{
"cells": [
{
"cell_type": "markdown",
"id": "5cbc8640",
"metadata": {},
"source": [
"### Demonstration of file storage of system concentration history, and stop-restart of simulation, for the reaction `A <-> B`,\n",
"with 1st-order kinetics in both directions, taken to equilibrium.\n",
"\n",
"Same as experiment `react_1_a`, but with CSV file storage of the concentration history, and reaction stop-restart."
]
},
{
"cell_type": "markdown",
"id": "5a3fe1d4-ffc9-4db9-ac0f-d51d2231d32b",
"metadata": {},
"source": [
"### TAGS : \"basic\", \"uniform compartment\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "97a57e9a-039b-479a-81dc-81399e22743a",
"metadata": {},
"outputs": [],
"source": [
"LAST_REVISED = \"Aug. 29, 2025\"\n",
"LIFE123_VERSION = \"1.0.0rc6\" # 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 ipynbname\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": 5,
"id": "e244883f-4075-4b46-9f1a-d42ed4a42402",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Initialize logging (for the system state)\n",
"csv_log_file = ipynbname.name() + \"_system_log.csv\" # Use the notebook base filename \n",
" # IN CASE OF PROBLEMS, set manually to any desired name"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac9eea69-174c-43e5-9eed-443cbc5e2ba7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2c2d4ca4-cbfe-4733-a20c-e2d5a1c35e7d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "e0529a0c",
"metadata": {},
"source": [
"## Initialize the Uniform-Compartment Simulation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4aac8eed-932a-4aae-9cad-bb76fc4dccb4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Instantiate the simulator and specify the chemicals\n",
"uc = life123.UniformCompartment() "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e59bd726-e9fb-48f4-8e31-cc39e4c3677d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-> CSV-format output will be LOGGED into the file 'react_1_b_system_log.csv' . An existing file by that name was over-written\n"
]
}
],
"source": [
"# We're now requesting that all System Concentration Data get logged in our previously-specified CSV file\n",
"uc.start_csv_log(csv_log_file)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "62181bcb-ef36-4e3b-bf3f-a7c429c7bba6",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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": [
"# Reaction A <-> B , with 1st-order kinetics in both directions\n",
"uc.add_reaction(reactants=\"A\", products=\"B\", kF=3., kR=2.)\n",
"\n",
"uc.describe_reactions()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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": "2ed62975-8865-427d-9734-74a0849e292d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "53d9d004-2e43-4e6f-8113-3c26e51364df",
"metadata": {
"tags": []
},
"source": [
"#### This time (contrasted to experiment `react_1_a`) we'll be running the simulation in two parts"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53903d59-906e-4546-9624-d5a820379c4e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "ff4f04a1-7723-4116-a906-1cad31c94662",
"metadata": {},
"source": [
"## Part 1 (early run)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "43735178-313b-48cf-a583-5181238feac3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"28 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': 12, 'norm_B': 8, 'norm_C': 8, 'norm_D': 8}\n",
"System Time is now: 0.2117\n"
]
}
],
"source": [
"uc.single_compartment_react(initial_step=0.1, target_end_time=0.2) # The first part of our run"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2d5df59c",
"metadata": {},
"outputs": [
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" step | \n",
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\n",
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" \n",
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" | 0 | \n",
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\n",
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\n",
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"uc.plot_history(show_intervals=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "23c4b3ba",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0: A <-> B\n",
"Current concentrations: [A] = 50.92 ; [B] = 39.08\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 0.767398\n",
" Formula used: [B] / [A]\n",
"2. Ratio of forward/reverse reaction rates: 1.5\n",
"Discrepancy between the two values: 48.84 %\n",
"Reaction is NOT in equilibrium (not within 1% tolerance)\n",
"\n"
]
},
{
"data": {
"text/plain": [
"{False: [0]}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We're nowhere near equilibrium yet!\n",
"uc.is_in_equilibrium()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33e46694-c5c0-4ced-bb59-e665e8aea022",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f59733f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "236f3df9-ca09-48fe-9afe-b4b9748dd1d9",
"metadata": {},
"source": [
"## Part 2 (late run)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a2299384-404b-4cb6-a709-04803cab93d2",
"metadata": {},
"outputs": [],
"source": [
"initial_step = 0.013759414272 # We're choosing this value simply FOR DEMONSTRATION PURPOSES,\n",
" # to remain in exact lockstep with the time course of experiment `react_1_a`\n",
"\n",
"'''\n",
"If you run experiment `react_1_a`, you can determine what the next time step would have been, had we not stopped early this time.\n",
"\n",
" In experiment `react_1_a`, after running the simulation, you can issue:\n",
"\n",
" list(uc.get_history(t_start=0.21, t_end=0.23, columns=\"SYSTEM TIME\"))\n",
"\n",
" and you will get: [0.211700785152, 0.225460199424]\n",
"\n",
" Their difference is the initial_step we're using here.\n",
"''';"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "16970ff9-f064-4620-b232-12af11dfed3f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"19 total variable step(s) taken in 0.00 min\n",
"Norm usage: {'norm_A': 14, 'norm_B': 14, 'norm_C': 14, 'norm_D': 14}\n",
"System Time is now: 1.1343\n"
]
}
],
"source": [
"uc.single_compartment_react(initial_step=0.013759414272, target_end_time=1.0) # The 2nd part of our run, to the final target end time"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "3adf0b32-3f63-4350-a853-4461014e418e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" A | \n",
" B | \n",
" step | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.000000 | \n",
" 80.000000 | \n",
" 10.000000 | \n",
" | \n",
" Set concentration | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.006400 | \n",
" 78.592000 | \n",
" 11.408000 | \n",
" 1 | \n",
" 1st reaction step | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.009600 | \n",
" 77.910528 | \n",
" 12.089472 | \n",
" 2 | \n",
" | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.013440 | \n",
" 77.105846 | \n",
" 12.894154 | \n",
" 3 | \n",
" | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.018048 | \n",
" 76.158767 | \n",
" 13.841233 | \n",
" 4 | \n",
" | \n",
"
\n",
" \n",
" | 5 | \n",
" 0.023578 | \n",
" 75.048458 | \n",
" 14.951542 | \n",
" 5 | \n",
" | \n",
"
\n",
" \n",
" | 6 | \n",
" 0.029107 | \n",
" 73.968846 | \n",
" 16.031154 | \n",
" 6 | \n",
" | \n",
"
\n",
" \n",
" | 7 | \n",
" 0.034637 | \n",
" 72.919083 | \n",
" 17.080917 | \n",
" 7 | \n",
" | \n",
"
\n",
" \n",
" | 8 | \n",
" 0.040166 | \n",
" 71.898344 | \n",
" 18.101656 | \n",
" 8 | \n",
" | \n",
"
\n",
" \n",
" | 9 | \n",
" 0.045696 | \n",
" 70.905827 | \n",
" 19.094173 | \n",
" 9 | \n",
" | \n",
"
\n",
" \n",
" | 10 | \n",
" 0.052332 | \n",
" 69.747735 | \n",
" 20.252265 | \n",
" 10 | \n",
" | \n",
"
\n",
" \n",
" | 11 | \n",
" 0.058967 | \n",
" 68.628067 | \n",
" 21.371933 | \n",
" 11 | \n",
" | \n",
"
\n",
" \n",
" | 12 | \n",
" 0.065603 | \n",
" 67.545546 | \n",
" 22.454454 | \n",
" 12 | \n",
" | \n",
"
\n",
" \n",
" | 13 | \n",
" 0.072238 | \n",
" 66.498940 | \n",
" 23.501060 | \n",
" 13 | \n",
" | \n",
"
\n",
" \n",
" | 14 | \n",
" 0.078874 | \n",
" 65.487058 | \n",
" 24.512942 | \n",
" 14 | \n",
" | \n",
"
\n",
" \n",
" | 15 | \n",
" 0.085509 | \n",
" 64.508749 | \n",
" 25.491251 | \n",
" 15 | \n",
" | \n",
"
\n",
" \n",
" | 16 | \n",
" 0.093472 | \n",
" 63.373726 | \n",
" 26.626274 | \n",
" 16 | \n",
" | \n",
"
\n",
" \n",
" | 17 | \n",
" 0.101434 | \n",
" 62.283893 | \n",
" 27.716107 | \n",
" 17 | \n",
" | \n",
"
\n",
" \n",
" | 18 | \n",
" 0.109397 | \n",
" 61.237449 | \n",
" 28.762551 | \n",
" 18 | \n",
" | \n",
"
\n",
" \n",
" | 19 | \n",
" 0.117360 | \n",
" 60.232668 | \n",
" 29.767332 | \n",
" 19 | \n",
" | \n",
"
\n",
" \n",
" | 20 | \n",
" 0.125322 | \n",
" 59.267889 | \n",
" 30.732111 | \n",
" 20 | \n",
" | \n",
"
\n",
" \n",
" | 21 | \n",
" 0.134877 | \n",
" 58.156249 | \n",
" 31.843751 | \n",
" 21 | \n",
" | \n",
"
\n",
" \n",
" | 22 | \n",
" 0.144433 | \n",
" 57.097717 | \n",
" 32.902283 | \n",
" 22 | \n",
" | \n",
"
\n",
" \n",
" | 23 | \n",
" 0.153988 | \n",
" 56.089758 | \n",
" 33.910242 | \n",
" 23 | \n",
" | \n",
"
\n",
" \n",
" | 24 | \n",
" 0.163543 | \n",
" 55.129955 | \n",
" 34.870045 | \n",
" 24 | \n",
" | \n",
"
\n",
" \n",
" | 25 | \n",
" 0.175009 | \n",
" 54.033218 | \n",
" 35.966782 | \n",
" 25 | \n",
" | \n",
"
\n",
" \n",
" | 26 | \n",
" 0.186475 | \n",
" 52.999357 | \n",
" 37.000643 | \n",
" 26 | \n",
" | \n",
"
\n",
" \n",
" | 27 | \n",
" 0.197941 | \n",
" 52.024769 | \n",
" 37.975231 | \n",
" 27 | \n",
" | \n",
"
\n",
" \n",
" | 28 | \n",
" 0.211701 | \n",
" 50.922312 | \n",
" 39.077688 | \n",
" 28 | \n",
" last reaction step | \n",
"
\n",
" \n",
" | 29 | \n",
" 0.225460 | \n",
" 49.895700 | \n",
" 40.104300 | \n",
" 1 | \n",
" 1st reaction step | \n",
"
\n",
" \n",
" | 30 | \n",
" 0.239220 | \n",
" 48.939717 | \n",
" 41.060283 | \n",
" 2 | \n",
" | \n",
"
\n",
" \n",
" | 31 | \n",
" 0.255731 | \n",
" 47.871459 | \n",
" 42.128541 | \n",
" 3 | \n",
" | \n",
"
\n",
" \n",
" | 32 | \n",
" 0.272242 | \n",
" 46.891393 | \n",
" 43.108607 | \n",
" 4 | \n",
" | \n",
"
\n",
" \n",
" | 33 | \n",
" 0.292056 | \n",
" 45.812407 | \n",
" 44.187593 | \n",
" 5 | \n",
" | \n",
"
\n",
" \n",
" | 34 | \n",
" 0.311869 | \n",
" 44.840314 | \n",
" 45.159686 | \n",
" 6 | \n",
" | \n",
"
\n",
" \n",
" | 35 | \n",
" 0.335646 | \n",
" 43.789365 | \n",
" 46.210635 | \n",
" 7 | \n",
" | \n",
"
\n",
" \n",
" | 36 | \n",
" 0.359422 | \n",
" 42.863355 | \n",
" 47.136645 | \n",
" 8 | \n",
" | \n",
"
\n",
" \n",
" | 37 | \n",
" 0.387953 | \n",
" 41.884245 | \n",
" 48.115755 | \n",
" 9 | \n",
" | \n",
"
\n",
" \n",
" | 38 | \n",
" 0.422191 | \n",
" 40.876927 | \n",
" 49.123073 | \n",
" 10 | \n",
" | \n",
"
\n",
" \n",
" | 39 | \n",
" 0.456429 | \n",
" 40.042050 | \n",
" 49.957950 | \n",
" 11 | \n",
" | \n",
"
\n",
" \n",
" | 40 | \n",
" 0.497514 | \n",
" 39.211704 | \n",
" 50.788296 | \n",
" 12 | \n",
" | \n",
"
\n",
" \n",
" | 41 | \n",
" 0.546817 | \n",
" 38.419979 | \n",
" 51.580021 | \n",
" 13 | \n",
" | \n",
"
\n",
" \n",
" | 42 | \n",
" 0.605980 | \n",
" 37.704113 | \n",
" 52.295887 | \n",
" 14 | \n",
" | \n",
"
\n",
" \n",
" | 43 | \n",
" 0.676975 | \n",
" 37.099191 | \n",
" 52.900809 | \n",
" 15 | \n",
" | \n",
"
\n",
" \n",
" | 44 | \n",
" 0.762170 | \n",
" 36.630965 | \n",
" 53.369035 | \n",
" 16 | \n",
" | \n",
"
\n",
" \n",
" | 45 | \n",
" 0.864404 | \n",
" 36.308436 | \n",
" 53.691564 | \n",
" 17 | \n",
" | \n",
"
\n",
" \n",
" | 46 | \n",
" 0.987084 | \n",
" 36.119241 | \n",
" 53.880759 | \n",
" 18 | \n",
" | \n",
"
\n",
" \n",
" | 47 | \n",
" 1.134300 | \n",
" 36.031470 | \n",
" 53.968530 | \n",
" 19 | \n",
" last reaction step | \n",
"
\n",
" \n",
"
\n",
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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 last reaction step\n",
"29 0.225460 49.895700 40.104300 1 1st reaction step\n",
"30 0.239220 48.939717 41.060283 2 \n",
"31 0.255731 47.871459 42.128541 3 \n",
"32 0.272242 46.891393 43.108607 4 \n",
"33 0.292056 45.812407 44.187593 5 \n",
"34 0.311869 44.840314 45.159686 6 \n",
"35 0.335646 43.789365 46.210635 7 \n",
"36 0.359422 42.863355 47.136645 8 \n",
"37 0.387953 41.884245 48.115755 9 \n",
"38 0.422191 40.876927 49.123073 10 \n",
"39 0.456429 40.042050 49.957950 11 \n",
"40 0.497514 39.211704 50.788296 12 \n",
"41 0.546817 38.419979 51.580021 13 \n",
"42 0.605980 37.704113 52.295887 14 \n",
"43 0.676975 37.099191 52.900809 15 \n",
"44 0.762170 36.630965 53.369035 16 \n",
"45 0.864404 36.308436 53.691564 17 \n",
"46 0.987084 36.119241 53.880759 18 \n",
"47 1.134300 36.031470 53.968530 19 last reaction step"
]
},
"execution_count": 16,
"metadata": {},
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"source": [
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"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
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}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
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"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
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}
},
"title": {
"x": 0.05
},
"xaxis": {
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"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
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},
"yaxis": {
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"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Reaction `A <-> B` . Changes in concentrations with time (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
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],
"range": [
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],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
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"autorange": true,
"domain": [
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],
"range": [
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83.88888888888889
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": 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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"uc.plot_history(show_intervals=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "4eac2789-754e-4cc6-92a6-7c087997b923",
"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": 18,
"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": "59fce89c-186f-4f41-a60a-955820ffde73",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "fec11ec2-e464-49ec-9722-41273fd87ba9",
"metadata": {},
"source": [
"#### As we requested, a log of the concentration data, in CSV format, has ben saved in the following file:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "58117fc0-250f-4136-93cc-63c9899dd9bb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'react_1_b_system_log.csv'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"csv_log_file"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "1d0f4922-ea2d-4493-9114-ecc66b1784cd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM TIME,A,B,step,caption\n",
"0.0,80.0,10.0,,Set concentration\n",
"0.006400000000000002,78.592,11.408000000000001,1,1st reaction step\n",
"0.009600000000000003,77.910528,12.089472,2,\n",
"0.013440000000000004,77.1058458624,12.894154137600001,3,\n",
"0.018048000000000005,76.15876717373031,13.841232826269698,4,\n",
"0.023577600000000004,75.04845757891101,14.951542421088993,5,\n",
"0.029107200000000007,73.96884582376929,16.031154176230725,6,\n",
"0.03463680000000001,72.91908317443371,17.080916825566298,7,\n",
"0.04016640000000001,71.89834436282696,18.101655637173042,8,\n",
"0.045696000000000014,70.90582693788352,19.094173062116482,9,\n",
"0.05233152000000001,69.7477353740692,20.252264625930806,10,\n",
"0.05896704000000001,68.62806650892247,21.371933491077524,11,\n",
"0.06560256000000002,67.54554556951605,22.45445443048395,12,\n",
"0.07223808000000002,66.49894007682887,23.501059923171127,13,\n",
"0.07887360000000003,65.48705844253587,24.512941557464124,14,\n",
"0.08550912000000004,64.50874861235279,25.491251387647203,15,\n",
"0.09347174400000004,63.373726382799354,26.62627361720064,16,\n",
"0.10143436800000004,62.2838929294738,27.716107070526196,17,\n",
"0.10939699200000004,61.237449146205506,28.762550853794487,18,\n",
"0.11735961600000004,60.23266755485373,29.767332445146263,19,\n",
"0.12532224000000003,59.26788945357223,30.732110546427762,20,\n",
"0.13487738880000003,58.15624872361806,31.84375127638193,21,\n",
"0.14443253760000002,57.09771745659816,32.90228254340183,22,\n",
"0.15398768640000002,56.0897583084074,33.910241691592596,23,\n",
"0.16354283520000001,55.12995515844305,34.87004484155694,24,\n",
"0.17500901376000003,54.033217749985546,35.96678225001445,25,\n",
"0.18647519232000004,52.999357276322066,37.00064272367793,26,\n",
"0.19794137088000005,52.02476894664434,37.97523105335565,27,\n",
"0.21170078515200005,50.92231177389454,39.07768822610545,28,\n",
"0.22546019942400006,49.89570042592975,40.10429957407024,1,1st reaction step\n",
"0.23921961369600006,48.93971693212988,41.06028306787011,2,\n",
"0.25573091082240007,47.871459377140354,42.12854062285964,3,\n",
"0.27224220794880005,46.89139341164059,43.1086065883594,4,\n",
"0.29205576450048004,45.81240721519991,44.18759278480008,5,\n",
"0.31186932105216003,44.84031378886753,45.15968621113246,6,\n",
"0.335645588914176,43.78936544572559,46.2106345542744,7,\n",
"0.359421856776192,42.863355249162076,47.13664475083792,8,\n",
"0.38795337821061116,41.88424541214457,48.11575458785542,9,\n",
"0.42219120393191417,40.87692656753266,49.123073432467336,10,\n",
"0.4564290296532172,40.042049758158775,49.95795024184122,11,\n",
"0.4975144205187808,39.21170378709872,50.78829621290127,12,\n",
"0.5468168895574572,38.41997915447455,51.580020845525446,13,\n",
"0.6059798524038689,37.704113470448206,52.29588652955179,14,\n",
"0.6769754078195629,37.099191058819024,52.90080894118097,15,\n",
"0.7621700743183957,36.630964980446095,53.3690350195539,16,\n",
"0.8644036741169949,36.30843587395681,53.69156412604318,17,\n",
"0.9870839938753141,36.11924081574702,53.88075918425297,18,\n",
"1.1343003775852971,36.031469807322495,53.9685301926775,19,\n",
"\n"
]
}
],
"source": [
"# Here's dump of the contents of that log file\n",
"with open(csv_log_file, 'r', encoding='utf8') as fh:\n",
" file_contents = fh.read()\n",
" print(file_contents)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c8f058df-f310-43eb-9b5c-a68a9918423b",
"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"
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},
"nbformat": 4,
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