{ "cells": [ { "cell_type": "markdown", "id": "5cbc8640", "metadata": {}, "source": [ "### A MINIMALIST, \"get-started\", demonstration for the reaction `A <-> B`,\n", "with 1st-order kinetics in both directions, taken to equilibrium.\n", "\n", "**\"No frills!\"** For advanced graphics, analysis, diagnostics, fine-tuning, etc, please see other experiments." ] }, { "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 = \"Dec. 15, 2024\"\n", "LIFE123_VERSION = \"1.0-rc.1\" # 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\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: {'B', 'A'}\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 TIMEABcaption
00.00000080.00000010.000000Set concentration
10.00640078.59200011.4080001st reaction step
20.00960077.91052812.089472
30.01344077.10584612.894154
40.01804876.15876713.841233
50.02357875.04845814.951542
60.02910773.96884616.031154
70.03463772.91908317.080917
80.04016671.89834418.101656
90.04569670.90582719.094173
100.05233269.74773520.252265
110.05896768.62806721.371933
120.06560367.54554622.454454
130.07223866.49894023.501060
140.07887465.48705824.512942
150.08550964.50874925.491251
160.09347263.37372626.626274
170.10143462.28389327.716107
180.10939761.23744928.762551
190.11736060.23266829.767332
200.12532259.26788930.732111
210.13487758.15624931.843751
220.14443357.09771732.902283
230.15398856.08975833.910242
240.16354355.12995534.870045
250.17500954.03321835.966782
260.18647552.99935737.000643
270.19794152.02476937.975231
280.21170150.92231239.077688
290.22546049.89570040.104300
300.23922048.93971741.060283
310.25573147.87145942.128541
320.27224246.89139343.108607
330.29205645.81240744.187593
340.31186944.84031445.159686
350.33564643.78936546.210635
360.35942242.86335547.136645
370.38795341.88424548.115755
380.42219140.87692749.123073
390.45642940.04205049.957950
400.49751439.21170450.788296
410.54681738.41997951.580021
420.60598037.70411352.295887
430.67697537.09919152.900809
440.76217036.63096553.369035
450.86440436.30843653.691564
460.98708436.11924153.880759
471.13430036.03147053.968530last reaction step
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" ], "text/plain": [ " SYSTEM TIME A B caption\n", "0 0.000000 80.000000 10.000000 Set concentration\n", "1 0.006400 78.592000 11.408000 1st reaction step\n", "2 0.009600 77.910528 12.089472 \n", "3 0.013440 77.105846 12.894154 \n", "4 0.018048 76.158767 13.841233 \n", "5 0.023578 75.048458 14.951542 \n", "6 0.029107 73.968846 16.031154 \n", "7 0.034637 72.919083 17.080917 \n", "8 0.040166 71.898344 18.101656 \n", "9 0.045696 70.905827 19.094173 \n", "10 0.052332 69.747735 20.252265 \n", "11 0.058967 68.628067 21.371933 \n", "12 0.065603 67.545546 22.454454 \n", "13 0.072238 66.498940 23.501060 \n", "14 0.078874 65.487058 24.512942 \n", "15 0.085509 64.508749 25.491251 \n", "16 0.093472 63.373726 26.626274 \n", "17 0.101434 62.283893 27.716107 \n", "18 0.109397 61.237449 28.762551 \n", "19 0.117360 60.232668 29.767332 \n", "20 0.125322 59.267889 30.732111 \n", "21 0.134877 58.156249 31.843751 \n", "22 0.144433 57.097717 32.902283 \n", "23 0.153988 56.089758 33.910242 \n", "24 0.163543 55.129955 34.870045 \n", "25 0.175009 54.033218 35.966782 \n", "26 0.186475 52.999357 37.000643 \n", "27 0.197941 52.024769 37.975231 \n", "28 0.211701 50.922312 39.077688 \n", "29 0.225460 49.895700 40.104300 \n", "30 0.239220 48.939717 41.060283 \n", "31 0.255731 47.871459 42.128541 \n", "32 0.272242 46.891393 43.108607 \n", "33 0.292056 45.812407 44.187593 \n", "34 0.311869 44.840314 45.159686 \n", "35 0.335646 43.789365 46.210635 \n", "36 0.359422 42.863355 47.136645 \n", "37 0.387953 41.884245 48.115755 \n", "38 0.422191 40.876927 49.123073 \n", "39 0.456429 40.042050 49.957950 \n", "40 0.497514 39.211704 50.788296 \n", "41 0.546817 38.419979 51.580021 \n", "42 0.605980 37.704113 52.295887 \n", "43 0.676975 37.099191 52.900809 \n", "44 0.762170 36.630965 53.369035 \n", "45 0.864404 36.308436 53.691564 \n", "46 0.987084 36.119241 53.880759 \n", "47 1.134300 36.031470 53.968530 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": "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", "Final 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 }