{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "### A simple `A <-> B` reaction between 2 species with initial uniform concentrations across 3 bins,\n", "with 1st-order kinetics in both directions, taken to equilibrium\n", "\n", "Diffusion NOT taken into account\n", "\n", "See also the experiment _\"reactions_single_compartment/react_1\"_ \n", "\n", "LAST REVISED: July 14, 2023" ] }, { "cell_type": "code", "execution_count": 1, "id": "7245be7a-c9db-45f5-b033-d6c521237a9c", "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": "3cddd49a", "metadata": { "tags": [] }, "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": "cc53849f-351d-49e0-bfa8-22f8d8e22f8e", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-> Output will be LOGGED into the file 'reaction_1.log.htm'\n" ] } ], "source": [ "# Initialize the HTML logging (for the graphics)\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": "8af818bc-9bac-4ec7-9672-b76223876595", "metadata": {}, "source": [ "# Initialize the System" ] }, { "cell_type": "code", "execution_count": 4, "id": "de47e1df-67cf-4200-a580-fbcf50ebd1c0", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "3 bins and 2 species:\n", " Species 0 (A). Diff rate: None. Conc: [10. 10. 10.]\n", " Species 1 (B). Diff rate: None. Conc: [50. 50. 50.]\n" ] } ], "source": [ "# Initialize the system\n", "chem_data = chem(names=[\"A\", \"B\"]) # Diffusion NOT taken into account\n", "bio = BioSim1D(n_bins=3, chem_data=chem_data) # We'll specify the reactions later\n", "\n", "bio.set_uniform_concentration(species_name=\"A\", conc=10.)\n", "bio.set_uniform_concentration(species_name=\"B\", conc=50.)\n", "\n", "bio.describe_state()" ] }, { "cell_type": "code", "execution_count": 5, "id": "5562fea2-834e-40a9-9b1d-5ea28a0100bf", "metadata": {}, "outputs": [], "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\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "3d22b349-c063-4c2d-9cde-ebd87bda6901", "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", "
SYSTEM TIMEABcaption
0010.050.0Initial state
\n", "
" ], "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": [ "bio.get_history()" ] }, { "cell_type": "code", "execution_count": 7, "id": "25155c63-e53c-41e1-be1e-41577158a4ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 1\n" ] } ], "source": [ "# Specify the reaction\n", "\n", "# Reaction A <-> B , with 1st-order kinetics in both directions\n", "chem_data.add_reaction(reactants=[\"A\"], products=[\"B\"], forward_rate=3., reverse_rate=2.)\n", "\n", "print(\"Number of reactions: \", chem_data.number_of_reactions())" ] }, { "cell_type": "code", "execution_count": 8, "id": "16396b37-96ce-4b6d-a415-dc8f72b04559", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 1 (at temp. 25 C)\n", "0: A <-> B (kF = 3 / kR = 2 / Delta_G = -1,005.13 / K = 1.5) | 1st order in all reactants & products\n" ] } ], "source": [ "chem_data.describe_reactions()" ] }, { "cell_type": "code", "execution_count": 9, "id": "00a39103-a470-490a-8165-bd58c6f70fb6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[GRAPHIC ELEMENT SENT TO LOG FILE `reaction_1.log.htm`]\n" ] } ], "source": [ "# Send a plot of the network of reactions 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": "0b46b395-3f68-4dbd-b0c5-d67a0e623726", "metadata": { "tags": [] }, "source": [ "### First step" ] }, { "cell_type": "code", "execution_count": 10, "id": "bcf652b8-e0dc-438e-bdbe-02216c1d52a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.1:\n", "3 bins and 2 species:\n", " Species 0 (A). Diff rate: None. Conc: [17. 17. 17.]\n", " Species 1 (B). Diff rate: None. Conc: [43. 43. 43.]\n" ] } ], "source": [ "# First step of reaction\n", "bio.react(time_step=0.1, n_steps=1)\n", "bio.describe_state()" ] }, { "cell_type": "markdown", "id": "7dc56592-179d-4e4c-b75a-8eb81dcafe71", "metadata": {}, "source": [ "NOTE: the concentration of species A is increasing, while that of species B is decreasing.\n", "All bins have identical concentrations; so, there's no diffusion (and we're not attempting to compute it): \n", "[[17. 17. 17.] \n", " [43. 43. 43.]]" ] }, { "cell_type": "code", "execution_count": 11, "id": "54ee2411-de4e-4895-91a9-dfb4cab743ec", "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
10.117.043.0
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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 17.0 43.0 " ] }, "execution_count": 11, "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))\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "82a62165-425d-4e8d-abac-01d579dfd1ae", "metadata": {}, "source": [ "### Numerous more steps" ] }, { "cell_type": "code", "execution_count": 12, "id": "cf6a7337-8e2e-4c02-9bb3-85052f37144f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 1.1:\n", "3 bins and 2 species:\n", " Species 0 (A). Diff rate: None. Conc: [23.99316406 23.99316406 23.99316406]\n", " Species 1 (B). Diff rate: None. Conc: [36.00683594 36.00683594 36.00683594]\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": "962acf15-3b50-40e4-9daa-3dcca7d3291a", "metadata": {}, "source": [ "### Equilibrium" ] }, { "cell_type": "markdown", "id": "809b4afa-fb2f-4ac3-92c9-083fc487c81b", "metadata": {}, "source": [ "NOTE: Consistent with the 3/2 ratio of forward/reverse rates (and the 1st order reactions),\n", " the systems settles in the following equilibrium:\n", "\n", "[A] = 23.99316406\n", " \n", "[B] = 36.00683594\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "490fdc0f-fa2a-48d8-ae04-7c80d298842a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'A': 23.9931640625, 'B': 36.0068359375}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.bin_snapshot(bin_address = 0)" ] }, { "cell_type": "code", "execution_count": 14, "id": "f22258b2-5181-4ff9-b379-f6a12ad5c8fb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A <-> B\n", "Final concentrations: [B] = 36.01 ; [A] = 23.99\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 1.50071\n", " Formula used: [B] / [A]\n", "2. Ratio of forward/reverse reaction rates: 1.5\n", "Discrepancy between the two values: 0.04749 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 14, "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": 15, "id": "382e3253-045e-40a5-beab-c1c6e8608334", "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 17.000000 43.000000 \n", "2 0.2 20.500000 39.500000 \n", "3 0.3 22.250000 37.750000 \n", "4 0.4 23.125000 36.875000 \n", "5 0.5 23.562500 36.437500 \n", "6 0.6 23.781250 36.218750 \n", "7 0.7 23.890625 36.109375 \n", "8 0.8 23.945312 36.054688 \n", "9 0.9 23.972656 36.027344 \n", "10 1.0 23.986328 36.013672 \n", "11 1.1 23.993164 36.006836 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save the state of the concentrations of all species at bin 0\n", "#bio.save_snapshot(bio.bin_snapshot(bin_address = 0))\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "cbf6c9c7-8cec-400f-9e70-49ff1a9f485c", "metadata": { "tags": [] }, "source": [ "## Plots of changes of concentration with time" ] }, { "cell_type": "code", "execution_count": 16, "id": "665dfff9-e943-44e1-b76d-af363d94c9f8", "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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}, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "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 concentrations with time" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0, 1.0999999999999999 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ 7.777777777777778, 52.22222222222222 ], "title": { "text": "concentration" }, "type": "linear" } } }, "image/png": 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xjW6G2dMuXy46+2wX1L7bauX2qZclO342cFhGJuatlvHFNzp/xhb/Myn1XRRiFhyCgF0ChNl29ZPZ2CNAmG1PL5mJfQKE2fb1lBnZI0CYbU8vmYldAoTZdvVTzSaOMFtd11+hHdzz2t9n2g+8G63M9vev9oNxvzNLFi2obPnRLLgOs2e2P64KtL/7/f2VG8Cv1Q/h33rnfedn6nX/QwXchNmG/M5EGmZPT7el58wvKuF274kXJTv2cdVRk3N/W8accHu9jC1V4fYlhshRBgLdEyDM7p4tIyPQiQBhdid6nItAdwUIs7vry+gIdCJAmN2JHuci0D0Bwuzu2cY1clxhdlzzTeJ12TNbQ9fiDbOnTyB/9p+kV+25fUJtTfKC5EaOV4fbc1a5D5T0wu3irCUaFBgCAbMECLPN6gfVIOALEGZzLyBgrgBhtrm9oTIECLO5BxAwU4Aw28y+dFIVYXYnetGcS5itwdm0MLt2Svnh992V2ydeELVyO3f+UNUhxTkrZGzROhlfsl7GF62TyYFVGlQYAoF4BQiz4/Xn6gjMJECYzb2BgLkChNnm9obKECDM5h5AwEwBwmwz+9JJVYTZnehFcy5htgZn08Ps2inmLhyX3uN/Jz0q3D75ouTP/ao63J611Am1VbitHiw5OfjbGpQYAoFoBQizo/XmagiEFSDMDivFcQhEL0CYHb05V0QgrABhdlgpjkMgWgHC7Gi9o7gaYXYUyp1dgzC7Mz/n7KSF2bVTVntsq3BbrdxWW5P0nH1HRMqVw4p9i7xw232o5MS8KzSoMQQC3RUgzO6uL6Mj0K4AYXa7cpyHQPcFCLO7b8wVEGhXgDC7XTnOQ6C7AoTZ3fWNY3TC7DjUW7smYXZrXnWPTnqYPT3cPisFtef2cXflds/pfxCRUuWwUu8CGVu8TiYW3+g8WHJi/udEJKNBkiEQ0CdAmK3PkpEQ0ClAmK1Tk7EQ0CtAmK3Xk9EQ0ClAmK1Tk7EQ0CevHuPHAAAgAElEQVRAmK3P0pSRCLNN6cTMdRBma+iRbWF2LUlm4rwTavsrtwuf/EykPDkVbhfmyfiiG5xV2064fdEXRDJZDbIMgUD7AoTZ7dtxJgLdFCDM7qYuYyPQmQBhdmd+nI1ANwUIs7upy9gItC9AmN2+nalnEmab2pmpugizNfTI9jB7WrhdHJHCyVcqD5Ts+finkimNVQ4r9wzI2MLrKw+UHL/4iyKZnAZphkAgvABhdngrjkQgSgHC7Ci1uRYCrQkQZrfmxdEIRClAmB2lNtdCILwAYXZ4q6QcSZhtfqcIszX0KG1h9rRwuzQmhY9ek8KJl6Rw4oAUTr0mmeKFqXA7P0fGF17rPVBynUxc/CUpZ3s0yDMEAjMLEGZzdyBgpgBhtpl9oSoElABhNvcBAuYKEGab2xsqS7cAYbZ9/SfMNr+nhNkaepT2MHsaYWlSCh+/4e63feIF6T31imQmhqfC7Vy/jF/yZXfl9uJ1ztflbK+GTjAEAlMChNncDQiYKUCYbWZfqAoBwmzuAQTMFiDMNrs/VJdeAcJs+3pPmG1+TwmzNfSIMLsJYrkkPad/Lr0nXpDCiRelcOolyY6dmQq3s70yfsk1Mr5onRtwL1wr5Vyfhs4wRJoFCLPT3H3mbrIAYbbJ3aG2tAuwMjvtdwDzN1mAMNvk7lBbmgUIs+3rPmF2dU9ff/Nd+da3d8if33en3LZhvRENJ8zW0AbC7FYRy9Jz9m0pHFfh9gvSe/IlyY6eCoTbPTKx4GoZU6u2Vbi96Hop52a1ehGOT7kAYXbKbwCmb6wAYbaxraEwBNhmhHsAAYMFCLMNbg6lpVqAMNu+9hNmV/f08d37nBdOnDotD2+5U/r7CrE3nTBbQwsIsztHzA/90tmWpHD8gBROviS5Cx9ODZrJy/iCNe6WJIvXy9iidVLumdP5RRnBagHCbKvby+QSLECYneDmUbr1AqzMtr7FTDDBAoTZCW4epVstQJhtX3sJs6d6emZoWLb/xdPyP3/za7Lz//y+bLnrDrl85dLYm06YraEFhNkaEGuGyJ//wAm1nXD7xEuivp/6yMrERZ+XscU3yrjzZ72UCnP1F8GIiRYgzE50+yjeYgHCbIuby9QSL0CYnfgWMgGLBQizLW4uU0u0AGF2ottXt/i4wuwPPjgrhw6djRx01ar5smLFYN3rqi1GXnj1H+SeTbeLWqF92fLFRmw1Qpit4TYhzNaA2GSI3MhJ6T3+vPtAyRMvSP7crwJnZGRi3me9YPtGGVvyFSn1zu9+UVzBaAHCbKPbQ3EpFiDMTnHzmbrxAoTZxreIAlMsQJid4uYzdaMFCLONbk9bxcUVZj/wwE/k0UdfaKvmTk565JGvyv3331h3CBVg33jt5+WaNatFBduP7d4nT+24W+YPDnRyyY7PJczumFCEMFsDYotDZEdPS+/J56Vw/EVn3221B7dIuTLK5LzVMrboRhlfosLtm6TUe3GLV+DwpAsQZie9g9RvqwBhtq2dZV42CBBm29BF5mCrAGG2rZ1lXkkXIMxOegen1x9XmP1Xf/X38vTTb0UO+qd/+kX5xjeumnbd9w4dk11PPiPb79/ohNdqy5HNW5+Qezfd7oTbcX4QZmvQJ8zWgNjhENnxc1I4obYkeVF6T74gPaf/QaRcnAq35/62s9f2xJL1Trhd7F/U4RU53XQBwmzTO0R9aRUgzE5r55l3EgQIs5PQJWpMqwBhdlo7z7xNFyDMNr1DrdcXV5jdeqXdPePZ/QfkwZ17p13kT/5og7PtSJwfhNka9AmzNSBqHiIzeV56T74sPccPSO/Jl6Tnk7+XTGliKtye8xkZX7ROxp1we50UZ6/UXAHDxS1AmB13B7g+AvUFCLO5MxAwV4Aw29zeUBkChNncAwiYKUCYbWZfOqmKMFtkZHRcHtq1V9ZefWXVHtm1q7U7ce7kXMLsTvS8cwmzNSB2eYhMcVQKp16VwvG/k8JJtXr7xaorFvsXy/jC6yRTnpTJgVVSmn2pTM5ZKcU5K5ygu9Q7r8sVMrxuAcJs3aKMh4AeAcJsPY6MgkA3BAizu6HKmAjoESDM1uPIKAjoFiDM1i0a/3iE2SIqtH5g+x55ZNtGuXzl0kpT/JD767feFOtWI4TZGn5PCLM1IMYwhAq01X7bzp9Tr0mmeGHGKsr5OVPhthNyr5TigAq6V8jkgAq72ZM7hhY2vCRhtmkdoR4EXAHCbO4EBMwVIMw2tzdUhgBhNvcAAmYKEGab2ZdOqiLM7kQvmnMJszU4E2ZrQDRgiMJHr0n+3PuSu3BEsuc+kPynhyU3fFjyw79uWl05N0uKc5ZXAu/SnMtkcs5yJ+wuDqwUtfKbj2gFCLOj9eZqCIQVIMwOK8VxCEQvQJgdvTlXRCCsAGF2WCmOQyBaAcLsaL2juBphdhTKnV2DMLszP+dswmwNiIYPkRs5KbnzhyWnAm71+fwhyQ0fcr7PDx+WTPHThjMoZ3vdLUtU4K22LVEB95yVXuC9Uoqz1X+2kTFcIVnlEWYnq19Umx4Bwuz09JqZJk+AMDt5PaPi9AgQZqen18w0WQKE2cnqV5hqCbPDKMV7DGG2Bn/CbA2ICR8iO3pacp8ekrwXdGfViu5PD1WC7+zEuSZhd48UZy1zwm61R3dxrgq43T27J9V2JrOWiWRyCVeKtnzC7Gi9uRoCYQUIs8NKcRwC0QsQZkdvzhURCCtAmB1WiuMQiFaAMDta7yiuRpgdhXJn1yDM7szPOZswWwOi5UNkJj+V/PD7TridP39IVNjtrOpWK7zPH5bs2CeNBTI5Kc5a6u3R7e7VXRy4zPvsfl/O9liu2Nr0CLNb8+JoBKISIMyOSprrINC6AGF262acgUBUAoTZUUlzHQRaEyDMbs0rCUcTZpvfJcJsDT0izNaAmPIhMsVRyQ3/xl3NrYLu87+R3Pkj7nYmarX36CkRKTdQyjj7crtbmbjbl6h9u52V3c7DKleK2uokTR+E2WnqNnNNkgBhdpK6Ra1pEyDMTlvHmW+SBAizk9Qtak2TAGG2fd0mzDa/p7GG2WeGhmXz1ifkrXfenyZ11RWr5Kkdd8v8wQHjFQmzjW9R4gvMFMedbUycB1J+eliyw7+p7Nft7ON94biIlBrOs9R7ibtlibN9yXJ3ZbcKv2evlMm5n5Fyrj/xTsEJEGZb1U4mY5EAYbZFzWQq1gkQZlvXUiZkkQBhtkXNZCpWCRBmW9VOZzKE2eb3NNYw+/Hd+xyhezbdbr5UgwoJsxPdPjuKL01K/oJaye2t5nYeTqlWdR9xtzL59EOR8mSTsHu+u42JWsk9e4WU5q6USbWdif9977xEWRFmJ6pdFJsiAcLsFDWbqSZOgDA7cS2j4BQJEGanqNlMNVEChNmJaleoYgmzQzHFelBsYbZalb3t0T2y5a475PKVS2NF8C8+MjouD+3a63z78JY7pb+v4Hz97P4D8uBO9/Vbbl5b9TP1GmG2Ee2jiEYC5ZITaLv7dLv7defOuau7nW1MPj0qmdJYQ8Nyz8BUuK1WdA94D6hUK70HVkip92KjekCYbVQ7KAaBigBhNjcDAuYKEGab2xsqQ4Awm3sAATMFCLPN7EsnVRFmd6IXzbmE2Z6zH2T/8McHqwLr1998Vx7bva+y5Um91eSE2dHcrFylmwJlyV044QXbbuCdVSu6hw8525qoFd6Z4oXGYXdulrN9ibOye84KZ89utXe3s7Jbhd/9i7s5gWljE2ZHys3FEAgtQJgdmooDEYhcgDA7cnIuiEBoAcLs0FQciECkAoTZkXJHcjHC7EiYO7pIbGG2qloFw5ctXyy3bVjf0SR0nOzXosY6+MbbldXXtTXWhtvqeMJsHR1gDNMFsmMfS97ZvuSw95BKtY2J+4BKtZVJZvJ847A72yvF2Wqv7hXOCu+S2rNbbWvi7OOt/lwqIhltDITZ2igZCAGtAoTZWjkZDAGtAoTZWjkZDAGtAoTZWjkZDAFtAoTZ2iiNGYgw222Fyj+/9e0dVX35kz/aYMRW0bGG2e8dOiZPP/u3smXzHZUtPeK4e4OrrdWWIn6YrWpR246svfrKSuCuan5g+x55ZNvGyvYohNlxdI1rmiaQHTvr7dPthtvZYXc7E3fP7sOSHTvTJOzukVL/pW647Wxj4j+gUu3jrcLu5SKZXOhpE2aHpuJABCIVIMyOlJuLIdCSAGF2S1wcjECkAoTZkXJzMQRCCxBmh6ZKzIGE2VNhdnCnCrVd9OatT8i9m26Xa9asjrWfsYXZPsJb77xfF+CqK1ZVtvboppAKr39z5ETlnYV6YfbXb72p0qh6YXY362NsBKwRmBwROfueyLnfiJw7JDLkfVbfDx0SGTnVeKoqyJ6zVGTuZSJzV4oMep/V1+o19X22xxoukycyPlkSFUjygQACCCCAAAIIIGCvAP/ms7e3zAwBBBBoJlC7M4W/PXNwwW+zMbr189jC7G5NqNVx1ars735//7TT1IMet/6vfyw7/vI/szK7VVSOR6BNgfzZf5L8p+7WJdnzRyQ/rB5SeVRy5z+Q3MiJpqMW+xa5D6acvUL6ln9Zzg58QUqlqdPK+X4p9QxKqXdQSn0Lm47HAQggoF+Aldn6TRkRAV0CrMzWJck4COgXYGW2flNGRECHACuzdSiaNUZsK7OHPnAX/kX9MbhKZO6KaVetDbPVouRtj+6RLXfdUdmpIupS/eulPsyuhQ+uzO7vK0zb15s9s+O6VbkuAiL5c7+W3KcfSv78B07YnRv+QPLqswrAPz3aFlGp9xIp9c6TUmGulAuDbthdGHS+Lvuv590AvFyYK6WCd2yP+jzY1jU5CYE0CxBmp7n7zN10AcJs0ztEfWkWIMxOc/eZu8kChNkmd6e92mILs198QOTVR9srupOz1j0icu39dcPs2j2zlyxaILt33kuYXW9D8e99Z2ts+6/Uhtm14XVwf22/0+yZ3clvDecioE8gd/43lXB7sHhSRs6eFBk/K5nxc5IdPyvZ8XOSGR+S7MSQZMdOa7lwuWfACbVVCO6E4c4fFYyrsHu+E4C7IbgXkve4gbj/WjnXr6UOBkEgKQKE2UnpFHWmUYAwO41dZ85JESDMTkqnqDNtAoTZ9nU8tjD7rb8Seefp6EGv+lORK75RN8wO7pmtDqi3wDf6gkViXZldD0HtSb3pvsfkrm9+rfLQxShhasNsdW312oM79zplqO1HHt5yZ9UDKwmzo+wQ10IgnECYB0BmihfcgHvMC7jHhyTjBN3qs3r9jGQnzol6uKUbiHs/V4G4+nryUxEphytohqPK2YKUvdXgzupvP+xWn3tV6O2tCq+3Otx7TSTTUQ2cjECUAoTZUWpzLQRaEyDMbs2LoxGIUoAwO0ptroVAeAHC7PBWSTkytjDbMKB6ma0pW43EFmb7G4cHH67o902B/eC556eFxob1tVIOYbapnaGuNAuECbM79ym7wfekG4CrFd9OMK6C7wk3AM+MqRXhNSG4t1LcCcxL4x2WkZFyfra7VUqPtyLcCcK9leJqxbgfinvbpFQCc2/FOKvDO2wBp7ckQJjdEhcHIxCpAGF2pNxcDIGWBAizW+LiYAQiEyDMjow6sgsRZrvU9cLs1K/MbpTmq9XZu558Rrbfv1HmDw5EdsO2eyHC7HblOA+B7glEE2Z3Xn+mOOqG3X7A7QXh/pYoThiuQnJvNbj7unrN3TolM3Few+rwnsrq8OotUbytUZwwfK6UnH3C/S1U3K1TnFXlvfOE1eGd3wtpGYEwOy2dZp5JFCDMTmLXqDktAoTZaek080yaAGF20jrWvF7C7Kkwu3bP7KuuWCVP7bg79qyWldnN7+OmRxBmNyXiAAQiF0hKmN05TFmy48PVAbe/JYq3P7gKxN0wfGrFuArQ/RA9UxrtuIxyfs7UfuGV0HtqD3EpzHeCcP/hmsEHbToBeX52xzUwQDIECLOT0SeqTKcAYXY6+86skyFAmJ2MPlFl+gQIs+3rOWG2+T2NLcxWNGov6n3PPV+V6se9Z3Y7LSPMbkeNcxDorkB6wmw9jtnRU9VbpXhbpzhbpTj7hp9xA3Pn+2Hn+8qK8YlzWooo9c53Vn9XHo7pP1CzZ1AmL/6CFGevnPk6mYyUc31SzvdLOdvrfd3nfZ6jpT4G0SNAmK3HkVEQ6IYAYXY3VBkTAT0ChNl6HBkFAd0ChNm6ReMfjzA7/h40qyDWMFsVp/ZbqV22/r3vbJVr1qxuVrsxPyfMNqYVFIJARYAwO9qbwd8WZaYtUdTDNP2HaFYf426xomN1eLMZq9Xj5ZwXdKvg2wm/1ed+Ef/1rHpNHdPv/jzXK9IzS8R/XX1WP3OO8b5Wx/hfq9edY/3xWXFe2xfC7GZ3Kj9HID4Bwuz47LkyAs0ECLObCfFzBOIRIMyOx72bVyXM7qaunrFjD7P1TCPeUQiz4/Xn6gjUEyDMTt59kR39yNn6pLKHuPcQTWdvcLWVSvGCZCbHRIqjTvidmRwVtee480d9rbZLcb4ek0xxxDtGfX0hdgw3SPdCbicQnwq83SDcf80L0r3wXPKznJ9VwnY/SA+G6ln180CArn7Wo8abFfu86xVAmG1kWygKAUeAMJsbAQFzBQizze0NlaVbgDDbvv4TZpvfU8JsDT0izNaAyBAIaBYgzNYMmvDhMqUJkaIXcnvBd0Z9P+mG3s7P1NdOUK6+ViH5iBeeq9f9r1sN0tV+5KXY9MrZgheG+0G6F5b7K8e9EN0Jw9X2LD1qm5apFemVr70V7M4x/tf+KnQvSHfH8EP13hkfCkqYHdvtwIURaCpAmN2UiAMQiE2AMDs2ei6MQEMBwmz7bhDCbPN7SpitoUeE2RoQGQIBzQKE2ZpBGa4jAbUNi6jV5F4o7q4mV0G6CtjdleXVK85VgD4m4ofq/jHOGDWr0iur09Wq9cCKdA0P9uxo0iJS7hnwtmXxwnK1pUtPn+R6+mSilBXJ5KWcyYtk81LO5kUkJ5LNuV+rn6nP6mfifpZs1ju+4Bzn/Eydn/G/zk2dVxk3JyJqLPeY6mt6xzvHemM415qqwb1GYFz1M0NXvXfaL85HgDCbewABcwUIs83tDZWlW4Aw277+E2ab39PIw+wzQ8OyeesT8m//zb+Qv/4v/6+89c77dZWuumJV1YMhTaYkzDa5O9SWVgHC7LR2nnkHBdxV5mNtBuneSvUZg3Rvpbu3tUt1kD6mouwUNCPrBe8q7J4ewrsheiCEDwb3flifqRPqOz/zwnfxxq0K9XNTIX6up06o770J4IXw7psFfqjvvTGg6g0G/oE3FtzAv/qNhak3HPzaAuOpOfBhhQBhthVtZBKWChBmW9pYppV4AcLsxLdw2gQIs83vaeRhtk+iQu1tj+6RLXfdIZevXFolpR4K+YPnnpeHt9wp/X0F4xUJs41vEQWmUIAwO4VNZ8pGCVRWoXsr0sXZy3xUCplxmd1TkqHhC5IpT4qUJiVTmhQR/+uiiBTd17w/7nFF93jnT1GkOOF87R4X+Jn62hsvUyq6Y/jHqc/qXOe1kjjbz1TGUDX41y1KplKPd01VZ7nk1au2j+GjmYDzcFVRYX3G+VN2Qm/1tfrsfu285vzxXveOKTvn+cf7P8uI83plzKljpo3tXE+9yZBxy/Svo65ZNfbU9d0xAjV6IX1VjWX3+HJNvf6cyv78KnNS1/fnXjN21p+LN39R49bM2RvPrVmNVe1VOT4wJ7de17xiWDV2YAz1esBzdn9BJksiY+NFp1Uenjjzchz9F2u+L3vXcw+qe2z1GE3Gc+btHaPGDly7Mk7wmMo1p2qeVq833rS5+OPXHW+G+To9nflnzrXDmATHCHjXtVbXVM9Q4CO1AoTZqW09EzdcgDDb8Aa1UR5hdhtoEZ9iZJj93qFjsuvJZ2T7/Rtl/uBAxCStX44wu3UzzkCg2wKE2d0WZnwE2hOwcc/szOSn4gTnKigPhON+IO6E7eKG6JWQPhCsuyG99/NKwK9C/amw3hk3+DM/2HdCfS/M94P8qjcJpgL6qfGmxnJDfS/w986repPAq9up3bum/yaBs988HwggYLWAehbCTOH61JsbHkHdNwxmeOOi8m6J+4ZP8BqN3zwJ8UaI9wZF0zccKm8mzPxGjVtLzRsXDd/cmeHNnKo3P0KY1LxZoubS25OV8Ymy+989BWtv9mZTYA4z2laN0djYfdNuBpNaK+e42vHcdlf1p403hxr2t9l4YUyq3myqvNtU9/chlMk0C29M541Np6nVn+vZ1by555/jWtSe7//VFPRvcn/WexOuhfu97v1V+8Zkg/Gmzq9Xu2dU29swJo3uhxnu5cr9FeKNyUIhJ709ORm+oBZG8GGDwILP/g82TMPqORgZZj+7/4AcfONtVmZbfesxOQS6K0CY3V1fRkegXQEbw+x2LWw9T63Adx58Wi47f5wV7Sp+UZ/Vaxn1uSQZ5+fu15WfS8lZO115XaU26lh/POfz1PFVY1eu6R/vnls5Xo1dVYtbj/Oa+pPxrutd363Pe82p2f162hhOtOS9XpmTU/j0sf3rO29+eGN6c6rM20l5vJpUfc5DZKeu78/HrSNo65sG5lE7duCa9eaRz6pplqVU9B9c620XpOblxk+Bz41+pjhdA/e0mvPrfl/vGs669sAYteP55dQb333NrcP5onoOLdZXXUethf994/m6Pa7ZgqmurWvGG0a2/i3JvBBAAAEEjBa4Nw3bJRrdgabFRR5mq1XXm+57TI6f/GTG4pYsWiC7d947bfuRprOJ6QBWZscEz2URaCBAmM3tgYCZAoTZZvaFqhBQAuyZzX3gvhlVE9ZPe/PBd6p5w6AqrK8J9oNvJtSMV3nDod6bH83eCHHehPLqCPPmSSWfqP/GQ9UbDn7Njd58maG+ypsfYUzqvLFSdb7XjgVzC3L63JjzhlNVj+q9MePVXm3b+ptLM/bGGT/seLVvSgXur+BcGno3e2OrXi3178GqOTW7v6rulzr3uzOVwBtmzcbzzUqBNwyD58z05lbAevq90eANsjD3Z9WblE3efAzzu1v3jcP6PZ/qhX9LN7i3a+usZ9LMP/i7UnP/VruGvLe9/qvF9blMRiaKBKBW/K9oJiu933jeiqnYPInIw2wfs9Ge2UkDJ8xOWseoNw0ChNlp6DJzTKIAYXYSu0bNaREgzE5Lp5lnEgXYMzuJXaPmNAiwZ7Z9XWbPbPN7GluYbT5N+AoJs8NbcSQCUQkQZkclzXUQaE2AMLs1L45GIEoBwuwotbkWAq0JEGa35sXRCEQlQJgdlXR01yHMjs663SsRZrcrFziPMFsDIkMgoFmAMFszKMMhoEmAMFsTJMMg0AUBwuwuoDIkApoECLM1QTIMApoFCLM1gxowHGG2AU1oUkKsYXaj/bOvumKVPLXjbpk/OGC8ImG28S2iwBQKEGansOlMOREChNmJaBNFplSAMDuljWfaiRAgzE5EmygyhQKE2fY1nTDb/J7GFmaPjI7LQ7v2ytqrr5QvfPa35Oln/1a2bL5D+vsK8vjufXLjtZ+Xa9asNl9QRAizE9EmikyZAGF2yhrOdBMjQJidmFZRaAoFCLNT2HSmnBgBwuzEtIpCUyZAmG1fwwmzze9pbGF28AGQimnXk8/I9vs3OiuxX3/zXfnBc8/Lw1vudMJt0z8Is03vEPWlUYAwO41dZ85JECDMTkKXqDGtAoTZae08806CAGF2ErpEjWkUIMy2r+uE2eb31Igw+6J5A7L9L56WbX/2DSfMVtuPBMNt0xkJs03vEPWlUYAwO41dZ85JECDMTkKXqDGtAoTZae08806CAGF2ErpEjWkUIMy2r+uE2eb3NLYwO7jNyG0b1jtbi1y2fLGor5/df0AOvvE2K7PNv3+oEAFjBQizjW0NhaVcgDA75TcA0zdagDDb6PZQXMoFCLNTfgMwfWMFCLONbU3bhRFmt00X2Ymxhdm1M1Tbjmze+oS89c77smTRAtm98165fOXSyCA6uRArszvR41wEuiNAmN0dV0ZFoFMBwuxOBTkfge4JEGZ3z5aREehUgDC7U0HOR6A7AoTZ3XGNc1TC7Dj1w13bmDA7XLlmHkWYbWZfqCrdAoTZ6e4/szdXgDDb3N5QGQKE2dwDCJgrQJhtbm+oLN0ChNn29Z8w2/yexhZmBx8AmZQV2DO1kzDb/BudCtMnQJidvp4z42QIEGYno09UmU4Bwux09p1ZJ0OAMDsZfaLK9AkQZtvXc8Js83tKmK2hR4TZGhAZAgHNAoTZmkEZDgFNAoTZmiAZBoEuCBBmdwGVIRHQJECYrQmSYRDQLECYrRnUgOEIsw1oQpMSYguzVV3qoY83Xvt5uWbNavOlGlRImJ3o9lG8pQKE2ZY2lmklXoAwO/EtZAIWCxBmW9xcppZ4AcLsxLeQCVgqQJhtX2MJs83vaaxh9nuHjsnTz/6tbNl8h/T3FczXmqFCwuzEto7CLRYgzLa4uUwt0QKE2YluH8VbLkCYbXmDmV6iBQizE90+irdYgDDbvuYSZpvf09jCbLVn9uatT8hb77xfV+mqK1bJUzvulvmDA8YrEmYb3yIKTKEAYXYKm86UEyFAmJ2INlFkSgUIs1PaeKadCAHC7ES0iSJTKECYbV/TCbPN72lsYbb5NOErJMwOb8WRCEQlQJgdlTTXQaA1AcLs1rw4GoEoBQizo9TmWgi0JkCY3ZoXRyMQlQBhdlTS0V2HMDs663avFFuYrVZmb3t0j2y56w65fOXSqvpff/Nd+cFzz8vDW+5MxPYjhNnt3n6ch0D3BAizu2fLyAh0IkCY3Yke5yLQXQHC7O76MjoCnQgQZneix7kIdE+AMLt7tnGNTJgdl3z46xoZZqu9tHc9+Yxsv38j24yE7yVHIoBAQIAwm9sBATMFCLPN7AtVIaAECLO5DxAwV4Aw29zeUFm6BQiz7es/Ybb5PTUyzH52/wE5+Mbbka3Mfnz3Pvnu988XwIcAACAASURBVPdXuvW972yVa9asrnyv6nlw517n+1tuXjutLlZmm3+jU2H6BAiz09dzZpwMAcLsZPSJKtMpQJidzr4z62QIEGYno09UmT4Bwmz7ek6YbX5PIw+z1arrTfc9JsdPfjKjzpJFC2T3znunbT/SDU613clfP/Mj2fzNP3S2NFH1PbB9jzyybaNzfbXlyWO791UeRqmCb/Vxz6bbK+UQZnejM4yJQGcChNmd+XE2At0SIMzulizjItC5AGF254aMgEC3BAizuyXLuAh0JkCY3ZmfiWcTZpvYleqaIg+z/cs32jM7TjZV1+atT8i9m253Vmer8Pqy5Yvltg3rnbJqw231GmF2nB3j2gjUFyDM5s5AwEwBwmwz+0JVCCgBwmzuAwTMFSDMNrc3VJZuAcJs+/pPmG1+T2MLs02lUWH1tu17nJXhSxddLA/t2itrr76yEmbXrtwmzDa1k9SVdgHC7LTfAczfVAHCbFM7Q10IEGZzDyBgsgBhtsndobY0CxBm29d9wmzze0qY7fUouP2Jv2f2yOi4E2Z//dabKnto1wuzh0cmze80FSKQMoE5fXm5MDYppXLKJh7BdMvlsmQymQiuxCVsFMhlM9Lbk5ULY0Ubp8ecEEi0QF9PVoplkYnJUqLnQfEI2Cgw0J+X8yOTEuU/bfk3n413EnPSLZDPZaQnl5WRcf5tq9s2rvHU37d8mC0Qa5jtb+nx1jvvT1O66opVlX2qoyQMbjPyudWrQq3MHr4wEWWJXAsBBEIIzO7Py4XRoqh/hPOhV6AsGclE+n+l9NbPaPEKOGF2IScXRnkjON5OcHUEpguo381SqUyYzc2BgIECc/p75PxItP+/k3/zGXgjUJJxAvlcVnryGRlhoYZxvWm3oIFZPe2eynkRCcQaZtd7mGJE8254meA+2eyZbUJHqAGB1gXYZqR1M85AIAoBthmJQplrINCeAHtmt+fGWQhEIcA2I1Eocw0EWhdgm5HWzUw/g21GTO+QSGxhtikPgFTbhvz4hTfk3/3xrU63/O1Gtm/b6GwtUvvAx3oBPA+ANP9Gp8L0CRBmp6/nzDgZAoTZyegTVaZTgDA7nX1n1skQIMxORp+oMn0ChNn29Zww2/yepj7M9vfF/uGPD1a65e+Z7b/w7P4D8uDOvc63t9y8Vh7ecqf09xUqxxNmm3+jU2H6BAiz09dzZpwMAcLsZPSJKtMpQJidzr4z62QIEGYno09UmT4Bwmz7ek6YbX5PYwuzFU3tFh7mc9WvkDA7qZ2jbpsFCLNt7i5zS7IAYXaSu0fttgsQZtveYeaXZAHC7CR3j9ptFiDMtq+7hNnm9zTWMFtt6fH0s38rWzbfUbXS2Xy26goJs5PWMepNgwBhdhq6zByTKECYncSuUXNaBAiz09Jp5plEAcLsJHaNmtMgQJhtX5cJs83vaWxhttoze/PWJ+Std96vq3TVFavkqR13y/zBAeMVCbONbxEFplCAMDuFTWfKiRAgzE5EmygypQKE2SltPNNOhABhdiLaRJEpFCDMtq/phNnm9zS2MNt8mvAVEmaHt+JIBKISIMyOSprrINCaAGF2a14cjUCUAoTZUWpzLQRaEyDMbs2LoxGISoAwOyrp6K5DmB2ddbtXIsxuVy5wHmG2BkSGQECzAGG2ZlCGQ0CTAGG2JkiGQaALAoTZXUBlSAQ0CRBma4JkGAQ0CxBmawY1YDjCbAOa0KSEWMPskdFxeWjXXvnhjw/KkkULZPfOe2Xpooud19ZefaXctmG9+YIiQpidiDZRZMoECLNT1nCmmxgBwuzEtIpCUyhAmJ3CpjPlxAgQZiemVRSaMgHCbPsaTphtfk9jDbMf371PLlu+WP7gq2tl11PPyDdu++dy+cql8vqb78oPnnteHt5yZyIeDEmYbf6NToXpEyDMTl/PmXEyBAizk9EnqkynAGF2OvvOrJMhQJidjD5RZfoECLPt6zlhtvk9jS3MVg+A3PboHtly1x3OauxgmP3eoWOy68lnZPv9G3kApPn3EBUiYKQAYbaRbaEoBIQwm5sAAXMFCLPN7Q2VIUCYzT2AgJkChNlm9qWTqgizO9GL5lwjw2xWZkfTfK6CgM0ChNk2d5e5JVmAMDvJ3aN22wUIs23vMPNLsgBhdpK7R+02CxBm29ddwmzzexpbmK1ont1/QA6+8bZs+7NvyF/u/b+dbUYumjcgm7c+IbffehN7Zpt//1AhAsYKEGYb2xoKS7kAYXbKbwCmb7QAYbbR7aG4lAsQZqf8BmD6xgoQZhvbmrYLI8xumy6yE2MNs9Us1Srsb317R9WEv/edrXLNmtWRIXR6IfbM7lSQ8xHQL0CYrd+UERHQIUCYrUORMRDojgBhdndcGRUBHQKE2ToUGQMB/QKE2fpN4x6RMDvuDjS/fuxhdvMSzT+CMNv8HlFh+gQIs9PXc2acDAHC7GT0iSrTKUCYnc6+M+tkCBBmJ6NPVJk+AcJs+3pOmG1+T2MNsx/fvU9OnDotD2+5U/r7Co7WyOi4PLRrr6y9+kq2GTH//qFCBIwVIMw2tjUUlnIBwuyU3wBM32gBwmyj20NxKRcgzE75DcD0jRUgzDa2NW0XRpjdNl1kJ8YWZvuh9ddvvWnaliI8ADKy/nMhBKwVIMy2trVMLOEChNkJbyDlWy1AmG11e5lcwgUIsxPeQMq3VoAw277WEmab39PYwuwzQ8Oy7dE9suWuO+TylUurpN47dEx2PfmMbL9/o8wfHDBekW1GjG8RBaZQgDA7hU1nyokQIMxORJsoMqUChNkpbTzTToQAYXYi2kSRKRQgzLav6YTZ5vc0tjCbldnm3xxUiECSBQizk9w9ardZgDDb5u4yt6QLEGYnvYPUb7MAYbbN3WVuSRYgzE5y9+rXTphtfk9jC7MVjdpOZNv2PbJ7572V1dlqVfam+x6Tu775NfbMNv/+oUIEjBUgzDa2NRSWcgHC7JTfAEzfaAHCbKPbQ3EpFyDMTvkNwPSNFSDMNrY1bRdGmN02XWQnxhpmq1n64fXxk59UJv2972ydto92ZCJtXIhtRtpA4xQEuixAmN1lYIZHoE0Bwuw24TgNgQgECLMjQOYSCLQpQJjdJhynIdBlAcLsLgPHMDxhdgzoLV4y9jC7xXqNPJww28i2UFTKBQizU34DMH1jBQizjW0NhSEghNncBAiYK0CYbW5vqCzdAoTZ9vWfMNv8nhJma+gRYbYGRIZAQLMAYbZmUIZDQJMAYbYmSIZBoAsChNldQE3YkGNjk1Iuu0WXvS+mf5+Z+nnTY0XK4h4007j+D9TPZ77mVE2VcWrGDY7j19+wdq8m/5recFM1eL1TP29uEjDTbuKOfdFAQU6fG5dSnb5Mq8+/7wLHhrH1+zSjiT831dUQ86z0PcSxXbtPfIO2TPy+Tt2b0+6TdkxKpcrvRLAv03pU73dnRsupWme+/ys3tXv9hr/n0/8eqK2v7u9c4Pem0e+9M1ZlLtNrr70XZ/q9rx6n8d81jX6XK3NpySTj/H2RzYrkMhmZKHoTStjf+5RbLaD6+dILd8JiuECsYfaZoWHZvPUJeeud96cxXXXFKnlqx90yf3DAcEIRwmzjW0SBKRQgzE5h05lyIgQIsxPRpo6KVGFYqeQGHer/r5edr8vOayqEcV93Uiv3tcqx7s+rX8+4P/fH8M7xx5hpbPd19zqV8dW1A7VMG2Pa2G54UVt31TX969SM7cxbTcW/vlTPtVwqSbHouXjXUP+nvsrCG9uvoVKL/3rFc2psdwx/HNfTtQ5aeCZqHKl+PZtR3iLF4lTQ4ociVaFEiCBLDd5OeOZHAfXCtDDBZG2IEwwoaufSSn31QpvOTAKB8AxBr/pd4gMBBBBAAAEEohUolx+K9oJcrWWBWMPsx3fvcwq+Z9PtLRdu0gmE2SZ1g1oQcAUIs7kTEDBTwMYw+8KFCSlOlp0AcLJYkuJkSSYnVVjpfp6cdMND5zXvZ+5x3mvqa+/Yymf1M28sFXoGx3PO88eeKEpRBaPedZzP/ljqeqVy5Wf1alNj++dU16ZqckNNdw7eNQJ1E7SZ+TtGVQjoFOjtzTvDZdzsXTLeF+rT1NfuWxD+9xI4tt55zrHeQdXjeJUHrjHzNafX4x/rXb5S9Iw1ZDKSyVTXXnusKrMyz0p57hUam1TXp8OktycrE5Ml502ooH2wJ7Ve7ZnMbOtOvPo+qO1n8575kFPXadQj75JV916wAa30IXhsa/eJX2vtZ/9mD2dSdX3/VLUMNOz9FPi9qf2dm34fTK91Jmf/ZlYlhbmf/Hprj62MU+/vgBlqr6qpcl5953p/XwT/LmluMv13V4dJbyEnfYWsDF/gDUjvtzvxn/71v/qdxM/B9gnEFmarVdnbHt0jW+66Qy5fuTTRzoTZiW4fxVsqQJhtaWOZVmIERkcnZWy0KCrwHB2dkFHv69JkSXqyGfn47NhUgFsVwJYD4WwwFA6ErircnSgGgmMvSPYC5Klw1g2BgyFuJVR2xpgePrvhbWC8QG2lojjjEeKGuw37+vKSzaqwKOP8J7gZ52txXlMrgNXr6jX3GO9173j3HP9192eV451za46vM3Y2552TUf8JsD+eX0/9Mdy6gmO7dVdq8epSB02NOTWn6a+7wYAzRu3Y2Wz1GN5xvlfQxRmjzjVdU3ds/3i3Xve6wborx9aYB8eeOzsvkyWRsfFiIKibSlkaBZPNwqhQYV69oNTPvSoXaBSAzRxM+qdPC1sahKoNQ50Ggasf7rVm0jhUVWP5wXK430COsk2APbNt6yjzsUWAPbNt6eTUPNgz2/yeEmZr6BFhtgZEhkBAswBhtmZQhkukwPh40QmUR71AuRIuj0zK2JgbNI+NTsqICp69r50QeqwooyMTXhDtHuOOocbzjy06IbV7XjEwRlHGx6f2XE0kXMiiVVCYy2cll81IPu9+nc9l3c+Br3O5qZ+p14PH5XJSdc7Uz9SYauyp8XI5b1x1LXWdXEbyPTn3OOdn3jnecVn12fs6p34WqM2paYafqeOccwNzCtatQmLnWt54yoEPOwTYM9uOPjILOwUIs+3sK7NKvgBhdvJ7WDsDwmzzexpbmK1o1DYjly1fLLdtWG++VIMKCbMT3T6Kt1SAMNvSxiZ0WsPnxiMPlE1YPTwwUBC1OrdX/enNSV9fj/T152VWf975z6SrwlU//A0Ev34oXAldc1IJguuFuNmsG/b6gbD7tRu6ugGs9/M6Ie7UNeqHuCqwduvJSH9/T0LvRMpGoLEAYTZ3CALmChBmm9sbKku3AGG2ff0nzDa/p7GG2e8dOiZPP/u3smXzHdLfVzBfa4YKCbMT2zoKt1iAMNvi5rYxNbWdxKhabaxWEvsri53Vx+4qY+dn3kpkZ8WyWonsbZPhbpGhznNXODvHj3irmp2v/THcFcruyuapLTach6/F9NGj9vDry0lfb3Wg7IbLeedn6mvn5yps7u9xXldf98+a+loF0iqIVq87xzsBdc47Nu8E1O4YU69X/pP+mrnbuGd2TO3lsghoFyDM1k7KgAhoEyDM1kbJQAhoFSDM1sppxGCE2Ua0oWERsYXZas/szVufkLfeeb9ugVddsUqe2nG3zB8cMF6RMNv4FlFgCgUIs5PX9I8/viDD58bk3NCYnFOfz43KuXPjcm5oVM4Pj8mICpsrW2ZMhcVuuDwVKAeDZmf7i5GJ2DHmzPGC4UqI7IfLeen3AmU/CHZDYzcUViuAK6GzEz73VELkYKCsgmQVKLtBtPraPdfED8JsE7tCTQi4AoTZ3AkImCtAmG1ub6gs3QKE2fb1nzDb/J7GFmabTxO+QsLs8FYciUBUAoTZUUm711HbaLjhsxdED405wfTQ0KgMD4/L0NkR52dVxw35YfVYJA/Umz27UL2y2F9lXCcI9kNhN1AueCub1Srn6tXHfrjshMnB1cre6uVZs8wMlKO9O6qvRpgdpz7XRqCxAGE2dwgC5goQZpvbGypLtwBhtn39J8w2v6eE2Rp6RJitAZEhENAsQJjdGuhHH11wVkQPnxuVIfV5eGqFtFoVffasen3UCajdcFqF1F5YfW68tYvNcPS8+X0yd676U5CBuX0yONgrA3MLzmvq60pwHFi57K5a9rbLcEJpbysMb1sNtY2GCrH5MEeAMNucXlAJArUChNncEwiYK0CYbW5vqCzdAoTZ9vWfMNv8nsYeZr/+5rvyrW/vqJL63ne2yjVrVpuv51VImJ2YVlFoigTSEmaXyyLnVbjsrIBWAfTUaufKSukhL6AObN2hQmt3O49xLauiVWDsBM+DvTJ3oM/9PLfX+Twwt1fmDfZP/dwJrN3X3cC6V1jBnJ5fTsLs9PSamSZPgDA7eT2j4vQIEGanp9fMNFkChNnJ6leYagmzwyjFe0ysYbYKsh/bva9qb2z1UMhN9z0md33za3LbhvXx6oS8OmF2SCgOQyBCgaSE2Wq/Zz9UdoNod69otR2HWiF9bsjdnsP5U7N1hzpOBdkq0O7kI9+TlbkDKnh2V0W7AXSfDPqB8zw3fFYrpFVgPTioPrtBtTpvcF6fzPSwv07q4lw7BQiz7ewrs7JDgDDbjj4yCzsFCLPt7CuzSr4AYXbye1g7A8Js83saW5g9MjouD+3aK1+/9aZpq7BVyP2D556Xh7fcKf195v/n4YTZ5t/oVJg+gSjCbBUiO0H0sLvK2d0j2v3sP7zQ2Y5Dbd3h7BcdeLihCq6Hx2VivNhRc1SIPHt2jxNEOyudvcDZXRnthdCD/pYdXmA9oAJrf3V0wdgHBXYEw8nGChBmG9saCkOAB0ByDyBgsABhtsHNobRUCxBm29d+wmzzexpbmH1maFi2PbpHttx1h1y+cmmVlFqdvevJZ2T7/Rtl/uBAVxX9UP2HPz5YuU7tNifP7j8gD+7c6/z8lpvXTgvZCbO72iIGR6AtgTBh9sjIRGVVtLPtRmAFtPp6aGjEfWChv2LaCa79FdKj8umnEx2viu4p5CoroCtbc3j7RjuBs/PHXQHt/3xqVbT7M1ZFt3WLcFJMAoTZMcFzWQRCCLAyOwQShyAQkwBhdkzwXBaBJgKE2fbdIoTZ5vc0tjDblJXZKlT/62d+JJu/+YfOKnC1Knzb9j2ye+e9TsheuxXK47v3OV29Z9Ptle4SZpt/o1NhOgQOHx6SD4+ckyNHhmT49IgcPTYsZytB9NTWHcPDo3Lm9KgWlIEBtVe0tz+0CqG9hxaqrTjmzeuTOWr/6MDWHXMDq6LVserhhXwgkCYBwuw0dZu5Jk2AMDtpHaPeNAkQZqep28w1SQKE2UnqVrhaCbPDOcV5VGxhtpq0WvG877nnjdozW4Xbm7c+Ifduut3Z/kSF15ctX1zZv7vePt+E2XHewlw7TQIfvH9Gjh45J0ePDMmHR8/JoUNnK+H1sQ+H26K4+OJZ3hYdai/oXhkIPLxQ7QXtBNWBVdHudh4FZzsPFUbzgQACrQkQZrfmxdEIRClAmB2lNtdCoDUBwuzWvDgagagECLOjko7uOoTZ0Vm3e6VYw2xVtAqHv/XtHVX1127z0e7k2jlPbXHywPY98si2jbJ00cXOvt5rr76yEmYHf+5vj0KY3Y405yAwXeBXvzztBNV+WH340JAc+9ANrU+d/LQp2SULZ8uy5XNl+fJBuWL1AumbVZA5XhA9f36/s7e0erChCqEvuWRW0/E4AAEE9AsQZus3ZUQEdAkQZuuSZBwE9AsQZus3ZUQEdAgQZutQNGsMwmyz+lGvmtjDbJOI/K1P/PC63lYo9cJsk+ZALQiYKjAyMinvvXdafvObs3Lo0JD3+Wzl+1OnGofV2WxGLr10QFaunCeXXTZPVq4c9D6736s/PT1ZU6dvVV3jkyVRgSQfCCCAAAIIIIAAAvYK8G8+e3vLzBBAAIEkC8QaZqstPE6cOl31QMXaQDkqXP+6ixdeVNkPu14trMyOqiNcJ2kCQ2fHnL2q/ZXVRw6fk6NHh+To4SE5evScnD3TeI/qfD4rS5cOyKXLB2X5irmybNlcWb5ynixbNijLVsyVSy+dK7lcJjRLmAdAhh6MAxFAQJsAK7O1UTIQAtoFWJmtnZQBEdAmwMpsbZQMhIBWAVZma+U0YjBWZhvRhoZFxBZmm/IASKVTL8j21dgz2/ybmAqjEfjkkxEvmB4SJ6g+oj5Phdfnz080LKRQyMmlKqBWwfQyFVgPOtuBXOptC7Jk6YBkwmfVTSdNmN2UiAMQiEWAMDsWdi6KQCgBwuxQTByEQCwChNmxsHNRBJoKEGY3JUrcAYTZ5rcstjBbPWhx26N7ZMtdd4i/97TPpVY/73ryGdl+/0aZPzjQVcVmK8FrH/iowm31cc+m2yt1sWd2V1vE4BEIlMsip06edx6uqFZXf+h9VmH1h2p19ZFhGRlpHFb39/fIsuUDsmz5oPNnxYp5cunyASewVt8vXDQ7gplMXYIwO1JuLoZAaAHC7NBUHIhA5AKE2ZGTc0EEQgsQZoem4kAEIhUgzI6UO5KLEWZHwtzRRWILs01Zma2C8033PSbHT35SBfknf7ShElg/u/+APLhzr/PzW25eW7UtinqNMLuje5CTIxAolcpy/Niws93HUW/7jyOHzjrfq1XW6iGL4+PFhpUMDBS8ldVuOK1WVquHLfrh9YIF/RHMJPwlCLPDW3EkAlEKEGZHqc21EGhNgDC7NS+ORiBKAcLsKLW5FgLhBQizw1sl5UjCbPM7FVuYrWjUqudt2/fI7p33VlZn++HyXd/8mty2Yb35goTZieiR7UVOTpacQNrdp/qcuEG1u6JabQeigmx1TKOPefP7nH2ql6mQetmgrFg5OBVeLxuUwXm9iWIkzE5Uuyg2RQKE2SlqNlNNnABhduJaRsEpEiDMTlGzmWqiBAizE9WuUMUSZodiivWgWMNsNfN6K6O/952tcs2a1bHCtHJxVma3osWx7QhMjBed7T9UWP3h0XNy2A+rvfD65InzolZfN/q4+OJZlf2p1Yrq5SvmOSur1TYgKz8zT/r68u2UZuw5hNnGtobCUi5AmJ3yG4DpGy1AmG10eygu5QKE2Sm/AZi+sQKE2ca2pu3CCLPbpovsxNjD7Mhm2sULEWZ3ETclQ4+NTcqhD4ac1dRTD1V0969W+1h//NGnova1nulDPThx4aI5lXBaPVRR7VntbwOiVlmrBzCm6YMwO03dZq5JEiDMTlK3qDVtAoTZaes4802SAGF2krpFrWkSIMy2r9uE2eb3lDBbQ48IszUgWj7E8LlxL5hW4bS7wtp90OKQ8/nM6dGGArlcRhYvGfC2AZnrbAOyfOU85/vlK9zv8z1ZyxVbmx5hdmteHI1AVAKE2VFJcx0EWhcgzG7djDMQiEqAMDsqaa6DQGsChNmteSXhaMJs87tEmK2hR4TZGhATPsTp0yPOCuqjh/3V1eec0NoJro8MiQqzG32oIHrpUvUwxQFn249lK+a5n9UDFlfMdX6mAm0+wgsQZoe34kgEohQgzI5Sm2sh0JoAYXZrXhyNQJQChNlRanMtBMILEGaHt0rKkYTZ5neKMFtDjwizNSAaPsRHpz51wmp/248jh8+64bW3DciFCxMNZ9Dbm3cepqjC6mXLB539qlVYfakXXqtV12qrED70CRBm67NkJAR0ChBm69RkLAT0ChBm6/VkNAR0ChBm69RkLAT0CRBm67M0ZSTCbFM6MXMdhNkaekSYrQHRgCF+9sZx+eD9M96DFofk2IfqQYtDzmvNPvr7eypBtRNWO6ur3Ycrqu8XLprdbAh+rlmAMFszKMMhoEmAMFsTJMMg0AUBwuwuoDIkApoECLM1QTIMApoFCLM1gxowHGG2AU1oUgJhtoYeEWZrQIxhiFdfOSIHXzkqB18+LK++8qGohzDO9DFnjgqr3WB6+Qr/s1pp7X69YEF/DDPgko0ECLO5PxAwU4Aw28y+UBUCSoAwm/sAAXMFCLPN7Q2VpVuAMNu+/hNmm99TwmwNPSLM1oDY5SFUUP3T147JKy8dlpdfOiIqyA5+qJXTqy6/SObN65PPrJrnPGzxM6sukkWL5zirqwfn9Xa5QobXLUCYrVuU8RDQI0CYrceRURDohgBhdjdUGRMBPQKE2XocGQUB3QKE2bpF4x+PMDv+HjSrgDC7mVCInxNmh0CK+JBPPx2X1w5+KK+8fFgOvnxEfv7mCZmcKFWqWLFynlx3/TK57oaVsvaGZU5gzYddAoTZdvWT2dgjQJhtTy+ZiX0ChNn29ZQZ2SNAmG1PL5mJXQKE2Xb1U82GMNv8nhJma+gRYbYGxA6HGD437gTXr3irrn/x1kkpFsuVUdWq67XXL5Prb1gp69avkEsWsod1h+TGn06YbXyLKDClAoTZKW08006EAGF2ItpEkSkVIMxOaeOZtvEChNnGt6jlAgmzWyaL/ATCbA3khNkaEFsc4szpUXn5pUNOeK1WXr/7zkdSnsqu5Xd+d4GsvX65XHfDCrnhxpXsad2irw2HE2bb0EXmYKMAYbaNXWVOtggQZtvSSeZhowBhto1dZU42CBBm29DF6jkQZpvfU8JsDT0izNaA2GSIj059Ki++oFZeq4c1HpFf/+p05YxMRmT1FZfIdV54ff26FTJvfl/3i+IKRgsQZhvdHopLsQBhdoqbz9SNFyDMNr5FFJhiAcLsFDefqRstQJhtdHvaKo4wuy22SE8izNbATZitAbFmiMOHh5wV1wdfOiyvvHxUDh86Wzkim83IZz+3sLLy+vobVsjA3IL+Ihgx0QKE2YluH8VbLECYbXFzmVriBQizE99CJmCxAGG2xc1laokWIMxOdPvqFk+YbX5PCbM19Igwu3PE9359Rg6qPa+dBzYelePHhiuD5vNZ+dznF1Ue2Hjtdctlzpyezi/KCFYLEGZb3V4ml2ABwuwEN4/SrRcgzLa+xUwwwQKE2QluHqVbLUCYbV97CbPN7ylhtoYeEWa3hqj2C1iekgAAHDVJREFUtv6ndz92tgxRq6/VtiEffXRhKrzuycqaNUtk7Q3L5LobVsq1ay+V/n7C69aUOZowm3sAATMFCLPN7AtVIaAECLO5DxAwV4Aw29zeUFm6BQiz7es/Ybb5PSXM1tAjwuzGiKVSWX7x1ikntHb2vD54VM6eGa2cVCjk5PeuXlpZeX3NtUultzevoTMMkWYBwuw0d5+5myxAmG1yd6gt7QKE2Wm/A5i/yQKE2SZ3h9rSLECYbV/3CbPN7ylhtoYeEWZXI05OluTnPzshr75yVF5+6ZD89LUPZXh4vHJQX19err5mqVx3wwrnoY1f/NJSUYE2HwjoFCDM1qnJWAjoEyDM1mfJSAjoFiDM1i3KeAjoEyDM1mfJSAjoFCDM1qlpxliE2Wb0oVEVhNkaepT2MHt8vCh//9NjcvCVo/LKS4fkjdePy8jIREV29uyCfOnLbni99vrl8nu/t0TyPVkN8gyBwMwChNncHQiYKUCYbWZfqAoBJUCYzX2AgLkChNnm9obK0i1AmG1f/wmzze8pYbaGHqUtzB4dnZTXX/3Q3fP6laPyszeOiQq0/Y+BgYJcc63a79pdef35NYsll8tokGYIBMILEGaHt+JIBKIUIMyOUptrIdCaAGF2a14cjUCUAoTZUWpzLQTCCxBmh7dKypGE2eZ3ijBbQ49sD7PPn5+Q1w66+12r8Pof3jwhaisR/2NwXq9cu3a5XHfDcll7/Qr53FULJZslvNZwazFEBwKE2R3gcSoCXRQgzO4iLkMj0KEAYXaHgJyOQBcFCLO7iMvQCHQgQJjdAZ6hpxJmG9qYQFmE2Rp6ZFuYPXR2TF55+bC88tIR56GN//iLU6Ie4uh/XHRRv1x7/XK53guvr7jyEsmQXWu4kxhCpwBhtk5NxkJAnwBhtj5LRkJAtwBhtm5RxkNAnwBhtj5LRkJApwBhtk5NM8YizDajD42qIMzW0KOkh9mffDIiL794SA6+fEReefmI/PLdj6U8lV3LJQtny9rr3JXX112/Qn5n9QINagyBQHcFCLO768voCLQrQJjdrhznIdB9AcLs7htzBQTaFSDMbleO8xDorgBhdnd94xidMDsO9dauSZjdmlfdo5MWZp88cV5eeuGws/r64MtH5f33TlfNa/GSOc5e1+4DG1fI5b81X4MSQyAQrQBhdrTeXA2BsAKE2WGlOA6B6AUIs6M354oIhBUgzA4rxXEIRCtAmB2tdxRXI8yOQrmzaxBmd+bnnG16mH3oN2edFdcHvfD6yOGhqlkvWz5X1nrhtQqxV142T4MKQyAQrwBhdrz+XB2BmQQIs7k3EDBXgDDb3N5QGQKE2dwDCJgpQJhtZl86qYowuxO9aM4lzNbgbFqY/atfnnb2unYf2HhEThw/XzVLFVb7K69v/MpKWbR4jgYFhkDALAHCbLP6QTUI+AKE2dwLCJgrQJhtbm+oDAHCbO4BBMwUIMw2sy+dVEWY3YleNOcSZmtwjjPMVntbv/P2R05orcLrV18+ImoP7OCH2iZEbReitg1R4fXFF8/SMGuGQMBsAcJss/tDdekVIMxOb++ZufkChNnm94gK0ytAmJ3e3jNzswUIs83uTzvVEWa3oxbtOYTZGryjDLOLxbL84h9OVsLr1149KkNnxyqzyGREfvt3FzgPalQPbFx342Uy/6I+DbNkCASSJUCYnax+UW16BAiz09NrZpo8AcLs5PWMitMjQJidnl4z02QJEGYnq19hqiXMDqMU7zGE2Rr8uxlmT06U5M03j8vBl47KKy8dkp++/qGcPz9RqTqbzcjqKy52Vl5ff8NyuX7dShmc16thVgyBQLIFCLOT3T+qt1eAMNve3jKz5AsQZie/h8zAXgHCbHt7y8ySLUCYnez+1aueMNv8nhJma+iRzjB7fLwob7x+rPLARvX16OhkpcpcLiOf/dxCb9uQ5c7WIQMDBQ2zYAgE7BIgzLarn8zGHgHCbHt6yUzsEyDMtq+nzMgeAcJse3rJTOwSIMy2q59qNoTZ5veUMFtDjzoJs0dGJuS1gx+6+12/clR+9rPjMjFerFSVz2flqi8sqjyw8drrlsns2YTXGtrGEJYLEGZb3mCml1gBwuzEto7CUyBAmJ2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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Same plot, but with a smoothed 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": "37879680-50e8-4564-a872-a1f94cedfd22", "metadata": {}, "source": [ "## For more in-depth analysis of this reaction, see the experiment _\"reactions_single_compartment/react_1\"_ " ] }, { "cell_type": "code", "execution_count": null, "id": "da56d751", "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 }