{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "## Exploration of variable time steps in the simulation of the coupled reactions `2 S <-> U` and `S <-> X` \n", "Both mostly forward. 1st-order kinetics throughout. \n", "\n", "Based on the reactions and initial conditions of the experiment `up_regulate_3`\n", "\n", "LAST REVISED: Mar. 6, 2023" ] }, { "cell_type": "code", "execution_count": 1, "id": "d9efa3fd-e95d-4e1c-878a-81ae932b2709", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Added 'D:\\Docs\\- MY CODE\\BioSimulations\\life123-Win7' to sys.path\n" ] } ], "source": [ "# Extend the sys.path variable, to contain the project's root directory\n", "import set_path\n", "set_path.add_ancestor_dir_to_syspath(2) # The number of levels to go up \n", " # to reach the project's home, from the folder containing this notebook" ] }, { "cell_type": "code", "execution_count": 2, "id": "01bae555-3dcf-42c1-bddc-9477a37f49f8", "metadata": { "tags": [] }, "outputs": [], "source": [ "from experiments.get_notebook_info import get_notebook_basename\n", "\n", "from src.modules.reactions.reaction_data import ReactionData as chem\n", "from src.modules.reactions.reaction_dynamics import ReactionDynamics\n", "\n", "import numpy as np\n", "import plotly.express as px\n", "from src.modules.visualization.graphic_log import GraphicLog" ] }, { "cell_type": "code", "execution_count": 3, "id": "cc53849f-351d-49e0-bfa8-22f8d8e22f8e", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-> Output will be LOGGED into the file 'variable_steps_1.log.htm'\n" ] } ], "source": [ "# Initialize the HTML logging\n", "log_file = get_notebook_basename() + \".log.htm\" # Use the notebook base filename for the log file\n", "\n", "# Set up the use of some specified graphic (Vue) components\n", "GraphicLog.config(filename=log_file,\n", " components=[\"vue_cytoscape_1\"],\n", " extra_js=\"https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.21.2/cytoscape.umd.js\")" ] }, { "cell_type": "markdown", "id": "d6d3ca49-589d-49b7-8424-37c7b01bcacf", "metadata": {}, "source": [ "### Initialize the system" ] }, { "cell_type": "code", "execution_count": 4, "id": "23c15e66-52e4-495b-aa3d-ecddd8d16942", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 2 (at temp. 25 C)\n", "0: 2 S <-> U (kF = 8 / kR = 2 / Delta_G = -3,436.56 / K = 4) | 1st order in all reactants & products\n", "1: S <-> X (kF = 6 / kR = 3 / Delta_G = -1,718.28 / K = 2) | 1st order in all reactants & products\n", "[GRAPHIC ELEMENT SENT TO LOG FILE `variable_steps_1.log.htm`]\n" ] } ], "source": [ "# Initialize the system\n", "chem_data = chem(names=[\"U\", \"X\", \"S\"])\n", "\n", "# Reaction 2 S <-> U , with 1st-order kinetics for all species (mostly forward)\n", "chem_data.add_reaction(reactants=[(2, \"S\")], products=\"U\",\n", " forward_rate=8., reverse_rate=2.)\n", "\n", "# Reaction S <-> X , with 1st-order kinetics for all species (mostly forward)\n", "chem_data.add_reaction(reactants=\"S\", products=\"X\",\n", " forward_rate=6., reverse_rate=3.)\n", "\n", "chem_data.describe_reactions()\n", "\n", "# Send the plot of the reaction network to the HTML log file\n", "graph_data = chem_data.prepare_graph_network()\n", "GraphicLog.export_plot(graph_data, \"vue_cytoscape_1\")" ] }, { "cell_type": "markdown", "id": "d1d0eabb-b5b1-4e15-846d-5e483a5a24a7", "metadata": {}, "source": [ "### Set the initial concentrations of all the chemicals" ] }, { "cell_type": "code", "execution_count": 5, "id": "e80645d6-eb5b-4c78-8b46-ae126d2cb2cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "3 species:\n", " Species 0 (U). Conc: 50.0\n", " Species 1 (X). Conc: 100.0\n", " Species 2 (S). Conc: 0.0\n" ] } ], "source": [ "dynamics = ReactionDynamics(reaction_data=chem_data)\n", "dynamics.set_conc(conc={\"U\": 50., \"X\": 100., \"S\": 0.})\n", "dynamics.describe_state()" ] }, { "cell_type": "code", "execution_count": 6, "id": "bcf652b8-e0dc-438e-bdbe-02216c1d52a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "44 total step(s) taken\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " SYSTEM TIME U X S caption\n", "0 0.0000 50.000000 100.000000 0.000000 Initial state\n", "1 0.0050 49.500000 98.500000 2.500000 \n", "2 0.0075 49.302500 97.798750 3.596250 \n", "3 0.0125 48.953325 96.439656 5.653694 \n", "4 0.0150 48.821632 95.801164 6.555571 \n", "5 0.0200 48.595639 94.560814 8.247909 \n", "6 0.0250 48.439599 93.389839 9.730964 \n", "7 0.0300 48.344441 92.280920 11.030197 \n", "8 0.0350 48.302205 91.227612 12.167978 \n", "9 0.0400 48.305902 90.224238 13.163959 \n", "10 0.0500 48.392901 88.307348 14.906851 \n", "11 0.0550 48.505246 87.429943 15.559566 \n", "12 0.0650 48.779906 85.740619 16.699569 \n", "13 0.0750 49.140273 84.170374 17.549079 \n", "14 0.0850 49.561394 82.698208 18.179004 \n", "15 0.0950 50.024487 81.308002 18.643025 \n", "16 0.1050 50.515439 79.987343 18.981779 \n", "17 0.1250 51.531906 77.465916 19.470272 \n", "18 0.1350 52.058890 76.310155 19.572066 \n", "19 0.1550 53.108064 74.080194 19.703677 \n", "20 0.1650 53.622197 73.040009 19.715597 \n", "21 0.1850 54.631805 71.023480 19.712910 \n", "22 0.2050 55.600598 69.127620 19.671183 \n", "23 0.2250 56.523964 67.340505 19.611568 \n", "24 0.2450 57.400856 65.653463 19.544825 \n", "25 0.2650 58.231994 64.059634 19.476378 \n", "26 0.2850 59.018935 62.553221 19.408909 \n", "27 0.3050 59.763603 61.129097 19.343697 \n", "28 0.3250 60.468050 59.782595 19.281305 \n", "29 0.3450 61.134337 58.509396 19.221930 \n", "30 0.3850 62.394608 56.101532 19.109253 \n", "31 0.4050 62.956304 55.028550 19.058842 \n", "32 0.4450 64.018629 52.999246 18.963496 \n", "33 0.4850 64.965457 51.190576 18.878510 \n", "34 0.5250 65.809344 49.578549 18.802763 \n", "35 0.5650 66.561481 48.141786 18.735252 \n", "36 0.6050 67.231843 46.861232 18.675082 \n", "37 0.6850 68.426800 44.578576 18.567824 \n", "38 0.7250 68.894360 43.685424 18.525856 \n", "39 0.8050 69.727810 42.093333 18.451046 \n", "40 0.8850 70.380030 40.847436 18.392504 \n", "41 1.0450 71.400826 38.897470 18.300879 \n", "42 1.2050 71.977686 37.795528 18.249100 \n", "43 1.5250 72.629663 36.550094 18.190579 \n", "44 2.1650 72.799461 36.225739 18.175339 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n", "#dynamics.verbose_list = [\"substeps\", \"variable_steps\"] # Uncomment for debug data\n", "\n", "dynamics.single_compartment_react(time_step=0.01, stop_time=2.0, \n", " variable_steps=True, thresholds={\"low\": 0.25, \"high\": 0.64})\n", "\n", "df = dynamics.get_history()\n", "df" ] }, { "cell_type": "code", "execution_count": 7, "id": "12da63da-9b3b-4c43-a68b-7dfb6585b9d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From time 0 to 0.005, in 1 reduced step of 0.005 (1/2 of requested step)\n", "From time 0.005 to 0.0075, in 1 FULL step of 0.0025\n", "From time 0.0075 to 0.0125, in 1 FULL step of 0.005\n", "From time 0.0125 to 0.015, in 1 FULL step of 0.0025\n", "From time 0.015 to 0.04, in 5 FULL steps of 0.005\n", "From time 0.04 to 0.05, in 1 FULL step of 0.01\n", "From time 0.05 to 0.055, in 1 FULL step of 0.005\n", "From time 0.055 to 0.105, in 5 FULL steps of 0.01\n", "From time 0.105 to 0.125, in 1 FULL step of 0.02\n", "From time 0.125 to 0.135, in 1 FULL step of 0.01\n", "From time 0.135 to 0.155, in 1 FULL step of 0.02\n", "From time 0.155 to 0.165, in 1 FULL step of 0.01\n", "From time 0.165 to 0.345, in 9 FULL steps of 0.02\n", "From time 0.345 to 0.385, in 1 FULL step of 0.04\n", "From time 0.385 to 0.405, in 1 FULL step of 0.02\n", "From time 0.405 to 0.605, in 5 FULL steps of 0.04\n", "From time 0.605 to 0.685, in 1 FULL step of 0.08\n", "From time 0.685 to 0.725, in 1 FULL step of 0.04\n", "From time 0.725 to 0.885, in 2 FULL steps of 0.08\n", "From time 0.885 to 1.205, in 2 FULL steps of 0.16\n", "From time 1.205 to 1.525, in 1 FULL step of 0.32\n", "From time 1.525 to 2.165, in 1 FULL step of 0.64\n" ] } ], "source": [ "(transition_times, step_sizes) = dynamics.explain_time_advance(return_times=True)" ] }, { "cell_type": "code", "execution_count": 8, "id": "438e4ec0-44f7-4c0d-b6a6-4a435da6e683", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.005 , 0.0025, 0.005 , 0.0025, 0.005 , 0.01 , 0.005 , 0.01 ,\n", " 0.02 , 0.01 , 0.02 , 0.01 , 0.02 , 0.04 , 0.02 , 0.04 ,\n", " 0.08 , 0.04 , 0.08 , 0.16 , 0.32 , 0.64 ])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(step_sizes)" ] }, { "cell_type": "code", "execution_count": 9, "id": "74d500e5-0b59-419c-90ae-4948eb7c8611", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 0.005 , 0.0075, 0.0125, 0.015 , 0.04 , 0.05 , 0.055 ,\n", " 0.105 , 0.125 , 0.135 , 0.155 , 0.165 , 0.345 , 0.385 , 0.405 ,\n", " 0.605 , 0.685 , 0.725 , 0.885 , 1.205 , 1.525 , 2.165 ])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(transition_times) # 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"standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Changes in concentration for `2 S <-> U` and `S <-> X` (time steps shown in dashed lines)" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ -0.001411342894393742, 2.166411342894394 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -5.555555555555555, 105.55555555555556 ], "title": { "text": "concentration" }, "type": "linear" } } }, "image/png": 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ILY9fMWn8CcqouWo774c8CKD+oZFdW1L5zIir1cxLq9kzTr7fvXxnq3zVhzhO7kHs3os6ORdTMRYp0PUj/FbnovZz7cMno/Pd6rrkpC6kBQEtgbQS6OpF02yaHb8IctFnts2a3elsXkbQ7a4NMxP9+q7ML2j6bZ7MLr7a/H4IdLM2qut5nKw3s/ul5KVeo8uDXeFg9iVgtXbLaNRJX6/RWjrVXj0bP7haCULZ16Ab3TTa/Trg7LngTfWQy0rQ2qlHhFDXTnP2Ih7MbhbN+qGddhqlcRKTw65A5/U4XcPr1n6ej1/TjLbyU8tMddNqt04RQl2UMOc2GwWlUpn7MdVdPxVVz41fA/mhn1ruREzZFe5Opuoa+TdVXzFa3mYkxO1eb8zs1J4bqa5vdurR3lNp26r/TrIrip18d/H61HK1687N3td+pu8rdh8UO+lTdtegm10D7bLU+8vonlB/XXQi0O30B7N7VifnjUiBbuZzbZ/lLIy2wnQy2GD3eo50IGBFIK0EOm9sqqk/6gnKb7LVJ5l2R9DVi4p2CrL2RkcrruyWye0xGlFR8+tvcPiXqdG0OV4G37aEP3AwGr2wO7Lnh0DXtlE7oqKfqqiKCv10OJ5fG5HfrkC3W2+q0R7OjB9WgXqMbjhS3YSYfbEZBdxKFXTJSRAZP7imuqCY3Xzpb5yM+r3dGyOrC1qqz9XzwO66Mn052vPdKAo/j17ttmx9XarvnQpsI3+75ZXq+qTezPkhwPRLL7TXbDdr0Hl+o+ufdhmQU8ZmPNVyU12n+XlgNYpmx1/qtdrpyLfah53mMzvn9ddo7XewqPNBrd/qYXOqm2gnYsquQOc2qdd0vU+5f/iWZvqH5VqOaj1Gu4BouVl9b1jdExjduxid26n6hp1gkGp/1Npt9PDGrkDXnvdW9wRaP+ivSWbnoxNbjPq/kz5lV6Br26K/X+Lb42l9bTRbVLvsQuVm1EdVe9S+50Sgcxvt3Isa+V87Em1nGYxogW52/eNt4sxSfb8ZfTep/cKP2WR2vgeQJv0JpJ1AV12mHUlS39NfwJ2Iae0NOi+Pl6Xu5+lWoGu/KFUbeVl8b1+jp+ZGAUGM9hrVr52xM93QL4GuvUnWnk76G2P9mj6zfdD12+hZ+dGsXqM1+zy9lpndEXSeT78Wz2gfdKPLin5tF+djVK++H4rcB90uVyP7zUYH9YxT3VRqp7yLvvSmCqaj1mNXtKRagydS6Iluu5fyjM4P0cJLa5/+uq0GfNLOznAygm50/eH2820N+U2vaL/pz89U3z1efCJDXtVPqR446KfRirLZ6kGe/jy3sw+6NuK2E4HO22S0vpe/b+dBjP47j+czEi7ac8JqH3RehjaNdpq69r7A6Bps1Ba710U71wmnotjOPYHWB3p2qWZ48DxObdH3X78EOq9H327eLj59X//9aJRO3etee10z+u4z2trX6LpuNiCgj+Gj71P6eyH+OW/L9+79la04FaIFuupDo+9wo63XtD5P9UDaycMXUddAlBMfAmkr0KPsQn4B8fMmOMpsYDsIgAAIgEA8CTiZIhtPQsmtBi/0AhDwj4CdGSb+1Y6S050ABHqIHtZPpdY+PRU9shNiM1E1CIAACIAACAghYDS9V0jBaVgIBHoaOhVNkoIArkNSuCGtjYBAD9G9RtPC7GzPEqLJqBoEQAAEQAAEQiVgtO1pqAZJWjkEuqSOgVmRJqAuO7Gzlj7SDYXxoRKAQA8VPyoHARAAARAAARAAARAAARAAARAAgQQBCHT0BBAAARAAARAAARAAARAAARAAARCQgAAEugROgAkgAAIgAAIgAAIgAAIgAAIgAAIgAIGOPgACIAACIAACIAACIAACIAACIAACEhCAQJfACTABBEAABEAABEAABEAABEAABEAABCDQ0QdAAARAAARAAARAAARAAARAAARAQAICEOgSOAEmgAAIgAAIgAAIgAAIgAAIgAAIgAAEOvoACIAACIAACIAACIAACIAACIAACEhAAAJdAifABBAAARAAARAAARAAARAAARAAARCAQEcfAAEQAAEQAAEQAAEQAAEQAAEQAAEJCECgS+AEmAACIAACIAACIAACIAACIAACIAACEOjoAyAAAiAAAiAAAiAAAiAAAiAAAiAgAQEIdAmcABNAAARAAARAAARAAARAAARAAARAAAIdfQAEQAAEQAAEQAAEQAAEQAAEQAAEJCAAgS6BE2ACCIAACIAACIAACIAACIAACIAACECgow+AAAiAAAiAAAiAAAiAAAiAAAiAgAQEINAlcAJMAAEQAAEQAAEQAAEQAAEQAAEQAAEIdPQBEAABEAABEAABEAABEAABEAABEJCAAAS6BE6ACSAAAiAAAiAAAiAAAiAAAiAAAiAAgY4+AAIgAAIgAAIgAAIgAAIgAAIgAAISEIBAl8AJMAEEQAAEQAAEQAAEQAAEQAAEQAAEINDRB0AABEAABEAABEAABEAABEAABEBAAgIQ6BI4ASaAAAiAAAiAAAiAAAiAAAiAAAiAAAQ6+gAIgAAIgAAIgAAIgAAIgAAIgAAISEAAAl0CJ8AEEAABEAABEAABEAABEAABEAABEIBARx8AARAAARAAARAAARAAARAAARAAAQkIQKBL4ASYAAIgAAIgAAIgAAIgAAIgAAIgAAIQ6OgDIAACIAACIAACIAACIAACIAACICABAQh0CZwAE0AABEAABEAABEAABEAABEAABEAAAh19AARAAARAAARAAARAAARAAARAAAQkIACBLoETYAIIgAAIgAAIgAAIgAAIgAAIgAAIQKCjD4AACIAACIAACIAACIAACIAACICABAQg0CVwAkwAARAAARAAARAAARAAARAAARAAAQh09AEQAAEQAAEQAAEQAAEQAAEQAAEQkIAABLoEToAJIAACIAACIAACIAACIAACIAACIACBjj4AAiAAAiAAAiAAAiAAAiAAAiAAAhIQgECXwAkwAQRAAARAAARAAARAAARAAARAAAQg0NEHQAAEQAAEQAAEQAAEQAAEQAAEQEACAhDoEjgBJoAACIAACIAACIAACIAACIAACIAABDr6AAiAAAiAAAiAAAiAAAiAAAiAAAhIQAACXQInwAQQAAEQAAEQAAEQAAEQAAEQAAEQgEBHHwABEAABEAABEAABEAABEAABEAABCQhAoEvgBJgAAiAAAiAAAiAAAiAAAiAAAiAAAhDo6AMgAAIgAAIgAAIgAAIgAAIgAAIgIAEBCHQJnAATQAAEQAAEQAAEQAAEQAAEQAAEQAACHX0ABEAABEAABEAABEAABEAABEAABCQgAIEugRNgAgiAAAiAAAiAAAiAAAiAAAiAAAhAoAvoA3UNrVTX2CagJBQBAvITKMzLotycLKo52iK/sbAQBAQRKCvJo6ONrdTc2iGoRBQDAnITyMrMoH6lebT3cJPchsI6EBBIoLQoh9raO6m+KZ739YPKCgTSRFFuCUCguyWnyQeBLgAiiogMAQj0yLgKhgokAIEuECaKigQBCPRIuAlGCiYAgQ6BLrhLuSoOAt0VtuRMEOgCIKKIyBCAQI+Mq2CoQAIQ6AJhoqhIEIBAj4SbYKRgAhDoEOiCu5Sr4iDQXWE7lun++++nb3/3Tnpq3i8pP7+App1/Mf116WLKzcun0WPG0+5dO6i4uETJ0NLSTEW9SmjPru00asxJtGrlcpo2fQaV9StXPn/6l/9JlbNuoTcWVSmv/NhSvZE2rvuMLphxpfI3z8OPyVPPUl5ff6WKpkw9kwYOHqb8veDpx+i6WXNZ/XlUV3skqSx9Uz9YtlixZ8KkUw3L0qY/eGAfLXnnTbqqco7y9oa1q5W28fYaHQur5ie1TZtm985t9NHKFXTplZWGeZe8+xYNHDSERo+d0OPzluZmevG5eTT7ptsN8+r56BNVPfckXTKzkopLSnvk17PWJ9Cz1n5uxdrQWM2b3Pc3fecHhsmsWJuVbcXaLG8q1nYFuhlrKx5/fvMV5RypGDHKKmmPz/X92nEBLjPozxEnxVj1aydlOU3rhbXTurTpvfRrL/W6zStCoIfF2m2bReQzu7aJKB9l+EfArkCPY7/2jzpKDptAnAU6v17fe++9YbsA9TMCsRHoq9dWU+WtD1DVE/fQhLEjkpx/xZy7adOWncp7J1QMpj/Mf9D25xDoEOi8s0Cg97yeQqDb/46BQLfPKqyUEOjuyEOgu+MmQy4IdBm8ABuCJgCBDoEedJ8zqi8WAv2cmbfRoZo6pf16gf7NOx+hg4dqu0U5F+tlfUvo14/epaS3+hwCHQIdAt34UgaBbv8SD4Fun1VYKSHQ3ZGHQHfHTYZcEOgyeAE2BE0AAh0CPeg+F1uBzhueagSdi/fvz72WZl50tsJn0dvL6KfzXqKlix5X/rb6XBFniOIuQ1+GDQERsDvFPSBzUA0IBEJAhEAPxFBUAgKCCNgV6IKqQzEgIAWBOAt07gBEcZeiG8Z7iruRaNe+x12knxZvlAcCXY7ODCuCIQCBHgxn1CIXAQh0ufwBa/wnAIHuP2PUIB8BCPTgg8TpZy/L1ytSW2S2hNpLO2IxxZ0DshLj6rp0xwK9+QC17lpMTcclgrjhAIF0J5CTnUn8xq2ppT3dm4r2gUA3gYK8bGppa6d2tj8uDhCIA4GMjAwqzM+i+sZ47gcdBx+jjT0J5OVmUUdHJ7W2dcQST3FhjvB28+XCH65am1Ru397F3bOVwxDofMb03Q8/Qw/+8MbuWdRuGg6B7oaaJo9fAp2vQb/rhP+mn229i/ILe9FXL76Ulix+h/JYFPdxJ02gHdu3UUlpImJ4M4tAXsIiuu/YsY3GjZ9IH6xYShey9OXlxymfP/pfD9GNN3+bXn7pBeWVH5s2bqDP13xKl8/8mvL3X1kefpxx5jnK6++qfkOnn3U2DR06XPn7V794lG685VtK/bVHjiSVpUf4/nvvKPZMPnWqYVna9Pv27aU/vfU6fWP2Dcrbaz77VGkbb6/R8cKCZ5Papk2zfftW+mD5Mrq68uuGef/I6hkydBiNP2lij8+bm5vomSf/R4mcb3To+ejTPPPUr+iaa7/R7RPt53rW+rx61trPrVgbGqt5k/v+zn/9sWEyK9ZmZVuxNsubirVdgW7G2orHq4t+r5wjJ4wabZW0x+f6fu24AJcZ9OeIk2Ks+rWTspym9cLaaV3a9F76tZd63eYVIdDDYu22zSLymV3bRJSPMvwjYFegx7Ff+0cdJYdNIM4CnV+vRUdxH3/uHNKKcdW/XLQf168P/eTHN1MYAl1UP4NA90jSyRp0/kRlzfvzlRqN1qBrP1cE+siH6edbvk95+YX05Qv/CdusMW7YZs1Zh8U2a8m8vGzbg23WnPU9L6yd1ZScGtuseaEXnbwIEhcdX+kttTvFPaxrSHTJwnKZCcR5irvobda4CN9YvaN7pDyV31WBzj9XR9pTiXrtSLw28DfXa2dPnUDLVq7uDgw+9/rLaejgcmWkXD3UPEa6UD/Sz/PfdsNVSsBw/QwAVSdCoHs8m1MBtIrSbvU5F+h3nrebfrWkL+XndNB5Xz6Nlv5jD/ZBxz7ojnosBDoE+ovPzaPZN93uqN+ISBzWzTUEugjvyV8GBLr8PkplIQR6dH0Hy90TgEAXF8Wdj55ffuGZyii52aFud60KYp6WC+5RI4ak3FXr8WcX0rznX00aUOU7dqkCXP1cP5Wel82309brQv3DBP75z57+nVI//+x7N13dvU03tzdVOe57XnLOWKxB126zxpuvfyrjZR90Xl79/g2U+ekPqWDvK9RUfinVjv4PaityPjVXlFNRDgj4SQBB4vyki7JlJYAgcbJ6Bnb5RcCuQPerfpQLAmEQiLNA57xFRXFXBbCdNd5GU9x/9NBT9PmGrYZiWu0XXN9dc9l5yii3OoKuPgwwGpjlZfIRdr5Tl1HMMTu28rq5+H/5tfd6lKPGMxPRb2Mh0EWAMiuDR3Fv2ruSStb/G91p4YEAACAASURBVOUdep8aBs9mIv1B6sjt63fVKB8EAicAgR44clQoAQEIdAmcABMCJQCBHihuVCYJAQh0MVHcRQp0NaCbURdRR91TCXSt6E4lrDdv3aVMg1enrRvVox/s5Wl4ekxxl+TENTJD3WYt7wALurb+bso5upqOHn+nMpKOAwTSjQAEerp5FO2xQwAC3Q4lpEknAhDo6eRNtMUuAQh0MQKd83Yyxb2sb0n3dHaeVzuCrgp0KwHN16DrR9BFCHTejtMmj+22Tzu9HgLd7pkVcDq+Bp1HFH9q3i8pP7+ALjilgJZ/uIYKMupo9OiRtKVlHBWzSOn8aGlppqJeJbRn13YaNeYkWrVyOU2bPoPK+pUrn/O1epWzbqE3FlUpr/zYUr2RNq77jC6YkdjGjefhx+SpZymvr79SRVOmnkkDBw9T/l7w9GN03ay5bA18HtXVHkkqS49GH0xLX5Y2/cED+2jJO2/SVZVzlLet1o8iSJyzjog16Mm8vKyLRpA4Z33PC2tnNSWntrqGeCnbj7wiBHpYrP3gYbdMrEG3S0q+dHYFehz7tXzegkWiCMRZoAcdJI6L8FRR3I2muJtNQfcygs77TuWtDxhuuWb0cAACXdTZ5mM5eoE+7fyL6cPFv6Oi1i00sc962pR1GRUMmKxYAIGecMTundvoo5Ur6NIrKw09s+Tdt2jgoCE0euyEHp+3sK3qzIJp6R9g6Auoeu5JumRmJRWXJLa+0x76hyH6z80eYFg9DLHqghDoyYS83PBBoFv1NnGsndWUnBoC3Qu96OSFQI+Or/SWQqBH13ew3D0BCHRxQeK4F4y2WVNFrxpAzmoNOi9HjaSuHUXnIv60yeOUfcy9CHS+dpzbcKimtjvivBokjgeH04t33iZ+YIq7+/PM95xGAv2vSxdTYcc+mpyziLY2jaLc4RdTe/5QCPQub0Cg9+yWEOjiRCMEurPLnpeHIc5qgkAPi7UXP3nNC4HulWB4+SHQw2OPmsMjAIEuVqBrxbXWq9rRcDsCPVU52m2x3U5xV4O7aYOG8/pUG/mDgFf/tKLbfL7uXY0gjynu4Z2rljWra9D1CXt/dgsV7nyeOrN60cEpi6ilz5mWZSEBCMhOAGvQZfcQ7PODgIgp7n7YhTJBwC8CdgW6X/WjXBAIg0CcBTrnLSqKexi+S6c6EcVdgDdTCXTqbKM+n86mgj2vKCL98MRn2TZslwmoEUWAQHgEINDDY4+awyMAgR4ee9QcDgEI9HC4o9ZwCUCgiwsSF64no107BLoA/6UU6LxsJtJ7r/0uFW6fT5SRTUfG/CfVD5sroFYUAQLhEIBAD4c7ag2XAAR6uPxRe/AEINCDZ44awycAgQ6BHn4vZJKxkx0yGBJVG1KtQc/Ny6fRY8bT7l07qLhXEeXW/JU6D66i0qwDtDnjfBp5ygz6+0cfI4q7geMRJC4ZipdgWlbr/c3Ou1QB+ewKdLOAfFbnu5e1uliDbkU3+XMvrJ3VJK5fe6nXbV4RAj0s1m7bLCIf1qCLoBhOGXYFehz7dTgeQa1BEIizQBcdxT0If6VrHRDoHj1rS6B3bbPWceBv1K9xCW2v60NjKspo2e5R9OULZmKbNZ0PINDFCRkIdI8nuIPs+q0IHWQlq90JnJTlNG1YN9deHjw5baOI9BDo7ihCoLvjJkMuCHQZvAAbgiYAgS4+SFzQPkyH+iDQPXrRiUDn26z17thMe7d9RqcULKX3aq+gc8+bRqUV5ypWYB/0hDMg0CHQvYhGjKA7u6h5Ye2sJnH92ku9bvNCoLsjB4HujpsMuSDQZfACbAiaAAQ6BHrQfc6oPgh0AV4wXYNuUH7+gT9Sr+qfUu7hZdTcdxodrbiTmvtfIMASFAEC/hOwO8Xdf0tQAwgER0CEQA/OWtQEAt4J2BXo3mtCCSAgD4E4C3TuBURxl6MvQqAL8INTgc6rzK35kHp98VPK3/c6tRZPoKPH30mNA68VYA2KAAF/CUCg+8sXpctJAAJdTr/AKv8IQKD7xxYly0sAAh1B4mTonRDoArzgRqDzarPr1zOR/qiyV3p7/hBFpCPCuwCHoAhfCUCg+4oXhUtKAAJdUsfALN8IQKD7hhYFS0wAAh0CXYbuCYHu0QtO16AX9SqhPbu206gxJ9Gqlctp2vkX0sj9/6Fsw3b/xvvoxmnN9NKq/lQ5O7EV25bqjbRx3Wd0wYwrlb95Hn5MnnqW8vr6K1U0ZeqZNHDwMOXvBU8/RtfNmku5eXlUV3uE3lhURZWzbjFspX6trr4sbSZ9ACyrAE8Lq+YnRajXlmUVuAxr0JPdZcXarAtbsTbLiyjuzi4OCBLnjJeXfu2sJjGpRQj0sNb7iyHgrhSsQXfHTYZcdgV6HPu1DP6BDf4QiLNARxR3f/qUm1Ih0N1Q0+TxLNCnz1CiuPeq/m/62ZsddHvFz2n+7lvouutvZKPqgyHQdf6xinatf4Chd6/Z1l/6hyH6vGYPMKwehlh1M7ObWC9CBgLdiry4zyHQnbH00q+d1SQmNQS6O44Q6O64yZALAl0GL8CGoAlAoKd3kLjVa6up8tYHqOqJe2jC2BHd3SvV+0H3P7U+CHSP5EUJdG4Gv5H5zolV9EL1RXTbqP+l2hP/kz5vPh0j6BofQaA767AQ6M54eUkNge6MHgS6M15RTQ2BHlXPEUGgR9d3sNw9AQh0CHStcHffk7zlhED3xk/J7XYNulHVmc17qfeab1H+/reUj5v6X0xHxv1CGU3HAQIyEMAadBm8ABuCJiBiBD1om1EfCHghYFege6kDeUFANgJxFujcF+kexR0j6LKdcT7aI1KgczMzOpvZmvRfU9H2Zyj76FolynvD0BupfsgN7MNMH1uCokHAmgAEujUjpEg/AhDo6edTtMicAAQ6ekgcCUCgiw8St6VmC/GfoI+K3hXEf7QHBHrQXgixPtECXW1K7uG/UuGOX1Phrt8obzUMmcNE+o3UWjo5xNai6rgTgECPew+IZ/sh0OPp9zi3GgI9zt6Pb9sh0MUL9PuX3E/3vX9f4J3qvnPvo3unJU/Zh0AP3A3hVCh6DTqPuK6NvL518xra/I8/UuXA3yij6Yvrr6PWksk0Yfq3lQYjinuy3xEkLpkH1qAHd13AGnRnrLEG3RmvqKbGGvSoeg5r0KPrOVjuhUCcBbpfUdznfzyfFnyywItbXOWdffJsmjNpTlJeCHRXKKOXyW+BrkYWv+SMcjbl/de0cs1uBimDTj35BDbt/QZ65Z3Psc2apttAoEOg67cPDOqqAoHujDQEujNeUU0NgR5Vz0GgR9dzsNwLAQj09A4Sx/vG+HPn0IM/vJFmXnR2d1dZ9PYyuvvhZ2jN+/O9dB9heREkziPKoAQ63wc9o6OBVr/zJOXU/oPOK/49tZR+iZ7fciVNOucq7IPe5UcIdAh0CHRnF7Ww9jCGQHfmp6imhkCPqucg0KPrOVjuhQAEevoL9G/e+QhtrN5BSxc93t1Vzpl5G40aMYR+/ehdXrqPsLwQ6AJQ+rUGPZVpWY1bqfdnt1Deob8oSRoHXM22ZHsIkd4F+BJFWBPAGnRrRkiRfgSwBj39fIoWmRPAGnT0kDgSiLNA5/5O9yjuap/mIv3DVWu7u/hpk8dKI865URDoAq4+QQt01eSibfOoZMM9lNF+lDoz8+noiB/Q0ePvUH7HAQJ+EYBA94ssypWZAAS6zN6BbX4QgED3gyrKlJ0ABLr4IHGy+1xG+yDQBXglLIHOTeej6SUbfkwFe15RWtJWOFIZTW8qv0xAy1AECPQkAIGOXhFHAhDocfR6vNsMgR5v/8e19RDoEOgy9H0IdI9eCHINOjdVv8ZajeJekb+JStd+n3766ZV0+/E/p6zS0bRj6L/TK+9VE48Mb3To1+rqI8Jr8+gDYFmtH11YNZ+mTZ9BZf3Ke1RtFVl8ybtv0cBBQ2j02Ak98rY0N9OLz82j2TfdbtgmrEFPxmLF2qz7p2JtV6BXPfckXTKzkopLSh2fZV7WRWMNujPcXlg7qyk5tdU1xEvZfuQVIdDDYu0HD7tlYg26XVLypbMr0OPYr+XzFiwSRSDOAt2vKO6ifBOnciDQPXpbFoE+cPAwos42eu6pn9F3Rz5Lhe3bqKa1N83fPZe+XjmTWosn9mgpBHoyEjViPg/IZ3SYPcCoqz2StD2e025ldhPrRchAoDv1hPv0iOLujJ2Xfu2sJjGpIdDdcYRAd8dNhlwQ6DJ4ATYETQACPf2DxAXdp9zUB4Huhpomj1QCndm14OnH6BtXX0i9D75CLVvfpBc2fpkJ9meoceDX2M811Nz33G7rIdAh0M26P0bQnV0cINCd8YJAd8Yrqqkh0KPqOURxj67nYLkXAhDoEOhe+o+ovBDoAkiGuQbdzPycuk+oYNfLbH36y5TVtJM6csuZSL9aEep8izYcIOCGgN0p7m7KRh4QkJWAiBF0WdsGu0DAiIDdEXTQA4F0IhBngc79GJco7rL3WV8EOt9L7lBNnWHbZdkAXqRjZBXoahtzD69QRHrB7pcps7WG2guOV4R6AxPqbb3GiUSBsmJAAAI9Bk5GE3sQgEBHp4gbAQj0uHkc7eUEINARJE6GM0G4QL9izt1U1rdEqr3k/AYtu0BX25934M+KSOdiPaOjla1LH6/sod444BpqL6zwGxPKTxMCEOhp4kg0wxEBCHRHuJA4DQhAoKeBE9EExwQg0CHQHXcaHzIIF+jjz51DD/7wRpp50dk+mCtfkTKuQb9u1lzKzcujVIHL+JZsBXt/R0tWN1FpTg1NruhQhPrLHxXT5NPOJSXgnO5AFHciBIlLdAq7Ah1R3O1fr6x2J7BfkvOUYUVgxhp0576KYg6sQY+i1xI22xXoYV1DoksWlstMIM4CHVHc5emZEOgefRFFga42+W9/eob6NyymMwsWKW/N330rTZkyhcom/DMEukG/gECHQDe7XCBInLOLKQS6M15RTQ2BHlXPQaBH13Ow3AsBCHQEifPSf0TlFS7Q+RT36edModtuuEqUjVKXE2WBnoji3otO67+eijc/xCK+n0fTyt6nIf3y6Ojxd1LDoOuIMrIV/hhBxwi6eiJiBN34kgSB7uxSDYHujFdUU0OgR9VzEOjR9Rws90IAAj29Bfrjzy6kec+/SvqYaDx+2tlTJ9BPfnyzl+4jLK9wgb7o7WX003kv0dJFjwszUvaCorIG3YxjRkcTFe58gYq2PEbZDZuVpG2FI6l+6C3UMOxG6szMl90NsC8gAnYFekDmoBoQCIQA1qAHghmVSETA7hR3iUyGKSDgmUCcBTqHF4co7j966Cnae+Bwd7y0b975iNJvfv3oXZ77j6gChAt0vgbd7EAUd1Gu86eczNYDbH36Hyh/zyLKO/iuUklrySnUeNwV1MR+2opO9KdilBoZAhDokXEVDBVIAAJdIEwUFQkCEOiRcBOMFEwAAj0eQeL4iPn3516r9J67H36mx4i64G7luDjhAt2xBWmQIR1G0I3ckL/vNSre9CDl1H2qfNyRU0oNQ2+i+iE3sK3ahqeB59AENwQg0N1QQ56oE4BAj7oHYb9TAhDoTokhfToQgED3QaDXbyE6yn6CPnpVEBWxH4ODz/jmwrxv72K65rLzpFuaDYHusbNEfw16CU2YdKpCIVUQtLz9f6b6z5+ktzYMo1uGzVPWpf+t8xv0RdsUOvuSGwwJLqyaT9Omz6CyfuU9Pt+9cxt9tHIFXXplpWHeJe++RQMHDaHRYyf0+Nwq2vWqlcuVPJOnnmVYtllk8S3VG2njus/oghlXGuZFkLgEFrsCHVHc7V9crPq1/ZKcpwwrAjPWoDv3VRRzYA16FL2WsNmuQA/rGhJdsrBcZgJxFui+RXFffT/R6vuCd/sEVueE1Gvq+dT2g4dq6Q/zHwzeNosafRHo6lMJbd3puvVaHAQ69yMPgPWXP75Mc6Zsofx9b9HqvaW0tbGCLhp7mJr7X0xN/S9i099Hd7scAt3ZuW52E+tFyFg9DDGzMpVohEA3poYgcc76vJd+7awmMalFjKDHUcjEQaDXtx6lQ40HHXe09s422n10p+N8PMMulq+9o81x3kNNB6mhtd5WvoyMDCrMz6L6xjaqZ3l4XqNj6I4BVFNaR3XF9sq1VXnAiQ41HlDaiCM8Aoeb5PBBZgZRJ8PQyf+L2XFD7f+je+/1IUhc9XyiLxYET/P42UQj5hjWq8ZM4x/GYgRdjY5X9cQ9NGHsCAXK6rXVVHnrAzT3+sulm0LgtbfESaAveedNuqpyDmXXr6PNq96gPbt20JW9n1QQthWewIT6RdRUfjE19z2PINCd9SwI9GReXoRMYneCYzNDnHnCfWoIdGfsINCd8XKbmgvANhMhxz/nQjHVsa9hLzW3NaX83ErwFa/Moc/HJgKPOjl4nbxuN8eOuq1usikim4ttHM4IVFIlfcz+rWP/cIAACESXwH10nz8CXUIksVuDzhts9CSCC/eXX3svLaO7p+sadKvzKaOtlvIPvK2MqOfvf4v43zzauyLS+zGx3n8GdeSWWRWDzyNGwO4IesSaBXNBwJBAczsTivV7qU9xLtU3tVFLawfpBaBexB5pPkJ1LUe6y2tubzYUm3wElI+EpjrsjLBur3UnRuFucQSKcnpR3wLn33VZbLnYwF6DXRkyiOXLykxsg+rk6JtfRkU5RbayaEfQC1kenjddj74F/WxzSVcGYberJK83leSWhm0GFRfmUHt7JzU0O5+hErrxHg0oZT4YM2iAx1Lkz66P2q6P6i5DC4RPcedR3I2ms6vT3hHFXQa3i7cht2Z5t1DPPrpWqaCl99SEUC+fQa3FE8VXihJDIQCBHgr22FXKR33103/1o736EU8+RfWwZhqukTDWC9r9fISYiXD1ONJcQ7VMYKfLwQVgtomQ6194HOVlpd5GkwtPLkBTHVaCr5jdcJfmOb/pzsvOp3Jmm5tjSLG7IKZWbXVjS5Tz2F2DHuU2wnYQ0BOI8xp0ziLdt1njYvzVP62I3z7oGEGP98Uuq/ELNprORtXZiHregXcUGO15g9hoOhtVZyPr/JVFmYs3pIi3HgI94g60Yb61iD3CRGxNd0lcCHOhqx5tne2G4lo71Vq/3tDtOl4bzfGUhIvX8qLjKJOtx+1kixL5skS9AORCkgtK9eCCVDsSlJuVZyg2+QgoHwk1O6zE5tASd2LUExRkjgUBCPRYuBmN1BGAQPchijt6mWMCwkfQsQb9Yvrr0sWUm5dPo8eMp91snXZxcYnimJaWZmVt7J5d22nUmJOIRxzXRjrn65ArZ91CbyyqUl75oY8sro9Sro8svuDpx+i6WXNZ/XlUV3skqSx979Cv1TWLUq5fX2u1fnRh1f/SBacU0dC299jI+puU2bJfqb6p31dpc+aFtKK6gC75pzmGHRZR3JOxWLE2O+sRJM7xNdF1hqDWoDe2NVAD/2mpV14b2ahxQ1s91St/s5+u95XfW9nn2veUtCxvVx7++blHz6FPMj6lT9o+ZuIzuKg4k2gSVbB/i9g/7ZGZkUlc0OZm5VI+E8eJ3zV/Z7PfM/PYqC/7UX9n4pinV97jeZT3u/J3pVHKU9J15c9haVk5/G+lHP7Ky1XLYr9nZ+V0m4Ygce5OjTgEiXNHRv5cdgW6l5gh8lOAhXEjEGeB7lsU97h1IgHtFS7QuU2I4g6Brg0Sl9W0kwp3PEtF2/+XCfW9tIVFf3//8IX0tTMLqWHIbBb9/cSkrgyBDoHu5YZP1iBx6jpldUq1Oj2bB9lqbKqnwo+zad3oasX5+xv2UFNbs/K7ut7ZS9Ass+8KowBP+lFZ/TRoZYSYrVVTDy6M9dORhxQPS6q2f+EAJpDzut9r2lVPjQeP0oQzpyrvuV3HK+B70FYREOi2MPVIBIHujpsMuSDQZfACbAiaAAS6D1Hcg3ZiGtTni0BPAy62mxDHKO4cjtWorlEU94yOJjaa/hod/OxFWr51AM0eMl/hzNeqNw76BjUOuJo6ckoJAj25+1mxNuusGEG3fSpbJlSjYavroNVtjhJ/t1PNoUPUf0cf+vC4vyniWhXiPMCYdo2zUUX5lE93sH8Ps392jhImkHkwF35oxbMaOCo7M4sGFiWmTmuDO2kDIfG1yTxI1cfvr6AJJ51KFSNG2alaWBov/VqYEQ4KgkB3AEuTFALdHTcZckGgy+AF2BA0AQh0CPSg+5xRfRDoArwQ1yjurtGxLX3y2fr0vIPs58C7bNu29UpR7QXD2BZt57N16hdQc9n51Jld7LoKZPSPQFTXoKvrqtUR6WN/b1Ngba/reu2Kiq0KcattquySVgNQ8aBahSzoljoKra5X1gbT0o42q2uQvQTNsmsj0qUmIEKggy8IRImAXYEepTbBVhCwIhBngc7ZpHuQOCv/y/K5MIHOo7fzfc7nPf+qadsQxV0W18thhzKqvucVKtz1GybYF3cb1Z4/mJoGXEmN5TOppc+ZchgLKxQCQQt0Pjpd11yrRNaua+16ball21glfpT3Na/K+yxdHXu/luVT03lxX2F2ERXnlSiBvxKvJeyV/Z5bTMVs1kcJe48LbO1rrxyWTn2fpc9ha6BxRJcABHp0fQfL3RGAQHfHDbmiTQACHUHiZOjBwgS6DI0JywaMoIshn1irPp+J9Rcoq/HY3r5thSOpceDV1HTcTGzXJga1p1K8CnQ1Wjcfma5le0UfajygRPyuZcKar8dWfmcRwvnvItddq9tNqVO71engyvsZWTSATQlXpoZ3bUulTh3nEbzNtqHyBBOZI0MAAj0yroKhgghAoAsCiWIiRQACHQJdhg4rXKCn2gedR3d/+bX3aOmix2VotzAbsAadb5vW8zBag66msloX/Zd3XqMhJfU0qXQ15R1aQjm1q7orqC2ZRv/z6Xl00zWnU3OfL/eoWB/lXp+g6rkn6ZKZlVRc0nNfXn3EfH1esyj3VhHzrTqc2TpNL2t1rVib2dXS3EwvPjePZt90e1IyI4HORTcX1nzdNZ86zgOiZX/URtXDttO25q3dgtvudHF94DK+j7MqrPkrP9RAZkO7gpGpQcly2LOd7IJcmnTK6UnprHzg9fOgorh7tVOf30tAPi+2eOnXXup1m1eEQA+Ltds2i8iHNegiKIZThl2BHsd+HY5HUGsQBOIs0BHFPYgeZq+OwAS6Gtk93aa4Q6CLF+jaIHEZ7UeZSP8L5TKhnnfwL9R+ZD099sUddNfIh9l69WnU0vfLideuafAQ6MknvgiB/pVrZyqRxLno5gK7hm2Zt79hH22t2d4tyrlA1x886Nl89q+G/dMe6r7SfISar7/m6635umw+bZyLbx7I7NCqXTRi9FgaXDFcec/JIWsUdzcPQ5y0223asG6uIdDdeixa+SDQo+UvrbUQ6NH1HSx3TwACHUHi3PcecTkDE+g/eugpWrZyNUbQY7MP+vykPd61XdZKNKaK4p7ZeoQ69y6mBa9voO9P/D3l1K1Riu3MzGECnQv1L9PyHcOJr1+fPPUsw7MEI+gJLG0dbYq4Vke9ufje37hP+ZuPgPOR8CMNh+mG5m/aiiyujnDzgGZ8uy0+LXzQ2n5Ucmo5Hdd3cJcQT7xvZ7q4F9EIge7sC8ILa2c1JaeGQPdCLzp5IdCj4yu9pRDo0fUdLHdPAAIdAt197xGXU4hAN9r33MjEB394I8286Gxx1ktSEtagB++IzJaDx0bWDy+l7KNru8R6ARtVP4dFgZ+mjKy3lkwO3rgQazzaUkcHmNDmW3txwc2FNh/t3l+/RxkB177X0p7YZ9vsKGaj2uVFA6h/QTn1Z+Kavw4uGUgDSwZQr6x+1I/9zQU5fz87K8eqOHwOApElIGKKe2QbD8NjScCuQI8lHDQ6bQnEWaBzp8Ylijtfkq09TqgYTH+Y/6A0/VqIQNe2JtUadGla7IMhEOg+QHVQZGbLPmWtei6bCs+nwWc3bEyIdbZNGx9VV6fCtxZPdFCqXEmb2hoV0a2Ibya0EyJ8T0J8q8K7S5Q3tNZbGl/EtvlSxHWvhLjm080TIpyJ8cKE6FY+Nxjx9hokztI4JAABCQlAoEvoFJjkKwEIdF/xonBJCUCgp3+QuHNm3kZnT51AP/nxzd298Io5d6e3QJf0fPPVLAh0X/E6Kpxv25Z34M9UsHcR5e99jfgaduXIyFbEeuNxX2PR4C+jjtwyR+X6nfhQI4teXp+Ycs6nmO+p35X4na39Vt+za4Myot01zZyv7+ZB1PjffC238n7X73bL06eDQHdLDvmiTAACPcreg+1uCECgu6GGPFEnAIGe3gJ99dpqqrz1Aap64h6aMHaEtN1V+Ai6tC31yTAEifM3SJzebakii6vptEHiVLFeuPMFRbTzvx/bcgfNHvI8FfY/kZrKL2U/lxHfxo0ffkdx5/t1f3FkM21hP5sPb6BN7If/zt+7s/l7dB/7Z3RMoklUwf79pWBpYjo5E91cbCuCu2uKuSq+1cjmajlW6/3NTgsnUdyNyjFb7291OnpZF4016FZ0kz/3wtpZTcmpsQbdC73o5MUa9Oj4Sm+pXYEe1jUkumRhucwE4izQ/YrivmULu8dmP0EfFRVE/Ed/8BH0vr1LpBox19soXKCrTyZSOQFR3Etoz67tNGrMScTF5LTpM6isX7mCi58YlbNuoTcWVSmv/NCLRn2Ucv3WXwuefoyumzWXcvPyyGrrL72QMdtGTL+FlNXNtZdt1lIFieM8nAh0bR/kI+l8RH3BG5uZQJ9PfTJ3dX/MBToX6p83n0Frd7TSBTOuMuy+drZZu+y6rzPRXc3E93raUsOEeM3GblHOR8lTHVycv9DnRbZ12LCE+GZ7cvNtw5StxfYTNR9qoPOmX+r42gaB7hiZ6wzYZs0ZOqtriLPS/E8tYgQ9jkIGAt3/vulXDRDofpFFuTITgEAXHyTu/vuJ7rsveK/zOu9N0Rz9GvS5119Ot91gfP8fvOVs4m8nO0RWOW6mkgAAIABJREFUrM7rP23yOPrpvJe6o7bzuf3Tz5kiVeNFtBsj6PKOoBv5l4/qXnrFldSv5SPK3/8a5e3/M2U17VSSrjs6hj6un0pXnNxIzf2mK1Pi2wuGdxejCvSsPnm0g01D31a3RZmOzn/fd2gvTdw7jn6e8XNqa2817FplbPuwoSzK+ZCS4TSspELZx5tHPedT0N//7at003d+YJjPi5CBQBdxltsrAwLdHic1lZd+7awmMakh0N1xhEB3x02GXBDoMngBNgRNAAJdvECfP59owYKgPUk0ezbRnDnW9T7+7EKa9/yrJFMwc+ECXQ0SN3L4IPrWj37WLdB5pHetYLfGFZ0UWIMeHV8ZWZpbs5JNgX+bCva9Stl1nyclqckZRBuyh9AHbaX0bkM7fX4ksUa8ub3JsNGleb2PiW8uxIs1QpyNjvPgbFE/sAY96h6E/W4IiBDobupFHhAIi4BdgR6WfagXBPwgEGeBznnGJYq7vu/wAeZrLjtPmoFk3wQ6306Ni3V1Sru6FVu6TXHnDoZA9+MS6X+ZNU2HlSBs6ig4HxHPrvuMypuraWT7Ppqc10Yn5x2z43AH0Uqmyz9uzaOtWQPocP5o6lUyWhkFH1aaEOJDew2n4rwS/40PsQYI9BDho+rQCECgh4YeFYdEAAI9JPCoNlQCEOjpHSSO69H/rXoraf25jBpVuEDnU9nHjR6uhK7X/v6jh56iZStXd4+oh3r2Ca4cAl0wUMHF8bXf6w+toXUHP2drwrsCs9VUKwHazI7hvQbRpWUD6JxeeTQhs56Gt++mola2ILzraC8YRi2lp1Ir+1FeS6ZQZ1Z6X9h40yHQBXdQFBcJAhDokXATjBRIAAJdIEwUFRkCEOjpfx/L9emmLYnlreoh2wCycIGuPwO1i/BlD2nv5uqBNejyrEGvbz1K7/zlVTrQsJ++KN2qCHIuzLXB2e6gO2g++1fD/vGDR0U/sWw8VZSOoOPbKqjocAGd+pVpdHzvEZSXlZ/UJd5Y+BydfmIBVeR+TrlH/k45R/5GmW21Spqa1t40f9dNdPNZ+7tFu5N9183WaXpZq4s16G7Oand5sAbdGTcv/dpZTWJSixDoCBInxhcoJRgCdgV6HPt1MB5ALWEQiLNA9yuKexh+jHqdvgv0qAOysh8CPRyBXvnNWxQBvmrPSvrH3r/RmgOfKH+fy/7x4332Tz240D6hz2j2cyJN2jaeBpxeQSMHjlFEuXZNuNNt1rLrNzCh/jcm1P9Ojfs+p9+sm0K3V/xcqbYzM5eNrE9JGmFvLzg+ZXeCQE9G4+WGD9usWV21xLF2VlNyagh0L/SikxdB4qLjK72lEOjR9R0sd08AAl18kDj33ohvTuECXQ0Sx9egx+GAQPdfoLd1tLGR8M/pk72raN2+1TTg8770SMYjxN/XH18rvJr6sb3B+4w+ThHkqjBX05ntze1UoGvrrqutIT7CfsN52V2j63+n7IZN3Uk6csuUKfAtvb+UmBJfcirx99QDAl2caIRAd3bl9fIwxFlNEOhhsfbiJ695IdC9EgwvPwR6eOxRc3gEINAh0MPrfcdqhkAX4AWsQRcAsauIxrYGJsbX0vqDbM04E+Ub2Kg4HxnfU39s33KeNDsrh07sM5bGsOnpJ5aNoxP7Jn54oDYZjszWGsqp+Vv3CHtu7d8ps+VAt2l8ND2xfv3YKDsfdY/CgTXoUfASbBRNQMQUd9E2oTwQ8JOAXYHupw0oGwSCJhBngc5ZxzWKe9D9zKo+4QI9Xfc7NwMJgW7VzYw/b+9opw1MjK9j68TXK+vF2Q973Vr7RY8Mo/uOJf4zhgtxLsrZ7yPZtPWoHBntRynv0FK2ndufKe/ge5Rdvz7J9Pb8wdTC9l1v6X0G+5lKTtavB80AAj1o4qhPBgIQ6DJ4ATYESQACPUjaqEsWAhDo6R8kTpa+ZmaHcIG+em110v7nUYDg1UYIdHsEeQR1JXAbGx1PjJJ/ThsPr+uReThbGz5GHRXnr324KB9HmRmZ9iqKQKqspp1MqC9mgv0dJtwXs9H1g0lWd+SUUisT6i2lTLD3OZNae41PmhIfZhMh0MOkj7rDIgCBHhZ51BsWAQj0sMij3jAJQKBDoIfZ/9S6hQt0bdR2owbKFsbeqxOwBt14DfpLv32Kik/qR9Wt1YlRckWUf66sG69g/3gwNx5NfWCvwYoYH81EuCLK2ej4wU920pAhFTR67IQe7mlpbqYXn5tHs2+63dB1q1YuV96fPPUsw8/9W4N+hN5YVEWVs25x1aX4Os07ruirRIbPPbyCrV8/tgXcx7WTaGtjBV06/G+J0fVSto69eKKypp0LebMDUdxducNVJkRxd4YNQeKc8YpqaqxBj6rniOwK9DjGVoiuV2G5FYE4C3REcbfqHcF9LlygB2e6HDVBoF9M+xr2JAS4Mk09IcbP2PclWti5kPawf+pRlt9PEeAn502igYfKaeL5pytryItzS5KcueTdt2jgoCGxE+g3fecH3Rwym/ey9esrKe/wUlq7aRftPJRBV5T/X49O31Z0IhPsk6ml+BTltbVkItuLvVd3Ogj04K4TEOjOWEOgO+MV1dQQ6FH1HAR6dD0Hy70QgEBHkDgv/UdUXuECPVUU98efXUgvv/YeLV30uCjbpSgnbgL9KzOvUNaMr13zMdXsO0DLClYof9c0HU7yx7cyvk3ryjbQoOOGsrXjx0bH+xX0JyvRCIGe3LW5kNmzYzNNP7WMcurWUvbRzynnKH9dQ5mth5ISd+T0prZe49iU+HHK69b6IfTh2sN0yVXXOz5fUs1WsDvF3Wy2gpUxXkZkEMXdim7y515YO6upZ7/evWsHTTvfeBaOl7L9yCtiintYrP3gYbdMCHS7pORLhxF0+XwCi/wnAIEOge5/L7OuITCBvujtZXT3w89Quk1x54jTdQ16fetRzfT0xJpxPjrOR8y1B99nnAdt46Pj3dPV+42jQb2GWPdApHBNgI+y5zCxns3EeuI18ZPZVpss2nPL2ZT48Uywj1XWsicE/FjqzE6euWDXELsC3W55SAcCUSAgQqBHoZ2wEQRUAnYFOoiBQDoRiLNA535EFHc5enNgAv1HDz1Fy1aulm4EXX1woHeH9kECj0y/actOJckJFYPpD/MfTEqeDgK9rb2V1h9OTE/XrhnfXrs1qa0ZlEEnMvHNA7epa8a5OK8oHSlHj465FTz4XEKwr1Fe1dH2jPb6JDLtBUMTgr2oS7hzAV88jjoz8iwJQqBbIkKCNCQAgZ6GTkWTTAlAoKODxJEABDqCxMnQ74UI9FQiV9/AB394I8286GwZ2t1tA7f9p/NeSvng4Jt3PkIHD9V2i3Iu1sv6ltCvH72ru4woCnQePV1dM67sOc5Gx3mUdf0xoveoxKg4n6aubHHGfx8rlQ9hjDmBrMat3SPsianxiZH2jI6WpIxthSOPTY9nYl0ZbS9ivs7ISEoHgY4eF0cCEOhx9Hq82wyBHm//x7X1EOgQ6DL0fSECXduQVGvQZWiskQ1WAv2cmbfR9+de2/1gQZ8+CmvQz5h5Yff0dO2+4xd2Xkg17N8H7B8/bsmaS/v7HaIBLEAbHyXnAdxO7DOWcrJySR8AyyrA08Kq+TRt+gwq61feAzvWoPfsiWbrNK1Ym51bqVhnth6hnJqVlFv7EeXU/oP9rCI++q49mjry6bEt36M7Tvv7sZF2Jtpzy1ifyC+lmqPJAl9vB9ag27/qWe1OYL8k5ynDWhftpV87b6X3HCIEelisvbfefQlYg+6eXdg57Qr0OPbrsH2D+v0jEGeBjiju/vUrpyULF+hODQg7vdHovzq9ne/pXnnrA1T1xD00YewIxVT9e7IJ9PlP/ZyGnT+WNtato+o9G2nQF/3oF/Q4NbU3JqEuLzyOLsu6nEpKetOg0ccro+Q7VmykL512Dg0cPKyHWyDQiV5/pYqmTD3TkE9drfdt1rRR3LUO8CJkrB6GaOvhe7HzyPE5R1Ypgr3j0Cf0+IZZdNfIh3v0h47iMdRSMIqNuLMo8jyCPIsm39ZrNBttz+5OC4Fu/+oGgW6fVVgpIdDdkYdAd8dNhlwQ6DJ4ATYETQACHUHigu5zRvXFXqDroWintNsV6CuG/5XO2nEWFRUV0cUzLqcli9+hvLx8GnfSBNqxfRuVlCb2qm5me3iXFJfQjh3baNz4ifTBiqV04cWXUnn5ccrnj/7XQ3Tjzd+ml196QXnlx6aNG+jzNZ/S5TO/pvz9V5aHH2eceQ7trd9Dr/7+/6h5cAt90vwJrd73CV1+4DL6OfvXxP71Zv/msH+/zPwljes3nk4ZMIUmHTeZvjToNOXvZUveV+yZfOpUpczfVf2GTj/rbBo6dHiPvrJv317601uv0zdm36B8tuazT5W2fZXZb3S8sODZpLZp02zfvpU+WL6Mrq78umHeP7J6hgwdRuNPmtjj8+bmJnrmyf+hb3/3TsO8Wj5GCZ556ld0zbXf6PaJNo2etT6/GZ/aI0eS/GZonMmb3Pd3/uuPDVNYsTary4q1Wd4E61/S7ddOoMy69ZTJA9DVfEJZR9cTtR3tmZWJ846S8dRRfCK1s+3enng3k65hszeKB/b0oxWfVxf9XjlHThjFRL/D4/333knq1w6zu06uP0ecFGTVr52U5TStF9ZO69Km99KvvdTrNm9BXja1tLVTe3un2yIoLNauDRaQ0ezaJqB4FOEjgQy2vKkwP4vqG9tMa4ljv/YRO4oOmUBebhZ1dHRSa1tHyJYEXz2/Xt97LwR68OQNbqk72SHaED4t/FBNnbHYeH++6OqElqeKcj6KbkegX//K9fTCpy8oNuRn59P04y+kK0ZfRZeecDkV5R7bj9qtkVyEVx/ezAK3racvaqqpumaT8vfmms1U32IgklhFXHxPKD+ZTukS4/x3bhsOEBBBICc7k7IbNlPrwdVMuK+jLDbirgh49kOdBjdyXLgXjaSOXiewn5HUWTgi8XdR4m8cIBAFAiIEehTaCRtBQCVgV6CDGAikE4E4C3Tux+LCnHRyZ2TbInwE3SiIWpTo6LeDM1qDrt0ubkftDvrNxy/RwvULaeWuFUlNVaKc9x1PeUwc980vo6KcIirOLaXSvFL2ey/qW1CmpD/SfIRqm2uoraOdttZW0xdHNtMWJsC3HKkmvtVZqqOElXM8i55+Qp8TaUL/U+jk8sk0vv9EpWwcIOAXgZRB4pg4V7Z7q1/P9mtfzQLRrafsxs2UVV/NAtI1GZvDxLsSnI6J9nYm2tsKRrAfHqxuNLUX9JzJ4VebUC4IWBEQMcXdqg58DgIyEbA7xV0mm2ELCHglEOcp7pwdtlnz2oPE5Bcu0KMWJI4L8KWLHu+mqX/A4CSK++6jO+n1Ta/Qm9WLeoh1t+5SRTjfxqyi90gayaKqc1HO/1YFvtuykQ8E3BBwGsU9o/0oZTVuY2J9C2U1sJ+mrWwEnr2y6PJZ7D39vu2qTR25ZdSeP5wJ9uHUXlihCHb+05Y/jP09nDozC92Yjzwg4IoABLorbMgUYQIQ6BF2Hkx3TQACHVHcXXcegRljL9C1e5xzrqdNHpu0hRp/z2wf9FRB4rJzc6hgUDHt2rmNMgvYGi42Et7R2k51mUfZ6GIW7SrcQ4MOHUer+3xOHYWdTGz3o3FrR9KRic3UZ2MRnXLJ2YoIr919iDau+4wumHGl4vZVK5crr5OnnqW86gOXLXj6Mbpu1lzKzcsjq8BlHyxbTEW9SmjCpFMNy9L2MwSJS/8gcfrrSqrAZXYFup0gcZmtNYpI52JdEfBdvy/6fCidXLSSxhZ+Yni568jtnxDu/IeJeC7Y2wq4iB9Gy1Zto6LiPt39WuD10rQo/TnipF4EiXNCK5y0IgR6HKNdI0hcOP1VRK12BXoc+7UIvihDTgJxFuiI4i5PnxQu0LmYnX7OFLrthqvkaaWPlsgWxR0CPfkBht71ZqJxS/XGpIch+rzpHsU9DIGe6tTkN3yjTxxNI8szKIdNmc9ia96zm6rZtPkNbAr9Zsps2ZvyrP7jgUupuCibJg9r7hp1ZyPu+YO7R+M78hJBGUUfEOjOiHrZncBZTWJSQ6C74wiB7o6bDLkg0GXwAmwImgAEOoLEBd3njOoTLtCt9hWXodEibYBAv9gQJ/ZBd9bLgt4H3Y51QYygmwn0UWNOoooRowyT8H3cFdHO1rhzwZ7dsFFZ857F/v7zrjOoNKeGTu/9gWHezsz8hGBXps4z8Z43TBmN72DvqaPydvjo00CgO6MGge6MV1RTQ6BH1XNEEOjR9R0sd08AAh0C3X3vEZdTuEDna9DNDnWPcXFNCL+kuoZWqrPYhiR8K2EBCIghYHeKu5janJfC93NPTJlna9zZene+/j2raWdizTv7m4t7q0NZ664V7UzEK2K+S9hzkY8jXgREjKDHixhaG3UCdgV61NsJ+0FASyDOAp1zQJA4Oc4H4QJdjmYFawUEerC8UVu4BGQX6FZ0uEDPZII9WxHvqoDn4p2918AEvMn0ebXsjtzjWOR5vv49MXWer31PBLBjI/EsuJ1f0+it2obP/SMAge4fW5QsJwEIdDn9Aqv8JQCBjiBx/vYwe6VDoNvjZJoKAl0ARBQRGQJRF+hWoPmWcInRdjbq3sBG35t5BHom3LmAVwT9VqsilM+5eO/MKaX2nH6KeO/IYcI9p68i3vlnHdm9FTGP7eRs4Qw9EQR66C6AAQETgEAPGDiqk4IABDoEugwd0ReBro16/uAPb6SZF51NfOq7UYR0GSB4sQFr0LEGnfcfq4j5Vn0Ma9CTCXmJCqzfncCKvZvP1Sn03aKdifgD+w/Rm5uOp5tHvMBG4Q86KrYhaxA9vnEO3XHqMmUtfEd2qSLo+ch8R26/xO9dwp7Y3vEiDy+svdiBNehe6EUnL9agR8dXekvtCvSwriHRJQvLZSYQZ4GOKO7y9EzhAl27jzjfY/z7c69VBPrjzy6kl197L2nPcXkwuLcEAh0CHQLd+Pyxs81aqjPPyw1fEALdyG5tkLiMtlrKajnA1rsfZGKdvSq/s59m9b39ic/Y31ns/eaWFnrsizvorpEPm1+MMrKOCXa21Zwi3hUBz37y2N/KKxuxz2G/s9eO7DKijEzTMr2wdn/lJIJA90IvOnkh0KPjKwj06PoKlosjAIGOIHHiepP7koQLdD5SXvXEPTRh7AjSCnQe3f3uh5+hdAsSB4EOgQ6BDoHOCXiJ4t52dDf99rdVdNPXpihr4PkIvBLsrmUnE/Hsdybm+ZR7/l5G+1HbV3wezO7YlHo2Ks/EfHs+H5Vn0+2Vn+PojeXbaPSoEVRx/MhA185DoNt2Y6QTQqBH130YQY+u72C5ewIQ6BDo7nuPuJzCBToX5f/zk+/1EOjpOoKuiDNEcRfXI1GS9ATSfQ267A7ga+Qzm/eytfFMyHeN0PNI9ZntLPgdF/Z87bzyWY2tgHf69nawdfOdfH08XzOf3UsR8Z1ZfGu6YWw0Piuxtp79zd/vyOKfJ8R+J/s9nQ+sQU9n76JtRgTsCnTQA4F0IhBngc79iCjucvRm4QL9Rw89RctWrlamsqsj6COHD6LKWx+gyy88k37y45vlaLlAKyDQBcJEUdITgECX3kVJBvKR9wy+bzybSs/XznNRr0zB50K+ZU/iMybo+ci807XzPcR9t5hnIr5rv/nOzLyEyM8uSoh+NorfmVWkrKlXRvgjIu4h0KPV72GtdwIQ6N4ZooToEYBAR5A4GXqtcIHOG6VOZ9c2cO71l9NtN1wlQ5uF2wCBLhwpCpSYAAS6xM4RYJo6jV4R7Xy0ngv89mYm4tlrZ7Myeq+KeWWUvo2N3Hel9VQ9C37HR+f5oYp3LuS5oFfeUz5ja/BZAD0eHV95j0XHV17Z2nv+AIAH0FPL8GSLQWYIdNFEUZ7sBCDQZfcQ7PODAAQ6BLof/cppmb4IdKdGRDk91qBjDTrvv4ji3vMsjnOQOKfXtJbmZnrxuXk0+6bbnWb1nF5kkDg+Kk+dbcp2dMRFPR+hb69PTL1nU/CV0Xr2Hv9sza582lZfTlf0/73yIED0oU7N58KdC3h+8Aj5nUzEc4HfkaWKfDZ1n3/WNbLPP1dFPl+/z0f5+SFCoItkLZqXX+VhDbpfZP0v165Aj2O/9p8+agiLQJwFOqK4h9XretYrXKB/885H6MNVa3sEg8M2a0QtLc1U1KuE9uzaTqPGnESrVi6nadNnUFm/csUz/MSonHULvbGoSnnlx5bqjbRx3Wd0wYwrlb95Hn5MnnqW8vr6K1U0ZeqZNHBw4iZzwdOP0XWz5lJuXp6laNRHu9aXpe0u+gBYVgGeFlbNT2qbtqzdO7fRRytX0KVXVhqeCUvefYsGDhpCo8dO6PG5lZDR89EXYCYa9az1ec34QKD3dCUEuv0LvVW/tl+S85Rh3VxrryEZHS1s2v1RZWSev2by1/a6hLhvS7xmdL0m/c3TavN1JPLz9KIOPkWfj+Rn5JQwUc/EPltrz9fbd/L1+V2vRn93ZBUro/qd2eyVzwRgr3/8819o1NiTqWLEKFHmSV8OBLr0LkppIAR6dH0Hy90TgEBHkDj3vUdcTuECna87v+ay83pMZ0/XIHEYQccIOj8dIdAh0L1EcY+7QBf3lXasJHU0X5mCz6bi84OvwedHYh0+F/Htylp85bOukX0+ms+n9yvvdc0IEGXfS7sr6eTij2lM8aYeU/GV4HuaPe7VGQBq3dpRf/6euqa/+3OWl88Q0B5tXUsA1Pe0MwJEtcmqHAh0K0Lyfg6BLq9vYJl/BCDQIdD96132SxYu0PlI+YM/vFHZ+1x7pOs2a7yNWINuv8MhZfQJYA169H2IFjgjkMmm5vctaKD6pjbqqP1Cydy95V0HE/lsfX7iPb5uvzkp4J4SpI9N+89oY+v1WTkyHfoAfdrlAIqdWXnUnjsgyWQlmr/maM9jDwUys7rfUZcKqG905LAdAVjcgFSHspxA82BCJj5xt8WuQI87J7Q/vQjEWaBzTyKKuxz9WbhAj9sIOncjBLocnRlWBEMAAj0YzqhFLgIi1qB3t4gJdnXkXn1PFfLq34rYbz+2Np+v3+ezAdRDmf7PZgIc+5sF9GN5tIcSC0BziJ4R4LeH+NIB/hAhpbjXxAjokcYiYCDfUYAvPUhZdkHygwhtOku7dLMhkvLynQuY3VE4INCj4CXYKJoABDqCxInuU27KEy7Q+VT2ec+/SlVP3KPshc6P1WurlW3W0jWSOwS6m66HPFElAIEeVc/Bbi8EhAp0L4YIzNs9C6CrTO1yAOWtrkB/3VVqZguo72W1sNkD7P1jfyeCAHb/zbb3SywnMD7UZQcCmxXpoqy2HdQvW9A2tpPNVOAzFlId6k4IRp8r2yHqHhxkZmRQr8Jsqq1vTcykYDMqDPP6uHuCrM7szGYzQ7p2k5DVRtjljgAEOgS6u54jNpdwgc7NM9pmzWjau9imhFMa1qBjDTrveViD3vP8Q5A4+9ckrEG3zyqslCIEelgB+cJixuv1sgZd3c4vpbg3295P/3BBV8ixOAQpHhw0bUuJjcc0yNDMZtAn1M+G0H7OZ0XoZzqE6R8RdXfHVui1TkRxKENSAnFZjpKZwWJ8MB90kH/bdkrqYnr4w6/QvfdiDboM/vFFoMvQsKBsgECHQIdANz7bINDtX4Ug0O2zCislBLo78l4Eursao50r0+ShA49loF8akST+NQERjShkNaZ+6KBfMsHzswF0ys/JosYWFmehK4iiUbkLN4ynk0s+oxNLU5cfba/0tF7GmBLpxhjtCZ7A/Rvvg0APHrthjRDoHh0BgQ6BDoEOgc4JIIq7s4up1VaNzkrzPzUEujvGEOjuuMmQy+4a9DjODJHBP0HbELUYFm75FBfmUHt7JzU2sR09unb5cFtW1PL98rcfQKBL4jRfBDoPFHeops6wiWveny9J08WZgTXo4liiJPkJYA26/D6CheIJiBDo4q1CiSDgHwG7At0/C1AyCARPAGvQsQY9+F7Xs0bhAv2KOXdTWd8S+vWjd8nQvkBsgEAPBDMqkYQABLokjoAZgRKAQA8UNyqTgAAEugROgAmBE4BAh0APvNMZVChcoKfaB12GxvplAwS6X2RRrowEINBl9Aps8psABLrfhFG+bAQg0GXzCOwJggAEOgR6EP3Mqg4IdCtCFp9jDTrWoPMugijuPU8UBImzf3FBkDj7rMJKKUKgx3GtLtagh9VjvddrV6DHsV97p4sSZCUQZ4HOr9eI4i5HzxQu0PkU9+nnTKHbbrhKjhb6bAUEOgQ6BLrxSQaBbv/iA4Fun1VYKSHQ3ZGHQHfHTYZcEOgyeAE2BE0AAh3brAXd54zqEy7Q+R7oP533Ei1d9LgM7fPdBgh0CHQIdAh0TgBR3J1dbhHF3RmvqKaGQI+q54gg0KPrO1jungAEOgS6+94jLqdwgc7XoJsdiOIuznkoCQTCIIA16GFQR51hExAxgh52G1A/CDghYFegOykTaUFAdgJxFujcN4PKsAZdhj4qXKDL0KigbUCQuKCJo74wCUCgh0kfdYdFAAI9LPKoNywCEOhhkUe9YRKAQIdAD7P/qXVDoAvwAgS6AIgoIjIEINAj4yoYKpAABLpAmCgqEgQg0CPhJhgpmAAEOgS64C7lqjhfBDpfh373w88kGfTgD2+kmRed7cpImTNhDTrWoPP+iSjuPc9SBImzf+VCkDj7rMJKKUKgxzHaNdagh9VjvddrV6DHsV97p4sSZCUQZ4GOKO7y9ErhAv3xZxfSvOdfpaon7qEJY0coLV29tpoqb32A5l5/edpFd4dAh0CHQDe+oEGg27/QQ6DbZxVWSgh0d+Qh0N1xkyEXBLoMXoANQROAQEeQuKD7nFF9wgX6OTNvo2suO6+HEOfC/eXX3ku76O4Q6BDoEOgQ6JwAorg7+0pDFHdnvKKaGgI9qp5DFPfoeg6WeyEAgQ6B7qX/iMorXKD8IuLeAAAgAElEQVTzKO5G09nVae+I4i7KdSgHBMIhgDXo4XBHreESEDGCHm4LUDsIOCNgdwTdWalIDQJyE4izQOeeQRR3OfqncIEetxF0ZfS0oZXqGtvk8CisAAGfCUCg+wwYxUtJAAJdSrfAKB8JQKD7CBdFS0sAAh1B4mTonMIFetzWoEOgy9CNYUOQBCDQg6SNumQhAIEuiydgR1AEINCDIo16ZCIAgQ6BLkN/FC7QeaMQxX0x5ebl0+gx42n3rh1UXFyi+LqlpZmKepXQnl3badSYk2jVyuU0bfoMKutXrnzO1+pVzrqF3lhUpbzyY0v1Rtq47jO6YMaVyt88Dz8mTz1LeX39lSqaMvVMGjh4mPL3gqcfo+tmzWX151lGFv9g2WLFngmTTjUsS9tB9etrrdaPLqyan9Q2bVm7d26jj1auoEuvrDQ8B5a8+xYNHDSERo+d0ONzq2Baej76AswCl+lZ6/PqWWs/RxT3nq5EkDjD7m34plW/tl+S85RhRWC2uoY4b4m/OUQI9LBY+0vGvHSsQQ+Tvre67Qr0OPZrb2SRW2YCcRboiOIuT8/0RaDL0zz/LUGQOASJ470MAh0CHUHinF1vIdCd8Ypqagj0qHoOQeKi6zlY7oUABDqCxHnpP6LyChfo37zzEfpw1VrSB4PjweNOmzyWfv3oXaJsl6IcCHQIdAh041MRI+j2L1EYQbfPKqyUGEF3Rx4C3R03GXJhBF0GL8CGoAlAoEOgB93njOoTLtARJE4Gt8IGEPCPANag+8cWJctLQIRAl7d1sAwEehKwK9DBDgTSiUCcBTr3I6K4y9GbhQt0bLMmh2NhBQj4RQAC3S+yKFdmAhDoMnsHtvlBAALdD6ooU3YCEOgIEidDHxUu0DGCLoNbYQMI+EcAAt0/tihZXgIQ6PL6Bpb5QwAC3R+uKFVuAhDoEOgy9FDhAj1u26xhDTrWoPMTGUHiel7OsAbd/iUea9DtsworpQiBHsdo11iDHlaP9V6vXYEex37tnS5KkJVAnAU6orjL0yuFC3TeNGyzhm3WsM2as5Pc7CbWS7Rrqy3tzKxMJRrtjqBDoNvvAxDo9lmFlRIC3R15CHR33GTIBYEugxdgQ9AEINARJC7oPmdUny8CXYaGBWUDRtAxgo4RdOOzDQLd/lUIAt0+q7BSQqC7Iw+B7o6bDLkg0GXwAmwImgAEOgR60H0OAt0n4nUNrVTX2OZT6eKL3bcvg/azn0MHiXbvylB+9u/PIP7+oYMZSRU2N5OS1uro27eTCousUvX8vLS0k0pKXeYr6XSckdvIbU11DBzUSdnZjotV2m5WrlmJQ4c5b4dzC8XlsDuCLq5GlAQC4RMQIdDDbwUsAAH7BOwKdPslIiUIyE8gzgKdewdR3OXooxhBF+AHmQT6Jx9nKiJ7/z6iHdsTopsLbPV1+zZrsS0ACYoIgEDfsk4qcvFQJCuLiD+IcHOUl7M6CzMoMzODWto6bBdRVNTp+gHGgIHEHpo4t5c/+OEPgJwenvgc10l5eU5rRPooEIBAj4KXYKNIAhDoImmirKgQgEBHkDgZ+ioEugAvhCXQuRB/b3Emrf4kk7gwX7M6k+rrrRtUwkRLeTlRfya2+OgtF139+rNXJi7479qDiw2ezuo4dCiDGmzUrS/nyJEMqj1iVXrPz3m+ulrn+err2SwBZmuqg88maHMxGYK33axcM0vx0MS5H5HDPwJuZ5H4Z1F4JZewWTrqDJ+c7Axqb++kDuvLYXgG+1xz1Gb7+ImDz5jiDx7T+cjIyKDC/Cyqj9AMwXT2B9oWDIG83CzqYBf6VgeDEMFY5n8tZ5zVQf90OUYZ/CdtXQMEujUj0xRBr0Ff/KcVtGljBr311rm08oNMmjNnAb3//jTasqVCsfPHP36Eli6/jY2Q5tGAATXUt/fzVD7oW4rwHjI0IcLVEb4Pli2mol4lNGHSqUre11+poilTz6SBg4f1aPPBA/toyTtv0lWVc5TPrAKXIUics47lJkgcf0Bj9UDm0IFttGnDcpp65nXdBrW3J5Y2WB3t7U20Yc08Gjvxju6kfCZGZ3smZWdlUkOT+ZOMusP/Q0WlX2ej7aXMzgw6fMiqxmOfF+T9nn05TqS29tG0ezd/aGJtr5p7QPmfqaW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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_step_sizes(show_intervals=True)" ] }, { "cell_type": "code", "execution_count": 14, "id": "3d012f8e-4066-40b6-9b9a-d1e9dd7532c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 S <-> U\n", "Final concentrations: [U] = 72.8 ; [S] = 18.18\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 4.0054\n", " Formula used: [U] / [S]\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.1349 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n", "S <-> X\n", "Final concentrations: [X] = 36.23 ; [S] = 18.18\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 1.99313\n", " Formula used: [X] / [S]\n", "2. Ratio of forward/reverse reaction rates: 2.0\n", "Discrepancy between the two values: 0.3437 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.is_in_equilibrium()" ] }, { "cell_type": "code", "execution_count": 15, "id": "9dd856c0-58e6-4048-8b03-90f68e725232", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reaction: 2 S <-> U\n" ] }, { "data": { "text/html": [ "
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TIMEDelta UDelta XDelta Sreactionsubsteptime_subdivisiondelta_timecaption
00.0000-1.0000000.02.0000000010.0100
10.0000-0.5000000.01.0000000010.0050
20.0050-0.1975000.00.3950000010.0025
30.0075-0.3491750.00.6983500010.0050
40.0125-0.1316930.00.2633850010.0025
50.0150-0.2259930.00.4519870010.0050
60.0200-0.1560400.00.3120800010.0050
70.0250-0.0951570.00.1903150010.0050
80.0300-0.0422370.00.0844730010.0050
90.03500.0036970.0-0.0073940010.0050
100.04000.0869990.0-0.1739970010.0100
110.05000.1123450.0-0.2246900010.0050
120.05500.2746600.0-0.5493210010.0100
130.06500.3603670.0-0.7207350010.0100
140.07500.4211210.0-0.8422420010.0100
150.08500.4630920.0-0.9261850010.0100
160.09500.4909520.0-0.9819050010.0100
170.10501.0164670.0-2.0329340010.0200
180.12500.5269840.0-1.0539670010.0100
190.13501.0491750.0-2.0983500010.0200
200.15500.5141330.0-1.0282660010.0100
210.16501.0096080.0-2.0192150010.0200
220.18500.9687930.0-1.9375870010.0200
230.20500.9233650.0-1.8467310010.0200
240.22500.8768920.0-1.7537850010.0200
250.24500.8311380.0-1.6622760010.0200
260.26500.7869410.0-1.5738820010.0200
270.28500.7446680.0-1.4893360010.0200
280.30500.7044470.0-1.4088950010.0200
290.32500.6662870.0-1.3325730010.0200
300.34501.2602710.0-2.5205410010.0400
310.38500.5616960.0-1.1233920010.0200
320.40501.0623250.0-2.1246500010.0400
330.44500.9468280.0-1.8936570010.0400
340.48500.8438870.0-1.6877730010.0400
350.52500.7521370.0-1.5042740010.0400
360.56500.6703620.0-1.3407250010.0400
370.60501.1949570.0-2.3899150010.0800
380.68500.4675600.0-0.9351190010.0400
390.72500.8334500.0-1.6669010010.0800
400.80500.6522200.0-1.3044400010.0800
410.88501.0207950.0-2.0415910010.1600
421.04500.5768600.0-1.1537210010.1600
431.20500.6519780.0-1.3039550010.3200
441.52500.1697980.0-0.3395950010.6400
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" ], "text/plain": [ " TIME Delta U Delta X Delta S reaction substep time_subdivision \\\n", "0 0.0000 -1.000000 0.0 2.000000 0 0 1 \n", "1 0.0000 -0.500000 0.0 1.000000 0 0 1 \n", "2 0.0050 -0.197500 0.0 0.395000 0 0 1 \n", "3 0.0075 -0.349175 0.0 0.698350 0 0 1 \n", "4 0.0125 -0.131693 0.0 0.263385 0 0 1 \n", "5 0.0150 -0.225993 0.0 0.451987 0 0 1 \n", "6 0.0200 -0.156040 0.0 0.312080 0 0 1 \n", "7 0.0250 -0.095157 0.0 0.190315 0 0 1 \n", "8 0.0300 -0.042237 0.0 0.084473 0 0 1 \n", "9 0.0350 0.003697 0.0 -0.007394 0 0 1 \n", "10 0.0400 0.086999 0.0 -0.173997 0 0 1 \n", "11 0.0500 0.112345 0.0 -0.224690 0 0 1 \n", "12 0.0550 0.274660 0.0 -0.549321 0 0 1 \n", "13 0.0650 0.360367 0.0 -0.720735 0 0 1 \n", "14 0.0750 0.421121 0.0 -0.842242 0 0 1 \n", "15 0.0850 0.463092 0.0 -0.926185 0 0 1 \n", "16 0.0950 0.490952 0.0 -0.981905 0 0 1 \n", "17 0.1050 1.016467 0.0 -2.032934 0 0 1 \n", "18 0.1250 0.526984 0.0 -1.053967 0 0 1 \n", "19 0.1350 1.049175 0.0 -2.098350 0 0 1 \n", "20 0.1550 0.514133 0.0 -1.028266 0 0 1 \n", "21 0.1650 1.009608 0.0 -2.019215 0 0 1 \n", "22 0.1850 0.968793 0.0 -1.937587 0 0 1 \n", "23 0.2050 0.923365 0.0 -1.846731 0 0 1 \n", "24 0.2250 0.876892 0.0 -1.753785 0 0 1 \n", "25 0.2450 0.831138 0.0 -1.662276 0 0 1 \n", "26 0.2650 0.786941 0.0 -1.573882 0 0 1 \n", "27 0.2850 0.744668 0.0 -1.489336 0 0 1 \n", "28 0.3050 0.704447 0.0 -1.408895 0 0 1 \n", "29 0.3250 0.666287 0.0 -1.332573 0 0 1 \n", "30 0.3450 1.260271 0.0 -2.520541 0 0 1 \n", "31 0.3850 0.561696 0.0 -1.123392 0 0 1 \n", "32 0.4050 1.062325 0.0 -2.124650 0 0 1 \n", "33 0.4450 0.946828 0.0 -1.893657 0 0 1 \n", "34 0.4850 0.843887 0.0 -1.687773 0 0 1 \n", "35 0.5250 0.752137 0.0 -1.504274 0 0 1 \n", "36 0.5650 0.670362 0.0 -1.340725 0 0 1 \n", "37 0.6050 1.194957 0.0 -2.389915 0 0 1 \n", "38 0.6850 0.467560 0.0 -0.935119 0 0 1 \n", "39 0.7250 0.833450 0.0 -1.666901 0 0 1 \n", "40 0.8050 0.652220 0.0 -1.304440 0 0 1 \n", "41 0.8850 1.020795 0.0 -2.041591 0 0 1 \n", "42 1.0450 0.576860 0.0 -1.153721 0 0 1 \n", "43 1.2050 0.651978 0.0 -1.303955 0 0 1 \n", "44 1.5250 0.169798 0.0 -0.339595 0 0 1 \n", "\n", " delta_time caption \n", "0 0.0100 \n", "1 0.0050 \n", "2 0.0025 \n", "3 0.0050 \n", "4 0.0025 \n", "5 0.0050 \n", "6 0.0050 \n", "7 0.0050 \n", "8 0.0050 \n", "9 0.0050 \n", "10 0.0100 \n", "11 0.0050 \n", "12 0.0100 \n", "13 0.0100 \n", "14 0.0100 \n", "15 0.0100 \n", "16 0.0100 \n", "17 0.0200 \n", "18 0.0100 \n", "19 0.0200 \n", "20 0.0100 \n", "21 0.0200 \n", "22 0.0200 \n", "23 0.0200 \n", "24 0.0200 \n", "25 0.0200 \n", "26 0.0200 \n", "27 0.0200 \n", "28 0.0200 \n", "29 0.0200 \n", "30 0.0400 \n", "31 0.0200 \n", "32 0.0400 \n", "33 0.0400 \n", "34 0.0400 \n", "35 0.0400 \n", "36 0.0400 \n", "37 0.0800 \n", "38 0.0400 \n", "39 0.0800 \n", "40 0.0800 \n", "41 0.1600 \n", "42 0.1600 \n", "43 0.3200 \n", "44 0.6400 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_rxn_data(rxn_index=0)" ] }, { "cell_type": "code", "execution_count": 16, "id": "5ff51045-dfa3-4f04-94f4-5d66f1352d4a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reaction: S <-> X\n" ] }, { "data": { "text/html": [ "
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TIMEDelta UDelta XDelta Sreactionsubsteptime_subdivisiondelta_timecaption
00.00000.0-3.0000003.0000001010.0100
10.00000.0-1.5000001.5000001010.0050
20.00500.0-0.7012500.7012501010.0025
30.00750.0-1.3590941.3590941010.0050
40.01250.0-0.6384920.6384921010.0025
50.01500.0-1.2403501.2403501010.0050
60.02000.0-1.1709751.1709751010.0050
70.02500.0-1.1089191.1089191010.0050
80.03000.0-1.0533081.0533081010.0050
90.03500.0-1.0033751.0033751010.0050
100.04000.0-1.9168901.9168901010.0100
110.05000.0-0.8774050.8774051010.0050
120.05500.0-1.6893241.6893241010.0100
130.06500.0-1.5702441.5702441010.0100
140.07500.0-1.4721671.4721671010.0100
150.08500.0-1.3902061.3902061010.0100
160.09500.0-1.3206591.3206591010.0100
170.10500.0-2.5214272.5214271010.0200
180.12500.0-1.1557611.1557611010.0100
190.13500.0-2.2299612.2299611010.0200
200.15500.0-1.0401851.0401851010.0100
210.16500.0-2.0165292.0165291010.0200
220.18500.0-1.8958601.8958601010.0200
230.20500.0-1.7871151.7871151010.0200
240.22500.0-1.6870421.6870421010.0200
250.24500.0-1.5938291.5938291010.0200
260.26500.0-1.5064131.5064131010.0200
270.28500.0-1.4241241.4241241010.0200
280.30500.0-1.3465021.3465021010.0200
290.32500.0-1.2731991.2731991010.0200
300.34500.0-2.4078642.4078641010.0400
310.38500.0-1.0729821.0729821010.0200
320.40500.0-2.0293042.0293041010.0400
330.44500.0-1.8086711.8086711010.0400
340.48500.0-1.6120271.6120271010.0400
350.52500.0-1.4367631.4367631010.0400
360.56500.0-1.2805541.2805541010.0400
370.60500.0-2.2826572.2826571010.0800
380.68500.0-0.8931510.8931511010.0400
390.72500.0-1.5920911.5920911010.0800
400.80500.0-1.2458981.2458981010.0800
410.88500.0-1.9499651.9499651010.1600
421.04500.0-1.1019421.1019421010.1600
431.20500.0-1.2454341.2454341010.3200
441.52500.0-0.3243540.3243541010.6400
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" ], "text/plain": [ " TIME Delta U Delta X Delta S reaction substep time_subdivision \\\n", "0 0.0000 0.0 -3.000000 3.000000 1 0 1 \n", "1 0.0000 0.0 -1.500000 1.500000 1 0 1 \n", "2 0.0050 0.0 -0.701250 0.701250 1 0 1 \n", "3 0.0075 0.0 -1.359094 1.359094 1 0 1 \n", "4 0.0125 0.0 -0.638492 0.638492 1 0 1 \n", "5 0.0150 0.0 -1.240350 1.240350 1 0 1 \n", "6 0.0200 0.0 -1.170975 1.170975 1 0 1 \n", "7 0.0250 0.0 -1.108919 1.108919 1 0 1 \n", "8 0.0300 0.0 -1.053308 1.053308 1 0 1 \n", "9 0.0350 0.0 -1.003375 1.003375 1 0 1 \n", "10 0.0400 0.0 -1.916890 1.916890 1 0 1 \n", "11 0.0500 0.0 -0.877405 0.877405 1 0 1 \n", "12 0.0550 0.0 -1.689324 1.689324 1 0 1 \n", "13 0.0650 0.0 -1.570244 1.570244 1 0 1 \n", "14 0.0750 0.0 -1.472167 1.472167 1 0 1 \n", "15 0.0850 0.0 -1.390206 1.390206 1 0 1 \n", "16 0.0950 0.0 -1.320659 1.320659 1 0 1 \n", "17 0.1050 0.0 -2.521427 2.521427 1 0 1 \n", "18 0.1250 0.0 -1.155761 1.155761 1 0 1 \n", "19 0.1350 0.0 -2.229961 2.229961 1 0 1 \n", "20 0.1550 0.0 -1.040185 1.040185 1 0 1 \n", "21 0.1650 0.0 -2.016529 2.016529 1 0 1 \n", "22 0.1850 0.0 -1.895860 1.895860 1 0 1 \n", "23 0.2050 0.0 -1.787115 1.787115 1 0 1 \n", "24 0.2250 0.0 -1.687042 1.687042 1 0 1 \n", "25 0.2450 0.0 -1.593829 1.593829 1 0 1 \n", "26 0.2650 0.0 -1.506413 1.506413 1 0 1 \n", "27 0.2850 0.0 -1.424124 1.424124 1 0 1 \n", "28 0.3050 0.0 -1.346502 1.346502 1 0 1 \n", "29 0.3250 0.0 -1.273199 1.273199 1 0 1 \n", "30 0.3450 0.0 -2.407864 2.407864 1 0 1 \n", "31 0.3850 0.0 -1.072982 1.072982 1 0 1 \n", "32 0.4050 0.0 -2.029304 2.029304 1 0 1 \n", "33 0.4450 0.0 -1.808671 1.808671 1 0 1 \n", "34 0.4850 0.0 -1.612027 1.612027 1 0 1 \n", "35 0.5250 0.0 -1.436763 1.436763 1 0 1 \n", "36 0.5650 0.0 -1.280554 1.280554 1 0 1 \n", "37 0.6050 0.0 -2.282657 2.282657 1 0 1 \n", "38 0.6850 0.0 -0.893151 0.893151 1 0 1 \n", "39 0.7250 0.0 -1.592091 1.592091 1 0 1 \n", "40 0.8050 0.0 -1.245898 1.245898 1 0 1 \n", "41 0.8850 0.0 -1.949965 1.949965 1 0 1 \n", "42 1.0450 0.0 -1.101942 1.101942 1 0 1 \n", "43 1.2050 0.0 -1.245434 1.245434 1 0 1 \n", "44 1.5250 0.0 -0.324354 0.324354 1 0 1 \n", "\n", " delta_time caption \n", "0 0.0100 \n", "1 0.0050 \n", "2 0.0025 \n", "3 0.0050 \n", "4 0.0025 \n", "5 0.0050 \n", "6 0.0050 \n", "7 0.0050 \n", "8 0.0050 \n", "9 0.0050 \n", "10 0.0100 \n", "11 0.0050 \n", "12 0.0100 \n", "13 0.0100 \n", "14 0.0100 \n", "15 0.0100 \n", "16 0.0100 \n", "17 0.0200 \n", "18 0.0100 \n", "19 0.0200 \n", "20 0.0100 \n", "21 0.0200 \n", "22 0.0200 \n", "23 0.0200 \n", "24 0.0200 \n", "25 0.0200 \n", "26 0.0200 \n", "27 0.0200 \n", "28 0.0200 \n", "29 0.0200 \n", "30 0.0400 \n", "31 0.0200 \n", "32 0.0400 \n", "33 0.0400 \n", "34 0.0400 \n", "35 0.0400 \n", "36 0.0400 \n", "37 0.0800 \n", "38 0.0400 \n", "39 0.0800 \n", "40 0.0800 \n", "41 0.1600 \n", "42 0.1600 \n", "43 0.3200 \n", "44 0.6400 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_rxn_data(rxn_index=1)" ] }, { "cell_type": "code", "execution_count": 17, "id": "03eec482-0b4a-4a15-ba33-1788f63fc60f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TIMEUXSis_primaryprimary_timestepn_substepssubstep_numbercaption
00.000050.000000100.0000000.000000True0.010010
10.005049.50000098.5000002.500000True0.010010
20.007549.30250097.7987503.596250True0.002510
30.012548.95332596.4396565.653694True0.005010
40.015048.82163295.8011646.555571True0.002510
50.020048.59563994.5608148.247909True0.005010
60.025048.43959993.3898399.730964True0.005010
70.030048.34444192.28092011.030197True0.005010
80.035048.30220591.22761212.167978True0.005010
90.040048.30590290.22423813.163959True0.005010
100.050048.39290188.30734814.906851True0.010010
110.055048.50524687.42994315.559566True0.005010
120.065048.77990685.74061916.699569True0.010010
130.075049.14027384.17037417.549079True0.010010
140.085049.56139482.69820818.179004True0.010010
150.095050.02448781.30800218.643025True0.010010
160.105050.51543979.98734318.981779True0.010010
170.125051.53190677.46591619.470272True0.020010
180.135052.05889076.31015519.572066True0.010010
190.155053.10806474.08019419.703677True0.020010
200.165053.62219773.04000919.715597True0.010010
210.185054.63180571.02348019.712910True0.020010
220.205055.60059869.12762019.671183True0.020010
230.225056.52396467.34050519.611568True0.020010
240.245057.40085665.65346319.544825True0.020010
250.265058.23199464.05963419.476378True0.020010
260.285059.01893562.55322119.408909True0.020010
270.305059.76360361.12909719.343697True0.020010
280.325060.46805059.78259519.281305True0.020010
290.345061.13433758.50939619.221930True0.020010
300.385062.39460856.10153219.109253True0.040010
310.405062.95630455.02855019.058842True0.020010
320.445064.01862952.99924618.963496True0.040010
330.485064.96545751.19057618.878510True0.040010
340.525065.80934449.57854918.802763True0.040010
350.565066.56148148.14178618.735252True0.040010
360.605067.23184346.86123218.675082True0.040010
370.685068.42680044.57857618.567824True0.080010
380.725068.89436043.68542418.525856True0.040010
390.805069.72781042.09333318.451046True0.080010
400.885070.38003040.84743618.392504True0.080010
411.045071.40082638.89747018.300879True0.160010
421.205071.97768637.79552818.249100True0.160010
431.525072.62966336.55009418.190579True0.320010
442.165072.79946136.22573918.175339True0.640010
\n", "
" ], "text/plain": [ " TIME U X S is_primary primary_timestep \\\n", "0 0.0000 50.000000 100.000000 0.000000 True 0.0100 \n", "1 0.0050 49.500000 98.500000 2.500000 True 0.0100 \n", "2 0.0075 49.302500 97.798750 3.596250 True 0.0025 \n", "3 0.0125 48.953325 96.439656 5.653694 True 0.0050 \n", "4 0.0150 48.821632 95.801164 6.555571 True 0.0025 \n", "5 0.0200 48.595639 94.560814 8.247909 True 0.0050 \n", "6 0.0250 48.439599 93.389839 9.730964 True 0.0050 \n", "7 0.0300 48.344441 92.280920 11.030197 True 0.0050 \n", "8 0.0350 48.302205 91.227612 12.167978 True 0.0050 \n", "9 0.0400 48.305902 90.224238 13.163959 True 0.0050 \n", "10 0.0500 48.392901 88.307348 14.906851 True 0.0100 \n", "11 0.0550 48.505246 87.429943 15.559566 True 0.0050 \n", "12 0.0650 48.779906 85.740619 16.699569 True 0.0100 \n", "13 0.0750 49.140273 84.170374 17.549079 True 0.0100 \n", "14 0.0850 49.561394 82.698208 18.179004 True 0.0100 \n", "15 0.0950 50.024487 81.308002 18.643025 True 0.0100 \n", "16 0.1050 50.515439 79.987343 18.981779 True 0.0100 \n", "17 0.1250 51.531906 77.465916 19.470272 True 0.0200 \n", "18 0.1350 52.058890 76.310155 19.572066 True 0.0100 \n", "19 0.1550 53.108064 74.080194 19.703677 True 0.0200 \n", "20 0.1650 53.622197 73.040009 19.715597 True 0.0100 \n", "21 0.1850 54.631805 71.023480 19.712910 True 0.0200 \n", "22 0.2050 55.600598 69.127620 19.671183 True 0.0200 \n", "23 0.2250 56.523964 67.340505 19.611568 True 0.0200 \n", "24 0.2450 57.400856 65.653463 19.544825 True 0.0200 \n", "25 0.2650 58.231994 64.059634 19.476378 True 0.0200 \n", "26 0.2850 59.018935 62.553221 19.408909 True 0.0200 \n", "27 0.3050 59.763603 61.129097 19.343697 True 0.0200 \n", "28 0.3250 60.468050 59.782595 19.281305 True 0.0200 \n", "29 0.3450 61.134337 58.509396 19.221930 True 0.0200 \n", "30 0.3850 62.394608 56.101532 19.109253 True 0.0400 \n", "31 0.4050 62.956304 55.028550 19.058842 True 0.0200 \n", "32 0.4450 64.018629 52.999246 18.963496 True 0.0400 \n", "33 0.4850 64.965457 51.190576 18.878510 True 0.0400 \n", "34 0.5250 65.809344 49.578549 18.802763 True 0.0400 \n", "35 0.5650 66.561481 48.141786 18.735252 True 0.0400 \n", "36 0.6050 67.231843 46.861232 18.675082 True 0.0400 \n", "37 0.6850 68.426800 44.578576 18.567824 True 0.0800 \n", "38 0.7250 68.894360 43.685424 18.525856 True 0.0400 \n", "39 0.8050 69.727810 42.093333 18.451046 True 0.0800 \n", "40 0.8850 70.380030 40.847436 18.392504 True 0.0800 \n", "41 1.0450 71.400826 38.897470 18.300879 True 0.1600 \n", "42 1.2050 71.977686 37.795528 18.249100 True 0.1600 \n", "43 1.5250 72.629663 36.550094 18.190579 True 0.3200 \n", "44 2.1650 72.799461 36.225739 18.175339 True 0.6400 \n", "\n", " n_substeps substep_number caption \n", "0 1 0 \n", "1 1 0 \n", "2 1 0 \n", "3 1 0 \n", "4 1 0 \n", "5 1 0 \n", "6 1 0 \n", "7 1 0 \n", "8 1 0 \n", "9 1 0 \n", "10 1 0 \n", "11 1 0 \n", "12 1 0 \n", "13 1 0 \n", "14 1 0 \n", "15 1 0 \n", "16 1 0 \n", "17 1 0 \n", "18 1 0 \n", "19 1 0 \n", "20 1 0 \n", "21 1 0 \n", "22 1 0 \n", "23 1 0 \n", "24 1 0 \n", "25 1 0 \n", "26 1 0 \n", "27 1 0 \n", "28 1 0 \n", "29 1 0 \n", "30 1 0 \n", "31 1 0 \n", "32 1 0 \n", "33 1 0 \n", "34 1 0 \n", "35 1 0 \n", "36 1 0 \n", "37 1 0 \n", "38 1 0 \n", "39 1 0 \n", "40 1 0 \n", "41 1 0 \n", "42 1 0 \n", "43 1 0 \n", "44 1 0 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_conc_data()" ] }, { "cell_type": "code", "execution_count": 18, "id": "703eae06-0fbe-42be-a5d1-562b5b8c3772", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TIMEDelta UDelta XDelta SL2actioncaptionstep_factor
00.0000-1.000000-3.0000005.0000003.888889ABORT0.5
10.0000-0.500000-1.5000002.5000000.972222OK0.5
20.0050-0.197500-0.7012501.0962500.192502OK2.0
30.0075-0.349175-1.3590942.0574440.689126OK0.5
40.0125-0.131693-0.6384920.9018780.137600OK2.0
50.0150-0.225993-1.2403501.6923370.494839OK1.0
60.0200-0.156040-1.1709751.4830550.399443OK1.0
70.0250-0.095157-1.1089191.2992340.325196OK1.0
80.0300-0.042237-1.0533081.1377810.267310OK1.0
90.03500.003697-1.0033750.9959810.222084OK2.0
100.04000.086999-1.9168901.7428920.746634OK0.5
110.05000.112345-0.8774050.6527150.134277OK2.0
120.05500.274660-1.6893241.1400040.469874OK1.0
130.06500.360367-1.5702440.8495100.368578OK1.0
140.07500.421121-1.4721670.6299250.304602OK1.0
150.08500.463092-1.3902060.4640210.262494OK1.0
160.09500.490952-1.3206590.3387540.233325OK2.0
170.10501.016467-2.5214270.4884930.847714OK0.5
180.12500.526984-1.1557610.1017940.180429OK2.0
190.13501.049175-2.2299610.1316120.676758OK0.5
200.15500.514133-1.0401850.0119190.149607OK2.0
210.16501.009608-2.016529-0.0026860.565078OK1.0
220.18500.968793-1.895860-0.0417270.503843OK1.0
230.20500.923365-1.787115-0.0596150.449993OK1.0
240.22500.876892-1.687042-0.0667420.402167OK1.0
250.24500.831138-1.593829-0.0684470.359529OK1.0
260.26500.786941-1.506413-0.0674690.321456OK1.0
270.28500.744668-1.424124-0.0652120.287435OK1.0
280.30500.704447-1.346502-0.0623930.257023OK1.0
290.32500.666287-1.273199-0.0593740.229833OK2.0
300.34501.260271-2.407864-0.1126770.822088OK0.5
310.38500.561696-1.072982-0.0504110.163259OK2.0
320.40501.062325-2.029304-0.0953470.583967OK1.0
330.44500.946828-1.808671-0.0849860.463888OK1.0
340.48500.843887-1.612027-0.0757460.368501OK1.0
350.52500.752137-1.436763-0.0675110.292728OK1.0
360.56500.670362-1.280554-0.0601710.232536OK2.0
370.60501.194957-2.282657-0.1072580.738883OK0.5
380.68500.467560-0.893151-0.0419680.113121OK2.0
390.72500.833450-1.592091-0.0748100.359443OK1.0
400.80500.652220-1.245898-0.0585430.220120OK2.0
410.88501.020795-1.949965-0.0916250.539198OK1.0
421.04500.576860-1.101942-0.0517780.172192OK2.0
431.20500.651978-1.245434-0.0585210.219956OK2.0
441.52500.169798-0.324354-0.0152410.014919OK2.0
\n", "
" ], "text/plain": [ " TIME Delta U Delta X Delta S L2 action caption step_factor\n", "0 0.0000 -1.000000 -3.000000 5.000000 3.888889 ABORT 0.5\n", "1 0.0000 -0.500000 -1.500000 2.500000 0.972222 OK 0.5\n", "2 0.0050 -0.197500 -0.701250 1.096250 0.192502 OK 2.0\n", "3 0.0075 -0.349175 -1.359094 2.057444 0.689126 OK 0.5\n", "4 0.0125 -0.131693 -0.638492 0.901878 0.137600 OK 2.0\n", "5 0.0150 -0.225993 -1.240350 1.692337 0.494839 OK 1.0\n", "6 0.0200 -0.156040 -1.170975 1.483055 0.399443 OK 1.0\n", "7 0.0250 -0.095157 -1.108919 1.299234 0.325196 OK 1.0\n", "8 0.0300 -0.042237 -1.053308 1.137781 0.267310 OK 1.0\n", "9 0.0350 0.003697 -1.003375 0.995981 0.222084 OK 2.0\n", "10 0.0400 0.086999 -1.916890 1.742892 0.746634 OK 0.5\n", "11 0.0500 0.112345 -0.877405 0.652715 0.134277 OK 2.0\n", "12 0.0550 0.274660 -1.689324 1.140004 0.469874 OK 1.0\n", "13 0.0650 0.360367 -1.570244 0.849510 0.368578 OK 1.0\n", "14 0.0750 0.421121 -1.472167 0.629925 0.304602 OK 1.0\n", "15 0.0850 0.463092 -1.390206 0.464021 0.262494 OK 1.0\n", "16 0.0950 0.490952 -1.320659 0.338754 0.233325 OK 2.0\n", "17 0.1050 1.016467 -2.521427 0.488493 0.847714 OK 0.5\n", "18 0.1250 0.526984 -1.155761 0.101794 0.180429 OK 2.0\n", "19 0.1350 1.049175 -2.229961 0.131612 0.676758 OK 0.5\n", "20 0.1550 0.514133 -1.040185 0.011919 0.149607 OK 2.0\n", "21 0.1650 1.009608 -2.016529 -0.002686 0.565078 OK 1.0\n", "22 0.1850 0.968793 -1.895860 -0.041727 0.503843 OK 1.0\n", "23 0.2050 0.923365 -1.787115 -0.059615 0.449993 OK 1.0\n", "24 0.2250 0.876892 -1.687042 -0.066742 0.402167 OK 1.0\n", "25 0.2450 0.831138 -1.593829 -0.068447 0.359529 OK 1.0\n", "26 0.2650 0.786941 -1.506413 -0.067469 0.321456 OK 1.0\n", "27 0.2850 0.744668 -1.424124 -0.065212 0.287435 OK 1.0\n", "28 0.3050 0.704447 -1.346502 -0.062393 0.257023 OK 1.0\n", "29 0.3250 0.666287 -1.273199 -0.059374 0.229833 OK 2.0\n", "30 0.3450 1.260271 -2.407864 -0.112677 0.822088 OK 0.5\n", "31 0.3850 0.561696 -1.072982 -0.050411 0.163259 OK 2.0\n", "32 0.4050 1.062325 -2.029304 -0.095347 0.583967 OK 1.0\n", "33 0.4450 0.946828 -1.808671 -0.084986 0.463888 OK 1.0\n", "34 0.4850 0.843887 -1.612027 -0.075746 0.368501 OK 1.0\n", "35 0.5250 0.752137 -1.436763 -0.067511 0.292728 OK 1.0\n", "36 0.5650 0.670362 -1.280554 -0.060171 0.232536 OK 2.0\n", "37 0.6050 1.194957 -2.282657 -0.107258 0.738883 OK 0.5\n", "38 0.6850 0.467560 -0.893151 -0.041968 0.113121 OK 2.0\n", "39 0.7250 0.833450 -1.592091 -0.074810 0.359443 OK 1.0\n", "40 0.8050 0.652220 -1.245898 -0.058543 0.220120 OK 2.0\n", "41 0.8850 1.020795 -1.949965 -0.091625 0.539198 OK 1.0\n", "42 1.0450 0.576860 -1.101942 -0.051778 0.172192 OK 2.0\n", "43 1.2050 0.651978 -1.245434 -0.058521 0.219956 OK 2.0\n", "44 1.5250 0.169798 -0.324354 -0.015241 0.014919 OK 2.0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_delta_data()" ] }, { "cell_type": "markdown", "id": "376ac947-fee3-467e-9dc5-b9c96b3b2a36", "metadata": {}, "source": [ "#### Notice how the first step got aborted, and re-run, because of the large adjusted L2 value in the concentrations " ] }, { "cell_type": "code", "execution_count": 19, "id": "a479c269-4740-4866-9ec3-e736b8b09cb6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TIMEDelta UDelta XDelta S
00.0000-1.000000-3.0000005.000000
10.0000-0.500000-1.5000002.500000
20.0050-0.197500-0.7012501.096250
30.0075-0.349175-1.3590942.057444
40.0125-0.131693-0.6384920.901878
50.0150-0.225993-1.2403501.692337
60.0200-0.156040-1.1709751.483055
70.0250-0.095157-1.1089191.299234
80.0300-0.042237-1.0533081.137781
90.03500.003697-1.0033750.995981
100.04000.086999-1.9168901.742892
110.05000.112345-0.8774050.652715
120.05500.274660-1.6893241.140004
130.06500.360367-1.5702440.849510
140.07500.421121-1.4721670.629925
150.08500.463092-1.3902060.464021
160.09500.490952-1.3206590.338754
170.10501.016467-2.5214270.488493
180.12500.526984-1.1557610.101794
190.13501.049175-2.2299610.131612
200.15500.514133-1.0401850.011919
210.16501.009608-2.016529-0.002686
220.18500.968793-1.895860-0.041727
230.20500.923365-1.787115-0.059615
240.22500.876892-1.687042-0.066742
250.24500.831138-1.593829-0.068447
260.26500.786941-1.506413-0.067469
270.28500.744668-1.424124-0.065212
280.30500.704447-1.346502-0.062393
290.32500.666287-1.273199-0.059374
300.34501.260271-2.407864-0.112677
310.38500.561696-1.072982-0.050411
320.40501.062325-2.029304-0.095347
330.44500.946828-1.808671-0.084986
340.48500.843887-1.612027-0.075746
350.52500.752137-1.436763-0.067511
360.56500.670362-1.280554-0.060171
370.60501.194957-2.282657-0.107258
380.68500.467560-0.893151-0.041968
390.72500.833450-1.592091-0.074810
400.80500.652220-1.245898-0.058543
410.88501.020795-1.949965-0.091625
421.04500.576860-1.101942-0.051778
431.20500.651978-1.245434-0.058521
441.52500.169798-0.324354-0.015241
\n", "
" ], "text/plain": [ " TIME Delta U Delta X Delta S\n", "0 0.0000 -1.000000 -3.000000 5.000000\n", "1 0.0000 -0.500000 -1.500000 2.500000\n", "2 0.0050 -0.197500 -0.701250 1.096250\n", "3 0.0075 -0.349175 -1.359094 2.057444\n", "4 0.0125 -0.131693 -0.638492 0.901878\n", "5 0.0150 -0.225993 -1.240350 1.692337\n", "6 0.0200 -0.156040 -1.170975 1.483055\n", "7 0.0250 -0.095157 -1.108919 1.299234\n", "8 0.0300 -0.042237 -1.053308 1.137781\n", "9 0.0350 0.003697 -1.003375 0.995981\n", "10 0.0400 0.086999 -1.916890 1.742892\n", "11 0.0500 0.112345 -0.877405 0.652715\n", "12 0.0550 0.274660 -1.689324 1.140004\n", "13 0.0650 0.360367 -1.570244 0.849510\n", "14 0.0750 0.421121 -1.472167 0.629925\n", "15 0.0850 0.463092 -1.390206 0.464021\n", "16 0.0950 0.490952 -1.320659 0.338754\n", "17 0.1050 1.016467 -2.521427 0.488493\n", "18 0.1250 0.526984 -1.155761 0.101794\n", "19 0.1350 1.049175 -2.229961 0.131612\n", "20 0.1550 0.514133 -1.040185 0.011919\n", "21 0.1650 1.009608 -2.016529 -0.002686\n", "22 0.1850 0.968793 -1.895860 -0.041727\n", "23 0.2050 0.923365 -1.787115 -0.059615\n", "24 0.2250 0.876892 -1.687042 -0.066742\n", "25 0.2450 0.831138 -1.593829 -0.068447\n", "26 0.2650 0.786941 -1.506413 -0.067469\n", "27 0.2850 0.744668 -1.424124 -0.065212\n", "28 0.3050 0.704447 -1.346502 -0.062393\n", "29 0.3250 0.666287 -1.273199 -0.059374\n", "30 0.3450 1.260271 -2.407864 -0.112677\n", "31 0.3850 0.561696 -1.072982 -0.050411\n", "32 0.4050 1.062325 -2.029304 -0.095347\n", "33 0.4450 0.946828 -1.808671 -0.084986\n", "34 0.4850 0.843887 -1.612027 -0.075746\n", "35 0.5250 0.752137 -1.436763 -0.067511\n", "36 0.5650 0.670362 -1.280554 -0.060171\n", "37 0.6050 1.194957 -2.282657 -0.107258\n", "38 0.6850 0.467560 -0.893151 -0.041968\n", "39 0.7250 0.833450 -1.592091 -0.074810\n", "40 0.8050 0.652220 -1.245898 -0.058543\n", "41 0.8850 1.020795 -1.949965 -0.091625\n", "42 1.0450 0.576860 -1.101942 -0.051778\n", "43 1.2050 0.651978 -1.245434 -0.058521\n", "44 1.5250 0.169798 -0.324354 -0.015241" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_delta_data_alt() # TODO: OBSOLETE!" ] }, { "cell_type": "code", "execution_count": 20, "id": "b4d5101f-f3c9-4e0c-a9ae-e2774136a88a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TIMEDelta UDelta XDelta SL2actioncaptionstep_factorL2_computedactions_computedstep_factors_computed
00.0000-1.000000-3.0000005.0000003.888889ABORT0.53.888889ABORT0.5
10.0000-0.500000-1.5000002.5000000.972222OK0.50.972222OK0.5
20.0050-0.197500-0.7012501.0962500.192502OK2.00.192502OK2.0
30.0075-0.349175-1.3590942.0574440.689126OK0.50.689126OK0.5
40.0125-0.131693-0.6384920.9018780.137600OK2.00.137600OK2.0
50.0150-0.225993-1.2403501.6923370.494839OK1.00.494839OK1.0
60.0200-0.156040-1.1709751.4830550.399443OK1.00.399443OK1.0
70.0250-0.095157-1.1089191.2992340.325196OK1.00.325196OK1.0
80.0300-0.042237-1.0533081.1377810.267310OK1.00.267310OK1.0
90.03500.003697-1.0033750.9959810.222084OK2.00.222084OK2.0
100.04000.086999-1.9168901.7428920.746634OK0.50.746634OK0.5
110.05000.112345-0.8774050.6527150.134277OK2.00.134277OK2.0
120.05500.274660-1.6893241.1400040.469874OK1.00.469874OK1.0
130.06500.360367-1.5702440.8495100.368578OK1.00.368578OK1.0
140.07500.421121-1.4721670.6299250.304602OK1.00.304602OK1.0
150.08500.463092-1.3902060.4640210.262494OK1.00.262494OK1.0
160.09500.490952-1.3206590.3387540.233325OK2.00.233325OK2.0
170.10501.016467-2.5214270.4884930.847714OK0.50.847714OK0.5
180.12500.526984-1.1557610.1017940.180429OK2.00.180429OK2.0
190.13501.049175-2.2299610.1316120.676758OK0.50.676758OK0.5
200.15500.514133-1.0401850.0119190.149607OK2.00.149607OK2.0
210.16501.009608-2.016529-0.0026860.565078OK1.00.565078OK1.0
220.18500.968793-1.895860-0.0417270.503843OK1.00.503843OK1.0
230.20500.923365-1.787115-0.0596150.449993OK1.00.449993OK1.0
240.22500.876892-1.687042-0.0667420.402167OK1.00.402167OK1.0
250.24500.831138-1.593829-0.0684470.359529OK1.00.359529OK1.0
260.26500.786941-1.506413-0.0674690.321456OK1.00.321456OK1.0
270.28500.744668-1.424124-0.0652120.287435OK1.00.287435OK1.0
280.30500.704447-1.346502-0.0623930.257023OK1.00.257023OK1.0
290.32500.666287-1.273199-0.0593740.229833OK2.00.229833OK2.0
300.34501.260271-2.407864-0.1126770.822088OK0.50.822088OK0.5
310.38500.561696-1.072982-0.0504110.163259OK2.00.163259OK2.0
320.40501.062325-2.029304-0.0953470.583967OK1.00.583967OK1.0
330.44500.946828-1.808671-0.0849860.463888OK1.00.463888OK1.0
340.48500.843887-1.612027-0.0757460.368501OK1.00.368501OK1.0
350.52500.752137-1.436763-0.0675110.292728OK1.00.292728OK1.0
360.56500.670362-1.280554-0.0601710.232536OK2.00.232536OK2.0
370.60501.194957-2.282657-0.1072580.738883OK0.50.738883OK0.5
380.68500.467560-0.893151-0.0419680.113121OK2.00.113121OK2.0
390.72500.833450-1.592091-0.0748100.359443OK1.00.359443OK1.0
400.80500.652220-1.245898-0.0585430.220120OK2.00.220120OK2.0
410.88501.020795-1.949965-0.0916250.539198OK1.00.539198OK1.0
421.04500.576860-1.101942-0.0517780.172192OK2.00.172192OK2.0
431.20500.651978-1.245434-0.0585210.219956OK2.00.219956OK2.0
441.52500.169798-0.324354-0.0152410.014919OK2.00.014919OK2.0
\n", "
" ], "text/plain": [ " TIME Delta U Delta X Delta S L2 action caption \\\n", "0 0.0000 -1.000000 -3.000000 5.000000 3.888889 ABORT \n", "1 0.0000 -0.500000 -1.500000 2.500000 0.972222 OK \n", "2 0.0050 -0.197500 -0.701250 1.096250 0.192502 OK \n", "3 0.0075 -0.349175 -1.359094 2.057444 0.689126 OK \n", "4 0.0125 -0.131693 -0.638492 0.901878 0.137600 OK \n", "5 0.0150 -0.225993 -1.240350 1.692337 0.494839 OK \n", "6 0.0200 -0.156040 -1.170975 1.483055 0.399443 OK \n", "7 0.0250 -0.095157 -1.108919 1.299234 0.325196 OK \n", "8 0.0300 -0.042237 -1.053308 1.137781 0.267310 OK \n", "9 0.0350 0.003697 -1.003375 0.995981 0.222084 OK \n", "10 0.0400 0.086999 -1.916890 1.742892 0.746634 OK \n", "11 0.0500 0.112345 -0.877405 0.652715 0.134277 OK \n", "12 0.0550 0.274660 -1.689324 1.140004 0.469874 OK \n", "13 0.0650 0.360367 -1.570244 0.849510 0.368578 OK \n", "14 0.0750 0.421121 -1.472167 0.629925 0.304602 OK \n", "15 0.0850 0.463092 -1.390206 0.464021 0.262494 OK \n", "16 0.0950 0.490952 -1.320659 0.338754 0.233325 OK \n", "17 0.1050 1.016467 -2.521427 0.488493 0.847714 OK \n", "18 0.1250 0.526984 -1.155761 0.101794 0.180429 OK \n", "19 0.1350 1.049175 -2.229961 0.131612 0.676758 OK \n", "20 0.1550 0.514133 -1.040185 0.011919 0.149607 OK \n", "21 0.1650 1.009608 -2.016529 -0.002686 0.565078 OK \n", "22 0.1850 0.968793 -1.895860 -0.041727 0.503843 OK \n", "23 0.2050 0.923365 -1.787115 -0.059615 0.449993 OK \n", "24 0.2250 0.876892 -1.687042 -0.066742 0.402167 OK \n", "25 0.2450 0.831138 -1.593829 -0.068447 0.359529 OK \n", "26 0.2650 0.786941 -1.506413 -0.067469 0.321456 OK \n", "27 0.2850 0.744668 -1.424124 -0.065212 0.287435 OK \n", "28 0.3050 0.704447 -1.346502 -0.062393 0.257023 OK \n", "29 0.3250 0.666287 -1.273199 -0.059374 0.229833 OK \n", "30 0.3450 1.260271 -2.407864 -0.112677 0.822088 OK \n", "31 0.3850 0.561696 -1.072982 -0.050411 0.163259 OK \n", "32 0.4050 1.062325 -2.029304 -0.095347 0.583967 OK \n", "33 0.4450 0.946828 -1.808671 -0.084986 0.463888 OK \n", "34 0.4850 0.843887 -1.612027 -0.075746 0.368501 OK \n", "35 0.5250 0.752137 -1.436763 -0.067511 0.292728 OK \n", "36 0.5650 0.670362 -1.280554 -0.060171 0.232536 OK \n", "37 0.6050 1.194957 -2.282657 -0.107258 0.738883 OK \n", "38 0.6850 0.467560 -0.893151 -0.041968 0.113121 OK \n", "39 0.7250 0.833450 -1.592091 -0.074810 0.359443 OK \n", "40 0.8050 0.652220 -1.245898 -0.058543 0.220120 OK \n", "41 0.8850 1.020795 -1.949965 -0.091625 0.539198 OK \n", "42 1.0450 0.576860 -1.101942 -0.051778 0.172192 OK \n", "43 1.2050 0.651978 -1.245434 -0.058521 0.219956 OK \n", "44 1.5250 0.169798 -0.324354 -0.015241 0.014919 OK \n", "\n", " step_factor L2_computed actions_computed step_factors_computed \n", "0 0.5 3.888889 ABORT 0.5 \n", "1 0.5 0.972222 OK 0.5 \n", "2 2.0 0.192502 OK 2.0 \n", "3 0.5 0.689126 OK 0.5 \n", "4 2.0 0.137600 OK 2.0 \n", "5 1.0 0.494839 OK 1.0 \n", "6 1.0 0.399443 OK 1.0 \n", "7 1.0 0.325196 OK 1.0 \n", "8 1.0 0.267310 OK 1.0 \n", "9 2.0 0.222084 OK 2.0 \n", "10 0.5 0.746634 OK 0.5 \n", "11 2.0 0.134277 OK 2.0 \n", "12 1.0 0.469874 OK 1.0 \n", "13 1.0 0.368578 OK 1.0 \n", "14 1.0 0.304602 OK 1.0 \n", "15 1.0 0.262494 OK 1.0 \n", "16 2.0 0.233325 OK 2.0 \n", "17 0.5 0.847714 OK 0.5 \n", "18 2.0 0.180429 OK 2.0 \n", "19 0.5 0.676758 OK 0.5 \n", "20 2.0 0.149607 OK 2.0 \n", "21 1.0 0.565078 OK 1.0 \n", "22 1.0 0.503843 OK 1.0 \n", "23 1.0 0.449993 OK 1.0 \n", "24 1.0 0.402167 OK 1.0 \n", "25 1.0 0.359529 OK 1.0 \n", "26 1.0 0.321456 OK 1.0 \n", "27 1.0 0.287435 OK 1.0 \n", "28 1.0 0.257023 OK 1.0 \n", "29 2.0 0.229833 OK 2.0 \n", "30 0.5 0.822088 OK 0.5 \n", "31 2.0 0.163259 OK 2.0 \n", "32 1.0 0.583967 OK 1.0 \n", "33 1.0 0.463888 OK 1.0 \n", "34 1.0 0.368501 OK 1.0 \n", "35 1.0 0.292728 OK 1.0 \n", "36 2.0 0.232536 OK 2.0 \n", "37 0.5 0.738883 OK 0.5 \n", "38 2.0 0.113121 OK 2.0 \n", "39 1.0 0.359443 OK 1.0 \n", "40 2.0 0.220120 OK 2.0 \n", "41 1.0 0.539198 OK 1.0 \n", "42 2.0 0.172192 OK 2.0 \n", "43 2.0 0.219956 OK 2.0 \n", "44 2.0 0.014919 OK 2.0 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_L2_data() # TODO: OBSOLETE!" ] }, { "cell_type": "code", "execution_count": null, "id": "c9469a67-c513-492a-8bff-a20d0958ba39", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }