{ "cells": [ { "cell_type": "markdown", "id": "3bbe8002-bdf3-490c-bde0-80dd3713a3d0", "metadata": {}, "source": [ "## Association/Dissociation reaction `A + B <-> C`\n", "#### with 1st-order kinetics for each species, taken to equilibrium.\n", "#### Exploration of debugging and diagnostics options\n", "(Adaptive variable time steps are used)\n", "\n", "_See also the experiment \"1D/reactions/reaction_4\"_ \n", "\n", "LAST REVISED: June 4, 2023" ] }, { "cell_type": "code", "execution_count": 1, "id": "13e55c1d-609f-4bf0-a004-6c45bcfcbc99", "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": "bdad128a-9214-46f5-aeb9-a7b77c81aa3e", "metadata": { "tags": [] }, "outputs": [], "source": [ "from experiments.get_notebook_info import get_notebook_basename\n", "\n", "from src.modules.reactions.reaction_data import ChemData as chem\n", "from src.modules.reactions.reaction_dynamics import ReactionDynamics\n", "\n", "import numpy as np\n", "import plotly.express as px\n", "from src.modules.visualization.graphic_log import GraphicLog" ] }, { "cell_type": "code", "execution_count": 3, "id": "83c3cc5f-de21-4f66-9988-2806fbf0666d", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-> Output will be LOGGED into the file 'react_3.log.htm'\n" ] } ], "source": [ "# Initialize the HTML logging (for the graphics)\n", "log_file = get_notebook_basename() + \".log.htm\" # Use the notebook base filename for the log file\n", "\n", "# Set up the use of some specified graphic (Vue) components\n", "GraphicLog.config(filename=log_file,\n", " components=[\"vue_cytoscape_1\"],\n", " extra_js=\"https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.21.2/cytoscape.umd.js\")" ] }, { "cell_type": "markdown", "id": "9329208b-070f-4902-8f37-0f11ddf75ed6", "metadata": {}, "source": [ "# Initialize the System\n", "Specify the chemicals and the reactions" ] }, { "cell_type": "code", "execution_count": 4, "id": "72b4245c-de4e-480d-a501-3495b7ed8bc4", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 1 (at temp. 25 C)\n", "0: A + B <-> C (kF = 5 / kR = 2 / Delta_G = -2,271.45 / K = 2.5) | 1st order in all reactants & products\n" ] } ], "source": [ "# Specify the chemicals\n", "chem_data = chem(names=[\"A\", \"B\", \"C\"])\n", "\n", "# Reaction A + B <-> C , with 1st-order kinetics for each species\n", "chem_data.add_reaction(reactants=[\"A\" , \"B\"], products=[\"C\"],\n", " forward_rate=5., reverse_rate=2.)\n", "\n", "chem_data.describe_reactions()" ] }, { "cell_type": "code", "execution_count": 5, "id": "cb582868-431c-4022-aa0e-a2f554f80d6c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[GRAPHIC ELEMENT SENT TO LOG FILE `react_3.log.htm`]\n" ] } ], "source": [ "# Send a plot of the network of reactions to the HTML log file\n", "graph_data = chem_data.prepare_graph_network()\n", "GraphicLog.export_plot(graph_data, \"vue_cytoscape_1\")" ] }, { "cell_type": "markdown", "id": "98a9fbe5-2090-4d38-9c5f-94fbf7c3eae2", "metadata": {}, "source": [ "# Start the simulation" ] }, { "cell_type": "code", "execution_count": 6, "id": "c2f4a554-807b-49f9-8ca2-8d929fe6eeef", "metadata": {}, "outputs": [], "source": [ "dynamics = ReactionDynamics(reaction_data=chem_data)" ] }, { "cell_type": "code", "execution_count": 7, "id": "ae304704-c8d9-4cef-9e0b-2587bb3909ef", "metadata": {}, "outputs": [], "source": [ "# Initial concentrations of all the chemicals, in index order\n", "dynamics.set_conc([10., 50., 20.], snapshot=True)" ] }, { "cell_type": "code", "execution_count": 8, "id": "a605dacf-2c67-403e-9aa9-5be25fc9f481", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "3 species:\n", " Species 0 (A). Conc: 10.0\n", " Species 1 (B). Conc: 50.0\n", " Species 2 (C). Conc: 20.0\n" ] } ], "source": [ "dynamics.describe_state()" ] }, { "cell_type": "code", "execution_count": 9, "id": "0ff2c242-a15b-456d-ad56-0ba1041c0b4c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SYSTEM TIMEABCcaption
00.010.050.020.0Initial state
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" ], "text/plain": [ " SYSTEM TIME A B C caption\n", "0 0.0 10.0 50.0 20.0 Initial state" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_history()" ] }, { "cell_type": "markdown", "id": "fc516ca2-e62d-4784-b826-5372ff7f4c75", "metadata": { "tags": [] }, "source": [ "## Run the reaction" ] }, { "cell_type": "code", "execution_count": 10, "id": "2502cd11-0df9-4303-8895-98401a1df7b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: the tentative time step (0.004) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.002) [Step started at t=0, and will rewind there]\n", "INFO: the tentative time step (0.002) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.001) [Step started at t=0, and will rewind there]\n", "INFO: the tentative time step (0.001) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.0005) [Step started at t=0, and will rewind there]\n", "Some steps were backtracked and re-done, to prevent negative concentrations or excessively large concentration changes\n", "39 total step(s) taken\n" ] } ], "source": [ "dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n", "\n", "# All of these settings are currently close to the default values... but subject to change; set for repeatability\n", "dynamics.set_thresholds(norm=\"norm_A\", low=0.5, high=0.8, abort=1.44)\n", "dynamics.set_thresholds(norm=\"norm_B\", low=0.08, high=0.5, abort=1.5)\n", "dynamics.set_step_factors(upshift=1.5, downshift=0.5, abort=0.5)\n", "dynamics.set_error_step_factor(0.5)\n", "\n", "dynamics.single_compartment_react(initial_step=0.004, reaction_duration=0.06,\n", " variable_steps=True, explain_variable_steps=False,\n", " snapshots={\"initial_caption\": \"1st reaction step\",\n", " \"final_caption\": \"last reaction step\"})" ] }, { "cell_type": "code", "execution_count": 11, "id": "80fbaee3-bd6f-4197-9270-23374d46a4a7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SYSTEM TIMEABCcaption
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_curves(colors=['red', 'violet', 'green'], show_intervals=True)" ] }, { "cell_type": "markdown", "id": "c02a8f55-a671-4771-86c9-fc4d1b126bf8", "metadata": {}, "source": [ "### Check the final equilibrium" ] }, { "cell_type": "code", "execution_count": 15, "id": "765f6f39-4b2e-4a86-b6a9-ace9d1941663", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A + B <-> C\n", "Final concentrations: [C] = 29.7 ; [A] = 0.2979 ; [B] = 40.3\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 2.47387\n", " Formula used: [C] / ([A][B])\n", "2. Ratio of forward/reverse reaction rates: 2.5\n", "Discrepancy between the two values: 1.045 %\n", "Reaction IS in equilibrium (within 2% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "dynamics.is_in_equilibrium(tolerance=2)" ] }, { "cell_type": "code", "execution_count": null, "id": "d28567a7-ae12-4df6-bf2f-8ea7b132596c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "5c747824-a170-439e-96a5-dd35bc81e08b", "metadata": {}, "source": [ "# Everthing below is just for diagnostic insight \n", "### into the adaptive variable time steps" ] }, { "cell_type": "code", "execution_count": 16, "id": "06c8c7e9-edd9-45e1-b4a0-508f0b379c42", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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START_TIMEDelta ADelta BDelta Cnorm_Anorm_Bactionstep_factortime_stepcaption
00.000000-9.840000-9.8400009.8400003.227520e+01NaNABORT0.50.004000excessive norm value(s)
10.000000-4.920000-4.9200004.9200008.068800e+00NaNABORT0.50.002000excessive norm value(s)
20.000000-2.460000-2.4600002.4600002.017200e+00NaNABORT0.50.001000excessive norm value(s)
30.000000-1.230000-1.2300001.2300005.043000e-010.123000OK (stay)1.00.000500
40.000500-1.048052-1.0480521.0480523.661378e-010.119504OK (stay)1.00.000500
50.001000-0.898988-0.8989880.8989882.693931e-010.116420OK (stay)1.00.000500
60.001500-0.775501-0.7755010.7755012.004672e-010.113660OK (stay)1.00.000500
70.002000-0.672223-0.6722230.6722231.506278e-010.111158OK (stay)1.00.000500
80.002500-0.585132-0.5851320.5851321.141264e-010.108857OK (stay)1.00.000500
90.003000-0.511163-0.5111630.5111638.709597e-020.106712OK (stay)1.00.000500
100.003500-0.447946-0.4479460.4479466.688532e-020.104686OK (stay)1.00.000500
110.004000-0.393622-0.3936220.3936225.164603e-020.102747OK (stay)1.00.000500
120.004500-0.346714-0.3467140.3467144.007008e-020.100866OK (stay)1.00.000500
130.005000-0.306037-0.3060370.3060373.121956e-020.099020OK (stay)1.00.000500
140.005500-0.270632-0.2706320.2706322.441392e-020.097188OK (stay)1.00.000500
150.006000-0.239713-0.2397130.2397131.915417e-020.095352OK (stay)1.00.000500
160.006500-0.212633-0.2126330.2126331.507090e-020.093495OK (stay)1.00.000500
170.007000-0.188852-0.1888520.1888521.188836e-020.091603OK (stay)1.00.000500
180.007500-0.167920-0.1679200.1679209.399084e-030.089663OK (stay)1.00.000500
190.008000-0.149459-0.1494590.1494597.445949e-030.087666OK (stay)1.00.000500
200.008500-0.133145-0.1331450.1331455.909198e-030.085601OK (stay)1.00.000500
210.009000-0.118706-0.1187060.1187064.697054e-030.083463OK (stay)1.00.000500
220.009500-0.105908-0.1059080.1059083.738831e-030.081245OK (stay)1.00.000500
230.010000-0.094549-0.0945490.0945492.979837e-030.078945OK (low)1.50.000500
240.010500-0.126684-0.1266840.1266845.349575e-030.114843OK (stay)1.00.000750
250.011250-0.106503-0.1065030.1065033.780970e-030.109075OK (stay)1.00.000750
260.012000-0.089630-0.0896300.0896302.677874e-030.103033OK (stay)1.00.000750
270.012750-0.075497-0.0754970.0754971.899922e-030.096755OK (stay)1.00.000750
280.013500-0.063639-0.0636390.0636391.349957e-030.090294OK (stay)1.00.000750
290.014250-0.053676-0.0536760.0536769.603761e-040.083718OK (stay)1.00.000750
300.015000-0.045297-0.0452970.0452976.839364e-040.077104OK (low)1.50.000750
310.015750-0.057364-0.0573640.0573641.096869e-030.105802OK (stay)1.00.001125
320.016875-0.043996-0.0439960.0439966.452305e-040.090749OK (stay)1.00.001125
330.018000-0.033769-0.0337690.0337693.801200e-040.076606OK (low)1.50.001125
340.019125-0.038901-0.0389010.0389015.044334e-040.095569OK (stay)1.00.001688
350.020813-0.025386-0.0253860.0253862.148211e-040.068957OK (low)1.50.001688
360.022500-0.024871-0.0248710.0248712.061837e-040.072560OK (low)1.50.002531
370.025031-0.017919-0.0179190.0179191.070309e-040.056368OK (low)1.50.003797
380.028828-0.005948-0.0059480.0059481.179398e-050.019829OK (low)1.50.005695
390.0345230.0014930.001493-0.0014937.433416e-070.005079OK (low)1.50.008543
400.043066-0.001682-0.0016820.0016829.429553e-070.005691OK (low)1.50.012814
410.0558810.0041030.004103-0.0041035.611315e-060.013963OK (low)1.50.019222
\n", "
" ], "text/plain": [ " START_TIME Delta A Delta B Delta C norm_A norm_B \\\n", "0 0.000000 -9.840000 -9.840000 9.840000 3.227520e+01 NaN \n", "1 0.000000 -4.920000 -4.920000 4.920000 8.068800e+00 NaN \n", "2 0.000000 -2.460000 -2.460000 2.460000 2.017200e+00 NaN \n", "3 0.000000 -1.230000 -1.230000 1.230000 5.043000e-01 0.123000 \n", "4 0.000500 -1.048052 -1.048052 1.048052 3.661378e-01 0.119504 \n", "5 0.001000 -0.898988 -0.898988 0.898988 2.693931e-01 0.116420 \n", "6 0.001500 -0.775501 -0.775501 0.775501 2.004672e-01 0.113660 \n", "7 0.002000 -0.672223 -0.672223 0.672223 1.506278e-01 0.111158 \n", "8 0.002500 -0.585132 -0.585132 0.585132 1.141264e-01 0.108857 \n", "9 0.003000 -0.511163 -0.511163 0.511163 8.709597e-02 0.106712 \n", "10 0.003500 -0.447946 -0.447946 0.447946 6.688532e-02 0.104686 \n", "11 0.004000 -0.393622 -0.393622 0.393622 5.164603e-02 0.102747 \n", "12 0.004500 -0.346714 -0.346714 0.346714 4.007008e-02 0.100866 \n", "13 0.005000 -0.306037 -0.306037 0.306037 3.121956e-02 0.099020 \n", "14 0.005500 -0.270632 -0.270632 0.270632 2.441392e-02 0.097188 \n", "15 0.006000 -0.239713 -0.239713 0.239713 1.915417e-02 0.095352 \n", "16 0.006500 -0.212633 -0.212633 0.212633 1.507090e-02 0.093495 \n", "17 0.007000 -0.188852 -0.188852 0.188852 1.188836e-02 0.091603 \n", "18 0.007500 -0.167920 -0.167920 0.167920 9.399084e-03 0.089663 \n", "19 0.008000 -0.149459 -0.149459 0.149459 7.445949e-03 0.087666 \n", "20 0.008500 -0.133145 -0.133145 0.133145 5.909198e-03 0.085601 \n", "21 0.009000 -0.118706 -0.118706 0.118706 4.697054e-03 0.083463 \n", "22 0.009500 -0.105908 -0.105908 0.105908 3.738831e-03 0.081245 \n", "23 0.010000 -0.094549 -0.094549 0.094549 2.979837e-03 0.078945 \n", "24 0.010500 -0.126684 -0.126684 0.126684 5.349575e-03 0.114843 \n", "25 0.011250 -0.106503 -0.106503 0.106503 3.780970e-03 0.109075 \n", "26 0.012000 -0.089630 -0.089630 0.089630 2.677874e-03 0.103033 \n", "27 0.012750 -0.075497 -0.075497 0.075497 1.899922e-03 0.096755 \n", "28 0.013500 -0.063639 -0.063639 0.063639 1.349957e-03 0.090294 \n", "29 0.014250 -0.053676 -0.053676 0.053676 9.603761e-04 0.083718 \n", "30 0.015000 -0.045297 -0.045297 0.045297 6.839364e-04 0.077104 \n", "31 0.015750 -0.057364 -0.057364 0.057364 1.096869e-03 0.105802 \n", "32 0.016875 -0.043996 -0.043996 0.043996 6.452305e-04 0.090749 \n", "33 0.018000 -0.033769 -0.033769 0.033769 3.801200e-04 0.076606 \n", "34 0.019125 -0.038901 -0.038901 0.038901 5.044334e-04 0.095569 \n", "35 0.020813 -0.025386 -0.025386 0.025386 2.148211e-04 0.068957 \n", "36 0.022500 -0.024871 -0.024871 0.024871 2.061837e-04 0.072560 \n", "37 0.025031 -0.017919 -0.017919 0.017919 1.070309e-04 0.056368 \n", "38 0.028828 -0.005948 -0.005948 0.005948 1.179398e-05 0.019829 \n", "39 0.034523 0.001493 0.001493 -0.001493 7.433416e-07 0.005079 \n", "40 0.043066 -0.001682 -0.001682 0.001682 9.429553e-07 0.005691 \n", "41 0.055881 0.004103 0.004103 -0.004103 5.611315e-06 0.013963 \n", "\n", " action step_factor time_step caption \n", "0 ABORT 0.5 0.004000 excessive norm value(s) \n", "1 ABORT 0.5 0.002000 excessive norm value(s) \n", "2 ABORT 0.5 0.001000 excessive norm value(s) \n", "3 OK (stay) 1.0 0.000500 \n", "4 OK (stay) 1.0 0.000500 \n", "5 OK (stay) 1.0 0.000500 \n", "6 OK (stay) 1.0 0.000500 \n", "7 OK (stay) 1.0 0.000500 \n", "8 OK (stay) 1.0 0.000500 \n", "9 OK (stay) 1.0 0.000500 \n", "10 OK (stay) 1.0 0.000500 \n", "11 OK (stay) 1.0 0.000500 \n", "12 OK (stay) 1.0 0.000500 \n", "13 OK (stay) 1.0 0.000500 \n", "14 OK (stay) 1.0 0.000500 \n", "15 OK (stay) 1.0 0.000500 \n", "16 OK (stay) 1.0 0.000500 \n", "17 OK (stay) 1.0 0.000500 \n", "18 OK (stay) 1.0 0.000500 \n", "19 OK (stay) 1.0 0.000500 \n", "20 OK (stay) 1.0 0.000500 \n", "21 OK (stay) 1.0 0.000500 \n", "22 OK (stay) 1.0 0.000500 \n", "23 OK (low) 1.5 0.000500 \n", "24 OK (stay) 1.0 0.000750 \n", "25 OK (stay) 1.0 0.000750 \n", "26 OK (stay) 1.0 0.000750 \n", "27 OK (stay) 1.0 0.000750 \n", "28 OK (stay) 1.0 0.000750 \n", "29 OK (stay) 1.0 0.000750 \n", "30 OK (low) 1.5 0.000750 \n", "31 OK (stay) 1.0 0.001125 \n", "32 OK (stay) 1.0 0.001125 \n", "33 OK (low) 1.5 0.001125 \n", "34 OK (stay) 1.0 0.001688 \n", "35 OK (low) 1.5 0.001688 \n", "36 OK (low) 1.5 0.002531 \n", "37 OK (low) 1.5 0.003797 \n", "38 OK (low) 1.5 0.005695 \n", "39 OK (low) 1.5 0.008543 \n", "40 OK (low) 1.5 0.012814 \n", "41 OK (low) 1.5 0.019222 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_decisions_data()" ] }, { "cell_type": "code", "execution_count": 17, "id": "4e3012c6-c870-411c-bb80-7e1076233ca3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reaction: A + B <-> C\n" ] }, { "data": { "text/html": [ "
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START_TIMEDelta ADelta BDelta Ctime_stepcaption
00.000000-9.840000-9.8400009.8400000.004000aborted: excessive norm value(s)
10.000000-4.920000-4.9200004.9200000.002000aborted: excessive norm value(s)
20.000000-2.460000-2.4600002.4600000.001000aborted: excessive norm value(s)
30.000000-1.230000-1.2300001.2300000.000500
40.000500-1.048052-1.0480521.0480520.000500
50.001000-0.898988-0.8989880.8989880.000500
60.001500-0.775501-0.7755010.7755010.000500
70.002000-0.672223-0.6722230.6722230.000500
80.002500-0.585132-0.5851320.5851320.000500
90.003000-0.511163-0.5111630.5111630.000500
100.003500-0.447946-0.4479460.4479460.000500
110.004000-0.393622-0.3936220.3936220.000500
120.004500-0.346714-0.3467140.3467140.000500
130.005000-0.306037-0.3060370.3060370.000500
140.005500-0.270632-0.2706320.2706320.000500
150.006000-0.239713-0.2397130.2397130.000500
160.006500-0.212633-0.2126330.2126330.000500
170.007000-0.188852-0.1888520.1888520.000500
180.007500-0.167920-0.1679200.1679200.000500
190.008000-0.149459-0.1494590.1494590.000500
200.008500-0.133145-0.1331450.1331450.000500
210.009000-0.118706-0.1187060.1187060.000500
220.009500-0.105908-0.1059080.1059080.000500
230.010000-0.094549-0.0945490.0945490.000500
240.010500-0.126684-0.1266840.1266840.000750
250.011250-0.106503-0.1065030.1065030.000750
260.012000-0.089630-0.0896300.0896300.000750
270.012750-0.075497-0.0754970.0754970.000750
280.013500-0.063639-0.0636390.0636390.000750
290.014250-0.053676-0.0536760.0536760.000750
300.015000-0.045297-0.0452970.0452970.000750
310.015750-0.057364-0.0573640.0573640.001125
320.016875-0.043996-0.0439960.0439960.001125
330.018000-0.033769-0.0337690.0337690.001125
340.019125-0.038901-0.0389010.0389010.001688
350.020813-0.025386-0.0253860.0253860.001688
360.022500-0.024871-0.0248710.0248710.002531
370.025031-0.017919-0.0179190.0179190.003797
380.028828-0.005948-0.0059480.0059480.005695
390.0345230.0014930.001493-0.0014930.008543
400.043066-0.001682-0.0016820.0016820.012814
410.0558810.0041030.004103-0.0041030.019222
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" ], "text/plain": [ " START_TIME Delta A Delta B Delta C time_step \\\n", "0 0.000000 -9.840000 -9.840000 9.840000 0.004000 \n", "1 0.000000 -4.920000 -4.920000 4.920000 0.002000 \n", "2 0.000000 -2.460000 -2.460000 2.460000 0.001000 \n", "3 0.000000 -1.230000 -1.230000 1.230000 0.000500 \n", "4 0.000500 -1.048052 -1.048052 1.048052 0.000500 \n", "5 0.001000 -0.898988 -0.898988 0.898988 0.000500 \n", "6 0.001500 -0.775501 -0.775501 0.775501 0.000500 \n", "7 0.002000 -0.672223 -0.672223 0.672223 0.000500 \n", "8 0.002500 -0.585132 -0.585132 0.585132 0.000500 \n", "9 0.003000 -0.511163 -0.511163 0.511163 0.000500 \n", "10 0.003500 -0.447946 -0.447946 0.447946 0.000500 \n", "11 0.004000 -0.393622 -0.393622 0.393622 0.000500 \n", "12 0.004500 -0.346714 -0.346714 0.346714 0.000500 \n", "13 0.005000 -0.306037 -0.306037 0.306037 0.000500 \n", "14 0.005500 -0.270632 -0.270632 0.270632 0.000500 \n", "15 0.006000 -0.239713 -0.239713 0.239713 0.000500 \n", "16 0.006500 -0.212633 -0.212633 0.212633 0.000500 \n", "17 0.007000 -0.188852 -0.188852 0.188852 0.000500 \n", "18 0.007500 -0.167920 -0.167920 0.167920 0.000500 \n", "19 0.008000 -0.149459 -0.149459 0.149459 0.000500 \n", "20 0.008500 -0.133145 -0.133145 0.133145 0.000500 \n", "21 0.009000 -0.118706 -0.118706 0.118706 0.000500 \n", "22 0.009500 -0.105908 -0.105908 0.105908 0.000500 \n", "23 0.010000 -0.094549 -0.094549 0.094549 0.000500 \n", "24 0.010500 -0.126684 -0.126684 0.126684 0.000750 \n", "25 0.011250 -0.106503 -0.106503 0.106503 0.000750 \n", "26 0.012000 -0.089630 -0.089630 0.089630 0.000750 \n", "27 0.012750 -0.075497 -0.075497 0.075497 0.000750 \n", "28 0.013500 -0.063639 -0.063639 0.063639 0.000750 \n", "29 0.014250 -0.053676 -0.053676 0.053676 0.000750 \n", "30 0.015000 -0.045297 -0.045297 0.045297 0.000750 \n", "31 0.015750 -0.057364 -0.057364 0.057364 0.001125 \n", "32 0.016875 -0.043996 -0.043996 0.043996 0.001125 \n", "33 0.018000 -0.033769 -0.033769 0.033769 0.001125 \n", "34 0.019125 -0.038901 -0.038901 0.038901 0.001688 \n", "35 0.020813 -0.025386 -0.025386 0.025386 0.001688 \n", "36 0.022500 -0.024871 -0.024871 0.024871 0.002531 \n", "37 0.025031 -0.017919 -0.017919 0.017919 0.003797 \n", "38 0.028828 -0.005948 -0.005948 0.005948 0.005695 \n", "39 0.034523 0.001493 0.001493 -0.001493 0.008543 \n", "40 0.043066 -0.001682 -0.001682 0.001682 0.012814 \n", "41 0.055881 0.004103 0.004103 -0.004103 0.019222 \n", "\n", " caption \n", "0 aborted: excessive norm value(s) \n", "1 aborted: excessive norm value(s) \n", "2 aborted: excessive norm value(s) \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 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_rxn_data(rxn_index=0)" ] }, { "cell_type": "code", "execution_count": 18, "id": "a5cee533-a63c-4ff8-9427-e9b64cf4885a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TIMEABCcaption
00.00000010.00000050.00000020.000000
10.0005008.77000048.77000021.230000
20.0010007.72194847.72194822.278052
30.0015006.82296046.82296023.177040
40.0020006.04745946.04745923.952541
50.0025005.37523645.37523624.624764
60.0030004.79010444.79010425.209896
70.0035004.27894144.27894125.721059
80.0040003.83099543.83099526.169005
90.0045003.43737343.43737326.562627
100.0050003.09065943.09065926.909341
110.0055002.78462242.78462227.215378
120.0060002.51399042.51399027.486010
130.0065002.27427742.27427727.725723
140.0070002.06164442.06164427.938356
150.0075001.87279241.87279228.127208
160.0080001.70487241.70487228.295128
170.0085001.55541341.55541328.444587
180.0090001.42226841.42226828.577732
190.0095001.30356241.30356228.696438
200.0100001.19765441.19765428.802346
210.0105001.10310541.10310528.896895
220.0112500.97642140.97642129.023579
230.0120000.86991840.86991829.130082
240.0127500.78028840.78028829.219712
250.0135000.70479140.70479129.295209
260.0142500.64115340.64115329.358847
270.0150000.58747640.58747629.412524
280.0157500.54217940.54217929.457821
290.0168750.48481640.48481629.515184
300.0180000.44081940.44081929.559181
310.0191250.40705040.40705029.592950
320.0208130.36814940.36814929.631851
330.0225000.34276340.34276329.657237
340.0250310.31789240.31789229.682108
350.0288280.29997340.29997329.700027
360.0345230.29402440.29402429.705976
370.0430660.29551840.29551829.704482
380.0558810.29383640.29383629.706164
390.0751030.29793940.29793929.702061
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" ], "text/plain": [ " TIME A B C caption\n", "0 0.000000 10.000000 50.000000 20.000000 \n", "1 0.000500 8.770000 48.770000 21.230000 \n", "2 0.001000 7.721948 47.721948 22.278052 \n", "3 0.001500 6.822960 46.822960 23.177040 \n", "4 0.002000 6.047459 46.047459 23.952541 \n", "5 0.002500 5.375236 45.375236 24.624764 \n", "6 0.003000 4.790104 44.790104 25.209896 \n", "7 0.003500 4.278941 44.278941 25.721059 \n", "8 0.004000 3.830995 43.830995 26.169005 \n", "9 0.004500 3.437373 43.437373 26.562627 \n", "10 0.005000 3.090659 43.090659 26.909341 \n", "11 0.005500 2.784622 42.784622 27.215378 \n", "12 0.006000 2.513990 42.513990 27.486010 \n", "13 0.006500 2.274277 42.274277 27.725723 \n", "14 0.007000 2.061644 42.061644 27.938356 \n", "15 0.007500 1.872792 41.872792 28.127208 \n", "16 0.008000 1.704872 41.704872 28.295128 \n", "17 0.008500 1.555413 41.555413 28.444587 \n", "18 0.009000 1.422268 41.422268 28.577732 \n", "19 0.009500 1.303562 41.303562 28.696438 \n", "20 0.010000 1.197654 41.197654 28.802346 \n", "21 0.010500 1.103105 41.103105 28.896895 \n", "22 0.011250 0.976421 40.976421 29.023579 \n", "23 0.012000 0.869918 40.869918 29.130082 \n", "24 0.012750 0.780288 40.780288 29.219712 \n", "25 0.013500 0.704791 40.704791 29.295209 \n", "26 0.014250 0.641153 40.641153 29.358847 \n", "27 0.015000 0.587476 40.587476 29.412524 \n", "28 0.015750 0.542179 40.542179 29.457821 \n", "29 0.016875 0.484816 40.484816 29.515184 \n", "30 0.018000 0.440819 40.440819 29.559181 \n", "31 0.019125 0.407050 40.407050 29.592950 \n", "32 0.020813 0.368149 40.368149 29.631851 \n", "33 0.022500 0.342763 40.342763 29.657237 \n", "34 0.025031 0.317892 40.317892 29.682108 \n", "35 0.028828 0.299973 40.299973 29.700027 \n", "36 0.034523 0.294024 40.294024 29.705976 \n", "37 0.043066 0.295518 40.295518 29.704482 \n", "38 0.055881 0.293836 40.293836 29.706164 \n", "39 0.075103 0.297939 40.297939 29.702061 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_diagnostic_conc_data()" ] }, { "cell_type": "code", "execution_count": null, "id": "fcc82495-c952-4200-b316-157aac1ef22f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }