{
"cells": [
{
"cell_type": "markdown",
"id": "5cbc8640",
"metadata": {},
"source": [
"### Demonstration of file storage of system 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 file storage of system 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 = \"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 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)"
]
},
{
"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 (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": [
"# 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": 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": "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",
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\n",
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" 0.000000 | \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",
"Final 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",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.000000 | \n",
" 80.000000 | \n",
" 10.000000 | \n",
" Set concentration | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.006400 | \n",
" 78.592000 | \n",
" 11.408000 | \n",
" 1st reaction step | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.009600 | \n",
" 77.910528 | \n",
" 12.089472 | \n",
" | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.013440 | \n",
" 77.105846 | \n",
" 12.894154 | \n",
" | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.018048 | \n",
" 76.158767 | \n",
" 13.841233 | \n",
" | \n",
"
\n",
" \n",
" | 5 | \n",
" 0.023578 | \n",
" 75.048458 | \n",
" 14.951542 | \n",
" | \n",
"
\n",
" \n",
" | 6 | \n",
" 0.029107 | \n",
" 73.968846 | \n",
" 16.031154 | \n",
" | \n",
"
\n",
" \n",
" | 7 | \n",
" 0.034637 | \n",
" 72.919083 | \n",
" 17.080917 | \n",
" | \n",
"
\n",
" \n",
" | 8 | \n",
" 0.040166 | \n",
" 71.898344 | \n",
" 18.101656 | \n",
" | \n",
"
\n",
" \n",
" | 9 | \n",
" 0.045696 | \n",
" 70.905827 | \n",
" 19.094173 | \n",
" | \n",
"
\n",
" \n",
" | 10 | \n",
" 0.052332 | \n",
" 69.747735 | \n",
" 20.252265 | \n",
" | \n",
"
\n",
" \n",
" | 11 | \n",
" 0.058967 | \n",
" 68.628067 | \n",
" 21.371933 | \n",
" | \n",
"
\n",
" \n",
" | 12 | \n",
" 0.065603 | \n",
" 67.545546 | \n",
" 22.454454 | \n",
" | \n",
"
\n",
" \n",
" | 13 | \n",
" 0.072238 | \n",
" 66.498940 | \n",
" 23.501060 | \n",
" | \n",
"
\n",
" \n",
" | 14 | \n",
" 0.078874 | \n",
" 65.487058 | \n",
" 24.512942 | \n",
" | \n",
"
\n",
" \n",
" | 15 | \n",
" 0.085509 | \n",
" 64.508749 | \n",
" 25.491251 | \n",
" | \n",
"
\n",
" \n",
" | 16 | \n",
" 0.093472 | \n",
" 63.373726 | \n",
" 26.626274 | \n",
" | \n",
"
\n",
" \n",
" | 17 | \n",
" 0.101434 | \n",
" 62.283893 | \n",
" 27.716107 | \n",
" | \n",
"
\n",
" \n",
" | 18 | \n",
" 0.109397 | \n",
" 61.237449 | \n",
" 28.762551 | \n",
" | \n",
"
\n",
" \n",
" | 19 | \n",
" 0.117360 | \n",
" 60.232668 | \n",
" 29.767332 | \n",
" | \n",
"
\n",
" \n",
" | 20 | \n",
" 0.125322 | \n",
" 59.267889 | \n",
" 30.732111 | \n",
" | \n",
"
\n",
" \n",
" | 21 | \n",
" 0.134877 | \n",
" 58.156249 | \n",
" 31.843751 | \n",
" | \n",
"
\n",
" \n",
" | 22 | \n",
" 0.144433 | \n",
" 57.097717 | \n",
" 32.902283 | \n",
" | \n",
"
\n",
" \n",
" | 23 | \n",
" 0.153988 | \n",
" 56.089758 | \n",
" 33.910242 | \n",
" | \n",
"
\n",
" \n",
" | 24 | \n",
" 0.163543 | \n",
" 55.129955 | \n",
" 34.870045 | \n",
" | \n",
"
\n",
" \n",
" | 25 | \n",
" 0.175009 | \n",
" 54.033218 | \n",
" 35.966782 | \n",
" | \n",
"
\n",
" \n",
" | 26 | \n",
" 0.186475 | \n",
" 52.999357 | \n",
" 37.000643 | \n",
" | \n",
"
\n",
" \n",
" | 27 | \n",
" 0.197941 | \n",
" 52.024769 | \n",
" 37.975231 | \n",
" | \n",
"
\n",
" \n",
" | 28 | \n",
" 0.211701 | \n",
" 50.922312 | \n",
" 39.077688 | \n",
" last reaction step | \n",
"
\n",
" \n",
" | 29 | \n",
" 0.225460 | \n",
" 49.895700 | \n",
" 40.104300 | \n",
" 1st reaction step | \n",
"
\n",
" \n",
" | 30 | \n",
" 0.239220 | \n",
" 48.939717 | \n",
" 41.060283 | \n",
" | \n",
"
\n",
" \n",
" | 31 | \n",
" 0.255731 | \n",
" 47.871459 | \n",
" 42.128541 | \n",
" | \n",
"
\n",
" \n",
" | 32 | \n",
" 0.272242 | \n",
" 46.891393 | \n",
" 43.108607 | \n",
" | \n",
"
\n",
" \n",
" | 33 | \n",
" 0.292056 | \n",
" 45.812407 | \n",
" 44.187593 | \n",
" | \n",
"
\n",
" \n",
" | 34 | \n",
" 0.311869 | \n",
" 44.840314 | \n",
" 45.159686 | \n",
" | \n",
"
\n",
" \n",
" | 35 | \n",
" 0.335646 | \n",
" 43.789365 | \n",
" 46.210635 | \n",
" | \n",
"
\n",
" \n",
" | 36 | \n",
" 0.359422 | \n",
" 42.863355 | \n",
" 47.136645 | \n",
" | \n",
"
\n",
" \n",
" | 37 | \n",
" 0.387953 | \n",
" 41.884245 | \n",
" 48.115755 | \n",
" | \n",
"
\n",
" \n",
" | 38 | \n",
" 0.422191 | \n",
" 40.876927 | \n",
" 49.123073 | \n",
" | \n",
"
\n",
" \n",
" | 39 | \n",
" 0.456429 | \n",
" 40.042050 | \n",
" 49.957950 | \n",
" | \n",
"
\n",
" \n",
" | 40 | \n",
" 0.497514 | \n",
" 39.211704 | \n",
" 50.788296 | \n",
" | \n",
"
\n",
" \n",
" | 41 | \n",
" 0.546817 | \n",
" 38.419979 | \n",
" 51.580021 | \n",
" | \n",
"
\n",
" \n",
" | 42 | \n",
" 0.605980 | \n",
" 37.704113 | \n",
" 52.295887 | \n",
" | \n",
"
\n",
" \n",
" | 43 | \n",
" 0.676975 | \n",
" 37.099191 | \n",
" 52.900809 | \n",
" | \n",
"
\n",
" \n",
" | 44 | \n",
" 0.762170 | \n",
" 36.630965 | \n",
" 53.369035 | \n",
" | \n",
"
\n",
" \n",
" | 45 | \n",
" 0.864404 | \n",
" 36.308436 | \n",
" 53.691564 | \n",
" | \n",
"
\n",
" \n",
" | 46 | \n",
" 0.987084 | \n",
" 36.119241 | \n",
" 53.880759 | \n",
" | \n",
"
\n",
" \n",
" | 47 | \n",
" 1.134300 | \n",
" 36.031470 | \n",
" 53.968530 | \n",
" last reaction step | \n",
"
\n",
" \n",
"
\n",
"
"
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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 last reaction step\n",
"29 0.225460 49.895700 40.104300 1st reaction step\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": 16,
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"source": [
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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",
"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": 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,caption\n",
"0.0,80.0,10.0,Set concentration\n",
"0.006400000000000002,78.592,11.408000000000001,1st reaction step\n",
"0.009600000000000003,77.910528,12.089472,\n",
"0.013440000000000004,77.1058458624,12.894154137600001,\n",
"0.018048000000000005,76.15876717373031,13.841232826269698,\n",
"0.023577600000000004,75.04845757891101,14.951542421088993,\n",
"0.029107200000000007,73.96884582376929,16.031154176230725,\n",
"0.03463680000000001,72.91908317443371,17.080916825566298,\n",
"0.04016640000000001,71.89834436282696,18.101655637173042,\n",
"0.045696000000000014,70.90582693788352,19.094173062116482,\n",
"0.05233152000000001,69.7477353740692,20.252264625930806,\n",
"0.05896704000000001,68.62806650892247,21.371933491077524,\n",
"0.06560256000000002,67.54554556951605,22.45445443048395,\n",
"0.07223808000000002,66.49894007682887,23.501059923171127,\n",
"0.07887360000000003,65.48705844253587,24.512941557464124,\n",
"0.08550912000000004,64.50874861235279,25.491251387647203,\n",
"0.09347174400000004,63.373726382799354,26.62627361720064,\n",
"0.10143436800000004,62.2838929294738,27.716107070526196,\n",
"0.10939699200000004,61.237449146205506,28.762550853794487,\n",
"0.11735961600000004,60.23266755485373,29.767332445146263,\n",
"0.12532224000000003,59.26788945357223,30.732110546427762,\n",
"0.13487738880000003,58.15624872361806,31.84375127638193,\n",
"0.14443253760000002,57.09771745659816,32.90228254340183,\n",
"0.15398768640000002,56.0897583084074,33.910241691592596,\n",
"0.16354283520000001,55.12995515844305,34.87004484155694,\n",
"0.17500901376000003,54.033217749985546,35.96678225001445,\n",
"0.18647519232000004,52.999357276322066,37.00064272367793,\n",
"0.19794137088000005,52.02476894664434,37.97523105335565,\n",
"0.21170078515200005,50.92231177389454,39.07768822610545,\n",
"0.22546019942400006,49.89570042592975,40.10429957407024,1st reaction step\n",
"0.23921961369600006,48.93971693212988,41.06028306787011,\n",
"0.25573091082240007,47.871459377140354,42.12854062285964,\n",
"0.27224220794880005,46.89139341164059,43.1086065883594,\n",
"0.29205576450048004,45.81240721519991,44.18759278480008,\n",
"0.31186932105216003,44.84031378886753,45.15968621113246,\n",
"0.335645588914176,43.78936544572559,46.2106345542744,\n",
"0.359421856776192,42.863355249162076,47.13664475083792,\n",
"0.38795337821061116,41.88424541214457,48.11575458785542,\n",
"0.42219120393191417,40.87692656753266,49.123073432467336,\n",
"0.4564290296532172,40.042049758158775,49.95795024184122,\n",
"0.4975144205187808,39.21170378709872,50.78829621290127,\n",
"0.5468168895574572,38.41997915447455,51.580020845525446,\n",
"0.6059798524038689,37.704113470448206,52.29588652955179,\n",
"0.6769754078195629,37.099191058819024,52.90080894118097,\n",
"0.7621700743183957,36.630964980446095,53.3690350195539,\n",
"0.8644036741169949,36.30843587395681,53.69156412604318,\n",
"0.9870839938753141,36.11924081574702,53.88075918425297,\n",
"1.1343003775852971,36.031469807322495,53.9685301926775,\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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}