{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "## Exploration of variable time steps in the simulation of the coupled reactions:\n", "### `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", "This experiment gets repeated, with very fine _fixed_ steps (as a proxy for the \"exact value\"), in `variable_steps_2`\n", "\n", "LAST REVISED: July 14, 2023" ] }, { "cell_type": "markdown", "id": "cdbeee8e-b67b-4462-9486-13a271636e9f", "metadata": {}, "source": [ "![Adaptive time steps](../../docs/variable_steps.png)" ] }, { "cell_type": "code", "execution_count": 1, "id": "d545a787-f84c-4d63-97a1-36a29f6c5dd6", "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": "386fc233", "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.modules.reactions.reaction_dynamics import ReactionDynamics\n", "\n", "import numpy as np\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(chem_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": [ "\n", "(STEP 0) ANALYSIS: Examining Conc. Changes from System Time 0 due to tentative single step of 0.01:\n", " Baseline: [ 50. 100. 0.]\n", " Deltas: [-1. -3. 5.]\n", " Relative Deltas: [-0.02 -0.03 inf]\n", " Norms: {'norm_A': 3.888888888888889}\n", " Thresholds: \n", " norm_A : low 0.25 | high 0.64 | abort 1.44 | (VALUE 3.8889)\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: abort (with step size factor of 0.5)\n", "* INFO: the tentative time step (0.01) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.005) [Step started at t=0, and will rewind there]\n", "\n", "(STEP 0) ANALYSIS: Examining Conc. Changes from System Time 0 due to tentative single step of 0.005:\n", " Baseline: [ 50. 100. 0.]\n", " Deltas: [-0.5 -1.5 2.5]\n", " Relative Deltas: [-0.01 -0.015 inf]\n", " Norms: {'norm_A': 0.9722222222222222}\n", " Thresholds: \n", " norm_A : low 0.25 | high 0.64 | (VALUE 0.97222) | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: high (with step size factor of 0.5)\n", "NOTICE: the tentative time step (0.005) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.0025) at the next round, because at least one norm is high\n", " [The current step started at System Time: 0, and will continue to 0.005]\n", "\n", "(STEP 1) ANALYSIS: Examining Conc. Changes from System Time 0.005 due to tentative single step of 0.0025:\n", " Baseline: [49.5 98.5 2.5]\n", " Deltas: [-0.1975 -0.70125 1.09625]\n", " Relative Deltas: [-0.0039899 -0.00711929 0.4385 ]\n", " Norms: {'norm_A': 0.19250243055555555}\n", " Thresholds: \n", " norm_A : (VALUE 0.1925) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.0025) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.005) at the next round, because all norms are low\n", " [The current step started at System Time: 0.005, and will continue to 0.0075]\n", "\n", "(STEP 2) ANALYSIS: Examining Conc. Changes from System Time 0.0075 due to tentative single step of 0.005:\n", " Baseline: [49.3025 97.79875 3.59625]\n", " Deltas: [-0.349175 -1.35909375 2.05744375]\n", " Relative Deltas: [-0.0070823 -0.01389684 0.5721081 ]\n", " Norms: {'norm_A': 0.6891259762586809}\n", " Thresholds: \n", " norm_A : low 0.25 | high 0.64 | (VALUE 0.68913) | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: high (with step size factor of 0.5)\n", "NOTICE: the tentative time step (0.005) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.0025) at the next round, because at least one norm is high\n", " [The current step started at System Time: 0.0075, and will continue to 0.0125]\n", "\n", "(STEP 3) ANALYSIS: Examining Conc. Changes from System Time 0.0125 due to tentative single step of 0.0025:\n", " Baseline: [48.953325 96.43965625 5.65369375]\n", " Deltas: [-0.13169275 -0.63849202 0.90187752]\n", " Relative Deltas: [-0.00269017 -0.00662064 0.15952005]\n", " Norms: {'norm_A': 0.1375997875121511}\n", " Thresholds: \n", " norm_A : (VALUE 0.1376) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.0025) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.005) at the next round, because all norms are low\n", " [The current step started at System Time: 0.0125, and will continue to 0.015]\n", "\n", "(STEP 4) ANALYSIS: Examining Conc. Changes from System Time 0.015 due to tentative single step of 0.005:\n", " Baseline: [48.82163225 95.80116423 6.55557127]\n", " Deltas: [-0.22599347 -1.24035033 1.69233727]\n", " Relative Deltas: [-0.00462896 -0.01294713 0.25815252]\n", " Norms: {'norm_A': 0.494838601385062}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.49484) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.005) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.015, and will continue to 0.02]\n", "\n", "(STEP 5) ANALYSIS: Examining Conc. Changes from System Time 0.02 due to tentative single step of 0.005:\n", " Baseline: [48.59563878 94.56081391 8.24790853]\n", " Deltas: [-0.15604005 -1.17097495 1.48305505]\n", " Relative Deltas: [-0.00321099 -0.0123833 0.17980983]\n", " Norms: {'norm_A': 0.3994425670227834}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.39944) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.005) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.02, and will continue to 0.025]\n", "\n", "(STEP 6) ANALYSIS: Examining Conc. Changes from System Time 0.025 due to tentative single step of 0.005:\n", " Baseline: [48.43959873 93.38983896 9.73096358]\n", " Deltas: [-0.09515744 -1.10891868 1.29923357]\n", " Relative Deltas: [-0.00196446 -0.01187408 0.13351541]\n", " Norms: {'norm_A': 0.32519593644806855}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.3252) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.005) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.025, and will continue to 0.03]\n", "\n", "(STEP 7) ANALYSIS: Examining Conc. Changes from System Time 0.03 due to tentative single step of 0.005:\n", " Baseline: [48.34444129 92.28092028 11.03019715]\n", " Deltas: [-0.04223653 -1.05330789 1.13778094]\n", " Relative Deltas: [-0.00087366 -0.01141415 0.10315146]\n", " Norms: {'norm_A': 0.2673096568217399}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.26731) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.005) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.03, and will continue to 0.035]\n", "\n", "(STEP 8) ANALYSIS: Examining Conc. Changes from System Time 0.035 due to tentative single step of 0.005:\n", " Baseline: [48.30220476 91.22761239 12.16797809]\n", " Deltas: [ 0.00369708 -1.00337484 0.99598069]\n", " Relative Deltas: [ 7.65405221e-05 -1.09985871e-02 8.18526039e-02]\n", " Norms: {'norm_A': 0.22208358683873192}\n", " Thresholds: \n", " norm_A : (VALUE 0.22208) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.005) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.01) at the next round, because all norms are low\n", " [The current step started at System Time: 0.035, and will continue to 0.04]\n", "\n", "(STEP 9) ANALYSIS: Examining Conc. Changes from System Time 0.04 due to tentative single step of 0.01:\n", " Baseline: [48.30590184 90.22423755 13.16395878]\n", " Deltas: [ 0.08699867 -1.9168896 1.74289227]\n", " Relative Deltas: [ 0.00180099 -0.02124584 0.13239879]\n", " Norms: {'norm_A': 0.7466342181101149}\n", " Thresholds: \n", " norm_A : low 0.25 | high 0.64 | (VALUE 0.74663) | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: high (with step size factor of 0.5)\n", "NOTICE: the tentative time step (0.01) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.005) at the next round, because at least one norm is high\n", " [The current step started at System Time: 0.04, and will continue to 0.05]\n", "\n", "(STEP 10) ANALYSIS: Examining Conc. Changes from System Time 0.05 due to tentative single step of 0.005:\n", " Baseline: [48.3929005 88.30734795 14.90685105]\n", " Deltas: [ 0.11234504 -0.87740469 0.65271461]\n", " Relative Deltas: [ 0.00232152 -0.00993581 0.04378622]\n", " Norms: {'norm_A': 0.13427741784149083}\n", " Thresholds: \n", " norm_A : (VALUE 0.13428) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.005) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.01) at the next round, because all norms are low\n", " [The current step started at System Time: 0.05, and will continue to 0.055]\n", "\n", "(STEP 11) ANALYSIS: Examining Conc. Changes from System Time 0.055 due to tentative single step of 0.01:\n", " Baseline: [48.50524554 87.42994326 15.55956566]\n", " Deltas: [ 0.27466034 -1.68932436 1.14000367]\n", " Relative Deltas: [ 0.00566249 -0.01932203 0.07326706]\n", " Norms: {'norm_A': 0.4698737184351335}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.46987) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.01) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.055, and will continue to 0.065]\n", "\n", "(STEP 12) ANALYSIS: Examining Conc. Changes from System Time 0.065 due to tentative single step of 0.01:\n", " Baseline: [48.77990588 85.7406189 16.69956934]\n", " Deltas: [ 0.36036743 -1.57024441 0.84950955]\n", " Relative Deltas: [ 0.00738762 -0.01831389 0.05087015]\n", " Norms: {'norm_A': 0.3685776282283221}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.36858) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.01) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.065, and will continue to 0.075]\n", "\n", "(STEP 13) ANALYSIS: Examining Conc. Changes from System Time 0.075 due to tentative single step of 0.01:\n", " Baseline: [49.14027331 84.17037449 17.54907888]\n", " Deltas: [ 0.42112084 -1.4721665 0.62992481]\n", " Relative Deltas: [ 0.00856977 -0.01749032 0.03589504]\n", " Norms: {'norm_A': 0.3046024715786003}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.3046) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.01) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.075, and will continue to 0.085]\n", "\n", "(STEP 14) ANALYSIS: Examining Conc. Changes from System Time 0.085 due to tentative single step of 0.01:\n", " Baseline: [49.56139416 82.69820799 18.1790037 ]\n", " Deltas: [ 0.46309241 -1.39020602 0.46402119]\n", " Relative Deltas: [ 0.00934381 -0.01681059 0.02552512]\n", " Norms: {'norm_A': 0.2624936691312418}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.26249) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.01) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.085, and will continue to 0.095]\n", "\n", "(STEP 15) ANALYSIS: Examining Conc. Changes from System Time 0.095 due to tentative single step of 0.01:\n", " Baseline: [50.02448657 81.30800197 18.64302489]\n", " Deltas: [ 0.49095226 -1.32065857 0.33875405]\n", " Relative Deltas: [ 0.00981424 -0.01624266 0.01817055]\n", " Norms: {'norm_A': 0.23332527475445466}\n", " Thresholds: \n", " norm_A : (VALUE 0.23333) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.01) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.02) at the next round, because all norms are low\n", " [The current step started at System Time: 0.095, and will continue to 0.105]\n", "\n", "(STEP 16) ANALYSIS: Examining Conc. Changes from System Time 0.105 due to tentative single step of 0.02:\n", " Baseline: [50.51543883 79.98734341 18.98177894]\n", " Deltas: [ 1.01646708 -2.52142713 0.48849298]\n", " Relative Deltas: [ 0.02012191 -0.03152283 0.02573484]\n", " Norms: {'norm_A': 0.847713943482735}\n", " Thresholds: \n", " norm_A : low 0.25 | high 0.64 | (VALUE 0.84771) | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: high (with step size factor of 0.5)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.01) at the next round, because at least one norm is high\n", " [The current step started at System Time: 0.105, and will continue to 0.125]\n", "\n", "(STEP 17) ANALYSIS: Examining Conc. Changes from System Time 0.125 due to tentative single step of 0.01:\n", " Baseline: [51.5319059 77.46591628 19.47027191]\n", " Deltas: [ 0.52698364 -1.15576117 0.1017939 ]\n", " Relative Deltas: [ 0.01022636 -0.01491961 0.00522817]\n", " Norms: {'norm_A': 0.18042862670354223}\n", " Thresholds: \n", " norm_A : (VALUE 0.18043) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.01) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.02) at the next round, because all norms are low\n", " [The current step started at System Time: 0.125, and will continue to 0.135]\n", "\n", "(STEP 18) ANALYSIS: Examining Conc. Changes from System Time 0.135 due to tentative single step of 0.02:\n", " Baseline: [52.05888954 76.3101551 19.57206582]\n", " Deltas: [ 1.04917495 -2.22996141 0.13161151]\n", " Relative Deltas: [ 0.02015362 -0.02922234 0.00672446]\n", " Norms: {'norm_A': 0.676757504987934}\n", " Thresholds: \n", " norm_A : low 0.25 | high 0.64 | (VALUE 0.67676) | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: high (with step size factor of 0.5)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.01) at the next round, because at least one norm is high\n", " [The current step started at System Time: 0.135, and will continue to 0.155]\n", "\n", "(STEP 19) ANALYSIS: Examining Conc. Changes from System Time 0.155 due to tentative single step of 0.01:\n", " Baseline: [53.10806449 74.08019369 19.70367733]\n", " Deltas: [ 0.5141329 -1.04018517 0.01191938]\n", " Relative Deltas: [ 0.00968088 -0.01404134 0.00060493]\n", " Norms: {'norm_A': 0.149606655239135}\n", " Thresholds: \n", " norm_A : (VALUE 0.14961) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.01) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.02) at the next round, because all norms are low\n", " [The current step started at System Time: 0.155, and will continue to 0.165]\n", "\n", "(STEP 20) ANALYSIS: Examining Conc. Changes from System Time 0.165 due to tentative single step of 0.02:\n", " Baseline: [53.62219739 73.04000852 19.71559671]\n", " Deltas: [ 1.00960758 -2.01652891 -0.00268625]\n", " Relative Deltas: [ 0.01882816 -0.02760855 -0.00013625]\n", " Norms: {'norm_A': 0.5650781675774635}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.56508) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.165, and will continue to 0.185]\n", "\n", "(STEP 21) ANALYSIS: Examining Conc. Changes from System Time 0.185 due to tentative single step of 0.02:\n", " Baseline: [54.63180496 71.02347962 19.71291046]\n", " Deltas: [ 0.96879347 -1.89585952 -0.04172743]\n", " Relative Deltas: [ 0.01773314 -0.02669342 -0.00211676]\n", " Norms: {'norm_A': 0.5038428113796767}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.50384) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.185, and will continue to 0.205]\n", "\n", "(STEP 22) ANALYSIS: Examining Conc. Changes from System Time 0.205 due to tentative single step of 0.02:\n", " Baseline: [55.60059844 69.12762009 19.67118303]\n", " Deltas: [ 0.92336535 -1.78711524 -0.05961545]\n", " Relative Deltas: [ 0.01660711 -0.02585241 -0.0030306 ]\n", " Norms: {'norm_A': 0.4499931616991674}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.44999) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.205, and will continue to 0.225]\n", "\n", "(STEP 23) ANALYSIS: Examining Conc. Changes from System Time 0.225 due to tentative single step of 0.02:\n", " Baseline: [56.52396378 67.34050485 19.61156758]\n", " Deltas: [ 0.87689226 -1.68704218 -0.06674234]\n", " Relative Deltas: [ 0.01551364 -0.02505241 -0.00340321]\n", " Norms: {'norm_A': 0.4021673223106198}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.40217) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.225, and will continue to 0.245]\n", "\n", "(STEP 24) ANALYSIS: Examining Conc. Changes from System Time 0.245 due to tentative single step of 0.02:\n", " Baseline: [57.40085605 65.65346267 19.54482524]\n", " Deltas: [ 0.8311378 -1.59382873 -0.06844686]\n", " Relative Deltas: [ 0.01447954 -0.02427638 -0.00350205]\n", " Norms: {'norm_A': 0.3595294483481833}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.35953) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.245, and will continue to 0.265]\n", "\n", "(STEP 25) ANALYSIS: Examining Conc. Changes from System Time 0.265 due to tentative single step of 0.02:\n", " Baseline: [58.23199384 64.05963394 19.47637838]\n", " Deltas: [ 0.78694079 -1.50641263 -0.06746894]\n", " Relative Deltas: [ 0.01351389 -0.02351579 -0.00346414]\n", " Norms: {'norm_A': 0.32145631944741265}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.32146) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.265, and will continue to 0.285]\n", "\n", "(STEP 26) ANALYSIS: Examining Conc. Changes from System Time 0.285 due to tentative single step of 0.02:\n", " Baseline: [59.01893463 62.55322131 19.40890943]\n", " Deltas: [ 0.74466812 -1.42412415 -0.0652121 ]\n", " Relative Deltas: [ 0.01261744 -0.0227666 -0.00335991]\n", " Norms: {'norm_A': 0.2874347575511702}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.28743) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.285, and will continue to 0.305]\n", "\n", "(STEP 27) ANALYSIS: Examining Conc. Changes from System Time 0.305 due to tentative single step of 0.02:\n", " Baseline: [59.76360275 61.12909716 19.34369733]\n", " Deltas: [ 0.70444746 -1.34650215 -0.06239278]\n", " Relative Deltas: [ 0.01178723 -0.02202719 -0.00322548]\n", " Norms: {'norm_A': 0.25702301402562294}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.25702) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.305, and will continue to 0.325]\n", "\n", "(STEP 28) ANALYSIS: Examining Conc. Changes from System Time 0.325 due to tentative single step of 0.02:\n", " Baseline: [60.46805022 59.78259501 19.28130456]\n", " Deltas: [ 0.66628672 -1.27319915 -0.05937429]\n", " Relative Deltas: [ 0.01101882 -0.02129715 -0.00307937]\n", " Norms: {'norm_A': 0.22983326503644058}\n", " Thresholds: \n", " norm_A : (VALUE 0.22983) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.04) at the next round, because all norms are low\n", " [The current step started at System Time: 0.325, and will continue to 0.345]\n", "\n", "(STEP 29) ANALYSIS: Examining Conc. Changes from System Time 0.345 due to tentative single step of 0.04:\n", " Baseline: [61.13433694 58.50939586 19.22193027]\n", " Deltas: [ 1.26027073 -2.40786424 -0.11267722]\n", " Relative Deltas: [ 0.02061478 -0.04115346 -0.00586191]\n", " Norms: {'norm_A': 0.8220876292375117}\n", " Thresholds: \n", " norm_A : low 0.25 | high 0.64 | (VALUE 0.82209) | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: high (with step size factor of 0.5)\n", "NOTICE: the tentative time step (0.04) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.02) at the next round, because at least one norm is high\n", " [The current step started at System Time: 0.345, and will continue to 0.385]\n", "\n", "(STEP 30) ANALYSIS: Examining Conc. Changes from System Time 0.385 due to tentative single step of 0.02:\n", " Baseline: [62.39460767 56.10153162 19.10925305]\n", " Deltas: [ 0.56169618 -1.07298153 -0.05041083]\n", " Relative Deltas: [ 0.00900232 -0.01912571 -0.00263803]\n", " Norms: {'norm_A': 0.16325924649115092}\n", " Thresholds: \n", " norm_A : (VALUE 0.16326) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.02) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.04) at the next round, because all norms are low\n", " [The current step started at System Time: 0.385, and will continue to 0.405]\n", "\n", "(STEP 31) ANALYSIS: Examining Conc. Changes from System Time 0.405 due to tentative single step of 0.04:\n", " Baseline: [62.95630385 55.02855009 19.05884222]\n", " Deltas: [ 1.0623252 -2.02930388 -0.09534652]\n", " Relative Deltas: [ 0.01687401 -0.03687729 -0.00500274]\n", " Norms: {'norm_A': 0.5839666694455721}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.58397) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.04) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.405, and will continue to 0.445]\n", "\n", "(STEP 32) ANALYSIS: Examining Conc. Changes from System Time 0.445 due to tentative single step of 0.04:\n", " Baseline: [64.01862905 52.99924621 18.96349569]\n", " Deltas: [ 0.9468283 -1.80867058 -0.08498602]\n", " Relative Deltas: [ 0.01478989 -0.03412635 -0.00448156]\n", " Norms: {'norm_A': 0.4638884123583411}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.46389) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.04) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.445, and will continue to 0.485]\n", "\n", "(STEP 33) ANALYSIS: Examining Conc. Changes from System Time 0.485 due to tentative single step of 0.04:\n", " Baseline: [64.96545735 51.19057563 18.87850968]\n", " Deltas: [ 0.84388651 -1.61202675 -0.07574626]\n", " Relative Deltas: [ 0.01298977 -0.03149069 -0.0040123 ]\n", " Norms: {'norm_A': 0.36850135438022225}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.3685) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.04) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.485, and will continue to 0.525]\n", "\n", "(STEP 34) ANALYSIS: Examining Conc. Changes from System Time 0.525 due to tentative single step of 0.04:\n", " Baseline: [65.80934386 49.57854888 18.80276341]\n", " Deltas: [ 0.75213678 -1.43676265 -0.06751092]\n", " Relative Deltas: [ 0.01142903 -0.02897952 -0.00359048]\n", " Norms: {'norm_A': 0.29272826302332505}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.29273) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.04) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.525, and will continue to 0.565]\n", "\n", "(STEP 35) ANALYSIS: Examining Conc. Changes from System Time 0.565 due to tentative single step of 0.04:\n", " Baseline: [66.56148064 48.14178623 18.73525249]\n", " Deltas: [ 0.67036235 -1.28055375 -0.06017094]\n", " Relative Deltas: [ 0.01007133 -0.02659963 -0.00321164]\n", " Norms: {'norm_A': 0.23253601370675195}\n", " Thresholds: \n", " norm_A : (VALUE 0.23254) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.04) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.08) at the next round, because all norms are low\n", " [The current step started at System Time: 0.565, and will continue to 0.605]\n", "\n", "(STEP 36) ANALYSIS: Examining Conc. Changes from System Time 0.605 due to tentative single step of 0.08:\n", " Baseline: [67.23184299 46.86123248 18.67508155]\n", " Deltas: [ 1.19495731 -2.28265665 -0.10725798]\n", " Relative Deltas: [ 0.01777368 -0.04871098 -0.00574337]\n", " Norms: {'norm_A': 0.7388831828204337}\n", " Thresholds: \n", " norm_A : low 0.25 | high 0.64 | (VALUE 0.73888) | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: high (with step size factor of 0.5)\n", "NOTICE: the tentative time step (0.08) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.04) at the next round, because at least one norm is high\n", " [The current step started at System Time: 0.605, and will continue to 0.685]\n", "\n", "(STEP 37) ANALYSIS: Examining Conc. Changes from System Time 0.685 due to tentative single step of 0.04:\n", " Baseline: [68.4268003 44.57857583 18.56782357]\n", " Deltas: [ 0.46755952 -0.89315144 -0.0419676 ]\n", " Relative Deltas: [ 0.00683299 -0.02003544 -0.00226023]\n", " Norms: {'norm_A': 0.11312140907262146}\n", " Thresholds: \n", " norm_A : (VALUE 0.11312) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.04) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.08) at the next round, because all norms are low\n", " [The current step started at System Time: 0.685, and will continue to 0.725]\n", "\n", "(STEP 38) ANALYSIS: Examining Conc. Changes from System Time 0.725 due to tentative single step of 0.08:\n", " Baseline: [68.89435982 43.68542439 18.52585598]\n", " Deltas: [ 0.83345025 -1.59209098 -0.07480952]\n", " Relative Deltas: [ 0.01209751 -0.03644444 -0.00403811]\n", " Norms: {'norm_A': 0.3594432769718806}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.35944) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.08) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.725, and will continue to 0.805]\n", "\n", "(STEP 39) ANALYSIS: Examining Conc. Changes from System Time 0.805 due to tentative single step of 0.08:\n", " Baseline: [69.72781007 42.0933334 18.45104645]\n", " Deltas: [ 0.65222012 -1.24589772 -0.05854252]\n", " Relative Deltas: [ 0.0093538 -0.02959846 -0.00317286]\n", " Norms: {'norm_A': 0.2201199373223358}\n", " Thresholds: \n", " norm_A : (VALUE 0.22012) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.08) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.16) at the next round, because all norms are low\n", " [The current step started at System Time: 0.805, and will continue to 0.885]\n", "\n", "(STEP 40) ANALYSIS: Examining Conc. Changes from System Time 0.885 due to tentative single step of 0.16:\n", " Baseline: [70.38003019 40.84743568 18.39250394]\n", " Deltas: [ 1.02079538 -1.94996535 -0.0916254 ]\n", " Relative Deltas: [ 0.01450405 -0.04773777 -0.00498167]\n", " Norms: {'norm_A': 0.5391981423603522}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.5392) | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: stay (with step size factor of 1)\n", "NOTICE: the tentative time step (0.16) results in norm values that leads to the following:\n", "ACTION: COMPLETE NORMALLY - we're inside the target range. No change to step size.\n", " [The current step started at System Time: 0.885, and will continue to 1.045]\n", "\n", "(STEP 41) ANALYSIS: Examining Conc. Changes from System Time 1.045 due to tentative single step of 0.16:\n", " Baseline: [71.40082557 38.89747033 18.30087853]\n", " Deltas: [ 0.57686034 -1.10194237 -0.05177831]\n", " Relative Deltas: [ 0.00807918 -0.02832941 -0.00282928]\n", " Norms: {'norm_A': 0.17219175887655633}\n", " Thresholds: \n", " norm_A : (VALUE 0.17219) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.16) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.32) at the next round, because all norms are low\n", " [The current step started at System Time: 1.045, and will continue to 1.205]\n", "\n", "(STEP 42) ANALYSIS: Examining Conc. Changes from System Time 1.205 due to tentative single step of 0.32:\n", " Baseline: [71.97768591 37.79552796 18.24910022]\n", " Deltas: [ 0.65197758 -1.24543442 -0.05852075]\n", " Relative Deltas: [ 0.00905805 -0.0329519 -0.00320677]\n", " Norms: {'norm_A': 0.21995626094117085}\n", " Thresholds: \n", " norm_A : (VALUE 0.21996) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.32) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 0.64) at the next round, because all norms are low\n", " [The current step started at System Time: 1.205, and will continue to 1.525]\n", "\n", "(STEP 43) ANALYSIS: Examining Conc. Changes from System Time 1.525 due to tentative single step of 0.64:\n", " Baseline: [72.62966349 36.55009354 18.19057947]\n", " Deltas: [ 0.16979763 -0.32435443 -0.01524084]\n", " Relative Deltas: [ 0.00233786 -0.00887424 -0.00083784]\n", " Norms: {'norm_A': 0.01491881249157113}\n", " Thresholds: \n", " norm_A : (VALUE 0.014919) | low 0.25 | high 0.64 | abort 1.44\n", " Step Factors: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5}\n", " => Action: low (with step size factor of 2.0)\n", "NOTICE: the tentative time step (0.64) results in norm values that leads to the following:\n", "ACTION: COMPLETE STEP NORMALLY and MAKE THE INTERVAL LARGER, multiplied by 2.0 (set to 1.28) at the next round, because all norms are low\n", " [The current step started at System Time: 1.525, and will continue to 2.165]\n", "44 total step(s) taken\n" ] } ], "source": [ "dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n", "\n", "# All of these settings are currently close to the default values... but subject to change; set for repeatability\n", "dynamics.set_thresholds(norm=\"norm_A\", low=0.25, high=0.64, abort=1.44)\n", "dynamics.set_thresholds(norm=\"norm_B\") # We are disabling norm_B (to conform to the original run)\n", "dynamics.set_step_factors(upshift=2.0, downshift=0.5, abort=0.5) # Note: upshift=2.0 seems to often be excessive. About 1.4 is currently recommended\n", "dynamics.set_error_step_factor(0.5)\n", "\n", "dynamics.single_compartment_react(initial_step=0.01, target_end_time=2.0, \n", " variable_steps=True, explain_variable_steps=True)" ] }, { "cell_type": "code", "execution_count": 7, "id": "8a57c6d4-32cc-4351-8ad8-2e8b30e9fecf", "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEUXScaption
00.000050.000000100.0000000.000000Initial state
10.005049.50000098.5000002.500000
20.007549.30250097.7987503.596250
30.012548.95332596.4396565.653694
40.015048.82163295.8011646.555571
50.020048.59563994.5608148.247909
60.025048.43959993.3898399.730964
70.030048.34444192.28092011.030197
80.035048.30220591.22761212.167978
90.040048.30590290.22423813.163959
100.050048.39290188.30734814.906851
110.055048.50524687.42994315.559566
120.065048.77990685.74061916.699569
130.075049.14027384.17037417.549079
140.085049.56139482.69820818.179004
150.095050.02448781.30800218.643025
160.105050.51543979.98734318.981779
170.125051.53190677.46591619.470272
180.135052.05889076.31015519.572066
190.155053.10806474.08019419.703677
200.165053.62219773.04000919.715597
210.185054.63180571.02348019.712910
220.205055.60059869.12762019.671183
230.225056.52396467.34050519.611568
240.245057.40085665.65346319.544825
250.265058.23199464.05963419.476378
260.285059.01893562.55322119.408909
270.305059.76360361.12909719.343697
280.325060.46805059.78259519.281305
290.345061.13433758.50939619.221930
300.385062.39460856.10153219.109253
310.405062.95630455.02855019.058842
320.445064.01862952.99924618.963496
330.485064.96545751.19057618.878510
340.525065.80934449.57854918.802763
350.565066.56148148.14178618.735252
360.605067.23184346.86123218.675082
370.685068.42680044.57857618.567824
380.725068.89436043.68542418.525856
390.805069.72781042.09333318.451046
400.885070.38003040.84743618.392504
411.045071.40082638.89747018.300879
421.205071.97768637.79552818.249100
431.525072.62966336.55009418.190579
442.165072.79946136.22573918.175339
\n", "
" ], "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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_history()" ] }, { "cell_type": "code", "execution_count": 8, "id": "12da63da-9b3b-4c43-a68b-7dfb6585b9d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From time 0 to 0.005, in 1 step of 0.005\n", "From time 0.005 to 0.0075, in 1 step of 0.0025\n", "From time 0.0075 to 0.0125, in 1 step of 0.005\n", "From time 0.0125 to 0.015, in 1 step of 0.0025\n", "From time 0.015 to 0.04, in 5 steps of 0.005\n", "From time 0.04 to 0.05, in 1 step of 0.01\n", "From time 0.05 to 0.055, in 1 step of 0.005\n", "From time 0.055 to 0.105, in 5 steps of 0.01\n", "From time 0.105 to 0.125, in 1 step of 0.02\n", "From time 0.125 to 0.135, in 1 step of 0.01\n", "From time 0.135 to 0.155, in 1 step of 0.02\n", "From time 0.155 to 0.165, in 1 step of 0.01\n", "From time 0.165 to 0.345, in 9 steps of 0.02\n", "From time 0.345 to 0.385, in 1 step of 0.04\n", "From time 0.385 to 0.405, in 1 step of 0.02\n", "From time 0.405 to 0.605, in 5 steps of 0.04\n", "From time 0.605 to 0.685, in 1 step of 0.08\n", "From time 0.685 to 0.725, in 1 step of 0.04\n", "From time 0.725 to 0.885, in 2 steps of 0.08\n", "From time 0.885 to 1.205, in 2 steps of 0.16\n", "From time 1.205 to 1.525, in 1 step of 0.32\n", "From time 1.525 to 2.165, in 1 step of 0.64\n", "(44 steps total)\n" ] } ], "source": [ "(transition_times, step_sizes) = dynamics.explain_time_advance(return_times=True)" ] }, { "cell_type": "code", "execution_count": 9, "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": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(step_sizes)" ] }, { "cell_type": "code", "execution_count": 10, "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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(transition_times) # Note: there will be one more transition time (the end time) than step sizes" ] }, { "cell_type": "markdown", "id": "cbf6c9c7-8cec-400f-9e70-49ff1a9f485c", "metadata": { "tags": [] }, "source": [ "## Plots of changes of concentration with time" ] }, { "cell_type": "code", "execution_count": 11, "id": "c388dae7-c4a6-4644-a390-958e3862d102", "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=U
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "U", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "U", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.005, 0.0075, 0.0125, 0.015000000000000001, 0.02, 0.025, 0.030000000000000002, 0.035, 0.04, 0.05, 0.055, 0.065, 0.075, 0.08499999999999999, 0.09499999999999999, 0.10499999999999998, 0.12499999999999999, 0.13499999999999998, 0.15499999999999997, 0.16499999999999998, 0.18499999999999997, 0.20499999999999996, 0.22499999999999995, 0.24499999999999994, 0.26499999999999996, 0.285, 0.305, 0.325, 0.34500000000000003, 0.385, 0.405, 0.445, 0.485, 0.525, 0.5650000000000001, 0.6050000000000001, 0.685, 0.7250000000000001, 0.805, 0.885, 1.045, 1.2049999999999998, 1.525, 2.165 ], "xaxis": "x", "y": [ 50, 49.5, 49.3025, 48.953325, 48.82163225, 48.595638778125, 48.439598731740624, 48.34444128763423, 48.30220476057483, 48.30590183654385, 48.39290050226037, 48.5052455391863, 48.7799058814107, 49.14027331067588, 49.56139415522215, 50.02448656789047, 50.51543882771388, 51.5319059043599, 52.05888953946266, 53.10806448877514, 53.6221973851961, 54.631804962718874, 55.60059843741017, 56.52396378475239, 57.400856045782184, 58.231993841938426, 59.018934628561645, 59.76360275298115, 60.468050216020096, 61.13433693633931, 62.39460766762712, 62.95630384813689, 64.01862904935517, 64.96545734689445, 65.8093438554794, 66.56148063896055, 67.23184298514343, 68.4268002988057, 68.89435981841929, 69.72781007217334, 70.38003019051833, 71.40082556698592, 71.97768590721373, 72.62966349214221, 72.7994611238083 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "orange", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.005, 0.0075, 0.0125, 0.015000000000000001, 0.02, 0.025, 0.030000000000000002, 0.035, 0.04, 0.05, 0.055, 0.065, 0.075, 0.08499999999999999, 0.09499999999999999, 0.10499999999999998, 0.12499999999999999, 0.13499999999999998, 0.15499999999999997, 0.16499999999999998, 0.18499999999999997, 0.20499999999999996, 0.22499999999999995, 0.24499999999999994, 0.26499999999999996, 0.285, 0.305, 0.325, 0.34500000000000003, 0.385, 0.405, 0.445, 0.485, 0.525, 0.5650000000000001, 0.6050000000000001, 0.685, 0.7250000000000001, 0.805, 0.885, 1.045, 1.2049999999999998, 1.525, 2.165 ], "xaxis": "x", "y": [ 100, 98.5, 97.79875, 96.43965625, 95.801164234375, 94.56081390882812, 93.38983895624335, 92.28092027930796, 91.22761238948105, 90.22423754631991, 88.30734794676586, 87.42994325902578, 85.7406189010111, 84.17037449415082, 82.69820799239614, 81.30800197445382, 79.98734340860611, 77.46591627640568, 76.31015510300598, 74.08019369499387, 73.0400085237914, 71.02347961706188, 69.12762009493821, 67.34050485287088, 65.65346267101354, 64.05963393924337, 62.55322130811434, 61.12909716179896, 59.782595011959664, 58.5093958579621, 56.10153161965286, 55.02855008788483, 52.99924620914057, 51.19057563015947, 49.578548876792716, 48.14178623051722, 46.86123248082995, 44.57857582889467, 43.685424387065844, 42.0933334026959, 40.84743568346842, 38.897470333478665, 37.79552796465638, 36.5500935427452, 36.2257391168805 ], "yaxis": "y" }, { "hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "S", "line": { "color": "blue", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "S", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.005, 0.0075, 0.0125, 0.015000000000000001, 0.02, 0.025, 0.030000000000000002, 0.035, 0.04, 0.05, 0.055, 0.065, 0.075, 0.08499999999999999, 0.09499999999999999, 0.10499999999999998, 0.12499999999999999, 0.13499999999999998, 0.15499999999999997, 0.16499999999999998, 0.18499999999999997, 0.20499999999999996, 0.22499999999999995, 0.24499999999999994, 0.26499999999999996, 0.285, 0.305, 0.325, 0.34500000000000003, 0.385, 0.405, 0.445, 0.485, 0.525, 0.5650000000000001, 0.6050000000000001, 0.685, 0.7250000000000001, 0.805, 0.885, 1.045, 1.2049999999999998, 1.525, 2.165 ], "xaxis": "x", "y": [ 0, 2.5, 3.59625, 5.65369375, 6.555571265625001, 8.247908534921876, 9.730963580275391, 11.03019714542356, 12.167978089369273, 13.163958780592365, 14.906851048713396, 15.559565662601617, 16.699569336167485, 17.5490788844974, 18.179003697159533, 18.643024889765208, 18.981778935966094, 19.470271914874488, 19.572065818068666, 19.703677327455825, 19.715596705816367, 19.712910457500335, 19.671183030241412, 19.611567577624296, 19.54482523742205, 19.476378376879737, 19.40890943476233, 19.343697332238698, 19.2813045560001, 19.221930269359245, 19.10925304509285, 19.058842215841338, 18.963495692149042, 18.878509676051582, 18.80276341224844, 18.735252491561642, 18.67508154888315, 18.56782357349389, 18.52585597609554, 18.451046452957367, 18.392503935494886, 18.300878532549454, 18.24910022091612, 18.190579472970335, 18.17533863550288 ], "yaxis": "y" } ], "layout": { "autosize": true, "legend": { "title": { "text": "Chemical" }, "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "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 concentration for `2 S <-> U` and `S <-> X`" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0, 2.165 ], "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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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_curves(colors=['green', 'orange', 'blue'])" ] }, { "cell_type": "code", "execution_count": 12, "id": "2f07ad6b-a1c9-4d99-8108-72b16727303d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Min abs distance found at data row: 28\n" ] }, { "data": { "text/plain": [ "(0.3183157284824908, 60.23261431038145)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.curve_intersection(\"U\", \"X\", t_start=0.3, t_end=0.35) # Compare with the value from experiment \"variable_steps_2\"" ] }, { "cell_type": "code", "execution_count": 13, "id": "a264d96b-31de-493d-9e92-742a84b4a453", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=U
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "U", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "U", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.005, 0.0075, 0.0125, 0.015000000000000001, 0.02, 0.025, 0.030000000000000002, 0.035, 0.04, 0.05, 0.055, 0.065, 0.075, 0.08499999999999999, 0.09499999999999999, 0.10499999999999998, 0.12499999999999999, 0.13499999999999998, 0.15499999999999997, 0.16499999999999998, 0.18499999999999997, 0.20499999999999996, 0.22499999999999995, 0.24499999999999994, 0.26499999999999996, 0.285, 0.305, 0.325, 0.34500000000000003, 0.385, 0.405, 0.445, 0.485, 0.525, 0.5650000000000001, 0.6050000000000001, 0.685, 0.7250000000000001, 0.805, 0.885, 1.045, 1.2049999999999998, 1.525, 2.165 ], "xaxis": "x", "y": [ 50, 49.5, 49.3025, 48.953325, 48.82163225, 48.595638778125, 48.439598731740624, 48.34444128763423, 48.30220476057483, 48.30590183654385, 48.39290050226037, 48.5052455391863, 48.7799058814107, 49.14027331067588, 49.56139415522215, 50.02448656789047, 50.51543882771388, 51.5319059043599, 52.05888953946266, 53.10806448877514, 53.6221973851961, 54.631804962718874, 55.60059843741017, 56.52396378475239, 57.400856045782184, 58.231993841938426, 59.018934628561645, 59.76360275298115, 60.468050216020096, 61.13433693633931, 62.39460766762712, 62.95630384813689, 64.01862904935517, 64.96545734689445, 65.8093438554794, 66.56148063896055, 67.23184298514343, 68.4268002988057, 68.89435981841929, 69.72781007217334, 70.38003019051833, 71.40082556698592, 71.97768590721373, 72.62966349214221, 72.7994611238083 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "orange", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.005, 0.0075, 0.0125, 0.015000000000000001, 0.02, 0.025, 0.030000000000000002, 0.035, 0.04, 0.05, 0.055, 0.065, 0.075, 0.08499999999999999, 0.09499999999999999, 0.10499999999999998, 0.12499999999999999, 0.13499999999999998, 0.15499999999999997, 0.16499999999999998, 0.18499999999999997, 0.20499999999999996, 0.22499999999999995, 0.24499999999999994, 0.26499999999999996, 0.285, 0.305, 0.325, 0.34500000000000003, 0.385, 0.405, 0.445, 0.485, 0.525, 0.5650000000000001, 0.6050000000000001, 0.685, 0.7250000000000001, 0.805, 0.885, 1.045, 1.2049999999999998, 1.525, 2.165 ], "xaxis": "x", "y": [ 100, 98.5, 97.79875, 96.43965625, 95.801164234375, 94.56081390882812, 93.38983895624335, 92.28092027930796, 91.22761238948105, 90.22423754631991, 88.30734794676586, 87.42994325902578, 85.7406189010111, 84.17037449415082, 82.69820799239614, 81.30800197445382, 79.98734340860611, 77.46591627640568, 76.31015510300598, 74.08019369499387, 73.0400085237914, 71.02347961706188, 69.12762009493821, 67.34050485287088, 65.65346267101354, 64.05963393924337, 62.55322130811434, 61.12909716179896, 59.782595011959664, 58.5093958579621, 56.10153161965286, 55.02855008788483, 52.99924620914057, 51.19057563015947, 49.578548876792716, 48.14178623051722, 46.86123248082995, 44.57857582889467, 43.685424387065844, 42.0933334026959, 40.84743568346842, 38.897470333478665, 37.79552796465638, 36.5500935427452, 36.2257391168805 ], "yaxis": "y" }, { "hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "S", "line": { "color": "blue", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "S", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.005, 0.0075, 0.0125, 0.015000000000000001, 0.02, 0.025, 0.030000000000000002, 0.035, 0.04, 0.05, 0.055, 0.065, 0.075, 0.08499999999999999, 0.09499999999999999, 0.10499999999999998, 0.12499999999999999, 0.13499999999999998, 0.15499999999999997, 0.16499999999999998, 0.18499999999999997, 0.20499999999999996, 0.22499999999999995, 0.24499999999999994, 0.26499999999999996, 0.285, 0.305, 0.325, 0.34500000000000003, 0.385, 0.405, 0.445, 0.485, 0.525, 0.5650000000000001, 0.6050000000000001, 0.685, 0.7250000000000001, 0.805, 0.885, 1.045, 1.2049999999999998, 1.525, 2.165 ], "xaxis": "x", "y": [ 0, 2.5, 3.59625, 5.65369375, 6.555571265625001, 8.247908534921876, 9.730963580275391, 11.03019714542356, 12.167978089369273, 13.163958780592365, 14.906851048713396, 15.559565662601617, 16.699569336167485, 17.5490788844974, 18.179003697159533, 18.643024889765208, 18.981778935966094, 19.470271914874488, 19.572065818068666, 19.703677327455825, 19.715596705816367, 19.712910457500335, 19.671183030241412, 19.611567577624296, 19.54482523742205, 19.476378376879737, 19.40890943476233, 19.343697332238698, 19.2813045560001, 19.221930269359245, 19.10925304509285, 19.058842215841338, 18.963495692149042, 18.878509676051582, 18.80276341224844, 18.735252491561642, 18.67508154888315, 18.56782357349389, 18.52585597609554, 18.451046452957367, 18.392503935494886, 18.300878532549454, 18.24910022091612, 18.190579472970335, 18.17533863550288 ], "yaxis": "y" } ], "layout": { "autosize": true, "legend": { "title": { "text": "Chemical" }, "tracegroupgap": 0 }, "shapes": [ { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0, "x1": 0, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.005, "x1": 0.005, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.0075, "x1": 0.0075, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.0125, "x1": 0.0125, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.015000000000000001, "x1": 0.015000000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.02, "x1": 0.02, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.025, "x1": 0.025, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.030000000000000002, "x1": 0.030000000000000002, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.035, "x1": 0.035, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.04, "x1": 0.04, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.05, "x1": 0.05, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.055, "x1": 0.055, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.065, "x1": 0.065, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.075, "x1": 0.075, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.08499999999999999, "x1": 0.08499999999999999, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.09499999999999999, "x1": 0.09499999999999999, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.10499999999999998, "x1": 0.10499999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.12499999999999999, "x1": 0.12499999999999999, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.13499999999999998, "x1": 0.13499999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.15499999999999997, "x1": 0.15499999999999997, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.16499999999999998, "x1": 0.16499999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.18499999999999997, "x1": 0.18499999999999997, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.20499999999999996, "x1": 0.20499999999999996, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.22499999999999995, "x1": 0.22499999999999995, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.24499999999999994, "x1": 0.24499999999999994, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.26499999999999996, "x1": 0.26499999999999996, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.285, "x1": 0.285, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.305, "x1": 0.305, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.325, "x1": 0.325, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.34500000000000003, "x1": 0.34500000000000003, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.385, "x1": 0.385, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.405, "x1": 0.405, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.445, "x1": 0.445, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.485, "x1": 0.485, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.525, "x1": 0.525, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.5650000000000001, "x1": 0.5650000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.6050000000000001, "x1": 0.6050000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.685, "x1": 0.685, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.7250000000000001, "x1": 0.7250000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.805, "x1": 0.805, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.885, "x1": 0.885, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 1.045, "x1": 1.045, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 1.2049999999999998, "x1": 1.2049999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 1.525, "x1": 1.525, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 2.165, "x1": 2.165, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" } ], "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "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 concentration for `2 S <-> U` and `S <-> X` (time steps shown in dashed lines)" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ -0.0013121212121212122, 2.1663121212121212 ], "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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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_curves(colors=['green', 'orange', 'blue'], show_intervals=True)" ] }, { "cell_type": "code", "execution_count": 14, "id": "75866674-1a8a-40a6-bdc4-ee52eb94a823", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=U
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "U", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "U", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.005, 0.0075, 0.0125, 0.015000000000000001, 0.02, 0.025, 0.030000000000000002, 0.035, 0.04, 0.05, 0.055, 0.065, 0.075, 0.08499999999999999, 0.09499999999999999, 0.10499999999999998, 0.12499999999999999, 0.13499999999999998, 0.15499999999999997, 0.16499999999999998, 0.18499999999999997, 0.20499999999999996, 0.22499999999999995, 0.24499999999999994, 0.26499999999999996, 0.285, 0.305, 0.325, 0.34500000000000003, 0.385, 0.405, 0.445, 0.485, 0.525, 0.5650000000000001, 0.6050000000000001, 0.685, 0.7250000000000001, 0.805, 0.885, 1.045, 1.2049999999999998, 1.525, 2.165 ], "xaxis": "x", "y": [ 50, 49.5, 49.3025, 48.953325, 48.82163225, 48.595638778125, 48.439598731740624, 48.34444128763423, 48.30220476057483, 48.30590183654385, 48.39290050226037, 48.5052455391863, 48.7799058814107, 49.14027331067588, 49.56139415522215, 50.02448656789047, 50.51543882771388, 51.5319059043599, 52.05888953946266, 53.10806448877514, 53.6221973851961, 54.631804962718874, 55.60059843741017, 56.52396378475239, 57.400856045782184, 58.231993841938426, 59.018934628561645, 59.76360275298115, 60.468050216020096, 61.13433693633931, 62.39460766762712, 62.95630384813689, 64.01862904935517, 64.96545734689445, 65.8093438554794, 66.56148063896055, 67.23184298514343, 68.4268002988057, 68.89435981841929, 69.72781007217334, 70.38003019051833, 71.40082556698592, 71.97768590721373, 72.62966349214221, 72.7994611238083 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "orange", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.005, 0.0075, 0.0125, 0.015000000000000001, 0.02, 0.025, 0.030000000000000002, 0.035, 0.04, 0.05, 0.055, 0.065, 0.075, 0.08499999999999999, 0.09499999999999999, 0.10499999999999998, 0.12499999999999999, 0.13499999999999998, 0.15499999999999997, 0.16499999999999998, 0.18499999999999997, 0.20499999999999996, 0.22499999999999995, 0.24499999999999994, 0.26499999999999996, 0.285, 0.305, 0.325, 0.34500000000000003, 0.385, 0.405, 0.445, 0.485, 0.525, 0.5650000000000001, 0.6050000000000001, 0.685, 0.7250000000000001, 0.805, 0.885, 1.045, 1.2049999999999998, 1.525, 2.165 ], "xaxis": "x", "y": [ 100, 98.5, 97.79875, 96.43965625, 95.801164234375, 94.56081390882812, 93.38983895624335, 92.28092027930796, 91.22761238948105, 90.22423754631991, 88.30734794676586, 87.42994325902578, 85.7406189010111, 84.17037449415082, 82.69820799239614, 81.30800197445382, 79.98734340860611, 77.46591627640568, 76.31015510300598, 74.08019369499387, 73.0400085237914, 71.02347961706188, 69.12762009493821, 67.34050485287088, 65.65346267101354, 64.05963393924337, 62.55322130811434, 61.12909716179896, 59.782595011959664, 58.5093958579621, 56.10153161965286, 55.02855008788483, 52.99924620914057, 51.19057563015947, 49.578548876792716, 48.14178623051722, 46.86123248082995, 44.57857582889467, 43.685424387065844, 42.0933334026959, 40.84743568346842, 38.897470333478665, 37.79552796465638, 36.5500935427452, 36.2257391168805 ], "yaxis": "y" }, { "hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "S", "line": { "color": "blue", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "S", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.005, 0.0075, 0.0125, 0.015000000000000001, 0.02, 0.025, 0.030000000000000002, 0.035, 0.04, 0.05, 0.055, 0.065, 0.075, 0.08499999999999999, 0.09499999999999999, 0.10499999999999998, 0.12499999999999999, 0.13499999999999998, 0.15499999999999997, 0.16499999999999998, 0.18499999999999997, 0.20499999999999996, 0.22499999999999995, 0.24499999999999994, 0.26499999999999996, 0.285, 0.305, 0.325, 0.34500000000000003, 0.385, 0.405, 0.445, 0.485, 0.525, 0.5650000000000001, 0.6050000000000001, 0.685, 0.7250000000000001, 0.805, 0.885, 1.045, 1.2049999999999998, 1.525, 2.165 ], "xaxis": "x", "y": [ 0, 2.5, 3.59625, 5.65369375, 6.555571265625001, 8.247908534921876, 9.730963580275391, 11.03019714542356, 12.167978089369273, 13.163958780592365, 14.906851048713396, 15.559565662601617, 16.699569336167485, 17.5490788844974, 18.179003697159533, 18.643024889765208, 18.981778935966094, 19.470271914874488, 19.572065818068666, 19.703677327455825, 19.715596705816367, 19.712910457500335, 19.671183030241412, 19.611567577624296, 19.54482523742205, 19.476378376879737, 19.40890943476233, 19.343697332238698, 19.2813045560001, 19.221930269359245, 19.10925304509285, 19.058842215841338, 18.963495692149042, 18.878509676051582, 18.80276341224844, 18.735252491561642, 18.67508154888315, 18.56782357349389, 18.52585597609554, 18.451046452957367, 18.392503935494886, 18.300878532549454, 18.24910022091612, 18.190579472970335, 18.17533863550288 ], "yaxis": "y" } ], "layout": { "autosize": true, "legend": { "title": { "text": "Chemical" }, "tracegroupgap": 0 }, "shapes": [ { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0, "x1": 0, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.005, "x1": 0.005, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.0075, "x1": 0.0075, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.0125, "x1": 0.0125, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.015000000000000001, "x1": 0.015000000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.04, "x1": 0.04, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.05, "x1": 0.05, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.055, "x1": 0.055, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.10499999999999998, "x1": 0.10499999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.12499999999999999, "x1": 0.12499999999999999, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.13499999999999998, "x1": 0.13499999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.15499999999999997, "x1": 0.15499999999999997, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.16499999999999998, "x1": 0.16499999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.34500000000000003, "x1": 0.34500000000000003, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.385, "x1": 0.385, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.405, "x1": 0.405, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.6050000000000001, "x1": 0.6050000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.685, "x1": 0.685, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.7250000000000001, "x1": 0.7250000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.885, "x1": 0.885, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 1.2049999999999998, "x1": 1.2049999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 1.525, "x1": 1.525, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 2.165, "x1": 2.165, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" } ], "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "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": "Critical values of time-step changes for reactions `2 S <-> U` and `S <-> X`" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ -0.0013121212121212122, 2.1663121212121212 ], "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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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Show the \"critical values\", i.e. times when the step size changes\n", "dynamics.plot_curves(colors=['green', 'orange', 'blue'], vertical_lines=transition_times, \n", " title=\"Critical values of time-step changes for reactions `2 S <-> U` and `S <-> X`\")" ] }, { "cell_type": "markdown", "id": "73277ff6-78f4-4b3c-9304-c22e4873c566", "metadata": {}, "source": [ "## Note: the dashed lines in the plots immediatly above and below are NOT the steps; they are the \"critical values\", i.e. times when the step size changes. \n", "The time steps were shown in an earlier plots" ] }, { "cell_type": "code", "execution_count": 15, "id": "ca14a2d7-5916-4144-a909-bfc3cf89c02c", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "x=%{x}
y=%{y}", "legendgroup": "", "line": { "color": "#636efa", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "", "orientation": "v", "showlegend": false, "type": "scatter", "x": [ 0, 0.005, 0.005, 0.0075, 0.0075, 0.0125, 0.0125, 0.015000000000000001, 0.015000000000000001, 0.04, 0.04, 0.05, 0.05, 0.055, 0.055, 0.10499999999999998, 0.10499999999999998, 0.12499999999999999, 0.12499999999999999, 0.13499999999999998, 0.13499999999999998, 0.15499999999999997, 0.15499999999999997, 0.16499999999999998, 0.16499999999999998, 0.34500000000000003, 0.34500000000000003, 0.385, 0.385, 0.405, 0.405, 0.6050000000000001, 0.6050000000000001, 0.685, 0.685, 0.7250000000000001, 0.7250000000000001, 0.885, 0.885, 1.2049999999999998, 1.2049999999999998, 1.525, 1.525, 2.165 ], "xaxis": "x", "y": [ 0.005, 0.005, 0.0024999999999999996, 0.0024999999999999996, 0.005000000000000001, 0.005000000000000001, 0.0025000000000000005, 0.0025000000000000005, 0.0049999999999999975, 0.0049999999999999975, 0.010000000000000002, 0.010000000000000002, 0.0049999999999999975, 0.0049999999999999975, 0.009999999999999995, 0.009999999999999995, 0.020000000000000004, 0.020000000000000004, 0.009999999999999995, 0.009999999999999995, 0.01999999999999999, 0.01999999999999999, 0.010000000000000009, 0.010000000000000009, 0.020000000000000018, 0.020000000000000018, 0.03999999999999998, 0.03999999999999998, 0.020000000000000018, 0.020000000000000018, 0.040000000000000036, 0.040000000000000036, 0.07999999999999996, 0.07999999999999996, 0.040000000000000036, 0.040000000000000036, 0.07999999999999996, 0.07999999999999996, 0.15999999999999992, 0.15999999999999992, 0.32000000000000006, 0.32000000000000006, 0.6400000000000001, 0.6400000000000001 ], "yaxis": "y" } ], "layout": { "autosize": true, "legend": { "tracegroupgap": 0 }, "margin": { "t": 60 }, "shapes": [ { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0, "x1": 0, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.005, "x1": 0.005, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.0075, "x1": 0.0075, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.0125, "x1": 0.0125, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.015000000000000001, "x1": 0.015000000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.02, "x1": 0.02, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.025, "x1": 0.025, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.030000000000000002, "x1": 0.030000000000000002, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.035, "x1": 0.035, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.04, "x1": 0.04, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.05, "x1": 0.05, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.055, "x1": 0.055, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.065, "x1": 0.065, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.075, "x1": 0.075, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.08499999999999999, "x1": 0.08499999999999999, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.09499999999999999, "x1": 0.09499999999999999, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.10499999999999998, "x1": 0.10499999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.12499999999999999, "x1": 0.12499999999999999, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.13499999999999998, "x1": 0.13499999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.15499999999999997, "x1": 0.15499999999999997, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.16499999999999998, "x1": 0.16499999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.18499999999999997, "x1": 0.18499999999999997, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.20499999999999996, "x1": 0.20499999999999996, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.22499999999999995, "x1": 0.22499999999999995, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.24499999999999994, "x1": 0.24499999999999994, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.26499999999999996, "x1": 0.26499999999999996, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.285, "x1": 0.285, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.305, "x1": 0.305, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.325, "x1": 0.325, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.34500000000000003, "x1": 0.34500000000000003, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.385, "x1": 0.385, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.405, "x1": 0.405, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.445, "x1": 0.445, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.485, "x1": 0.485, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.525, "x1": 0.525, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.5650000000000001, "x1": 0.5650000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.6050000000000001, "x1": 0.6050000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.685, "x1": 0.685, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.7250000000000001, "x1": 0.7250000000000001, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.805, "x1": 0.805, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 0.885, "x1": 0.885, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 1.045, "x1": 1.045, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 1.2049999999999998, "x1": 1.2049999999999998, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 1.525, "x1": 1.525, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" }, { "line": { "color": "gray", "dash": "dot", "width": 1 }, "type": "line", "x0": 2.165, "x1": 2.165, "xref": "x", "y0": 0, "y1": 1, "yref": "y domain" } ], "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "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": "Simulation step sizes" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ -0.0012856294536817102, 2.166285629453682 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -0.03291666666666667, 0.6754166666666668 ], "title": { "text": "Step size" }, "type": "linear" } } }, "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+sAAAFoCAYAAAAvu2oWAAAAAXNSR0IArs4c6QAAIABJREFUeF7tnWuYFdWVsFc39gUUkFuDKIKYiagxEIhINCKiRkRFhwQwkhglgY5mJl4IDJjH4TH5EhgIyqijIgPxMgmKjo4hEs0EQ1QSxBtRg5iMoogEvIAGFGig+3uqOqc9fbq6q07tVZdd5+0fM6FP7V1rv2udat9Ttdcpa2hoaBB+IAABCEAAAhCAAAQgAAEIQAACEEgNgTJkPTW5IBAIQAACEIAABCAAAQhAAAIQgIBLAFmnECAAAQhAAAIQgAAEIAABCEAAAikjgKynLCGEAwEIQAACEIAABCAAAQhAAAIQQNapAQhAAAIQgAAEIAABCEAAAhCAQMoIIOspSwjhQAACEIAABCAAAQhAAAIQgAAEkHVqAAIQgAAEIAABCEAAAhCAAAQgkDICyHrKEkI4EIAABCAAAQhAAAIQgAAEIAABZJ0agAAEIAABCEAAAhCAAAQgAAEIpIwAsp6yhBAOBCAAAQhAAAIQgAAEIAABCEAAWacGIAABCEAAAhCAAAQgAAEIQAACKSOArKcsIYQDAQhAAAIQgAAEIAABCEAAAhBA1qkBCEAAAhCAAAQgAAEIQAACEIBAyggg6ylLCOFAAAIQgAAEIAABCEAAAhCAAASQdWoAAhCAAAQgAAEIQAACEIAABCCQMgLIesoSQjgQgAAEIAABCEAAAhCAAAQgAAFknRqAAAQgAAEIQAACEIAABCAAAQikjACynmBCbli4TNau2yC3zblaunTumGAknBoCEIAABCAAAQhAAAIQgAAE0kQgk7L+4Ion5Lq5S1pwvnPBDDlx0ICm3zuyvOLxp2Xh3KlydN/eseclrKzv+HCnXD7jRhk6aIBcUzu+WdyvvblFaqfPl9EjT2rxWuwLbOWEYdcdd/y5Oiqsm7jj4HwQgAAEIAABCEAAAhCAQOkRyJSs795TJ7PmLZHnX/5LCwF3BHHx0hXyw+mTZOzo4W6mkfVkCh5ZT4Y7Z4UABCAAAQhAAAIQgAAE7CGQKVl/Zt0GufSqOdLanVDnrvP/bdwsZ48YmooMhZXWtu6sp2JhPkGEXbcNayNGCEAAAhCAAAQgAAEIQAACGgQyJevOY8u33vVw4MfaC6UxJ8Hjzx/hss1/lD53Rz53hz4HP/9OvfM7J4Zly1e12IfufJAwc/aiZrEVnj/3ZMAjK9c0y+25ZwyT66dNkvbVlZKL8aVXXvc8Zsu299zH4K/4xgVNTxA4B3qNK/xQI/cIvTO2cP3FPApeyOiEY/s38Sh8zTnPYT27NeOS+9Alt8D88bnf5dj9eOZkuXb2IsnxyGfl9wYpPI9zfP46C+vJ6/jWYgyyBq98FxO/3/p4HQIQgAAEIAABCEAAAhCwl0CmZD0nSEGFpzVZd8QvX8Lz98AX/r7wwwFTWZ93270yceyZTXvoc0LnlFihsLe1Zz1f1vMlPLcFIMcqfz254/667f0W6w/6IUhrH0Dkx9/WnXWvrQleH8LkpD9f5L1YtfbW9HoKo/A8QT788eIYZA3F5tXeSwyRQwACEIAABCAAAQhAAAJhCGRK1h0AXs3lCu/c5kC1dWc9J7XOsfl33P1+byLrbYll/l35IA3mcrLelsAWyqiX1Le1/sJ424rrsVVrZfiwQe7TAa3Jeu78s2dObtYI0Gve1ubw2wrRWu5zv3/51Y3SvrrK/bDET9Zz8Q7+zD80fZASdA2tsXby9cSadanZqhHmosIYCEAAAhCAAAQgAAEIQMCcQOZkPYck/y5x7neF0p5WWff7wKEYWW/tgwaHSaHY+sm61538/BLMf6y7cHtA/nGtiXZrH3Q4Y50xW9/Z3iTFfsJfuA2g8K2SY9zWUxhtyXqOqzNv/lfvBV3Dnr173Y7+zlMcxWwxMH/LMwMEIAABCEAAAhCAAAQgYAOBzMp6IXyvu6Bpk/WcAL63/cMWe7jD3llvTcAdPoWvmcq6M6fXPmy/D0lyufLaz56fx3yxbk3W2/pwojVhz/+93zaH/Fi9vvavmDV49RHw2p9vw4WEGCEAAQhAAAIQgAAEIAABXQIlI+sOtkLBS5ust3Ynt7A5XVrvrHuVZu4DgO5dOzdrMrd23YYWTfjauitdOLfpnfXC+fI/ZMjd6W4tH7m78l5PDxSzhsIYiu25oHspYDYIQAACEIAABCAAAQhAIE0EMiXrzr7oTx11RFNztnzQXnu3o5J1r2ZsQbrBt7UPO//Oem4tvWq6yjW145vVU+Hdcc09636PwTsfIjiPdQ8fNrBZTF6N21rrmN/WV+/lTxqUVWtvtvw99LljCtl5yXpOqL/51dEt2DvzBN0z75xr95698pljjmoWIl9rl6bLI7FAAAIQgAAEIAABCEAgOQKZkvXW9iHnhPX5l//S5lenFdNIzkmZ1/FtdV73exzcS/Ryv/Mb25pwOr/PxTR65ElNgtlWN/jWvvYtiKw7+7Dzj/P6sKAtoc09Rl64j9sZ8+TTLzbF7yW1Xuts7a3lNd6vG3wu30f2rmnaO+81f5A1eNVJW09MJHeJ4MwQgAAEIAABCEAAAhCAQBIEMiXr+Xc2C2F63QmN4s66VwzOuU896bO+37NezNjCveG5/dxa37Pu1fXeT9bzPxhwvv4t9+PFPr+Jnt/3rDvzeH1YsXjpihbvmbYa2+Uf7LW3vnC/uJe8Xzd3ief7NMwavL63PWj8SVwsOCcEIAABCEAAAhCAAAQgEB+BzMl6fOg4U5IEeFw8SfqcGwIQgAAEIAABCEAAAhCImgCyHjVh5o+EALIeCVYmhQAEIAABCEAAAhCAAARSQgBZT0kiCKM4Ash6cbw4GgIQgAAEIAABCEAAAhCwiwCyble+iBYCEIAABCAAAQhAAAIQgAAESoAAsl4CSWaJEIAABCAAAQhAAAIQgAAEIGAXAWTdrnwRLQQgAAEIQAACEIAABCAAAQiUAAFkvQSSzBIhAAEIQAACEIAABCAAAQhAwC4CyLpd+SJaCEAAAhCAAAQgAAEIQAACECgBAsh6CSSZJUIAAhCAAAQgAAEIQAACEICAXQSQdbvyRbQQgAAEIAABCEAAAhCAAAQgUAIEkPUSSDJLhAAEIAABCEAAAhCAAAQgAAG7CCDrduWLaCEAAQhAAAIQgAAEIAABCECgBAgg6yWQZJYIAQhAAAIQgAAEIAABCEAAAnYRQNbtyhfRQgACEIAABCAAAQhAAAIQgEAJEEDWSyDJLBECEIAABCAAAQhAAAIQgAAE7CKArNuVL6KFAAQgAAEIQAACEIAABCAAgRIggKyXQJJZIgQgAAEIQAACEIAABCAAAQjYRQBZtytfRAsBCEAAAhCAAAQgAAEIQAACJUAAWS+BJLNECEAAAhCAAAQgAAEIQAACELCLALJuV76IFgIQgAAEIAABCEAAAhCAAARKgACyXgJJZokQgAAEIAABCEAAAhCAAAQgYBcBZN2ufBEtBCAAAQhAAAIQgAAEIAABCJQAAWS9BJLMEiEAAQhAAAIQgAAEIAABCEDALgLIul35IloIQAACEIAABCAAAQhAAAIQKAECyHoJJJklQgACEIAABCAAAQhAAAIQgIBdBJB1u/JFtBCAAAQgAAEIQAACEIAABCBQAgSQ9RJIMkuEAAQgAAEIQAACEIAABCAAAbsIIOt25YtoIQABCEAAAhCAAAQgAAEIQKAECCDrJZBklggBCEAAAhCAAAQgAAEIQAACdhFA1u3KF9FCAAIQgAAEIAABCEAAAhCAQAkQQNYVkrxz937Z+fE+hZmYAgJ2EujUoULqGxpk1+79di6AqCGgQKC8TKSmS3vZun23wmxMAQF7CXQ5pFL27Dsgu/cesHcRRA4BQwIHtSuTrh2r5J0P9hjOZPfw3t3a272AhKNH1g0TcP3118t3vjtV7rjtZjmookLGfHmiPPLQUqmorJJTTz9bnv79Khl84sny0rpn5ZhjT5CNr/9ZjujTT7Zt3SJdunSTjz7aJcO+eLobxZqnHpeDD+kk299/Vw7rfYR8+tgT3N+veHiZDBw8VA7v08/99/0/Wyxnjb5QDu3Szf33nQsXyMTLrpCKykqpq9srS++8Xb4x5Ur3tR3b35OVj/5CvnLxJM+Vvrr+RTeW4SNHua8/v3a1NIjIkKGneB7vF0v+oMJYCifcvGmjy+WcMeM8z1VsLIWT5HMpfK1YLoXjn/ztY9KjppcMOH6gZ+z/vfSncvqXzpOu3XoUXWGmXPxOePeim2TCJVOkqqra79AWrxfWS+6AoLJuwmXL5jflhWfXyLkXTig6bmfAo8sfkOM/O1j69O0farzJoKdW/Vq6dq+R4z4zqOhp9u/bJ/csuUUuq7266LEaA+5ZfIuMu3iSVLfvoDFdUXM8eN9dctrIUdKtR8+ixiV1sJasm9RLUmvXOO9fNrwsb2/eJCPOHK0xHXMkSCCorCd5fUkQD6cuEQLIusiiW+bKrFmzSiTj0SwTWTfkiqw3/+AgHyeyjqwXvr2QdWS9mEsusl58vRTDN23HIutpy0j4eJD18OwYmR0CyDqyrlHNyLohRWQdWfcqIRMp5c6695uSO+vcWTe8XEc+nDvrZoiRdTN+aRqNrKcpG8SSFAFkHVnXqD1kXYGis1/d2bfODwRKlUDQx+BLlQ/rLg0CWrJeGrRYZZYJBJX1LDNgbRBA1htrgD3rZu8FZN2MnzsaWVeAyBRWE0DWrU4fwSsRQNaVQDKN9QSQdetTyAIUCCDryLpCGQmyrkARWVeAyBRWE0DWrU4fwSsRQNaVQDKN9QSQdetTyAIUCCDryLpCGSHrphDZs86eda8aYs+69zvLhAt71tmzbnq9jnq8lqzTDZ5u8FHXatTzB5V1usFHnQnmT5IAss6edY364866IUVkHVlH1kWC3llH1ovv7s1Xt/HVbYZ/pqwZToM5a1LlGyiy7ouIA0qAALKOrGuUObJuSBFZR9aRdWTd7zJicqcUWUfW/eorK68j61nJpAiynp1cspLwBJB1ZD189XwyEllXoMiedQWITGE1gaB31q1eJMFDwIeA1mPwgIaA7QSCyPraZ8vllyvKbV8q8UMAAm0QGHFavXxtbBWMDAgg6wbwckORdQWITGE1AWTd6vQRvBIBZF0JJNNYTwBZtz6FLAACKgSQdXOMyLo5Q766TYEhU9hNAFm3O39Er0MAWdfhyCz2EyhG1j8/pF7GnFtv/6JZAQQKCPAYfCMQvmfd7K2BrJvxE/ass2fdq4RMGqlt3rRRXlr3rJwzZpxndT6/drU0iMiQoaeEqt67F90kEy6ZIlVV1UWPf3X9i7Jt6xYZPnJUs7FBZd2EC93g6QZfdMHGPEBL1k16HMS8ZNXTsWddFWeikwWV9Rf+cJP07PstuXBM8X+PEl0gJ4dAAALIOnvWA5SJ7yHIui+itg9A1pF1ZJ0Gc36XERP5osEcDeb86isrryPrWclksAZzzp51ZD07OWclLQkg68i6xvsCWTekiKwj68g6su53GUHW/Qh5v/7gfXfJaSOR9XD07BuFrNuXs9Yi5s56dnLJSsITQNaR9fDV88lIZF2BIg3mFCAyhdUEgj4Gb/UiCR4CPgS0HoMHNARsJxBU1p1u8OxZtz3bxN8aAWS9kQx71s3eI8i6GT93NLKuAJEprCaArFudPoJXIoCsK4FkGusJIOvWp5AFKBBA1pF1hTISZF2BIrKuAJEprCaArFudPoJXIoCsK4FkGusJIOvWp5AFKBBA1pF1hTIqTVl/Zt0GufSqOS6/E47tL7fNuVq6dO7YKs/de+pk1rwl8sjKNe4xP5w+ScaOHu7+b/ass2fdq3BMup7TDd77rUg3eLrBa/zRi3IOLVk36XEQ5fqinps961ETjm/+oLJOg7n4csKZ4ieArLNnXaPqSu7O+mtvbpHvz14kP5o5WY7u21seXPGErHluvVw/bZK0r65swTQn6sOGHNck6PkHIevIOrJOgzm/i7GJfNENngZzfvWVldeR9axkkm7w2ckkKzEhgKwj6yb1kxtbcrLuyPkbb22Va2rHuwwK5b0QauHxha8j68g6so6s+12MkXU/Qt6v0w1+UDhwlo5C1i1NnEfY3FnPTi5ZSXgCyDqyHr56PhlZcrJ+w8Jl7upzsr7jw51y+YwbZWrteDlx0IAWTJ3jFy9d0fT7w3p2k4Vzp7p35XM/u3bvl1179mvkgzkgYCWBju0PkvoGkY94H1iZP4LWIeA8Bt+9c7W888EenQlDzdIgImWhRjIIAloEDj24Qvbuq5fddQdanfLpZ8pk+SPlcuLnG+SC8+q1Ts08EEgNAUfWDz24Ut77297UxJREIL26VCdx2sycsyRlvV+fXk2PtLcl67lH4MedP6JJ5J077cuWr2q2z72hoUEanP8+4gcCJUqgrMyRA94HJZp+lp1HoLy8TOqdT64S+tl3oEEq2iHrCeHntH8n4PxNaJAG589Cqz+rVtfLzx+ol+Enl8vXxpXDDgKZJJD034Q0QHUY8BOeQEnKuoMryJ11L1n3knu6wYcvQEZmgwDd4LORR1ZhRkCrwZxZFIyGQPIEgj4Gz/esJ58rIoiOAI/BN7Lle9bNaqzkZL3YPevOY/CFd+Jn/niRTLviIvdRePass2fd6y1IN3jvC5MJF7rB0w3e7M9d9KO1ZN2kx0H0q4zuDOxZj45t3DMHlXW6wcedGc4XJwFknT3rGvVWcrLu1w2+8DF352veZs5e1LRPvbB7PLKOrCPrNJjzuxibyBfd4OkG71dfWXkdWc9KJukGn51MshITAsg6sm5SP7mxJSfrzsLb+p51rz3pzu+um7vEZVb4vezIOrKOrCPrfhdjZN2PkPfrdIOnG3y4ymFU0gS4s550Bjh/Gggg68i6Rh2WpKxrgMufgz3r2kSZzzYC7Fm3LWPEGwUBrcfgo4iNOSEQJ4Ggss6e9TizwrniJoCsNxJnz7pZ5SHrZvzc0ci6AkSmsJoAsm51+gheiQCyrgSSaawngKxbn0IWoEAAWUfWFcpIkHUFisi6AkSmsJoAsm51+gheiQCyrgSSaawngKxbn0IWoEAAWUfWFcoIWTeFyJ519qx71ZBJ1/PNmzbKS+uelXPGjPMsz+fXrna/unbI0FNCle/di26SCZdMkaqq6qLHv7r+Rdm2dYsMHzmq2digsm7ChW7wdIMvumBjHqAl6yY9DmJesurpaDCnijPRyYLKOt3gE00TJ4+YALLOnnWNEuPOuiFFZB1ZR9ZpMOd3GTGRL7rB0w3er76y8jqynpVM0g0+O5lkJSYEkHVk3aR+cmORdUOKyDqyjqwj636XEWTdj5D363SDpxt8uMphVNIEuLOedAY4fxoIIOvIukYdIusKFNmzrgCRKawmEPQxeKsXSfAQ8CGg9Rg8oCFgO4Ggsk43eNszTfxtEUDWG+nQDd7sfYKsm/FzRyPrChCZwmoCyLrV6SN4JQLIuhJIprGeALJufQpZgAIBZB1ZVygjGsxpQETWNSgyh80EkHWbs0fsWgSQdS2SzGM7AWTd9gwSvwYBZB1Z16gj7qwbUmTPOnvWvUrIpOs53eC935R0g6cbvOHlOvLhWrJu0uMg8kVGeAIazEUIN+apg8o63eBjTgyni5UAss6edY2CQ9YNKSLryDqyToM5v8uIiXzRDZ5u8H71lZXXkfWsZJJu8NnJJCsxIYCsI+sm9ZMbi6wbUkTWkXVkHVn3u4wg636EvF+nGzzd4MNVDqOSJsCd9aQzwPnTQABZR9Y16hBZV6DInnUFiExhNQH2rFudPoJXIqD1GLxSOEwDgcQIBJV1usEnliJOHAMBZL0RMt3gzYoNWTfj545G1hUgMoXVBJB1q9NH8EoEkHUlkExjPQFk3foUsgAFAsg6sq5QRnSD14CIrGtQZA6bCSDrNmeP2LUIIOtaJJnHdgLIuu0ZJH4NAsg6sq5RR9xZN6TInnX2rHuVEN3gvd9YJlzoBk83eMPLdeTDtWTdpMdB5IuM8AQ0mIsQbsxTB5V1usHHnBhOFysBZJ096xoFh6wbUkTWkXVknQZzfpcRE/miGzzd4P3qKyuvI+tZySTd4LOTSVZiQgBZR9ZN6ic3Flk3pIisI+vIOrLudxlB1v0Ieb9ON3i6wYerHEYlTYA760lngPOngQCyjqxr1CGyrkCRPesKEJnCagLsWbc6fQSvREDrMXilcJgGAokRCCrrdINPLEWcOAYCyHojZLrBmxUbsm7Gzx2NrCtAZAqrCSDrVqeP4JUIIOtKIJnGegLIuvUpZAEKBJB1ZF2hjOgGrwERWdegyBw2E0DWbc4esWsRQNa1SDKP7QSQddszSPwaBJB1ZF2jjrizbkiRPevsWfcqIZOu55s3bZSX1j0r54wZ51mdz69dLQ0iMmToKaGq9+5FN8mES6ZIVVV10eNfXf+ibNu6RYaPHNVsbFBZN+FCN3i6wRddsDEP0JJ1kx4HMS9Z9XQ0mFPFmehkQWWdbvCJpomTR0wAWWfPukaJIeuGFJF1ZB1Zp8Gc32XERL7oBk83eL/6ysrryHpWMkk3+OxkkpWYEEDWkXWT+smNRdYNKSLryDqyjqz7XUaQdT9C3q/TDZ5u8OEqh1FJE+DOetIZ4PxpIICsI+sadYisK1Bkz7oCRKawmkDQx+CtXiTBQ8CHgNZj8ICGgO0Egso63eBtzzTxt0UAWW+kQzd4s/cJsm7Gzx2NrCtAZAqrCSDrVqeP4JUIIOtKIJnGegLIuvUpZAEKBJB1ZF2hjOgGrwERWdegyBw2E0DWbc4esWsRQNa1SDKP7QSQddszSPwaBJB1ZF2jjrizbkiRPevsWfcqIZOu53SD935T0g2ebvCGl+vIh2vJukmPg8gXGeEJaDAXIdyYpw4q63SDjzkxnC5WAsg6e9Y1Cg5ZN6SIrCPryDoN5vwuIybyRTd4usH71VdWXkfWs5JJusFnJ5OsxIQAso6sm9RPbiyybkgRWUfWkXVk3e8ygqz7EfJ+nW7wdIMPVzmMSpoAd9aTzgDnTwMBZB1Z16hDZF2BInvWFSAyhdUE2LNudfoIXomA1mPwSuEwDQQSIxBU1ukGn1iKOHEMBJD1Rsh0gzcrNmTdjJ87GllXgMgUVhNA1q1OH8ErEUDWlUAyjfUEkHXrU8gCFAgg68i6QhnRDV4DIrKuQZE5bCaArNucPWLXIoCsa5FkHtsJIOu2Z5D4NQgg68i6Rh1xZ92QInvW2bPuVUJ0g/d+Y5lwoRs83eANL9eRD9eSdZMeB5EvMsIT0GAuQrgxTx1U1ukGH3NiOF2sBJB19qxrFByybkgRWUfWkXUazPldRkzki27wdIP3q6+svI6sZyWTdIPPTiZZiQkBZB1ZN6mf3Fhk3ZAiso6sI+vIut9lBFn3I+T9Ot3g6QYfrnIYlTQB7qwnnQHOnwYCyDqyrlGHyLoCRfasK0BkCqsJsGfd6vQRvBIBrcfglcJhGggkRiCorNMNPrEUceIYCCDrjZDpBm9WbMi6GT93NLKuAJEprCaArFudPoJXIoCsK4FkGusJIOvWp5AFKBBA1pF1hTKiG7wGRGRdgyJz2EwAWbc5e8SuRQBZ1yLJPLYTQNZtzyDxaxBA1pF1jTrizrohRfass2fdq4RMup5v3rRRXlr3rJwzZpxndT6/drU0iMiQoaeEqt67F90kEy6ZIlVV1UWPf3X9i7Jt6xYZPnJUs7FBZd2EC93g6QZfdMHGPEBL1k16HMS8ZNXT0WBOFWeikwWVdbrBJ5omTh4xAWSdPesaJYasG1JE1pF1ZJ0Gc36XERP5ohs83eD96isrryPrWckk3eCzk0lWYkIAWUfWTeonNxZZN6SIrCPryDqy7ncZQdb9CHm/Tjd4usGHqxxGJU2AO+tJZ4Dzp4EAso6sa9Qhsq5AkT3rChCZwmoCQR+Dt3qRBA8BHwJaj8EDGgK2Ewgq63SDtz3TxN8WAWS9kQ7d4M3eJ8i6GT93NLKuAJEprCaArFudPoJXIoCsK4FkGusJIOvWp5AFKBBA1pF1hTKiG7wGRGRdgyJz2EwAWbc5e8SuRQBZ1yLJPLYTQNZtzyDxaxBA1pF1jTrizrohRfass2fdq4RMup7TDd77TUk3eLrBG16uIx+uJesmPQ4iX2SEJ6DBXIRwY546qKzTDT7mxHC6WAkg6+xZ1yg4ZN2QIrKOrCPrNJjzu4yYyBfd4OkG71dfWXkdWc9KJukGn51MshITAsg6sm5SP7mxyLohRWQdWUfWkXW/ywiy7kfI+3W6wdMNPlzlMCppAtxZTzoDnD8NBJB1ZF2jDpF1BYrsWVeAyBRWE2DPutXpI3glAlqPwSuFwzQQSIxAUFmnG3xiKeLEMRBA1hsh0w3erNiQdTN+7mhkXQEiU1hNAFm3On0Er0QAWVcCyTTWE0DWrU8hC1AggKwj6wplRDd4DYjIugZF5rCZALJuc/aIXYsAsq5FknlsJ4Cs255B4tcggKwj6xp1xJ11Q4rsWWfPulcJ0Q3e+41lwoVu8HSDN7xcRz5cS9ZNehxEvsgIT0CDuQjhxjx1UFmnG3zMieF0sRJA1tmzrlFwscr67j11MmveEnlk5Ro5rGc3WTh3qvTu2d393bAhx8nY0cM11hTrHMg6so6s02DO76JjIl90g6cbvF99ZeV1ZD0rmaQbfHYyyUpMCCDryLpJ/eTGxirrNyxcJv369JJzRg6TebfdKxPHnilH9+0tz6zbIPcvXyXXT5sk7asrNdbV5hzO+S69ao57zAnH9pfb5lwtXTp39D1vbtydC2bIiYMGuMcj68g6so6s+108kHU/Qt6v0w2ebvDhKodRSRPgznrSGeD8aSCArCPrGnUYm6zv+HCnzPzxIpl2xUXu3fR8WX/tzS0y79Z7Zfa1kwNJs8nCnXN9f/Yi+dHMye4HBQ+ueELWPLfe94OCfMHPl3UnFvasm2SEsVkgwJ71LGSRNZgS0HoM3jQOxkMgaQJBZZ1u8ElnivNHSQBZb6RLN3izKkuFrMd5Z92FTnqcAAAgAElEQVSR8zfe2irX1I53yRXKuxfO3IcJ07/zVbl29iKZWju+6c46sm5WgIzOBgFkPRt5ZBVmBJB1M36Mzg4BZD07uWQl4Qkg68h6+Or5ZGRssu6cMncXe+Z3J8rNSx5yH4PvemhHuXzGjTL+/BGx7Fl3HsV3fnKy7tzxd85fKOA5RPkyn4sVWdcoPebIEgFkPUvZZC1hCSDrYckxLmsEkPWsZZT1hCGArCPrYeqmcEyssu6cPP9x8lwwhY+VayystTly++ZzzezakvX8R/edR+a9jnX2rP/Lv8yQG2+8QSoqKmTSpG/KXXfdKVVVVXLeeefLb37zvzJ8+GmyZs0fZNCgz8mGDa9I//5Hy+bNb0n37j1k166dctZZX3LD/fWvH5NOnTrLO+9skyOP7CuDBjXuV/yv/7pHTj75FOnfv7/771tv/Q8ZP36CdO/e3f33nDmz5ZprpkplZaXs3btXFiy40Y3J+Xn33XflgQful8svv8ITyQsvvODGcv75Y9zXf/e734lIg5x22gjP4/1iyR9UGEvhhK+99prLZeLEr3meq9hYCifJ51L4WrFcCsf/8pfLpXfvw2Xw4MGesd9++20yduyXpaampuhyNuXid8K5c/9NvvvdK6W6utrv0BavF9ZL7oCyssb/1dDQ9pQmXDZu3ChPPfWkfP3rlxQdtzPg5z//mQwdepJ86lOfCjXeZNCKFY9ITU1P+fznP1/0NPv27ZOf/GSezJx5bdFjNQY4577iiu9Ihw4dNKYrao477lgoY8ZcIL169SpqXJIHO+8Fv/eBX3xG9XKgXiralfudIpWv//GPf5Q33tgoF1xwYSrjI6jgBNz3gftHofUxv32qXn63cr4c9elvyze+6t83KPjZORIC6SGg8TchPaspPpIf/OB6mTVrVvEDGdFEIHZZT5p9MXfWnbvqtdPny1+3vd8i7NwHDG6DuSunyh233iwHVVTIBV+ZKL98cKlUVFbJqSPPlqdXr5IhQ0+WF194Vo457gR547U/yxFH9pOtf90iXbp2k48/2iXDvni6O/8fnnxcDunYSba/9670OvwIOebYE9zfP/I/y2TgkKFyRJ9+7r+X/ddi+dLoC+XQrt3cf/904QL52mVXSEVlpdTV7ZWf//R2ubT2Sve1Hdvfk9/86hcybuIkT/Svrn/RjeW0M0a5rz+3drX7x3XISad4Hu8XS/6gwlgKJ9y8aaPLZfQF4zzPVWwshZPkcyl8rVguheOfePwxqenZSwYcP9Az9gd+/lMZefZ50rVbj6JL3pSL3wnvuuMmuegbU6SqqnhZL6yX3Lk6tq+Q+oYG+WjP/jZPb8Ll7bfelHXPrpFz/3GC3xI9X//VLx6QzwwcLH36Nn7oFeeP22CuW40cd0LxDcOcbvB3L75FJn07ma9uu/s/b5HxEydJdfv4Zf3Be++S4WeMku49esaZrtDncu6s9zi0vWzbsTv0HM5Ak3pxPijIfXhmFEQCg/+84WXZ8tYmGXHW6ATOzik1CRx6cKXs3XdAdtcdaHXap58pF+er23r1+5ZcOKb4v0ea8TIXBKIg4NxZ73JIlbz74Z4oprdizjtunousG2aq5GQ9zJ71HOPW7qx/57tT5Y7bGmV9zJcnyiMP/V3WTz9bnv79Khl84sny0rpnXfne+PqfXenetnWLdOnSTT7Kk/U1Tz0uBx/SSba//64c1vsI+fTfZX3Fw8tk4OChcvjfZf3+nzXvwH7nwgUyMU/Wl955u3xjyieyvvLRX8hXLm5d1p1Yho9slPXn1652PwgfMtRb1v1iya9HR9bzYymsVUdKHS7njPGW9WJjKZw/n0vha46sF8OlcPyTv31MetS0Lusm3yduysXvmnD3optkwiXhZT2/XnLnCvoYvAkXvmc9GVm/Z/EtMu7ihGT9vrvktJF8dZvfezorr/PVbVnJJF/dlp1MshITAjwGTzd4k/rJjY1N1nOie2Tvmhad19PUDd6R+WXLV3l+nVtrj8zTDV6jFJnDZgJBZd3mNRI7BPwIsGfdjxCvlwoB9qyXSqZZZ1sEkPVGOnSDN3ufxCrrzle39e7VXe57+HHJ36cep6w7uNr6nnVk3aygGF2aBJD10sw7q25OAFmnIiDQSABZpxIgIIKsI+sa74PYZd35nvVcV3VnAbfNuVq2f7Aztu9Z14BWOAd31qOgypw2EUDWbcoWsUZFAFmPiizz2kYAWbctY8QbBQFkHVnXqKtEZN3prO785O5wT7hgpGzZ+p7MvnaydOlsV0dQt8Ece9bl0C6Nze7yf9izHr7BnMlefr8LA3vWE2ow171GjvtMuAZz9yy5RS6rZc+6X20n/bqWrLsN5kLWS9IMTM7PnnUTeukaG1TWnQZzPfvSYC5d2SMaLQLIOnvWNWopUVl3FrB7T53MmrdENm15x3OfuMYio5wDWW/e7A5ZbyRg0kiNBnPe71gazCHrUV7LNeZG1s0oIutm/NI0GllPUzaIJSkCyDqyrlF7scm6RrBpnANZR9a96hJZ9363mnBB1pH1NP4NyI8JWTfLELJuxi9No5H1NGWDWJIigKwj6xq1h6wrUGTPugJEprCaAHvWrU4fwSsR0JJ1pXCYBgKJEQgq679cUS6fH1IvY86tTyxWTgyBqAgg641k6QZvVmHIuhk/dzSyrgCRKawmgKxbnT6CVyKArCuBZBrrCSDr1qeQBSgQQNaRdYUykshlPffd5JdNGCU/ve9ReemV1z3jPuHY/lbuWUfWNcqQOWwngKzbnkHi1yCArGtQZI4sEEDWs5BF1mBKAFlH1k1ryBkfuaxrBJnmOdizzp51r/o02ZtNgznvdzx71tmznua/BU5sWrJON/jRaU818fkQCCrrdIOnlLJMAFlnz7pGfSPrhhSRdWQdWRcJemfd5EMMZB1ZN7xcRz4cWTdDTIM5M35pGo2spykbxJIUAWQdWdeovVhl/YaFy2TrO9vl+mmT3Nidr2x7ZOUaOaxnN1k4d6rkvn9dY2FxzYGsI+vIOrLud70xuVO6f98+4XvWe/ohTsXryLpZGpB1M35pGo2spykbxJIUAWQdWdeovdhkPbd3fWrteDlx0AB5Zt0GuX/5KlfcX97wetP/bl9dqbGuWOegwVysuDlZCgkEvbOewtAJCQJqBLRkXS0gJoJAQgSCyjrd4BNKEKeNhQCy3oiZbvBm5RarrM/88SKZdsVF7h105y6783NN7Xh57c0tMu/We2X2tZOlS+eOZitKYDSyngB0TpkqAsh6qtJBMAkRQNYTAs9pU0cAWU9dSggoAQLIOrKuUXaxyfruPXXuY+/jzh8hnzrqcLl8xo2Sf5d9/sJldIPXyChzQCABAsh6AtA5ZeoIIOupSwkBJUQAWU8IPKdNFQFkHVnXKMjYZN0J1rmDXjt9vvx12/vyza+Odu+q5x6PHzpogPtv237Ys86eda+aNWmkRjd476sADeZoMJf2vw9asm7S4yDtjNqKjz3rNmeveexBZZ1u8NnJOStpSQBZZ8+6xvsiVlnXCDhtcyDryDqyToM5v+uSiXzRYG6UdOtBgzm/GsvC68h6FrLYuAZkPTu5ZCXhCSDryHr46vlkJLJuSBFZR9aRdWTd7zKCrPsR8n79wfvuktNGIuvh6Nk3Clm3L2etRYysZyeXrCQ8AWQdWQ9fPci6BrumOWgwp4qTySwkwJ51C5NGyOoEtB6DVw+MCSEQM4Ggsk43+JgTw+liJYCsN+KmG7xZ2XFn3YyfOxpZV4DIFFYTQNatTh/BKxFA1pVAMo31BJB161PIAhQIIOvIukIZCbKuQBFZV4DIFFYTQNatTh/BKxFA1pVAMo31BJB161PIAhQIIOvIukIZIeumENmzzp51rxqiG7z3O8uEC93g6QZver2OeryWrJv0OIh6jVHO/6sVf5IPt78pNUecG+VpmDsGAtWV7WR/fYPs31/f6tne3lImH79/k/Tq+y25YEx1DFFxCgjESwBZZ8+6RsVxZ92QIrKOrCPrNJjzu4yYyBfd4Gkw51dfWXn95pvXy4G6N2X7R+dnZUmsw4fA4V0WIOtUSWYJIOvIukZxxy7rD654Qq6bu6Qp9sN6dpOFc6fK0X17a6wn9jmQdWQdWUfW/S48yLofIe/X6QY/KBw4S0flZP0fjjtX2rdvsHQVhO0QCHJn3TnujQ03y0mnfkuOP54761RO9ggg68i6RlXHKuuOqC9bvkpum3O1dOnc0Y3/tTe3SO30+TJ75mQ5cdAAjTXFPgd71mNHzglTRoA96ylLCOEkQkDrMfhEgk/BSW+8uZ3s2FEmV/7TAenWFVlPQUpChxBkz3royRkIAUsIIOuNiaIbvFnBxibrOz7cKZfPuFGm1o5vIeXPrNsg9y9fJddPmyTtqyvNVpTAaGQ9AeicMlUEkPVUpYNgEiKArJuBR9bN+KVpNLKepmwQS1IEkHVkXaP2YpX1mT9eJNOuuKjFI+/O3fV5t94rs6+d3HTHXWNxcc2BrMdFmvOklQCyntbMEFecBJB1M9rIuhm/NI1G1tOUDWJJigCyjqxr1F5ssr57T53MmrdExp0/osWddZtlnT3r7Fn3eiOadD3fvGmjvLTuWTlnzDjP9/jza1eL84DokKGnhLoG3L3oJplwyRSpqip+j+Cr61+UbVu3yPCRo5qdO6ism3ChGzzd4EMVfIyDtGTdpMdBjMtVP1Vuz/rXLxvNY/DqdOOdMKis37P4Fhl38SSpbt8h3gA5GwRiIICss2ddo8xik3Un2NYed3f2sr/x1la5pna8xppinQNZR9aRdRrM+V10TOSLbvB0g/err6y8jqxnJZMiyHp2cslKwhNA1pH18NXzycjYZD23Z/2lV173jfuEY/s3a0LnOyDBA5B1ZB1ZR9b9LkHIuh8h79fpBl+a3eC5sx7u/ZKmUch6mrJBLEkRQNaRdY3ai03WNYJN6xzsWU9rZogrLgJBH4OPKx7OA4EkCGg9Bp9E7Gk4J3vW05AFnRiCyrrO2ZgFAukkgKw35oVu8Gb1iayb8XNHI+sKEJnCagLIutXpI3glAsi6GUhk3YxfmkYj62nKBrEkRQBZR9Y1ai9WWc81mXtk5Ro5rGc3WTh3qvTu2d1tPDdsyHEydvRwjTXFPgeyHjtyTpgyAsh6yhJCOIkQQNbNsCPrZvzSNBpZT1M2iCUpAsg6sq5Re7HK+g0Ll0m/Pr3knJHDZN5t98rEsWe6X+Nm8/ess2edPeteb0STrud0g/e+tNENnm7wGn/0opxDS9ZNehxEub6o56bBXNSE45s/qKzTDT6+nHCm+Akg6+xZ16i62GTdaTCX+5515256vqzz1W2nu7lc89TjcvAhnWT7++/KYb2PkE8fe4L7+xUPL5OBg4fK4X36uf++/2fNBfnOhQtk4mVXSEVlpdTV7ZWld94u35hypXvsju3vycpHfyFfuXiSZ70UfhWX39eC+cWSf5LCWAoDMJXSwlgK58/nUvhasVwKxz/528ekR00vGXD8QE+uyLr35cmEC7KOrGv80YtyDmTdjC6ybsYvTaOR9TRlg1iSIoCsI+satZcKWefOOrLuVczFfnCArAe7JPA96/2DgVI8yuROKV/dxle3KZZiqqdC1lOdnqKCQ9aLwsXBGSWArCPrGqUdm6w7wTrfp77mufUy87sT5eYlD7mPwXc9tKNcPuNGGX/+CPasa2SUOSCQAAH2rCcAnVOmjoDWnfXULSymgNizHhPoGE4TVNZjCIVTQCAxAsh6I3q6wZuVYKyy7oTq3EW/9Ko5zaK+c8EMOXHQALOVJDiaBnMJwufUqSCArKciDQSRMAFk3SwByLoZvzSNRtbTlA1iSYoAso6sa9RebLKev2fdaSqX/2PzY/DOOpB1jVJkDpsJIOs2Z4/YtQgg62YkkXUzfmkajaynKRvEkhQBZB1Z16i9VMg6DebYs+5VzOxZH+f5Hvfj4ndhYM86e9b9aiT/9SS7NT94311y2kj2rBeTL5uPZc+6zdlrHntQWU/y+pId2qwkrQSQdfasa9RmKmQ9t5f9+mmTpH11pca6YpuDr27jq9u8is2k67lpl3y/4kfWkXW/GkHWiyH0ybFad9ZNGhKGizwdo5D1dORBIwpkXYMic9hOAFlH1jVqOHJZd+6a106fL3/d9n6r8R7Ws5ssnDvV/c51236QdWQdWRcJ+hi8yYcYfHUbX92W9r8PyLpZhpB1M35pGo2spykbxJIUAWQdWdeovchlPRdkW3vWNRaS5BzsWU+SPudOA4Ggsp6GWIkBAlER0JL1qOJL+7zsWU97hoLHF1TWg8/IkRCwjwCy3pgzusGb1W5ssm4WZrpHI+vpzg/RRU8AWY+eMWdIPwFk3SxHyLoZvzSNRtbTlA1iSYoAso6sa9Qesq5AEVlXgMgUVhNA1q1OH8ErEUDWzUAi62b80jQaWU9TNoglKQLIOrKuUXuxyLrTQO7Wux5uti89//vWfzh9kowdPVxjPbHPwZ519qx7FZ3J3mwazHm/jdmzzp712C/wRZ5QS9ZpMDdaunVtKJI+h6eJQFBZpxt8mrJGLNoEkHX2rGvUVCyyfsPCZW6s19SOd/9//v713j27y6x5S2Tc+SPkxEEDNNYU6xzIOrKOrNNgzu+iYyJf+/ftk3uW3CKX1SLrfpyTfh1ZN8sADebM+KVpNLKepmwQS1IEkHVkXaP2Ipd1R8wvn3GjTK0d3yTjzl31+5evktxXtRX+W2Nhcc2BrCPryDqy7ne9Qdb9CHm/zvesDwoHztJRyLqlifMIG1nPTi5ZSXgCyDqyHr56PhkZi6zP/PEimXbFRU1fzVZ4p935erd5t94rs6+dLF06d9RYV6xzsGc9VtycLIUE2LOewqQQUuwEtO6sxx54Sk7InvWUJEIhjKCyrnAqpoBAagkg642poRu8WYkmJuv9+vRq2qeOrJslkdEQSJoAsp50Bjh/Gggg62ZZQNbN+KVpNLKepmwQS1IEkHVkXaP2Ipf13Xvqmu1JL/y3swjnMfj5C5fJbXOu5s66RlaZAwIxE0DWYwbO6VJJAFk3SwuybsYvTaOR9TRlg1iSIoCsI+satRe5rDtBOt3g1zy33t2j/vKG11uIeeFj8RoLi2sO9qyzZ92r1ugG7/0ONOFCN3gazMV1XQ97Hi1ZN+lxEDb2NIxjz3oasqATQ1BZpxu8Dm9mSScBZJ096xqVGYusO4E6Qr546Qo35jsXzGjWbO7Sq+Y0+53GwuKaA1lH1pF1Gsz5XW9M5Itu8KOkW4+efohT8TqybpYGZN2MX5pGI+tpygaxJEUAWUfWNWovNlnXCDaNcyDryDqyjqz7XZuQdT9C3q/TDZ5u8OEqh1FJE0DWk84A508DAWQdWdeoQ2RdgSLd4BUgMoXVBNizbnX6CF6JgNaddaVwrJuGPevWpazVgIPKenZWzEog0JIAst7IhG7wZu+OkpR1p6Gd8+i983PCsf3bbGyXf6xz/LlnDGv6fvgcemTdrAgZbT8BZN3+HLICcwLIuhlDZN2MX5pGI+tpygaxJEUAWUfWNWqv5GTd+Zq4789eJD+aOdn93vf85nftqytbMHVe79O7xt1jn+tk36umq1xTO77pWGRdoxSZw2YCyLrN2SN2LQLIuhlJZN2MX5pGI+tpygaxJEUAWUfWNWqv5GTdke833traJNuF8u4HtVDu2bPOnnWvmjHper5500Z5ad2zcs6YcZ7l+Pza1dIgIkOGnuJXrp6v373oJplwyRSpqqouevyr61+UbVu3yPCRo5qNDSrrJlzoBk83+KILNuYBWrJu0uMg5iWrno4Gc6o4E50sqKzTDT7RNHHyiAkg6+xZ1yixkpP1wq+J2/HhTrl8xo0ytXZ8U4f6tsAWjkfWkXVknQZzfhdjE/miGzzd4P3qKyuvI+tZyaQIsp6dXLKS8ASQdWQ9fPV8MrIkZb1fn14ydvRwl0Ixsu7sX5+/cFmzPe6OrE+f/i+yYMGNUlFRId+49DL5r3vulqqqKhk9+lxZ+fhKOfXUU+XpNU/LwEGD5NUNr8hR/Y+Wtze/Jd2795Cdu3bKmWee5cbyv//7a+ncqbO888426XNkXxk4cKD7+5//7GfyhZO/IEcd1d/998Lbb5Mvf2WcdO/e3f33vLn/JldedbVUVlbK3r175eab/l2+N226+9q7774rDz343zKl9tue9bJu3To3lnPPO999/cknnpAGaZDhw0/zPN4vlvxBhbEUTvj666+5XL568cWe5yo2lsJJ8rkUvlYsl8LxKx75pRzW+3D53Oc+5xn7ojsWygUX/qPU1NQU/T415eJ3wvk/mSff+ad/lurq4u+sF9ZL7lzlZWXu/6xvcO75t/5jwuWNNzbK6qdWy8Svfc1viZ6v37t0qZw49EQ5+uhPhRpvMuhXv1ohPWt6yuAhQ4qeZt++fXLjDfNl+r/MKHqsxgDn3LXfvlw6dOigMV1Rcyz+z0XutalXr15FjUvy4IPalcv+A/VGIZjUy/4DIge1Mzp9YoNn/Wid7Nvzhlx15YVS0/jnjR9LCZSXl4nz56DB529CktcXS9EStlUEyqRdeZkcqDf7m2DVkguC/dH/+6HMmjXL5iUkHntJyrpDPbfnPKisO6I+c/YiWTh3qrvXPf/noz375aM9BxJPJgFAICkCh7Q/yBX1j3kfJJUCzpsCAs5j8F07Vcl7H+5NQTT2hTBvQZns2FEmU69skG5d2/7gz77VlVbEnTtUyN79B2RPXelKSmllnNV6EXA+OO18cKW8/7e6kgZUc2hVSa/fdPElJ+th9qy3JepOAmgwZ1qGjLedQNA967avk/gh0BYBrT3rpUqZBnPZyXzQx+Czs2JWAoGWBHgMvpEJX91m9u4oOVn36wbvyPyy5auaHnX3evS9EDmyblaEjLafALJufw5ZgTkBZN2MIbJuxi9No5H1NGWDWJIigKwj6xq1V3Ky7kBr63vWC2XdaSi3eOmKZqwP69mt6XF4GszRYM7rjWjS9Zxu8N6XNrrB0w1e449elHNoybpJQ8Io1xf13DSYi5pwfPMHlXW6wceXE84UPwFknQZzGlVXkrKuAS43B7KOrCPrdIP3u6aYyBfd4OkG71dfWXkdWc9KJukGn51MshITAsg6sm5SP7mxyLohRWQdWUfWkXW/ywiy7kfI+/UH77tLThuJrIejZ98oZN2+nLUWMXfWs5NLVhKeALKOrIevnk9GIusKFNmzrgCRKawmwJ51q9NH8EoEtB6DVwrHumnYs25dyloNOKisZ2fFrAQCLQkg641MaDBn9u5A1s34uaORdQWITGE1AWTd6vQRvBIBZN0MJLJuxi9No5H1NGWDWJIigKwj6xq1h6wrUETWFSAyhdUEkHWr00fwSgSQdTOQyLoZvzSNRtbTlA1iSYoAso6sa9Qesm5IkT3r7Fn3KiG6wXu/sUy40A2ebvCGl+vIh2vJukmPg8gXGeEJ2LMeIdyYpw4q63SDjzkxnC5WAsg6e9Y1Cg5ZN6SIrCPryDoN5vwuIybyRTd4Gsz51VdWXkfWs5JJusFnJ5OsxIQAso6sm9RPbiyybkgRWUfWkXVk3e8ygqz7EWr5+isbyuXxx34q23edJ/sO9Cx+AotHdDn4UanbXyMf7R1s8SqKD/3gqpek6qA35OuXjZZuXRuKn4ARqSHAnfXUpIJAEiSArCPrGuWHrCtQZM+6AkSmsJoAe9atTl8qg3dkfemy8lTGRlDRErjynw4g69Eijnz2oLIeeSCcAAIJEkDWG+HTDd6sCJF1M37uaGRdASJTWE0AWbc6fakMPifrA46pl4sn1KcyxsKgtPasW7FYgoRAGwSQdcoDAiLIOrKu8T5A1hUoIusKEJnCagLIutXpS2XwyHoq00JQEAhEAFkPhImDMk4AWUfWNUocWTekyJ519qx7lZBJ1/PNmzbKS+uelXPGjPOszufXrhZnN+eQoaeEqt67F90kEy6ZIlVV1UWPf3X9i7Jt6xYZPnJUs7FBZd2EC93gS6sbfG7Peqfuo+XrE3sUXatJDNC6s27S4yCJdWud8y8bXpa3N2+SEWeO1pqSeRIiEFTW6QafUII4bSwEkHX2rGsUGrJuSBFZR9aRdRrM+V1GTOSrVLvBI+s1ctxnBvmVVqZeR9azk05kPTu5ZCXhCSDryHr46vlkJLJuSBFZR9aRdWTd7zKCrPsRavk6so6sF181jEgLAWQ9LZkgjiQJIOvIukb9IesKFNmzrgCRKawmEPQxeKsXSfCxEmDPeqy4ORkEVAkElXXVkzIZBFJGAFlvTAjd4M0KE1k34+eORtYVIDKF1QSQdavTl8rgkfVUpoWgIBCIALIeCBMHZZwAso6sa5Q4sq5AEVlXgMgUVhNA1q1OXyqDR9ZTmRaCgkAgAsh6IEwclHECyDqyrlHiyLohRfass2fdq4RMup7TDd77TUk3eLrBG16uIx9ON3gzxDSYM+OXptFBZZ1u8GnKGrFoE0DW2bOuUVPIuiFFZB1ZR9ZpMOd3GaHBnB+hlq/TYI4Gc8VXDSPSQgBZT0smiCNJAsg6sq5Rf8i6IUVkHVlH1pF1v8sIsu5HCFnPJ2BSL8WTTs8I7qynJxemkSDrpgQZnwUCyDqyrlHHyLoCRfasK0BkCqsJsGfd6vSlMnj2rKcyLQQFgUAEgsp6oMk4CAKWEkDWGxNHN3izAkbWzfi5o5F1BYhMYTUBZN3q9KUyeGQ9lWkhKAgEIoCsB8LEQRkngKwj6xoljqwrUETWFSAyhdUEkHWr05fK4JH1VKaFoCAQiACyHggTB2WcALKOrGuUOLJuSJE96+xZ9yohusF7v7FMuNANnm7whpfryIfTDd4MMXvWzfilaXRQWacbfJqyRizaBJB19qxr1BSybkgRWUfWkXUazPldRkwahu3ft0/uWXKLXFaLrPtxTvp1ZN0sA8i6Gb80jUbW05QNYkmKALKOrGvUHrJuSBFZR9aRdUQBCBgAABx7SURBVGTd7zKCrPsRavk6X93GV7cVXzWMSAsBZD0tmSCOJAkg68i6Rv0h6woU2bOuAJEprCbAnnWr05fK4Nmznsq0EBQEAhEIKuuBJuMgCFhKAFlvTBzd4M0KGFk34+eORtYVIDKF1QSQdavTl8rgkfVUpoWgIBCIALIeCBMHZZwAso6sa5Q4sq5AEVlXgMgUVhNA1q1OXyqDR9ZTmRaCgkAgAsh6IEwclHECyDqyrlHiyLohRfass2fdq4RMup5v3rRRXlr3rJwzZpxndT6/drU0iMiQoaeEqt67F90kEy6ZIlVV1UWPf3X9i7Jt6xYZPnJUs7FBZd2EC93gaTBXdMHGPIAGc2bAaTBnxi9No4PKOt3g05Q1YtEmgKyzZ12jppB1Q4rIOrKOrNNgzu8yQoM5P0ItX6fBHA3miq8aRqSFALKelkwQR5IEkHVkXaP+kHVDisg6so6sI+t+lxFk3Y8Qsp5PwKReiiednhHcWU9PLkwjQdZNCTI+CwSQdWRdo46RdQWK7FlXgMgUVhMI+hh8Uovc/HaZ3LG4XVKnd897yCEi06/ZX3QMj/1vuaz+Q3nR47IyYMAx9XLxhHorlqP1GLwViyVICLRBIKisAxECWSaArDdml27wZlWOrJvxc0cj6woQmcJqAsi6f/qQdX9GXkcg6+G4MQoCSRJA1pOkz7nTQgBZR9Y1ahFZV6CIrCtAZAqrCdgi64f3bpDabx2IlfXOnSLzbjzI+M76l86sly+ebMcd5lgBp+hk3FlPUTIIJVECyHqi+Dl5Sggg68i6Riki64YU2bPOnnWvEjLpek43eO83pUk3eOcx+IeWPSCVHYZIbW0/w3d9ccMdWf/PO34jZe1q5KqrPlvcYBF59LED8uafb5Ljh0xNRNaT7Nb84H13yWkjR0m3Hj2L5pbEAC1ZZ8/66CTSxzkVCQSV9SSvL4rLZSoIeBJA1tmzrvHWQNYNKSLryDqynv4Gc8h6+Atdkv8xjawPCp84C0fSYM7CpLUSMrKenVyykvAEkHVkPXz1fDISWTekiKwj68g6st7WZYQ76+Evssg6sh6+ehiZJAFkPUn6nDstBJB1ZF2jFpF1BYrsWVeAyBRWE2DPeuvpY8+61aVdVPBaj8EXdVIOhkAKCQSV9RSGTkgQUCOArDeipBu8WUkh62b83NHIugJEprCaALKOrFtdwErBI+tKIJnGegLIuvUpZAEKBJB1ZF2hjARZV6CIrCtAZAqrCSDryLrVBawUPLKuBJJprCeArFufQhagQABZR9YVyghZN4XInnX2rHvVEN3gvd9ZJlzoBk83eNPrddTjtWSdbvB0g4+6VqOeP6isJ9nAMmoGzA8BZJ096xrvAu6sG1JE1pF1ZJ0Gc21dRmgwF/4iS4M5GsyFrx5GJkkAWU+SPudOCwFkHVnXqEVk3ZAiso6sI+vIOrJueCFtZTiyjqxHU1nMGjUBZD1qwsxvAwFkHVnXqFNkXYEie9YVIGZoijc2lcnKx8uNVzRmdL30qGloMc+Su8ulob7MeH7NCZw/SA0NIgfqW8areZ6wc9XVifx1a5kc3rtBar91IOw0ocbt2lUmc29o547te2TxfHZ8IPK3v5XJ2Wc1yClfiDf2UAsu4UFaj8GXMEKWnhECQWU9I8tlGRDwJICsN2KhG7zZGwRZN+PnjkbWFSBmaIpXNpTL0mXmsj75sgPSp09LufvXHxyUIVrxLiVpWTdZLbJuQi+esch6PJw5S/oJIOvpzxERRk8AWUfWNaoMWVegiKwrQMzQFDlZ79unQc44vfg7qct/VSbvvlsmfrJ+2SX1kpb76x2q27l31nfvTfed38qqBul9WPE5MS3PN94w//CmS5cG6dw5/thN115K45H1Uso2a22LALJOfUBABFlH1jXeB8i6IUX2rLNnvbCE1r9SLqt+vVg69RgjX7u4W9EVdseiN2XPzmfkwnHj5EiPO+s/mfcHd86p3/uClIWw9bsX3SQTLpkiVVXVRcf26voXZdvWLTJ85KhmY4N+dVtS3eCdYB9d/oAc/9nB0qdv/6LXbTrApLv3/n375J4lt8hltVebhhFqfJLdmtmzzp71UEXLoMQJBJX1JK8viUMigMwTQNbZs65R5Mi6IUVkHVlH1tPfYA5ZD3+hS/I/ppF1ZD185TIySQLIepL0OXdaCCDryLpGLSLrhhSRdWQdWUfW/S4j3Fn3I+T9OrKOrIerHEYlTQBZTzoDnD8NBJB1ZF2jDpF1BYpp3rP+p1fKZds2/32uJ39BpLqqQR5f5f9cdefOZTLkc/Xyzrtl8vKfFABmbIp33yuXP60vkwED6uXi8fVFr+6OJe1k8+YyGTywXjp57FFe9URjZ/Ef/Ov+oueOakDQx+CjOj/zQiANBNiznoYsEEMaCASV9TTESgwQiIoAst5Ilm7wZhWGrJvxc0enWdaXPdBOXl7vL+Dfu+qAdOrUIEE6jR9xRINMmXRAnA8C7rvfvHGWQgpSOYWprPstCln3I8TrEIiXALIeL2/Oll4CyHp6c0Nk8RFA1pF1jWpD1hUo2iDrxw2olxqP7+z+/Zpyqasrk0JZHzG8ZVdv57uen19XLoWy3qN7vRx/nP/dewXUVk1R00PkM8cXz+W5F8rlww/9x40c4X9MXMC4sx4Xac6TZgLIepqzQ2xxEkDW46TNudJKAFlH1jVqE1kPQPHBFU/IdXOXuEeee8YwuX7aJGlfXen+22TPupR3lz0f75JPHzfSnevV9SulurqT7Nr5jnTpeqT07nOC+/vn19wrfY8eJt169HP//ftVi2Tg58fKwYc0dhr/7aM3yKln/pMcdFCl7N+/V578za1y+qjGztErV70nez/4HzntS9/0FOp///eXpX7f2/Kl0aPk4A5l8sD9q91x35v2hRZknEezH7p/mZRXnSTnju4nGzeVySsvLJJDe46Viy86tMXxdXV7Zemdt8s3plzpSXnzpo3y0rpn5Zwx4zxff37tanF0dMjQUzxfX/HwMhk4eKgc3qeRS+HPnQsXyMTLrpCKysZc5f/s2P6erHz0F/KViyd5jm2t63nu4Cd/+5j0qOklA44f6DnepOu5KRfPgPJ+STd4usH71Uj+6zSYC05LS9ZNehwEjzZ9R/5lw8vy9uZNMuLM0ekLjoiKIhBU1pO8vhS1IA6GQAgCyDp71kOUTYshyLoPxWfWbZD5C5fJbXOuli6dO8oNC5e5I66pHe/+fxNZf25dD6k/sEs+3H2GO9ehHX4j+w90ksqD3pE9+46Uj+s+6/6+R8el8rfdw2Tv/qPcf/fqfIe8t3Os7K/v7v77iC4/kbd3fFcapFLKy/bKYZ1vkbc/mOq+VtHuPel28IO+sr7j43Pd4zu1f9L9/23JemEsyHrLIkLWvd9YJly2bH5TXnh2jZx74YRQ1z6+ui0UNknyP6ZpMEeDuXBVy6ikCSDrSWeA86eBALKOrGvUIbLuQ9GR8359esnY0cPdIwvl3ZH1yo5TpW7nzSJlFVLe/utSv/vnImWVUl51jtTv/a2UVZ0iDXXPSFnFZ6Vh/6tS1u4oaTjwtnzwt+5SXrZLOndvvLP+t/dXNpP1Hr0a76w37L5XpOIkKTuoUdbrP75Dyqq/LGXljXfW6z+aL2Ud/lnKyipFGvZK/Uf/IeWHXNO4svr3pH7PQ3LW6G9Jv34tm53dc9fLsmf321Je3Xgno6HuKef/yremnNyCjNNQ7hcPLGsRy5Gf+rKc/SXurOcDM5FS7qx7vymRdb5nXeOPXpRzcGfdjC531s34pWk0sp6mbBBLUgSQdWRdo/aQ9TYo7t5TJ7PmLZFhQ45rkvXX3twi35+9SH40c7Ic3be3PPyrA7L80eI7fuef9gfXNYjTAu66H37SCO7Qzg0y83saKWYOCERPoEN1O2loENm9t2Wvg+jPzhkgkA4CZWUiXTpWyva/1aUjIKKAQEIEOrY/SOr218vefWb/fZRQ+JwWAioE2pWLdOxQKR/sKu2/Cd06tdyOqgK4RCZB1gPI+rjzR8iJgwa4RxbK+nvbRZb9z355/o8tm32NOaedHHO0fyf2Y/6h8ZhX//LJHBUVIv37+Y8tkTplmRCAAAQgAAEIQAACEIAABEqKALIeQNbburPuDD9woEH216enM3dJVTCLTQUB51Ev5876Ad4HqcgHQSRDwPl4taKiXOq4m5hMAjhraghUtCuT+oYGOcCN9dTkhEDiJ+A8bVXRrtx9yqSUf6oq+Jpnk/wj6z70guxZ/853p8odt90sB1VUyJgvT5RHHloqFZVVcurpZ8vTv18lg0882e16fsyxJ8jG1/8sR/TpJ9u2bpEuXbrJRx/tkmFfPN2NYs1Tj8vBh3SS7e+/K4f1PkI+fWzjnvXCruf3/2yxnDX6Qjm0S+Oe9fyu54Ud2Ivtel5sB/bCWPJx0g3+POnarUfR70/2rHsjY886e9aLfjPFPIA962bA2bNuxi9No9mznqZsEEtSBNizzp51jdpD1n0oRtkNHlnnq9u8yg9ZR9bzCezft0/uWXKLXFaLrGv80YtyDmTdjC6ybsYvTaOR9TRlg1iSIoCsI+satYesB6AY1fesI+vIOrIe4A3490O4s46sB6+WZI5E1s24I+tm/NI0GllPUzaIJSkCyDqyrlF7yLoCxZ0f75Odu/crzMQUELCTQKcOFe7+xF28D+xMIFGrENCSdZVgmAQCCRIIKusJhsipIRA5AWS9EXHvbu0jZ53lEyDrCtlF1hUgMoXVBJB1q9NH8EoEkHUlkExjPQFk3foUsgAFAsg6sq5QRoKsK1BE1hUgMoXVBJB1q9NH8EoEkHUlkExjPQFk3foUsgAFAsg6sq5QRsi6KcTrr79e6Ab/SWf6fJ50g6cbfOH767+X/lRO/1I4LuxZZ8+66fU66vFasv7Uql9L1+41ctxnBkUdcqrmZ896qtJhFExQWb9n8S0y7uJJUt2+g9H5GAyBNBJA1tmzrlGX3Fk3pIisN/8aOWS9kYCJlNIN3vtNiawj64aX68iHI+tmiJF1M35pGo2spykbxJIUAWQdWdeoPWTdkKIr61d+T+649Sa+Z72AJXfWw91BRtaR9XwCfHXbKOnWo6fhlTqe4ci6GWdk3YxfmkYj62nKBrEkRQBZR9Y1ag9Z16DIHBCAAAQgAAEIQAACEIAABCAAAUUCyLoiTKaCAAQgAAEIQAACEIAABCAAAQhoEEDWNSgyBwQgAAEIQAACEIAABCAAAQhAQJEAsq4Ik6kgAAEIQAACEIAABCAAAQhAAAIaBJD1kBR3fLhTLp9xo7z0yuvuDHcumCEnDhoQcjaGQSD9BB5c8YRcN3eJG+i5ZwyT66dNkvbVlZ6Bv/bmFqmdPl/+uu39ptdPOLa/3DbnaunSuWP6F0uEEAhBwPm7MPPHi2TaFRfJ0X17h5iBIRCwi4Dzd+GNt7bKNbXj2ww8/+9H7sBvfnW07zi7aBAtBBoJFP43EP/9Q2WYEEDWQ9DbvadOZs1bIsOGHCdjRw9335Tfn71IfjRzMv+BFoInQ9JP4Jl1G2T+wmVNsn3DwmVu0K39BxrvifTnlAj1COT+Jjyyco0c1rObLJw7lb8FeniZKYUEnL8Jl141x40siHQ7sr7mufVtfsibwmUSEgRCEXDeH29tecd1BOfH+W+mre9sp/5D0WQQsh6iBhwRmXfrvTL72snuXcJCeQ8xJUMgkGoCzh+afn16Nf3hKZT3wuCR9VSnk+AiIsCd9YjAMm1qCRRzZx1ZT20aCSxiAn7/zRTx6ZnecgLIeogEer3p/O40hjgNQyCQCgJeH0b5yTiPgKUidQQRMwFkPWbgnC5xAsXIem4blRN0kLvxiS+OACCgRIAnS5RAlug0yHqIxDuyfv/yVc0eZ0HWQ4BkiBUEcrI+7vwRTX0Z/GS9cGE8AmZFqgnSkACybgiQ4dYRCCrr+QvL9fwZf/6Ipqe1rFs4AUMgIIFi/3sp4LQcVkIEkPUQyebOeghoDLGWQJg764WLLdw6Yi0MAodAGwSQdcqj1AiEkXWHUdhxpcaX9dpNIPeU4eyZk2lCbXcqE40eWQ+Bnz3rIaAxxGoCxe5ZR9atTjfBhySArIcExzBrCYSV7rDjrAVF4CVHAFEvuZRHtmBkPQRausGHgMYQqwn4dYMvfMz9sVVr5VNHHdHUEZttIlann+ADEkDWA4LisMwQaE268/8mOIv970d+J18+9zT36z5zj8FPrR3P3cbMVAILySfAo+/UgyYBZD0kTb5nPSQ4hllLoK3vWS+U9fyv9XEW7Pe97NZCIXAIiDR9I4jz1W25H2qe0sgygcJrvLPWOxfMaJLvwr8Jzr8XL13RhOSH0yexXz3LBVLia8v/76V8FPnvkRJHxPKLIICsFwGLQyEAAQhAAAIQgAAEIAABCEAAAnEQQNbjoMw5IAABCEAAAhCAAAQgAAEIQAACRRBA1ouAxaEQgAAEIAABCEAAAhCAAAQgAIE4CCDrcVDmHBCAAAQgAAEIQAACEIAABCAAgSIIIOtFwOJQCEAAAhCAAAQgAAEIQAACEIBAHASQ9Tgocw4IQAACEIAABCAAAQhAAAIQgEARBJD1ImBxKAQgAAEIQAACEIAABCAAAQhAIA4CyHoclDkHBCAAAQhAAAIQgAAEIAABCECgCALIehGwOBQCEIAABCAAAQhAAAIQgAAEIBAHAWQ9DsqcAwIQgAAEIAABCEAAAhCAAAQgUAQBZL0IWBwKAQhAAAIQgAAEIAABCEAAAhCIgwCyHgdlzgEBCEAAAhCAAAQgAAEIQAACECiCALJeBCwOhQAEIAABCEAAAhCAAAQgAAEIxEEAWY+DMueAAAQgAAEIQAACEIAABCAAAQgUQQBZLwIWh0IAAhCAAAQgAAEIQAACEIAABOIggKzHQZlzQAACEIAABCAAAQhAAAIQgAAEiiCArBcBi0MhAAEIQAACEIAABCAAAQhAAAJxEEDW46DMOSAAAQhAAAIQgAAEIAABCEAAAkUQQNaLgMWhEIAABCBQGgQeXPGEXDd3SbPFnnBsf7ltztXyfxvflkuvmiN3LpghJw4a0OyYGxYuk7XrNrjHdencUdqaZ/sHO6V2+nz567b3W4X6w+mTpE/vGvd8Xj+5GJ5Zt8E95twzhsn10yZJ++rKpsPbes05aMeHO+XyGTfKS6+83moc3/zqaOnXp1czJk5sY0cPl9fe3OKuo3vXzk3rzk3k9VounrbWUxpVxiohAAEIQAACbRNA1qkQCEAAAhCAQB6BQuHOveT8/tSTPusKuiPhy5avaianjph+f/Yi+dHMyXJ0394SZJ588M6ca55b7ynbM2cvkoVzp7rzev3kBPiwnt2aHbd7T53MmrdEHlm5xlPkW5tr/sJlLcTbOdY5T2EsOSF3PnQo/ADDYbB46QrJfdDhfIDhNQcFCAEIQAACEIBASwLIOlUBAQhAAAIQ+DuB3F3m8eePcO8at/aTk+BeNV3lmtrxkvv3sCHHueOCzqMp645gjzzlc7Lro91uTDm5vn/5KjnkkA6ya9fHLT4I0JJ150OK8846WV5c/1rTORyJn3frvTL4hH+Qx1e/0CT/yDpvNwhAAAIQgEAwAsh6ME4cBQEIQAACJUCgUMLbWnLujvLsmZPlrS3vNLvTXsw8uXOY3ll3ZH1q7XiZf/t9ze7uO4+vv/HWVtn6zvZIZX3qtydILobc0wfOeZ3z5z+FgKyXwBuJJUIAAhCAgAoBZF0FI5NAAAIQgEBWCBTuqc5/hLtwjbnHvJ3fFz4CXsw8zvi2ZD3InvXco+s/vfdXbpgXjPqie2d79rWTxfld1LLuPP7/xz/9n/so/9VTxsn1N9wl0664yP1doaz7rScrtcQ6IAABCEAAAiYEkHUTeoyFAAQgAIHMEsjf7+0ssnA/uPO73OPuQwcNaHr0vBBIkHn8ZD3InvWcrDuN65zH0p2f8WNOdx/Ldz5UiEPWux7a0W1W17njwXLM0X1cJoX7+7mzntm3DAuDAAQgAAFlAsi6MlCmgwAEIACB7BFo7bH2wr3qfitv6/F4jcfgc13oC5vbxSXrTgM8Zx233vVwU6M7ZN2vKngdAhCAAAQg4E0AWacyIAABCEAAAn8n4Nwpd77CbPiwgS2YOMLr/OSatzn/uzVZL3YeZy5NWXf20//fxs1y9oihbsxxyrqz9hUr18jEsWe550bWeXtBAAIQgAAEwhFA1sNxYxQEIAABCGSQQO6x9iN71zRrxpbbf164L70tWXceBw86j7asF6YmTlkvPDeynsE3CkuCAAQgAIFYCCDrsWDmJBCAAAQgYAuBnLA7d9hzP1771Z3X2noMvph5/GTdryGb82FCa9+N7sytIeuOdF83d0kTkx9On+Tuhy/8fvkgsu63HltqhTghAAEIQAACURJA1qOky9wQgAAEIAABCEAAAhCAAAQgAIEQBJD1ENAYAgEIQAACEIAABCAAAQhAAAIQiJIAsh4lXeaGAAQgAAEIQAACEIAABCAAAQiEIICsh4DGEAhAAAIQgAAEIAABCEAAAhCAQJQEkPUo6TI3BCAAAQhAAAIQgAAEIAABCEAgBAFkPQQ0hkAAAhCAAAQgAAEIQAACEIAABKIkgKxHSZe5IQABCEAAAhCAAAQgAAEIQAACIQgg6yGgMQQCEIAABCAAAQhAAAIQgAAEIBAlAWQ9SrrMDQEIQAACEIAABCAAAQhAAAIQCEEAWQ8BjSEQgAAEIAABCEAAAhCAAAQgAIEoCSDrUdJlbghAAAIQgAAEIAABCEAAAhCAQAgCyHoIaAyBAAQgAAEIQAACEIAABCAAAQhESQBZj5Iuc0MAAhCAAAQgAAEIQAACEIAABEIQQNZDQGMIBCAAAQhAAAIQgAAEIAABCEAgSgLIepR0mRsCEIAABCAAAQhAAAIQgAAEIBCCALIeAhpDIAABCEAAAhCAAAQgAAEIQAACURJA1qOky9wQgAAEIAABCEAAAhCAAAQgAIEQBJD1ENAYAgEIQAACEIAABCAAAQhAAAIQiJIAsh4lXeaGAAQgAAEIQAACEIAABCAAAQiEIICsh4DGEAhAAAIQgAAEIAABCEAAAhCAQJQEkPUo6TI3BCAAAQhAAAIQgAAEIAABCEAgBAFkPQQ0hkAAAhCAAAQgAAEIQAACEIAABKIkgKxHSZe5IQABCEAAAhCAAAQgAAEIQAACIQgg6yGgMQQCEIAABCAAAQhAAAIQgAAEIBAlAWQ9SrrMDQEIQAACEIAABCAAAQhAAAIQCEEAWQ8BjSEQgAAEIAABCEAAAhCAAAQgAIEoCSDrUdJlbghAAAIQgAAEIAABCEAAAhCAQAgCyHoIaAyBAAQgAAEIQAACEIAABCAAAQhESQBZj5Iuc0MAAhCAAAQgAAEIQAACEIAABEIQQNZDQGMIBCAAAQhAAAIQgAAEIAABCEAgSgLIepR0mRsCEIAABCAAAQhAAAIQgAAEIBCCALIeAhpDIAABCEAAAhCAAAQgAAEIQAACURJA1qOky9wQgAAEIAABCEAAAhCAAAQgAIEQBJD1ENAYAgEIQAACEIAABCAAAQhAAAIQiJIAsh4lXeaGAAQgAAEIQAACEIAABCAAAQiEIICsh4DGEAhAAAIQgAAEIAABCEAAAhCAQJQEkPUo6TI3BCAAAQhAAAIQgAAEIAABCEAgBAFkPQQ0hkAAAhCAAAQgAAEIQAACEIAABKIkgKxHSZe5IQABCEAAAhCAAAQgAAEIQAACIQj8fzJCUJX1LDq5AAAAAElFTkSuQmCC", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_step_sizes(show_intervals=True)" ] }, { "cell_type": "code", "execution_count": 16, "id": "3d012f8e-4066-40b6-9b9a-d1e9dd7532c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: 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", "1: 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": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.is_in_equilibrium()" ] }, { "cell_type": "code", "execution_count": 17, "id": "9dd856c0-58e6-4048-8b03-90f68e725232", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reaction: 2 S <-> U\n" ] }, { "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \n", "
START_TIMEDelta UDelta XDelta Stime_stepcaption
00.0000-1.0000000.02.0000000.0100aborted: excessive norm value(s)
10.0000-0.5000000.01.0000000.0050
20.0050-0.1975000.00.3950000.0025
30.0075-0.3491750.00.6983500.0050
40.0125-0.1316930.00.2633850.0025
50.0150-0.2259930.00.4519870.0050
60.0200-0.1560400.00.3120800.0050
70.0250-0.0951570.00.1903150.0050
80.0300-0.0422370.00.0844730.0050
90.03500.0036970.0-0.0073940.0050
100.04000.0869990.0-0.1739970.0100
110.05000.1123450.0-0.2246900.0050
120.05500.2746600.0-0.5493210.0100
130.06500.3603670.0-0.7207350.0100
140.07500.4211210.0-0.8422420.0100
150.08500.4630920.0-0.9261850.0100
160.09500.4909520.0-0.9819050.0100
170.10501.0164670.0-2.0329340.0200
180.12500.5269840.0-1.0539670.0100
190.13501.0491750.0-2.0983500.0200
200.15500.5141330.0-1.0282660.0100
210.16501.0096080.0-2.0192150.0200
220.18500.9687930.0-1.9375870.0200
230.20500.9233650.0-1.8467310.0200
240.22500.8768920.0-1.7537850.0200
250.24500.8311380.0-1.6622760.0200
260.26500.7869410.0-1.5738820.0200
270.28500.7446680.0-1.4893360.0200
280.30500.7044470.0-1.4088950.0200
290.32500.6662870.0-1.3325730.0200
300.34501.2602710.0-2.5205410.0400
310.38500.5616960.0-1.1233920.0200
320.40501.0623250.0-2.1246500.0400
330.44500.9468280.0-1.8936570.0400
340.48500.8438870.0-1.6877730.0400
350.52500.7521370.0-1.5042740.0400
360.56500.6703620.0-1.3407250.0400
370.60501.1949570.0-2.3899150.0800
380.68500.4675600.0-0.9351190.0400
390.72500.8334500.0-1.6669010.0800
400.80500.6522200.0-1.3044400.0800
410.88501.0207950.0-2.0415910.1600
421.04500.5768600.0-1.1537210.1600
431.20500.6519780.0-1.3039550.3200
441.52500.1697980.0-0.3395950.6400
\n", "
" ], "text/plain": [ " START_TIME Delta U Delta X Delta S time_step \\\n", "0 0.0000 -1.000000 0.0 2.000000 0.0100 \n", "1 0.0000 -0.500000 0.0 1.000000 0.0050 \n", "2 0.0050 -0.197500 0.0 0.395000 0.0025 \n", "3 0.0075 -0.349175 0.0 0.698350 0.0050 \n", "4 0.0125 -0.131693 0.0 0.263385 0.0025 \n", "5 0.0150 -0.225993 0.0 0.451987 0.0050 \n", "6 0.0200 -0.156040 0.0 0.312080 0.0050 \n", "7 0.0250 -0.095157 0.0 0.190315 0.0050 \n", "8 0.0300 -0.042237 0.0 0.084473 0.0050 \n", "9 0.0350 0.003697 0.0 -0.007394 0.0050 \n", "10 0.0400 0.086999 0.0 -0.173997 0.0100 \n", "11 0.0500 0.112345 0.0 -0.224690 0.0050 \n", "12 0.0550 0.274660 0.0 -0.549321 0.0100 \n", "13 0.0650 0.360367 0.0 -0.720735 0.0100 \n", "14 0.0750 0.421121 0.0 -0.842242 0.0100 \n", "15 0.0850 0.463092 0.0 -0.926185 0.0100 \n", "16 0.0950 0.490952 0.0 -0.981905 0.0100 \n", "17 0.1050 1.016467 0.0 -2.032934 0.0200 \n", "18 0.1250 0.526984 0.0 -1.053967 0.0100 \n", "19 0.1350 1.049175 0.0 -2.098350 0.0200 \n", "20 0.1550 0.514133 0.0 -1.028266 0.0100 \n", "21 0.1650 1.009608 0.0 -2.019215 0.0200 \n", "22 0.1850 0.968793 0.0 -1.937587 0.0200 \n", "23 0.2050 0.923365 0.0 -1.846731 0.0200 \n", "24 0.2250 0.876892 0.0 -1.753785 0.0200 \n", "25 0.2450 0.831138 0.0 -1.662276 0.0200 \n", "26 0.2650 0.786941 0.0 -1.573882 0.0200 \n", "27 0.2850 0.744668 0.0 -1.489336 0.0200 \n", "28 0.3050 0.704447 0.0 -1.408895 0.0200 \n", "29 0.3250 0.666287 0.0 -1.332573 0.0200 \n", "30 0.3450 1.260271 0.0 -2.520541 0.0400 \n", "31 0.3850 0.561696 0.0 -1.123392 0.0200 \n", "32 0.4050 1.062325 0.0 -2.124650 0.0400 \n", "33 0.4450 0.946828 0.0 -1.893657 0.0400 \n", "34 0.4850 0.843887 0.0 -1.687773 0.0400 \n", "35 0.5250 0.752137 0.0 -1.504274 0.0400 \n", "36 0.5650 0.670362 0.0 -1.340725 0.0400 \n", "37 0.6050 1.194957 0.0 -2.389915 0.0800 \n", "38 0.6850 0.467560 0.0 -0.935119 0.0400 \n", "39 0.7250 0.833450 0.0 -1.666901 0.0800 \n", "40 0.8050 0.652220 0.0 -1.304440 0.0800 \n", "41 0.8850 1.020795 0.0 -2.041591 0.1600 \n", "42 1.0450 0.576860 0.0 -1.153721 0.1600 \n", "43 1.2050 0.651978 0.0 -1.303955 0.3200 \n", "44 1.5250 0.169798 0.0 -0.339595 0.6400 \n", "\n", " caption \n", "0 aborted: excessive norm value(s) \n", "1 \n", "2 \n", "3 \n", "4 \n", "5 \n", "6 \n", "7 \n", "8 \n", "9 \n", "10 \n", "11 \n", "12 \n", "13 \n", "14 \n", "15 \n", "16 \n", "17 \n", "18 \n", "19 \n", "20 \n", "21 \n", "22 \n", "23 \n", "24 \n", "25 \n", "26 \n", "27 \n", "28 \n", "29 \n", "30 \n", "31 \n", "32 \n", "33 \n", "34 \n", "35 \n", "36 \n", "37 \n", "38 \n", "39 \n", "40 \n", "41 \n", "42 \n", "43 \n", "44 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_rxn_data(rxn_index=0)" ] }, { "cell_type": "code", "execution_count": 18, "id": "5ff51045-dfa3-4f04-94f4-5d66f1352d4a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reaction: S <-> X\n" ] }, { "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \n", "
START_TIMEDelta UDelta XDelta Stime_stepcaption
00.00000.0-3.0000003.0000000.0100aborted: excessive norm value(s)
10.00000.0-1.5000001.5000000.0050
20.00500.0-0.7012500.7012500.0025
30.00750.0-1.3590941.3590940.0050
40.01250.0-0.6384920.6384920.0025
50.01500.0-1.2403501.2403500.0050
60.02000.0-1.1709751.1709750.0050
70.02500.0-1.1089191.1089190.0050
80.03000.0-1.0533081.0533080.0050
90.03500.0-1.0033751.0033750.0050
100.04000.0-1.9168901.9168900.0100
110.05000.0-0.8774050.8774050.0050
120.05500.0-1.6893241.6893240.0100
130.06500.0-1.5702441.5702440.0100
140.07500.0-1.4721671.4721670.0100
150.08500.0-1.3902061.3902060.0100
160.09500.0-1.3206591.3206590.0100
170.10500.0-2.5214272.5214270.0200
180.12500.0-1.1557611.1557610.0100
190.13500.0-2.2299612.2299610.0200
200.15500.0-1.0401851.0401850.0100
210.16500.0-2.0165292.0165290.0200
220.18500.0-1.8958601.8958600.0200
230.20500.0-1.7871151.7871150.0200
240.22500.0-1.6870421.6870420.0200
250.24500.0-1.5938291.5938290.0200
260.26500.0-1.5064131.5064130.0200
270.28500.0-1.4241241.4241240.0200
280.30500.0-1.3465021.3465020.0200
290.32500.0-1.2731991.2731990.0200
300.34500.0-2.4078642.4078640.0400
310.38500.0-1.0729821.0729820.0200
320.40500.0-2.0293042.0293040.0400
330.44500.0-1.8086711.8086710.0400
340.48500.0-1.6120271.6120270.0400
350.52500.0-1.4367631.4367630.0400
360.56500.0-1.2805541.2805540.0400
370.60500.0-2.2826572.2826570.0800
380.68500.0-0.8931510.8931510.0400
390.72500.0-1.5920911.5920910.0800
400.80500.0-1.2458981.2458980.0800
410.88500.0-1.9499651.9499650.1600
421.04500.0-1.1019421.1019420.1600
431.20500.0-1.2454341.2454340.3200
441.52500.0-0.3243540.3243540.6400
\n", "
" ], "text/plain": [ " START_TIME Delta U Delta X Delta S time_step \\\n", "0 0.0000 0.0 -3.000000 3.000000 0.0100 \n", "1 0.0000 0.0 -1.500000 1.500000 0.0050 \n", "2 0.0050 0.0 -0.701250 0.701250 0.0025 \n", "3 0.0075 0.0 -1.359094 1.359094 0.0050 \n", "4 0.0125 0.0 -0.638492 0.638492 0.0025 \n", "5 0.0150 0.0 -1.240350 1.240350 0.0050 \n", "6 0.0200 0.0 -1.170975 1.170975 0.0050 \n", "7 0.0250 0.0 -1.108919 1.108919 0.0050 \n", "8 0.0300 0.0 -1.053308 1.053308 0.0050 \n", "9 0.0350 0.0 -1.003375 1.003375 0.0050 \n", "10 0.0400 0.0 -1.916890 1.916890 0.0100 \n", "11 0.0500 0.0 -0.877405 0.877405 0.0050 \n", "12 0.0550 0.0 -1.689324 1.689324 0.0100 \n", "13 0.0650 0.0 -1.570244 1.570244 0.0100 \n", "14 0.0750 0.0 -1.472167 1.472167 0.0100 \n", "15 0.0850 0.0 -1.390206 1.390206 0.0100 \n", "16 0.0950 0.0 -1.320659 1.320659 0.0100 \n", "17 0.1050 0.0 -2.521427 2.521427 0.0200 \n", "18 0.1250 0.0 -1.155761 1.155761 0.0100 \n", "19 0.1350 0.0 -2.229961 2.229961 0.0200 \n", "20 0.1550 0.0 -1.040185 1.040185 0.0100 \n", "21 0.1650 0.0 -2.016529 2.016529 0.0200 \n", "22 0.1850 0.0 -1.895860 1.895860 0.0200 \n", "23 0.2050 0.0 -1.787115 1.787115 0.0200 \n", "24 0.2250 0.0 -1.687042 1.687042 0.0200 \n", "25 0.2450 0.0 -1.593829 1.593829 0.0200 \n", "26 0.2650 0.0 -1.506413 1.506413 0.0200 \n", "27 0.2850 0.0 -1.424124 1.424124 0.0200 \n", "28 0.3050 0.0 -1.346502 1.346502 0.0200 \n", "29 0.3250 0.0 -1.273199 1.273199 0.0200 \n", "30 0.3450 0.0 -2.407864 2.407864 0.0400 \n", "31 0.3850 0.0 -1.072982 1.072982 0.0200 \n", "32 0.4050 0.0 -2.029304 2.029304 0.0400 \n", "33 0.4450 0.0 -1.808671 1.808671 0.0400 \n", "34 0.4850 0.0 -1.612027 1.612027 0.0400 \n", "35 0.5250 0.0 -1.436763 1.436763 0.0400 \n", "36 0.5650 0.0 -1.280554 1.280554 0.0400 \n", "37 0.6050 0.0 -2.282657 2.282657 0.0800 \n", "38 0.6850 0.0 -0.893151 0.893151 0.0400 \n", "39 0.7250 0.0 -1.592091 1.592091 0.0800 \n", "40 0.8050 0.0 -1.245898 1.245898 0.0800 \n", "41 0.8850 0.0 -1.949965 1.949965 0.1600 \n", "42 1.0450 0.0 -1.101942 1.101942 0.1600 \n", "43 1.2050 0.0 -1.245434 1.245434 0.3200 \n", "44 1.5250 0.0 -0.324354 0.324354 0.6400 \n", "\n", " caption \n", "0 aborted: excessive norm value(s) \n", "1 \n", "2 \n", "3 \n", "4 \n", "5 \n", "6 \n", "7 \n", "8 \n", "9 \n", "10 \n", "11 \n", "12 \n", "13 \n", "14 \n", "15 \n", "16 \n", "17 \n", "18 \n", "19 \n", "20 \n", "21 \n", "22 \n", "23 \n", "24 \n", "25 \n", "26 \n", "27 \n", "28 \n", "29 \n", "30 \n", "31 \n", "32 \n", "33 \n", "34 \n", "35 \n", "36 \n", "37 \n", "38 \n", "39 \n", "40 \n", "41 \n", "42 \n", "43 \n", "44 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_rxn_data(rxn_index=1)" ] }, { "cell_type": "code", "execution_count": 19, "id": "03eec482-0b4a-4a15-ba33-1788f63fc60f", "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TIMEUXScaption
00.000050.000000100.0000000.000000
10.005049.50000098.5000002.500000
20.007549.30250097.7987503.596250
30.012548.95332596.4396565.653694
40.015048.82163295.8011646.555571
50.020048.59563994.5608148.247909
60.025048.43959993.3898399.730964
70.030048.34444192.28092011.030197
80.035048.30220591.22761212.167978
90.040048.30590290.22423813.163959
100.050048.39290188.30734814.906851
110.055048.50524687.42994315.559566
120.065048.77990685.74061916.699569
130.075049.14027384.17037417.549079
140.085049.56139482.69820818.179004
150.095050.02448781.30800218.643025
160.105050.51543979.98734318.981779
170.125051.53190677.46591619.470272
180.135052.05889076.31015519.572066
190.155053.10806474.08019419.703677
200.165053.62219773.04000919.715597
210.185054.63180571.02348019.712910
220.205055.60059869.12762019.671183
230.225056.52396467.34050519.611568
240.245057.40085665.65346319.544825
250.265058.23199464.05963419.476378
260.285059.01893562.55322119.408909
270.305059.76360361.12909719.343697
280.325060.46805059.78259519.281305
290.345061.13433758.50939619.221930
300.385062.39460856.10153219.109253
310.405062.95630455.02855019.058842
320.445064.01862952.99924618.963496
330.485064.96545751.19057618.878510
340.525065.80934449.57854918.802763
350.565066.56148148.14178618.735252
360.605067.23184346.86123218.675082
370.685068.42680044.57857618.567824
380.725068.89436043.68542418.525856
390.805069.72781042.09333318.451046
400.885070.38003040.84743618.392504
411.045071.40082638.89747018.300879
421.205071.97768637.79552818.249100
431.525072.62966336.55009418.190579
442.165072.79946136.22573918.175339
\n", "
" ], "text/plain": [ " TIME U X S caption\n", "0 0.0000 50.000000 100.000000 0.000000 \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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_conc_data()" ] }, { "cell_type": "code", "execution_count": 20, "id": "703eae06-0fbe-42be-a5d1-562b5b8c3772", "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
START_TIMEDelta UDelta XDelta Snorm_Anorm_Bactionstep_factortime_stepcaption
00.0000-1.000000-3.0000005.0000003.888889NoneABORT0.50.0100excessive norm value(s)
10.0000-0.500000-1.5000002.5000000.972222NoneOK (high)0.50.0050
20.0050-0.197500-0.7012501.0962500.192502NoneOK (low)2.00.0025
30.0075-0.349175-1.3590942.0574440.689126NoneOK (high)0.50.0050
40.0125-0.131693-0.6384920.9018780.137600NoneOK (low)2.00.0025
50.0150-0.225993-1.2403501.6923370.494839NoneOK (stay)1.00.0050
60.0200-0.156040-1.1709751.4830550.399443NoneOK (stay)1.00.0050
70.0250-0.095157-1.1089191.2992340.325196NoneOK (stay)1.00.0050
80.0300-0.042237-1.0533081.1377810.267310NoneOK (stay)1.00.0050
90.03500.003697-1.0033750.9959810.222084NoneOK (low)2.00.0050
100.04000.086999-1.9168901.7428920.746634NoneOK (high)0.50.0100
110.05000.112345-0.8774050.6527150.134277NoneOK (low)2.00.0050
120.05500.274660-1.6893241.1400040.469874NoneOK (stay)1.00.0100
130.06500.360367-1.5702440.8495100.368578NoneOK (stay)1.00.0100
140.07500.421121-1.4721670.6299250.304602NoneOK (stay)1.00.0100
150.08500.463092-1.3902060.4640210.262494NoneOK (stay)1.00.0100
160.09500.490952-1.3206590.3387540.233325NoneOK (low)2.00.0100
170.10501.016467-2.5214270.4884930.847714NoneOK (high)0.50.0200
180.12500.526984-1.1557610.1017940.180429NoneOK (low)2.00.0100
190.13501.049175-2.2299610.1316120.676758NoneOK (high)0.50.0200
200.15500.514133-1.0401850.0119190.149607NoneOK (low)2.00.0100
210.16501.009608-2.016529-0.0026860.565078NoneOK (stay)1.00.0200
220.18500.968793-1.895860-0.0417270.503843NoneOK (stay)1.00.0200
230.20500.923365-1.787115-0.0596150.449993NoneOK (stay)1.00.0200
240.22500.876892-1.687042-0.0667420.402167NoneOK (stay)1.00.0200
250.24500.831138-1.593829-0.0684470.359529NoneOK (stay)1.00.0200
260.26500.786941-1.506413-0.0674690.321456NoneOK (stay)1.00.0200
270.28500.744668-1.424124-0.0652120.287435NoneOK (stay)1.00.0200
280.30500.704447-1.346502-0.0623930.257023NoneOK (stay)1.00.0200
290.32500.666287-1.273199-0.0593740.229833NoneOK (low)2.00.0200
300.34501.260271-2.407864-0.1126770.822088NoneOK (high)0.50.0400
310.38500.561696-1.072982-0.0504110.163259NoneOK (low)2.00.0200
320.40501.062325-2.029304-0.0953470.583967NoneOK (stay)1.00.0400
330.44500.946828-1.808671-0.0849860.463888NoneOK (stay)1.00.0400
340.48500.843887-1.612027-0.0757460.368501NoneOK (stay)1.00.0400
350.52500.752137-1.436763-0.0675110.292728NoneOK (stay)1.00.0400
360.56500.670362-1.280554-0.0601710.232536NoneOK (low)2.00.0400
370.60501.194957-2.282657-0.1072580.738883NoneOK (high)0.50.0800
380.68500.467560-0.893151-0.0419680.113121NoneOK (low)2.00.0400
390.72500.833450-1.592091-0.0748100.359443NoneOK (stay)1.00.0800
400.80500.652220-1.245898-0.0585430.220120NoneOK (low)2.00.0800
410.88501.020795-1.949965-0.0916250.539198NoneOK (stay)1.00.1600
421.04500.576860-1.101942-0.0517780.172192NoneOK (low)2.00.1600
431.20500.651978-1.245434-0.0585210.219956NoneOK (low)2.00.3200
441.52500.169798-0.324354-0.0152410.014919NoneOK (low)2.00.6400
\n", "
" ], "text/plain": [ " START_TIME Delta U Delta X Delta S norm_A norm_B action \\\n", "0 0.0000 -1.000000 -3.000000 5.000000 3.888889 None ABORT \n", "1 0.0000 -0.500000 -1.500000 2.500000 0.972222 None OK (high) \n", "2 0.0050 -0.197500 -0.701250 1.096250 0.192502 None OK (low) \n", "3 0.0075 -0.349175 -1.359094 2.057444 0.689126 None OK (high) \n", "4 0.0125 -0.131693 -0.638492 0.901878 0.137600 None OK (low) \n", "5 0.0150 -0.225993 -1.240350 1.692337 0.494839 None OK (stay) \n", "6 0.0200 -0.156040 -1.170975 1.483055 0.399443 None OK (stay) \n", "7 0.0250 -0.095157 -1.108919 1.299234 0.325196 None OK (stay) \n", "8 0.0300 -0.042237 -1.053308 1.137781 0.267310 None OK (stay) \n", "9 0.0350 0.003697 -1.003375 0.995981 0.222084 None OK (low) \n", "10 0.0400 0.086999 -1.916890 1.742892 0.746634 None OK (high) \n", "11 0.0500 0.112345 -0.877405 0.652715 0.134277 None OK (low) \n", "12 0.0550 0.274660 -1.689324 1.140004 0.469874 None OK (stay) \n", "13 0.0650 0.360367 -1.570244 0.849510 0.368578 None OK (stay) \n", "14 0.0750 0.421121 -1.472167 0.629925 0.304602 None OK (stay) \n", "15 0.0850 0.463092 -1.390206 0.464021 0.262494 None OK (stay) \n", "16 0.0950 0.490952 -1.320659 0.338754 0.233325 None OK (low) \n", "17 0.1050 1.016467 -2.521427 0.488493 0.847714 None OK (high) \n", "18 0.1250 0.526984 -1.155761 0.101794 0.180429 None OK (low) \n", "19 0.1350 1.049175 -2.229961 0.131612 0.676758 None OK (high) \n", "20 0.1550 0.514133 -1.040185 0.011919 0.149607 None OK (low) \n", "21 0.1650 1.009608 -2.016529 -0.002686 0.565078 None OK (stay) \n", "22 0.1850 0.968793 -1.895860 -0.041727 0.503843 None OK (stay) \n", "23 0.2050 0.923365 -1.787115 -0.059615 0.449993 None OK (stay) \n", "24 0.2250 0.876892 -1.687042 -0.066742 0.402167 None OK (stay) \n", "25 0.2450 0.831138 -1.593829 -0.068447 0.359529 None OK (stay) \n", "26 0.2650 0.786941 -1.506413 -0.067469 0.321456 None OK (stay) \n", "27 0.2850 0.744668 -1.424124 -0.065212 0.287435 None OK (stay) \n", "28 0.3050 0.704447 -1.346502 -0.062393 0.257023 None OK (stay) \n", "29 0.3250 0.666287 -1.273199 -0.059374 0.229833 None OK (low) \n", "30 0.3450 1.260271 -2.407864 -0.112677 0.822088 None OK (high) \n", "31 0.3850 0.561696 -1.072982 -0.050411 0.163259 None OK (low) \n", "32 0.4050 1.062325 -2.029304 -0.095347 0.583967 None OK (stay) \n", "33 0.4450 0.946828 -1.808671 -0.084986 0.463888 None OK (stay) \n", "34 0.4850 0.843887 -1.612027 -0.075746 0.368501 None OK (stay) \n", "35 0.5250 0.752137 -1.436763 -0.067511 0.292728 None OK (stay) \n", "36 0.5650 0.670362 -1.280554 -0.060171 0.232536 None OK (low) \n", "37 0.6050 1.194957 -2.282657 -0.107258 0.738883 None OK (high) \n", "38 0.6850 0.467560 -0.893151 -0.041968 0.113121 None OK (low) \n", "39 0.7250 0.833450 -1.592091 -0.074810 0.359443 None OK (stay) \n", "40 0.8050 0.652220 -1.245898 -0.058543 0.220120 None OK (low) \n", "41 0.8850 1.020795 -1.949965 -0.091625 0.539198 None OK (stay) \n", "42 1.0450 0.576860 -1.101942 -0.051778 0.172192 None OK (low) \n", "43 1.2050 0.651978 -1.245434 -0.058521 0.219956 None OK (low) \n", "44 1.5250 0.169798 -0.324354 -0.015241 0.014919 None OK (low) \n", "\n", " step_factor time_step caption \n", "0 0.5 0.0100 excessive norm value(s) \n", "1 0.5 0.0050 \n", "2 2.0 0.0025 \n", "3 0.5 0.0050 \n", "4 2.0 0.0025 \n", "5 1.0 0.0050 \n", "6 1.0 0.0050 \n", "7 1.0 0.0050 \n", "8 1.0 0.0050 \n", "9 2.0 0.0050 \n", "10 0.5 0.0100 \n", "11 2.0 0.0050 \n", "12 1.0 0.0100 \n", "13 1.0 0.0100 \n", "14 1.0 0.0100 \n", "15 1.0 0.0100 \n", "16 2.0 0.0100 \n", "17 0.5 0.0200 \n", "18 2.0 0.0100 \n", "19 0.5 0.0200 \n", "20 2.0 0.0100 \n", "21 1.0 0.0200 \n", "22 1.0 0.0200 \n", "23 1.0 0.0200 \n", "24 1.0 0.0200 \n", "25 1.0 0.0200 \n", "26 1.0 0.0200 \n", "27 1.0 0.0200 \n", "28 1.0 0.0200 \n", "29 2.0 0.0200 \n", "30 0.5 0.0400 \n", "31 2.0 0.0200 \n", "32 1.0 0.0400 \n", "33 1.0 0.0400 \n", "34 1.0 0.0400 \n", "35 1.0 0.0400 \n", "36 2.0 0.0400 \n", "37 0.5 0.0800 \n", "38 2.0 0.0400 \n", "39 1.0 0.0800 \n", "40 2.0 0.0800 \n", "41 1.0 0.1600 \n", "42 2.0 0.1600 \n", "43 2.0 0.3200 \n", "44 2.0 0.6400 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_decisions_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 value of `norm_A`" ] }, { "cell_type": "code", "execution_count": null, "id": "c9469a67-c513-492a-8bff-a20d0958ba39", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "id": "a479c269-4740-4866-9ec3-e736b8b09cb6", "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
START_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": [ " START_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": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_decisions_data_ALT() # TODO: OBSOLETE!" ] }, { "cell_type": "code", "execution_count": null, "id": "94832b6d", "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 }