{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "## Exploration of variable time steps in the simulation of the 2 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: June 23, 2024 (using v. 1.0 beta36)" ] }, { "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 life123 import ChemData as chem\n", "from life123 import UniformCompartment\n", "\n", "import numpy as np\n", "from life123 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_2\"],\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 = -3,436.6 / K = 4) | 1st order in all reactants & products\n", "1: S <-> X (kF = 6 / kR = 3 / delta_G = -1,718.3 / K = 2) | 1st order in all reactants & products\n", "Set of chemicals involved in the above reactions: {'S', 'U', 'X'}\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\", 1)], 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", "chem_data.plot_reaction_network(\"vue_cytoscape_2\")" ] }, { "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", "Set of chemicals involved in reactions: {'S', 'U', 'X'}\n" ] } ], "source": [ "dynamics = UniformCompartment(chem_data=chem_data, preset=None)\n", "dynamics.set_conc(conc={\"U\": 50., \"X\": 100.})\n", "dynamics.describe_state()" ] }, { "cell_type": "code", "execution_count": 6, "id": "5480fb93-fb31-4269-99a0-72e0c6bcd944", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters used for the automated adaptive time step sizes -\n", " THRESHOLDS: [{'norm': 'norm_A', 'low': 0.25, 'high': 0.64, 'abort': 1.44}]\n", " STEP FACTORS: {'upshift': 2.0, 'downshift': 0.5, 'abort': 0.5, 'error': 0.5}\n" ] } ], "source": [ "dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n", "\n", "# All of these settings are typically managed by a preset... but set explitly here for demonstration of low-level control\n", "# Here we're setting just \"norm_A\" (a measure of concentration changes across a single step), but typically multiple norms are used\n", "dynamics.set_thresholds(norm=\"norm_A\", low=0.25, high=0.64, abort=1.44)\n", "dynamics.set_step_factors(upshift=2.0, downshift=0.5, abort=0.5, error=0.5) # Note: upshift=2.0 seems to often be excessive; smaller values are recommented\n", "\n", "dynamics.show_adaptive_parameters()" ] }, { "cell_type": "code", "execution_count": 7, "id": "08082b84-eb83-4970-b5a0-117f100578ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "(STEP 0 aborted) SYSTEM TIME 0 : Examining Conc. changes due to tentative Δt=0.01 ...\n", " Previous: None\n", " Baseline: [ 50. 100. 0.]\n", " Deltas: [-1. -3. 5.]\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, 'error': 0.5}\n", " => Action: 'ABORT' (with step size factor of 0.5)\n", " * INFO: the tentative time step (0.01) leads to a value of ['norm_A'] > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER Δt, x0.5 (now set to 0.005) [Step started at t=0, and will rewind there]\n", "\n", "(STEP 0 completed) SYSTEM TIME 0 : Examining Conc. changes due to tentative Δt=0.005 ...\n", " Previous: None\n", " Baseline: [ 50. 100. 0.]\n", " Deltas: [-0.5 -1.5 2.5]\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, 'error': 0.5}\n", " => Action: 'HIGH' (with step size factor of 0.5)\n", " INFO: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.0025) at the next round, because ['norm_A'] is high\n", " [The current step started at System Time: 0, and will continue to 0.005]\n", "\n", "(STEP 1 completed) SYSTEM TIME 0.005 : Examining Conc. changes due to tentative Δt=0.0025 ...\n", " Previous: [ 50. 100. 0.]\n", " Baseline: [49.5 98.5 2.5]\n", " Deltas: [-0.1975 -0.70125 1.09625]\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, 'error': 0.5}\n", " => Action: 'LOW' (with step size factor of 2.0)\n", " INFO: 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 completed) SYSTEM TIME 0.0075 : Examining Conc. changes due to tentative Δt=0.005 ...\n", " Previous: [49.5 98.5 2.5]\n", " Baseline: [49.3025 97.79875 3.59625]\n", " Deltas: [-0.349175 -1.35909375 2.05744375]\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, 'error': 0.5}\n", " => Action: 'HIGH' (with step size factor of 0.5)\n", " INFO: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.0025) at the next round, because ['norm_A'] is high\n", " [The current step started at System Time: 0.0075, and will continue to 0.0125]\n", "\n", "(STEP 3 completed) SYSTEM TIME 0.0125 : Examining Conc. changes due to tentative Δt=0.0025 ...\n", " Previous: [49.3025 97.79875 3.59625]\n", " Baseline: [48.953325 96.43965625 5.65369375]\n", " Deltas: [-0.13169275 -0.63849202 0.90187752]\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, 'error': 0.5}\n", " => Action: 'LOW' (with step size factor of 2.0)\n", " INFO: 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 completed) SYSTEM TIME 0.015 : Examining Conc. changes due to tentative Δt=0.005 ...\n", " Previous: [48.953325 96.43965625 5.65369375]\n", " Baseline: [48.82163225 95.80116423 6.55557127]\n", " Deltas: [-0.22599347 -1.24035033 1.69233727]\n", " Norms: {'norm_A': 0.494838601385062}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.49484) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.02 : Examining Conc. changes due to tentative Δt=0.005 ...\n", " Previous: [48.82163225 95.80116423 6.55557127]\n", " Baseline: [48.59563878 94.56081391 8.24790853]\n", " Deltas: [-0.15604005 -1.17097495 1.48305505]\n", " Norms: {'norm_A': 0.3994425670227834}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.39944) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.025 : Examining Conc. changes due to tentative Δt=0.005 ...\n", " Previous: [48.59563878 94.56081391 8.24790853]\n", " Baseline: [48.43959873 93.38983896 9.73096358]\n", " Deltas: [-0.09515744 -1.10891868 1.29923357]\n", " Norms: {'norm_A': 0.32519593644806855}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.3252) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.03 : Examining Conc. changes due to tentative Δt=0.005 ...\n", " Previous: [48.43959873 93.38983896 9.73096358]\n", " Baseline: [48.34444129 92.28092028 11.03019715]\n", " Deltas: [-0.04223653 -1.05330789 1.13778094]\n", " Norms: {'norm_A': 0.2673096568217399}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.26731) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.035 : Examining Conc. changes due to tentative Δt=0.005 ...\n", " Previous: [48.34444129 92.28092028 11.03019715]\n", " Baseline: [48.30220476 91.22761239 12.16797809]\n", " Deltas: [ 0.00369708 -1.00337484 0.99598069]\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, 'error': 0.5}\n", " => Action: 'LOW' (with step size factor of 2.0)\n", " INFO: 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 completed) SYSTEM TIME 0.04 : Examining Conc. changes due to tentative Δt=0.01 ...\n", " Previous: [48.30220476 91.22761239 12.16797809]\n", " Baseline: [48.30590184 90.22423755 13.16395878]\n", " Deltas: [ 0.08699867 -1.9168896 1.74289227]\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, 'error': 0.5}\n", " => Action: 'HIGH' (with step size factor of 0.5)\n", " INFO: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.005) at the next round, because ['norm_A'] is high\n", " [The current step started at System Time: 0.04, and will continue to 0.05]\n", "\n", "(STEP 10 completed) SYSTEM TIME 0.05 : Examining Conc. changes due to tentative Δt=0.005 ...\n", " Previous: [48.30590184 90.22423755 13.16395878]\n", " Baseline: [48.3929005 88.30734795 14.90685105]\n", " Deltas: [ 0.11234504 -0.87740469 0.65271461]\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, 'error': 0.5}\n", " => Action: 'LOW' (with step size factor of 2.0)\n", " INFO: 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 completed) SYSTEM TIME 0.055 : Examining Conc. changes due to tentative Δt=0.01 ...\n", " Previous: [48.3929005 88.30734795 14.90685105]\n", " Baseline: [48.50524554 87.42994326 15.55956566]\n", " Deltas: [ 0.27466034 -1.68932436 1.14000367]\n", " Norms: {'norm_A': 0.4698737184351335}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.46987) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.065 : Examining Conc. changes due to tentative Δt=0.01 ...\n", " Previous: [48.50524554 87.42994326 15.55956566]\n", " Baseline: [48.77990588 85.7406189 16.69956934]\n", " Deltas: [ 0.36036743 -1.57024441 0.84950955]\n", " Norms: {'norm_A': 0.3685776282283221}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.36858) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.075 : Examining Conc. changes due to tentative Δt=0.01 ...\n", " Previous: [48.77990588 85.7406189 16.69956934]\n", " Baseline: [49.14027331 84.17037449 17.54907888]\n", " Deltas: [ 0.42112084 -1.4721665 0.62992481]\n", " Norms: {'norm_A': 0.3046024715786003}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.3046) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.085 : Examining Conc. changes due to tentative Δt=0.01 ...\n", " Previous: [49.14027331 84.17037449 17.54907888]\n", " Baseline: [49.56139416 82.69820799 18.1790037 ]\n", " Deltas: [ 0.46309241 -1.39020602 0.46402119]\n", " Norms: {'norm_A': 0.2624936691312418}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.26249) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.095 : Examining Conc. changes due to tentative Δt=0.01 ...\n", " Previous: [49.56139416 82.69820799 18.1790037 ]\n", " Baseline: [50.02448657 81.30800197 18.64302489]\n", " Deltas: [ 0.49095226 -1.32065857 0.33875405]\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, 'error': 0.5}\n", " => Action: 'LOW' (with step size factor of 2.0)\n", " INFO: 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 completed) SYSTEM TIME 0.105 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [50.02448657 81.30800197 18.64302489]\n", " Baseline: [50.51543883 79.98734341 18.98177894]\n", " Deltas: [ 1.01646708 -2.52142713 0.48849298]\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, 'error': 0.5}\n", " => Action: 'HIGH' (with step size factor of 0.5)\n", " INFO: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.01) at the next round, because ['norm_A'] is high\n", " [The current step started at System Time: 0.105, and will continue to 0.125]\n", "\n", "(STEP 17 completed) SYSTEM TIME 0.125 : Examining Conc. changes due to tentative Δt=0.01 ...\n", " Previous: [50.51543883 79.98734341 18.98177894]\n", " Baseline: [51.5319059 77.46591628 19.47027191]\n", " Deltas: [ 0.52698364 -1.15576117 0.1017939 ]\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, 'error': 0.5}\n", " => Action: 'LOW' (with step size factor of 2.0)\n", " INFO: 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 completed) SYSTEM TIME 0.135 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [51.5319059 77.46591628 19.47027191]\n", " Baseline: [52.05888954 76.3101551 19.57206582]\n", " Deltas: [ 1.04917495 -2.22996141 0.13161151]\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, 'error': 0.5}\n", " => Action: 'HIGH' (with step size factor of 0.5)\n", " INFO: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.01) at the next round, because ['norm_A'] is high\n", " [The current step started at System Time: 0.135, and will continue to 0.155]\n", "\n", "(STEP 19 completed) SYSTEM TIME 0.155 : Examining Conc. changes due to tentative Δt=0.01 ...\n", " Previous: [52.05888954 76.3101551 19.57206582]\n", " Baseline: [53.10806449 74.08019369 19.70367733]\n", " Deltas: [ 0.5141329 -1.04018517 0.01191938]\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, 'error': 0.5}\n", " => Action: 'LOW' (with step size factor of 2.0)\n", " INFO: 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 completed) SYSTEM TIME 0.165 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [53.10806449 74.08019369 19.70367733]\n", " Baseline: [53.62219739 73.04000852 19.71559671]\n", " Deltas: [ 1.00960758 -2.01652891 -0.00268625]\n", " Norms: {'norm_A': 0.5650781675774635}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.56508) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.185 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [53.62219739 73.04000852 19.71559671]\n", " Baseline: [54.63180496 71.02347962 19.71291046]\n", " Deltas: [ 0.96879347 -1.89585952 -0.04172743]\n", " Norms: {'norm_A': 0.5038428113796767}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.50384) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.205 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [54.63180496 71.02347962 19.71291046]\n", " Baseline: [55.60059844 69.12762009 19.67118303]\n", " Deltas: [ 0.92336535 -1.78711524 -0.05961545]\n", " Norms: {'norm_A': 0.4499931616991674}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.44999) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.225 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [55.60059844 69.12762009 19.67118303]\n", " Baseline: [56.52396378 67.34050485 19.61156758]\n", " Deltas: [ 0.87689226 -1.68704218 -0.06674234]\n", " Norms: {'norm_A': 0.4021673223106198}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.40217) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.245 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [56.52396378 67.34050485 19.61156758]\n", " Baseline: [57.40085605 65.65346267 19.54482524]\n", " Deltas: [ 0.8311378 -1.59382873 -0.06844686]\n", " Norms: {'norm_A': 0.3595294483481833}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.35953) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.265 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [57.40085605 65.65346267 19.54482524]\n", " Baseline: [58.23199384 64.05963394 19.47637838]\n", " Deltas: [ 0.78694079 -1.50641263 -0.06746894]\n", " Norms: {'norm_A': 0.32145631944741265}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.32146) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.285 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [58.23199384 64.05963394 19.47637838]\n", " Baseline: [59.01893463 62.55322131 19.40890943]\n", " Deltas: [ 0.74466812 -1.42412415 -0.0652121 ]\n", " Norms: {'norm_A': 0.2874347575511702}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.28743) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.305 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [59.01893463 62.55322131 19.40890943]\n", " Baseline: [59.76360275 61.12909716 19.34369733]\n", " Deltas: [ 0.70444746 -1.34650215 -0.06239278]\n", " Norms: {'norm_A': 0.25702301402562294}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.25702) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.325 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [59.76360275 61.12909716 19.34369733]\n", " Baseline: [60.46805022 59.78259501 19.28130456]\n", " Deltas: [ 0.66628672 -1.27319915 -0.05937429]\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, 'error': 0.5}\n", " => Action: 'LOW' (with step size factor of 2.0)\n", " INFO: 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 completed) SYSTEM TIME 0.345 : Examining Conc. changes due to tentative Δt=0.04 ...\n", " Previous: [60.46805022 59.78259501 19.28130456]\n", " Baseline: [61.13433694 58.50939586 19.22193027]\n", " Deltas: [ 1.26027073 -2.40786424 -0.11267722]\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, 'error': 0.5}\n", " => Action: 'HIGH' (with step size factor of 0.5)\n", " INFO: COMPLETE STEP NORMALLY and MAKE THE INTERVAL SMALLER, multiplied by 0.5 (set to 0.02) at the next round, because ['norm_A'] is high\n", " [The current step started at System Time: 0.345, and will continue to 0.385]\n", "\n", "(STEP 30 completed) SYSTEM TIME 0.385 : Examining Conc. changes due to tentative Δt=0.02 ...\n", " Previous: [61.13433694 58.50939586 19.22193027]\n", " Baseline: [62.39460767 56.10153162 19.10925305]\n", " Deltas: [ 0.56169618 -1.07298153 -0.05041083]\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, 'error': 0.5}\n", " => Action: 'LOW' (with step size factor of 2.0)\n", " INFO: 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 completed) SYSTEM TIME 0.405 : Examining Conc. changes due to tentative Δt=0.04 ...\n", " Previous: [62.39460767 56.10153162 19.10925305]\n", " Baseline: [62.95630385 55.02855009 19.05884222]\n", " Deltas: [ 1.0623252 -2.02930388 -0.09534652]\n", " Norms: {'norm_A': 0.5839666694455721}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.58397) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.445 : Examining Conc. changes due to tentative Δt=0.04 ...\n", " Previous: [62.95630385 55.02855009 19.05884222]\n", " Baseline: [64.01862905 52.99924621 18.96349569]\n", " Deltas: [ 0.9468283 -1.80867058 -0.08498602]\n", " Norms: {'norm_A': 0.4638884123583411}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.46389) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. 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 completed) SYSTEM TIME 0.485 : Examining Conc. changes due to tentative Δt=0.04 ...\n", " Previous: [64.01862905 52.99924621 18.96349569]\n", " Baseline: [64.96545735 51.19057563 18.87850968]\n", " Deltas: [ 0.84388651 -1.61202675 -0.07574626]\n", " Norms: {'norm_A': 0.36850135438022225}\n", " Thresholds: \n", " norm_A : low 0.25 | (VALUE 0.3685) | high 0.64 | abort 1.44\n", " => Action: 'STAY' (with step size factor of 1)\n", " INFO: COMPLETE NORMALLY - we're inside the target range of all norms. No change to step size.\n", " [The current step started at System Time: 0.485, and will continue to 0.525]\n", "44 total step(s) taken\n", "Number of step re-do's because of negative concentrations: 0\n", "Number of step re-do's because of elective soft aborts: 1\n", "Norm usage: {'norm_A': 23, 'norm_B': 15, 'norm_C': 15, 'norm_D': 15}\n" ] } ], "source": [ "dynamics.single_compartment_react(initial_step=0.01, target_end_time=2.0, \n", " variable_steps=True, explain_variable_steps=[0, 0.5]) # Detailed printout for the early steps" ] }, { "cell_type": "code", "execution_count": 8, "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
00.000050.000000100.0000000.000000Initialized 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 Initialized 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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_history()" ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(step_sizes)" ] }, { "cell_type": "code", "execution_count": 11, "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": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(transition_times) # Note: there will be 1 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": 12, "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_history(colors=['green', 'orange', 'blue'])" ] }, { "cell_type": "code", "execution_count": 13, "id": "2f07ad6b-a1c9-4d99-8108-72b16727303d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.3183157284824908, 60.23261431038145)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.curve_intersect(\"U\", \"X\", t_start=0.3, t_end=0.35) # Compare with the value from experiment \"variable_steps_2\"" ] }, { "cell_type": "code", "execution_count": 14, "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": [ -0.0012646028037383177, 2.1662646028037384 ], "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_history(colors=['green', 'orange', 'blue'], show_intervals=True)" ] }, { "cell_type": "code", "execution_count": 15, "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": [ -0.0012646028037383177, 2.1662646028037384 ], "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_history(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 plot immediatly above 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": 16, "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": [ -0.0012371428571428572, 2.1662371428571428 ], "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": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_step_sizes(show_intervals=True)" ] }, { "cell_type": "code", "execution_count": 17, "id": "3d012f8e-4066-40b6-9b9a-d1e9dd7532c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: 2 S <-> U\n", "Final concentrations: [S] = 18.18 ; [U] = 72.8\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\n", "Discrepancy between the two values: 0.1349 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n", "1: S <-> X\n", "Final concentrations: [S] = 18.18 ; [X] = 36.23\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\n", "Discrepancy between the two values: 0.3437 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.is_in_equilibrium()" ] }, { "cell_type": "code", "execution_count": 18, "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": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_rxn_data(rxn_index=0)" ] }, { "cell_type": "code", "execution_count": 19, "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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_rxn_data(rxn_index=1)" ] }, { "cell_type": "code", "execution_count": 20, "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": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_conc_data()" ] }, { "cell_type": "code", "execution_count": 21, "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": 21, "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": 22, "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": 22, "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 }