkarOrAwGg8FgMOSJTIzE0FNDcSyAS0L7+PNjSEnKlJxcoNI21pAhQ7B79240bNgQDRo0yNJpct68eT8sYEHDtrEYDAaDUdy5F3IPvY/2xvvI91AXqmNl65WY0GACBAJBUYtWZOTl81sly86RI0cwcOBA7Nq1S5XLGQwGg8Fg5AIiwro76zDjwgykSFNQwaACDvU8BKeyTkUtWtEREwNERgJZGFqUoZLtS0tLSy78l8FgMBgMRv7zLvIdfrv0G1KkKejh0AOPRj76uRWds2eB6tWBPn2AbJL0ZkYlZadv3744ffq0KpeWWgYNGgSBQACBQAB1dXVUqFAB06dPR2JiIj/m/fv3GDp0KCpUqABNTU3Y2tpi3rx5SE5OLnR5jxw5Ant7e0gkEtSoUQNnzpzJdvyxY8fQunVrmJqaQk9PD87Ozjh//rzcmGvXrqFTp06wtLSEQCDAiRMn5PpTUlIwY8YM1KhRA9ra2rC0tISHhwdCQ0Pz+/YYDAajVFDRsCI2td+Eje024kivIzCQGBS1SEVDeDjQvz/Qvj3w4QMQGgqEhOT6cpWUnRUrVsDAwAAdO3bEsWPHcP/+fTx69Ejh+NmQVQR/+/Yt1q5di23btsn5Lb148QJSqRTbtm3Ds2fPsHbtWmzduhW//fZbocp569Yt9O3bF0OHDoWvry+6du2Krl27wt/fP8trrl27htatW+PMmTN4+PAhXF1d0alTJ/j6+vJj4uLiUKtWrSyrUMfHx+PRo0eYM2cOHj16hGPHjiEwMDDbelEMBoPxMyElKVbeXIk7H+/wbUNqD8HY+mN/Tv8cImDfPsDBAThwABAKgcmTAX9/IC/JjVVJ5BMdHU19+/YloVCo9BAIBCQUClWZutDJr6SCyiqCd+/enWrXrp3tdStWrKAKFSr80Np5xd3dnTp06CDX1qBBg1xV1c5I1apVacGCBUr7ANDx48dznOPevXsEgIKCgvK0NoPBYJQ2vsR+oXb72hHmg2zW2lBMUkxRi1S0vHtH1LYtEafyENWsSXTvHt9dIFXPMzJkyBAcP34cffr0yTYa62fG398ft27d4usHZUVUVBSMjIyyHdOuXTtcv349y34bGxs8e/Ys17Ldvn1bofRE27ZtFbadskMqlSImJiZH2XMiKioKAoGgUAuSMhgMRnHjWtA19P27L0JjQiERSTCr6Sxoq2vnfGFpJC0N2LABmDULiI8HNDSAuXOBadOATAW2c4tKys758+cxfvx4rF27VqVFSyunT5+Gjo4OUlNTkZSUBKFQmG1toNevX2PDhg185eas2L59OxISErLsz1xdPSc+ffrEF36UUaZMGbk6TDmxatUqxMbGwt3dPU9rZyQxMREzZsxA3759Wdg/g8H4KZGSFEuvL8XcK3MhJSnsjO1wuNdh1CxTs6hFKxqePgWGDQPu3ePOmzUDvLyA/xekVhWVlB09Pb18rQhdWnB1dcWWLVsQFxeHtWvXQiQS8RW7MxMSEgI3Nzf06tULw4cPz3be4lbF+cCBA1iwYAFOnjwpV8k6L6SkpMDd3R1EhC1btuSzhAwGg1H8iU2ORfdD3eHz1gcAMLDmQGzusBk6Yp0ilqwIIALmzweWLAFSUwE9PWDlSk7xEf540kSVZhg+fDj++usvliE5E9ra2qhUqRJq1aqFP//8E3fv3sWOHTsUxoWGhsLV1RWNGjWCl5dXjvO2a9cOOjo6WR7VqlXLk5zm5ub4/PmzXNvnz5/5CuTZcfDgQQwbNgyHDx9Gq1at8rSuDJmiExQUBB8fH2bVYTAYPyXa6trQVNeEpkgTO7vsxJ5ue35ORQcABALg2zdO0enSBXj+HBgxIl8UHUBFy07VqlVx8uRJ1KlTB56enrC2toaamprCuO7du/+wgCUVoVCI3377DZMnT0a/fv2gqakJgLPouLq6om7duti5cyeEufhD5vc2lrOzMy5evIhJkybxbT4+PnB2ds72ur/++gtDhgzBwYMH0aFDhzytKUOm6Lx69QqXL1+GsbGxSvMwGAxGSSRNmobktGRoqmtCIBBgZ5ed+BT7CVVNqxa1aIVPdDSXIFC2e7FkCdCqFafs5HPkmUrlInLzAS0QCEqE5Se/ykUMGjQIkZGRck6+qampKF++PCZNmoSpU6ciJCQEzZs3h42NDXbv3i2nIObGqpJf3Lp1Cy4uLli2bBk6dOiAgwcPYsmSJXj06BGqV68OAJg5cyZCQkKwZ88eANzWlaenJ/744w85JVZTU5N3UI+NjcXr168BALVr18aaNWvg6uoKIyMjlCtXDikpKejZsycePXqE06dPy/kNGRkZQSwWF9YjYDAYjEInLCYM/Y71Q1ndstjbbe/PGUou4+ZNoHdvoHJl4NIllZSbAi8XcfnyZVUu++kQiUQYN24cVqxYgdGjR8PHxwevX7/G69evYWVlJTdWBZ1TZRo1aoQDBw5g9uzZ+O2331C5cmWcOHGCV3QAICwsDB8+fODPvby8kJqairFjx2Ls2LF8u6enJ1825MGDB3B1deX7ZBFfsjEhISE4deoUAMDR0VFOpsuXL6N58+b5fKcMBoNRPPjvzX8YcGwAwuPDoa2ujbcRb2FrZFvUYhUdFhbA9+9AcDAQFgZYWhbocipZdkoTrBAog8FgMAqKVGkq5l2eh6U3loJAqFmmJg71PAR7E/uiFq1wIQJu3wYaNUpvu3YNcHIC/u/mkVfy8vmtkufP9+/f8eTJkyz7nz59ioiICFWmZjAYDAajVPAx+iNa7G6BJTeWgEAYWXck7gy98/MpOm/fAq1bA40bcwqOjGbNVFZ08opKys4vv/yCESNGZNk/cuRITJ06VWWhGAwGg8EoyUhJinb72+H6h+vQFeviYI+D2NpxKzTVC+fDvViQmgqsXs0V7rx4EZBIOMWnCFBJ2bl06VK29Yw6deqECxcuqCwUg8FgMBglGaFAiHVt16GeZT08GvkIvav3LmqRChc/P6BhQ2DqVCAhAWjRgksYOGhQkYijkrITHh4OExOTLPuNjY3x5csXlYViMBgMBqOk8SHqA/578x9/3rJiS9wddheVjH6iJLwJCcDMmUC9esDDh4CBAbBjB3DhAlCEyYhVisaysLCQq3admYcPH8LU1FRloRgMBoPBKEmcCjyFQScGIVWaikcjH/EKjlCQP0nxSgRXrgDDhwP/T0GCXr2A9euBQkytkhUq/RW6du2KHTt28GHEGTl58iR27tyJbt26/bBwDAaDwWAUZ5LTkvHLuV/Q5WAXRCRGwN7EHiKhSnaEkktkJKfkuLpyio6lJXDiBHD4cLFQdAAVQ8+joqLQpEkTPH/+HLVq1eLzs/j7++Px48dwcHDAjRs3SkQlaxZ6zmAwGAxVeBfxDr2P9sb90PsAgF8a/oJlrZZBrPYTJUi9cgXo2xeQFZIeNQpYtgz4f7LZgqTAQ8/19fVx584dzJ49GykpKTh69CiOHj2KlJQUzJkzB3fv3i0Rig6DwWAwGKrw9/O/UXtbbdwPvQ9DiSFO9jmJNW3X/FyKDsAlB4yIAKpU4cLKt2wpFEUnr+TaspOWlqa0/lVJh1l2GAwGg5FXppyfgjV31sDZyhkHex5EOf1yRS1S4SCVAnfvAhlrKV69CjRowIWWFyIFYtkxNjZG7969sXfvXoSHh/+wkAwGg8FglCQy2gaWtlqKP9z+wNVBV38eRScxkQshb9wYuHMnvd3FpdAVnbySa2Vn0aJFiI6OxsiRI2FhYYEGDRpg4cKFePjwYUHKx2AwGAxGkXPQ/yDaH2iPlLQUAIBYTYwJDSZAXU29iCUrRCQSwMaGy3pcRMkBVSXPDsoJCQm4cOECzp49izNnzuDDhw8wNzeHm5sbOnbsiNatW0NXV7eg5M132DYWg8FgMLIiISUBk85NgtcjLwDApvabMMZpTBFLVYjcv8/55ciKV3/7BsTEAOXLF6lYQN4+v3+4EKi/vz/+/fdfnD17Frdu3YJAIECTJk3Qvn17dOjQAfb2xbsGCFN2GAwGg6GMwK+BcD/qjiefn0AAAX5r+hvmN5//c4SWx8UBc+cC69YB7dsDp04BAkFRSyVHgUdjZaR69eqYMWMGrly5gvDwcOzduxfW1tZYuXIlqlWrhuXLl//oEgwGg8FgFCr7nuxDXa+6ePL5Ccy0zXB+wHn83uL3n0PR+e8/rp7VmjWcQ7KeHpCUVNRS/RA/bNnJjvv3udwDTk5OBbXED8MsOwwGg8HIyOJrizH78mwAgGt5V+zvvh8WuhZFLFUh8O0bMGUKsHs3d25tDWzdyll2iiEFbtmJiYlBcHCwXFtoaCjmzp2LGTNmyCk5xVnRYTAYDAYjMz2r9oSehh7mu8yHz0Cf0q/oEAEHDwIODpyiIxAAEyYAz54VW0Unr6hk2enbty/evXuHO/8PPYuOjkb16tXx8eNHCIVCiEQinDt3Ds2bN89vefMdZtlhMBiMnxsiwpPPT1DLvBbf9i3+G4y1jItQqkIiOBgYMwY4fZo7r1oV2L5dPo9OMaXALTs3btxAx44d+fN9+/YhNDQUt27dQkREBGrWrInff/9dlakZDAaDwSg0YpNj4XHCA3W86uDq+6t8e6lXdKRSYNMmTrk5fRpQVwcWLAB8fUuEopNXVFJ2vn79irJly/Lnp06dQpMmTdCwYUPo6urCw8MDjx8/zjchGQwGg8HIb558foJ6XvWw78k+AID/F/8ilqiQiI8HmjYFxo0DYmOBRo0APz8u+kpcOstdqKTsGBgY4NP/i34lJCTg+vXraNOmDd8vEokQHx+fPxIyGAwGg5GPEBG8Hnqhvnd9BH4LRFndsrjieQVj648tatEKBy0tzvlYRwfYuBG4fp2z8JRiVIqha9SoETZv3gx7e3ucO3cOiYmJ6NKlC9//8uVLOcsPg8FgMBjFgeikaIw8PRIH/Q8CANpVaoc93fbARMukiCUrYO7c4bIfW/zf2Xr9emDlSk7p+QlQybKzfPlyqKuro0ePHvD29sbkyZNRrVo1AFzB0CNHjsDFxSVfBWUwGAwG40c5+eIkDvofhJpADStarcDpfqdLv6Lzxx/cVtWECeltZmY/jaIDqGjZqVSpEgIDA/H8+XPo6+ujfIa00fHx8di4cSNq1aqV9QQMBoPBYBQBA2oOgO8nX/Sq2gvO1qXPEVcpLi6AmhqgrQ2kpHDOyD8ZBZpUsCTAQs8ZDAaj9BKZGInZl2ZjkesiGGoaFrU4hUN4OHD1KtCzZ3rbmzeArW3RyVQAFEq5iOjoaCxbtgxt27ZF7dq1ce/ePQDA9+/fsWbNGrx+/VrVqRkMBoPB+GHuh9xHnW11sOn+Joz+d3RRi1PwEAH79nHJAfv2BZ48Se8rZYpOXlFpG+vjx49wcXFBcHAwKleujBcvXiA2NhYAYGRkhG3btiEoKAh//PFHvgrLYDAYDEZOEBH+uPsHpvtMR4o0BRUMKmCK85SiFqtgCQoCRo0Czp3jzmvU4JQfBgAVlZ1p06YhJiYGfn5+MDMzg5mZmVx/165dcVqWjZHBYDAYjELie8J3DD45GKcCTwEAejj0wPbO22EgMShawQqKtDQufHzWLK5SuYYGly9n2rSf0jcnK1RSdv777z/88ssvqFq1Kr59+6bQX7FiRYXaWQwGg8FgFCRPPz9Fx7864kPUB4jVxFjTZg3GOI2BQCAoatEKBn9/YNgw4O5d7rxpU8DbG7CzK1q5iiEqKTsJCQkwNTXNsj8mJkZlgRgMBoPBUAVLXUsQEWwNbXG412HUsahT1CIVDElJwOLFwNKlQGoqoKcHrFgBDB8OCFV2xS3VqPRUqlatimvXrmXZf+LECdSuXVtloRgMBiMr4uK4oswCAfc74+cmJikGsqBiYy1jnO1/Fo9GPiq9is6NG4CjI7BoEafodOkCPH8OjBzJFJ1sUOnJTJo0CQcPHsTy5csRFRUFAJBKpXj9+jUGDhyI27dv45dffslXQRkMBoPByMj1oOtw2OSAXX67+LZqZtWgp1FK04isXMltVb14AZibA0ePAsePA6xiQY6opOwMGDAACxcuxOzZs1GlShUAgJubG+zs7HDw4EEsWbIEXbt2zfO8S5cuhZOTE3R1dWFmZoauXbsiMDAwx+uOHDkCe3t7SCQS1KhRA2fOnMnz2gwGg8EoGUhJiiXXl8B1tytCYkKw4d4GpEnTilqsgkeWHHDoUM6a06MHZ+Jk5MgPJRX88OED/v77b7x+/RpSqRS2trbo3r07KlasqNJ8bm5u6NOnD5ycnJCamorffvsN/v7+eP78ObS1tZVec+vWLTRr1gxLly5Fx44dceDAASxfvhyPHj1C9erVc1yTT0rUty/0bG259NnW1oCVFfdTX5+9mBiMYkRcHFe/EOAKNmfxr4FRSvkS9wUDjg2Az1sfAFxG5C0dtkBHrFPEkhUAnz8Dt24B3bqlt5XC5ICqkpekgsU6g3J4eDjMzMxw9epVNGvWTOmY3r17Iy4uTi7UvWHDhnB0dMTWrVtzXIN/WACUPip9feDrV0D0f1/u48eBiAh5hUinFL7JGIxiSkZlJzAQ+L9xmfETcPndZfQ71g+fYj9BU6SJje03YrDj4NIZbfX+PVCnDveC9/PjEgUy5MiLsqNSNFZhIfMHMjIyynLM7du3MXnyZLm2tm3b4sSJE0rHJyUlISkpiT+Pjo4GACT/OgcpX79CEBIMwcdgCEOCIfj+HVJNLSQkiYD/XyJZ8wfUblyVm5MMDEBlrSEtZ4Okw6d4S5DgRQCgrg4qawVIJHm6dwaDoRwvr/TfHRyADRsAT8+ik4dROHyICkKbfW2QKk2FvXFV7Ol0GFVNqiE+vqglKyBMbKBR3xmCT2FIikkDMWd8BfIUoEC5QCAQkFAozNOhpqaWm6mzJC0tjTp06ECNGzfOdpy6ujodOHBArm3Tpk1kZmamdPy8efMIgJIjirh0k+mHFmLJGkFybYswi87AjZ6iGkVCT+6CEFjIjb0MF/7kM0zpAerQcXSh9RhHU7BSbqwAaQrrs4Md7GAHOzIcrnMIXQYT1GOLXpZ8PtSQQhOwjgzxjW8zxDdSQ0qRy1Z8jygCQFFRUTnqFLmy7MydO7fQzYRjx46Fv78/bty4ka/zzpw5U84SFB0dDessytzHQxvxkHcImIPf5c51EQ1rBMMawZAgUa4vGWLEQQvaiIcZwmGGcNTFIwBAKCywGlP5sRfQCvZ48f+ZuOMjrBAMawTBBvfQ4Ifum8FgMEocFS8AkeWB75W488sLAJS+LStH+GI7hqEuHqEWHmMo/gQARCDrXQ1G3siVsjN//vwCFkOecePG4fTp07h27RqsrKyyHWtubo7Pnz/LtX3+/Bnm5uZKx2toaEBDQ0OhPTSUy8uUd/QAVPv/kZn/ACLERURw22If/3+EBMNEpI7Y2ekjNWsEQfguDJYIQwPck5tFamGJhFch/Ll48jgIvn2F1MoaVNYaZMUd0rLWgKkpy7XAKLWEhHBbV1JpepuaGheYwqJvSw+p0lQsvjUfq+4sQa0ytXGx7y1oiDRQ6hSdhASoL10A9T9WQZCWBjI0RP8lTdF7YFELVjKIjgYsLXM3tlj57BARxo8fj+PHj+PKlSuoUKFCjtc4Ozvj4sWLmDRpEt/m4+MDZ2fnPK2trV1QUR0CQMcIsDYCUEuuR5zx5M5tIDhY6SE0NZWXzecM8O6d8uXKl5fv8/LiEk/JosysrQEjIxZhxiiRVKnC+eiMHcudq6kB27YxJ+XSREh0CPr+3RfXP1wHADSwcoKWNkFSrD6t8oHLl4ERI4DXr7lzd3cI/vgDGubmUPw6zlBGWh6yDagUjbVnz55cjfPw8MjTvGPGjMGBAwdw8uRJ2GWo7aGvrw9NTU1+zrJly2Lp0qUAuNBzFxcXLFu2DB06dODz/OQ59DwX3tzFhuPHOYVGphB9/Mj9DAsD6tcH7txJH1uxoqJipKnJRZLVrQv89Vd6+927nMYnC7lnMIohLBqr9HL21Vl4nPDA1/iv0BXrwquTF/pU71PUYuUvERFckc4dO7jzsmWBzZuBzp2LVq4SSIGHnguz2SbJ6NuTlhe1K9O1Gdm5cycGDRoEAGjevDnKly+PXbt28f1HjhzB7Nmz8f79e1SuXBkrVqxA+/btc7VmiVR2siIlBYiKAkxM0tumTOHyMsiUoi9f0vsaNJBXjCpU4MIdAUBXNz203toaqFEDmDgxfWxSElddl8EoZFiendJHSloKZl+ajRW3VgAAapvXxqGeh1DZuHIRS5aPEAHHjgHjxgGfPnFto0dz9a3Yl0uVKHBlJygoSKEtLS0N79+/x+bNm/Hhwwfs3r0bDiUgL0CpUnZyQ2Ii5/gQHMz59sjyFxFxVqG3b4Hv3xWva9gQuH07/bxCBU6xyrg9JjsqV+bGMxgFAFN2Sh/Jaclo8mcT3A+9j7FOY7GqzSpIRKUoXUdICKfkyFKi2NkB27cDTZoUqVglnSJPKtihQweUL18emzZtyu+p852fTtnJDXFx6VtjssPMDBg1iuuXSrm8QSkpyq/PbDHq3JlTrJQpRpaWgLp6wd8Tg8EodhARb9F/F/EOj8IeoUfVHkUsVT7z6hVQrx7nTSsSAb/+CsyaxXKv5QNFruxs2bIFc+bMwdevX/N76nyHKTsqEhWl6DMkO2rUANau5cblpBg5O3Pp0GUsXMh9bc+oEJmbc56oDAajVJCcloxfL/wKiUiCJS2XFLU4BQsR0Lo1EBPDWXNq1ChqiUoNRZ5B+c2bN3JZihmlEH197sjJCVwq5faplUWaffzI+QVlHPv774qKkUjEWYDatQMylgA5d47zT7JmIfcMRknhXcQ79Pm7D+6F3IMAAnjU8oC9iX1Ri5V/pKQAGzcCQ4ak11Y8fJj7nX1pKzJUUnauXbumtD0yMhLXrl3D+vXrVap6ziiFiERAx47K+6RSICEh/Tw5mXOCzqgQhYZyofMfPnBRDDLS0oBOnbg+ABCL0x2qrayApk2BkSPTx0dGsqKuDEYRcyzgGIacHIKopCgYSAywq8uu0qXoAEDfvsDffwMvXwJbtnBt2ZQ8YhQOKik7zZs3Vxo5RURQU1NDr169sGHDhh8WjlHKEQrlvUslEmDlSvkxaWlc5EJwMKClld4eE8MVyQsO5vqTkznn6rdvuf6UlHRlJy2Ns/yoqytWtbe2BmrWZA7VDEYBkpSahKn/TcXG+xsBAA2tGuJgj4OwMbApYskKgLFjgatXgcaNi1oSRgZU8tm5evWqQptAIIChoSFsbGxKlO8L89kpBSQnczmGMlqEKlcGunXj+j99Aiwssr7e3R04dIj7PS2NU6IsLJQ7VFtbyytdDAYjW4gIzXc3x7UgbkdgeqPp+L3F71BXKyWBCT4+nAU6YzXauDgWJlgIFLjPjouLi0qCMRgFglgM2NhwhzLMzbntso8fFZ2pg4PlrTqfPwNPnnCHMjIrRuPHy1uJZFYjloOIwQDAfREeVnsYnn15hj3d9qB95dzlQCv2fPvG5THbvZv7AtSsGZeSA2CKTjFEJWXn3bt38Pf3R6dOnZT2//PPP6hRowbKly//I7IxGPmHRAJUqsQd2WFoCPz3X5alO5CxaOznz+l78pkxM+McFP+f6RtpaZyTIgu5Z/wEJKQk4H3keziYcrnWBtYaiA5VOsBIsxT4rhBx7+UJE7gkrQIBMHSofDJXRrFDJWVn6tSpiI6OzlLZ2bRpEwwMDHDw4MEfEo7BKHQ0NbkwUWUQyRdjUVcH5sxRVIgSErh/gjLnaYBTjPr1Sz8XCLitMplVqEsXYOD/q/9Jpdy2HAu5Z5RAAr8Gwv2oO77Gf4XfSD+YapsCQOlQdIKDgTFjgNOnufOqVblw8jzWYmQUPiopO7dv35YrvJmZli1bYt26dSqKxGAUUwQCLrpMhqkplxcoI0RcBurgYPkU8ImJgItLesh9cjK3zx8aCty7l27+BjhFx8oqPeQ+8xaZszPg5FSw98pgqMC+J/sw6vQoxKXEwVTLFO8i3/HKTolGKuXSXvz6Kxccoa7OJQb89Ve2ZV1CUEnZiYiIgK6ubpb9Ojo6+Pbtm8pCMRglFoEAMDbmjoxUrAhcucL9LpUC4eHy1qA6ddLHfvrEWXRkIfcfPsjPNXVqurITGsqF2WdWiGS/V6jA6u4wCpz4lHiMPzMef/r9CQBoXr459nffD0tdyyKWLB8ICACGDUtPfurszFlzqlYtWrkYeUIlZadcuXK4efMmRo8erbT/+vXrsMqYLI7BYKQjFAJlynBHvXqK/XXrcpYgWch9ZqfqjFadDx/kQ+4zM3Vqejj/ly/AjBnKlSKmEDFU5Hn4c7gfccez8GcQQIC5LnMxp9kcqAlL+BZscjKwbBmweDH3u44Odz56NEtgWgJRSdnp27cvFi1ahPr162PcuHF8FfS0tDRs3LgRhw4dwqxZs/JVUAbjp0Ik4pSRnL40VK8OXLumXCkKDgbKlUsf+/YtsGuX8nl0dTn/o2nTuPPISOD4cRZyz8iR5TeX41n4M5jrmGN/9/1oUaFFUYuUP/Toke6b0749F4yQ8f3EKFGolGcnKSkJHTp0wKVLl2Bqago7OzsAQGBgIMLDw9G8eXOcPXsWGiVgL5Pl2WGUaojSs0YHBQH79ysqRLLM1H/8wUWYAFwh18xOl0ZG6YrP4MFA9+5ce2IiV9WZhdz/lEQlRmHy+clY0nIJyuiUKWpx8o/z57mggfXrgd69Wfb1YkihFAKVSqXYvXs3jh07hjdv3gAAbG1t0aNHD3h4ePDWnuIOU3YYPz1xcZzSY2TEhcwDwMOHnAOmTCGKiZG/JivFyMxMcZusTRvA0bHQbodRsDz9/BR7Hu/BitYrlGbSL7GcPcsp/hmjJllywGJNoRQCFQqFGDx4MAYPHqzqFAwGozigrQ3YZ6pPVLcuV2hVRsYq98HBQJMm6X3fv3Mh+7KQ+y9fOGVJhqZmurJz9y5nEVKWmdrKCrCz43IdMYodRITtj7ZjwrkJSExNhJ2JHYbVGVbUYuUP585xW1W6ulxyQNn2MVN0Sg0/VPU8KSkJjx49wpcvX9C4cWOYsKRKDEbpJLsq9+3bc9+AZSH3mY+MVp2goPSQ+7t3Fedav57LSg0A/v6cc6gypcjMjG0rFCLRSdEYeXokDvpzudPaVWqHLnZdiliqfKR1a06Br1+fKdulFJWVnfXr12P+/PmIjIyEQCCAj48PWrRoga9fv8Le3h4rVqzAkCFD8lNWBoNRXMkYcp/dllW7dlxeoYzKUEbH6oxZ1589A7JKTCoWA15e6fWIgoKAM2fkFSIjI6YQ5QO+Yb5wP+qO199fQ02ghiUtl2Bqo6kQCkqGq4JS3r8HFi3ilGttbS7Vw6VLLKt5KUYlZWfnzp2YNGkS+vTpgzZt2sgpNSYmJmjRogUOHjzIlB0GgyGPri4XOp+bpIiOjsCqVYoKkazKvYFB+tg7d7jMthnR0kr3G5o5E2jZkmuPiOAsS9bWAPPTy5a9j/di2D/DkJyWDGs9axzseRCNrBsVtViqk5YGbNjA+aPFx3PK+YoVXB9TdEo1Kik7q1evRpcuXXDgwAGlyQPr1q2L9evX/7BwDAbjJ8bOjjsyI8s+nTFxo4kJ0LlzulIUHs59mL18yR2yrTGAq33Wpw/3u56e4jZZz56AA1fTSS6a7SekgmEFpEnT0KlKJ+zquqtkl3x4+pRLDnjvHnferBlX04rxU6CSsvP69WtMkEViKMHIyIhlUGYwGAWDWCy/3QVwVhuZ5QbgwuEzWoMyWpISEjirUGQkEB3NbZc9e5beX7NmurJz+DBnMVLmUG1tDdSqJW9hKgVEJUZBX8IlmWxSrgluD72Nepb1Sm7kVVIS8PvvXELA1FROwV25klN8SkjUMOPHUUnZMTAwwNevX7Psf/78OczNzVUWisFgMH6I7KrcDxrEHbGxypMxZiwDEBzMOV5//w48fqw418mTnEUJAHx8gM2blStFlpbyddWKIUSE9XfXY/7V+bg55CaqmnLPwals0dRhi4vjkhYD3J9KpcCoGzeA4cOBFy+48y5dgE2bgLJl801ORslApXdf+/bt4eXlhTGZ98gBPHv2DN7e3sxfh8FgFG90dDgLjsyKo4xRowA3N+VRZsHBgI1N+tjHj4ETJ5TPIxQC//7LzQUAvr5crbSMClGZMkVW5f57wncMOTkEJwNPAgB2+e3CitYrikSWfCE6mvPT2ryZOzc3BzZu5NIelFQLFeOHUCmpYGhoKBo0aAAiQqdOneDl5YUBAwYgLS0Nf//9NywsLHDv3r0SEYrOkgoyGIx8wd8/vXRH5mizlBTg0SOgdm1u7MqVwPTp8teLRJzFwcqKS9pYty7XHhLCOWVbWwOmpvn+YX3n4x30PtobH6I+QKwmxuo2qzHWaWyRb1upbNn55x+uflVICHc+bBjnhMxCyksdBZ5U0NLSEg8fPsRvv/2GQ4cOgYiwd+9e6Orqom/fvli2bFmJUHQYDAYj36heXXkeIqmUS7RolMG5t3JlwN09XSEKDeX8SYKCuCOjL8mBA+mKkYZGes00mUVoxIh0H6bUVM46lAtFRUpSrLm9BjMvzkSqNBW2hrY41PMQ6lrWVf0ZFDWnTnFbVQC3henlBbi6Fq1MjGKByuUiMhIeHg6pVApTU9MSUyZCBrPsMBiMIic1Nb3KfXAwl6hRZtZYtQpYvRr4/JmLDstMZovR/PlZO1Q7O/Ph9nse74HnCS5PkXs1d3h19OIdk4sDKll2UlO55IDNmwPz5nHZuxmllkIpF5ERU1PT/JiGwWAwfk4yVrnPXIB16lTukIXcZ94mq1AhfWxwMBdyHxjIHZnJoBj1f5QKh2PG0K7oAAdBNQhij8snZSwJVe7fvAGWLuX8cSQS7jneuFHsncEZhU+uXhELFy7M88QCgQBz5szJ83UMBoPBUIIs5D5z2H1Gli/nCrQqcaamjx+xL+Iq3FOrQkOkATVfPzg9+QY8uQGcuKE4l58fF1oPcJFmDx7IF3gt6ir3aWlA27acwmNpCcg+p5iiw1BCrraxVNmaEggESEtLU0mowoRtYzEYjNLOl7gvGHh8IP578x8mNpiIdW7rOGXm0SPlUWaxsVxiRpnv5YQJXObhzMiq3B89mq6EvXgBfPv2wyH3GbexAgOBKlWUDPr7by7iyssLsLVVaR1GySXft7GkUmm+CFaciYsrsqhPBoPBKDCufbiCIf/2w6e4MGiKNGFnUBNxcQAqO3JHZoi4KvcSfSCOa1Kr7Qy1/tEQfgyGICQYgo/BECQm8lXu44S6/Fjxuk1Q37aRm0ooBJlbgKysQWWtQWWtkDz1t3QlKj6e235S8oXayyv9dwcHYMPqZAwLngtp9ZpIc+/HdbTtzh0CAb8+4+chLg9/83xxUC7JyDRDIAoAs+wwGIxSgiANaLYYcFkACKVAuANw+AgQXi0fJicY4xusEQxrBOMfdALARYAtxm/oi79QFiEQI0XhSmN8xXdwpT7WYzxGYStCUPb/M1njI6wQAHvswmB+TgAQIg1BsIEEiaiIt4hh/68ZiAaQO8vODyk7cXFxuHr1KoKCggAANjY2cHFxgbZKqS6LBqbsMBiMUofOJ6B7f6DiJe7cdzBwZgOQUnj/mwWQogw+wwofeaXIEqH4FcsgU2KOoRu64YTCtZfRHC1wWaH9PFpDHam4hUb4CCteQQqGNSJgiIzKEeNnoBCUnQ0bNmD27NmIjY1Fxil0dXWxePFijBs3TpVpCx2ZshMaynx2GAxG6eBd5Fs03lMbqdJU/NF6K/pWG1jUIiknNRWCT2HctljIRwhCgiH8GIyQV3Gw9dkKKdJ9C9SQivcoDyuEKJ0qLugrXxxW7fABCF8GcttnVtaQluV+Qle3UG6LwREXxyUGB7jMCfltB4mOjoalZQGGnu/ZswcTJ06Es7MzJkyYAIf/p1sPCAjAhg0bMHHiROjr62PgwGL6BlOCtnb+/yEYDAajsCAiPutxde2KONzrMGwMbGBvYl/EkmWHCNC3Buys05tCQ1F+3Dh4YQRGYhvSIIKakLBtVgis7Fcod6iOj4e2tVG6YefkYa5uWWb09TnH6du3072f79zhHLLLlSs5IfclkIL4jM1LDJRKlh1HR0cYGBjg4sWLUMvk1ZuWloaWLVsiMjISfn5+eZ260GHRWAwGo6QTEh2CAccHYGaTmWhj26aoxVENqRTYvh2YNg2IjgaJRJiWugTrMRH+gWLl0VgykpLkw+B37gTu3pVXiCIjuT5NTc7kIMsy3aULl3lZhrGxfIbqdeu4sH+AKwiro5N+zsiWfCnmmg0FnlQwMDAQq1atUlB0AEBNTQ29evXC1KlTVZmawWAwGHng3OtzGHh8IL7Gf0VwVDBejHsBkbCE5Zp5+ZIre3H1Kndevz4S1m/H6oY1AOSiSHnmfD+DB3NHRmJiuDplX7/Kl9OwtubCvWQh99++ccfjx5yVZ9Om9LGDBnG1t8qUUcxObWXFlQApYVUEfhZUekfo6+vj/fv3Wfa/f/+eWUkYDAajAElJS8Gcy3Ow/OZyAICjuSMO9TxU8hSdlBSgVStO2dDSAhYvBsaPByXmcy4QXV3lFe43cmHyfMh9pu0xOcXo8+f0n58/c4kWZWhrA717p58PGwY8e6aoEMl+t7D4qSqwh4RkkSupkFDpXdGhQwds2LABdevWRZ8+feT6Dh06hI0bN6J///75ImBhwfLsMBiMksLH6GAMOt0Hd0JvAQCGO47B0uarIRFJ8pR7pHigDrUFyyDavxvJ67eBbMoDifI5VArnngSAugFQ0QCoWCPD4hmGXLwDfP0KoSzX0MdgCEI/QvgxGBAIkBSfrrxI7j+E2hM/zicoE6StjfhPMbyPkWj9agjCv3C5iKy4fETSsgVT5b4wUciVtAHw9My/+Qs8z054eDhcXFwQGBgIc3NzVK5cGQDw6tUrfPr0Cfb29rh69WqJqHzOQs8ZDEaJQjcEGF0T0PoOJOoBp3YAz3sWtVS5RgtxWIB5uIOG+BsyuWUfQyX3gz0zjvBFRbyFNYLlwu+tEYxIGKAmnvJjH6E2asNPYY5EaOAF7OX62uIc1JDGzxYJA5Sm55Y3CiH0PDExEdu2bcPZs2fl8uy0b98eI0aMgEQiUWXaQocpOwwGo8TRdRBg+gw4egiIqFjU0uSJKViFVZiGTygDW7xBPH6+MFgBpCCk+/aMxFY4IEBOKbLAJwBAAOxRFQH82MyKUSy0ecUnEHaYgPSyHmXxEVHQRyxKa8h9ISUVLA2wPDsMBqO4ExT1HtrqOjDR4qzl8SnxUBOoQUNUhIU4VSUpCRq9uyB11HikuXXIclhB52gp9iQnQxAaAsTGgqqnb6uJR3hC6P+EK93x/ZvcJdIq9kh4lK4YSRrVhtoTP5C+PqisNaSysh1W1pBWrIS0XvJuKPlJSAi3dZWx2pSaGvD8eS4cznNJXvLsMGWHhZ4zGIxizPGA4xh8cjAal2uMf/r+A6GgBEX7EAFHjgB//slFMamr5/rSgg5bLhXEx3MRZh8/cg7VIhGQ0V+2UiWuKrwyHBw4zUNGy5ZcaH3mKDNray4HkazQax7YvBkYO5b7XU0N2LYNGDo0z9NkSYGHngPAjRs38Oeff+Lt27eIiIhAZp1JIBDg8ePHqk7PYDAYPzVJqUmY5jMNG+5x2xLf4r8hKjEKhpqGRSxZLvn4ERgzhlNyAGDHDmDUqFxfrq3N6UqMbNDS4kKcsgpzev06PeQ+cyJGMzP5sY8fcyH3yvLjZVaMpkwBUlMVlSILC7kq956e6crO8+clMBprzZo1mDZtGiQSCezs7GBkZJTfcjEYDMZPy5vvb9D7aG88DHsIAJjqPBVLWi6BulruLSNFhlQKbN0K/Por90Grrg7Mng0MGVLUkv2cyELulYXdZ+TyZeXZqYODgf8HIfHs2sVZgTIjFAIuLsClS3xTf+xDAjRhHWYN6Fpze5NFkItIpW0sCwsLVK5cGf/888//nXtLLmwbi8FgFCcOPzuMYaeGISY5BsaaxtjddTc6VMnat6VYERAADB8O3LzJnTdqBHh7A1WrFq1cjPyDiEu0KFOEZFajjx85a0+bNsD58wC4rchEHWMYI4NiJBJxTjvW1tzrY/ny9L6AAMDEhDtyEXJf4NtY8fHx6N+/f4lXdBgMBqOgUMXnJDE1ETMvzkRMcgwaWzfGwZ4HYaVnVbCC5gfJycCyZVxCwORk7saXLQNGj2YZhUsbAgGgrNC3VMp5kiclpbelpeEiWsIawWhQ9iOEYaGcQhQUxB2Z65A1acJZjDQ05BMwWlsDjo5Ar14qi62SsuPq6oqnT5/mPJDBYDAYuUYikuBQz0M4HnAcC1wXlIxsyHfupGcLBoAOHYAtW7gPKMbPg1DI+exkRE0NvXEYABAbCGhrpAJhYelWIQOD9LFJSekO7ElJnGN1RufqNm3klZ3y5fNUo0ylbazg4GC0adMGQ4cOxZAhQ0q0zw7bxmIwGAVBbi07B54eQHxKPIbVGVZ4wuUHsbHArFlcWlwiLtvv+vVcyYQSnPWXkX+oFFGXnMzFrWf2G3JwSLcoxccD2tr/z7KDgg09X7duHaZOnQoigkQiUSgKKhAIEBUVpcrUhQpTdhgMRkGQ0z/6+JR4TDw7Edt9t0OsJobfSD84mObgRFqcWLyYczwGAA8PYM0armI4g1HQpKUBb98i+sUL6HfuXHA+O3PnzsXixYtRtmxZ1KtXj/nuMBgMRh4ICA+A+1F3+H/xhwACzGwyE1WMizAuVxUmT+aqlE+dym0xMBiFhZoaFyEmyzqZC1Sy7JiZmaFhw4Y4ceIEhCXc+YxZdhgMRkGQ0bITGJieY2S3326MOTMG8SnxKKNdBgd6HECLCi2KTtDcQATs3w/89Rdw6hSrmswoFhR4NFZycjI6dOhQ4hWdjLCq5wwGIz/JXPF5/XrCfYvh2P10BwDA1aYVtrffhzLaZYp/pfLwr9AaOxaC6Ggkee9B6sDBRS0Rg1HwVc8HDBgAANi3b19eLy12sEKgDAaj0Ki/AXCbBFxZAFyfCVDx/YaVuVjlIOyEBcKwEtOQihKQ3JDxE1DAhUBfvXqF3r17o2HDhhg6dCjKlSun4KAMoEREaTFlh8FgFB5SwMIPCKtT1IJkS3U8hTeGYwHm4RzaFbU4DEYWFLCyk3H7SpBNiGFaWlpepy50WNVzBoOR33AVnwlSafr/x/yu+FwgJCZCfcViqK9ZBkFqKtKq10TibT8WSs4oluSl6rnK0VjZKTklEW1tVlWXwWDkD/F6fjBxP4wvhxYCJIJQSNi2TVCkhRBz5MYNrtTDixfcedeuUNu0Cdo6pet/PaP0kBd7isp5dgqCa9euYeXKlXj48CHCwsJw/PhxdO3aNcvxV65cgaurq0J7WFgYzM3Nc7Umi8ZiMBj5BRFh64Ot+OX8L0hKSwI+OgEn/0TgjerFV9GJjuaKdm7Zwp2bm3O1j7p3L1q5GIwcyMvnd7EKp4qLi0OtWrWwadOmPF0XGBiIsLAw/jDLXLqewWAwCpioxCj0PtobY86MQVJaEtrbdgL2nwXCqxffratTp7ginTJFZ9gwbq+NKTqMUkaxKrzSrl07tGuXd2c4MzMzGGSsscFgMBiFzLiz43Dk+RGIhCIsb7UcI2r8At2BxXQL6PNnYMIE4DBXtwiVKnGx8kos5QxGaaBYWXZUxdHRERYWFmjdujVu3rxZ1OIwGIyfkKUtl6KuRV3cGHwDk50nF0+/RiJg504u8c/hw5zX9IwZwJMnTNFhlGqKlWUnr1hYWGDr1q2oV68ekpKSsH37djRv3hx3795FnTrKQzuTkpKQlKEEfXR0dGGJy2AwShERCRE4FXgKno6eAAArPSvcH36/eCo5Mj59AsaP57Kx1akDbN8O1K5d1FIxGAVOiVZ27OzsYGdnx583atQIb968wdq1a7F3716l1yxduhQLFiwoLBEZDEYp5O7Hu+h9tDeCooJgIDFAF/suAORTcWhrc4aUIkcqBWTpQiwsgNWrOafkX34BRCX6I4DByDWlYhsrI/Xr18fr16+z7J85cyaioqL4Izg4uBClYzAYJRkiwupbq9FkZxMERQXB1tAWVnpWRS1W1vj6AvXrAxcvpreNHAlMm8YUHcZPRYG82j98+AADA4MiCeX28/ODhYVFlv0aGhrQ0NAoRIkYDEZp4Fv8Nww6OQinX54GALhXc4d3J2/oaRTjlBU7dgAP/9fefUdFcXZxAP4tCEtvAmIBRFQUG1YUY0dRsWCvEewm9q4xijUYu7GCDY29oEaxo9iNFbEgioItNAsgSJHd+/0xH6srRVgXFvA+5+w5zMw7M3dn2+Wdt9wSupZfv86DA7IfVr4kO+XLl4eOjg5GjRqFKVOm5HraiMTERLlamfDwcAQFBcHExARWVlaYPn06Xr9+jW3btgEAVqxYARsbG1SrVg0pKSnYuHEjzp49i1OnTuXH02KM/aAuv7iM3gd641XCK4jVxVjRdgWG1x1eONvnpKUBmprC33/8IYy85unJiQ77oeVLsjNgwAAkJSXB19cX69evR1xcXK72u3nzptwggRMmTAAAuLu7w9fXF5GRkXjx4oVse1paGiZOnIjXr19DR0cHNWvWxJkzZ7IcaJAxxhT134f/8CrhFSqZVMLeHnvhYOGg6pAyi4sTbk89ewacOSMkNwYGn8fQYewHlu8jKD98+BD29vb5eYrvwiMoM8ayQkRyNTdbg7aia9Wu0BfrqzCqbPj5ASNHCr2tAGHqh8aNVRsTY/msUI2gXJgTHcYYy8r5iPOo61MXkR8iZevcHdwLX6Lz33/CaMfdugmJTuXKwIULnOgw9pVi1xuLMcYUJZFKMO/8PLTc1hJ3ou5g1rlZqg4pa1IpsGGDMNXDwYNCz6oZM4C7d4EmTVQdHWOFjkJtdogIPj4+2LRpE549e4b3799nKiMSiZCenv7dATLGWEGISoxCf7/+CAgXuml7OHhgRdsVqg0qK48fA8OGAefPC8v16wuDA9asqdq4GCvEFEp2pkyZgmXLlsHBwQH9+/eHsbGxsuNijLECE/AsAP38+iE6KRo6GjpY57oOA2oNUHVY8j59ApYsAebMAVJTAR0dYMECYURkdXVVR8dYoaZQsrN161Z069YNezMmkWOMsSLqYMhBdNvbDQRCdfPq2Nt9L6qaVVV1WPJu3hRmJL97V1hu0wbw9gbKl1dpWIwVFQolO8nJyXB2dlZ2LIwxVuBa27aGnakdmlg1wcq2K6Gtoa3qkDLz9hYSHRMTYMUKoH9/HjeHsTxQqIFyq1atcOPGDWXHwhhjBeLG6xuQkhQAoKeph2uDr8Gno0/hSnTS0j7/vWgR8MsvQEgI8PPPnOgwlkcKJTtr167FtWvX8Mcff+Dt27fKjokxxvJFujQd089MR4ONDbDs6jLZekMtQxVG9ZW3bwEPD6BTp88ziRobA2vXAubmKg2NsaJKodtYdnZ2kEqlmDlzJmbOnAktLS2of9VATiQSIT4+XilBMsbY93oZ/xJ9DvTB5ZeXAQCvEl6pOKJsvHkD7N4t1OzcvCn0tmKMfReFkp1u3boVzjlhGGMsC/6P/THg0AC8S34HA7EBNnXahO723VUd1mcfPgD6/x+w0M5OqMWpWpUTHcaUJN+niyjseLoIxoqvNEkafgv4DUuvLgUA1CtTD3u670EF4woqjuz/pFJg/Xrgt9+AEyeAhg1VHRFjRUahmi6CMcZUJSQ2BH/9+xcAYKzjWFwaeKnwJDohIcJoxyNHAvHxgI+PqiNirNj6rlnPL1y4IBtB+esKIpFIhPHjx39XcIwx9j1qWdTC6varYa5rDrcqbqoOR5CWBixcKAwImJYG6OkJy7/8ourIGCu2FLqNFRQUhF69eiEsLCxTkiM7sEgEiUTy3QHmN76NxVjxkZqeit8CfsPPtX6Gg4WDqsPJ7OpVYOhQ4MEDYdnVFVi3DrC0VG1cjBVBefn9VqhmZ8iQIYiJicH69evh6OgIQ8NC1G2TMfZDevruKXrt74Vbkbdw9MlR3P/lPjTUNVQdluDDB2GiztWrhe7kZmbAX38BvXrxmDmMFQCFkp0HDx5g7ty5GDp0qLLjYYyxPNv3YB+GHBmChNQEmGibYFmbZYUn0Tl+HBgxAnjxQlh2dweWLgVKllRtXIz9QBRKdipVqsRdzxljKpeSnoIJJydg3c11AIDGlo2xq9suWBoWgttCsbHAuHHAzp3CcvnyQiPk1q1VGRVjPySFemPNnj0ba9aswevXr5UdD2OM5UpsUiwabWokS3Sm/zQdgR6BhSPRAYCYGGDfPkBNDZgwAbh/nxMdxlREoZqdrl27IiUlBXZ2dmjVqhXKlSuX5QjKK1euVEqQjDH2NRNtE5jqmMJMxwx/d/kbLhVdVB2S/OCA1aoBa9YADg48OCBjKqZQb6zz58+jU6dO+PDhQ/YH5t5YjDEl+/jpI0QQySbsjEmKQbo0HWX0y6g2MCKhwbGnJ3D2LFCnjmrjYewHkO+DCo4ePRoGBgY4efIk4uLiIJVKMz2KQqLDGCs6QmJD4LjREeNOjJOtM9c1V32iAwg9qq5eFQYH3LhR1dEwxr6iULITFhaGyZMno3Xr1lwbwhjLd1uDtqLehnq4H3Mfh0MPIzYpVtUhASkpwPv3n5dXrhQaIK9erbqYGGNZUijZqVatGs9ozhjLd0lpSfA45AGPwx74+OkjWtm0QtCIIJjpmqk2sEuXgNq1geHDP68rVUoYMFCNZ+FhrLBR6FO5ZMkSeHt74/r168qOhzHGAAD3Y+6j/ob62Hp3K9REapjXYh5O9j8JCz0L1QWVkAD8+qswp9WjR8DFi0B0tOriYYzlikK9sZYuXQp9fX00atQI9vb2sLKyyrI31uHDh5USJGPsx5ImSUO7He3wKuEVyuiXwc6uO9GsfDPVBvXPP0KikzHkxuDBwOLFgLGxauNijH2TQslOcHAwRCIRrKyskJiYiIcPH2Yqw4MOMsYUpamuifWu67Hmxhpsdduq2ttW0dHAmDHA3r3Csq2t0DanZUvVxcQYyxOFup4XJ9z1nLHC4W7UXcQkxaC17eeB94hIdf84EQG+vsDEiUJDZHV1YNIkoXu5trZqYmKMyeT7RKCMMaYsRATvW94Yd2IcdDR0EDQiCFaGVgBUWEP89KnQ+DggQFiuXVvoUs7j5zBWJCmU7LzImNDuG6ysrBQ5PGPsBxGfEo9hR4dh7wPhFlFr29bQ1dBVXUBSKbBsGTBrFpCcDGhpAXPnAuPHAyX4f0PGiiqFPr3ly5fP1X9cPLAgYyw7t/67hZ77e+LZ+2cooVYCfzr/ifENx6u2vZ9IJHQrT04W2uR4ewMVK6ouHsaYUiiU7GzevDnTF5JEIkFERAS2bdsGc3NzjBw5UikBMsaKn1X/rsKk05OQJkmDtaE19nTfA8dyjqoJJjkZSE0FjIyEZGfNGqBTJ2DgQGGZMVbkKZTseHh4ZLtt6tSpcHR05EEHGWPZehD7AGmSNLhVccPmTpthrK2i7tvXrgE//ww4OgLbtwvrypYFBg1STTyMsXyh9KE+dXV1MXDgQCxfvlzZh2aMFWFfdvxc7rIcW922wq+nn+oSHUAY7fjZMyAwEHj7VnVxMMbyVb6May6VShEVFZUfh2aMFTFEhGVXl6H9zvaQSIV2fNoa2hhQa4Bq2ueEhn7+u0EDYfycBw+AkiULPhbGWIFQarKTkJCAo0ePYvHixahdu7YyD80YK4LefnyLTrs7YeKpiTgRdgJ+IX6qC+a//4CuXYGaNYGQkM/ru3UDDA1VFxdjLN8p1GZHTU0t2//IiAhWVlZYu3btdwXGGCvarry8gt77e+NlwkuI1cVY0XYFutt3L/hApFJhjJzJk4W5rUqUENrqVK1a8LEwxlRCoWRn1qxZmZIdkUgEY2Nj2Nraok2bNijBY1Iw9kOSkhSLLy/GjLMzICEJKplUwt4ee+Fg4VDwwTx+DAwbBpw/Lyw3aCAkPjVqFHwsjDGV4ekieLoIxpRq1LFRWHNjDQCgb42+WO+6Hvpi/YIN4tMnYMkSYM4coVu5jg6wYAEwerQw7QNjrMjLy+93vjRQZoz9uIbVHQYTbRNs7LgR27tsL/hE5+ZNoF494LffhESnTRuhAfK4cZzoMPaDytW9pkEKjDkhEomwadOmPO/HGCtaJFIJbv53UzYoYM1SNRExNqLgk5ykJGGSzuXLhXY6JUsCK1YA/frx4ICM/eByleycPXs2z11EVTrkO2OsQEQnRqP/wf4IjAjEpYGXZAlPgSc6Fy8C7u5AeLiw3LevkOiYmRVsHIyxQilXyU5EREQ+h8EYK2rOhp9F3wN9EZ0UDR0NHbxKeAVHqGjKBzU1IdGxtATWrwfat1dNHIyxQom7TDHG8kQilWDu+bmYd2EeCITq5tWxt/teVDUrwK7cREJPKzs7YblxY2FwwLZtAf0CrlVijBV635XshIeH4/jx43j+/DkAwNraGu3atYONjY1SgmOMFS7/ffgP/fz6ITAiEAAwpPYQrGy3EjoaOgUXRGIi0KcPcOYMcO/e51nJe/QouBgYY0WKwsnOxIkTsXLlSkilUrn1ampqGDduHJYsWfLdwTHGChe/ED8ERgRCT1MP3h280bdG34IPQldXmKlcIhEGB8xIdhhjLBsKJTtLly7F8uXL0b17d0ycOBFV/z8SaUhICJYvX47ly5ejbNmyGD9+vFKDZYyp1sj6I/E87jmG1h2KyiUrF9yJQ0KAMmWEaR1EImFgwI8fAXv7gouBMVZkKTSoYJUqVVClShUcOnQoy+1ubm549OgRHj169L3x5TseVJCx7L1KeIXfz/6OVe1WFXwPKwBISwMWLhQGBBw0CFi3ruBjYIwVSvk+qGBERARcXFyy3e7i4sI9uBgr4vwf+8NhvQO23t2KiacmFnwA164BdeoIY+ekpQGvXgHp6QUfB2OsyFMo2TE3N8fdu3ez3X737l2Y8fgWjBVJnySfMPnUZHTY1QFvk9+ibum6mNp4asEFkJgIjB0LODkJIx+bmQG7dgH//CNM4skYY3mkULLTo0cPbNy4EQsXLkRSUpJsfVJSEv78809s3LgRvXr1UlqQjLGC8TzuOZr6NsWSq0IHgzENxuDyoMuwNbEtmACOHweqVQP++kvoXj5ggNBep3dvHgWZMaYwhdrsfPz4ER07dsS5c+dQokQJlClTBgDw33//IT09HS1atMCRI0ego1OA3VEVxG12GBNcfH4RnXZ3QlxKHIy0jLCl8xa4VXErmJPHxgLjxwM7dgjL5csD3t7CvFaMMZaFvPx+K1QnrKOjg4CAABw+fFhunJ22bduiffv26NixI08XwVgRU6lkJYjVxXAs64jd3XejvFH5/D8pkZDgjBsHvH0rjIQ8bhwwd67QxZwxxpRAoZqd4oRrdtiP7O3HtyipU1K2/OjNI1QwrgBNdc38P3l8vHB76sQJYblGDaFLeYMG+X9uxliRl++9scLDw3HkyJFstx85coR7YzFWyO1/uB8V/qqAPff3yNZVMa1SMIkOIEzrkJQEiMVC1/JbtzjRYYzlC4Vqdrp164aEhAScPn06y+1t27aFkZERdu/e/d0B5jeu2WE/mpT0FEw8ORFrb64FALSr2A7+ff0L5tbz/ftCexw9PWE5LEwYCTljjivGGMulfK/ZuXr1Klq3bp3t9latWuHixYuKHJoxlo+evH2CRpsayRKdaY2n4XDvwwWT6Pz1F1C7NjBz5ud1FStyosMYy3cKNVB+//499HOYWVhPTw9v375VOCjGmPLturcLw44OQ2JaIkx1TPF3l7/RtmLbggvAzk4YFPDFC0AqFRojM8ZYAVDo28bKygqXL1/OdvvFixdRrly5PB/3woUL6NixI8qUKQORSJTtdBRfCgwMRJ06dSAWi1GxYkX4+vrm+byMFXfB0cHo69cXiWmJaGrdFEHDg/I/0YmPB86f/7zs4gL8+y+wfz8nOoyxAqXQN06fPn2wa9cu/PXXX3KznkskEqxcuRJ79uxB3755nw05KSkJtWrVwpo1a3JVPjw8HK6urmjRogWCgoIwbtw4DBkyBCdPnszzuRkrzmqWqolJjSZhZtOZCBgQgLIGZfP3hP/8IwwO2LGjMM1DhgYNeHBAxliBU6iBcmpqKlxdXXH27FmYmZnB7v/33ENDQxEbG4vmzZvj+PHjEIvFigcmEuHgwYNwc3PLtszUqVPh7++P+/fvy9b17t0bcXFxOJHRnfUbuIEyK652BO9AE+smsDK0AgAQUf63zYmKAsaMAfbtE5YrVgT27BHmuGKMMSXK9wbKYrEYp06dwqZNm9CgQQO8efMGb968QYMGDbB582acOXPmuxKd3Lp69SqcnZ3l1rm4uODq1avZ7pOamoqEhAS5B2PFSVJaEgYdHoT+B/ujz4E++CT5BAD5m+gQAZs3A1WrComOujowdSoQHMyJDmNM5RSeVU9NTQ0DBw7EwIEDlRlPnkRFRaFUqVJy60qVKoWEhAQkJydDW1s70z5eXl6YM2dOQYXIWIF6EPMAPff3xMPYh1ATqcHF1gVqonxuH/P0KTBsGHD2rLBcp44wOGDt2vl7XsYYy6UfrpXg9OnTER8fL3u8fPlS1SEx9t2ICJvvbEb9DfXxMPYhSuuVRsCAAMxqNgvqaur5c9L0dGDxYmHk47NnAW1tYfnffznRYYwVKgrX7BQGFhYWiI6OllsXHR0NAwODLGt1AOEWXEHcYmOsoCSlJWGE/whsD94OAHCxdcG2Lttgrmuefye9cwcYMgS4fVtYbtkS8PEBbAtodnTGGMuDIl2z06hRIwQEBMitO336NBo1aqSiiBgreGoiNQRHB0NdpA6vVl441u9Y/iY6S5YA9esLiY6RkdBW58wZTnQYY4VWoarZSUxMRFhYmGw5PDwcQUFBMDExgZWVFaZPn47Xr19j27ZtAIARI0Zg9erVmDJlCgYNGoSzZ89i79698Pf3V9VTYKxAEBEIBDWRGrQ1tLG3+17EfozFT1Y/5f/JK1cWpnjo0UMYFdnCIv/PyRhj36FQzXoeGBiIFi1aZFrv7u4OX19feHh4ICIiAoGBgXL7jB8/Hg8fPkS5cuUwc+ZMeHh45Pqc3PWcFTXxKfEYdnQYapjXwO9Nf8//E75/L8xp1aTJ53XXr/OknYwxlcrL73ehSnZUgZMdVpTc+u8Weu3vhafvn0KrhBaejXmG0vql8++EoaFA8+ZASgoQEsK1OIyxQiNfx9lJTk7GgwcPMjUM/lJ4eLjsVhNj7PsREVb9uwpOm53w9P1TWBta45z7ufxNdAChHU6ZMkCpUkBMTP6eizHG8kmekp05c+agZMmSqFmzJsqUKYMWLVrIjV6c4cqVKyodf4ex4iQuJQ7d93XHmBNjkCZJg1sVN9wZfgcNyzVU/smkUmDHDqEmBwBKlAAOHQKCgoCaNZV/PsYYKwC5Tnb27t2LOXPmwMnJCStXrsS0adMQEhKC+vXrY+fOnfkZI2M/rHRpOpw2OcEvxA8aahpY2XYl/Hr6wVjbWPkne/xY6ELevz+wYMHn9ZaWgJaW8s/HGGMFJNe9sZYvX46WLVvizJkzsnWTJk1C37598fPPP+O///7DpEmT8iVIxn5UJdRKYKzjWCy6sgh7uu9BvTL1lH+ST5+E7uRz5gCpqYCOjnDbijHGiolc1+w8evQI3bp1k1tnbGyMY8eOYcSIEZg6dSonO4wpwbvkd3gQ80C2PKzuMASPCM6fROfmTaBePeC334REp00b4MEDYNQo5Z+LMcZUJNc1OxoaGkhPT8+0XiQSYc2aNTA3N8ecOXMQGxubZfdxxti3XXl5Bb3394a6mjruDL8DIy0jiEQi6GrqKvdESUmApyewfLnQTsfEBFixQriFld8zozPGWAHLdc1O1apVce7cuWy3e3p6YtWqVdixYwemTZumlOAY+1FISYo/L/2Jplua4mXCS2ioaSAmKZ96P50+LcxntXSpkOj07St0K//5Z050GGPFUq6TnQ4dOuDYsWM5Tpw5cuRI7NixA+/fv1dKcIz9CGKTYtFhZwdMC5gGCUnQp3of3Bp2C5VLVlbuid6+BTw8hFtV4eFCw2N/f6H3lXk+Ti/BGGMqluvbWIMHD4a9vT2kUmmO5Xr16oUKFSrg4cOH3x0cY8XdhecX0OdAH/z34T9oldDCqnarMLj2YIiUXcPy4IHQ0yomRqi9GTVK6HGlr6/c8zDGWCHEIyjzCMpMhdx2u+Fw6GFUMa2Cvd33okapGvlzorQ0oSGyRAJs3AjwZLmMsSIuL7/fCk0EmpqaisuXLyMkJAQJCQnQ19eHvb09GjduDLFYrFDQjP2INnXahAoXK2Bui7nQ09RT3oEzBgfs1QvQ1BQeR44I0z3wZ5Qx9oPJU7JDRFiyZAn+/PNPvH//Hl9WColEIhgbG8u6oCu9Gp6xYuBs+Fn4P/bHkjZLIBKJUFKnJJa5LFP+ibp3Bw4eBJ4/B37//2Sh1tbKPw9jjBUBeUp2+vXrh927d6NSpUoYPXo0atWqBX19fXz48AF3797Fzp07MW3aNAQFBWHHjh35FTNjRY5EKsHc83Mx78I8EAiO5RzRs1rP/Dth9+5Cryszs/w7B2OMFRG5brPz999/w93dHZMmTYKXlxfU1dUzlZFKpfjtt9+wePFibN26Ff3791d6wMrGbXZYfvvvw3/o59cPgRGBAIDBtQfjr3Z/QUdDR3knuXoViI8H2rYVlomExsg8EjJjrJjKy+93rpOdpk2bQl1dPcexdjK0bNkS6enpuHDhQu4iViFOdlh+OvX0FPr79Ufsx1joaujCu4M3+tXsp7wTfPgAzJgBrF4t1OKEhAgDBDLGWDGXl9/vXI+zExwcnGm6iOx07doVwcHBuT00Y8XS4suL0XZ7W8R+jEWtUrVwe/ht5SY6x48D1asDq1YJNTnt2/OggIwxloVcJzufPn2CVi5nPhaLxVlOLcHYj6R26doAgF/q/YJrQ64pb5DA2FigXz8huXnxArCxAU6dArZsAYzzYTZ0xhgr4nKd7FSsWDHXt6UuXryIChUqKBwUY0XVl1M8OFdwxr1f7mGt61polcjdPwo5IgK2bweqVgV27gTU1ICJE4F794DWrb//+IwxVkzlOtnp3r07du3aBX9//xzL+fv7Y9euXejRo8d3B8dYUfFJ8gmTT01G5VWV8fTdU9n6aubVlHOC58+FmpyffxamfahZE/j3X2DJEkBXyZOEMsZYMZPrZGfixImws7ODm5sbhg0bhosXLyIhIQFEhISEBFy6dAnDhg2Dm5sb7OzsMHHixPyMm7FC43ncczTZ0gRLri5BfGo8jjw+oryDSyTAypVAtWrAiRPCgIB//AHcvCmMiMwYY+yb8jRdRExMDAYMGIBTp05lOWggEcHZ2Rnbtm2DhYWFUgPNL9wbi32PQ48OYeDhgYhLiYOh2BCbO29G16pdlXeC9u2FhsgA0LQpsGEDUFnJE4QyxlgRlG/TRZibm+PEiRP4999/ceTIETx8+BAfPnyAvr4+qlatig4dOqARz7nDfgBpkjRMOT0FK/9dCQBoULYBdnfbDRtjG+WeqEcP4PJlYPFiYMgQoZ0OY4yxPOGJQLlmhylg2dVlmHhKuFU7oeEEeDl7QVNd8/sPfPEikJoKODsLy0RC7ytz8+8/NmOMFSP5PhEoYz+6UQ1G4fSz0/i13q/oaNdROQc9dAjo0gUoUwZ4+BAwNBTGzeFEhzHGvkuuk51OnTrl6cAikQiHDx/Oc0CMFUYp6SlYe2MtRjcYDQ11DWiqa+J4v+PKPYmLC1CpEtCsmXKPyxhjP7hcJzvBwcG5msk8JSUF0dHRPOs5KzaevH2CXvt74U7UHcQmxcLL2Us5B46OFkY/njMHUFcHtLWBW7cAfX3lHJ8xxhiAPCQ7EREROW5PSUmBt7c3/vzzTwDCXFqMFXW77+/G0CNDkZiWCFMdUzS1VsL7mgjw9RUGBHz/XpjTauxYYRsnOowxpnTf3WYnNTUV69evx6JFixAVFYVmzZph165daMZV8awIS/6UjHEnxsHntg8AoIlVE+zqtgtlDcp+34GfPgWGDwcCAoTl2rWBJk2+M1rGGGM5UTjZ+TLJiYyMRPPmzbFr1y6u0WFF3uO3j9F9b3fci7kHEUSY0WQGPJt7ooTad/xvkJ4OrFgBzJoFJCcDWlrA3LnA+PFACe4nwBhj+SnP37KpqalYt26drCanRYsW2L17N5rwf6esmJCSFM/eP4O5rjl2dN0B5wrO33fAO3eEMXJu3xaWW7YEfHwAW9vvD5Yxxtg35TrZSUlJwbp167B48WJERUWhZcuW2LNnDyc5rFiQkhRqImHAviqmVeDXyw81zGugtH5pxQ+anCw0Pl6yRJj2wcgIWLYM8PAQupQzxhgrELlOdmxsbBATEwMHBwesW7dOluS8e/cu231MTEy+P0LG8tmDmAfoc6APVrdfLWuA3Ma2zfcd9Nw5YNgwICxMWO7RA/jrL6CITKPCGGPFSa5HUFb7Ypj63HYrl0gkikVVgHgE5R8XEWHznc0YfXw0ktOTUa9MPVwfcv37h03Yswfo3Vv4u0wZYO1aoHPn7w+YMcaYTL6MoOzp6fndgTFWWHxI/YBf/H/Bjns7AAg1OX93+Vs540O5ugI2NkDbtoCXlzASMmOMMZXhubG4ZueHczfqLnru74nHbx9DXaSOeS3mYepPU2VtdvLsv/+AdeuE9jkZNaAfPvCYOYwxlo94bizGshESGwLHjY5IlaSirH5Z7O6+Gz9Z/aT4AdPSgAYNgNevgbJlgREjhPWc6DDGWKHByQ77oVQxrYJOdp2Q9CkJW922wlTH9PsOqKkJTJkCbN8OODkpJ0jGGGNKxbex+DZWsXcn8g5sjG1gpGUEQBgdWVxCrNhtq0+fgKVLhdqcli2FdRkN8dXVlRMwY4yxb8rL77eCjRQYK/yICKuvr0bDTQ0x5J8hyMjrtTW0FUt0bt4E6tcHpk8Hhg4VxtEBhCSHEx3GGCu0+DYWK5biUuIw+J/B8AvxAwCkS9ORkp4CbQ3tvB8sKQnw9ASWLwekUqBkSaExspaWkqNmjDGWHzjZYcXO9dfX0Wt/L0TERUBDTQOLWy/GGMcxinUrP3NGGBwwPFxY7ttXSHrMzZUbNGOMsXzz3cnOhw8fEB8fD6lUmmmblZXV9x6esVwjIqy4tgJTz0zFJ+kn2BjZYE/3Pahftn7eD/buHTBxIuDrKyxbWgLr1wPt2ys1ZsYYY/lP4WRn3bp1WLZsGZ49e5ZtmaIwgjIrPuJT47Hs2jJ8kn5Ct6rdsLHTRlmj5FwjAvbuBcaMAWJihDmsRo0CFizg7uSMMVZEKZTsrF+/HiNHjoSLiwsGDRqEGTNmYPz48dDS0oKvry9KlSqFMWPGKDtWxnJkpGWEXd124W7UXfxa/9e837Z69Qr49VfgyBFh2d4e2LgRaNRI+cEyxhgrMAp1Pa9WrRqsrKxw/PhxvH37FmZmZjhz5gxatmyJ+Ph41KtXDyNGjMDEiRPzI2al4q7nRZeUpFhyZQks9CwwoNaA7ztYcrIwxUN0NKChAcyYAUybBojFygmWMcaYUuV71/OnT5+iY8eOAAANDQ0AQFpaGgDA0NAQQ4YMwdq1axU5NGO5EpsUiw47O2Dqman4xf8XvIx/+X0H1NYW2ug0agQEBQm9rzjRYYyxYkGhZMfQ0BDp6ekAAAMDA+jo6ODly88/Nvr6+oiKilJOhIx95eLzi3DwdsDxsOPQKqGFFS4rUM6gXN4OkpYGzJ0LXLz4ed2ECcKyvb1yA2aMMaZSCiU71atXx927d2XLDRs2xLp16/D69Wu8fPkS3t7eqFy5stKCZAwQblstuLAAzbc2x38f/oNdSTv8O+RfDK07NO/tc+bPF2pvhg4VEh+ABwdkjLFiSqEGyv3798f69euRmpoKsViMOXPmwNnZWdbVXENDAwcOHFBqoOzHJpFK4LrTFSefngQA/FzzZ6x1XQs9TT3FDjh+PPDPP0K7nP/fimWMMVY8KW1urGfPnuHIkSNQV1dHmzZtikzNDjdQLjp+P/s7ll9bjjXt18DDwSNvOx8/Dvj5AT4+QndyQOhmrshAg4wxxlQuL7/feU52UlJS4OPjAwcHBzRt2vS7Ai0MONkpvCRSCd4lv4OZrhkAYcqH8PfhqFSyUu4PEhsLjBsH7NwpLO/ZA/TsqfxgGWOMFah87Y2lpaWFqVOnIjQ0VOEAGfuWyA+RaP13a7Tb0Q6p6akAgBJqJXKf6BAB27cDVasKiY6amtAA2dU1H6NmjDFWGCnUZqd69eqIiIhQciiMCU49PYX+fv0R+zEWuhq6uBt9Fw3KNsj9ASIigBEjgJNC+x7UrCkMDlhfgWkjGGOMFXkK9cZasGABvL29cebMGWXHw35g6dJ0zAiYgbbb2yL2YyxqlqqJW8Nu5T7RkUiAFSuAatWEREcsFqZ5uHmTEx3GGPuBKVSzs3r1apiYmMDFxQU2NjawsbGBtra2XBmRSITDhw8rJUhW/L1KeIW+B/ri4gth3JvhdYdjuctyaGtof2PP/7t3DxgyBLh+XVhu2lRojGxnl08RM8YYKyoUSnaCg4MhEolgZWUFiUSCsLCwTGXyPO4J+6ENPTIUF19chL6mPjZ03IBe1XvlbseUFKH2ZuFCID0dMDAAFi8WEh81hSouGWOMFTMKJTvcXocp25r2azDknyHw6eiDiiYVc7/jrFlCcgMAbm7AmjVAmTL5EiNjjLGiqVD+67tmzRqUL18eWlpacHR0xPWMWxNZ8PX1hUgkkntoaWkVYLRMES/iX2Dj7Y2y5QrGFXDW/WzeEh0AmDJFaIB84ABw8CAnOowxxjJRqGYnw/nz5+Hv74/nz58DAKytreHq6opmzZopfMw9e/ZgwoQJWL9+PRwdHbFixQq4uLggNDQU5ubmWe5jYGAg1xWeb6EVbv+E/gOPQx6IS4mDlaEV2ti2ycPO/wgDBK5dKwwIaGoqTNzJrzljjLFsKJTspKWloU+fPjh06BCICEZGRgCAuLg4LF26FF26dMGuXbtkM6LnxbJlyzB06FAMHDgQALB+/Xr4+/tj8+bNmDZtWpb7iEQiWFhYKPJUWAFKk6Rh6umpWPHvCgBA/TL1UckkDwMEvnwJdO8OfPoEtG0LdO4srOdEhzHGWA4Uuo01Z84cHDx4EBMnTkRkZCTevXuHd+/eISoqCpMmTYKfnx/mzp2b5+OmpaXh1q1bcHZ2/hygmhqcnZ1x9erVbPdLTEyEtbU1LC0t0blzZzx48CDbsqmpqUhISJB7sPwX/j4cP23+SZbojG84HpcGXYKNsU3uD2JpCcycCUydCrTJQ20QY4yxH5pCyc7OnTvh7u6ORYsWoVSpUrL15ubm+PPPPzFgwAD8/fffeT7umzdvIJFI5I4JAKVKlUJUVFSW+9jZ2WHz5s04fPgwtm/fDqlUCicnJ7x69SrL8l5eXjA0NJQ9LC0t8xwny5tDjw6htndt3PjvBoy1jHG492Esc1kGTXXNnHd8+lSowbl9+/O6mTOFnlfaueySzhhj7IenULITGRkJR0fHbLc7Ojpmm5woW6NGjTBgwAA4ODigWbNm8PPzg5mZGby9vbMsP336dMTHx8seL1++LJA4f2QJqQmIT41Ho3KNEDQiCJ3sOuW8Q3o6sGQJUKOGMDjgmDEFEyhjjLFiSaE2O+XKlUNgYCBGjBiR5fbz58+jXLlyeT6uqakp1NXVER0dLbc+Ojo6121yNDQ0ULt27SzH/gEAsVgMsVic59hY3kikEqirqQMABtQaAK0SWuhSpQs01L/RjisoCBg8+HNtTsuWQDaJK2OMMZYbCtXsuLu7Y+/evRgxYgRCQ0MhkUgglUoRGhqKX375Bfv27YOHh0eej6upqYm6desiICBAtk4qlSIgIACNGjXK1TEkEgnu3buH0qVL5/n8TDl239+NGutq4M3HN7J1Pav1zDnRSU4Gpk8H6tUTEh0jI2DTJuDMGaBiHrujM8YYY18iBaSnp5O7uzuJRCJSU1OjEiVKUIkSJUhNTY1EIhF5eHiQRCJR5NC0e/duEovF5OvrSw8fPqRhw4aRkZERRUVFERHRzz//TNOmTZOVnzNnDp08eZKePn1Kt27dot69e5OWlhY9ePAgV+eLj48nABQfH69QvOyzj2kfadg/wwizQZgNmn5meu52PHeOqGJFImGucqIePYgiI/M1VsYYY0VbXn6/FbqNpa6uDl9fX0yYMAHHjh2TG2enffv2qFmzpsLJV69evRAbG4tZs2YhKioKDg4OOHHihKzR8osXL6D2xTQA79+/x9ChQxEVFQVjY2PUrVsXV65cgb29vcIxsLx79OYReu7riXsx9yCCCL81+Q2zm8/Oeae4OGDyZGFGckAYEHDt2s9dyhljjDElEBERqToIVUpISIChoSHi4+NhYGCg6nCKpL/v/o1f/H9B0qckmOuaY3uX7Wht2zrnnfz8gJEjgYyG7CNGCL2sDA3zP2DGGGNFXl5+v79rBGXGvG96Y4S/0FC9RfkW2NF1B0rrf6O91NOnQM+egEQCVK4s1Ow0aVIA0TLGGPsR5SrZUVNTU2gKBolEkud9WNHSu3pvLLm6BP1r9MfvTX+X9cDKka2t0BiZCPj9d4DnMmOMMZaPcpXszJo1K1Oyc/DgQTx48AAuLi6ws7MDADx69AinTp1C9erV4ebmpvRgmeoREc6Gn0VLm5YQiUQw1DJE8IhgaGvkMMjf48fCLatly4SxcwBg3ryCCZgxxtgPL1fJzuzZs+WWfXx8EBMTg/v378sSnQwhISFo2bIlyvDs08VOYloifvX/FX8H/4217dfil/q/AEDOiQ4gjHp85gwwdixw9mwBRMoYY4x9ptA4O4sXL8aoUaMyJToAULVqVYwaNQqLFi367uBY4REcHYx6PvXwd/DfUBOpIelTUs47fNnufdkyoFs3YPPm/A2SMcYYy4JCDZRfvXqV44zmGhoa2c5NxYoWIoLPLR+MPTEWqZJUlNUvi13ddqGJdTYNipOSAE9PIDYW2LpVWFe2LLB/f8EFzRhjjH1BoZqd6tWrY+3atXj9+nWmba9evcLatWtRI6NtBiuyElIT0OdAH4zwH4FUSSraVWyHoBFB2Sc6p08LbXKWLgW2bQPu3CnYgBljjLEsKFSzs3z5cri4uKBy5cro0qULKv5/OP8nT57g0KFDICJs375dqYGygnc/5j72PdwHdZE6vFp5YaLTRKiJssiP374FJk78XJNjaQmsXw/Url2wATPGGGNZUHhQwfv372PmzJk4deoUkpOTAQDa2tpwcXHBnDlzikzNDg8qmLN1N9bBwcIBjSyzmJuMCNi7V5iVPCYGEImAUaOABQsAff2CD5YxxtgPIy+/3989grJUKkVsbCwAwMzMTG4qh6KAk53P4lLiMPr4aPz202+oalY158IvXwK//gocPSos29sLgwPmcsJWxhhj7HsU6AjKampqsnmrWNF14/UN9NrfC+Fx4XgY+xA3h97MeiBJqVS4RTVtGvDhA6ChAcyYISyLxQUfOGOMMfYNCic779+/x65du/Ds2TO8f/8eX1cQiUQibNq06bsDZPmLiLDy35WYcnoKPkk/obxReax3XZ91ohMSAgwZAly5Iiw3aiTU5vCkq4wxxgoxhZKdkydPonv37khKSoKBgQGMjY0zlVFkeglWsN4lv8PAwwPxT+g/AICuVbtiU6dNMNIyynqH6dOFREdPT5i085dfgCJ225IxxtiPR6FkZ+LEibCwsICfn1+RaYjM5IW/D0fzrc3xIv4FNNU1sazNMvxa/9fMSSqR0PAYAFauBDQ1gSVLACurAo+ZMcYYU4RCyU5YWBgWL17MiU4RZmloCStDK2ioaWBvj72oU7qOfIEPH4S2OCkpgI+PsM7aWuh9xRhjjBUhCt2DqFSpEj58+KDsWFg+e/vxLdIkaQCAEmolsK/HPtwefjtzogMA9+4Bq1YBGzYADx8WcKSMscKufPnyWLFiharDYAUkMDAQIpEIcXFxqg5FIQolO/Pnz8fatWsRERGh5HBYfrn4/CJqra+FqaenytZZ6FnAQPxFd71Pnz7/7eQkzEx+6hQ3QGaskPPw8JBN2CwSiQrku/nGjRsYNmxYrssXxR/L5s2bY9y4cXLrIiIiZLf7Z8+eDQ8Pj4IPTAWcnJwQGRkJQ0NDVYeiEIVuYwUEBMDMzAxVq1ZF69atYWlpCXV1dbkyIpEIK1euVEqQTHFSkmLhpYWYdW4WJCTBiacnMD9tPnQ1dT8XIgJ27AB++02Ylfz/I2Lj999VEzRjrNAzMzNTyXmJCBKJBCVKfPfIKQVOIpFAJBLly3h0aWlp0NTUVPpxM2hqasLCwiLfjp/vSAEikeibDzU1NUUOXeDi4+MJAMXHx6s6FKWLToym1ttaE2aDMBvU368/fUj9IF8oPJzIxYVISHmIRoxQSayMMcW5u7uTp6cnEREBoPDwcCIiOnfuHAGgM2fOUN26dUlbW5saNWpEjx49ktt/7dq1VKFCBdLQ0KDKlSvTtm3bvnlOa2trWr58uWwZAG3YsIHc3NxIW1ubKlasSIcPHyYiovDwcAIg93B3dyciIolEQn/88QeVL1+etLS0qGbNmrRv3z7ZcTOew7Fjx6hOnTqkoaFB586dI4lEQn/++SfZ2tqSpqYmWVpa0vz582X7vXjxgnr06EGGhoZkbGxMnTp1kl2XjGvWuXNnmj17NpmampK+vj4NHz6cUlNTZdu/jjk8PFz2XIiIPD09Zc8jK1u2bCFDQ0M6fPgwVa1aldTV1Sk8PJxSUlJo4sSJVKZMGdLR0aEGDRrQuXPn5Pb18fGhcuXKkba2Nrm5udHSpUvJ0NBQtt3T05Nq1apFGzZsoPLly5NIJCIiovfv39PgwYNlz6lFixYUFBQk2y8oKIiaN29Oenp6pK+vT3Xq1KEbN24QEVFERAR16NCBjIyMSEdHh+zt7cnf31/udXj//r3sWPv37yd7e3vS1NQka2trWrJkSab3yIIFC2jgwIGkp6dHlpaW5O3tne31yqu8/H4rlOwUJ8U12Tn77CxZLLEgzAZpz9emzbc3k1Qq/VwgPZ1o+XIiHR0hyRGLiRYsIEpLU1nMjDHFfCvZcXR0pMDAQHrw4AE1adKEnJycZPv6+fmRhoYGrVmzhkJDQ2np0qWkrq5OZ8+ezfGcWSU75cqVo507d9KTJ09ozJgxpKenR2/fvqX09HQ6cOAAAaDQ0FCKjIykuLg4IiKaP38+ValShU6cOEFPnz6lLVu2kFgspsDAQLnnULNmTTp16hSFhYXR27dvacqUKWRsbEy+vr4UFhZGFy9epA0bNhARUVpaGlWtWpUGDRpEwcHB9PDhQ+rbty/Z2dnJJTN6enrUq1cvun//Ph09epTMzMzot99+IyKiuLg4atSoEQ0dOpQiIyMpMjKS0tPT85zsaGhokJOTE12+fJkePXpESUlJNGTIEHJycqILFy5QWFgYLV68mMRiMT1+/JiIiC5dukRqamq0ePFiCg0NpTVr1pCJiUmmZEdXV5fatm1Lt2/fprt37xIRkbOzM3Xs2JFu3LhBjx8/pokTJ1LJkiXp7du3RERUrVo16t+/P4WEhNDjx49p7969smTI1dWVWrduTcHBwfT06VM6cuQInT9/Xu51yEh2bt68SWpqajR37lwKDQ2lLVu2kLa2Nm3ZskXuPWJiYkJr1qyhJ0+ekJeXF6mpqckl282aNcvxGuaEk508KI7JTnxKPBkvNCbMBtmvsaf70fflCwQHEzVo8Lk2p2lToq/+02OMFX1f1uxk8Pf3JwCUnJxMREROTk40dOhQuf169OhB7du3z/HYWSU7v//+u2w5MTGRANDx48flYvmyZiAlJYV0dHToypUrcscePHgw9enTR26/Q4cOybYnJCSQWCyWJTdf+/vvv8nOzk7uH7zU1FTS1tamkydPEpGQ7JiYmFBSUpKszLp160hPT48kEgkRCT/EY8eOzfE65GTLli0EQK5m5fnz56Surk6vX7+WK9uqVSuaPn06ERH16tWLXF1d5bb369cvU7KjoaFBMTExsnUXL14kAwMDSklJkdvX1tZWVqOir69Pvr6+WcZbo0YNmj17dpbbvn79+vbtS61bt5YrM3nyZLK3t5ctW1tbU//+/WXLUqmUzM3Nad26dbJ1P//8M02bNi3Lc35LXn6/eUS4YshAbADvDt4Y6DAQ14dcRzXzasKGlBRg5kygTh3g+nXAwADw9gbOnQPs7FQbNGMs39SsWVP2d+nSpQEAMTExAICQkBA0btxYrnzjxo0REhICANixYwf09PRkj4sXL+bqPLq6ujAwMJCdJythYWH4+PEjWrduLXeObdu24enTp3Jl69WrJ/s7JCQEqampaNWqVZbHvXv3LsLCwqCvry87pomJCVJSUuSOW6tWLejo6MiWGzVqhMTERLx8+TLbmPNKU1NT7rrcu3cPEokElStXlnvO58+fl8UWGhqKBg0ayB3n62UAsLa2lms7dffuXSQmJqJkyZJyxw4PD5cde8KECRgyZAicnZ2xcOFCuesxZswYzJ8/H40bN4anpyeCg4OzfV7ZvW+ePHkCiUQiW/flcxeJRLCwsJB7T2zbtg1eXl7ZnkdZFG7hdfz4cSxbtgy3b99GfHx8pukiAMg9YZa/zjw7AzWRGlratAQA9KjWAz2q9fhc4NIlYOhQ4NEjYblzZ2DNGqBsWRVEyxgrSBoaGrK/M3oSSaXSXO3bqVMnODo6ypbL5vCd8eV5Ms6V03kSExMBAP7+/pmOK/5qrj1d3c+dKrS1tXOMOTExEXXr1sWOHTsybSvohtXa2tpyg7UmJiZCXV0dt27dytSxR09PL0/H/vKaZBy7dOnSCAwMzFTWyMgIgNCDrG/fvvD398fx48fh6emJ3bt3o0uXLhgyZAhcXFzg7++PU6dOwcvLC0uXLsXo0aPzFNeX8vqeyC8KJTsHDhxAz549Ua1aNfTu3Rvr1q1D3759QUQ4fPgwKlWqBDc3NyWHyrKSLk3H7MDZ+OPiHzDTNUPQ8CCU1i/9uUBCgjBJ57p1wrKFBbB6NdC16+eRkRljP6yqVavi8uXLcHd3l627fPky7P8/5IS+vj709fW/+zwZPYW+/CfY3t4eYrEYL168QLNmzXJ9rEqVKkFbWxsBAQEYMmRIpu116tTBnj17YG5unuNs2Hfv3kVycrIsebp27Rr09PRgaWkpi1nZ/7TXrl0bEokEMTExaNKkSZZl7OzscOPGDbl1Xy9npU6dOoiKikKJEiVQvnz5bMtVrlwZlStXxvjx49GnTx9s2bIFXbp0AQBYWlpixIgRGDFiBKZPn44NGzZkmexkvG++dPnyZVSuXDlTElcYKHQby8vLCw0aNMCdO3cwZ84cAMCgQYOwY8cO3L9/H5GRkbCxsVFqoCyz1wmv0XJrSyy4uAAEgpudW+Z5rW7f/pzoDB4sDBDYrRsnOowxAMDkyZPh6+uLdevW4cmTJ1i2bBn8/PwwadIkpZ7H2toaIpEIR48eRWxsLBITE6Gvr49JkyZh/Pjx2Lp1K54+fYrbt29j1apV2Lp1a7bH0tLSwtSpUzFlyhTZLa9r167JJp/u168fTE1N0blzZ1y8eBHh4eEIDAzEmDFj8OrVK9lx0tLSMHjwYDx8+BDHjh2Dp6cnRo0aJesaXr58efz777+IiIjAmzdvlFIjUblyZfTr1w8DBgyAn58fwsPDcf36dXh5ecHf3x8AMHr0aBw7dgzLli3DkydP4O3tjePHj39zzklnZ2c0atQIbm5uOHXqFCIiInDlyhXMmDEDN2/eRHJyMkaNGoXAwEA8f/4cly9fxo0bN1C1alUAwLhx43Dy5EmEh4fj9u3bOHfunGzb1yZOnIiAgADMmzcPjx8/xtatW7F69eo8v28GDBiA6dOn52kfhSjSKEhbW5tWrFhBREI3N5FIRCdOnJBtnzNnjlwjpcKsqDZQPvb4GJkuMiXMBun9oUe77u36vPHrHlWenkQBAQUaH2NM9bJqFHznzh25HltEyut6fvDgQbkyhoaGcr1z5s6dSxYWFiQSiWQ9cKRSKa1YsYLs7OxIQ0ODzMzMyMXFJdteQBkkEgnNnz+frK2tSUNDg6ysrOiPP/6QbY+MjKQBAwaQqakpicViqlChAg0dOlT2XZ/R9XzWrFlUsmRJ0tPTo6FDh8o17g0NDaWGDRuStrZ2pmuWGxldz7+WlpZGs2bNovLly5OGhgaVLl2aunTpQsHBwbIyPj4+VLZsWVnX8/nz55OFhYVse0bX868lJCTQ6NGjqUyZMqShoUGWlpbUr18/evHiBaWmplLv3r3J0tKSNDU1qUyZMjRq1ChZY/VRo0aRra0ticViMjMzo59//pnevHmT7euQ0fU84/ovXrxYLpav3yNERLVq1ZL1HCQquN5YIqIsGtt8g6mpKebNm4dffvkFgHBPct26dbKRJL29vTF+/Hh8/PhRKQlZfkpISIChoSHi4+NzrO4sLKQkxfQz07HoyiIAQG2L2tjTfQ8qlawk9K3auhWYNQu4eFGYy4oxxlgmHh4eiIuLw6FDh1QdSq4MHToUjx49yrGB+I8mL7/fCt3GsrOzw8Mv5ktycHDA33//jfT0dKSkpGDnzp2w4lmx84WaSA1RSVEAgJH1R+LK4CtCogMIyc7mzcDLl8Dy5SqMkjHG2PdYsmSJrFdZxm29L9tVsbxRqIFyly5d8Ndff2HJkiUQi8WYMWMGOnfuDCMjI4hEIiQlJWHz5s3KjvWHli5NRwk14eVa034Netj3QIfKHYD0dCA5GdDWBtTUhIk7//kHGD9exREzxhhT1PXr17Fo0SJ8+PABFSpUwF9//ZVlY2yWOwrdxsrKxYsX4efnB3V1dbi6uqJFixbKOGy+K+y3sdIkaZh2ZhrC3oXhcO/D8g3U7twBhgwBmjQBePZhxhhjP5C8/H7nKdlJSUnB4cOHER4eDlNTU7i6usoGqCqqCnOyE/4+HL3298KN/4Quh2cHnEULmxZCTc6cOcCSJYBEApQsCYSFAf8fR4Exxhgr7vLy+53r21gxMTFwcnJCeHi4bABBHR0dHDp0CM7Ozt8XMcvEL8QPgw4PQnxqPIy0jODb2VdIdM6dA4YNE5IbAOjRA/jrL050GGNZKmoNcRnLD7luoDxv3jxERERg/PjxOHr0KFasWAFtbW0MHz48P+P74aSmp2L0sdHotrcb4lPj0bBcQwQND0LnUk2FEZBbthQSnTJlgEOHgL17hYECGWOsiCEizJo1C6VLl4a2tjacnZ3x5MmTHPeRSCSYOXMmbGxsoK2tDVtbW8ybNy/TKP4hISHo1KkTDA0Noauri/r16+PFixey7T4+PmjevDkMDAwgEokQFxf3zXg9PDwgEokgEomgoaEBGxsbTJkyBSkpKbIyERERGDx4sFx8np6eSEtLy9vFUYLbt2+jdevWMDIyQsmSJTFs2DDZqNXZiY6OhoeHB8qUKQMdHR20bds202syfPhw2NraQltbG2ZmZujcuTMeZYzO/38BAQFwcnKCvr4+LCwsMHXqVKSnpyv9OeZabvuzV65cmQYOHCi3bvfu3ZlmMC1qCts4O932dCPMBmE2aPKpyZSWnka0fz+RhcXniTt/+YXo/zMGM8ZYTjLGkymMFi5cSIaGhnTo0CG6e/cuderUiWxsbGTjvmRlwYIFVLJkSTp69CiFh4fTvn37SE9Pj1auXCkrExYWRiYmJjR58mS6ffs2hYWF0eHDhyk6OlpWZvny5eTl5UVeXl5ZjuOTFXd3d2rbti1FRkbSixcv6ODBg2RgYEBTpkyRlTl+/Dh5eHjQyZMn6enTp3T48GEyNzeniRMnKnaRFPT69WsyNjamESNG0KNHj+j69evk5ORE3bp1y3YfqVRKDRs2pCZNmtD169fp0aNHNGzYMLKysqLExERZOW9vbzp//jyFh4fTrVu3qGPHjmRpaUnp6elERBQUFESampo0Z84cevLkCQUGBlKVKlWUfg3yZdZzLS0t2rRpk9y6V69ekUgkkg3+VBQVtmTn+qvrVHpJaToaepTo9WuiLl0+Jzl2dkQXLqg6RMZYIbNv3z6qXr06aWlpkYmJCbVq1Ur24/R1spOSkkKjR48mMzMzEovF1LhxY7p+/bpse8bgcUePHqUaNWqQWCwmR0dHunfvntw5L168SD/99BNpaWlRuXLlaPTo0XI/iN8ilUrJwsJCbiC6uLg4EovFtGvXrmz3c3V1pUGDBsmt69q1K/Xr10+23KtXL7nZtnOS3aCFWckqcezatSvVrl07x/0WLVpENjY2uYpHWby9vcnc3Fw2gzsRUXBwMAGgJ0+eZLlPaGgoAaD79+/L1kkkEjIzM8t2hnkiort37xIACgsLIyKi6dOnU7169eTK/PPPP6SlpUUJCQnf87Tk5Mus56mpqdDS0pJbl7Gs0qqpIi75UzLOR5yXLdcvWx/PRofBNfA1ULUqcPAgUKIE8PvvQFCQ0POKMcb+LzIyEn369MGgQYMQEhKCwMBAdO3aNcvJmQFgypQpOHDgALZu3Yrbt2+jYsWKcHFxwbt37+TKTZ48GUuXLsWNGzdgZmaGjh074tOnTwCAp0+fom3btujWrRuCg4OxZ88eXLp0CaNGjZLtP3v27BznZwoPD0dUVJRcm09DQ0M4Ojri6tWr2e7n5OSEgIAAPH78GIAwv9WlS5fQrl07AMIEp/7+/qhcuTJcXFxgbm4OR0fHfGmzdP/+fVy5ckU271d24uPjYWJikmOZdu3ayc1U/vWjWrVqeYotNTUVmpqasqkvgM8TqF66dCnbfQDI/darqalBLBZnu09SUhK2bNkCGxsb2ZxiWeUL2traSElJwa1bt/L0PJQmtxmUSCSiBQsW0K1bt2SPs2fPkkgkovXr18utz3gUBaqs2XkU+4hqrqtJ4nliuhN55/OG9HQiR0ehNqdBA6IvhhBnjLEv3bp1iwBQREREltu/rI1ITEwkDQ0N2rFjh2x7WloalSlThhYtWkREn2s6du/eLSvz9u1b0tbWpj179hAR0eDBg2nYsGFy57l48SKpqanJbkGtWrWKWrZsmW3cly9fJgD033//ya3v0aMH9ezZM9v9JBIJTZ06lUQiEZUoUYJEIlGmaSIAkI6ODi1btozu3LlDXl5eJBKJKDAwMNPx8lqzo66uTrq6uiQWiwkAqamp0f79+7Pd58mTJ2RgYEA+Pj45HvvVq1f05MmTbB/Zvb7ZuX//PpUoUYIWLVpEqamp9O7dO+rWrRsBkLteX0pLSyMrKyvq0aMHvXv3jlJTU2nhwoUEgNq0aSNXds2aNaSrq0sAyM7OTlarQ0R08uRJUlNTo507d1J6ejq9evWKmjRpQgBo586deXoeOcmX21gikYjU1NQyPbJan7GuKFBVsrP97nbSXaBLmA0yW2RGgY9PE315n/rePaJly4TEhzHGspGenk6tWrUifX196t69O/n4+NC7d+9k279MdjJuN3z9w+nm5iZrk5nx4//8+XO5Mg4ODjR79mwiIqpXrx5pamqSrq6u7KGjo0MA6OHDh7mKW9FkZ9euXVSuXDnatWsXBQcH07Zt28jExIR8fX2JSGirAoD69Okjt1/Hjh2pd+/emY6X12TH2dmZnjx5QkFBQeTu7k6DBw/OtvyrV6/I1tY2xzLKMHz4cLnXIsOOHTuoVKlSpK6uTpqamjRp0iQqVaoULVy4MNtj3bx5k2rVqkUASF1dnVxcXKhdu3bUtm1buXJxcXH0+PFjOn/+PHXs2JHq1Kkj19Zq6dKlZGBgQOrq6qSjoyNrG/VlEv298vL7neuu51u2bFFOVdIP7uOnjxhzfAw23RFm521evjn2VpgGsx6TABcX4M8/hYLVqwsPxhjLgbq6Ok6fPo0rV67g1KlTWLVqFWbMmIF///0XNjY2+XLOxMREDB8+HGPGjMm0LbdTBVn8vxdpdHS03Hht0dHRcHBwyHa/yZMnY9q0aejduzcAoEaNGnj+/Dm8vLzg7u4OU1NTlChRAvb29nL7Va1aNdtbMXmhq6uLihUrAgA2b96MWrVqYdOmTRg8eLBcuf/++w8tWrSAk5MTfHx8vnncdu3a5TjvlbW1NR48eJDltrlz52Y523jfvn3Rt29fREdHQ1dXFyKRCMuWLUOFChWyPU/dunURFBSE+Ph4pKWlwczMDI6OjqhXr55cOUNDQxgaGqJSpUpo2LAhjI2NcfDgQfTp0wcAMGHCBIwfPx6RkZEwNjZGREQEpk+fnuO581Oukx2ek+P7PYx9iJ77euJB7AOIIMKsZrMws+lMqPsfA+7eBaKihLY5+vqqDpUxVoSIRCI0btwYjRs3xqxZs2BtbY2DBw9iwoQJcuVsbW2hqamJy5cvw/r/EwV/+vQJN27cwLhx4+TKXrt2TZa4vH//Ho8fP0bVqlUBAHXq1MHDhw9lP/qKsLGxgYWFBQICAmTJTUJCAv7991/ZJNNZ+fjxo1w7FEBI+KRSKQBAU1MT9evXR2hoqFyZx48fy56zsqipqeG3337DhAkT0LdvX1mbmNevX6NFixaoW7cutmzZkinerGzcuBHJycnZbtfQ0Mh2m7m5OczNzbPdXqpUKQBCcqalpYXWrVt/Mx5DQ0MAwJMnT3Dz5k3Mmzcv27Ik3CWStfnJIBKJUKZMGQDArl27YGlpiTp16nzz3PlCafVJRVRB3sb648IfhNkgiyUWdOH6V/d4V60i+qJbJGOM5ca1a9dowYIFdOPGDXr+/Dnt3buXNDU16dixY0SUuQfR2LFjqUyZMnT8+HF68OABubu7k7GxsezWV8ZtnWrVqtGZM2fo3r171KlTJ7KysqLU1FQiEm6HaWtr08iRI+nOnTv0+PFjOnToEI0cOVJ2nm+12SESup4bGRnR4cOHKTg4mDp37pyp63nLli1p1apVsmV3d3cqW7asrOu5n58fmZqaynX/9vPzIw0NDfLx8aEnT57QqlWrSF1dnS5evCgrExkZSXfu3KENGzYQALpw4QLduXOH3r59m228WfXG+vTpE5UtW1bWq+zVq1dUsWJFatWqFb169YoiIyNlj4K2atUqunXrFoWGhtLq1atJW1tbros+EZGdnR35+fnJlvfu3Uvnzp2jp0+f0qFDh8ja2pq6du0q2/706VP6448/6ObNm/T8+XO6fPkydezYkUxMTOS69i9atIiCg4Pp/v37NHfuXNLQ0KCDBw8q9fnlS5ud4qogk510STrN9xtPH/v2JDI2JlLBm58xVrw8fPiQXFxcZF3JK1eunCk5+PIHOjk5mUaPHk2mpqY5dj0/cuQIVatWjTQ1NalBgwZ09+5dufNev36dWrduTXp6eqSrq0s1a9akBQsWyLZ7enqStbV1jrFLpVKaOXMmlSpVisRiMbVq1YpCQ0PlylhbW5Onp6dsOSEhgcaOHUtWVlakpaVFFSpUoBkzZsgSsQybNm2iihUrkpaWFtWqVYsOHTokt93T05MAZHps2bIl23izG7PIy8uLzMzMKDExkbZs2ZLlcVVRt/Dzzz+TiYkJaWpqUs2aNWnbtm2Zynz9nFeuXEnlypUjDQ0NsrKyot9//13u2r5+/ZratWtH5ubmpKGhQeXKlaO+fftmGm+vRYsWZGhoSFpaWuTo6ChLvpUpL7/fSpsItKjKz7mx7kXfw9wLc7HNbRu0S2gJox2PGQPExAAiEbBlC8C3BxljhUhgYCBatGiB9+/fw4inoWGFWL7MjcVyj4iw8fZGjDkxBinpKaidZorfdr8Cjh4VCtjbAxs3Ao0aqTZQxhhj7AfAyY6SJaQmYPjR4dh9fzdEUmDVc3v8unQ78CER0NAQGiBPnQqIxaoOlTHGGPshcLKjRHci76Dn/p4IexeG6rFqOH7BEuXuPRQ2OjkBGzYItTqMMVZINW/ePNvRlxkrqjjZUZKDIQfR+0BvUGoaltwwwPhzyVBLew7o6QELFwK//ALkovshY4wxxpSLkx0lqVemHppFaWHDYTGsXyUIK11dgXXrgP/PF8IYY4yxgsdVDd/hdcJr2d+WhpbYVnc+rF99AMzMgF27gCNHONFhjDHGVIyTHQUQEVZeW4kKf1XAiSvbZOstPEYBK1cCISFA795C93LGGGOMqRSPs5PHcXbeJb/DoMODcPbuYaz1Bzo/14L+05eAqWkBRMsYY4wxIG+/31yzkwfXXl1Dbe/aOBx6GFKxBtoml4FeYhpw+rSqQ2OMMcZYNriBci5ISYplV5dh3YFpiNSRwNbMFnu674FpZzXg0yegQQNVh8gYY4yxbHCykwsXnp3Dq7mTEXwWONqtOtr9fhkGYgOgjKojY4wxxti3cLLzLffuofmQ39D8urDY821piDT0VBsTY4wxxnKN2+xkQUpS/BW4CIlTxwN16gDXrwMGBoC3N0QnTvDggIwxxlgRUih/tdesWYPy5ctDS0sLjo6OuH79eo7l9+3bhypVqkBLSws1atTAsWPHFD53TFIMJs1qiNY9pkJv0QogPR1wcxO6kw8bxokOY4wxVsQUul/uPXv2YMKECfD09MTt27dRq1YtuLi4ICYmJsvyV65cQZ8+fTB48GDcuXMHbm5ucHNzw/379/N87ov3/HGstQ2Wzb+Bqm+Aj6aGoP37gYMHgTLcQIcxxhgrigrdODuOjo6oX78+Vq9eDQCQSqWwtLTE6NGjMW3atEzle/XqhaSkJBw9elS2rmHDhnBwcMD69eu/eb6MfvqbZnZHm5X7Ue7/Mz2869cNJqs2AMbGynlijDHGGFOaIjvOTlpaGm7dugVnZ2fZOjU1NTg7O+Pq1atZ7nP16lW58gDg4uKSbfnsdJ8nJDrRpfWRfNIfJtv3c6LDGGOMFQOFqjfWmzdvIJFIUKpUKbn1pUqVwqNHj7LcJyoqKsvyUVFRWZZPTU1FamqqbDk+Ph4A8A7Aw35tYL90Gz5pa+NTQsJ3PBPGGGOM5aeE//9O5+YGVaFKdgqCl5cX5syZk2m9DQDsOAXssCjwmBhjjDGmmA8fPsDQ0DDHMoUq2TE1NYW6ujqio6Pl1kdHR8PCIuskxMLCIk/lp0+fjgkTJsiW4+LiYG1tjRcvXnzzYrHvk5CQAEtLS7x8+TJX85AxxfG1Ljh8rQsOX+uCUVSuMxHhw4cPKJOLDkSFKtnR1NRE3bp1ERAQADc3NwBCA+WAgACMGjUqy30aNWqEgIAAjBs3Trbu9OnTaNSoUZblxWIxxGJxpvWGhoaF+kUtTgwMDPhaFxC+1gWHr3XB4WtdMIrCdc5tJUWhSnYAYMKECXB3d0e9evXQoEEDrFixAklJSRg4cCAAYMCAAShbtiy8vLwAAGPHjkWzZs2wdOlSuLq6Yvfu3bh58yZ8fHxU+TQYY4wxVkgUumSnV69eiI2NxaxZsxAVFQUHBwecOHFC1gj5xYsXUPtiYD8nJyfs3LkTv//+O3777TdUqlQJhw4dQvXq1VX1FBhjjDFWiBS6ZAcARo0ale1tq8DAwEzrevTogR49eih0LrFYDE9PzyxvbTHl4mtdcPhaFxy+1gWHr3XBKI7XudANKsgYY4wxpkyFalBBxhhjjDFl42SHMcYYY8UaJzuMMcYYK9Y42WGMMcZYsfZDJDtr1qxB+fLloaWlBUdHR1y/fj3H8vv27UOVKlWgpaWFGjVq4NixYwUUadGXl2vt6+sLkUgk99DS0irAaIuuCxcuoGPHjihTpgxEIhEOHTr0zX0CAwNRp04diMViVKxYEb6+vvkeZ1GX1+scGBiY6T0tEomynauPfebl5YX69etDX18f5ubmcHNzQ2ho6Df34+/rvFPkWhf17+tin+zs2bMHEyZMgKenJ27fvo1atWrBxcUFMTExWZa/cuUK+vTpg8GDB+POnTtwc3ODm5sb7t+/X8CRFz15vdaAMEJnZGSk7PH8+fMCjLjoSkpKQq1atbBmzZpclQ8PD4erqytatGiBoKAgjBs3DkOGDMHJkyfzOdKiLa/XOUNoaKjc+9rc3DyfIiw+zp8/j5EjR+LatWs4ffo0Pn36hDZt2iApKSnbffj7WjGKXGugiH9fUzHXoEEDGjlypGxZIpFQmTJlyMvLK8vyPXv2JFdXV7l1jo6ONHz48HyNszjI67XesmULGRoaFlB0xRcAOnjwYI5lpkyZQtWqVZNb16tXL3JxccnHyIqX3Fznc+fOEQB6//59gcRUnMXExBAAOn/+fLZl+PtaOXJzrYv693WxrtlJS0vDrVu34OzsLFunpqYGZ2dnXL16Nct9rl69KlceAFxcXLItzwSKXGsASExMhLW1NSwtLdG5c2c8ePCgIML94fD7umA5ODigdOnSaN26NS5fvqzqcIqk+Ph4AICJiUm2Zfh9rRy5udZA0f6+LtbJzps3byCRSGRTTWQoVapUtvfQo6Ki8lSeCRS51nZ2dti8eTMOHz6M7du3QyqVwsnJCa9evSqIkH8o2b2vExISkJycrKKoip/SpUtj/fr1OHDgAA4cOABLS0s0b94ct2/fVnVoRYpUKsW4cePQuHHjHKf+4e/r75fba13Uv68L5XQR7MfQqFEjudnpnZycULVqVXh7e2PevHkqjIwxxdjZ2cHOzk627OTkhKdPn2L58uX4+++/VRhZ0TJy5Ejcv38fly5dUnUoxV5ur3VR/74u1jU7pqamUFdXR3R0tNz66OhoWFhYZLmPhYVFnsozgSLX+msaGhqoXbs2wsLC8iPEH1p272sDAwNoa2urKKofQ4MGDfg9nQejRo3C0aNHce7cOZQrVy7Hsvx9/X3ycq2/VtS+r4t1sqOpqYm6desiICBAtk4qlSIgIEAuQ/1So0aN5MoDwOnTp7MtzwSKXOuvSSQS3Lt3D6VLl86vMH9Y/L5WnaCgIH5P5wIRYdSoUTh48CDOnj0LGxubb+7D72vFKHKtv1bkvq9V3UI6v+3evZvEYjH5+vrSw4cPadiwYWRkZERRUVFERPTzzz/TtGnTZOUvX75MJUqUoCVLllBISAh5enqShoYG3bt3T1VPocjI67WeM2cOnTx5kp4+fUq3bt2i3r17k5aWFj148EBVT6HI+PDhA925c4fu3LlDAGjZsmV0584dev78ORERTZs2jX7++WdZ+WfPnpGOjg5NnjyZQkJCaM2aNaSurk4nTpxQ1VMoEvJ6nZcvX06HDh2iJ0+e0L1792js2LGkpqZGZ86cUdVTKDJ++eUXMjQ0pMDAQIqMjJQ9Pn78KCvD39fKoci1Lurf18U+2SEiWrVqFVlZWZGmpiY1aNCArl27JtvWrFkzcnd3lyu/d+9eqly5MmlqalK1atXI39+/gCMuuvJyrceNGycrW6pUKWrfvj3dvn1bBVEXPRldnL9+ZFxfd3d3atasWaZ9HBwcSFNTkypUqEBbtmwp8LiLmrxe5z///JNsbW1JS0uLTExMqHnz5nT27FnVBF/EZHWdAci9T/n7WjkUudZF/ftaRERUcPVIjDHGGGMFq1i32WGMMcYY42SHMcYYY8UaJzuMMcYYK9Y42WGMMcZYscbJDmOMMcaKNU52GGOMMVascbLDGGOMsWKNkx3GWL7x9fWFSCRCRESEqkMpcmbPng2RSKTqMAAAIpEIs2fPVnUYrJC5cOECOnbsiDJlykAkEuHQoUN52j8lJQUeHh6oUaMGSpQoATc3t0xlAgMDIRKJMj3yOrM9JzuswGT1hs3qERgYiIiIiBzLLFy4UHbc5s2bQyQSoVKlSlme9/Tp07L99u/fL1uf8UOc8dDS0kLlypUxatSoTJMLAsCLFy8wYsQIlC9fHmKxGObm5nBzc8Ply5fzfA2WLl2aaVtGPDdv3sy07fLly+jSpQtKlSoFsViM8uXLY/jw4Xjx4kWmsgEBARg0aBAqV64MHR0dVKhQAUOGDEFkZGSu42SsoEmlUpiZmWHRokUqi+H48ePQ0NCAtrZ2jrOA82dMkJSUhFq1amHNmjUK7S+RSKCtrY0xY8bA2dk5x7KhoaGIjIyUPczNzfN0rhIKRciYAv7++2+55W3btuH06dOZ1letWhXJyckAgD59+qB9+/aZjlW7dm25ZS0tLYSFheH69eto0KCB3LYdO3ZAS0sLKSkpWcY1d+5c2NjYICUlBZcuXcK6detw7Ngx3L9/Hzo6OgCEZCMjjiFDhsDe3h5RUVHw9fVFkyZNsHLlSowePTrX12Lx4sX45ZdfZMfPyapVqzB27FhUqFABo0ePRunSpRESEoKNGzdiz549OHbsGJycnGTlp06dinfv3qFHjx6oVKkSnj17htWrV+Po0aMICgriGaFZoXT9+nW8efMGrq6uKjn/rVu30LNnT9jZ2SE5ORmdO3fG5cuXUaVKlUxl+TMmaNeuHdq1a5ft9tTUVMyYMQO7du1CXFwcqlevjj///BPNmzcHAOjq6mLdunUAhO/YuLi4bI9lbm4OIyMjxYNV9XwV7Mc1cuRIyu4tGB4eTgBo8eLF3zxOs2bNqFq1amRnZ0fjxo2T25acnEwGBgbUrVs3AkD79u2TbduyZQsBoBs3bsjtM2HCBAJAO3fuJCKid+/ekYWFBZUqVYrCwsLkyn78+JGaNGlCampqdPny5W/GCoAcHBwIAC1dulRuW1bxXLp0idTU1KhJkyaUlJQkVz4sLIxKlSpFpUuXpnfv3snWnz9/niQSiVzZ8+fPEwCaMWPGN2NUpoznFB4eXqDnLQ48PT2z/XwUNADk6emZr+eYOXMmWVtb5+s5shMeHk4WFhZUvXp1iomJoefPn1OFChWofPnysomMv1SYPmOFBQA6ePCg3LohQ4aQk5MTXbhwgcLCwmjx4sUkFovp8ePHmfZ3d3enzp07Z1qfMT+dtbU1WVhYkLOzM126dCnP8fFtLFZs9OnTB3v27IFUKpWtO3LkCD5+/IiePXvm+jgtW7YEAISHhwMAvL29ERUVhcWLF8PW1laurLa2NrZu3QqRSIS5c+fm6viNGzdGy5YtsWjRIlkNVnbmzZsHkUiErVu3ZqoFsrW1xaJFixAZGQlvb2/Z+qZNm0JNTf6j3bRpU5iYmCAkJCRXMf77779o3749jI2Noauri5o1a2LlypVyZc6ePYsmTZpAV1cXRkZG6Ny5c66On137j/Lly8PDw0O2nHFb79KlSxgzZgzMzMxgZGSE4cOHIy0tDXFxcRgwYACMjY1hbGyMKVOmgL6Y6i/jVuiSJUvg4+MDW1tbiMVi1K9fHzdu3MjVdcgu/lGjRmHfvn2wt7eHtrY2GjVqhHv37gEQ3i8VK1aElpYWmjdvnmV7pX379qFu3brQ1taGqakp+vfvj9evX+fq/Nu3b5fta2Jigt69e+Ply5eZyn3rNWzevLnsP+wveXh4oHz58t+M4/Xr1xg0aJDs1mq1atWwefPmTOVWrVqFatWqQUdHB8bGxqhXrx527tyZqZy/v3+mWp3jx4+jWbNm0NfXh4GBAerXry+3b/PmzVG9enUEBwejWbNm0NHRQcWKFWW3q8+fPw9HR0doa2vDzs4OZ86cyXTed+/eoV27djAzM8PZs2dhZmYGKysrBAYGQk1NDa6urkhKSpLbRxmfseLuxYsX2LJlC/bt24cmTZrA1tYWkyZNwk8//YQtW7bk+jilS5fG+vXrceDAARw4cACWlpZo3rw5bt++nad4ONlhhdrHjx/x5s2bTI/09PRMZfv27YvIyEgEBgbK1u3cuROtWrXK0/3dp0+fAgBKliwJQEiYtLS0sk2YbGxs8NNPP+Hs2bPfTF4yzJ49G9HR0bIq3Kx8/PgRAQEBaNKkCWxsbLIs06tXL4jFYhw9ejTH8yUmJiIxMRGmpqbfjO306dNo2rQpHj58iLFjx2Lp0qVo0aKF3DnOnDkDFxcXxMTEYPbs2ZgwYQKuXLmCxo0bK70x8ujRo/HkyRPMmTMHnTp1go+PD2bOnImOHTtCIpHgjz/+wE8//YTFixdnuiUKCO+BxYsXY/jw4Zg/fz4iIiLQtWtXfPr0SeGYLl68iIkTJ8Ld3R2zZ89GSEgIOnTogDVr1uCvv/7Cr7/+ismTJ+Pq1asYNGiQ3L6+vr7o2bMn1NXV4eXlhaFDh8LPzw8//fRTjtX4ALBgwQIMGDAAlSpVwrJlyzBu3DgEBASgadOmcvvm5jX8HtHR0WjYsCHOnDmDUaNGYeXKlahYsSIGDx6MFStWyMpt2LABY8aMgb29PVasWIE5c+bAwcEB//77r9zxoqKicOfOHblb1r6+vnB1dcW7d+8wffp0LFy4EA4ODjhx4oTcvu/fv0eHDh3g6OiIRYsWQSwWo3fv3tizZw969+6N9u3bY+HChUhKSkL37t3x4cMH2b6pqano3LkzNDU1ZYlOBktLSwQGBiIuLg49evTI8jvnS3n5jP0I7t27B4lEgsqVK0NPT0/2OH/+vOw7Njfs7OwwfPhw1K1bF05OTti8eTOcnJywfPnyvAWU57ogxpQkN7exsntcvXpVVjbjNhYRUb169Wjw4MFERPT+/XvS1NSkrVu3yqpCs7qNdebMGYqNjaWXL1/S7t27qWTJkqStrU2vXr0iIiIjIyOqVatWjs9lzJgxBICCg4NzLAeARo4cSURELVq0IAsLC/r48aNcPBm3sYKCgggAjR07Nsdj1qxZk0xMTHIsM2/ePAJAAQEBOZZLT08nGxsbsra2pvfv38ttk0qlsr8dHBzI3Nyc3r59K1t39+5dUlNTowEDBsjWZXUbC9ncErG2tiZ3d/dM+7q4uMidu1GjRiQSiWjEiBFycZcrV46aNWsmW5fxHipZsqTcbb7Dhw8TADpy5EiO1yI7AEgsFss9J29vbwJAFhYWlJCQIFs/ffp0ueeflpZG5ubmVL16dUpOTpaVO3r0KAGgWbNmydZ9fRsrIiKC1NXVacGCBXLx3Lt3j0qUKCFbn9vXsFmzZnLXK4O7u3um20lfv2aDBw+m0qVL05s3b+TK9e7dmwwNDWXv6c6dO8s+mznZtGkTaWtry/aLi4sjfX19cnR0lLtOWT0HfHHLmYjo0aNHBIDU1NTo2rVrsvUnT54kALRly5ZvxqOI3H7Giit8dRtr9+7dpK6uTo8ePaInT57IPSIjIzPtn91trKxMmjSJGjZsmKf4uGaHFWrDhg3D6dOnMz3s7e2zLN+3b1/4+fkhLS0N+/fvh7q6Orp06ZLjOZydnWFmZgZLS0v07t0benp6OHjwIMqWLQsA+PDhA/T19XM8Rsb2hISEXD+32bNnIyoqCuvXr89ye8Z/oLk5d07nvXDhAubMmYOePXvKbtFl586dOwgPD8e4ceMyNQbM6AYdGRmJoKAgeHh4wMTERLa9Zs2aaN26NY4dO5bjOfJq8ODBcl2wHR0dQUQYPHiwbJ26ujrq1auHZ8+eZdq/V69eMDY2li03adIEALIsm1utWrWSu9Xj6OgIAOjWrZvc65WxPuNcN2/eRExMDH799VdoaWnJyrm6uqJKlSrw9/fP9px+fn6QSqXo2bOnXC2nhYUFKlWqhHPnzgHI3Wv4PYgIBw4cQMeOHUFEcrG4uLggPj5edovByMgIr169+uZtw2PHjqFFixbQ1tYGINRMffjwAdOmTZO7Tlk9Bz09PfTu3Vu2bGdnByMjI1StWlV2/YHMr4Uy5eUz9qOoXbs2JBIJYmJiULFiRbnH9zbgDgoKQunSpfO0D/fGYoVapUqVvtkl8Uu9e/fGpEmTcPz4cezYsQMdOnT4ZrKwZs0aVK5cGSVKlECpUqVgZ2cndz9eX19fruo7K7lNTL7UtGlTtGjRAosWLcKIESMybc84Vm7Ond15Hz16hC5duqB69erYuHHjN2PKqF6uXr16tmWeP38OQPhR+VrVqlVx8uRJJCUlQVdX95vnyw0rKyu5ZUNDQwDCbYav179///6b+2ckPlmVzY+YvjxXTteuSpUqOXZ3fvLkCYgo2yEWNDQ0AOTuNfwesbGxiIuLg4+PD3x8fLIsExMTA0DotXTmzBk0aNAAFStWRJs2bdC3b180btxYVvbTp084ffo0vLy8ZOvy8hzKlSuXKQEyNDT85muhLHn9jBUniYmJCAsLky2Hh4cjKCgIJiYmqFy5Mvr164cBAwZg6dKlqF27NmJjYxEQEICaNWvK2mc9fPgQaWlpePfuHT58+ICgoCAAgIODAwBgxYoVsLGxQbVq1ZCSkoKNGzfi7NmzOHXqVJ5i5WSHFSulS5dG8+bNsXTpUly+fBkHDhz45j4NGjRAvXr1st1etWpV3LlzB6mpqRCLxVmWCQ4OhoaGRrY/RNnx9PRE8+bN4e3tnem/8IoVK6JEiRIIDg7Odv/U1FSEhoZmGf/Lly/Rpk0bGBoa4tixY3lKxFRBIpFkuV5dXT3X6+mLBsrf2j+rsrmVl5i+91wZpFIpRCIRjh8/nuV59PT08nQ8kUiUZVzZvQ5fxgEA/fv3h7u7e5ZlatasCUD47ISGhuLo0aM4ceIEDhw4gLVr12LWrFmYM2cOAODSpUtISEjIcoiJ3FDFa5GhqH3GlO3mzZto0aKFbHnChAkAAHd3d/j6+mLLli2YP38+Jk6ciNevX8PU1BQNGzZEhw4dZPu0b99e9k8A8HlYkYzXKS0tTba/jo4OatasiTNnzsidNzc42WHFTt++fTFkyBAYGRkp/AX6pQ4dOuDq1avYt28f+vfvn2l7REQELl68CGdnZ1k1fG41a9YMzZs3x59//olZs2bJbdPV1UWLFi1w9uxZPH/+HNbW1pn237t3L1JTU+W+PADg7du3aNOmDVJTUxEQEJDrKt+M3mb379/PtkYtI47Q0NBM2x49egRTU9Mca3WMjY0zNcRNS0v7IQZk+/LafX27IzQ0NMvXOIOtrS2ICDY2NqhcuXKO5YCcX0NAeB2yuqXz5Q9PVszMzKCvrw+JRJKrWlddXV306tULvXr1QlpaGrp27YoFCxZg+vTp0NLSgr+/P+zt7eVuC375HCpWrPjNc6iCop+x4qR58+Y5Jo8aGhqYM2eOLLHNyrc6NEyZMgVTpkxRNEQZbrPDip3u3bvD09MTa9euhaam5ncfb/jw4TA3N8fkyZMz/TikpKRg4MCBIKJMyUpuZbTdyeqWwO+//w4igoeHR6aeXuHh4ZgyZQpKly6N4cOHy9YnJSWhffv2eP36NY4dO5an2qY6derAxsYGK1asyJSQZHyplS5dGg4ODti6datcmfv37+PUqVPfTDBtbW1x4cIFuXU+Pj7frFEoDurVqwdzc3OsX78eqampsvXHjx9HSEhIjgPqde3aFerq6pgzZ06mHxgiwtu3bwHk7jUEhNfh0aNHiI2Nla27e/fuN0cEV1dXR7du3XDgwAHcv38/0/Yvj5cRUwZNTU3Y29uDiGS94Y4dO5bpebdp0wb6+vrw8vLKNBioMmtmFPU9nzGmGlyzwwq127dvY/v27ZnW29raolGjRlnuY2hoqNR5fEqWLIn9+/fD1dUVderUyTSCclhYGFauXCk3inFeNGvWDM2aNcP58+czbWvatCmWLFmCCRMmoGbNmvDw8EDp0qXx6NEjbNiwAVKpFMeOHZNrgNuvXz9cv34dgwYNQkhIiNy4H3p6elnOP5NBTU0N69atQ8eOHeHg4ICBAwfKzvfgwQOcPHkSgDACdLt27dCoUSMMHjwYycnJWLVqVa6u/ZAhQzBixAh069YNrVu3xt27d3Hy5EmVdtmNiIiAjY2NrPo9v2hoaODPP//EwIED0axZM/Tp0wfR0dFYuXIlypcvj/Hjx2e7r62tLebPn4/p06cjIiICbm5u0NfXR3h4OA4ePIhhw4Zh0qRJuX4NBw0ahGXLlsHFxQWDBw9GTEwM1q9fj2rVqn2zof3ChQtx7tw5ODo6YujQobC3t8e7d+9w+/ZtnDlzBu/evQMgJC0WFhZo3LgxSpUqhZCQEKxevRqurq6y2ENCQjINwWBgYIDly5djyJAhqF+/Pvr27QtjY2PcvXsXHz9+xNatW7/zlfg+3/MZYyqSp75bjCnR93Q9/7KL8pddz7OTU9fzr0dQzk54eDgNHTqUrKysSENDg0xNTalTp0508eLFXO1PJN/1PKv4sovnwoUL1LlzZzI1NSUNDQ2ysrKioUOHUkRERKay1tbW2V633I5Qe+nSJWrdujXp6+uTrq4u1axZk1atWiVX5syZM9S4cWPS1tYmAwMD6tixIz18+FCuTFZdzyUSCU2dOpVMTU1JR0eHXFxcKCwsLNuu519fj4wu2bGxsXLr3d3dSVdXV7ac0yjc+Kor9b179wgATZs27ZvXJqvXMLtzZfW+IyLas2cP1a5dm8RiMZmYmFC/fv1kQx18/Ty/duDAAfrpp59IV1eXdHV1qUqVKjRy5EgKDQ2VK5eb13D79u1UoUIF0tTUJAcHBzp58mSuup4TEUVHR9PIkSPJ0tKSNDQ0yMLCglq1akU+Pj6yMt7e3tS0aVMqWbIkicVisrW1pcmTJ1N8fDwREa1evZoMDQ3p06dPmZ4nEdE///xDTk5OsvdYgwYNaNeuXbLt2X32ra2tydXVNdP67D5/eaWMzxgrWCKiQlAnyBhjKrR27VpMmTIFT58+RalSpVQdzg+jffv20NPTw969e1UdCivm+DYWY+yHd+7cOYwZM4YTnQLWvHlz2bhHjOUnrtlhjDHGWLHGvbEYY4wxVqxxssMYY4yxYo2THcYYY4wVa5zsMMYYY6xY42SHMcYYY8UaJzuMMcYYK9Y42WGMMcZYscbJDmOMMcaKNU52GGOMMVascbLDGGOMsWKNkx3GGGOMFWv/Aw2ySIxOrmGzAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#EXTRA. Archiving output files to make downloading easier" - ], - "metadata": { - "id": "cKn-MLMuen1q" - } - }, - { - "cell_type": "code", - "source": [ - "import zipfile\n", - "import zlib\n", - "import glob" - ], - "metadata": { - "id": "v-VbL8CNXsLL" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "list_jpg = glob.glob('*'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg')\n", - "\n", - "with zipfile.ZipFile('fig_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", - "+POI_name+'.zip', 'w') as fig_zip:\n", - " for name in list_jpg: fig_zip.write(name)" - ], - "metadata": { - "id": "4e1JLotAX9OC" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "list_data = glob.glob('*'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'*.txt')\n", - "\n", - "with zipfile.ZipFile('data_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", - "+POI_name+'.zip', 'w') as data_zip:\n", - " for name in list_data: data_zip.write(name)" - ], - "metadata": { - "id": "JVkprHdiYpuS" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file