{ "cells": [ { "cell_type": "markdown", "id": "76d0cfef-8918-4004-ba88-d27425d05675", "metadata": {}, "source": [ "### One-bin `A <-> 3B` reaction, with 1st-order kinetics in both directions,\n", "### taken to equilibrium\n", "\n", "Diffusion not applicable (just 1 bin)\n", "\n", "LAST REVISED: Dec. 6, 2023\n", "\n", "* [First Step](#reaction_2_sec_2_first_step)\n", "* [Numerous more steps](#reaction_2_sec_2)\n", "* [Equilibrium](#reaction_2_sec_2_equilibrium)" ] }, { "cell_type": "code", "execution_count": 1, "id": "6e80c184-9e85-4f2a-8426-35aaba9f2628", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Added 'D:\\Docs\\- MY CODE\\BioSimulations\\life123-Win7' to sys.path\n" ] } ], "source": [ "import set_path # Importing this module will add the project's home directory to sys.path" ] }, { "cell_type": "code", "execution_count": 2, "id": "decd55ad", "metadata": {}, "outputs": [], "source": [ "from experiments.get_notebook_info import get_notebook_basename\n", "\n", "from src.modules.chemicals.chem_data import ChemData as chem\n", "from src.life_1D.bio_sim_1d import BioSim1D\n", "\n", "import plotly.express as px\n", "from src.modules.visualization.graphic_log import GraphicLog" ] }, { "cell_type": "code", "execution_count": 3, "id": "cdc446c6-4b9f-4938-b119-8855f4c530fe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-> Output will be LOGGED into the file 'reaction_2.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": "code", "execution_count": 4, "id": "55ae0052-2f52-4d9b-8f13-cb65adb9d04b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "1 bins and 2 species:\n", " Species 0 (A). Diff rate: None. Conc: [10.]\n", " Species 1 (B). Diff rate: None. Conc: [50.]\n" ] } ], "source": [ "# Initialize the system\n", "chem_data = chem(names=[\"A\", \"B\"]) # NOTE: Diffusion not applicable (just 1 bin)\n", "\n", "# Reaction A <-> 3B , with 1st-order kinetics in both directions\n", "chem_data.add_reaction(reactants=[\"A\"], products=[(3,\"B\",1)], forward_rate=5., reverse_rate=2.)\n", "\n", "bio = BioSim1D(n_bins=1, chem_data=chem_data)\n", "\n", "bio.set_uniform_concentration(species_index=0, conc=10.)\n", "bio.set_uniform_concentration(species_index=1, conc=50.)\n", "\n", "bio.describe_state()" ] }, { "cell_type": "code", "execution_count": 5, "id": "025dc8d4-cc77-48fe-90f6-eab7f3383310", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 1 (at temp. 25 C)\n", "0: A <-> 3 B (kF = 5 / kR = 2 / delta_G = -2,271.4 / K = 2.5) | 1st order in all reactants & products\n", "Set of chemicals involved in the above reactions: {'A', 'B'}\n" ] } ], "source": [ "chem_data.describe_reactions()" ] }, { "cell_type": "code", "execution_count": 6, "id": "fd1b6f4c-1327-4f5c-8ac0-ad0a61cc728e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SYSTEM TIMEABcaption
0010.050.0Initial state
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" ], "text/plain": [ " SYSTEM TIME A B caption\n", "0 0 10.0 50.0 Initial state" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save the state of the concentrations of all species at bin 0\n", "bio.add_snapshot(bio.bin_snapshot(bin_address = 0), caption=\"Initial state\")\n", "bio.get_history()" ] }, { "cell_type": "code", "execution_count": 7, "id": "6aa601cb-5f4a-4a16-bde8-abb514bb25c2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[GRAPHIC ELEMENT SENT TO LOG FILE `reaction_2.log.htm`]\n" ] } ], "source": [ "# Send the plot 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": "c505025d-9bfd-485f-8465-2204353575a7", "metadata": { "tags": [] }, "source": [ "### First step" ] }, { "cell_type": "code", "execution_count": 8, "id": "1ec91d5e-717e-45f5-a88c-f8e8df328e48", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.1:\n", "1 bins and 2 species:\n", " Species 0 (A). Diff rate: None. Conc: [15.]\n", " Species 1 (B). Diff rate: None. Conc: [35.]\n" ] } ], "source": [ "# First step\n", "bio.react(time_step=0.1, n_steps=1, snapshots={\"sample_bin\": 0})\n", "bio.describe_state()" ] }, { "cell_type": "markdown", "id": "7a9b3abf-86e7-4ef3-9ddc-23e4a2698c7c", "metadata": {}, "source": [ "_Early in the reaction :_ \n", "[A] = 15. [B] = 35." ] }, { "cell_type": "code", "execution_count": 9, "id": "23c2fb5d-c5ea-4873-a4fa-cd28a55865b0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEABcaption
00.010.050.0Initial state
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" ], "text/plain": [ " SYSTEM TIME A B caption\n", "0 0.0 10.0 50.0 Initial state\n", "1 0.1 15.0 35.0 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.get_history()" ] }, { "cell_type": "markdown", "id": "8eaf0873-53fd-4928-804c-2a2385fc4931", "metadata": {}, "source": [ "### Numerous more steps" ] }, { "cell_type": "code", "execution_count": 10, "id": "6c178528-e88b-4be4-ab61-a5f1f6d57c36", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 1.1:\n", "1 bins and 2 species:\n", " Species 0 (A). Diff rate: None. Conc: [14.54545455]\n", " Species 1 (B). Diff rate: None. Conc: [36.36363636]\n" ] } ], "source": [ "# Numerous more steps\n", "bio.react(time_step=0.1, n_steps=10, snapshots={\"sample_bin\": 0})\n", "\n", "bio.describe_state()" ] }, { "cell_type": "markdown", "id": "260cfdf1-a424-44cb-af29-7cdc693d16f6", "metadata": { "tags": [] }, "source": [ "### Equilibrium" ] }, { "cell_type": "markdown", "id": "17457676-5007-41b3-b8c2-a83a3d72ee47", "metadata": {}, "source": [ "Consistent with the 5/2 ratio of forward/reverse rates (and the 1st order reactions),\n", "the systems settles in the equilibrium: [A] = 14.54545455 , [B] = 36.36363636" ] }, { "cell_type": "code", "execution_count": 11, "id": "bb44d88c-bb34-41d7-b8c2-ca15938495d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A <-> 3 B\n", "Final concentrations: [A] = 14.55 ; [B] = 36.36\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 2.5\n", " Formula used: [B] / [A]\n", "2. Ratio of forward/reverse reaction rates: 2.5\n", "Discrepancy between the two values: 6.875e-10 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "bio.reaction_dynamics.is_in_equilibrium(conc=bio.bin_snapshot(bin_address = 0))" ] }, { "cell_type": "code", "execution_count": 12, "id": "0f7ccafe-3fa4-4934-8f6f-8f6b774d1cf6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " SYSTEM TIME A B caption\n", "0 0.0 10.000000 50.000000 Initial state\n", "1 0.1 15.000000 35.000000 \n", "2 0.2 14.500000 36.500000 \n", "3 0.3 14.550000 36.350000 \n", "4 0.4 14.545000 36.365000 \n", "5 0.5 14.545500 36.363500 \n", "6 0.6 14.545450 36.363650 \n", "7 0.7 14.545455 36.363635 \n", "8 0.8 14.545454 36.363637 \n", "9 0.9 14.545455 36.363636 \n", "10 1.0 14.545455 36.363636 \n", "11 1.1 14.545455 36.363636 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.get_history()" ] }, { "cell_type": "markdown", "id": "7fbc0ddf-9903-48c2-af88-5fa2d2048371", "metadata": {}, "source": [ "Note how the simulation initially **OVERSHOT** the equilibrium values; the first step was too large!" ] }, { "cell_type": "markdown", "id": "715af433-0129-4fdf-a172-77f9d3d127a2", "metadata": { "tags": [] }, "source": [ "# Plots of changes of concentration with time" ] }, { "cell_type": "code", "execution_count": 13, "id": "9ee92b39-26ee-4fa0-a93b-a0ee5048293d", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
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ofsD9+dPP509vVkU4VTgSJTshkGqBai+vSTWW5uSx1gRkdwQQQAABBBBAoECAcMKR5RB0KnfYxzI6MgWGgQACjghU8+QUR4Zcc8PwL6GZML7zuEev1txkGDACCCCAAAIIIOCAAOGEA01gCAgggAACCCCAAAIIIIAAAgikWYBwIs3dZ+4IIIAAAggggAACCCCAAAIIOCBAOOFAExgCAggggAACCCCAAAIIIIAAAmkWIJxIc/eZOwIIIIAAAggggAACCCCAAAIOCBBOONAEhoAAAggggAACCCCAAAIIIIBAmgUIJ9LcfeaOAAIIIIAAAggggAACCCCAgAMChBMONIEhIIAAAggggAACCCCAAAIIIJBmAcKJNHefuSOAAAIIIIAAAggggAACCCDggADhhANNYAgIIIAAAggggAACCCCAAAIIpFmAcCLN3WfuCCCAAAIIIIAAAggggAACCDggQDjhQBMYAgIIIIAAAggggAACCCCAAAJpFiCcSHP3mTsCCCCAAAIIIIAAAggggAACDggQTjjQBIaAAAIIIIAAAggggAACCCCAQJoFCCfS3H3mjgACCCCAAAIIIIAAAggggIADAoQTDjSBISCAAAIIIIAAAggggAACCCCQZgHCiTR3n7kjgAACCCCAAAIIIIAAAggg4IAA4YQDTWAICCCAAAIIIIAAAggggAACCKRZgHAizd1n7ggggAACCCCAAAIIIIAAAgg4IEA44UATGAICCCCAAAIIIIAAAggggAACaRYgnEhz95k7AggggAACCCCAAAIIIIAAAg4IEE440ASGgAACCCCAAAIIIIAAAggggECaBQgn0tx95o4AAggggAACCCCAAAIIIICAAwKEEw40gSEggAACCCCAAAIIIIAAAgggkGYBwok0d5+5I4AAAggggAACCCCAAAIIIOCAAOGEA01gCAgggAACCCCAAAIIIIAAAgikWYBwIs3dZ+4IIIAAAggggAACCCCAAAIIOCBAOOFAExgCAggggAACCCCAAAIIIIAAAmkWIJxIc/eZOwIIIIAAAggggAACCCCAAAIOCBBOONAEhoAAAggggAACCCCAAAIIIIBAmgUIJ9LcfeaOAAIIIIAAAggggAACCCCAgAMChBMONIEhIIAAAggggAACCCCAAAIIIJBmAcKJNHefuSOAAAIIIIAAAggggAACCCDggADhhANNYAgIIIAAAggggAACCCCAAAIIpFmAcCLN3WfuCCCAAAIIIIAAAggggAACCDggQDjhQBMYAgIIIIAAAggggAACCCCAAAJpFiCcSHP3mTsCCCCAAAIIIIAAAggggAACDggQTjjQBIaAAAIIIIAAAggggAACCCCAQJoFCCfS3H3mjgACCCCAAAIIIIAAAggggIADAoQTDjSBISCAAAIIIIAAAggggAACCCCQZgHCiTR3n7kjgAACCCCAAAIIIIAAAggg4IAA4YQDTWAICCCAAAIIIIAAAggggAACCKRZgHAizd1n7ggggAACCCCAAAIIIIAAAgg4IEA44UATGAICCCCAAAIIIIAAAggggAACaRYgnEhz95k7AggggAACCCCAAAIIIIAAAg4IEE440ASGgAACCCCAAAIIIIAAAggggECaBQgn0tx95o4AAggggAACCCCAAAIIIICAAwKEEw40gSEggAACCCCAAAIIIIAAAgggkGYBwgkD3d+9t8dAFUogUL1A5+gmGcjk5GjvYPU7swcCBgTGdzTL0b6M9PZnDFSjBALVC0zobJGDRwZkYDBb/c7sgYABgUnjWuWDg32SyeYMVKMEAtULnDC+Td7b1yOswOrt/D2mdLVF35k9jQkQThigJJwwgEiJSAKEE5HY2MmgAOGEQUxKRRIgnIjExk4GBQgnDGJSKpIA4UQktmE7EU7oG5qoQDhhQJFwwgAiJSIJEE5EYmMngwKEEwYxKRVJgHAiEhs7GRQgnDCISalIAoQTkdgIJ/TZjFcgnDBASjhhAJESkQQIJyKxsZNBAcIJg5iUiiRAOBGJjZ0MChBOGMSkVCQBwolIbIQT+mzGKxBOiMgd6zbKd3+weRjut25cKNfOu9j72AObt8otqzd4/33VZXPltmULpa21Ob894YTxdUnBkAKEEyGh2Cw2AcKJ2GgpHFKAcCIkFJvFJkA4ERsthUMKEE6EhCqzGZd16BuaqEA4cSycUJg3LJ5/nOkz23fK2nUb5Z5V18u4zg4vyCjelnDCxFKkRhQBwokoauxjUoBwwqQmtaIIEE5EUWMfkwKEEyY1qRVFgHAiitrwfQgn9A1NVCCcqBBOqDBi2smT82dRFIcVqgmEEyaWIjWiCBBORFFjH5MChBMmNakVRYBwIooa+5gUIJwwqUmtKAKEE1HUCCf01cxXIJwIuKzDv6Sjp7dfbl2zQebOOTMfTrz6xm65eeV6+faKRTJj6hSvI4QT5hcmFcMJEE6Ec2Kr+AQIJ+KzpXI4AcKJcE5sFZ8A4UR8tlQOJ0A4Ec6p3Fa2zpwI+sW3/mxGvsL+g92yZPmdsnTxfDl/9szIAyCcKKJT4cPiG9fKyhWL5OyZ071w4otXX5JHPi6c+Jc75PC0fy+50UNBBS8ERlKgtaleMjmRgcHsSB6WYyGQF2hrbpCBTFYG1ULklUiBXC4ndXV1zs5tVEuD9A1kJZNlDTrbpIQPrL21UY72DQpLMOGNdnh6HW2NcrhnUPguGL1JyjCOl//L7gcf3zasvP/LcJvhhLqv4sZNW/K3L9CZP+GEjl6Fff1LOa68dG7lMyfW1km2Y6oc/cLPCChi7AmlgwVamhskm80RTrBArAm0tTTIwGBOBjMEZNaaEPOBc1IndQ7/yDuqtVF6+zPe90JeCNgQGN3WKEd7M6KCPF4I2BBob2uSwz0DNg6dmGN2jGoyPhf/l97zLr1w2L0N1Rv5Fbevl2XXLZB9+w8Nu7+h8UGMUEHCiRihC+8zUfGeE+tPFTn0umRGT5MPrnxYMu2nxDgySiMwXIDLOlgRtgW4rMN2Bzg+l3WwBmwLcFmH7Q5wfC7r0F8Dpi/r8M+YmDxxfOBDF/wR+2dOqMshVqxcL+/u2et96t67lg+7PMIPOvzPf+NL8/J1/WDg6394hXzvhw/LCzt2eTXU2RnnnnWad1VAUF115sS2Z18a9iTK4uPMOmO6d2aFeqnLNvza6t9BY+CyDs21qJq5+fFt8uVrP+tVKr5so+LTOg6/I5n7Pi4Nh9+UzKgp8sG8JwgoNHvC7uEFCCfCW7FlPAKEE/G4UjW8AOFEeCu2jEeAcCIeV6qGFyCcCG9VakvT4UThrQLK3YNBvdf82jdXyVWXzc2HBMWXWxS/Py0OPvxwQs3Nf8KkX9cPF9RTJ4vf1xaHE0FjfmTLL+W0U0+S8WM75Hv3PyRLvvoH0tbaLP4x5199iXdvRs6c0F+DXoWg64CKkyrVuFtWb/C2L1w4/hD2vPWqdG3+rDQefs0LKPbOe1QG2081NELKIFBagHCC1WFbgHDCdgc4PuEEa8C2AOGE7Q5wfMIJ/TVgOpxQQYA6E2Ld6qX5hygEjTLonhPFYUTxmfyqTuF+6t/FN6MMCguKP1YcTqjjqNcNi+eHAlX7v/7We972hBOhyEZmI/W0jvre92WCCigOvSzZ1onywe//lIBiZPhTfRTCiVS334nJE0440YZUD4JwItXtd2LyhBNOtCHVgyCc0G+/q+HElEkTvHsgFt9QU824+JKLwksqqg0nVL3iB0EUqxZf8qE+7//ivrevj6d16C9DMxX8R4nW9+2VCQ9e+ruA4spHZbDzdDMHoQoCAQKEEywL2wKEE7Y7wPEJJ1gDtgUIJ2x3gOMTTuivAdPhRDWXdaxdt3HYEzMKz5zww4m5c870Lp8IeoUJItR+5c6cqBRO+FcSFF5hUHjmBeGE/ho0VsEPJ1RBFVB0PfQ5aTqwQ7ItXd49KAgojFFTqEiAcIIlYVuAcMJ2Bzg+4QRrwLYA4YTtDnB8wgn9NWA6nKh0Q0z/Xg5BT+sIuqxDzbDU5RYmwgl1H4lyl3UEXVpCOKG/7mKpUBhODAUUB6Tr4Sukaf/zXkCx98p/koGxZ8RybIqmW4BwIt39d2H2hBMudCHdYyCcSHf/XZg94YQLXUj3GAgn9PtvOpxQIyr1KFH1Rn/zE09796MIE074N7dUT9/wz55QgYR/g8qgsxbCBBbF95zwj1N4doQfovz44Sflvff35W/a6c/to2ef7n2MMyf016CxCsXhhCpcN9Dt3YPCCyiax8reeY8TUBgTp5AvQDjBWrAtQDhhuwMcn3CCNWBbgHDCdgc4PuGE/hqII5xQowp6+EK5J2j4ocbNK9fLt1csyt9MM+h+D35YESaIUHUr3RBTbeMHFL6oP9bWlpZh975Q95o458wZ8vxLrxJO6C8/sxWCwgk/oOh6ZJ40f/DsUEBxxcMyMP4cswenWqoFCCdS3X4nJk844UQbUj0IwolUt9+JyRNOONGGVA+CcEK//XGFE/ojS1eFulwul0vXlM3PtlQ44QUUg4el65HPS/Nvt0muqUM+uPJRAgrzLUhtRcKJ1LbemYkTTjjTitQOhHAita13ZuKEE860IrUDIZzQbz3hhL6hiQqEEwYUy4UTXkCR6ZGuh6/KBxR7L98s/RPmGDgyJdIuQDiR9hVgf/6EE/Z7kPYREE6kfQXYnz/hhP0epH0EhBP6K4BwQt/QRAXCCQOKlcIJP6AY/+gXpOW9rZJrGC17L/+J9E/8mIGjUyLNAoQTae6+G3MnnHCjD2keBeFEmrvvxtwJJ9zoQ5pHQTih333CCX1DExUIJwwohgknvIAi2yfjH/t30rL7CcnVt8j+S/9eek+60sAIKJFWAcKJtHbenXkTTrjTi7SOhHAirZ13Z96EE+70Iq0jIZzQ7zzhhL6hiQqEEwYUw4YTQwHFgIx7YoG0vv2QeuioHPjkOjl62pcNjIISaRQgnEhj192aM+GEW/1I42gIJ9LYdbfmTDjhVj/SOBrCCf2uE07oG5qoQDhhQLGacMI7XC4rY59aLKNeuc/758EL75AjZ/yJgZFQIm0ChBNp67h78yWccK8naRsR4UTaOu7efAkn3OtJ2kZEOKHfccIJfUMTFQgnDChWHU4cO2bHv/yFdLx4h/ev7ll/Lt1z/tLAaCiRJgHCiTR12825Ek642Zc0jYpwIk3ddnOuhBNu9iVNoyKc0O824YS+oYkKhBMGFKOGE+rQo3d8RzqfvsEbhbq848An/0Zd/GFgVJRIgwDhRBq67PYcCSfc7k8aRkc4kYYuuz1Hwgm3+5OG0RFO6HeZcELf0EQFwgkDijrhhDq8urxj7JOLRSQrPVOvkQOf+lvJ1TcZGBklki5AOJH0Drs/P8IJ93uU9BESTiS9w+7Pj3DC/R4lfYSEE/odJpzQNzRRgXDCgKJuOKGGoG6QOe6J/+A90aNv8sWy7zP/ILnG0QZGR4kkCxBOJLm7tTE3wona6FOSR0k4keTu1sbcCCdqo09JHiXhhH530xZO7D/YLUuW3ymnTJkoty1bKG2tzfqIBioQThhANBFOqGE0v//P0vXI56Uuc0QGxp8re694WLLNnQZGSImkChBOJLWztTMvwona6VVSR0o4kdTO1s68CCdqp1dJHSnhhH5n0xZOPLN9p/xo0xY5dPioLLtugcyYOkUf0UAFwgkDiKbCCTWUpn3PSdcjV0l93z4Z7PywF1Bk2iYbGCUlkihAOJHErtbWnAgnaqtfSRwt4UQSu1pbcyKcqK1+JXG0hBP6XU1bOHHHuo1y0YXnyM+ffl6mnTxZrp13sT6igQqEEwYQTYYTajiN3a9K18NXSsORtyUz+iT5YN6jkhk91cBIKZE0AcKJpHW09uZDOFF7PUvaiAknktbR2psP4UTt9SxpIyac0O9oXOHE3r098sILe/QHWGWFrq5RMmvWxMC91CUdK//qPlnxZ1+WV157xzuDwpVLOwgnqmx00Oamwwl1jIae96Rr82e9oCLb0iV7P/dk8AdbAAAgAElEQVQTGeg6z8BoKZEkAcKJJHWzNudCOFGbfUvSqAknktTN2pwL4URt9i1Joyac0O9mXOHEpk2/kc9//gf6A6yywtVXf1h+8pMvBe6lLulQZ0zcsHi++PeeWLp4vpw/e2aVRzG/OeGEAdM4wgk1rPrefdL1T1d5l3rk6ltl/6X3Se9JVxoYMSWSIkA4kZRO1u48CCdqt3dJGTnhRFI6WbvzIJyo3d4lZeSEE/qdjCuc+MUv3pKbb35Cf4BVVvjYx06S22+/7Li9enr75dY1G+SLV1+SDyPUJR7qpcIK2y/CCQMdiCucUEOryxyV8Y8vkJbdj6l/yaHfu10On/1fDIyaEkkQIJxIQhdrew6EE7XdvySMnnAiCV2s7TkQTtR2/5IwesIJ/S7GFU7oj8xshVff2C2Lb1wr7+7ZO6zwrDOmyz2rrpdxnR1mD1hlNcKJKsGCNo8znPCOl8tK5y/+s4x++XveP4+c/nU5+PG/FqmrNzB6StSyAOFELXcvGWMnnEhGH2t5FoQTtdy9ZIydcCIZfazlWRBO6HcvLeHEA5u3yrZnXxp2j4mgsyn0RaNVIJyI5jZsr9jDiWNHa9/xv2TM0/9VRLLSN+Uzsu/Sv5dcY7uBGVCiVgUIJ2q1c8kZN+FEcnpZqzMhnKjVziVn3IQTyellrc6EcEK/c2kIJ/wQYu6cM497OocKLV5/6z3rl3YQTuivZRmpcEINtfXth2TcE1+WumyvDI6dKXs/94+SGeXGc2kNUFKiSgHCiSrB2Ny4AOGEcVIKVilAOFElGJsbFyCcME5KwSoFCCeqBAvYPA3hhL5S/BUIJwwYj2Q4oYbbtPffpOvRL0h97/uSbZ0oey//RxkYd7aBmVCi1gQIJ2qtY8kbL+FE8npaazMinKi1jiVvvIQTyetprc2IcEK/Y4QT+oYmKhBOGFAc6XBCDbnh6G7p+qffl8YDOyXXMEr2XXa/d6kHr3QJEE6kq98uzpZwwsWupGtMhBPp6reLsyWccLEr6RoT4YR+vwkn9A1NVCCcMKBoI5xQw64bPCzjH/v30vLeVvXgUe8mmUc+/HUDM6JErQgQTtRKp5I7TsKJ5Pa2VmZGOFErnUruOAknktvbWpkZ4YR+pwgn9A1NVCCcMKBoK5zwhp7LyNgnF8uoV//e++eRmYvl4AVrROobDcyMEq4LEE643qHkj49wIvk9dn2GhBOudyj54yOcSH6PXZ8h4YR+hwgn9A1NVCCcMKBoNZw4Nv72F++UMf/yFyqtkP4PXSD7PvN/JNsywcDsKOGyAOGEy91Jx9gIJ9LRZ5dnSTjhcnfSMTbCiXT02eVZEk7od4dwQt/QRAWr4cT+g92yZPmd8sKOXcfNZdYZ0+WeVdfLuM4OE/OMtYYL4YSaoPckj599TeoGuiXTNln2feYfZKDrvFjnTnG7AoQTdv05ugjhBKvAtgDhhO0OcHzCCdaAbQHCCf0OEE7oG5qoYDWcuGPdRm8ONyyeb2Iu1mq4Ek4ogMbuXTL+0S9I46GXJVffLAc/cY8cnfElazYcOF4Bwol4faleWYBworIRW8QrQDgRry/VKwsQTlQ2Yot4BQgn9H0JJ/QNTVSwFk6osyZW3L5ell23QGZMnWJiLto1enr75dY1G7w6ty1bKG2tzd5/P7B5q9yyeujjV102d9jn1MdcCifUeOoGj8i4LX/snUmhXkc+8h/l4IV3cB8K7RXiXgHCCfd6krYREU6krePuzZdwwr2epG1EhBNp67h78yWc0O8J4YS+oYkKhBPHFP1g4sHHtw0LIJ7ZvlPWrtuYv8Qk6GwP18IJf2F0vLBWOp69VUSy3n0o9l96v3e5B6/kCBBOJKeXtToTwola7Vxyxk04kZxe1upMCCdqtXPJGTfhhH4vCSf0DU1UsBZOqMGrN/rTTp4s18672MRctGr4Y1FFtj37Uv7siOIxFocVantXwwk1tpZ3fybjfrpA6vsPejfIVDfKVEEFr2QIEE4ko4+1PAvCiVruXjLGTjiRjD7W8iwIJ2q5e8kYO+GEfh/TEk4E3fPRpXs9Wg0nXn1jt9z3wGOybMmC/CUU+kur+gqFZ0OoSzj8cEJVUpd5zJ1zZj5AUWO+eeV6+faKRfnLUVwOJ9QcGg6/KeMf+4I0Hdjh3Yfi0IVr5chHvlE9FHs4J0A44VxLUjcgwonUtdy5CRNOONeS1A2IcCJ1LXduwoQT+i1JWzixdPF8OX/2TA/OpftAWgsnyj2pQyGNVIKjwojX33ovf1POoHDii1dfkm9eUDih/+UwAhUGe0Qe/rrIr384dLAPzxe54rsiTe0jcHAOgQACCCBQqwL9g1lpbqyv1eEzbgQQQAABBBA4JuC/By8MJwrf//r3XLQFZi2csDXh4uOqpOi7P9h83HDUjS+X/+evyKq//ruaP3OicHLtL/1PGfPMTSK5Qcm0T5X9n/o+l3m4shgjjIMzJyKgsYtRAc6cMMpJsQgCnDkRAY1djApw5oRRTopFEODMiQhoRbvEduZEz16RD17QH2C1Fdq6RCbMOm6voHDCpVstpD6cKO5YcXJU6/ecCFrHzXuekvFP/KHU9+0TqWuQ7tk3S/c5N4rU8Zuxar/ubW9POGG7AxyfcII1YFuAcMJ2Bzg+4QRrwLYA4YR+B2ILJ17dJPL/Pq8/wGorTL9a5As/KRlOvLBj17DPfevGhU7cB9J6OKFuMPm1b64ahnPvXcvzl1FU2wfd7YvDiVp+Wkc5i4aePTJ269el5d0t3mb9Ez8u+z91r2RGn6RLyP4jKEA4MYLYHCpQgHCChWFbgHDCdgc4PuEEa8C2AOGEfgdiCyd2/0LkyZv1B1hthRM+JnLR7SXDicLLOoLOpqj2cKa2txpOBD35Qt3TYfGNa+W6r15jJb0JuuZGfeyW1Rs8c3W5x23LFg67gafrN8Qst1jad9wtHc/8hdRleyXbNEYOXLReek+52tT6ok7MAoQTMQNTvqIA4URFIjaIWYBwImZgylcUIJyoSMQGMQsQTugDxxZO6A/NaIVSQYQrl3ZYCyd6evu9J2EU3mzSl1ehxY82bTkuBDDaGYPFajmcUAyNB34t47Z8RZoO/MpTOXraH8vBj90huYZRBpUoFYcA4UQcqtSsRoBwohotto1DgHAiDlVqViNAOFGNFtvGIUA4oa+a5nCCMydERCGsuH29LLtuQf6RnP6yUmdPrLn7fll50yIZ19mhv9pirlDr4YTiqcsOSMez/03af/VXIpKTwfbpsv/T/1sGus6LWY/yOgKEEzp67GtCgHDChCI1dAQIJ3T02NeEAOGECUVq6AgQTujoDe2btnCi+J4TNm+rUNg9zpzQX8uShHDCZ2jZ86SM/ekfSUPvHpG6Ruk++7/I4dk3Sa6hzYAUJUwLEE6YFqVetQKEE9WKsb1pAcIJ06LUq1aAcKJaMbY3LUA4oS+alnBCXyreCtbCCTUtdS+HjZu2yD2rrs+fIWH7nhNRuJMUTqj51/cflM6n/kTa3vixx5FpP0UOfOx/Sd+Jl0XhYZ8YBQgnYsSldCgBwolQTGwUowDhRIy4lA4lQDgRiomNYhQgnNDHJZzQNzRRwWo4oSbg2tM6oqAmLZzwDVQ4MebppdJwdLf3oZ6p/04OzV0jmbbJUZjYJwYBwokYUClZlQDhRFVcbByDAOFEDKiUrEqAcKIqLjaOQYBwQh+VcELf0EQF6+GEiUnYrpHUcEK51g0elo5nb5P2nd8RyWW8J3p0z/mWHJn5H9VnbdOn/viEE6lfAtYBCCestyD1AyCcSP0SsA5AOGG9BakfAOGE/hIgnNA3NFGBcMKAYpLDCZ+naf+LMvZnC6XpwIvehwa6PioHLlonA2PPMiBIiagChBNR5djPlADhhClJ6kQVIJyIKsd+pgQIJ0xJUieqAOFEVLnf7Uc4oW9oogLhhAHFNIQTHlMuK6N/vU7GPPvfpW6g27th5uGz/lS6Z98sucbRBiQpUa0A4US1YmxvWoBwwrQo9aoVIJyoVoztTQsQTpgWpV61AoQT1Yodvz3hhL6hiQojHk74z1H9+h9eId/74cNS/BgTf1Kzzpg+7EaZJiYbV43UhBPHABt69siYbX8ubW/8g/eRbMsE6T5vhRyZuSQuYuoGCDQcfl06ZZ8M9h2Rvt5+qZNBkWxG6rKD6jam3mU4ddmM9zGRwWP/PTj08dyxv73PqW2G9pX6RpG6epG6BsnVNYjU1Q39d32DiKh/H/uc+rf6bzm2Xb36eMG/1X/XH/uc2k/VDfOqq5NcfYvkGlq8v6Wxedi/c03tYaqwzQgKEE6MIDaHChQgnGBh2BYgnLDdAY5POKG/Bggn9A1NVBjxcMIftAopVty+XpZdt0BmTJ0ybC7qJpk/2rRFblu2UNpam03MM9YaaQsnfMzWdx6Vzqeuk4aj73gfUjfKPHLOn8vhM66L1TvpxRsPvyb1R3Z7rg1Hdkv9kXeG/vvoHqnveU8aet6XusyRpDNUnF+uvtULPXJeoKL+bhr6d536WwUq6uPHPuZt11KxJhtUL9DUUCeZXE6y2er3ZQ8ETAg0NdZJJpOTbM5ENWogUL1Ac2O9DGSykmMNVo/HHkYEWprqpX8gKyzB6JwtX9kafWf2NCbgZDihHie65u77ZeVNi/KPGDU24xgKpTWc8Cnbd9wto59fKw097w6FFK2T5PC5/1WOnPEnMWgno2TDkbel8dCr0nhghzR0vyZNB34ljQdfloYjb4WeYGb0SSLtJ0quoVUyOf9sh8bfvTE/dsZCzjuLQZ25cOwNu3rjLkPb5c9uqFNv5BuGzqCQrNTl1JkX2aGzKXJZqTt2Job33/7ZFse2qVPvStV/+/t5+xTs522fk7A/tdVljkrdYJ/UZdWffpHMsf8e7CeUCb062BABBBBAAAEEEEAgtMBSop3QVjFu6GQ48cDmrbLt2Zc4cyLGxsdRevSO70j7c/9DGnr3DIUUo6bI4XOWyZGZi+M4XE3UrO87IE17/1XUDUUb926XpoO/kcb9L0ldtrfk+DNtJ0hm9IlDf9pOlGz7SZIZPUUyo06UbOtEybZNlGxzp7d/mu854YUYGXU5yqDU5QaOBSuDUpdR/50ZuswlMzB0CUv22N/8f8f4103HqEbpG8h6v7HhhYANAfV98EjvoAxm+AK34c8xRcZ1NMvBI/2cQcZisCbQNaZZ9h3q58wJjQ50nfU5jb3Z1ZTAiIcT6qyIxTeulXf37C05hxMmdcm61UuPu9zD1KRN10n7mROFnuq33aN+c6+0P79GGo7u9j6l7klxdPoC6fnwH8nAuFmm+d2ol8tK46FXpGn/89K49zlp2veC98c/m6R4kNnmsTLY+REZ7DxdMp0fkYHOD8vgGPVnRvj7M6Q8nHCj8YyCe06wBmwLcM8J2x3g+NxzgjVgW4B7Tuh3gHtO6BuaqDDi4YQ/6HL3nDAxsZGsQTgRrD16598MnUlx7HIPtdXA2DOl57Q/kqOnf0WyLV0j2SZjx6rL9EqTF0A8L437npOm/b+S5t8+HVg/M/pkL4QYGHuGZMaqEEIFEh+WbOuHjIwnzWdOGAGkiLYA4YQ2IQU0BQgnNAHZXVuAcEKbkAKaAoQTmoAiQjihb2iigrVwwsTgXalBOFG+E61v/qO0vfoDaXvj/w7bsPfEK6Tn9P8gfZMvlWzreFfamR+Humyg8cCvpbF719D9IfY+PxRIdL9y3FgHO06TgXFnyqAXQswcOhNi7Eck1zAq1nkRTsTKS/EQAoQTIZDYJFYBwolYeSkeQoBwIgQSm8QqQDihz0s4oW9oogLhhAFFwolwiPX9h6Rt1/1eUFF8poE6w6B/wu/JwIcukIEPfVT6J8yJ/Y29P+qGI29I0/6XpGn/DmnwwohXvUs06nvfP25iKmwYGHeWDEz4PRlUIcT4c2Sg6xzJNbSFQzC8FeGEYVDKVS1AOFE1GTsYFiCcMAxKuaoFCCeqJmMHwwKEE/qghBP6hiYqWA0nyt1/YtYZ0+WeVdfztA4TXXawhjoboe3lv5PWtx+Spn3PBY5wYOzZMtB1nmTGnibZxg7JNQ39ybaMlVzjaMk2jZFcc4dk1ccbRw+rUd9/UOr790l97z6pGzgs9X37pS5zWFRAov7dcOg1aTy4U5r2vVjyCRCZ9qneZSgD48+Vwa7Z3qUZ6h4RLr0IJ1zqRjrHQjiRzr67NGvCCZe6kc6xEE6ks+8uzZpwQr8bhBP6hiYqWAsnenr75dY1G2TunDPl3LNOk/seeEyWLVkgba3Ncse6jXLRhefI+bNnmphj7DU4c0KP2LuHw77t0vzBv0rjB88O/X3w13pFQ+6da2yXwfZpkumYKoPtp0pm7IeHzowYf85xgUfIkiO6GeHEiHJzsAABwgmWhW0BwgnbHeD4hBOsAdsChBP6HSCc0Dc0UcFaOFF4Q0w1kTV33y8rb1rknSnxzPad8qNNW3iUqIkO12iNusEj0rz336Tpg2elvmeP1PV3S/1At9SpP4NHRJ0Z4Z0R4X8s0zNspip0yLaMk2zzOMm2TZDMqBOGHss5+gTvEaeZ0SdJpn2at00tvwgnarl7yRg74UQy+ljLsyCcqOXuJWPshBPJ6GMtz4JwQr97hBP6hiYqOBFOjB/bISv/6j5Z8Wdf9sIJdblHYVhhYqJx1uDMiTh1qV1OgHCC9WFbgHDCdgc4PuEEa8C2AOGE7Q5wfMIJ/TVAOKFvaKKCtXCi8LKOa+dd7F3KMe3kyaL++4HNW2Xbsy9x5oSJDlMj0QKEE4lub01MjnCiJtqU6EESTiS6vTUxOcKJmmhTogdJOKHfXsIJfUMTFayFE8WDV5d5LFl+p7ywY5ecMKlL1q1eKjOmTjExx9hrcOZE7MQcoIQA4QRLw7YA4YTtDnB8wgnWgG0BwgnbHeD4hBP6a4BwQt/QRAVnwgkTk7FVg3DCljzHJZxgDdgWIJyw3QGOTzjBGrAtQDhhuwMcn3BCfw0QTugbmqhgLZwovCFmrZwhUQqccMLEUqRGFAHCiShq7GNSgHDCpCa1oggQTkRRYx+TAoQTJjWpFUWAcCKK2vB9CCf0DU1UIJwwoEg4YQCREpEECCcisbGTQQHCCYOYlIokQDgRiY2dDAoQThjEpFQkAcKJSGzDdiKc0Dc0UcFaOKEGr26CedGF58j5s2eamIu1GoQT1uhTf2DCidQvAesAhBPWW5D6ARBOpH4JWAcgnLDegtQPgHBCfwkQTugbmqhgNZxQjwy974HHZNmSBdLW2mxiPlZqEE5YYeegIkI4wTKwLUA4YbsDHJ9wgjVgW4BwwnYHOD7hhP4aIJzQNzRRwVo4Ufh0jqCJzDpjutyz6noZ19lhYp6x1iCciJWX4mUECCdYHrYFCCdsd4DjE06wBmwLEE7Y7gDHJ5zQXwOEE/qGJipYCydMDN6VGoQTrnQifeMgnEhfz12bMeGEax1J33gIJ9LXc9dmTDjhWkfSNx7CCf2eE07oG5qoYC2cKPe0jme275Qfbdoity1bWBOXexBOmFiK1IgiQDgRRY19TAoQTpjUpFYUAcKJKGrsY1KAcMKkJrWiCBBORFEbvg/hhL6hiQpOhhPqXhRr7r5fVt60iMs6THSZGokVIJxIbGtrZmKEEzXTqsQOlHAisa2tmYkRTtRMqxI7UMIJ/dYSTugbmqjgZDjxwOatsu3Zl0bszAn11JDv/mBz3vPeu5YPe4KIGs8tqzd4n7/qsrnHjYszJ0wsRWpEESCciKLGPiYFCCdMalIrigDhRBQ19jEpQDhhUpNaUQQIJ6KoDd+HcELf0ESFEQ8n1FkRi29cK+/u2Vty/CdM6pJ1q5fKjKlTTMyxbA11ecn37n9Ilnz1D7xLSNT4bl65Xr69YpF3fHWJydp1G/M351RBhnrdsHh+vi7hROxt4gAlBAgnWBq2BQgnbHeA4xNOsAZsCxBO2O4Axyec0F8DhBP6hiYqjHg44Q+63D0nTEwsag3/KSJLF8/3zp5QYcS0kyfLtfMu9koWhxXqY4QTUbXZT1eAcEJXkP11BQgndAXZX1eAcEJXkP11BQgndAXZX1eAcEJXUIRwQt/QRAVr4YSJwcdRQ4UPK1au987cmDJpgty6ZoPMnXNmPpwoPrOCcCKOLlAzrADhRFgptotLgHAiLlnqhhUgnAgrxXZxCRBOxCVL3bAChBNhpUpvRzihb2iiAuHEMcXCy038e0709PZ74cQXr74kfw+KoHCiu2fQRC+ogUDVAq1N9ZLJiQwMZqvelx0QMCHQ1twgA5msDKqFyCuRArlcTurq6pyd26iWBukbyEomyxp0tkkJH1h7a6Mc7RsUlmDCG+3w9DraGuVwz6DwXTB6k5QhL/sCVsMJ/xKKF3bsOk5i1hnT8/d5GEmmwss6zp45PdSZE91HB0ZyiBwLgbxAS3ODZLM5wgnWhDWBtpYGGRjMyWCGgMxaE2I+cE7qpM7hH3lHtTZKb3/G+17ICwEbAqPbGuVob0ZUkMcLARsC7W1NcriH9yM69h2jmnR2Z19DAlbDiaCbSxqal1aZwvtMcM8JLUp2jlmAyzpiBqZ8RQEu66hIxAYxC3BZR8zAlK8owGUdFYnYIGYBLuvQB+ayDn1DExWshROu3BBTXabx+M+flf/0las9T//yjpUrFnmXcvC0DhPLjBpxCRBOxCVL3bAChBNhpdguLgHCibhkqRtWgHAirBTbxSVAOKEvSzihb2iiQurDCf++Eg8+vi3v6d9zwv/AA5u3yi2rN3j/vOqyuXLbsoXeY0f9F0/rMLEUqRFFgHAiihr7mBQgnDCpSa0oAoQTUdTYx6QA4YRJTWpFESCciKI2fB/CCX1DExWshRNq8MWXTJiYkI0ahBM21DmmEiCcYB3YFiCcsN0Bjk84wRqwLUA4YbsDHJ9wQn8NEE7oG5qoYDWcUJdQ3PfAY7JsyYJhZyKYmNhI1iCcGEltjlUoQDjBerAtQDhhuwMcn3CCNWBbgHDCdgc4PuGE/hognNA3NFHBWjhR7kkdamK2ntYRBZVwIooa+5gQIJwwoUgNHQHCCR099jUhQDhhQpEaOgKEEzp67GtCgHBCX5FwQt/QRAVr4YSJwbtSg3DClU6kbxyEE+nruWszJpxwrSPpGw/hRPp67tqMCSdc60j6xkM4od9zwgl9QxMVCCcMKBJOGECkRCQBwolIbOxkUIBwwiAmpSIJEE5EYmMngwKEEwYxKRVJgHAiEtuwnQgn9A1NVLAaThQ+KeOESV2ybvVSmTJpgty6ZoPMnXOmXDvvYhNzjL0G4UTsxByghADhBEvDtgDhhO0OcHzCCdaAbQHCCdsd4PiEE/prgHBC39BEBavhhP+0jisvnStr7rlfvnztZ2TG1CnyzPad8qNNW457ZKeJCcdRg3AiDlVqhhEgnAijxDZxChBOxKlL7TAChBNhlNgmTgHCiTh1qR1GgHAijFL5bQgn9A1NVLAWTqgbYq64fb0su26Bd7ZEYTihnuKx5u77ZeVNi2RcZ4eJecZag3AiVl6KlxEgnGB52BYgnLDdAY5POMEasC1AOGG7AxyfcEJ/DRBO6BuaqOBkOMGZEyZaS400CBBOpKHLbs+RcMLt/qRhdIQTaeiy23MknHC7P2kYHeGEfpcJJ/QNTVSwFk6owT+weatse/YlWfFnX5a/3vB/vcs6xo/tkCXL75T5V1/CPSdMdJgaiRYgnEh0e2ticoQTNdGmRA+ScCLR7a2JyRFO1ESbEj1Iwgn99hJO6BuaqGA1nFATUGdJfO2bq4bN5d67lsv5s2eamN+I1OCyjhFh5iABAoQTLAvbAoQTtjvA8QknWAO2BQgnbHeA4xNO6K8Bwgl9QxMVrIcTJiZhuwbhhO0OpPf4hBPp7b0rMyeccKUT6R0H4UR6e+/KzAknXOlEesdBOKHfe8IJfUMTFayGE+ppHe+9v2/YUzn8x4vyKFET7aVG0gUIJ5LeYffnRzjhfo+SPkLCiaR32P35EU6436Okj5BwQr/DhBP6hiYqWAsn/BDii1dfctwlHNwQ00RrqZEGAcKJNHTZ7TkSTrjdnzSMjnAiDV12e46EE273Jw2jI5zQ7zLhhL6hiQrWwonCR4nOmDpl2Fx4lKiJ1lIjDQKEE2nosttzJJxwuz9pGB3hRBq67PYcCSfc7k8aRkc4od9lwgl9QxMVrIUTnDlhon3USLsA4UTaV4D9+RNO2O9B2kdAOJH2FWB//oQT9nuQ9hEQTuivAMIJfUMTFayFE2rw6vKNFSvXy7rVS8U/e0KdNbH4xrVy3Vev4VGiJjpMjUQLEE4kur01MTnCiZpoU6IHSTiR6PbWxOQIJ2qiTYkeJOGEfnsJJ/QNTVSwGk6oCfhhxLt79ubnw6NETbSWGmkQIJxIQ5fdniPhhNv9ScPoCCfS0GW350g44XZ/0jA6wgn9LhNO6BuaqGA9nDAxCds1eJSo7Q6k9/iEE+ntvSszJ5xwpRPpHQfhRHp778rMCSdc6UR6x0E4od97wgl9QxMVCCcMKBJOGECkRCQBwolIbOxkUIBwwiAmpSIJEE5EYmMngwKEEwYxKRVJgHAiEtuwnQgn9A1NVLAaTqgndixZfqe8sGPXcXOZdcZ0uWfV9TKus8PEPGOtQTgRKy/FywgQTrA8bAsQTtjuAMcnnGAN2BYgnLDdAY5POKG/Bggn9A1NVLAaTtyxbqM3hxsWzzcxF2s1CCes0af+wIQTqV8C1gEIJ6y3IPUDIJxI/RKwDkA4Yb0FqR8A4YT+EiCc0Dc0UcFaOKHOmlhx+3pZdt2C/JM6TEzIRg3CCRvqHFMJEE6wDmwLEE7Y7gDHJ5xgDdgWIJyw3QGOTzihvwYIJ/QNTVQgnDCgSDhhAJESkQQIJyKxsZNBAcIJg5iUiiRAOBGJjZ0MChBOGMSkVCQBwolIbMN2IpzQNzRRwVo4oQavLuuYdvJkuXbexSbmYq0G4YQ1+tQfmHAi9Y/JKhkAACAASURBVEvAOgDhhPUWpH4AhBOpXwLWAQgnrLcg9QMgnNBfAoQT+oYmKlgNJ159Y7fc98BjsmzJAmlrbTYxHys1CCessHNQLutgDTggQDjhQBNSPgTCiZQvAAemTzjhQBNSPgTCCf0FQDihb2iigrVwotyTOtTEeFqHifZSI+kCnDmR9A67Pz/CCfd7lPQREk4kvcPuz49wwv0eJX2EhBP6HSac0Dc0UcFaOGFi8K7U4MwJVzqRvnEQTqSv567NmHDCtY6kbzyEE+nruWszJpxwrSPpGw/hhH7PCSf0DU1UIJwwoEg4YQCREpEECCcisbGTQQHCCYOYlIokQDgRiY2dDAoQThjEpFQkAcKJSGzDdiKc0Dc0UcF6OPHM9p3ytW+uGjaXe+9aLufPnmlifiNSg3BiRJg5SIAA4QTLwrYA4YTtDnB8wgnWgG0BwgnbHeD4hBP6a4BwQt/QRAWr4YQKJtau2yj3rLpexnV2ePNRN8lcfONaue6r19TMUzwIJ0wsRWpEESCciKLGPiYFCCdMalIrigDhRBQ19jEpQDhhUpNaUQQIJ6KoDd+HcELf0EQFa+FET2+/3Lpmg3zx6kuOO0tChRY/2rRFblu2sCae4kE4YWIpUiOKAOFEFDX2MSlAOGFSk1pRBAgnoqixj0kBwgmTmtSKIkA4EUWNcEJfzXwFa+GEelrHitvXy7LrFsiMqVOGzUydPbHm7vtl5U2L8mdUmJ/6UEU/JHnw8W35QxRfVvLA5q1yy+oN3uevumzucaEJ4URc3aFuJQHCiUpCfD5uAcKJuIWpX0mAcKKSEJ+PW4BwIm5h6lcSIJyoJFT585w5UdloJLawFk64cuaECkm+d/9DsuSrf+CdpaHO2lixcr2sW73UC02KLz25Y91Gry83LJ6f7w/hxEgsVY4RJEA4wbqwLUA4YbsDHJ9wgjVgW4BwwnYHOD7hhP4aIJzQNzRRwVo4oQavzkjYuGmLU/ecUGHFkuV3ytLF873LTVQYMe3kyfn7XwTdJ4NwwsRSpEYUAcKJKGrsY1KAcMKkJrWiCBBORFFjH5MChBMmNakVRYBwIora8H0IJ/QNTVSwGk6oCbj2tA51ScnNK9fLt1cskimTJnj3xZg758x8OFH4ef9yFMIJE0uRGlEECCeiqLGPSQHCCZOa1IoiQDgRRY19TAoQTpjUpFYUAcKJKGqEE/pq5itYDyfMTyl6Rf9SEz+MCLr0JCiciH5E9kQAAQQQQMBtgf7BrDQ31rs9SEaHAAIIIIAAAjUvYDWcUJdMvPf+vmE3mCwOCEZK2D/u5Inj8/eTCBoLZ06MVEc4ThgBzpwIo8Q2cQpw5kScutQOI8CZE2GU2CZOAc6ciFOX2mEEOHMijFL5bbisQ9/QRAVr4YQrN8RUiEHBhI/LPSdMLDNqxCVAOBGXLHXDChBOhJViu7gECCfikqVuWAHCibBSbBeXAOGEvizhhL6hiQrWwgnXHiVaeF+JQlie1mFimVEjLgHCibhkqRtWgHAirBTbxSVAOBGXLHXDChBOhJViu7gECCf0ZQkn9A1NVLAWTrhy5oS6TGPxjWvl3T17h3l+40vz8pd3qKeK3LJ6g/f5qy6bO+wyFPUxbohpYilSI4oA4UQUNfYxKUA4YVKTWlEECCeiqLGPSQHCCZOa1IoiQDgRRW34PoQT+oYmKlgLJ9Tg1VkJK1aul3Wrl4r/5As/LLjuq9fkn5BhYqJx1iCciFOX2uUECCdYH7YFCCdsd4DjE06wBmwLEE7Y7gDHJ5zQXwOEE/qGJipYDSfUBILOXLj3ruVy/uyZJuY3IjUIJ0aEmYMECBBOsCxsCxBO2O4AxyecYA3YFiCcsN0Bjk84ob8GCCf0DU1UsB5OmJiE7RqEE7Y7kN7jE06kt/euzJxwwpVOpHcchBPp7b0rMyeccKUT6R0H4YR+7wkn9A1NVCCcMKBIOGEAkRKRBAgnIrGxk0EBwgmDmJSKJEA4EYmNnQwKEE4YxKRUJAHCiUhsw3YinNA3NFGBcMKAIuGEAURKRBIgnIjExk4GBQgnDGJSKpIA4UQkNnYyKEA4YRCTUpEECCcisRFO6LMZr0A4YYCUcMIAIiUiCRBORGJjJ4MChBMGMSkVSYBwIhIbOxkUIJwwiEmpSAKEE5HYCCf02YxXIJwwQEo4YQCREpEECCcisbGTQQHCCYOYlIokQDgRiY2dDAoQThjEpFQkAcKJSGyEE/psxisQThggJZwwgEiJSAKEE5HY2MmgAOGEQUxKRRIgnIjExk4GBQgnDGJSKpIA4UQkNsIJfTbjFQgnDJASThhApEQkAcKJSGzsZFCAcMIgJqUiCRBORGJjJ4MChBMGMSkVSYBwIhIb4YQ+m/EKhBMGSAknDCBSIpIA4UQkNnYyKEA4YRCTUpEECCcisbGTQQHCCYOYlIokQDgRiY1wQp/NeAXCCQOkhBMGECkRSYBwIhIbOxkUIJwwiEmpSAKEE5HY2MmgAOGEQUxKRRIgnIjERjihz2a8AuGEAVLCCQOIlIgkQDgRiY2dDAoQThjEpFQkAcKJSGzsZFCAcMIgJqUiCRBORGIjnNBnM16BcMIAKeGEAURKRBIgnIjExk4GBQgnDGJSKpIA4UQkNnYyKEA4YRCTUpEECCcisRFO6LMZr0A4YYCUcMIAIiUiCRBORGJjJ4MChBMGMSkVSYBwIhIbOxkUIJwwiEmpSAKEE5HYCCf02YxXIJwwQEo4EQ3xjdcPyLu7u2Xf3h451N0nhw72yqGDfXLokPrTK92H+r2/+/oy0q/+DAz9PaD+7h/6M9CflSNH+ocNYNSoJmlorJPGxnppbGw49ne9NDTUS1NTnfe3+lyD93n1sQZR+4wZ0yId3p9m6RzT5v09Zkyr97f6eHt7s3R0NEt7R4uMH98WbdKG9yKcMAxKuaoFCCeqJmMHwwKEE4ZBKVe1AOFE1WTsYFiAcEIfdEqXGz/b68+ktisQThjoH+FEacQ33zwor/xmr+x6dZ+8tmu/vP7agWN/9huQt19ChRQqrFChhQowRo9uko6OFmlX//Y+1poPNDram2VMZ6v3uc7OFu+/dUMOwgn7ayDtIyCcSPsKsD9/wgn7PUj7CAgn0r4C7M+fcEK/B4QT+oYmKhBOGFAknBhCfP65PbLjpd/Kzpd+K89tf09efH7PcWc1FHJPnDRapk0bJ51j1Rv1Fukc0yqdY9WZCkNv3MeMaZaxY9ukuaVBmpsavL/VWQ7F/1YhgP/q7R2UwcGcZAazMjCY8f4eHMxKJpOVgYGhjw9mjn0sv01Ourv78mdqHD7cP3QWx6HffezokQE5cLBXDnf3y+HDfXLwQJ+BlTNUwjtrwwsrWmRsZ5u0jWqUtrYmaW1VfzdKa1uT93fbqGZvWxWAjBrV7P096UOjJFdXJ339GWPjsVkom82J/yeXy0kmU/Bv/3Pex7OSzYrkCj7m7VewvffvXM7bxt9efczbJzdUV9XPqUK8Igu0tTTIwGDO+7rihYANgVGtjdLbn/G+pnkhYENgdFujHO3NiPr/Fi8EbAi0tzXJ4Z4BG4dOzDHXrv5sYuZSyxMhnDDQvbSFE319g/Li8+/L88+95/3Z8avfygvP7wmUHDuuVc46e6JMO3WcnDp9vEyfMU5Omdopp04f5735ruVXT8+AF1Z4wUZ3v6gAQwUa/sfU34e6hwINtY0fbHiXrhzsk4OHeo2GHLVsydgRQAABBBBAAAEEELAlkMvdauvQHLdAgHDCwHJIcjihzkRQwcPz24eCiF+98L53dkTQ65SpY+WsWRNl1jmT5OxZk+TMsz4kJ0zpMCCc7BIq2Dh44NiZGt19XsjR2zMgPT2DovxVCNLbM/S3Oqvj6NEBOXpk6O++3kEZGMhKJiG/Mayrq5P6epH6evV3nah/NzQM/bf372N/13sfr/e2zX+svk4aCrcp2Lbe2/Z3tb19Cmone4XFOzvOnIjXl+qVBThzorIRW8QrwJkT8fpSvbIAZ05UNqq0BWdOVBIamc8TThhwTlI4cWB/r/zzU2/KM798R7b94i3v8oyg1+kfHi+zzp0s55w7Wc49b7KcedYkaW9vMqBJiWoEuOdENVpsG4cA95yIQ5Wa1Qhwz4lqtNg2DgHuORGHKjWrEeCeE9VoBW/LPSf0DU1UIJwwoFjL4YS6YeUz296Rbf/8pvxy29vyysv7homo31rPOH28F0KoP7POneSdGaHuicDLvgDhhP0epH0EhBNpXwH25084Yb8HaR8B4UTaV4D9+RNO6PeAcELf0EQFwgkDirUUTnzwwVH5+ZY35Oc/e02eevJNefutQ8ME1H0gzpszRS648ES58GMnywVzTySIMLBG4ipBOBGXLHXDChBOhJViu7gECCfikqVuWAHCibBSbBeXAOGEvizhhL6hiQqEEwYUXQ4nug/1y1NPviFPblV/XpeXfzP8zAh1w8rzL1BBxCleIKEu0WhsrDegQomRECCcGAlljlFOgHCC9WFbgHDCdgc4PuEEa8C2AOGEfgcIJ/QNTVQgnDCg6FI4oW6g+PQ/vy1P/vx1eWrrm97NLAsfrzZ6dLNc+LGT5KJPTZVPXDRVzjxrotTVGUCghBUBwgkr7By0QIBwguVgW4BwwnYHOD7hBGvAtgDhhH4HCCf0DU1UIJwwoGg7nPjNzr3y0yd2yU8f3+UFE/39mfysmpobZM7vTZGLPjVNPnHRKXLeR0/gzAgDPXelBOGEK51I7zgIJ9Lbe1dmTjjhSifSOw7CifT23pWZE07od4JwQt/QRAXCCQOKIx1OqEdPPvmz1+WJx3fJlidek93vdOdnoR6XqG5Y+YmLT5FPXjTNO0tC3UeCVzIFCCeS2ddamhXhRC11K5ljJZxIZl9raVaEE7XUrWSOlXBCv6+EE/qGJioQThhQHIlw4sUX3pctj7/mBRLPPvOODA5m8yP/0IdGySWXnSqfvmy6XPLp6dI5tsXArChRCwKEE7XQpWSPkXAi2f2thdkRTtRCl5I9RsKJZPe3FmZHOKHfJcIJfUMTFQgnDCjGEU6oe0ds3fK6/NPDr8jjj74q7+85kh+pumHlR+dMkU9/Zrp8+rJTvTMleKVTgHAinX13adaEEy51I51jIZxIZ99dmjXhhEvdSOdYCCf0+044oW9oogLhhAFFU+GECiAe3vyyPPrIK/LUz9+Uvr7B/OgmTW6XT196qhdIfOqSU6VjTLOBkVOi1gUIJ2q9g7U/fsKJ2u9hrc+AcKLWO1j74yecqP0e1voMCCf0O0g4oW9oogLhhAHFqOFELiey/V/f9cII9eelX/02Pxr1BI1zZk+Wz15+mvfn7FkTDYyUEkkTIJxIWkdrbz6EE7XXs6SNmHAiaR2tvfkQTtRez5I2YsIJ/Y4STugbmqhAOGFAsZpw4ujRAfnZE695YcQTj+2S3/72aH4Eo0Y1ycWXTJPPXH6afO6K06Srq83A6CiRZAHCiSR3tzbmRjhRG31K8igJJ5Lc3dqYG+FEbfQpyaMknNDvLuGEvqGJCoQTBYp3rNso006eLNfOu3iY7QObt8otqzd4H7vqsrly27KF0tb6u8sqKoUTBw/0ySMPvywP/uTX3n0kCh/1eeJJY+Qzn5vhnR3xyYtOEfXoT14IhBUgnAgrxXZxCRBOxCVL3bAChBNhpdguLgHCibhkqRtWgHAirFTp7Qgn9A1NVCCcEJHC8OFbNy4cFk48s32nrF23Ue5Zdb2M6+wQFWCo1w2L5+f9g8KJ375/RB568GV5cNNO2faLt/NP11CP+jxvzgleGKFCiTPO/JCJPlIjpQKEEyltvEPTJpxwqBkpHQrhREob79C0CSccakZKh0I4od94wgl9QxMVCCcKFIPOnCj+WHFYoXb3w4l33j4kD276tTz4k9/Is//yjqh7SqhXY1O9fOKTp8i83/+IXHHV6TJhwigTvaMGAkI4wSKwLUA4YbsDHJ9wgjVgW4BwwnYHOD7hhP4aIJzQNzRRgXCiTDjR09svt67ZIHPnnJk/m+LVN3bLzSvXy7dXLJIZU6fIzp0fyL1/94I8+JOd8uIL7+ertbY2yqc+Pc0LJC6/8nSermFitVLjOAHCCRaFbQHCCdsd4PiEE6wB2wKEE7Y7wPEJJ/TXAOGEvqGJCoQTIcKJL159iZw/e6a3ZXE4UVd3W75CR0eLXH7lDLnmmjPkc5fPkLZRTSZ6RA0ESgq0NtVLJicyMJhFCQErAm3NDTKQycqgWoi8EimQy+WkTj1CytHXqJYG6RvISibLGnS0RYkfVntroxztGxSWYOJb7ewEO9oa5XDPoPBdMHqLlCEv+wKEEyHCiXJnTnz849+V007vks9fM1M+fek0aeaGlvZXdYpG0NLcINlsjnAiRT13baptLQ0yMJiTwQwBmWu9MTWenNRJncM/8o5qbZTe/oz3vZAXAjYERrc1ytHejKggjxcCNgTa25rkcM+AjUMn5pgd/FLZiV4STpQJJ9SnqrnnhBMdZRCpEuCyjlS128nJclmHk21J1aC4rCNV7XZyslzW4WRbUjUoLuvQbzeXdegbmqhAOFEhnIj6tA4TzaEGApUECCcqCfH5uAUIJ+IWpn4lAcKJSkJ8Pm4Bwom4halfSYBwopJQ5c8TTlQ2GoktCCeKHiWq0E+Y1CXrVi/1bnipXoWPGr3qsrly27KF0tbanO9P0KNER6J5HAMBwgnWgG0BwgnbHeD4hBOsAdsChBO2O8DxCSf01wDhhL6hiQqEEwYUCScMIFIikgDhRCQ2djIoQDhhEJNSkQQIJyKxsZNBAcIJg5iUiiRAOBGJbdhOhBP6hiYqEE4YUCScMIBIiUgChBOR2NjJoADhhEFMSkUSIJyIxMZOBgUIJwxiUiqSAOFEJDbCCX024xUIJwyQEk4YQKREJAHCiUhs7GRQgHDCICalIgkQTkRiYyeDAoQTBjEpFUmAcCISG+GEPpvxCoQTBkgJJwwgUiKSAOFEJDZ2MihAOGEQk1KRBAgnIrGxk0EBwgmDmJSKJEA4EYmNcEKfzXgFwgkDpIQTBhApEUmAcCISGzsZFCCcMIhJqUgChBOR2NjJoADhhEFMSkUSIJyIxEY4oc9mvALhhHFSCiKAAAIIIIAAAggggAACCCCAQDUChBPVaLEtAggggAACCCCAAAIIIIAAAggYFyCcME5KQQQQQAABBBBAAAEEEEAAAQQQqEaAcKIaLbZFAAEEEEAAAQQQQAABBBBAAAHjAoQTEUkf2LxVblm9wdv7qsvmym3LFkpba3PEauyGQLBAT2+/3Lpmgzz4+DZvg2/duFCunXdxSa471m2U7/5gc/7zlbbHHYFKAtWuwcJ6aj3+cvtOuWfV9TKus6PSofg8AoECUdbgq2/slsU3rpV39+yVEyZ1ybrVS2XG1CkIIxBJYP/Bblmy/E55Yccub/9771ou58+eWbJW4foL8//uSINiJwQKBNT/b6edPLnsz4iAIVALAoQTEbr0zPadsnbdxvwP3OobgnrdsHh+hGrsgkBpgcK15f9wtHTx/MAfitQP8Pd8///J1xdc6b0R9H84WrliUdkfovBHoJxANWuwOJhQQdmsM6YTTrDEtASqXYPqe9/NK9fLt1csIpDQkmdnJeCHY3PnnOm98au0vor/X13p/90oI6AjUPjLUn4hpSPJvq4IEE5E6ERxOlkcVkQoyS4IHCegfqBZcft6WXbdgvwP2NUEYcU/UEGMQLUCUdeg+mHp9bfek4suPGdYkFvt8dkegWrXoP9974tXX0Ioy/IxIqDCiDV33y8rb1rkBf+V/t9aHF5U2t7IICmSegHOnEj9EkgMAOFEla0M+p9MpRS9ykOwOQKeQNC6Um/6tj37UqjLiPhtDQtJVyDKGixcoy/u3EU4oduElO9f7RosPv1e8XHpZcoXkeb0g34BVekXBerzm5942rucSL0Kww3N4bA7AoEChBMsjKQIEE5U2cmg38oQTlSJyOahBIp/W6N2qiacqPTDU6hBsFGqBapdg+qH+B9t2pIPzzirLNXLx8jkq12DpX7LPXnieC69NNKR9BUp/r6mBCr9/9X73vedH8oH+w9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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Same plot, but with smooth line\n", "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"B\"], \n", " title=\"Changes in concentrations with time (smoothed)\",\n", " color_discrete_sequence = ['navy', 'darkorange'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"},\n", " line_shape=\"spline\")\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "8d1fb634-3575-44fb-aa0c-0a9110fe54bc", "metadata": {}, "source": [ "The early **OVERSHOOTING** of the equilibrium values shows prominently in the last plot!" ] }, { "cell_type": "code", "execution_count": null, "id": "a84d9709", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }