{ "cells": [ { "cell_type": "markdown", "id": "5cbc8640", "metadata": {}, "source": [ "### A MINIMALIST, \"get-started\", demonstration of the unimolecular elementary 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 = \"Feb. 15, 2026\"\n", "LIFE123_VERSION = \"1.0.0rc7\" # 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) # To check compatibility" ] }, { "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": [ "add_reaction(): detected reaction type `ReactionUnimolecular`\n", "Number of reactions: 1\n", "0: A <-> B Elementary Unimolecular reaction (kF = 3 / kR = 2 / delta_G = -1,005.1 / K = 1.5 / Temp = 25 C)\n", "Chemicals involved in the above reactions: ['A', 'B']\n" ] } ], "source": [ "# Instantiate the simulator and specify the reaction and the chemicals\n", "uc = life123.UniformCompartment() \n", "\n", "# Elementary Reaction A <-> B\n", "uc.add_reaction(reactants=\"A\", products=\"B\", kF=3., kR=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": "code", "execution_count": null, "id": "b0a496fa-380e-4689-9584-9234a65419a3", "metadata": {}, "outputs": [], "source": [] }, { "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 0.084 sec\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
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80.04016671.89834418.1016568
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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", 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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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }