Tutorial
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“cell_type”: “markdown”, “metadata”: {
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}, “source”: [
“Copyright 2022, The Johns Hopkins University Applied Physics Laboratory LLCn”, “n”, “All rights reserved.n”, “n”, “Distributed under the terms of the BSD 3-Clause License.n”
]
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“cell_type”: “markdown”, “metadata”: {
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“name”: “#%% mdn”
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}, “source”: [
“# PyBAMOCS Tutorialn”, “n”, “PyBAMOCS uses a simplified box model to simulate the Atlantic meridional overturning circulation (AMOC) simulation, used by [Gnanadesikan et al.](https://journals.ametsoc.org/view/journals/clim/31/22/jcli-d-18-0388.1.xml) to examine the stability of the AMOC. For information about installing PyBAMOCS and the PyBAMOCS API, please see the included [README](../README.md).n”, “n”, “This tutorial is meant to be a tool for getting started using the PyBAMOCS package and the included box_model functionality, and is not meant to be a comprehensive overview of all functions or capabilities of the included code. For a more thorough understanding, please consider viewing the code itself, and the examples in the [scripts](../scripts) folder.n”, “n”, “This tutorial assumes that you have downloaded the PyBAMOCs code from the PACMANs repository on GitHub and installed PyBAMOCS and [Jupyter Notebook](https://jupyter.org) in your Python environment.n”
]
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“cell_type”: “code”, “execution_count”: 1, “metadata”: {
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“name”: “#%%n”
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“import timen”, “from matplotlib import pyplot as pltn”, “from typing import Unionn”, “n”, “# Import the box_model function and its argumentsn”, “n”, “from pybamocs.box_model import box_modeln”, “from pybamocs.box_model import NORTH_IDX, SOUTH_IDX, LOW_IDX, DEEP_IDXn”, “from pybamocs.box_model_args import (n”, ” BoxModelBoxDimensions,n”, ” BoxModelInitConditions,n”, ” BoxModelTimeStep,n”, ” BoxModelParametersn”, “)n”, “from pybamocs.box_model_args import dict_from_box_args, box_args_from_dictn”
]
}, {
“cell_type”: “code”, “execution_count”: 2, “metadata”: {
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“name”: “#%%n”
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}, “outputs”: [], “source”: [
“# Box model settings are divided into for groups:n”, “n”, “box_model_dimensions = BoxModelBoxDimensions()n”, “box_model_initial_conditions = BoxModelInitConditions()n”, “box_model_time_settings = BoxModelTimeStep()n”, “box_model_parameters = BoxModelParameters()n”, “n”, “# And there is one object for each group as shown above.n”
]
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“cell_type”: “markdown”, “metadata”: {
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“name”: “#%% mdn”
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}, “source”: [
“Notice that no arguments are required to initialize the objects in the previous cell, this is because each argument has a default value. Not passing in a value for an argument sets it to the default value.n”, “n”, “The following cells show the default values of each object. For more information about what each argument represents, see [pybamocs.box_model_args.py](../pybamocs/box_model_args.py).n”
]
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“cell_type”: “code”, “execution_count”: 3, “metadata”: {
- “pycharm”: {
“name”: “#%%n”
}
}, “outputs”: [], “source”: [
“def print_box_model_argument_settings(argument: Union[BoxModelBoxDimensions, BoxModelInitConditions,n”, ” BoxModelTimeStep, BoxModelParameters]) -> None:n”, ” """n”, ” Print box model settings to console for a given box_model argument.n”, ” :param argument: One of the box_model argument objects.n”, ” """n”, ” n”, ” settings_dict = argument.to_dict() # Get the settings as a python dictionaryn”, “n”, ” print("vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv")n”, ” # Print each key, value pair in the dictionaryn”, ” for key, value in settings_dict.items():n”, ” print(f"{key}={value}")n”, ” print("^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^")n”, ” print()n”
]
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“cell_type”: “code”, “execution_count”: 4, “metadata”: {
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“name”: “#%%n”
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“name”: “stdout”, “output_type”: “stream”, “text”: [
“————————————————————n”, “Box Dimensions:n”, “vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvn”, “area=360000000000000.0n”, “area_low=200000000000000.0n”, “area_s=100000000000000.0n”, “area_n=60000000000000.0n”, “D_high=100n”, “^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^n”, “n”
]
}
], “source”: [
“print("————————————————————")n”, “print("Box Dimensions:")n”, “print_box_model_argument_settings(box_model_dimensions)”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “metadata”: {
- “pycharm”: {
“name”: “#%%n”
}
}, “outputs”: [
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“name”: “stdout”, “output_type”: “stream”, “text”: [
“————————————————————n”, “Initial Conditionsn”, “vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvn”, “D_low0=400.0n”, “T_north0=2.0n”, “T_south0=4.0n”, “T_low0=17.0n”, “T_deep0=3.0n”, “S_north0=35.0n”, “S_south0=36.0n”, “S_low0=36.0n”, “S_deep0=34.5n”, “^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^n”, “n”
]
}
], “source”: [
“print("————————————————————")n”, “print("Initial Conditions")n”, “print_box_model_argument_settings(box_model_initial_conditions)”
]
}, {
“cell_type”: “code”, “execution_count”: 6, “metadata”: {
- “pycharm”: {
“name”: “#%%n”
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}, “outputs”: [
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“name”: “stdout”, “output_type”: “stream”, “text”: [
“————————————————————n”, “Model Time Step Parameters:n”, “vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvn”, “N=4000n”, “time_step_size_in_years=0.25n”, “^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^n”, “n”
]
}
], “source”: [
“print("————————————————————")n”, “print("Model Time Step Parameters:")n”, “print_box_model_argument_settings(box_model_time_settings)”
]
}, {
“cell_type”: “code”, “execution_count”: 7, “metadata”: {
- “pycharm”: {
“name”: “#%%n”
}
}, “outputs”: [
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“name”: “stdout”, “output_type”: “stream”, “text”: [
“————————————————————n”, “Model Parametersn”, “vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvn”, “K_v=1e-05n”, “A_GM=1000.0n”, “M_ek=25000000.0n”, “A_Redi=1000.0n”, “M_SD=15000000.0n”, “Fws=1000000.0n”, “Fwn=50000.0n”, “epsilon=0.00012n”, “^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^n”, “n”
]
}
], “source”: [
“print("————————————————————")n”, “print("Model Parameters")n”, “print_box_model_argument_settings(box_model_parameters)”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “pycharm”: {
“name”: “#%% mdn”
}
}, “source”: [
“If preferred, included are helper functions to deal with all settings combined in regular Python dictionariesn”
]
}, {
“cell_type”: “code”, “execution_count”: 8, “metadata”: {
- “pycharm”: {
“name”: “#%%n”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“All box model settings in one dictionaryn”, “n”, “{‘area’: 360000000000000.0, ‘area_low’: 200000000000000.0, ‘area_s’: 100000000000000.0, ‘area_n’: 60000000000000.0, ‘D_high’: 100, ‘D_low0’: 400.0, ‘T_north0’: 2.0, ‘T_south0’: 4.0, ‘T_low0’: 17.0, ‘T_deep0’: 3.0, ‘S_north0’: 35.0, ‘S_south0’: 36.0, ‘S_low0’: 36.0, ‘S_deep0’: 34.5, ‘K_v’: 1e-05, ‘A_GM’: 1000.0, ‘M_ek’: 25000000.0, ‘A_Redi’: 1000.0, ‘M_SD’: 15000000.0, ‘Fws’: 1000000.0, ‘Fwn’: 50000.0, ‘epsilon’: 0.00012, ‘N’: 4000, ‘time_step_size_in_years’: 0.25}n”
]
}
], “source”: [
“print("All box model settings in one dictionary")n”, “print()n”, “box_args_dict = dict_from_box_args(box_model_dimensions, box_model_initial_conditions, box_model_parameters, box_model_time_settings)n”, “print(box_args_dict)”
]
}, {
“cell_type”: “code”, “execution_count”: 9, “metadata”: {
- “pycharm”: {
“name”: “#%%n”
}
}, “outputs”: [
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“name”: “stdout”, “output_type”: “stream”, “text”: [
“Getting box model argument objects from a dictionaryn”, “n”, “[(‘box_dimensions’, <pybamocs.box_model_args.BoxModelBoxDimensions object at 0x12068fc70>), (‘init_conditions’, <pybamocs.box_model_args.BoxModelInitConditions object at 0x12068fdf0>), (‘box_params’, <pybamocs.box_model_args.BoxModelParameters object at 0x12068f280>), (‘time_step’, <pybamocs.box_model_args.BoxModelTimeStep object at 0x12068f1f0>)]n”
]
}
], “source”: [
“print("Getting box model argument objects from a dictionary")n”, “print()n”, “box_args = box_args_from_dict(box_args_dict)n”, “print([(arg, obj) for arg, obj in box_args.items()])”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“You can update the parameters in a box model argument object to run a new experiment, or make copies and update those parameters.n”
]
}, {
“cell_type”: “code”, “execution_count”: 10, “metadata”: {
- “pycharm”: {
“name”: “#%%n”
}
}, “outputs”: [
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“name”: “stdout”, “output_type”: “stream”, “text”: [
“epsilon: 0.002n”, “updated…n”, “epsilon: 0.005n”, “copied…n”, “same object epsilon: 0.005n”, “copied object’s epsilon after update: 100000n”
]
}
], “source”: [
“new_param_arg = BoxModelParameters(epsilon=0.002)n”, “print(‘epsilon:’, new_param_arg.epsilon)n”, “new_param_arg.epsilon = 0.005n”, “print(‘updated…’)n”, “print(‘epsilon:’, new_param_arg.epsilon)n”, “n”, “new_param_arg_copy = new_param_arg.copy()n”, “new_param_arg_copy.epsilon = 100000n”, “n”, “print(‘copied…’)n”, “n”, “print(‘same object epsilon:’, new_param_arg.epsilon)n”, “print(‘copied object\’s epsilon after update:’, new_param_arg_copy.epsilon)”
]
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“cell_type”: “markdown”, “metadata”: {
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“name”: “#%% mdn”
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“## Running a box model simulation”
]
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“name”: “stdout”, “output_type”: “stream”, “text”: [
“Box model output:n”, “<pybamocs.box_model_args.BoxModelResult object at 0x12068f670>n”, “n”, “——————————————————n”, “Shape of M_n: (4000,)n”, “n”, “——————————————————n”, “First 3 values of each output:n”, “M_n: [21477028.72709675 21079480.89969894 20812257.02873391]n”, “M_upw: [5000000. 5001245.50358385 5002293.70599358]n”, “M_eddy: [10000000. 9997509.61318945 9995414.69148277]n”, “D_low: [400. 399.90038453 399.81658766]n”, “T_north: [2. 2.46273224 2.78629748]n”, “T_south: [4. 4.173448 4.29444505]n”, “T_low: [17. 16.94705068 16.89830683]n”, “T_deep: [3. 3.00002853 3.0001507 ]n”, “S_north: [35. 35.02854932 35.05579254]n”, “S_south: [36. 35.9243136 35.85232083]n”, “S_low: [36. 36.00278966 36.00531313]n”, “S_deep: [34.5 34.50031742 34.50062474]n”, “sigma0_north: [1027.97173704 1027.9560741 1027.94929663]n”, “sigma0_south: [1028.58210098 1028.50303197 1028.43244304]n”, “sigma0_low: [1026.28211113 1026.29692278 1026.31050239]n”, “sigma0_deep: [1027.48594718 1027.48619799 1027.48643218]n”
]
}
], “source”: [
“# To run the box model, simply call the box_model function with your argument objectsn”, “n”, “results = box_model(box_model_dimensions, box_model_initial_conditions, box_model_parameters, box_model_time_settings)n”, “n”, “# Note that the results are contained in a BoxModelResult objectn”, “n”, “print("Box model output:")n”, “print(results)n”, “print()n”, “n”, “# You can access specific results directly from the objectn”, “n”, “print("——————————————————")n”, “print("Shape of M_n:", results.M_n.shape)n”, “print()n”, “n”, “# or you can "unpack" them all at oncen”, “n”, “M_n, M_upw, M_eddy, D_low, T, S, sigma0 = results.unpack()n”, “n”, “print("——————————————————")n”, “print("First 3 values of each output:")n”, “print("M_n:", M_n[:3])n”, “print("M_upw:", M_upw[:3])n”, “print("M_eddy:", M_eddy[:3])n”, “print("D_low:", D_low[:3])n”, “print("T_north:", T[NORTH_IDX, :3])n”, “print("T_south:", T[SOUTH_IDX, :3])n”, “print("T_low:", T[LOW_IDX, :3])n”, “print("T_deep:", T[DEEP_IDX, :3])n”, “print("S_north:", S[NORTH_IDX, :3])n”, “print("S_south:", S[SOUTH_IDX, :3])n”, “print("S_low:", S[LOW_IDX, :3])n”, “print("S_deep:", S[DEEP_IDX, :3])n”, “print("sigma0_north:", sigma0[NORTH_IDX, :3])n”, “print("sigma0_south:", sigma0[SOUTH_IDX, :3])n”, “print("sigma0_low:", sigma0[LOW_IDX, :3])n”, “print("sigma0_deep:", sigma0[DEEP_IDX, :3])”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Here is an example of how one might visualize the results using the pyplot submodule from [Matplotlib]n”, “(https://matplotlib.org)n”
]
}, {
“cell_type”: “code”, “execution_count”: 12, “metadata”: {
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“name”: “#%%n”
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- {
- “data”: {
“image/png”: 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”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}, {
- “data”: {
“image/png”: 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”, “text/plain”: [
“<Figure size 432x288 with 4 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}, {
- “data”: {
“image/png”: 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gBVVtiGgrHEMlmvrtsq/Oa/pjE59zlNi8l5PLi7rSdFoRzUgnnnz729/mxBNP5LrrrouqfI8ePdq8HjZvgGcGNqLDRlCxw88+FQwGOfvsszn77LMZO3Ys9913X6tlMzIyCLllBe2tYequfhWNF1+L6y3cqAjxHN4vBj5qo5krgD81a7cj9dtlf1hBORNf38JsintkmaNEklFUVMRll13GQw89dDjvtNNO489//jMATz31FGeccVRgBAAKCgrYv39/t8iZ6ogIIhZJIplZsWIFq1Z9HOVqwYIFDB8+nPXr17N69WoAnnjiCc466yzAM+fNnz8fgOeee+5wPT/7VTQmvv7AWyKyCHgfeENVp+OttVgMLAZKgJ8BiEiFiDwYriwi5XjRld9p1m6L9TvL/jrPxJefHQzfl9EDCk1BJSHf+c53Dk/WAtx777088sgjjBs3jieeeIJf//rXLdb7zGc+wwsvvHDEZK7ReYIiNoJKYmpqapg6dSqjR49m3LhxLF26lF/84hc88sgjXHrppYwdO5ZAIMC0adMAuO222/jWt75FRUXFEWZzP/tVuyY+VV0ETGgh/9xWys8jYnGgqq4HBkZbv7PUNTQBHLE1wJgBPXlo1loONYbIykipoBkpR01NzeF03759qa392NlzyJAh/POf/zyqzqOPPnrE+YgRI1i0aNHh8+Yjrch7GO0TDIh58SUxJ5100hHrl8Kcd955fPjhh0fln3HGGaxceXTgET/7Vcr8atc1eLbTIxVUIQ1NyqrtZvYxjI4SDAhNtlLX8JEUUlBHj6BGO08+M/MZRscJio2gDH9JPQUVYcobWtyDvKygLdiNgnT1cEvX546GQEAskkQXSOfvVqyePXUUVOPRJr5AQBjV3yJKtEdOTg7V1dVp16HC+9bk5OT4LUpCYnNQnSdd+xTEtl+lTIiFlkx84M1DPf/BZkIhPbwJm3EkZWVlVFZWsmPHDr9F6XbCO38aRxMQsf2gOkk69ymIXb9KIQUVIisYOLzAMMyYAYU8/t4GNu6qpbyk7UWd6UpmZmZS7ihrxJeMgNh+UJ3E+lRsSB0TX0MT2ZlHP87ow1tvmJnPMDpCMGAjKMNfUkZB1Tc2HWXeAxjRL5+MgNjeUIbRQQIBiyRh+EvKKKi6hhA5LYygsjOCHNMnn6UWNNYwOoRFkjD8JoUUVBM5GS1HtR4zoKeZ+AyjgwTMi8/wmdRSUC2Y+MBzlNixv57t+9uO0GsYxsdk2Doow2dSRkEdbGhq0cQHcPxAz1FicaXNQxlGtAREaDQFZfhIyiioQ40hslsx8R0/sJBgQPhw457uFcowkpigjaAMn0kZBdXQpGQGW16Im5eVwaj+BXy4aXc3S2UYyYtFkjD8JoUUVIjMYOuPM2FQbxZu2mteSYYRJQHz4jN8Jn0U1OBe1NQ3snq77QlkGNGQERBbB2X4SgopqNZNfAATBvcG4IONZuYzjGgIBIRG2w/K8JGUUVCNTSEy2hhBlRfn0Tsvkw9NQRlGVATFRlCGv7SroEQkR0TmishCEVkiIre7/EdFZJ2ILHDH+FbqN0WUeTkif6iIzBGR1SLytIhkdeVBDjVpmyY+EWHC4N7myWcYUeLF4jMFZfhHNCOoeuBcVT0BGA9MEZFJ7totqjreHQtaqX8wosxFEfl3Anep6jHAbuBLnXsEj8ZQqE0TH8CEQb1Ytb2GPbWHunIrw0gLAqagDJ9pV0GpR9izINMdXfrWiogA5wLPuqzHgIu70mZDY9tOEgCnDCsGYM66XV25lWGkBVlBW6hr+EtUc1AiEhSRBcB24A1VneMu/VxEFonIXSKS3Ur1HBGZJyKzReRil1cM7FHVRndeCQxs5d43uvrz2tr8qyHUtokP4IRBPcnOCDB7bXWb5QzDgMxggAbbb8PwkagUlKo2qep4oAyYKCLHA7cCI4GTgSLg+61UH6KqFcCVwN0iMrwjAqrqA6paoaoVpaWlrZbz3MzbNvFlZwSpKO/Ne2tMQRlGe2QEAzSYF5/hIx3y4lPVPcBbwBRVrXLmv3rgEWBiK3U2u9e1wNvABKAa6CUi4R19y4DNnXkAgKaQokq7IyiASUOLWb51P7sP2DyUYbRFZlBsBGX4SjRefKUi0sulc4HzgeUi0t/lCd780Uct1O0dNv2JSAkwGViqqoqn6C5xRacCL3X2IcKdKKOdERTAqcPD81A2ijKMtsgyE5/hM9GMoPoDb4nIIuB9vDmo6cBTIrIYWAyUAD8DEJEKEXnQ1R0FzBORhXgK6RequtRd+z5ws4isxpuTeqizDxHuRFlRjKDGlfUiNzPI7LXmKGEYbZFpJj7DZzLaK6Cqi/DMcs3zz22l/DzgBpd+FxjbSrm1tGIW7CjhTpQRaH8ElZURoKK8NzNXte5wYRiGU1CNNoIy/CMlIkk0uhFUZkZ0j3P2cX1Ys+MAm3bVxlMsw0hqMoNCQ8gUlOEfKaGgDoUVVCC6xznnOM8b8O0V2+Mmk2EkO2biM/wmJRRUuBNlZrRv4gMYWtKDIcV5vLXCzHyG0RqZwQBNIbVoEoZvpISCCpv4MqIcQYkI5xzXh3fX7KSuoSmeohlG0hL+w2eefIZfpISCOmzii8KLL8w5I/tQ1xCyRbtGUtLVIM7REDaZW7gjwy/a9eJLBsJ71rQXSSKSU4YWkZcV5PWl2zhnZJ94iWYY8SIcxLlGRDKBWSLyqrt2i6o+20bdqAj3p4bGELQWyMww4khKjKAaOjGCyskMct6ovvz9o6rDJkLDSBbiEcS5OWGvWDPxGX6RIgoqPILq2ON8amx/dtc28J4FjzWSkK4EcY4mCHO4Px0yBWX4RIooqPAIKnoTH8DZx5XSIyvI3xZVxUMsw4grXQniHE0Q5nBkFtv23fCLlFBQjaGOm/jAM/N9YnRf/r5kq5kxjKSlM0GcoyEc29L6huEXKaGgDjW6UEcdHEEBfHrcAPbUNvC2rYkykoiuBHGOFjPxGX6TEgqqsyMo8Mx8JfnZPP3+pliLZRjxpENBnDtD2MR3yOLxGT6REm7m4ZXuwSiCxTYnMxjg308ayIMz17F9Xx19CnNiLZ5hxJyOBnHuDDmZQQDqGkxBGf6QEiOosIKKJpp5S1xWMYimkPLcB53eM9EwUo68LE9BHWxo9FkSI11JCQUVXukekM4pqOGl+UwsL+LP72+0uGOG4ch1Cqr2kIUDM/whJRRUKNR5J4kwU08rZ0N1Lf9Yti1WYhlGUpPrTHwHTUEZPpESCqqxC3NQYS4c05ey3rn8YcbaWIllGElN7mETnykowx9SQkEddpLopIkPICMY4EunD2Xeht18sHF3rEQzjKQlz0x8hs+0q6C6EjVZRMaLyHuu3iIR+ULEtZhFXf7YSaJr+vayikH0zM3kN/9c3aV2DCMVyMkwE5/hL9G4mXclanItcI2qrhKRAcB8EXnNrXyPpn5UHB5BdWEOCqBHdgZfOWsYv/z7Cuat30VFeVFXRTOMpCUQEHIyA2biM3yj3SFHV6Imq+pKVV3l0lvwglq2HPirCzTGwMQX5trTyiktyOaXr61A1Tz6jPQmNzNI7SFzMzf8ISqbWFeiJke0MRHIAtZEZMck6nKTiyTRFSeJMHlZGXzj3GOYu24X/1i2vcvtGUYyk5eVwcFDtlDX8IeoFFRXoiYDuPhgTwDXqWr42x6zqMvhUGGdXajbnCsmDua4vgX89OUl9u/RSGtys2wEZfhHh7wKOhM1WUQKgb8BP1LV2RFtxSzqcngEFYiRgsoMBvjZ545n856D3POmOUwY6UvP3Ez2HmzwWwwjTYnGi6/TUZNFJAt4AXi8uTNELKMuN4Y0ZqOnMCeXF3FZRRl/mLnW3M6NtKV3Xha7DhzyWwwjTYlmBNWhqMkiUiEiD7q6lwFnAte24E4es6jLTaoxmX9qzo8+NZp+hTl8688fsr/O/kUa6UdRj0z21Np33/CHdt3MOxo1WVXnATe49JPAk62Ui1nU5aam2I+gwDNv3HPFeC69/z1ufX4x914xAYmBp6BhJAu987LYVXsIVbXvvtHtpEQkicaQxmz+qTknDSnilgtHMn1RFb9+c1Vc7mEYiUrvHlkcagzZWijDF1JiP6iQxmcEFWbaWcNYs6OGu/+xioG9crm0YlDc7mUYiURRXhYA1TWHyCtKiZ8LI4lIiW9cY0gJdjHMUVuICP/9ubFs3VvH955bhIhwyUllcbufYSQKA3rlAlC5+yCDivJ8lsZIN1LCxNfUpHRit/cOkZUR4A/XVDB5eAm3PLuQx95dH98bGkYCMKTYU0qbdtX6LImRjqSGglLtcqDYaMjNCvLg1ArOG9mX215ewo9fXExDk62yN1KX/j1zCAaEDbsO+C2KkYakhoIKxcfNvCVyMoP8/uqT+MpZw3hy9kY+99t/sWrb/m65t2F0NxnBAMNKerB0yz6/RTHSkJRQUI3dqKDAi/l367+N4v4vnsSWPXV86t5Z/Pofq2xbAiMlOWlIbz7YuOfwztWG0V2khIIKdbOCCjPl+H78/dtn8IlRfbjrHys59//e5s9zN1LfaIrKSB0mDStm78EG5ltEFaObSQkF1RgKxdXNvC36FOTw26tO4ukbJ1FakM0Pnl/M6Xe+xb1vrmLLnoO+yGQYseT80X3Jywry1OwNfotipBkpoaCaQkrA51Xupwwr5qWvT+bJL53CyH4F/N8bK5l85z+54oHZPPHeejZWmxeUkZz0yM7g6klDeGnhFotLaXQrKbEOqimkZHRxN91YICKcfmwJpx9bwobqA7zw4WZe/HAz//nSEmAJw0p6MHFoEScM6sUJZb0Y0TefjHj7xxtGDPj6uccwfVEVNz31AS/eNJk+BTl+i2SkASmhoLrbSSIahhT34NufGMG3zjuWdTsP8M7KHcxYuYO/L9nKn9/fBEBmUBhclMew0nyGlfRgUFEefQtz6FuYTd/CHErysxPuuYz0pDAnk99ffRKX3v8eV/5hDn/88immpIy4kxIKqimkMdnuPR6IiKeASvO5bvJQVJWNu2pZsGkPy7fuZ+2OGtbuOMA7K3ZwqNmaqoBAYW4mhTmZFOZmUJDtXnMyyc0MkpMZIDsjSHZGgJzMINmZAXIyvNdgQAiKeK+RR0ReICBkBISAeIcI3oE42UEOPwfQLD8cPFQOX/fqRn4UXptHl0sEAiL0LbQf2Wg5fmBPHr3uZK579H0+/9t3+cM1FYzqX+i3WEYKkzoKKklGGiLCkOIeDCnuwWcj8ptCys6aerbtq2PbPu91+7469h5sYF9dI/sONrCvroEN1bXsO9jAwYYm6htD1DU0Yd6/naMgO4PFt1/otxhJxSnDivnTlydx4xPz+Pxv3+WOz47hkpPKLNK5ERdSRkFlZyb3XE4wIM681/F/9A1NocPKKvzaFNIjD/04HQopjeG8Ju9VVVGFsK7z0hqRDud/nAdemcPpZuXUK3BUuUTB5v86xwmDevHXm07npj99yC3PLuKVxVXc8dnjLVafEXNSQkE1hpTcNP4HlxkMkBkMkJ+dEh+nkQT0Kczhz1+exGPvreeXf1/Buf/3NledMoSvnj3czKZGzEiJX7R4b7dhGMbRBALCdZOH8smx/bn7H6t4YvYGnpy9gSnH9+OLk4ZwcnlR0pjejcQkJRRUY1N8t9swDKN1+hbm8D+fH8u0s4bx+HsbeGbeJqYvqqK0IJt/O74fZx9XSkV5EYU5mX6LaiQZ7SooEckBZgDZrvyzqnqbiDwKnAXsdUWvVdUFLdSfCvzYnf5MVR9z+ScBjwK5wCvAt1Q7N0vhOUl0pqZhGLFiSHEP/vPTo7n5/BG8uXw7ryyq4un3N/H4exsICIwZ0JNxZT0Z2b+Q0f0LOLZvgSkto02iGUHVA+eqao2IZAKzRORVd+0WVX22tYoiUgTcBlTgTZfPF5GXVXU38Dvgy8AcPAU1BXi1tbbaoru22zAMo316ZGdw0QkDuOiEAdQ1NPHBxt3MXruLueuqeXnhFp6as/Fw2cKcDMp65zGoKJcBvXIp7pFF7x5ZFOVlUeTSPbIz6JEVJC8rg6wM6+fpRLsKyo1qatxppjuiHelcCLyhqrsAROQNYIqIvA0Uqupsl/84cDGdVVBJ5GZuGOlETmaQ04aXcNrwEsDz7tyyt45lW/axZkcNlbsPUrm7lrU7DvCv1dXU1De22V5GQMhzyiovK0hmMEBGUMgIBsgKChmBAJkZATIDcvhaZjBweN1ewK3189b9iUt76/cCbs1e+HrkefP1fS3R1uX267ZdoK367f7ytXPzWP9yHts3n0+PGxCTtqKagxKRIDAfOAa4T1XniMhXgZ+LyE+AN4EfqGp9s6oDgU0R55Uub6BLN89v6d43AjcCDB48uEX5TEEZRnIgIgzslcvAXrl8gr5HXa9raGJPbQPVB+rZfaCBXbWHqK1vpPZQE7WHwq8fpxublIamEA0hpaExRGMoRO3BJhqbQjQ0hbzroZC3BEI9BRlySyhC7lzVc7RSvJ0RwsslQqpevtLuEglt4z97+3Xbud5GA+3XbadAHPjk2H7dq6BUtQkYLyK9gBdE5HjgVmArkAU8AHwfuCMmUh157wdc+1RUVLT4ds/43jltfoiGYSQHOZlB+vUM0q+nuaonK7H8Le6QQVdV9wBvAVNUtUo96oFHgIktVNkMDIo4L3N5m126eX6nsZXshmEY/hPL3+J2FZSIlLqREyKSC5wPLBeR/i5P8OaPPmqh+mvABSLSW0R6AxcAr6lqFbBPRCa5+tcAL8XgeQzDMIwUIRoTX3/gMTcPFQCeUdXpIvJPESnFm2NbAEwDEJEKYJqq3qCqu0Tkv4D3XVt3hB0mgK/xsZv5q3TSQcIwDMNITSSZ5m5EZAfQ2raeJcDObhSnLRJJFkgseZJFliGqWtqdwviF9atOkUiyQGLJE7N+lVQKqi1EZJ6qVvgtBySWLJBY8pgsyUUivUcmS+skkjyxlMVWvRmGYRgJiSkowzAMIyFJJQX1gN8CRJBIskBiyWOyJBeJ9B6ZLK2TSPLETJaUmYMyDMMwUotUGkEZhmEYKYQpKMMwDCMhSXoFJSJTRGSFiKwWkR/E8T4Pi8h2EfkoIq9IRN4QkVXutbfLFxG5x8m0SEROjKgz1ZVf5fbK6owsg0TkLRFZKiJLRORbfskjIjkiMldEFjpZbnf5Q0Vkjrvn0yKS5fKz3flqd708oq1bXf4KEbmwM++NaycoIh+KyHS/ZUlW0q1fJVKfcm1Yv4JwNN/kPIAgsAYYhhe0diEwOk73OhM4EfgoIu+XeFHcAX4A3OnSn8SLjCHAJGCOyy8C1rrX3i7duxOy9AdOdOkCYCUw2g95XJv5Lp2Jt7/XJOAZ4HKXfz/wVZf+GnC/S18OPO3So93nlw0MdZ9rsJOf1c3AH4Hp7tw3WZLxSMd+lUh9yrVj/Uo16RXUqXix/cLntwK3xvF+5c060gqgf8QXfIVL/x64onk54Arg9xH5R5Trglwv4cVI9FUeIA/4ADgFbyV5RvPPCS8+46kuneHKSfPPLrJcB2Uow9v+5VxgumvbF1mS9bB+lTh9yrWRtv0q2U18re031V30VS/wLXhbj4Q3uGlrH6yYyuuGzxPw/mH5Io8b+i8AtgNv4P0z2qOq4d3nIts9fE93fS9QHCtZgLuB7wEhd17soyzJit/P72u/SoQ+5eRI+36V7AoqYVDvL0G3+uyLSD7wHPBtVd3nlzyq2qSq4/H+ZU0ERnbHfZsjIp8GtqvqfD/ub8Se7u5XidKn3P3Svl8lu4Jqbb+p7mKbfLztSH+8fzptyRUzeUUkE68jPaWqz/stDxyxX9ipQC8RCUfLj2z38D3d9Z5AdYxkmQxcJCLrgT/jmSN+7ZMsyYzfz+/L9zgR+xSkeb+Kl125Ow48++ZavAm38GTumDjer5wjbeW/4sgJ1F+69Kc4cgJ1rssvAtbhTZ72dumiTsghwOPA3c3yu10eoBTo5dK5wEzg08BfOHIC9Wsu/XWOnEB9xqXHcOQE6lq64JgAnM3Hk7m+ypJsRzr2q0TqU64d61ea5E4S7qE/iedxswb4URzv8yegCmjAs51+Cc+u+iawCvhH+IvovrT3OZkWAxUR7VwPrHbHdZ2U5XQ8U8MivL24Frj3odvlAcYBHzpZPgJ+4vKHAXNdu38Bsl1+jjtf7a4Pi2jrR07GFcC/dfHziuxIvsqSjEe69atE6lOuDetXqhbqyDAMw0hMkn0OyjAMw0hRTEEZhmEYCYkpKMMwDCMhMQVlGIZhJCSmoAzDMIyExBSUYRiGkZCYgjIMwzASElNQhmEYRkJiCsowDMNISExBGYZhGAmJKSjDMAwjIclov0jiUFJSouXl5X6LYaQB8+fP36mqpV1pQ0RygBl40ZszgGdV9baI6/cA16tqfgt1y4FleEE1AWar6jQRKcCLbB2mDHhSVb8tItfiRd8Ob2HwG1V9sD05rV8Z3UVH+1VSKajy8nLmzZvntxhGGiAiG2LQTD1wrqrWuL2GZonIq6o6W0Qq8LZjaIs16m1YdxhV3Q8czhOR+cDzEUWeVtWbOiKk9Suju+hovzITn2HECfWocaeZ7lARCeKNdL7XlfZFZATQhyNHVIaRMpiCMow4IiJBEVmAtxPrG6o6B7gJeFlVq9qpPlREPhSRd0TkjBauX443YorcM+ffRWSRiDwrIoNaqGMYbRKjfb5iQrsmvtbs6CIiwM+AS4Em4Heqek8L9acCP3anP1PVx1z+ScCjeLtFvgJ8Szv5ZG8u28b3n1vEw9eezLiyXp1pwjDigqo2AeNFpBfwgoiciddnzm6nahUwWFWrXV95UUTGqOq+iDKXA1dHnP8V+JOq1ovIV4DH8LbnPgoRuRG4EWDw4MEdf7Ak4FBjiF0HDrGzpp5dBw5RU99ITX0jB+obqT3URE19I7X1jRw41ERDU4iGphCHGvVwuqEpxKEmpaHRSzepogohVe8IeT/mocN54fOP8yLLR/66tfhDp81Pj8xo6dexeVbzn9CW7hPvLQA/ObYfv73qpJi0Fc0cVIt2dGAU3v7yI1U1JCJ9mlcUkSLgNqAC772aLyIvq+pu4HfAl4E5eApqCt4Wyh3mxQVb2FlziCdnb+CXl/TqTBNGOzQ0NFBZWUldXZ3fosSUnJwcysrKyMzMjOt9VHWPiLwFnAMcA6z2/uORJyKrVfWYZuXr8foeqjpfRNYAI4B5ACJyApChqvMj6lRHNPEg8Ms25HkAeACgoqIiKXctbQop63bWsGJrDRt31bJpdy2bdtWyefdBdtTUs7+usc36GQGhR3YGeVlBsjICZAa9Iyso3mtGgLyscL4QDAgBEQICAREkIh0IcOS5COLSOUHlhKIQBZmKxOF9iEebXWk0IxBg3bp1MelX7SooN6o5yo4OfBW4UlVDrtz2FqpfiGfW2AUgIm8AU0TkbaBQVWe7/MeBi+mkglpe5f2pfG9tdTsljc5SWVlJQUEB5eXluB/WpEdVqa6uprKykqFDh8a8fREpBRqccsoFzgfuVNV+EWVqmiuniLq7VLVJRIYBxwJrI4pcgbddemSd/hFmw4vwvABThp019cxeW83stdUsrtzLim37qWsIHb7eOy+TQUV5jOpfyJkF2RT3yKI4P5uiHlkU52dRmJNJXlaQ/OwM8rKDZGcEu0XudevWUVBQQHFxccr0nbaIZb+KyovPTerOx/vnd5+qzhGR4cAXRORzwA7gm6q6qlnVgcCmiPNKlzfQpZvnt3Tvdk0R104u58UPN/P++t3sqT1Er7ysaB7L6AB1dXUppZzA+8dbXFzMjh074nWL/sBjrv8EgGdUdXob8lwEVKjqT4AzgTtEpAEIAdPCf/QclwGfbNbEN10bjcAu4NqYPYlPrNq2n1cWb+XVj6pYvnU/APnZGYwd2JOrThnCqP6FjOxXwJDiPApy4jsK7iyp2HfaIpb9KioF1YId/Xi8Oak6Va0Qkc8DDwMtTeR2iWhMEVedMoRBvfO45uG5LK3ax2nDS2IthgEp2cHi+UyqugiY0E6Z/Ij0y8DLLv0c8Fwb9Ya1kHcrcGtn5U0U6hqaeHnBFh57bz1LtuxDBE4eUsT3p4zk1OHFHD+gkIxgcvl3pWLfaYtYPW+H1kFF2NGn4I16wusvXgAeaaHKZo6cDC4D3nb5Zc3yN9MFRvUvBGBZ1X5TUIaRhNQ1NPH4e+v53dtr2F3bwHF9C/jpZ0bzybH96VOY47d4hg9E48XXoh0deBFvwncdcBawsoXqrwH/LSLhBYkXALeq6i4R2Scik/CcJK4B7u3Kg5QWZFOSn83SLfvaL2wkJfn5+dTU1LRf0EgqVJXpi6r42d+Wsm1fPWeOKOVrZw/nlKFFaTfyiBfBYJCxY8fS0NBARkYG11xzDf/xH/9BIJDYI9FoRlAt2tFFZBbwlIj8B54TxQ0AboX8NFW9wSmi/wLed23dEWFH/xofu5m/SicdJCIZ1b+Aldv2d7UZwzC6ie376vjhC4v5x7LtjCvryT2XT+CUYcV+i5Vy5ObmsmDBAgC2b9/OlVdeyb59+7j99tv9Fawd2lWfqrpIVSeo6jhVPV5V73D5e1T1U6o6VlVPVdWFLn+eqt4QUf9hVT3GHY9E5M9z7Q1X1Zs6uwYqkuP6egqqKZSUXrNGJ1iwYAGTJk1i3LhxfO5zn2P37t1s376dk07y1mEsXLgQEWHjxo0ADB8+nNraWj9FNhxz1+3iU/fOYtbqnfz4U6N44WuTTTl1A3369OGBBx7gN7/5DapKU1MTt9xyCyeffDLjxo3j97///eGyv/rVrw7n33abF0Zy/fr1jBw5kquuuopRo0ZxySWXxK1PJVUsvvYY0a+A+sYQG3fVMrSkh9/ipCy3/3VJzE2powcUcttnxnS43jXXXMO9997LWWedxU9+8hNuv/127r77burq6ti3bx8zZ86koqKCmTNncvrpp9OnTx/y8vJiKrvRcZ55fxO3vrCYwUV5PPmlUziuX4HfInULidJ3hg0bRlNTE9u3b+ell16iZ8+evP/++9TX1zN58mQuuOACVq1axapVq5g7dy6qykUXXcSMGTMYPHgwK1as4KGHHmLy5Mlcf/31/Pa3v+W73/1uTJ8LUizU0XF9vS/5iq1m5ksH9u7dy549ezjrrLMAmDp1KjNmzADgtNNO41//+hczZszghz/8ITNmzGDmzJmccUbMHU2NDvLgzLV877lFnDa8mJdumpw2yilRef3113n88ccZP348p5xyCtXV1axatYrXX3+d119/nQkTJnDiiSeyfPlyVq3yVhINGjSIyZMnA/DFL36RWbNmxUW2lBpBHds3HxFYuW0/U47v134Fo1N0ZqTT3Zx55pnMnDmTDRs28NnPfpY777wTEeFTn/qU36KlNY/8ax0/+9syPjW2P3d9YTxZGSn1H7ldEqXvrF27lmAwSJ8+fVBV7r33Xi688MIjyrz22mvceuutfOUrXzkif/369Uc5r8TLmSWlvh15WRkMLsqzEVSa0LNnT3r37s3MmV4w7yeeeOLwaOqMM87gySef5NhjjyUQCFBUVMQrr7zC6aef7qfIac1fF27hjulLuXBMX+65YkLaKadEYceOHUybNo2bbroJEeHCCy/kd7/7HQ0NDQCsXLmSAwcOcOGFF/Lwww8f9pzdvHkz27d7AYM2btzIe++9B8Af//jHuPWrlBpBAYzoW8AK8+RLSWpraykr+3j53M0338xjjz3GtGnTqK2tZdiwYTzyiOeHU15ejqpy5plnAnD66adTWVlJ797tbcFkxIPFlXv5zjMLOXlIEb++fALBgLmPdycHDx5k/Pjxh93Mr776am6++WYAbrjhBtavX8+JJ56IqlJaWsqLL77IBRdcwLJlyzj11FMBb5nHk08+STAY5LjjjuO+++7j+uuvZ/To0Xz1q1+Ni9wSy9Do8aaiokLb21jtf19bwe/eWcPSOy7stlhb6cCyZcsYNWqU32LEhZaeTUTmq2qFTyJ1K9H0q66wt7aBT/9mJo1Nyt++eQZFPdIrFFmq9Z3169fz6U9/mo8++qjNcrHoVyk3xh7Rr4CmkLJ2xwG/RTEMA/jhC4up2lPHb648Me2Uk9E1Uk5BjexnnnyGkSj8/aMq/ra4iv84fwQnDTHzaipQXl7e7ugpVqScgiov7kFmUGweKg4kkzk4WlLxmRKFvbUN/OdLSxjdv5Abzzwqtm1akW7fs1g9b8opqKyMAMNK8llpI6iYkpOTQ3V1dUp1tPC+NTk5Fog0Htz95kqqa+r55SXjyEyy6OOxJBX7TlvEsl+lnBcfePNQH27c7bcYKUVZWRmVlZXx3DvJF8I76hqxZUP1AZ6cvYHLKgZx/MCefovjK6nad9oiVv0qJRXUyH4F/HXhFmrqG8nPTslH7HYyMzPjsuuskZr86rUVZAQC3Hz+CL9F8R3rO50nJcfdI1zII4tsbhjdz4qt+5m+qIobzhhq+zgZXSIlFVQ4Jp/NQxlG9/P7d9aQlxXkS6fbqMHoGimpoMp655KXFTRPPsPoZip31/LSwi1cMXEwvfJszZPRNVJSQQUCwrF9bfNCw+huHp61noDADWfY6MnoOu0qKBHJEZG5IrJQRJaIyO0u/1ERWSciC9wxvoW650RcXyAidSJycbT1u8JxffNtsa5hdCN1DU08O38TU47vT/+euX6LY6QA0bi41QPnqmqNiGQCs0QkvD37Lar6bGsVVfUtYDyAiBQBq4HXI4q0Wb8rjOhbwDPzKtlZU09JfnY8bmEYRgR/W1TFvrpGrpw42G9RjBQhmi3fVVVr3GmmOzqz4uwS4FVV7Zb9tsOboJmZz/CT1iwQEdfvEZGaVuqWi8jBCCvD/RHX3haRFRHX+rj8bBF5WkRWi8gcESmP6wNG8Ke5GxlW0oNJw4q665ZGihPVHJSIBEVkAbAdeENV57hLPxeRRSJyl4i0N0y5HPhTs7x264vIjSIyT0TmdWSh22EFZWY+w1/CFogT8KwJU0RkEoCIVADtBahbo6rj3TGt2bWrIq5td3lfAnar6jHAXcCdMXuSNli9fT/zNuzm8omD4rZ5nZF+RKWgVLVJVccDZcBEETkeuBUYCZwMFAHfb62+iPQHxgKvRWRHVV9VH1DVClWtKC0tjUZcAErzs+mdl2mefIavtGaBEJEg8CvgezG+5WeBx1z6WeA86QaN8fLCKgICF08YGO9bGWlEh7z4VHUP8BYwRVWrXOerBx4BJrZR9TLgBVVtiGirI/U7jIgwom8By20EZfhMKxaIm4CXVbWqnepDReRDEXlHRM5odu0RZ977zwglNBDYBKCqjcBeoLgFmTplmWgJVWX6wi2cMrSYPgW2MNeIHdF48ZWKSC+XzgXOB5a7URGuY1wMtBV//Qqamfc6WL9TjBnQk2VV+2hsCsW6acOImhYsEGcClwL3tlO1ChisqhOAm4E/ikihu3aVqo4FznDH1R2UqVOWiZZYWrWPtTsP8JkTBnSpHcNoTjQjqP7AWyKyCHgf7x/gdOApEVkMLAZKgJ+BZ1cXkQfDld0k7SDgnWbttlg/lpwwqCd1DSFWbmtxDtowupUIC8Q5wDHAahFZD+SJyOoWyterarVLzwfWACPc+Wb3uh/4Ix9bIDbj9TdEJAPoCVTH7aGA6YuqCAaEKcf3i+dtjDSkXTdzVV0ETGgh/9xWys8Dbog4X49ndoiqfiw5oawXAAsr9zB6QGHbhQ0jDohIKdCgqnsiLBB3qmq/iDI1zqmhpbq7VLVJRIYBxwJrneLppao73dKPTwP/cNVeBqYC7+F5zv5T47zPw2tLtnLa8GLbLdeIOSkZSSLMkOI8euZmsqhyj9+iGOlLaxaIFhGRi0TkDnd6JrDIzV89C0xT1V1ANvCaa3MB3qjpD67OQ0CxG5HdDPwg9o/0MRuqD7B2xwHOG9knnrcx0pSU3otCRBhX1pMFm/b6LYqRprRmgWhWJj8i/TLeKAhVfQ54roXyB4CTWmmrDm9+q1t4a7nn3X6OKSgjDqT0CAo8M9/Kbfs5eKjJb1EMI+X454odDCvtwZDiHn6LYqQgqa+gBvWiKaQs2WKjKMOIJbWHGpm9tppzj7PRkxEfUl9BlXnbTS+sNAVlGLFkztpdHGoMcbYpKCNOpLyC6lOYw4CeOXywcbffohhGSjF7bTVZwQAV5e1FazKMzpHyCgqgoryI99ftIs7etoaRVsxeW834wb3IyQz6LYqRoqSFgjp5aBHb99ezcVe3BFI3jJRnf10DizfvZdJQi1xuxI+0UFATy71ONHfdLp8lMYzUYN6G3YQUJg07KsyfYcSMtFBQx/bJp2duJu+vNwVlGLFg9tpqMoPChME2/2TEj7RQUIGAcHJ5b95fb44ShhEL5q7bxQllvcjNsvknI36khYICOLm8iHU7D7B9f53fohhGUnOoMcSSzfs4aYiNnoz4kjYKauJQm4cyjFiwfOs+DjWFOGFQL79FMVKctFFQYwf2pCAng5krd/otimEkNQs37QEwBWXEnbRRUBnBAJOHlzBz1Q5bD2UYXeDDTXsoyc9mQE/bPdeIL2mjoADOHFHKlr11rNlhGxgaRmdZuGkP4wf14uNd5g0jPqSVgjrj2BIAZpiZzzA6xb66BtbsOMD4QT39FsVIA9pVUCKSIyJzRWShiCwRkdtd/qMisk5EFrhjfCv1myLKvByRP1RE5ojIahF5WkTivh3noKI8hpb0YMaqHfG+lWGkJItd0OVxbrdqw4gn0Yyg6oFzVfUEYDwwRUQmuWu3qOp4dyxopf7BiDIXReTfCdzltrreDXypc4/QMc48toTZa6upa7D9oQyjoyyr2gfA8QNtBGXEn3YVlHqEJ20y3dElLwPxjNfn4m1jDfAYcHFX2oyWc0f1pa4hxKxVZuYzjI6ytGoffQuzKeoRd4OHYUQ3ByUiQRFZAGwH3lDVOe7Sz0VkkYjcJSLZrVTPEZF5IjJbRC52ecXAHlVtdOeVwMBW7n2jqz9vx46um+ZOHVZMQU4Gry3Z2uW2DCPdWF61n5H9Cv0Ww0gTolJQqtqkquOBMmCiiBwP3AqMBE4GioDvt1J9iKpWAFcCd4vI8I4IqKoPqGqFqlaUlpZ2pGqLZGUEOG9kH/6xbBuNTaEut2cY6UJDU4jV22sY2b/Ab1GMNKFDXnyqugd4C5iiqlXO/FcPPAJMbKXOZve6FngbmABUA71EJMMVKwM2d+YBOsOFY/qxu7aBuRY81jCiZt3OAxxqCjG6v42gjO4hGi++UhHp5dK5wPnAchHp7/IEb/7ooxbq9g6b/kSkBJgMLFVvpexbwCWu6FTgpa4+TLScdVwp2RkBXl+yrbtuaaQhrXnARly/R0RaXJQnIuUicjDCA/Z+l58nIn8TkeWuzV9E1LlWRHZE1Lkhls8TdpAwE5/RXUQzguoPvCUii4D38eagpgNPichiYDFQAvwMQEQqRORBV3cUME9EFuIppF+o6lJ37fvAzSKyGm9O6qFYPVR75GVlcPZxpUxfVGVmPiOetOoBKyIVQHvRVtdEeMBOi8j/X1UdiWeNmCwi/xZx7emIOg8SQ5ZV7SczKAwr7RHLZg2jVTLaK6Cqi/A6QvP8c1spPw+4waXfBca2Um4trZgFu4PPn1jGa0u2MXP1Ts45ro9fYhgpjLMUHOUBKyJB4Fd487Kf62CbtXh/9lDVQyLyAZ6JPO4s37qPY/oUkBlMq/X9ho+k7TftnOP60Csvk+c/6LapLyMNacUD9ibgZVWtaqf6UBH5UETeEZEzWmi7F/AZ4M2I7H93nrXPisigNuTqsHfsyq37GdnPHCSM7iNtFVRWRoCLThjA60u2sq+uwW9xjBSlBQ/YM4FLgXvbqVoFDFbVCcDNwB9F5PDkj3Mw+hNwj7NGAPwVKFfVccAbeOsLW5OrQ96xB+ob2bK3jmP65Ldb1jBiRdoqKPDMfPWNIf66cIvfohgpToQH7DnAMcBqEVkP5Ll52Obl61W12qXnA2uAERFFHgBWqerdEXWqnVctwIPASbGSf93OAwAMK7H5J6P7SGsFdUJZT0b1L+SJ9zbYFhxGzGnFA3a+qvZT1XJVLQdqXbivluoGXXoYcCyw1p3/DOgJfLtZnf4RpxcBy2L1LOEdAIaV2gjK6D7SWkGJCNedVs7yrfuZvdbWRBkxpzUP2BYRkYtE5A53eiawyM1fPQtMU9VdIlIG/AgYDXzQzJ38m871fCHwTeDaWD3I2h0HCAgMKc6LVZOG0S7tevGlOheNH8D/vLqMx95dz6nDi/0Wx0ghWvOAbVYmPyL9MvCySz8HPNdC+UqgxY2YVPVWvAgvMWfNjhrKeueRkxmMR/OG0SJpPYICyMkMcvnEwby+dCsbqg/4LY5hJCRrdxyw9U9Gt5P2Cgrg2tPKyQgGuO+to+aqDSPtCYWUtTtrGG7zT0Y3YwoK6FuYw5UTB/P8B5vZtKvWb3EMI6Go2ldHXUPIRlBGt2MKyjHtrOEERPjNP20UZRiRrA178JXYCMroXkxBOfr1zOGqSYP5y/xNh4NiGoYBa7Z7Cmp4HxtBGd2LKagIvnXesRTmZnLHX5fauijDcKzdeYCC7AxK81vbk9Qw4oMpqAh65WXxnfNH8N7aal5ZbDvuGgZ4LubD+uTj7axjGN2HKahmXDFxMGMGFHLbyx9RXVPffgXDSHHW7jjAcAtxZPiAKahmZAQD/N9lJ7D3YAP/+dJHZuoz0poD9Y1U7a0zDz7DF0xBtcDIfoV8+xMjeGXxVp5+f5Pf4hiGbxwOEmtroAwfMAXVCtPOGs4Zx5bwk5eW8OHG3X6LYxi+sN5FVykvthGU0f20q6BEJEdE5orIQheI8naX/6iIrHPBKheIyPgW6o4XkfdcvUUi8oWIa+3W95NgQLjn8gn0Kcxm2pPzqdxtC3iN9GNDtfe9Ly+xILFG9xPNCKoeOFdVTwDGA1NEZJK7douqjnfHghbq1gLXqOoYYApwd3j7gSjr+0rvHlk8OLWCg4ea+OKDc9ix35wmjPRi3c4D9CnIJi8r7eNKGz7QroJSjxp3mumOqDwHVHWlqq5y6S142163v31nAjGyXyGPXDeRbfvquerB2VTtPei3SIbRbazfeYBy8+AzfCKqOSgRCbp9abbj7Wkzx136uTPd3SUiba7iE5GJQBbezqBh2q0vIjeKyDwRmbdjx45oxI05Jw3pzUPXVrBlTx3//tt3WbVtvy9yGEZ3s766lqE2/2T4RFQKSlWbVHU8UAZMFJHj8fadGQmcDBQB32+tvtvp8wngOlUNueyo6qvqA6paoaoVpaX+Db5OG17C01+ZRENIufi+f/HSgs2+yWIY3cH+ugZ21tQzxOafDJ/okBefqu4B3gKmqGqVM//VA48AE1uqIyKFwN+AH6nq7Ii2oqqfSIwZ0JOXb5rMqP6FfOvPC7jlLwvZfeCQ32IZRlwIO0jYCMrwi2i8+ErDjg0ikgucDyx3oyLEi39yMfBRC3WzgBeAx1X12WbX2q2fiPTvmcufbpzE188ZzvMfbua8//cOz7y/icamUPuVDSOJOOxibnNQhk9EM4LqD7wlIouA9/HmoKYDT4nIYmAxUAL8DEBEKkTkQVf3MuBM4NoW3MlbrJ8MZAYD3HLhSKZ/43TKi/P43nOLOP+uGTw3v5JDjaaojNRgvVukO6TYTHyGP7TrO6qqi4AJLeSf20r5ecANLv0k8GQr5Vqsn0yM6l/Ic189jdeXbuPuf6ziO39ZyP+8uoxLThrEpRVltgOpkdRs3nOQkvwsczE3fMO+eV1ERLhwTD/OH9WXGat28NScjTwwYw33v7OGY/rkc+GYvpxxbCnjB/UiJzPot7iGETVb9tTRv2eu32IYaYwpqBgRCAhnH9eHs4/rw9a9dfz9oypeX7qN+99Zy31vrSEzKIwr68UJZb04rl8+x/UrZETffPt3muKISA4wA8jG62/PquptEdfvAa5X1aOG2yJSDiwDVris2ao6zV07CXgUyAVeAb6lqioiRcDTQDmwHrhMVTsVq6tq70ELcWT4iv06xoF+PXO4dvJQrp08lL21DczbsIu563fx/rpd/GnuRg42NB0uW5KfzcDeuZT1ymVg71z6FGTTOy+Loh5Z9O6RRVFeFj1zM8nNCpKVYaETk5BwJJYaEckEZonIq6o6W0QqgN7t1F/jlng053fAl4E5eApqCvAq8APgTVX9hYj8wJ23ugSkLar21HHqsOLOVDWMmGAKKs70zMvkvFF9OW9UXwBCIWXjrlqWb93P6u37qdx9kMrdB1latY83lm1r08kiIyDkZQXJy8rwXrOD5GQEyQgKmcEAmcEAGQEv/XGekBHwzgUhICACARFExKU5fA3xXgMiCN7IEMLlwRVpE69mO2VisPddNBvotVUiMyPA1ZOGdF2QNlBvv5ajIrGISBD4FXAl8LmOtOk8YAvDyzZE5HE8T9hXgc8CZ7uijwFv0wkFtb+ugf31jfTvZSY+wz9MQXUzgYBQXtLDue72O+KaqrKvrpHdBw6xq/YQe2oPsetAA3tqD3HwUBO1DU3e66FGDhzy0nUNTTQ2KTWNjTQ0hWhsUu81pDQ0hmgIKY0uX909QgqKe1VFFUIavu7Hu+IPBdkZcVdQ4EViAeYDxwD3qeocEfkW8LKqVrWjaIeKyIfAPuDHqjoTGAhURpSpdHkAfVW1yqW3An1bkelG4EaAwYMHH3V92746APr3zInmEQ0jLpiCSiBEhJ65mfTMzaQc/2z/YSUWilBe8PF5m3WjbL/rbURTKIoy3YCqNgHj3XrCF0TkTOBSPh7ptEYVMFhVq92c04siMqYD91URafFdUNUHgAcAKioqjiozvDSfhbddQFbQzMqGf5iCMo5CRAgKBKMw1RnRo6p7ROQt4By80dRqN3rKE5HVqnpMs/L1eHNYqOp8EVkDjAA244UdC1Pm8gC2iUh/NzLrjxc/s8OE/ywZhp/Y3yPDiCOtRGKZr6r9VLVcVcuB2ubKKaJu0KWHAccCa50Jb5+ITHKRWK4BXnLVXgamuvTUiHzDSDpsBGUY8aU/8JhTNAHgGReJpUVE5CKgQlV/gheF5Q4RaQBCwDRV3eWKfo2P3cxfdQfAL4BnRORLwAa8aC6GkZRIe/MBiYSI7MDrdC1RAuzsRnHaIpFkgcSSJ1lkGaKqSbV3WWexftUpEkkWSCx5YtavkkpBtYWIzFPVCr/lgMSSBRJLHpMluUik98hkaZ1EkieWstgclGEYhpGQmIIyDMMwEpJUUlAP+C1ABIkkCySWPCZLcpFI75HJ0jqJJE/MZEmZOSjDMAwjtUilEZRhGIaRQpiCMgzDMBKSpFdQIjJFRFaIyGq3vUC87vOwiGwXkY8i8opE5A0RWeVee7t8EZF7nEyLROTEiDpTXflVIjK1pXtFIcsgEXlLRJaKyBIXeNQXeUQkR0TmishCJ8vtLn+oiMxx93xaRLJcfrY7X+2ul0e0davLXyEiF3bmvXHtBEXkQxGZ7rcsyUq69atE6lOuDetXEI5mnZwHEATWAMOALGAhMDpO9zoTOBH4KCLvl8APXPoHwJ0u/Um8lf0CTALmuPwiYK177e3SvTshS3/gRJcuAFYCo/2Qx7WZ79KZePsTTQKeAS53+fcDX3XprwH3u/TlwNMuPdp9ftnAUPe5Bjv5Wd0M/BGY7s59kyUZj3TsV4nUp1w71q9Uk15BnQq8FnF+K3BrHO9X3qwjrQD6R3zBV7j074ErmpcDrgB+H5F/RLkuyPUSXow3X+UB8oAPgFPwVpJnNP+cgNeAU106w5WT5p9dZLkOylAGvAmcC0x3bfsiS7Ie1q8Sp0+5NtK2XyW7iW8gsCniPHJfnO6gtb13WpMr5vK64fMEvH9Yvsjjhv4L8CJnv4H3z2iPqja20O7he7rre4HiWMkC3A18Dy92Ha5tv2RJVvx+fl/7VSL0KSdH2verZFdQCYN6fwm61WdfRPKB54Bvq+o+v+RR1Sb1tiUvAyYCI7vjvs0RkU8D21V1vh/3N2JPd/erROlT7n5p36+SXUFtBgZFnEfui9MdbBNvz53wNtzhvXdakytm8opIJl5HekpVn/dbHvD2OwLewhvu9xKRcLT8yHYP39Nd7wlUx0iWycBFIrIe+DOeOeLXPsmSzPj9/L58jxOxT0Ga96t42ZW748Czb67Fm3ALT+aOieP9yjnSVv4rjpxA/aVLf4ojJ1DnuvwiYB3e5Glvly7qhBwCPA7c3Sy/2+UBSoFeLp0LzAQ+DfyFIydQv+bSX+fICdRnXHoMR06grqULjgl4u9WGJ3N9lSXZjnTsV4nUp1w71q80yZ0k3EN/Es/jZg3wozje5094W3A34NlOv4RnV30TWAX8I/xFdF/a+5xMi/H29wm3cz2w2h3XdVKW0/FMDYuABe74pB/yAOOAD50sHwE/cfnDgLmu3b8A2S4/x52vdteHRbT1IyfjCuDfuvh5RXYkX2VJxiPd+lUi9SnXhvUrVQt1ZBiGYSQmyT4HZRiGYaQopqAMwzCMhMQUlGEYhpGQmIIyDMMwEhJTUIZhGEZCYgrKMAzDSEhMQRmGYRgJyf8HMj6UMHWM28kAAAAASUVORK5CYII=n”, “text/plain”: [
“<Figure size 432x288 with 4 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}, {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 4 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“plt.plot(M_n, label=’M_n’)n”, “plt.plot(M_upw, label=’M_upw’)n”, “plt.plot(M_eddy, label=’M_eddy’)n”, “plt.plot(D_low, label=’Dlow’)n”, “plt.legend()n”, “plt.title("1D Box Model Outputs")n”, “plt.show()n”, “fig, ax = plt.subplots(nrows=2, ncols=2)n”, “ax[0, 0].plot(T[NORTH_IDX], label=’North’)n”, “ax[0, 1].plot(T[SOUTH_IDX], label=’South’)n”, “ax[1, 0].plot(T[LOW_IDX], label=’Low’)n”, “ax[1, 1].plot(T[DEEP_IDX], label=’Deep’)n”, “ax[0, 0].legend()n”, “ax[0, 1].legend()n”, “ax[1, 0].legend()n”, “ax[1, 1].legend()n”, “plt.suptitle("Temperature in the 4 different boxes")n”, “plt.tight_layout()n”, “plt.show()n”, “fig1, ax1 = plt.subplots(nrows=2, ncols=2)n”, “ax1[0, 0].plot(S[NORTH_IDX], label=’North’)n”, “ax1[0, 1].plot(S[SOUTH_IDX], label=’South’)n”, “ax1[1, 0].plot(S[LOW_IDX], label=’Low’)n”, “ax1[1, 1].plot(S[DEEP_IDX], label=’Deep’)n”, “ax1[0, 0].legend()n”, “ax1[0, 1].legend()n”, “ax1[1, 0].legend()n”, “ax1[1, 1].legend()n”, “plt.suptitle("Salinity in the 4 different boxes")n”, “plt.tight_layout()n”, “plt.show()n”, “fig2, ax2 = plt.subplots(nrows=2, ncols=2)n”, “ax2[0, 0].plot(sigma0[NORTH_IDX], label=’North’)n”, “ax2[0, 1].plot(sigma0[SOUTH_IDX], label=’South’)n”, “ax2[1, 0].plot(sigma0[LOW_IDX], label=’Low’)n”, “ax2[1, 1].plot(sigma0[DEEP_IDX], label=’Deep’)n”, “ax2[0, 0].legend()n”, “ax2[0, 1].legend()n”, “ax2[1, 0].legend()n”, “ax2[1, 1].legend()n”, “plt.suptitle("Density in the 4 different boxes")n”, “plt.tight_layout()n”, “plt.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “pycharm”: {
“name”: “#%% mdn”
}
}, “source”: [
“## Running multiple experiments with the box model”
]
}, {
“cell_type”: “code”, “execution_count”: 13, “metadata”: {
- “pycharm”: {
“name”: “#%%n”
}
}, “outputs”: [
- {
- “data”: {
“image/png”: 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”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Total time to collect data: 5.134157180786133 secondsn”
]
}
], “source”: [
“# Consider an example of some data collectionn”, “n”, “Fwn_values_to_test = [10000, 50000, 100000, 500000, 1000000]n”, “alternate_north_starting_temp = 4.0n”, “n”, “box_dims = BoxModelBoxDimensions()n”, “params = BoxModelParameters()n”, “time_step = BoxModelTimeStep()n”, “init = BoxModelInitConditions(T_north0=alternate_north_starting_temp)n”, “n”, “# Collect the data… (should take a few seconds)n”, “n”, “start_time = time.time()n”, “results = []n”, “for fwn in Fwn_values_to_test:n”, ” params.Fwn = fwnn”, ” results.append(box_model(box_dims, init, params, time_step))n”, “time_to_collect_data = time.time() - start_timen”, “n”, “# Let’s look at the difference in M_n for each runn”, “n”, “plt.figure()n”, “for i in range(len(Fwn_values_to_test)):n”, ” plt.plot(results[i].M_n, label=f"Fwn={Fwn_values_to_test[i]}")n”, “plt.title(f"M_n for different Northern Fluxes and T_north0={alternate_north_starting_temp}")n”, “plt.legend()n”, “plt.show()n”, “n”, “print(f"Total time to collect data: {time_to_collect_data} seconds")”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “pycharm”: {
“name”: “#%% mdn”
}
}, “source”: [
“Note that collecting data this way is embarrassingly parallel. To more quickly collect data on multiple configurations, consider a simple [multiprocessing](https://docs.python.org/3/library/multiprocessing.html) approach.n”
]
}, {
“cell_type”: “code”, “execution_count”: null, “metadata”: {}, “outputs”: [], “source”: []
}
], “metadata”: {
- “kernelspec”: {
“display_name”: “Python 3 (ipykernel)”, “language”: “python”, “name”: “python3”
}, “language_info”: {
- “codemirror_mode”: {
“name”: “ipython”, “version”: 3
}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.9.12”
}
}, “nbformat”: 4, “nbformat_minor”: 1
}