{ "cells": [ { "cell_type": "markdown", "id": "5cbc8640", "metadata": {}, "source": [ "### A MINIMALIST, \"get-started\", demonstration of the reaction `A <-> B`,\n", "with 1st-order kinetics in both directions, taken to equilibrium.\n", "\n", "**\"No frills!\"** Please see other experiments for advanced graphics, analysis, diagnostics, fine-tuning, etc." ] }, { "cell_type": "markdown", "id": "5a3fe1d4-ffc9-4db9-ac0f-d51d2231d32b", "metadata": {}, "source": [ "### TAGS : \"quick-start\", \"uniform compartment\"" ] }, { "cell_type": "code", "execution_count": 1, "id": "97a57e9a-039b-479a-81dc-81399e22743a", "metadata": {}, "outputs": [], "source": [ "LAST_REVISED = \"Jan. 28, 2025\"\n", "LIFE123_VERSION = \"1.0.0rc3\" # Library version this experiment is based on" ] }, { "cell_type": "code", "execution_count": 2, "id": "b5b8a8b0-d417-4432-b6a8-c196af57b105", "metadata": {}, "outputs": [], "source": [ "#import set_path # Using MyBinder? Uncomment this before running the next cell!\n", " # Importing this module will add the project's home directory to sys.path" ] }, { "cell_type": "code", "execution_count": 3, "id": "a29db1c7", "metadata": { "tags": [] }, "outputs": [], "source": [ "#import sys, os\n", "#os.getcwd()\n", "#sys.path.append(\"C:/some_path/my_env_or_install\") # CHANGE to the folder containing your venv or libraries installation!\n", "# NOTE: If any of the imports below can't find a module, uncomment the lines above, or try: import set_path\n", "\n", "import life123" ] }, { "cell_type": "code", "execution_count": 4, "id": "ccd6701b-ae96-4d20-b537-40c2c40df9aa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OK\n" ] } ], "source": [ "life123.check_version(LIFE123_VERSION)" ] }, { "cell_type": "code", "execution_count": null, "id": "ac9eea69-174c-43e5-9eed-443cbc5e2ba7", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "e0529a0c", "metadata": {}, "source": [ "## Initialize the System" ] }, { "cell_type": "code", "execution_count": 5, "id": "78077d8c", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 1 (at temp. 25 C)\n", "0: A <-> B (kF = 3 / kR = 2 / delta_G = -1,005.1 / K = 1.5) | 1st order in all reactants & products\n", "Set of chemicals involved in the above reactions: {'A', 'B'}\n" ] } ], "source": [ "# Instantiate the simulator and specify the chemicals\n", "uc = life123.UniformCompartment() \n", "\n", "# Reaction A <-> B , with 1st-order kinetics in both directions\n", "uc.add_reaction(reactants=\"A\", products=\"B\", \n", " forward_rate=3., reverse_rate=2.)\n", "\n", "uc.describe_reactions()" ] }, { "cell_type": "code", "execution_count": 6, "id": "9fc3948d", "metadata": {}, "outputs": [], "source": [ "# Set the initial concentrations of all the chemicals\n", "uc.set_conc({\"A\": 80., \"B\": 10.})" ] }, { "cell_type": "markdown", "id": "987af2c5", "metadata": { "tags": [] }, "source": [ "## Run the reaction" ] }, { "cell_type": "code", "execution_count": 7, "id": "43735178-313b-48cf-a583-5181238feac3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "47 total variable step(s) taken in 0.00 min\n", "Number of step re-do's because of elective soft aborts: 3\n", "Norm usage: {'norm_A': 26, 'norm_B': 22, 'norm_C': 22, 'norm_D': 22}\n", "System Time is now: 1.1343\n" ] } ], "source": [ "uc.single_compartment_react(initial_step=0.1, target_end_time=1.) # Using defaults for all other parameters" ] }, { "cell_type": "code", "execution_count": 8, "id": "2d5df59c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SYSTEM TIMEABstepcaption
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10.00640078.59200011.40800011st reaction step
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30.01344077.10584612.8941543
40.01804876.15876713.8412334
50.02357875.04845814.9515425
60.02910773.96884616.0311546
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90.04569670.90582719.0941739
100.05233269.74773520.25226510
110.05896768.62806721.37193311
120.06560367.54554622.45445412
130.07223866.49894023.50106013
140.07887465.48705824.51294214
150.08550964.50874925.49125115
160.09347263.37372626.62627416
170.10143462.28389327.71610717
180.10939761.23744928.76255118
190.11736060.23266829.76733219
200.12532259.26788930.73211120
210.13487758.15624931.84375121
220.14443357.09771732.90228322
230.15398856.08975833.91024223
240.16354355.12995534.87004524
250.17500954.03321835.96678225
260.18647552.99935737.00064326
270.19794152.02476937.97523127
280.21170150.92231239.07768828
290.22546049.89570040.10430029
300.23922048.93971741.06028330
310.25573147.87145942.12854131
320.27224246.89139343.10860732
330.29205645.81240744.18759333
340.31186944.84031445.15968634
350.33564643.78936546.21063535
360.35942242.86335547.13664536
370.38795341.88424548.11575537
380.42219140.87692749.12307338
390.45642940.04205049.95795039
400.49751439.21170450.78829640
410.54681738.41997951.58002141
420.60598037.70411352.29588742
430.67697537.09919152.90080943
440.76217036.63096553.36903544
450.86440436.30843653.69156445
460.98708436.11924153.88075946
471.13430036.03147053.96853047last reaction step
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" ], "text/plain": [ " SYSTEM TIME A B step caption\n", "0 0.000000 80.000000 10.000000 Set concentration\n", "1 0.006400 78.592000 11.408000 1 1st reaction step\n", "2 0.009600 77.910528 12.089472 2 \n", "3 0.013440 77.105846 12.894154 3 \n", "4 0.018048 76.158767 13.841233 4 \n", "5 0.023578 75.048458 14.951542 5 \n", "6 0.029107 73.968846 16.031154 6 \n", "7 0.034637 72.919083 17.080917 7 \n", "8 0.040166 71.898344 18.101656 8 \n", "9 0.045696 70.905827 19.094173 9 \n", "10 0.052332 69.747735 20.252265 10 \n", "11 0.058967 68.628067 21.371933 11 \n", "12 0.065603 67.545546 22.454454 12 \n", "13 0.072238 66.498940 23.501060 13 \n", "14 0.078874 65.487058 24.512942 14 \n", "15 0.085509 64.508749 25.491251 15 \n", "16 0.093472 63.373726 26.626274 16 \n", "17 0.101434 62.283893 27.716107 17 \n", "18 0.109397 61.237449 28.762551 18 \n", "19 0.117360 60.232668 29.767332 19 \n", "20 0.125322 59.267889 30.732111 20 \n", "21 0.134877 58.156249 31.843751 21 \n", "22 0.144433 57.097717 32.902283 22 \n", "23 0.153988 56.089758 33.910242 23 \n", "24 0.163543 55.129955 34.870045 24 \n", "25 0.175009 54.033218 35.966782 25 \n", "26 0.186475 52.999357 37.000643 26 \n", "27 0.197941 52.024769 37.975231 27 \n", "28 0.211701 50.922312 39.077688 28 \n", "29 0.225460 49.895700 40.104300 29 \n", "30 0.239220 48.939717 41.060283 30 \n", "31 0.255731 47.871459 42.128541 31 \n", "32 0.272242 46.891393 43.108607 32 \n", "33 0.292056 45.812407 44.187593 33 \n", "34 0.311869 44.840314 45.159686 34 \n", "35 0.335646 43.789365 46.210635 35 \n", "36 0.359422 42.863355 47.136645 36 \n", "37 0.387953 41.884245 48.115755 37 \n", "38 0.422191 40.876927 49.123073 38 \n", "39 0.456429 40.042050 49.957950 39 \n", "40 0.497514 39.211704 50.788296 40 \n", "41 0.546817 38.419979 51.580021 41 \n", "42 0.605980 37.704113 52.295887 42 \n", "43 0.676975 37.099191 52.900809 43 \n", "44 0.762170 36.630965 53.369035 44 \n", "45 0.864404 36.308436 53.691564 45 \n", "46 0.987084 36.119241 53.880759 46 \n", "47 1.134300 36.031470 53.968530 47 last reaction step" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "uc.get_history() # The system's history, saved during the run of single_compartment_react()" ] }, { "cell_type": "code", "execution_count": null, "id": "6409d11e-26a0-4785-bf66-88566aac56ea", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "03866901", "metadata": { "tags": [] }, "source": [ "## Plots changes of concentration with time \n", "Notice that adaptive variable time steps were automatically taken" ] }, { "cell_type": "code", "execution_count": 9, "id": "6b033cc7-078f-4b94-a466-1aa91e2fca4d", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
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"Reaction `A <-> B` . Changes in concentrations with time (time steps shown in dashed lines)" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ -0.0009132853281685162, 1.1352136629134657 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ 6.111111111111111, 83.88888888888889 ], "title": { "text": "Concentration" }, "type": "linear" } } }, "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "uc.plot_history(show_intervals=True)" ] }, { "cell_type": "code", "execution_count": 10, "id": "23c4b3ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A <-> B\n", "Current concentrations: [A] = 36.03 ; [B] = 53.97\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 1.49782\n", " Formula used: [B] / [A]\n", "2. Ratio of forward/reverse reaction rates: 1.5\n", "Discrepancy between the two values: 0.1456 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "uc.is_in_equilibrium()" ] }, { "cell_type": "code", "execution_count": null, "id": "7f59733f", "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": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 5 }