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"## Tour of Life123's process of automatic detection - and stepsize reduction - in case any concentration goes negative\n",
"### Case study of two coupled reactions: `2 S <-> U` and `S <-> X` (both mostly forward)\n",
"\n",
"1st-order kinetics throughout. \n",
"\n",
"LAST REVISED: Nov. 21, 2023 **THIS IS AN ARCHIVED EXPERIMENT** \n",
"(Note: streamlined graphic functions and other improvements were added in later versions of Life123; but this is an ARCHIVED experiment that depends on an old version,\n",
"as well as depending on special code changes for illustrative purpose. So, this experiment is NOT meant to be run - and for the most part it's not reflecting later changes in the software libraries, though some of the function calls got updated.)\n",
"\n",
"* [RUN 1 : negative concentrations are detected but otherwise ignored](#negative_concentrations_1_run_1) \n",
"(_\"DEMONIC\"_ in figure below)\n",
"\n",
"* [RUN 2 : we restored some, but not all, of the code that had been disabled in Run #1](#negative_concentrations_1_run_2) \n",
"Negative concentrations from *individual* reactions are now automatically corrected - but negative concentrations from *combined* (synergistic) reactions can still slip thru \n",
"(_\"POSSESSED\"_ in figure below)\n",
"\n",
"* [RUN 3 : we restored ALL the code that had been disabled in the earlier runs](#negative_concentrations_1_run_3) \n",
"Negative concentrations from *individual* reactions are now automatically corrected - and so are negative concentrations from *combined* (synergistic) reactions \n",
"(_\"DISTURBED\"_ in figure below)\n",
"\n",
"* [RUN 4 : same as the previous run, but with slightly finer time resolution](#negative_concentrations_1_run_4) \n",
"(_\"HEALING\"_ in figure below)\n",
"\n",
"For even more accurate solutions - _\"HEALTHY\"_ in figure below - see the experiment `large_time_steps_2`\n"
]
},
{
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"id": "6925549c-3198-4a02-92de-710ce1ff9edf",
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"source": [
""
]
},
{
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"id": "5ca2c020-f5fb-4882-86d7-2f04948bf43a",
"metadata": {},
"source": [
"# IMPORTANT: DO NOT ATTEMPT TO RUN THIS NOTEBOOK! \n",
"## This is a **\"frozen run\"** that depends on Life123 _code changes_ for demonstration purposes \n",
"(a code change that turned off the automatic correction of time steps that lead to negative concentrations.) \n",
"If you bypass the execution exit in the first cell, and run the other cells, you WILL NOT REPLICATE the results below!"
]
},
{
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"execution_count": 1,
"id": "3edd830c-6023-454d-98e7-5b2ba544aa80",
"metadata": {},
"outputs": [
{
"ename": "StopExecution",
"evalue": "",
"output_type": "error",
"traceback": []
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"source": [
"# To stop the current and subsequent cells: USED TO PREVENT ACCIDENTAL RUNS OF THIS NOTEBOOK!\n",
"\n",
"class StopExecution(Exception):\n",
" def _render_traceback_(self):\n",
" return []\n",
"\n",
"raise StopExecution # See: https://stackoverflow.com/a/56953105/5478830"
]
},
{
"cell_type": "markdown",
"id": "df711d07-f89b-4972-b64c-da1a141b7fef",
"metadata": {},
"source": [
"## The `StopExecution` above is on purpose, TO PREVENT ACCIDENTAL RUNS OF THIS NOTEBOOK!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afd82fd6-5051-44c9-bc52-9094a36347e7",
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"id": "b5a6d5ea",
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"\n",
"from src.modules.chemicals.chem_data import ChemData as chem\n",
"from src.modules.reactions.reaction_dynamics import ReactionDynamics\n",
"\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "23c15e66-52e4-495b-aa3d-ecddd8d16942",
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"text": [
"Number of reactions: 2 (at temp. 25 C)\n",
"0: 2 S <-> U (kF = 8 / kR = 2 / Delta_G = -3,436.56 / K = 4) | 1st order in all reactants & products\n",
"1: S <-> X (kF = 6 / kR = 3 / Delta_G = -1,718.28 / K = 2) | 1st order in all reactants & products\n"
]
}
],
"source": [
"# Initialize the system\n",
"chem_data = chem(names=[\"U\", \"X\", \"S\"])\n",
"\n",
"# Reaction 2 S <-> U , with 1st-order kinetics for all species (mostly forward)\n",
"chem_data.add_reaction(reactants=[(2, \"S\")], products=\"U\",\n",
" forward_rate=8., reverse_rate=2.)\n",
"\n",
"# Reaction S <-> X , with 1st-order kinetics for all species (mostly forward)\n",
"chem_data.add_reaction(reactants=\"S\", products=\"X\",\n",
" forward_rate=6., reverse_rate=3.)\n",
"\n",
"chem_data.describe_reactions()"
]
},
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"id": "a6d597b4-c72c-4965-b276-0f60ad87736e",
"metadata": {},
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{
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"id": "bb39a3ec-5b20-45a7-b6b7-c7d834fc208c",
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"source": [
"# RUN 1 : negative concentrations are detected but otherwise ignored\n",
"This run required the disabling of multiple software features that detect, and automatically correct, such issues. \n",
"It cannot be replicated with current versions of Life123. DO NOT ATTEMPT TO RE-RUN!"
]
},
{
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"execution_count": 5,
"id": "f64798a2-1e2b-4984-b3da-b5c9d386ade0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (U). Conc: 50.0\n",
" Species 1 (X). Conc: 100.0\n",
" Species 2 (S). Conc: 0.0\n"
]
}
],
"source": [
"dynamics = ReactionDynamics(chem_data=chem_data)\n",
"dynamics.set_conc(conc={\"U\": 50., \"X\": 100., \"S\": 0.})\n",
"dynamics.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "eca11aad-d41e-4705-9252-4cb4ba0b2066",
"metadata": {},
"outputs": [
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"text": [
"+++++++++++ SYSTEM STATE ERROR: FAILED TO CATCH negative concentration upon advancing reactions from system time t=0.45\n",
"The computation took 2 extra step(s) - automatically added to prevent negative concentrations\n",
"10 total step(s) taken\n",
"From time 0 to 0.1, in 1 FULL step of 0.1\n",
"From time 0.1 to 0.15, in 1 substep of 0.05 (1/2 of full step)\n",
"From time 0.15 to 0.65, in 5 FULL steps of 0.1\n",
"From time 0.65 to 0.7, in 1 substep of 0.05 (1/2 of full step)\n",
"From time 0.7 to 0.9, in 2 FULL steps of 0.1\n",
"(for a grand total of the equivalent of 9 FULL steps)\n"
]
}
],
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"dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n",
"\n",
"# ************ DO NOT RUN : Life123 automatically catches and corrects these errors;\n",
"# this run required a code change for demonstration purposes! \n",
"# If you run it, you WILL NOT REPLICATE the results below.\n",
"# This is meant to be a \"frozen run\"\n",
"dynamics.single_compartment_react(time_step=0.1, stop_time=0.8)\n",
"\n",
"dynamics.explain_time_advance()"
]
},
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"id": "efc858d3-3cf2-4c63-9a8f-63f6efe1656d",
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"id": "bddd6d35-49dd-4866-9f58-1a7dbd2ea559",
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"source": [
"## Notice how all concentrations dips into negative values various times!"
]
},
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig0 = dynamics.plot_history(colors=['green', 'orange', 'blue']) # Prepare, but don't show, the main plot\n",
"\n",
"# Add a second plot, with a horizontal red line at concentration = 0\n",
"fig1 = px.line(x=[0,0.9], y=[0,0], color_discrete_sequence = ['red'])\n",
"\n",
"# Combine the plots, and display them \n",
"# (NOTE: streamlined graphic functions were added in later versions, to simplify this kind of plotting; but this is an ARCHIVED experiment -\n",
"# and it's not reflecting those improvements)\n",
"all_fig = go.Figure(data=fig0.data + fig1.data, layout = fig0.layout) # Note that the + is concatenating lists\n",
"all_fig.update_layout(title=\"Changes in concentrations; notice the several dips into negative concentrations (red line)\")\n",
"all_fig.show()"
]
},
{
"cell_type": "markdown",
"id": "0c1e8dc5-cc40-416c-8da2-aa8eefa0fb9e",
"metadata": {},
"source": [
"# There are 3 separate scenarios that lead to negative concentrations\n",
"\n",
"[More information](https://life123.science/reactions)\n",
"\n",
"#### Scenario 1 : A reaction causes a dip into the negative, and the combined other reactions fail to remedy it\n",
"\n",
"#### Scenario 2 : A reaction causes a dip into the negative, and the other reactions counterbalance it enough to remedy it\n",
"(though \"counterbalanced\", this is still regarded as a sign of instability)\n",
"\n",
"#### Scenario 3 : No single reaction causes any negative concentration, but - combined - they do"
]
},
{
"cell_type": "markdown",
"id": "75e3bad6-b3fb-4aac-a181-cf12f2ffd7d1",
"metadata": {},
"source": [
"**Example of scenario 1** (all the necessary diagnostic data in the next several rows) \n",
"The step from t=0.1 to 0.2 : \n",
"[S] is initially 50 \n",
"* Reaction 0 causes a Delta_S of -64.000000 , which would send it into the negative \n",
"(leading to message _\"*** DETECTING NEGATIVE CONCENTRATION in chemical `S` from reaction 2 S <-> U\"_) \n",
"* Reaction 1 causes a Delta_S of -9.000000 , which is fine by itself (no error message) but fails to remedy the negative (in fact, makes it worse) \n",
"\n",
"The net result is [S] = 50 -64 -9 = -23 at the final t=0.2 \n",
"That's what had led to the message _\"+++ SYSTEM STATE ERROR: FAILED TO CATCH negative concentration upon advancing reactions from system time t=0.1\"_"
]
},
{
"cell_type": "markdown",
"id": "dcbc6e51-5603-44c4-8ae3-853e49220acb",
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"source": [
"**Example of scenario 2** (all the necessary diagnostic data in the next several rows) \n",
"The step from t=0.4 to 0.5 : \n",
"[S] is initially -67.99 \n",
"* Reaction 0 causes a Delta_S of 146.96 , which would remedy the negative when combined with the initial value \n",
"* Reaction 1 causes a Delta_S of 63.927 , sending [S] into the negative (or, in this case, keeping it into the negative); \n",
"(leading to message _\"*** DETECTING NEGATIVE CONCENTRATION in chemical `S` from reaction S <-> X\"_)\n",
"\n",
"The net result is [S] = -67.99 +146.96 +63.927 = 142.897 at the final t=0.5 \n",
"The value is now positive, so there's no SYSTEM STATE ERROR"
]
},
{
"cell_type": "markdown",
"id": "35f17ed7-0ef6-4cc7-81e9-f096f92343ab",
"metadata": {},
"source": [
"**Example of scenario 3** \n",
"This scenario doesn't occur in this run, but scroll down to RUN 2 - where it occurs. \n",
"An example would be if the initial concentration were 20, and each of the two reactions caused a delta_concentration of -15 ; \n",
"combined, they'll bring to concentration to 20 -15 -15 = -10"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "af7fb1fb-fb68-4a30-821d-d96f882320bd",
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"Reaction: 2 S <-> U\n"
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" TIME Delta U Delta X Delta S reaction substep time_subdivision \\\n",
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" delta_time caption \n",
"0 0.10 \n",
"1 0.05 \n",
"2 0.10 \n",
"3 0.10 \n",
"4 0.10 \n",
"5 0.10 \n",
"6 0.10 \n",
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"metadata": {},
"output_type": "execute_result"
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],
"source": [
"dynamics.get_diagnostic_rxn_data(rxn_index=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73470fcc-c408-433c-b667-e5caa6961b2b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "c3660bf1-f2b9-418b-8646-15ab368183cf",
"metadata": {},
"source": [
"##################################################################################"
]
},
{
"cell_type": "markdown",
"id": "b9ce3ea9-1711-45d0-861d-8caf5ae662d6",
"metadata": {},
"source": [
"# RUN 2 : we restored some, but not all, of the code that had been disabled in Run #1. \n",
"## Negative concentrations from *individual* reactions are now automatically corrected - but negative concentrations from *combined* (synergistic) reactions can still slip thru\n",
"### (With the restored code) Life123 automatically detects whenever an individual reaction would cause any chemical concentration to go negative. \n",
"At that point, it raises an `ExcessiveTimeStep` exception.\n",
"That exception gets caught - which results in the following: \n",
"* the (partial) execution of the last step gets discarded\n",
"* the step size gets halved\n",
"* the run automatically resumes from the previous time\n",
"\n",
"Such an automatic detection and remediation eliminates \"Scenarios 1 and 2\" (see earlier in notebook for definitions) BUT NOT \"scenario 3\" \n",
"IMPORTANT: scenario 3 normally gets caught and remedied, too - but that feature got disabled by a code change in this run, _FOR DEMONSTRATION PURPOSES_\n",
"\n",
"This run required the disabling of multiple software features that detect, and automatically correct, such issues. \n",
"It cannot be replicated with current versions of Life123. **DO NOT ATTEMPT TO RE-RUN!**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "03aff58b-7354-4d15-84ea-4a74bff4a832",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (U). Conc: 50.0\n",
" Species 1 (X). Conc: 100.0\n",
" Species 2 (S). Conc: 0.0\n"
]
}
],
"source": [
"# Same as for Run #1\n",
"dynamics = ReactionDynamics(chem_data=chem_data)\n",
"dynamics.set_conc(conc={\"U\": 50., \"X\": 100., \"S\": 0.})\n",
"dynamics.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f0f74d20-1c57-45dc-bd76-4d1e3c00041f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"****** DETECTING NEGATIVE CONCENTRATION in chemical `S` from reaction 2 S <-> U\n",
" [System Time: 0.1 : Baseline value: 50 ; delta conc: -64]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"+++++++++++ SYSTEM STATE ERROR: FAILED TO CATCH negative concentration upon advancing reactions from system time t=0.1\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `S` from reaction 2 S <-> U\n",
" [System Time: 0.3 : Baseline value: 80.1 ; delta conc: -112.48]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"+++++++++++ SYSTEM STATE ERROR: FAILED TO CATCH negative concentration upon advancing reactions from system time t=0.3\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `S` from reaction S <-> X\n",
" [System Time: 0.4 : Baseline value: -67.99 ; delta conc: 63.927]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `S` from reaction 2 S <-> U\n",
" [System Time: 0.5 : Baseline value: 142.9 ; delta conc: -219.85]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"+++++++++++ SYSTEM STATE ERROR: FAILED TO CATCH negative concentration upon advancing reactions from system time t=0.5\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `U` from reaction 2 S <-> U\n",
" [System Time: 0.6 : Baseline value: 131.89 ; delta conc: -153.37]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `S` from reaction S <-> X\n",
" [System Time: 0.6 : Baseline value: -158.74 ; delta conc: 123.73]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `X` from reaction S <-> X\n",
" [System Time: 0.6 : Baseline value: 94.966 ; delta conc: -123.73]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"+++++++++++ SYSTEM STATE ERROR: FAILED TO CATCH negative concentration upon advancing reactions from system time t=0.6\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `S` from reaction 2 S <-> U\n",
" [System Time: 0.7 : Baseline value: 271.73 ; delta conc: -443.36]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"+++++++++++ SYSTEM STATE ERROR: FAILED TO CATCH negative concentration upon advancing reactions from system time t=0.7\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `U` from reaction 2 S <-> U\n",
" [System Time: 0.8 : Baseline value: 200.2 ; delta conc: -314.68]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `S` from reaction S <-> X\n",
" [System Time: 0.8 : Baseline value: -343.3 ; delta conc: 248.85]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"****** DETECTING NEGATIVE CONCENTRATION in chemical `X` from reaction S <-> X\n",
" [System Time: 0.8 : Baseline value: 142.9 ; delta conc: -248.85]\n",
" IGNORING AND CONTINUING (for demonstration purposes)\n",
"+++++++++++ SYSTEM STATE ERROR: FAILED TO CATCH negative concentration upon advancing reactions from system time t=0.8\n",
"9 total step(s) taken\n",
"From time 0 to 0.9, in 9 FULL steps of 0.1\n",
"(for a grand total of the equivalent of 9 FULL steps)\n"
]
}
],
"source": [
"dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n",
"\n",
"# ************ DO NOT RUN : Life123 automatically catches and corrects these errors;\n",
"# this run required a code change for demonstration purposes! \n",
"# If you run it, you WILL NOT REPLICATE the results below.\n",
"# This is meant to be a \"frozen run\"\n",
"dynamics.single_compartment_react(time_step=0.1, stop_time=0.8)\n",
"\n",
"dynamics.explain_time_advance()"
]
},
{
"cell_type": "markdown",
"id": "e9643a15-97a4-4066-aae6-bd446c4a64cd",
"metadata": {},
"source": [
"### Notice how the system automatically temporarily slowed down from the requested time step of 0.1 at t=0.1 and again at t=0.65 \n",
"Those actions intercepted, and automatically remedied, some - but NOT all - of the negative concentrations (explained below in the notebook.) \n",
"We now took a total of 10 steps, instead of the 9 ones of Run #1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b568da5d-8f10-4128-8472-8ab34dd917a4",
"metadata": {},
"outputs": [
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" 28.550000 | \n",
" | \n",
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\n",
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" 67.320000 | \n",
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\n",
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\n",
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\n",
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" | \n",
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\n",
" \n",
" | 8 | \n",
" 0.70 | \n",
" 72.280162 | \n",
" 45.083834 | \n",
" 10.355842 | \n",
" | \n",
"
\n",
" \n",
" | 9 | \n",
" 0.80 | \n",
" 66.108803 | \n",
" 37.772189 | \n",
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" | \n",
"
\n",
" \n",
" | 10 | \n",
" 0.90 | \n",
" 76.895206 | \n",
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\n",
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\n",
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"text/plain": [
" SYSTEM TIME U X S caption\n",
"0 0.00 50.000000 100.000000 0.000000 Initial state\n",
"1 0.10 40.000000 70.000000 50.000000 \n",
"2 0.15 56.000000 74.500000 13.500000 \n",
"3 0.25 55.600000 60.250000 28.550000 \n",
"4 0.35 67.320000 59.305000 6.055000 \n",
"5 0.45 58.700000 45.146500 37.453500 \n",
"6 0.50 67.811400 49.610575 14.766625 \n",
"7 0.60 66.062420 43.587378 24.287783 \n",
"8 0.70 72.280162 45.083834 10.355842 \n",
"9 0.80 66.108803 37.772189 30.010204 \n",
"10 0.90 76.895206 44.446655 1.762933 "
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},
"execution_count": 6,
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"source": [
"## Notice how [S] dips into negative values at t=0.55!\n",
"But all the multitude of other negative dips we had in Run #1 are now gone :)"
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig0 = dynamics.plot_history(colors=['green', 'orange', 'blue']) # Prepare, but don't show, the main plot\n",
"\n",
"# Add a second plot, with a horizontal red line at concentration = 0\n",
"fig1 = px.line(x=[0,0.9], y=[0,0], color_discrete_sequence = ['red'])\n",
"\n",
"# Combine the plots, and display them\n",
"all_fig = go.Figure(data=fig0.data + fig1.data, layout = fig0.layout) # Note that the + is concatenating lists\n",
"all_fig.update_layout(title=\"Changes in concentrations; notice how S dips into negative concentrations (red line) at t=0.55\")\n",
"all_fig.show()"
]
},
{
"cell_type": "markdown",
"id": "1e3ac47c-4446-48d8-94cb-67f164d32d6a",
"metadata": {},
"source": [
"## With this partially-restored functionality, we're averting what we called \"scenarios 1 and 2\" earlier in the notebook - but are still afflicted by \"scenario 3\" (no single reaction causes any negative concentration, but - combined - they do)"
]
},
{
"cell_type": "markdown",
"id": "b5b816f5-1c0f-41bc-810a-3172c8b8abfd",
"metadata": {},
"source": [
"**That's exactly what happens to [S] at t=0.55** (all the necessary diagnostic data in the next few rows) \n",
"The step from t=0.45 to 0.55 : \n",
"[S] is initially 37.453500 \n",
"* Reaction 0 causes a Delta_S of -36.445600 , which by itself would cause no problem \n",
"* Reaction 1 causes a Delta_S of -8.928150 , by itself would cause no problem - but leads to a negative value when combined with reaction 0! \n",
"\n",
"The net result is [S] = 37.453500 -36.445600 -8.928150 = -7.92025 at the final t=0.55 \n",
"That's what had led to the message _\"+++ SYSTEM STATE ERROR: FAILED TO CATCH negative concentration upon advancing reactions from system time t=0.45\"_"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "dfe04bbd-c449-452a-9286-86dfd1e6972f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" U | \n",
" X | \n",
" S | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 50.000000 | \n",
" 100.000000 | \n",
" 0.000000 | \n",
" Initial state | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.1 | \n",
" 40.000000 | \n",
" 70.000000 | \n",
" 50.000000 | \n",
" | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.2 | \n",
" 72.000000 | \n",
" 79.000000 | \n",
" -23.000000 | \n",
" | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.3 | \n",
" 39.200000 | \n",
" 41.500000 | \n",
" 80.100000 | \n",
" | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.4 | \n",
" 95.440000 | \n",
" 77.110000 | \n",
" -67.990000 | \n",
" | \n",
"
\n",
" \n",
" | 5 | \n",
" 0.5 | \n",
" 21.960000 | \n",
" 13.183000 | \n",
" 142.897000 | \n",
" | \n",
"
\n",
" \n",
" | 6 | \n",
" 0.6 | \n",
" 131.885600 | \n",
" 94.966300 | \n",
" -158.737500 | \n",
" | \n",
"
\n",
" \n",
" | 7 | \n",
" 0.7 | \n",
" -21.481520 | \n",
" -28.766090 | \n",
" 271.729130 | \n",
" | \n",
"
\n",
" \n",
" | 8 | \n",
" 0.8 | \n",
" 200.198088 | \n",
" 142.901215 | \n",
" -343.297391 | \n",
" | \n",
"
\n",
" \n",
" | 9 | \n",
" 0.9 | \n",
" -114.479442 | \n",
" -105.947584 | \n",
" 534.906469 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SYSTEM TIME U X S caption\n",
"0 0.0 50.000000 100.000000 0.000000 Initial state\n",
"1 0.1 40.000000 70.000000 50.000000 \n",
"2 0.2 72.000000 79.000000 -23.000000 \n",
"3 0.3 39.200000 41.500000 80.100000 \n",
"4 0.4 95.440000 77.110000 -67.990000 \n",
"5 0.5 21.960000 13.183000 142.897000 \n",
"6 0.6 131.885600 94.966300 -158.737500 \n",
"7 0.7 -21.481520 -28.766090 271.729130 \n",
"8 0.8 200.198088 142.901215 -343.297391 \n",
"9 0.9 -114.479442 -105.947584 534.906469 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a090c46a-2fb0-4895-ab4f-c035640be6c9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reaction: 2 S <-> U\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
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\n",
" \n",
" \n",
" | \n",
" TIME | \n",
" Delta U | \n",
" Delta X | \n",
" Delta S | \n",
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" TIME Delta U Delta X Delta S reaction substep time_subdivision \\\n",
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"7 0.7 221.679608 0.0 -443.359216 0 0 1 \n",
"8 0.8 -314.677530 0.0 629.355061 0 0 1 \n",
"\n",
" delta_time caption \n",
"0 0.1 \n",
"1 0.1 \n",
"2 0.1 \n",
"3 0.1 \n",
"4 0.1 \n",
"5 0.1 \n",
"6 0.1 \n",
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"8 0.1 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dynamics.get_diagnostic_rxn_data(rxn_index=0)"
]
},
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"source": [
"dynamics.get_diagnostic_rxn_data(rxn_index=1)"
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},
{
"cell_type": "code",
"execution_count": null,
"id": "4906c37c-38cd-4600-9713-c9da1c492a00",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "a710a1ae-fa4a-4cd2-abc6-89e79a0cae7c",
"metadata": {},
"source": [
"##################################################################################"
]
},
{
"cell_type": "markdown",
"id": "93338e23-b272-4e28-b3f9-c509f738c47c",
"metadata": {},
"source": [
"# RUN 3 : we restored ALL the code that had been disabled in the earlier runs.\n",
"## Negative concentrations from *individual* reactions are now automatically corrected - and so are negative concentrations from *combined* (synergistic) reactions\n",
"### (With the restored code) Life123 automatically detects whenever any chemical concentration goes negative from any cause. \n",
"At that point, it raises an `ExcessiveTimeStep` exception.\n",
"That exception gets caught - which results in the following: \n",
"* the (partial) execution of the last step gets discarded\n",
"* the step size gets halved\n",
"* the run automatically resumes from the previous time"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4c0c3806-2294-4d8f-8e1c-834a01a7378e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (U). Conc: 50.0\n",
" Species 1 (X). Conc: 100.0\n",
" Species 2 (S). Conc: 0.0\n"
]
}
],
"source": [
"# Same as for the previous runs\n",
"dynamics = ReactionDynamics(chem_data=chem_data)\n",
"dynamics.set_conc(conc={\"U\": 50., \"X\": 100., \"S\": 0.})\n",
"dynamics.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fc94d565-ba29-41c9-966b-43d76f70f761",
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"******** CAUTION: negative concentration in chemical `S` (resulting from reaction 2 S <-> U)\n",
" upon advancing from system time t=0.1 [Baseline value: 50 ; delta conc: -64]\n",
" It will be AUTOMATICALLY CORRECTED with a reduction in the time step size\n",
"******** CAUTION: negative concentration resulting from the combined effect of multiple reactions, upon advancing reactions from system time t=0.45\n",
" It will be AUTOMATICALLY CORRECTED with a reduction in the time step size\n",
"The computation took 2 extra step(s) - automatically added to prevent negative concentrations\n",
"10 total step(s) taken\n",
"From time 0 to 0.1, in 1 FULL step of 0.1\n",
"From time 0.1 to 0.15, in 1 substep of 0.05 (1/2 of full step)\n",
"From time 0.15 to 0.45, in 3 FULL steps of 0.1\n",
"From time 0.45 to 0.5, in 1 substep of 0.05 (1/2 of full step)\n",
"From time 0.5 to 0.9, in 4 FULL steps of 0.1\n",
"(for a grand total of the equivalent of 9 FULL steps)\n"
]
}
],
"source": [
"dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n",
"\n",
"dynamics.single_compartment_react(time_step=0.1, stop_time=0.8)\n",
"\n",
"dynamics.explain_time_advance()"
]
},
{
"cell_type": "markdown",
"id": "c83c9ba3-efcf-4d21-ada3-fe7459b16af0",
"metadata": {},
"source": [
"### Notice how the system automatically temporarily slowed down from the requested time step of 0.1 at t=0.1 and again at t=0.45 \n",
"Those actions intercepted, and automatically remedied, ALL the negative concentrations, whether caused by any single reaction, or by the cumulative effect of multiple ones. \n",
"We now took a total of 10 steps, instead of the 9 ones of Run #1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "241b5266-3cd9-42fc-a0e4-0096d0405743",
"metadata": {},
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" SYSTEM TIME U X S \\\n",
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"\n",
" caption \n",
"0 Initial state \n",
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"2 \n",
"3 Interm. step, due to the fast rxns: [0, 1] \n",
"4 \n",
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"6 \n",
"7 \n",
"8 \n",
"9 \n",
"10 \n",
"11 \n",
"12 "
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"execution_count": 10,
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"source": [
"df = dynamics.get_history()\n",
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},
{
"cell_type": "markdown",
"id": "812cc2c8-a0fa-47e9-bd32-1bd35652ae2c",
"metadata": {},
"source": [
"## Notice how negative concentrations are no longer seen anywhere :)"
]
},
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"cell_type": "code",
"execution_count": 7,
"id": "acea7d10-a21b-4e33-9c29-0f218f2a189f",
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig0 = dynamics.plot_history(colors=['green', 'orange', 'blue']) # Prepare, but don't show, the main plot\n",
"\n",
"# Add a second plot, with a horizontal red line at concentration = 0\n",
"fig1 = px.line(x=[0,0.9], y=[0,0], color_discrete_sequence = ['red'])\n",
"\n",
"# Combine the plots, and display them\n",
"all_fig = go.Figure(data=fig0.data + fig1.data, layout = fig0.layout) # Note that the + is concatenating lists\n",
"all_fig.update_layout(title=\"Changes in concentrations; notice negative concentrations are totally absent now\")\n",
"all_fig.show()"
]
},
{
"cell_type": "markdown",
"id": "21f7a55d-09c9-4c06-8893-bebcbfc4b5a1",
"metadata": {},
"source": [
"## With the completely-restored functionality, we're averting what we called \"scenarios 1, 2 and 3\" earlier in the notebook :)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13f8e2e7-55c9-409b-a6a4-bc2dffb67ead",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "329e3e69-9db6-4f5a-876a-a63f7f5cb7bc",
"metadata": {},
"source": [
"##################################################################################"
]
},
{
"cell_type": "markdown",
"id": "04e74b37-abd8-4005-bd53-f4b0f1e9a2c7",
"metadata": {},
"source": [
"# RUN 4 : same as the previous run, but with slightly finer time resolution\n",
"\n",
"In run 3, we demonstrated catching - and resolving - all cases of negative concentrations that would slip in if it weren't for Life123's detection and automatic remediation. But we still have a lot of instability, especially in [S].\n",
"\n",
"A slight improvement in the time steps will make a profound difference in this run"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b261a4c1-2185-46d2-a9e5-839ba57b22e2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (U). Conc: 50.0\n",
" Species 1 (X). Conc: 100.0\n",
" Species 2 (S). Conc: 0.0\n"
]
}
],
"source": [
"# Same as for the previous runs\n",
"dynamics = ReactionDynamics(chem_data=chem_data)\n",
"dynamics.set_conc(conc={\"U\": 50., \"X\": 100., \"S\": 0.})\n",
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "25bf72e9-2a5f-416e-aa3b-0a2ad40f1aeb",
"metadata": {},
"source": [
"## Rather than decreasing (for the whole run) the time step of 0.1, we'll simply opt to AUTOMATICALLY take 2 substeps whenever any of the reactions are \"fast changing\" based on a threshold we provide"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "95dc4ede-e840-4221-a315-f37ffe65446b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"single_compartment_react(): setting rel_fast_threshold to 800.0\n",
"9 total step(s) taken\n",
"From time 0 to 0.3, in 6 substeps of 0.05 (each 1/2 of full step)\n",
"From time 0.3 to 0.9, in 6 FULL steps of 0.1\n",
"(for a grand total of the equivalent of 9 FULL steps)\n"
]
}
],
"source": [
"dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n",
"\n",
"dynamics.single_compartment_react(time_step=0.1, stop_time=0.8, \n",
" dynamic_substeps=2, abs_fast_threshold=80.0)\n",
"\n",
"dynamics.explain_time_advance()"
]
},
{
"cell_type": "markdown",
"id": "730d32ea-1493-4abe-8f2c-12db25785d3c",
"metadata": {},
"source": [
"### Note how the system automatically splits the time steps into substeps for up to t=0.3 \n",
"that's when most of the change is occurring"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "97e67baa-3f77-4d4c-b996-d0867a53c618",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" U | \n",
" X | \n",
" S | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 50.000000 | \n",
" 100.000000 | \n",
" 0.000000 | \n",
" Initial state | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.1 | \n",
" 40.000000 | \n",
" 70.000000 | \n",
" 50.000000 | \n",
" | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.2 | \n",
" 72.000000 | \n",
" 79.000000 | \n",
" -23.000000 | \n",
" | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.3 | \n",
" 39.200000 | \n",
" 41.500000 | \n",
" 80.100000 | \n",
" | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.4 | \n",
" 95.440000 | \n",
" 77.110000 | \n",
" -67.990000 | \n",
" | \n",
"
\n",
" \n",
" | 5 | \n",
" 0.5 | \n",
" 21.960000 | \n",
" 13.183000 | \n",
" 142.897000 | \n",
" | \n",
"
\n",
" \n",
" | 6 | \n",
" 0.6 | \n",
" 131.885600 | \n",
" 94.966300 | \n",
" -158.737500 | \n",
" | \n",
"
\n",
" \n",
" | 7 | \n",
" 0.7 | \n",
" -21.481520 | \n",
" -28.766090 | \n",
" 271.729130 | \n",
" | \n",
"
\n",
" \n",
" | 8 | \n",
" 0.8 | \n",
" 200.198088 | \n",
" 142.901215 | \n",
" -343.297391 | \n",
" | \n",
"
\n",
" \n",
" | 9 | \n",
" 0.9 | \n",
" -114.479442 | \n",
" -105.947584 | \n",
" 534.906469 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SYSTEM TIME U X S caption\n",
"0 0.0 50.000000 100.000000 0.000000 Initial state\n",
"1 0.1 40.000000 70.000000 50.000000 \n",
"2 0.2 72.000000 79.000000 -23.000000 \n",
"3 0.3 39.200000 41.500000 80.100000 \n",
"4 0.4 95.440000 77.110000 -67.990000 \n",
"5 0.5 21.960000 13.183000 142.897000 \n",
"6 0.6 131.885600 94.966300 -158.737500 \n",
"7 0.7 -21.481520 -28.766090 271.729130 \n",
"8 0.8 200.198088 142.901215 -343.297391 \n",
"9 0.9 -114.479442 -105.947584 534.906469 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = dynamics.get_history()\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "45dfb42e",
"metadata": {},
"outputs": [],
"source": [
"dynamics.plot_history(colors=['green', 'orange', 'blue'])"
]
},
{
"cell_type": "markdown",
"id": "8912283d-9fa2-49eb-8160-ae3f0a47c525",
"metadata": {},
"source": [
"## The resolution is still coarse, especially up to t=0.1, but all the instability is gone :)"
]
},
{
"cell_type": "markdown",
"id": "909f4875-91aa-41db-a799-df82b806c73a",
"metadata": {},
"source": [
"### Note: For even more accurate solutions, see the experiment `large_time_steps_2`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc0bc3d5",
"metadata": {},
"outputs": [],
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