{
"cells": [
{
"cell_type": "markdown",
"id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f",
"metadata": {},
"source": [
"## Coupled pair of reactions: `S <-> P` , and `S + E <-> P + E`\n",
"A direct reaction and the same reaction, catalyzed\n",
"### Re-run from same initial concentrations of S (\"Substrate\") and P (\"Product\"), for various concentations of the enzyme `E`: from zero to hugely abundant \n",
"#### Given the initial [S]=20 and P=[0], the times at which [S] = [P] are computed for various concentrations of `E`\n",
"\n",
"1. No enzyme `E` \n",
"2. [E] = 0.2 (1/100 of the initial [S]) \n",
"3. [E] = 1 \n",
"4. [E] = 5 \n",
"5. [E] = 20 \n",
"6. [E] = 30 \n",
"7. [E] = 100 \n",
"8. [E] = 2000 (100 times the initial [S])\n",
"\n",
"LAST REVISED: May 31, 2024 (using v. 1.0 beta32)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "cbb1af2e-3564-460e-a4ae-41e4ec4f719f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Added 'D:\\Docs\\- MY CODE\\BioSimulations\\life123-Win7' to sys.path\n"
]
}
],
"source": [
"import set_path # Importing this module will add the project's home directory to sys.path"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "087c0d08",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from src.modules.chemicals.chem_data import ChemData\n",
"from src.modules.reactions.reaction_dynamics import ReactionDynamics\n",
"from src.modules.movies.movies import MovieTabular\n",
"\n",
"import pandas as pd\n",
"import plotly.express as px"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "23c15e66-52e4-495b-aa3d-ecddd8d16942",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of reactions: 2 (at temp. 25 C)\n",
"0: S <-> P (kF = 1 / kR = 0.2 / delta_G = -3,989.7 / K = 5) | 1st order in all reactants & products\n",
"1: S + E <-> P + E (kF = 10 / kR = 2 / delta_G = -3,989.7 / K = 5) | Enzyme: E | 1st order in all reactants & products\n",
"Set of chemicals involved in the above reactions (not counting enzymes): {'P', 'S'}\n",
"Set of enzymes involved in the above reactions: {'E'}\n"
]
}
],
"source": [
"# Initialize the system\n",
"chem_data = ChemData(names=[\"S\", \"P\", \"E\"])\n",
"\n",
"# Reaction S <-> P , with 1st-order kinetics, favorable thermodynamics in the forward direction, \n",
"# and a forward rate that is much slower than it would be with the enzyme - as seen in the next reaction, below\n",
"chem_data.add_reaction(reactants=\"S\", products=\"P\",\n",
" forward_rate=1., delta_G=-3989.73)\n",
"\n",
"# Reaction S + E <-> P + E , with 1st-order kinetics, and a forward rate that is much faster than it was without the enzyme\n",
"# Thermodynamically, there's no change from the reaction without the enzyme\n",
"chem_data.add_reaction(reactants=[\"S\", \"E\"], products=[\"P\", \"E\"],\n",
" forward_rate=10., delta_G=-3989.73)\n",
"\n",
"chem_data.describe_reactions() # Notice how the enzyme `E` is noted in the printout below"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "919280b2-fd0f-4557-9008-e154b64a2b66",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "0e771dda-1c0f-4fc0-ab21-049740643897",
"metadata": {},
"source": [
"# 1. Set the initial concentrations of all the chemicals - starting with no enzyme"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5563e467-a637-44fa-9ba1-d35ddd82c887",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (S). Conc: 20.0\n",
" Species 1 (P). Conc: 0.0\n",
" Species 2 (E). Conc: 0.0\n",
"Set of chemicals involved in reactions (not counting enzymes): {'P', 'S'}\n",
"Set of enzymes involved in reactions: {'E'}\n"
]
}
],
"source": [
"dynamics = ReactionDynamics(chem_data=chem_data)\n",
"dynamics.set_conc(conc={\"S\": 20.},\n",
" snapshot=True) # Initially, no enzyme `E`\n",
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "651941bb-7098-4065-a598-e50c0b641ab3",
"metadata": {
"tags": []
},
"source": [
"### Advance the reactions (for now without enzyme) to equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "76f24d9a-a788-41d8-90a4-db87386f91aa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* INFO: the tentative time step (0.1) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.04) [Step started at t=0, and will rewind there]\n",
"35 total step(s) taken\n"
]
}
],
"source": [
"dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n",
"\n",
"\n",
"# This setting is a preset that can be adjusted make the time resolution finer or coarser;\n",
"# it will stay in effect from now on, unless explicitly changed later\n",
"dynamics.use_adaptive_preset(preset=\"mid\")\n",
"\n",
"\n",
"# Perform the reactions\n",
"dynamics.single_compartment_react(duration=4.0,\n",
" initial_step=0.1, variable_steps=True, explain_variable_steps=False)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e58db01b-b932-4f60-91c2-a578353a3702",
"metadata": {},
"outputs": [],
"source": [
"#dynamics.explain_time_advance()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4a19ad2a-fbd2-420a-b958-2daf88bcc841",
"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=S
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "S",
"line": {
"color": "cyan",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "S",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.04000000000000001,
0.08800000000000002,
0.11200000000000003,
0.13600000000000004,
0.16000000000000003,
0.18400000000000002,
0.20800000000000002,
0.232,
0.256,
0.28,
0.3088,
0.3376,
0.37216,
0.40671999999999997,
0.448192,
0.49795839999999997,
0.5477248,
0.6074444800000001,
0.6791080960000001,
0.7651044352,
0.86830004224,
0.97149564928,
1.095330377728,
1.2191651061759998,
1.3429998346239997,
1.4916015087615997,
1.6402031828991996,
1.8185251918643197,
1.9968472008294396,
2.2108336115875833,
2.467617304497356,
2.775757735989083,
3.1455262537791557,
3.589248475127243,
4.121715140744947
],
"xaxis": "x",
"y": [
20,
19.2,
18.286079992027837,
17.855440879717655,
17.4372041716963,
17.031012678782055,
16.63651949883985,
16.25338772051438,
15.881290135495687,
15.51990895907152,
15.168935558727751,
14.759897116932596,
14.36499504124019,
13.907489926459018,
13.468958460515475,
12.96454482995423,
12.389371888673928,
11.848548085448902,
11.23831690423155,
10.55851712534211,
9.812909729386089,
9.0105129276718,
8.307480698814572,
7.568313772292425,
6.938988269515275,
6.403181573848885,
5.855759648907261,
5.40595508448429,
4.962441654807947,
4.613834055651702,
4.285021667864934,
3.991767176044116,
3.7482980458747632,
3.5641677342495646,
3.4412545964073753,
3.372295395810298
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=P
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "P",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "P",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.04000000000000001,
0.08800000000000002,
0.11200000000000003,
0.13600000000000004,
0.16000000000000003,
0.18400000000000002,
0.20800000000000002,
0.232,
0.256,
0.28,
0.3088,
0.3376,
0.37216,
0.40671999999999997,
0.448192,
0.49795839999999997,
0.5477248,
0.6074444800000001,
0.6791080960000001,
0.7651044352,
0.86830004224,
0.97149564928,
1.095330377728,
1.2191651061759998,
1.3429998346239997,
1.4916015087615997,
1.6402031828991996,
1.8185251918643197,
1.9968472008294396,
2.2108336115875833,
2.467617304497356,
2.775757735989083,
3.1455262537791557,
3.589248475127243,
4.121715140744947
],
"xaxis": "x",
"y": [
0,
0.8000000000000002,
1.7139200079721628,
2.144559120282345,
2.5627958283036976,
2.968987321217942,
3.3634805011601463,
3.7466122794856176,
4.118709864504308,
4.480091040928475,
4.831064441272243,
5.240102883067398,
5.6350049587598035,
6.092510073540975,
6.531041539484517,
7.035455170045762,
7.610628111326063,
8.151451914551089,
8.761683095768442,
9.441482874657881,
10.187090270613902,
10.989487072328192,
11.692519301185419,
12.431686227707566,
13.061011730484715,
13.596818426151106,
14.14424035109273,
14.5940449155157,
15.037558345192044,
15.386165944348289,
15.714978332135056,
16.008232823955876,
16.251701954125227,
16.435832265750427,
16.558745403592617,
16.627704604189695
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=E
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "E",
"line": {
"color": "violet",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "E",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.04000000000000001,
0.08800000000000002,
0.11200000000000003,
0.13600000000000004,
0.16000000000000003,
0.18400000000000002,
0.20800000000000002,
0.232,
0.256,
0.28,
0.3088,
0.3376,
0.37216,
0.40671999999999997,
0.448192,
0.49795839999999997,
0.5477248,
0.6074444800000001,
0.6791080960000001,
0.7651044352,
0.86830004224,
0.97149564928,
1.095330377728,
1.2191651061759998,
1.3429998346239997,
1.4916015087615997,
1.6402031828991996,
1.8185251918643197,
1.9968472008294396,
2.2108336115875833,
2.467617304497356,
2.775757735989083,
3.1455262537791557,
3.589248475127243,
4.121715140744947
],
"xaxis": "x",
"y": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"shapes": [
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0,
"x1": 0,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.04000000000000001,
"x1": 0.04000000000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.08800000000000002,
"x1": 0.08800000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.11200000000000003,
"x1": 0.11200000000000003,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.13600000000000004,
"x1": 0.13600000000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.16000000000000003,
"x1": 0.16000000000000003,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.18400000000000002,
"x1": 0.18400000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.20800000000000002,
"x1": 0.20800000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.232,
"x1": 0.232,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.256,
"x1": 0.256,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.28,
"x1": 0.28,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.3088,
"x1": 0.3088,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.3376,
"x1": 0.3376,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.37216,
"x1": 0.37216,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.40671999999999997,
"x1": 0.40671999999999997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.448192,
"x1": 0.448192,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.49795839999999997,
"x1": 0.49795839999999997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.5477248,
"x1": 0.5477248,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.6074444800000001,
"x1": 0.6074444800000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.6791080960000001,
"x1": 0.6791080960000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.7651044352,
"x1": 0.7651044352,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.86830004224,
"x1": 0.86830004224,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.97149564928,
"x1": 0.97149564928,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.095330377728,
"x1": 1.095330377728,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.2191651061759998,
"x1": 1.2191651061759998,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.3429998346239997,
"x1": 1.3429998346239997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.4916015087615997,
"x1": 1.4916015087615997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.6402031828991996,
"x1": 1.6402031828991996,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.8185251918643197,
"x1": 1.8185251918643197,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.9968472008294396,
"x1": 1.9968472008294396,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 2.2108336115875833,
"x1": 2.2108336115875833,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 2.467617304497356,
"x1": 2.467617304497356,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 2.775757735989083,
"x1": 2.775757735989083,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 3.1455262537791557,
"x1": 3.1455262537791557,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 3.589248475127243,
"x1": 3.589248475127243,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 4.121715140744947,
"x1": 4.121715140744947,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
}
],
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "With ZERO enzyme
Changes in concentration for `S <-> P` and `S + E <-> P + E` (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-0.0029315185922794786,
4.124646659337227
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.1111111111111112,
21.11111111111111
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(colors=['cyan', 'green', 'violet'], show_intervals=True, title_prefix=\"With ZERO enzyme\")"
]
},
{
"cell_type": "markdown",
"id": "9a793ebf-f544-43ad-a1d3-059f74f3c02b",
"metadata": {},
"source": [
"### The reactions, lacking enzyme, are proceeding slowly towards equilibrium, just like the reaction that was discussed in part 1 of the experiment \"enzyme_1\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7b253509-903b-4c1a-8771-cd283e7fbf4c",
"metadata": {},
"outputs": [],
"source": [
"# To save up snapshots of crossover times at various Enzyme concentrations\n",
"crossover_points = MovieTabular(parameter_name=\"Enzyme concentration\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4677fcaa-e90b-4975-a567-cb55b66f91f2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.7435259497128707, 9.999999999999996)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_crossover = dynamics.curve_intersect(\"S\", \"P\", t_start=0, t_end=1.0)\n",
"crossover_points.store(par=dynamics.get_chem_conc(\"E\"), \n",
" data_snapshot = {\"crossover time\": new_crossover[0]})\n",
"new_crossover"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d684e2c3-c717-4c95-8852-62729914e48f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0: S <-> P\n",
"Final concentrations: [S] = 3.372 ; [P] = 16.63\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 4.93068\n",
" Formula used: [P] / [S]\n",
"2. Ratio of forward/reverse reaction rates: 5.00001\n",
"Discrepancy between the two values: 1.387 %\n",
"Reaction IS in equilibrium (within 2% tolerance)\n",
"\n",
"1: S + E <-> P + E\n",
"Final concentrations: [S] = 3.372 ; [E] = 0 ; [P] = 16.63\n",
"Reaction IS in equilibrium because it can't proceed in either direction due to zero concentrations in both some reactants and products!\n",
"\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium(tolerance=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf52d120-927e-44bb-893d-1673e9fd8bd6",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "27401e5d-8f3e-4c27-8438-129d3e3408a2",
"metadata": {},
"source": [
"# 2. Re-start all the reactions from the same initial concentrations - except for now having a tiny amount of enzyme (two orders of magnitude less than the starting [S])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3d00754a-1fcf-4296-8fd5-4240f6590ec7",
"metadata": {},
"outputs": [],
"source": [
"dynamics = ReactionDynamics(chem_data=chem_data) # A brand-new simulation "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "373ce459-3b57-4ab2-a136-4eb8bedcdd91",
"metadata": {},
"outputs": [],
"source": [
"dynamics.set_conc(conc={\"S\": 20., \"E\": 0.2},\n",
" snapshot=True) # A tiny bit of enzyme `E`"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e80645d6-eb5b-4c78-8b46-ae126d2cb2cf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (S). Conc: 20.0\n",
" Species 1 (P). Conc: 0.0\n",
" Species 2 (E). Conc: 0.2\n",
"Set of chemicals involved in reactions (not counting enzymes): {'P', 'S'}\n",
"Set of enzymes involved in reactions: {'E'}\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "0b46b395-3f68-4dbd-b0c5-d67a0e623726",
"metadata": {
"tags": []
},
"source": [
"### Re-take the new system (now with a tiny amount of enzyme) to equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "dde62826-d170-4b39-b027-c0d56fb21387",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* INFO: the tentative time step (0.05) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.02) [Step started at t=0, and will rewind there]\n",
"Some steps were backtracked and re-done, to prevent negative concentrations or excessively large concentration changes\n",
"33 total step(s) taken\n"
]
}
],
"source": [
"dynamics.single_compartment_react(duration=1.5, \n",
" initial_step=0.05, variable_steps=True, explain_variable_steps=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b0543cac-f3cd-453c-ae9b-c00f01e61fa8",
"metadata": {},
"outputs": [],
"source": [
"#dynamics.explain_time_advance()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "8cc14786-cc9f-4399-9203-290526d3a326",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "S",
"line": {
"color": "cyan",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "S",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.020000000000000004,
0.04000000000000001,
0.05000000000000001,
0.06000000000000001,
0.07,
0.08000000000000002,
0.09000000000000002,
0.10000000000000003,
0.11000000000000004,
0.12000000000000005,
0.13200000000000006,
0.14640000000000006,
0.16080000000000005,
0.17808000000000004,
0.19881600000000005,
0.21955200000000005,
0.24443520000000005,
0.27429504000000005,
0.31012684800000007,
0.3459586560000001,
0.3889568256000001,
0.43195499520000014,
0.47495316480000016,
0.5265509683200001,
0.5781487718400001,
0.6400661360640001,
0.7143669731328001,
0.8035279776153601,
0.9105211829944321,
1.0389130294493185,
1.192983245195182,
1.3778675040902184,
1.599728614764262
],
"xaxis": "x",
"y": [
20,
18.8,
17.686399985052194,
17.16968957118063,
16.67158072899024,
16.191403802016357,
15.72851324142287,
15.28228673812775,
14.852124386172044,
14.437447876207584,
14.037699718019134,
13.575271045639056,
13.04432893701177,
12.54091086253387,
11.968125799319516,
11.323541891919753,
10.72707590704908,
10.064747852581355,
9.341151409944567,
8.566175630319313,
7.891167453423617,
7.185644440929404,
6.589331722873586,
6.085324268236473,
5.574135745896077,
5.157901544902552,
4.7512001363102385,
4.37194414552246,
4.038570377857188,
3.7669292552139786,
3.5665153054334233,
3.4371787001974874,
3.3680590476865446,
3.3403213214741454
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=P
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "P",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "P",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.020000000000000004,
0.04000000000000001,
0.05000000000000001,
0.06000000000000001,
0.07,
0.08000000000000002,
0.09000000000000002,
0.10000000000000003,
0.11000000000000004,
0.12000000000000005,
0.13200000000000006,
0.14640000000000006,
0.16080000000000005,
0.17808000000000004,
0.19881600000000005,
0.21955200000000005,
0.24443520000000005,
0.27429504000000005,
0.31012684800000007,
0.3459586560000001,
0.3889568256000001,
0.43195499520000014,
0.47495316480000016,
0.5265509683200001,
0.5781487718400001,
0.6400661360640001,
0.7143669731328001,
0.8035279776153601,
0.9105211829944321,
1.0389130294493185,
1.192983245195182,
1.3778675040902184,
1.599728614764262
],
"xaxis": "x",
"y": [
0,
1.2000000000000002,
2.3136000149478053,
2.8303104288193683,
3.328419271009758,
3.808596197983641,
4.271486758577128,
4.717713261872247,
5.147875613827954,
5.562552123792415,
5.962300281980864,
6.424728954360942,
6.955671062988229,
7.459089137466128,
8.031874200680482,
8.676458108080245,
9.272924092950918,
9.935252147418645,
10.658848590055433,
11.433824369680687,
12.108832546576384,
12.814355559070597,
13.410668277126415,
13.914675731763529,
14.425864254103924,
14.84209845509745,
15.248799863689763,
15.628055854477543,
15.961429622142814,
16.233070744786023,
16.433484694566577,
16.56282129980251,
16.631940952313453,
16.659678678525854
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=E
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "E",
"line": {
"color": "violet",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "E",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.020000000000000004,
0.04000000000000001,
0.05000000000000001,
0.06000000000000001,
0.07,
0.08000000000000002,
0.09000000000000002,
0.10000000000000003,
0.11000000000000004,
0.12000000000000005,
0.13200000000000006,
0.14640000000000006,
0.16080000000000005,
0.17808000000000004,
0.19881600000000005,
0.21955200000000005,
0.24443520000000005,
0.27429504000000005,
0.31012684800000007,
0.3459586560000001,
0.3889568256000001,
0.43195499520000014,
0.47495316480000016,
0.5265509683200001,
0.5781487718400001,
0.6400661360640001,
0.7143669731328001,
0.8035279776153601,
0.9105211829944321,
1.0389130294493185,
1.192983245195182,
1.3778675040902184,
1.599728614764262
],
"xaxis": "x",
"y": [
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2,
0.2
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"shapes": [
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0,
"x1": 0,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.020000000000000004,
"x1": 0.020000000000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.04000000000000001,
"x1": 0.04000000000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.05000000000000001,
"x1": 0.05000000000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.06000000000000001,
"x1": 0.06000000000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.07,
"x1": 0.07,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.08000000000000002,
"x1": 0.08000000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.09000000000000002,
"x1": 0.09000000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.10000000000000003,
"x1": 0.10000000000000003,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.11000000000000004,
"x1": 0.11000000000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.12000000000000005,
"x1": 0.12000000000000005,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.13200000000000006,
"x1": 0.13200000000000006,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.14640000000000006,
"x1": 0.14640000000000006,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.16080000000000005,
"x1": 0.16080000000000005,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.17808000000000004,
"x1": 0.17808000000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.19881600000000005,
"x1": 0.19881600000000005,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.21955200000000005,
"x1": 0.21955200000000005,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.24443520000000005,
"x1": 0.24443520000000005,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.27429504000000005,
"x1": 0.27429504000000005,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.31012684800000007,
"x1": 0.31012684800000007,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.3459586560000001,
"x1": 0.3459586560000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.3889568256000001,
"x1": 0.3889568256000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.43195499520000014,
"x1": 0.43195499520000014,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.47495316480000016,
"x1": 0.47495316480000016,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.5265509683200001,
"x1": 0.5265509683200001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.5781487718400001,
"x1": 0.5781487718400001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.6400661360640001,
"x1": 0.6400661360640001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.7143669731328001,
"x1": 0.7143669731328001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.8035279776153601,
"x1": 0.8035279776153601,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.9105211829944321,
"x1": 0.9105211829944321,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.0389130294493185,
"x1": 1.0389130294493185,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.192983245195182,
"x1": 1.192983245195182,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.3778675040902184,
"x1": 1.3778675040902184,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.599728614764262,
"x1": 1.599728614764262,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
}
],
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "With a tiny amount of enzyme
Changes in concentration for `S <-> P` and `S + E <-> P + E` (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-0.0011377870659774268,
1.6008664018302394
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.1111111111111112,
21.11111111111111
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(colors=['cyan', 'green', 'violet'], show_intervals=True, title_prefix=\"With a tiny amount of enzyme\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e4632751-75db-40d8-95ee-6ddede100b41",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.24710707675961718, 10.0)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_crossover = dynamics.curve_intersect(\"S\", \"P\", t_start=0, t_end=0.5)\n",
"crossover_points.store(par=dynamics.get_chem_conc(\"E\"), \n",
" data_snapshot = {\"crossover time\": new_crossover[0]})\n",
"new_crossover"
]
},
{
"cell_type": "markdown",
"id": "3171fd78-a103-4523-8c43-6e29695f49d2",
"metadata": {},
"source": [
"## Notice how even a tiny amount of enzyme (1/100 of the initial [S]) makes a very pronounced difference!"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c3afbcc8-bdae-4938-a3f1-ce00d62816f2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0: S <-> P\n",
"Final concentrations: [S] = 3.34 ; [P] = 16.66\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 4.98745\n",
" Formula used: [P] / [S]\n",
"2. Ratio of forward/reverse reaction rates: 5.00001\n",
"Discrepancy between the two values: 0.2511 %\n",
"Reaction IS in equilibrium (within 1% tolerance)\n",
"\n",
"1: S + E <-> P + E\n",
"Final concentrations: [S] = 3.34 ; [E] = 0.2 ; [P] = 16.66\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 4.98745\n",
" Formula used: ([P][E]) / ([S][E])\n",
"2. Ratio of forward/reverse reaction rates: 5.00001\n",
"Discrepancy between the two values: 0.2511 %\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",
"dynamics.is_in_equilibrium()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b116cbea-f62a-4072-9255-24e109f894a2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "fe1c77c1-0e3e-4f6a-b774-d86398cde6aa",
"metadata": {},
"source": [
"# 3. Re-start all the reactions from the same initial concentrations - except for now having a more substantial amount of enzyme"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7c3d57f9-00cc-4377-ad12-a3d9af0fac18",
"metadata": {},
"outputs": [],
"source": [
"dynamics = ReactionDynamics(chem_data=chem_data) # A brand-new simulation "
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "61deddc2-1435-4261-8e40-66043b73365e",
"metadata": {},
"outputs": [],
"source": [
"dynamics.set_conc(conc={\"S\": 20., \"E\": 1.},\n",
" snapshot=True) # A more substantial amount of enzyme `E`"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "b4377e1a-8c8e-41a3-8087-2f4d617a2c2e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (S). Conc: 20.0\n",
" Species 1 (P). Conc: 0.0\n",
" Species 2 (E). Conc: 1.0\n",
"Set of chemicals involved in reactions (not counting enzymes): {'P', 'S'}\n",
"Set of enzymes involved in reactions: {'E'}\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "4c4616a5-4cd6-44b9-82bf-2e46689f512b",
"metadata": {
"tags": []
},
"source": [
"### Re-take the new system (now with a more substantial amount of enzyme) to equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "a4dd3c4e-06b3-4823-9c62-7528460f7d4a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* INFO: the tentative time step (0.02) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.008) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (0.008) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.0032) [Step started at t=0, and will rewind there]\n",
"Some steps were backtracked and re-done, to prevent negative concentrations or excessively large concentration changes\n",
"39 total step(s) taken\n"
]
}
],
"source": [
"dynamics.single_compartment_react(duration=0.5, \n",
" initial_step=0.02, variable_steps=True, explain_variable_steps=False)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "12ed7017-11b0-460d-8c53-34dd3962d9a7",
"metadata": {},
"outputs": [],
"source": [
"#dynamics.explain_time_advance()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "50c6521a-7c67-437d-9666-fef5782cf4ef",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "S",
"line": {
"color": "cyan",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "S",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.0032,
0.00704,
0.008960000000000001,
0.01088,
0.0128,
0.01472,
0.016640000000000002,
0.018560000000000004,
0.020480000000000005,
0.022400000000000007,
0.02432000000000001,
0.02662400000000001,
0.02938880000000001,
0.03215360000000001,
0.035471360000000014,
0.03878912000000002,
0.04277043200000002,
0.047548006400000016,
0.052325580800000014,
0.05805867008000001,
0.06493837721600002,
0.07319402577920002,
0.08310080405504001,
0.09300758233088001,
0.10291436060672,
0.114802494537728,
0.126690628468736,
0.1409563891859456,
0.1552221499031552,
0.1723410627638067,
0.1894599756244582,
0.21000267105724002,
0.2346539055765782,
0.264235386999784,
0.299733164707631,
0.3423304979570474,
0.393447297856347,
0.4547874577355066,
0.5283956495904981
],
"xaxis": "x",
"y": [
20,
19.296,
18.486884345826358,
18.10283274233119,
17.72851454099106,
17.363683058504424,
17.008097863510258,
16.66152461813889,
16.323734923578602,
15.994506169556145,
15.673621387632068,
15.360869108214093,
14.995078043790421,
14.569478440753352,
14.159411245757763,
13.685289268542746,
13.231931190426279,
12.71172696399301,
12.120288036611122,
11.56614759669416,
10.943114624865931,
10.2520540027292,
9.498089271081705,
8.691927146846888,
7.991186400706535,
7.382081136963941,
6.746737653321917,
6.2110943916506836,
5.6691883937250855,
5.2293276352736235,
4.800889889466515,
4.469266054274173,
4.161241742754644,
3.8918425215309402,
3.673757248229557,
3.5142432642882953,
3.4125187564223087,
3.3590871084457223,
3.338232200338282,
3.3334695225212725
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=P
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "P",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "P",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.0032,
0.00704,
0.008960000000000001,
0.01088,
0.0128,
0.01472,
0.016640000000000002,
0.018560000000000004,
0.020480000000000005,
0.022400000000000007,
0.02432000000000001,
0.02662400000000001,
0.02938880000000001,
0.03215360000000001,
0.035471360000000014,
0.03878912000000002,
0.04277043200000002,
0.047548006400000016,
0.052325580800000014,
0.05805867008000001,
0.06493837721600002,
0.07319402577920002,
0.08310080405504001,
0.09300758233088001,
0.10291436060672,
0.114802494537728,
0.126690628468736,
0.1409563891859456,
0.1552221499031552,
0.1723410627638067,
0.1894599756244582,
0.21000267105724002,
0.2346539055765782,
0.264235386999784,
0.299733164707631,
0.3423304979570474,
0.393447297856347,
0.4547874577355066,
0.5283956495904981
],
"xaxis": "x",
"y": [
0,
0.704,
1.5131156541736428,
1.8971672576688081,
2.271485459008939,
2.6363169414955747,
2.991902136489742,
3.3384753818611075,
3.6762650764213975,
4.0054938304438545,
4.326378612367932,
4.639130891785906,
5.004921956209578,
5.430521559246646,
5.840588754242235,
6.314710731457251,
6.76806880957372,
7.288273036006988,
7.879711963388876,
8.433852403305838,
9.056885375134067,
9.747945997270799,
10.501910728918293,
11.30807285315311,
12.008813599293463,
12.617918863036058,
13.253262346678083,
13.788905608349317,
14.330811606274915,
14.770672364726378,
15.199110110533487,
15.53073394572583,
15.83875825724536,
16.108157478469064,
16.326242751770447,
16.48575673571171,
16.5874812435777,
16.640912891554287,
16.661767799661728,
16.666530477478737
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=E
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "E",
"line": {
"color": "violet",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "E",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.0032,
0.00704,
0.008960000000000001,
0.01088,
0.0128,
0.01472,
0.016640000000000002,
0.018560000000000004,
0.020480000000000005,
0.022400000000000007,
0.02432000000000001,
0.02662400000000001,
0.02938880000000001,
0.03215360000000001,
0.035471360000000014,
0.03878912000000002,
0.04277043200000002,
0.047548006400000016,
0.052325580800000014,
0.05805867008000001,
0.06493837721600002,
0.07319402577920002,
0.08310080405504001,
0.09300758233088001,
0.10291436060672,
0.114802494537728,
0.126690628468736,
0.1409563891859456,
0.1552221499031552,
0.1723410627638067,
0.1894599756244582,
0.21000267105724002,
0.2346539055765782,
0.264235386999784,
0.299733164707631,
0.3423304979570474,
0.393447297856347,
0.4547874577355066,
0.5283956495904981
],
"xaxis": "x",
"y": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"shapes": [
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0,
"x1": 0,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0032,
"x1": 0.0032,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00704,
"x1": 0.00704,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.008960000000000001,
"x1": 0.008960000000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.01088,
"x1": 0.01088,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0128,
"x1": 0.0128,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.01472,
"x1": 0.01472,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.016640000000000002,
"x1": 0.016640000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.018560000000000004,
"x1": 0.018560000000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.020480000000000005,
"x1": 0.020480000000000005,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.022400000000000007,
"x1": 0.022400000000000007,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.02432000000000001,
"x1": 0.02432000000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.02662400000000001,
"x1": 0.02662400000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.02938880000000001,
"x1": 0.02938880000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.03215360000000001,
"x1": 0.03215360000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.035471360000000014,
"x1": 0.035471360000000014,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.03878912000000002,
"x1": 0.03878912000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.04277043200000002,
"x1": 0.04277043200000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.047548006400000016,
"x1": 0.047548006400000016,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.052325580800000014,
"x1": 0.052325580800000014,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.05805867008000001,
"x1": 0.05805867008000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.06493837721600002,
"x1": 0.06493837721600002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.07319402577920002,
"x1": 0.07319402577920002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.08310080405504001,
"x1": 0.08310080405504001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.09300758233088001,
"x1": 0.09300758233088001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.10291436060672,
"x1": 0.10291436060672,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.114802494537728,
"x1": 0.114802494537728,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.126690628468736,
"x1": 0.126690628468736,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.1409563891859456,
"x1": 0.1409563891859456,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.1552221499031552,
"x1": 0.1552221499031552,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.1723410627638067,
"x1": 0.1723410627638067,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.1894599756244582,
"x1": 0.1894599756244582,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.21000267105724002,
"x1": 0.21000267105724002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.2346539055765782,
"x1": 0.2346539055765782,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.264235386999784,
"x1": 0.264235386999784,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.299733164707631,
"x1": 0.299733164707631,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.3423304979570474,
"x1": 0.3423304979570474,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.393447297856347,
"x1": 0.393447297856347,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.4547874577355066,
"x1": 0.4547874577355066,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.5283956495904981,
"x1": 0.5283956495904981,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
}
],
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "With a more substantial amount of enzyme
Changes in concentration for `S <-> P` and `S + E <-> P + E` (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-0.0003758148290117341,
0.5287714644195098
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.1111111111111112,
21.11111111111111
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(colors=['cyan', 'green', 'violet'], show_intervals=True, title_prefix=\"With a more substantial amount of enzyme\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "11d21c1a-bf34-4572-a5ff-d360617380c0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.06769827987210066, 10.0)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_crossover = dynamics.curve_intersect(\"S\", \"P\", t_start=0, t_end=0.5)\n",
"crossover_points.store(par=dynamics.get_chem_conc(\"E\"), \n",
" data_snapshot = {\"crossover time\": new_crossover[0]})\n",
"new_crossover"
]
},
{
"cell_type": "markdown",
"id": "f01eaeb0-e3e6-40fc-9e3e-c79a9fe54285",
"metadata": {},
"source": [
"## Notice the continued - and substantial - speedup of the reaction, over the earlier runs"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "aa2f5b84-0a08-42e1-8b26-0b953806a900",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium(explain=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5e6c18d4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "90c86d7d-cff3-40dd-829f-083a3fc71f85",
"metadata": {},
"source": [
"# 4. Re-start all the reactions from the same initial concentrations - except for now having a good amount of enzyme"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "4d585598-f502-409d-be2c-992808f3177e",
"metadata": {},
"outputs": [],
"source": [
"dynamics = ReactionDynamics(chem_data=chem_data) # A brand-new simulation "
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "d505b176-3c55-494a-8e72-74dff8a741db",
"metadata": {},
"outputs": [],
"source": [
"dynamics.set_conc(conc={\"S\": 20., \"E\": 5.},\n",
" snapshot=True) # A good amount of enzyme `E`"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "db20d234-bd14-4df4-b803-ae8d47ff1093",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (S). Conc: 20.0\n",
" Species 1 (P). Conc: 0.0\n",
" Species 2 (E). Conc: 5.0\n",
"Set of chemicals involved in reactions (not counting enzymes): {'P', 'S'}\n",
"Set of enzymes involved in reactions: {'E'}\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "45a4f23a-e9c3-423d-b2bb-f9e48464dcc7",
"metadata": {
"tags": []
},
"source": [
"### Re-take the new system to equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "99099e14-885d-4a75-a3f5-0dc69709a608",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* INFO: the tentative time step (0.01) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.004) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (0.004) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.0016) [Step started at t=0, and will rewind there]\n",
"Some steps were backtracked and re-done, to prevent negative concentrations or excessively large concentration changes\n",
"36 total step(s) taken\n"
]
}
],
"source": [
"dynamics.single_compartment_react(duration=0.2, \n",
" initial_step=0.01, variable_steps=True, explain_variable_steps=False)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "cc595321-b1d5-4188-a47c-bce6e6b682e2",
"metadata": {},
"outputs": [],
"source": [
"#dynamics.explain_time_advance()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "02d64d04-0373-4235-b2dc-44bc24b549fe",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "S",
"line": {
"color": "cyan",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "S",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.0016,
0.0024000000000000002,
0.0032,
0.004,
0.0048000000000000004,
0.005600000000000001,
0.006400000000000001,
0.0072000000000000015,
0.008000000000000002,
0.008800000000000002,
0.009760000000000003,
0.010912000000000003,
0.012064000000000004,
0.013446400000000004,
0.015105280000000004,
0.017095936000000003,
0.019086592000000003,
0.021475379200000002,
0.0238641664,
0.0262529536,
0.02911949824,
0.03198604288,
0.035425896448000004,
0.038865750016000004,
0.04299357429760001,
0.047946963435520004,
0.053891030401024005,
0.061023910759628805,
0.06958336718995456,
0.07985471490634546,
0.09218033216601455,
0.10697107287761747,
0.12471996173154096,
0.14601862835624915,
0.171577028305899,
0.20224710824547878
],
"xaxis": "x",
"y": [
20,
18.368,
17.63190270617627,
16.931844729623094,
16.266061585672176,
15.632875178809629,
15.030689573063714,
14.457986969474355,
13.913323880505697,
13.395327491759417,
12.902692201818507,
12.340473157436628,
11.705448089275388,
11.115193804734018,
10.456825867049425,
9.733624118617495,
8.95388833431943,
8.26914629949335,
7.547560864478567,
6.931466711126278,
6.405441673235793,
5.866493516077486,
5.422094382371302,
4.982369884014856,
4.635215748181122,
4.306329835814853,
4.011367637969289,
3.764713415435605,
3.576400859394863,
3.449071185944923,
3.3763158218153433,
3.3438907674021445,
3.3343316400583647,
3.333244115441855,
3.3333566505010914,
3.333315668180226,
3.333343413562571
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=P
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "P",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "P",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.0016,
0.0024000000000000002,
0.0032,
0.004,
0.0048000000000000004,
0.005600000000000001,
0.006400000000000001,
0.0072000000000000015,
0.008000000000000002,
0.008800000000000002,
0.009760000000000003,
0.010912000000000003,
0.012064000000000004,
0.013446400000000004,
0.015105280000000004,
0.017095936000000003,
0.019086592000000003,
0.021475379200000002,
0.0238641664,
0.0262529536,
0.02911949824,
0.03198604288,
0.035425896448000004,
0.038865750016000004,
0.04299357429760001,
0.047946963435520004,
0.053891030401024005,
0.061023910759628805,
0.06958336718995456,
0.07985471490634546,
0.09218033216601455,
0.10697107287761747,
0.12471996173154096,
0.14601862835624915,
0.171577028305899,
0.20224710824547878
],
"xaxis": "x",
"y": [
0,
1.6320000000000001,
2.3680972938237304,
3.068155270376906,
3.7339384143278256,
4.367124821190373,
4.969310426936286,
5.542013030525647,
6.086676119494305,
6.604672508240585,
7.097307798181495,
7.659526842563374,
8.294551910724616,
8.884806195265986,
9.543174132950579,
10.266375881382508,
11.046111665680574,
11.730853700506653,
12.452439135521438,
13.068533288873727,
13.594558326764211,
14.133506483922519,
14.577905617628701,
15.017630115985147,
15.36478425181888,
15.69367016418515,
15.988632362030714,
16.2352865845644,
16.42359914060514,
16.55092881405508,
16.62368417818466,
16.65610923259786,
16.66566835994164,
16.66675588455815,
16.666643349498912,
16.666684331819777,
16.666656586437433
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=E
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "E",
"line": {
"color": "violet",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "E",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.0016,
0.0024000000000000002,
0.0032,
0.004,
0.0048000000000000004,
0.005600000000000001,
0.006400000000000001,
0.0072000000000000015,
0.008000000000000002,
0.008800000000000002,
0.009760000000000003,
0.010912000000000003,
0.012064000000000004,
0.013446400000000004,
0.015105280000000004,
0.017095936000000003,
0.019086592000000003,
0.021475379200000002,
0.0238641664,
0.0262529536,
0.02911949824,
0.03198604288,
0.035425896448000004,
0.038865750016000004,
0.04299357429760001,
0.047946963435520004,
0.053891030401024005,
0.061023910759628805,
0.06958336718995456,
0.07985471490634546,
0.09218033216601455,
0.10697107287761747,
0.12471996173154096,
0.14601862835624915,
0.171577028305899,
0.20224710824547878
],
"xaxis": "x",
"y": [
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"shapes": [
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0,
"x1": 0,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0016,
"x1": 0.0016,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0024000000000000002,
"x1": 0.0024000000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0032,
"x1": 0.0032,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004,
"x1": 0.004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0048000000000000004,
"x1": 0.0048000000000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.005600000000000001,
"x1": 0.005600000000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.006400000000000001,
"x1": 0.006400000000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0072000000000000015,
"x1": 0.0072000000000000015,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.008000000000000002,
"x1": 0.008000000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.008800000000000002,
"x1": 0.008800000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.009760000000000003,
"x1": 0.009760000000000003,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.010912000000000003,
"x1": 0.010912000000000003,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.012064000000000004,
"x1": 0.012064000000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.013446400000000004,
"x1": 0.013446400000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.015105280000000004,
"x1": 0.015105280000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.017095936000000003,
"x1": 0.017095936000000003,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.019086592000000003,
"x1": 0.019086592000000003,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.021475379200000002,
"x1": 0.021475379200000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0238641664,
"x1": 0.0238641664,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0262529536,
"x1": 0.0262529536,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.02911949824,
"x1": 0.02911949824,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.03198604288,
"x1": 0.03198604288,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.035425896448000004,
"x1": 0.035425896448000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.038865750016000004,
"x1": 0.038865750016000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.04299357429760001,
"x1": 0.04299357429760001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.047946963435520004,
"x1": 0.047946963435520004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.053891030401024005,
"x1": 0.053891030401024005,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.061023910759628805,
"x1": 0.061023910759628805,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.06958336718995456,
"x1": 0.06958336718995456,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.07985471490634546,
"x1": 0.07985471490634546,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.09218033216601455,
"x1": 0.09218033216601455,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.10697107287761747,
"x1": 0.10697107287761747,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.12471996173154096,
"x1": 0.12471996173154096,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.14601862835624915,
"x1": 0.14601862835624915,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.171577028305899,
"x1": 0.171577028305899,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.20224710824547878,
"x1": 0.20224710824547878,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
}
],
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "With a good amount of enzyme
Changes in concentration for `S <-> P` and `S + E <-> P + E` (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-0.0001438457384391741,
0.20239095398391796
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.1111111111111112,
21.11111111111111
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(colors=['cyan', 'green', 'violet'], show_intervals=True, title_prefix=\"With a good amount of enzyme\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "193fcaa3-b936-4221-bedc-d4c0e516b1ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.014494267066104415, 10.000000000000002)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_crossover = dynamics.curve_intersect(\"S\", \"P\", t_start=0, t_end=0.5)\n",
"crossover_points.store(par=dynamics.get_chem_conc(\"E\"), \n",
" data_snapshot = {\"crossover time\": new_crossover[0]})\n",
"new_crossover"
]
},
{
"cell_type": "markdown",
"id": "9c8cc734-7144-4a60-bdfe-a1e707a8899d",
"metadata": {},
"source": [
"## Notice the continued - and substantial - speedup of the reaction, over the earlier runs"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "8bc8ff02-307d-4842-a34a-58b458850264",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium(explain=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff8b0763-358c-4765-a134-274a843b959a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "711d3422-71b5-42dc-a121-5c4fba686ea3",
"metadata": {},
"source": [
"# 5. Re-start all the reactions from the same initial concentrations - except for now having a lot of enzyme (same as the initial [S])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "bbae7051-3d61-45ea-b4be-a79dad90342a",
"metadata": {},
"outputs": [],
"source": [
"dynamics = ReactionDynamics(chem_data=chem_data) # A brand-new simulation "
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "a1abec42-c74c-4adf-8580-914df5188021",
"metadata": {},
"outputs": [],
"source": [
"dynamics.set_conc(conc={\"S\": 20., \"E\": 20.},\n",
" snapshot=True) # A lot of enzyme `E`"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "52260372-0585-4c38-abf8-c4764cb45368",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (S). Conc: 20.0\n",
" Species 1 (P). Conc: 0.0\n",
" Species 2 (E). Conc: 20.0\n",
"Set of chemicals involved in reactions (not counting enzymes): {'P', 'S'}\n",
"Set of enzymes involved in reactions: {'E'}\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "c3e69edd-0089-4135-be74-01d88c06bda5",
"metadata": {
"tags": []
},
"source": [
"### Re-take the new system (now with a lot of enzyme) to equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "29c08e79-d275-41b1-a2a0-c4db6521dfe7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* INFO: the tentative time step (0.005) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.002) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (0.002) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.0008) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (0.0008) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.00032) [Step started at t=0, and will rewind there]\n",
"Some steps were backtracked and re-done, to prevent negative concentrations or excessively large concentration changes\n",
"39 total step(s) taken\n"
]
}
],
"source": [
"dynamics.single_compartment_react(duration=0.05, \n",
" initial_step=0.005, variable_steps=True, explain_variable_steps=False)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "b0c435d2-4cb9-438c-8c21-1f4eea081b99",
"metadata": {},
"outputs": [],
"source": [
"#dynamics.explain_time_advance()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "f05b1cff-a69e-439e-870f-5ede95c9a69c",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "S",
"line": {
"color": "cyan",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "S",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.00032,
0.00048000000000000007,
0.00064,
0.0008,
0.00096,
0.0011200000000000001,
0.00128,
0.00144,
0.0016,
0.00176,
0.0019520000000000002,
0.002144,
0.0023744,
0.00265088,
0.00292736,
0.0032591359999999997,
0.0036572671999999997,
0.004135024639999999,
0.004708333567999999,
0.0052816424959999985,
0.005854951423999998,
0.006542922137599998,
0.007230892851199998,
0.007918863564799997,
0.008744428421119997,
0.009735106248703997,
0.010725784076287997,
0.011914597469388798,
0.013341173541109757,
0.015053064827174908,
0.01710733437045309,
0.019572457822386907,
0.022530605964707486,
0.026080383735492183,
0.030340117060433817,
0.03545179705036378,
0.04158581303827974,
0.04894663222377889,
0.05777961524637786
],
"xaxis": "x",
"y": [
20,
18.7136,
18.12004474021111,
17.54939596104501,
17.000769655754446,
16.47331593317465,
15.96621770113501,
15.478689400680508,
15.009975789142073,
14.559350770170676,
14.126116268922742,
13.626298127000727,
13.149626759153822,
12.60411092321086,
11.985870715244666,
11.408859070056517,
10.7626200819234,
10.04919112783155,
9.275288421661857,
8.45362205754468,
7.745577435137566,
7.135442702583104,
6.50452587035793,
5.978302435573177,
5.539399732121711,
5.100113519391636,
4.677938141908037,
4.356642183312555,
4.0632159954800855,
3.8120696131582568,
3.614394331616922,
3.4751300652733894,
3.390817761821262,
3.3498002653312797,
3.335698706404907,
3.33326544716137,
3.3333455914217174,
3.3333231891454043,
3.3333360800711422,
3.33332408486962
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=P
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "P",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "P",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.00032,
0.00048000000000000007,
0.00064,
0.0008,
0.00096,
0.0011200000000000001,
0.00128,
0.00144,
0.0016,
0.00176,
0.0019520000000000002,
0.002144,
0.0023744,
0.00265088,
0.00292736,
0.0032591359999999997,
0.0036572671999999997,
0.004135024639999999,
0.004708333567999999,
0.0052816424959999985,
0.005854951423999998,
0.006542922137599998,
0.007230892851199998,
0.007918863564799997,
0.008744428421119997,
0.009735106248703997,
0.010725784076287997,
0.011914597469388798,
0.013341173541109757,
0.015053064827174908,
0.01710733437045309,
0.019572457822386907,
0.022530605964707486,
0.026080383735492183,
0.030340117060433817,
0.03545179705036378,
0.04158581303827974,
0.04894663222377889,
0.05777961524637786
],
"xaxis": "x",
"y": [
0,
1.2864,
1.8799552597888893,
2.450604038954988,
2.9992303442455515,
3.5266840668253474,
4.0337822988649865,
4.521310599319488,
4.990024210857923,
5.4406492298293205,
5.873883731077255,
6.373701872999269,
6.850373240846173,
7.395889076789135,
8.01412928475533,
8.591140929943478,
9.237379918076595,
9.950808872168444,
10.724711578338137,
11.546377942455315,
12.254422564862429,
12.86455729741689,
13.495474129642066,
14.02169756442682,
14.460600267878284,
14.899886480608359,
15.322061858091958,
15.64335781668744,
15.93678400451991,
16.18793038684174,
16.385605668383075,
16.524869934726606,
16.609182238178732,
16.650199734668714,
16.664301293595088,
16.666734552838623,
16.666654408578275,
16.66667681085459,
16.66666391992885,
16.666675915130373
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=E
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "E",
"line": {
"color": "violet",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "E",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.00032,
0.00048000000000000007,
0.00064,
0.0008,
0.00096,
0.0011200000000000001,
0.00128,
0.00144,
0.0016,
0.00176,
0.0019520000000000002,
0.002144,
0.0023744,
0.00265088,
0.00292736,
0.0032591359999999997,
0.0036572671999999997,
0.004135024639999999,
0.004708333567999999,
0.0052816424959999985,
0.005854951423999998,
0.006542922137599998,
0.007230892851199998,
0.007918863564799997,
0.008744428421119997,
0.009735106248703997,
0.010725784076287997,
0.011914597469388798,
0.013341173541109757,
0.015053064827174908,
0.01710733437045309,
0.019572457822386907,
0.022530605964707486,
0.026080383735492183,
0.030340117060433817,
0.03545179705036378,
0.04158581303827974,
0.04894663222377889,
0.05777961524637786
],
"xaxis": "x",
"y": [
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20,
20
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"shapes": [
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0,
"x1": 0,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00032,
"x1": 0.00032,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00048000000000000007,
"x1": 0.00048000000000000007,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00064,
"x1": 0.00064,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0008,
"x1": 0.0008,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00096,
"x1": 0.00096,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0011200000000000001,
"x1": 0.0011200000000000001,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00128,
"x1": 0.00128,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00144,
"x1": 0.00144,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0016,
"x1": 0.0016,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00176,
"x1": 0.00176,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0019520000000000002,
"x1": 0.0019520000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.002144,
"x1": 0.002144,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0023744,
"x1": 0.0023744,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00265088,
"x1": 0.00265088,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00292736,
"x1": 0.00292736,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0032591359999999997,
"x1": 0.0032591359999999997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0036572671999999997,
"x1": 0.0036572671999999997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004135024639999999,
"x1": 0.004135024639999999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004708333567999999,
"x1": 0.004708333567999999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0052816424959999985,
"x1": 0.0052816424959999985,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.005854951423999998,
"x1": 0.005854951423999998,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.006542922137599998,
"x1": 0.006542922137599998,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.007230892851199998,
"x1": 0.007230892851199998,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.007918863564799997,
"x1": 0.007918863564799997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.008744428421119997,
"x1": 0.008744428421119997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.009735106248703997,
"x1": 0.009735106248703997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.010725784076287997,
"x1": 0.010725784076287997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.011914597469388798,
"x1": 0.011914597469388798,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.013341173541109757,
"x1": 0.013341173541109757,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.015053064827174908,
"x1": 0.015053064827174908,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.01710733437045309,
"x1": 0.01710733437045309,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.019572457822386907,
"x1": 0.019572457822386907,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.022530605964707486,
"x1": 0.022530605964707486,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.026080383735492183,
"x1": 0.026080383735492183,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.030340117060433817,
"x1": 0.030340117060433817,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.03545179705036378,
"x1": 0.03545179705036378,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.04158581303827974,
"x1": 0.04158581303827974,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.04894663222377889,
"x1": 0.04894663222377889,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.05777961524637786,
"x1": 0.05777961524637786,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
}
],
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "With a lot of enzyme
Changes in concentration for `S <-> P` and `S + E <-> P + E` (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-4.1095032180923086e-05,
0.057820710278558785
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.1111111111111112,
21.11111111111111
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(colors=['cyan', 'green', 'violet'], show_intervals=True, title_prefix=\"With a lot of enzyme\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "e0869320-aee5-4273-a6e3-dd46d173d80d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.003687634618431487, 9.999999999999996)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_crossover = dynamics.curve_intersect(\"S\", \"P\", t_start=0, t_end=0.5)\n",
"crossover_points.store(par=dynamics.get_chem_conc(\"E\"), \n",
" data_snapshot = {\"crossover time\": new_crossover[0]})\n",
"new_crossover"
]
},
{
"cell_type": "markdown",
"id": "ae56f399-c58b-4c2c-b994-5ef3e7d498ac",
"metadata": {},
"source": [
"## Notice the continued - and substantial - speedup of the reaction, over the earlier runs"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "6594d68a-ff8b-428c-b42f-f1b5fa22b5d2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium(explain=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f165e29-6c26-47f7-9f0c-143305a11c67",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "3a3c7ae9-3a86-4afa-8e2a-17a2cb8e5b6c",
"metadata": {},
"source": [
"# 6. Re-start all the reactions from the same initial concentrations - except for now having a very large amount of enzyme (more than the initial substrate concentration [S])"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "cce1bc79-60e5-459d-842e-f5f722d0f637",
"metadata": {},
"outputs": [],
"source": [
"dynamics = ReactionDynamics(chem_data=chem_data) # A brand-new simulation "
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "84b38ef9-cdbd-4169-b76c-58ca3557371c",
"metadata": {},
"outputs": [],
"source": [
"dynamics.set_conc(conc={\"S\": 20., \"E\": 30.},\n",
" snapshot=True) # A very large amount of enzyme `E`"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "5ba56162-8c48-47b4-b105-5fe2376d1797",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (S). Conc: 20.0\n",
" Species 1 (P). Conc: 0.0\n",
" Species 2 (E). Conc: 30.0\n",
"Set of chemicals involved in reactions (not counting enzymes): {'P', 'S'}\n",
"Set of enzymes involved in reactions: {'E'}\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "74d8f3bc-49c7-4d39-a04d-95400f5d819f",
"metadata": {
"tags": []
},
"source": [
"### Re-take the new system to equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "a99f0ac6-14e7-4138-86f4-3873d6bf2bb9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* INFO: the tentative time step (0.001) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.0004) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (0.0004) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.00016) [Step started at t=0, and will rewind there]\n",
"Some steps were backtracked and re-done, to prevent negative concentrations or excessively large concentration changes\n",
"36 total step(s) taken\n"
]
}
],
"source": [
"dynamics.single_compartment_react(duration=0.02, \n",
" initial_step=0.001, variable_steps=True, explain_variable_steps=False)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "801f144e-9727-4985-a53f-601204d5c053",
"metadata": {},
"outputs": [],
"source": [
"#dynamics.explain_time_advance()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "07b46ddc-0c1d-475d-80bf-bedd0e247851",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "S",
"line": {
"color": "cyan",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "S",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.00016,
0.000352,
0.000448,
0.000544,
0.0006399999999999999,
0.0007359999999999999,
0.0008319999999999998,
0.0009279999999999998,
0.0010239999999999997,
0.0011199999999999997,
0.0012351999999999997,
0.0013503999999999996,
0.0014886399999999996,
0.0016545279999999995,
0.0018204159999999994,
0.0020194815999999994,
0.002258360319999999,
0.002545014783999999,
0.002889000140799999,
0.003232985497599999,
0.003645767925759999,
0.0040585503539199985,
0.004471332782079999,
0.004966671695871999,
0.005462010609663999,
0.006056417306214399,
0.006650824002764798,
0.007364112038625278,
0.008220057681657855,
0.009247192453296945,
0.010479754179263853,
0.011958828250424143,
0.013733717135816491,
0.01586358379828731,
0.01841942379325229,
0.02148643178721027
],
"xaxis": "x",
"y": [
20,
19.0368,
17.947758293723425,
17.4410001730254,
16.951813988474136,
16.47959042977477,
16.023741314585124,
15.583698855899836,
15.158914954838115,
14.74886051795419,
14.353024798220163,
13.894492750988471,
13.45504032898887,
12.949640283690663,
12.373443160834105,
11.831771040824373,
11.220712066823461,
10.540165286482798,
9.793972674126744,
8.991254097978974,
8.28827128959663,
7.549504538360782,
6.92088568401952,
6.385992006993214,
5.839820837458296,
5.391368639008238,
4.949508536141351,
4.602515647856376,
4.275523288765867,
3.9842278722137676,
3.7427442676531273,
3.560472737153488,
3.439123909502799,
3.371300806448285,
3.3420899185660846,
3.334003446462275,
3.3332578987680797
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=P
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "P",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "P",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.00016,
0.000352,
0.000448,
0.000544,
0.0006399999999999999,
0.0007359999999999999,
0.0008319999999999998,
0.0009279999999999998,
0.0010239999999999997,
0.0011199999999999997,
0.0012351999999999997,
0.0013503999999999996,
0.0014886399999999996,
0.0016545279999999995,
0.0018204159999999994,
0.0020194815999999994,
0.002258360319999999,
0.002545014783999999,
0.002889000140799999,
0.003232985497599999,
0.003645767925759999,
0.0040585503539199985,
0.004471332782079999,
0.004966671695871999,
0.005462010609663999,
0.006056417306214399,
0.006650824002764798,
0.007364112038625278,
0.008220057681657855,
0.009247192453296945,
0.010479754179263853,
0.011958828250424143,
0.013733717135816491,
0.01586358379828731,
0.01841942379325229,
0.02148643178721027
],
"xaxis": "x",
"y": [
0,
0.9632000000000001,
2.0522417062765745,
2.5589998269745986,
3.048186011525863,
3.520409570225229,
3.976258685414877,
4.416301144100164,
4.841085045161884,
5.25113948204581,
5.646975201779836,
6.105507249011529,
6.544959671011129,
7.0503597163093366,
7.626556839165894,
8.168228959175627,
8.779287933176539,
9.459834713517202,
10.206027325873256,
11.008745902021026,
11.71172871040337,
12.450495461639218,
13.07911431598048,
13.614007993006787,
14.160179162541706,
14.608631360991764,
15.050491463858652,
15.397484352143627,
15.724476711234136,
16.015772127786235,
16.257255732346877,
16.439527262846518,
16.560876090497207,
16.62869919355172,
16.657910081433922,
16.66599655353773,
16.666742101231925
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=E
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "E",
"line": {
"color": "violet",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "E",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.00016,
0.000352,
0.000448,
0.000544,
0.0006399999999999999,
0.0007359999999999999,
0.0008319999999999998,
0.0009279999999999998,
0.0010239999999999997,
0.0011199999999999997,
0.0012351999999999997,
0.0013503999999999996,
0.0014886399999999996,
0.0016545279999999995,
0.0018204159999999994,
0.0020194815999999994,
0.002258360319999999,
0.002545014783999999,
0.002889000140799999,
0.003232985497599999,
0.003645767925759999,
0.0040585503539199985,
0.004471332782079999,
0.004966671695871999,
0.005462010609663999,
0.006056417306214399,
0.006650824002764798,
0.007364112038625278,
0.008220057681657855,
0.009247192453296945,
0.010479754179263853,
0.011958828250424143,
0.013733717135816491,
0.01586358379828731,
0.01841942379325229,
0.02148643178721027
],
"xaxis": "x",
"y": [
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30,
30
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"shapes": [
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0,
"x1": 0,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00016,
"x1": 0.00016,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.000352,
"x1": 0.000352,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.000448,
"x1": 0.000448,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.000544,
"x1": 0.000544,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0006399999999999999,
"x1": 0.0006399999999999999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0007359999999999999,
"x1": 0.0007359999999999999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0008319999999999998,
"x1": 0.0008319999999999998,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0009279999999999998,
"x1": 0.0009279999999999998,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0010239999999999997,
"x1": 0.0010239999999999997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0011199999999999997,
"x1": 0.0011199999999999997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0012351999999999997,
"x1": 0.0012351999999999997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0013503999999999996,
"x1": 0.0013503999999999996,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0014886399999999996,
"x1": 0.0014886399999999996,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0016545279999999995,
"x1": 0.0016545279999999995,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0018204159999999994,
"x1": 0.0018204159999999994,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0020194815999999994,
"x1": 0.0020194815999999994,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.002258360319999999,
"x1": 0.002258360319999999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.002545014783999999,
"x1": 0.002545014783999999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.002889000140799999,
"x1": 0.002889000140799999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.003232985497599999,
"x1": 0.003232985497599999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.003645767925759999,
"x1": 0.003645767925759999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0040585503539199985,
"x1": 0.0040585503539199985,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004471332782079999,
"x1": 0.004471332782079999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004966671695871999,
"x1": 0.004966671695871999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.005462010609663999,
"x1": 0.005462010609663999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.006056417306214399,
"x1": 0.006056417306214399,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.006650824002764798,
"x1": 0.006650824002764798,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.007364112038625278,
"x1": 0.007364112038625278,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.008220057681657855,
"x1": 0.008220057681657855,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.009247192453296945,
"x1": 0.009247192453296945,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.010479754179263853,
"x1": 0.010479754179263853,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.011958828250424143,
"x1": 0.011958828250424143,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.013733717135816491,
"x1": 0.013733717135816491,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.01586358379828731,
"x1": 0.01586358379828731,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.01841942379325229,
"x1": 0.01841942379325229,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.02148643178721027,
"x1": 0.02148643178721027,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
}
],
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "With a very large amount of enzyme
Changes in concentration for `S <-> P` and `S + E <-> P + E` (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.5281957174402752e-05,
0.02150171374438467
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.6666666666666665,
31.666666666666668
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(colors=['cyan', 'green', 'violet'], show_intervals=True, title_prefix=\"With a very large amount of enzyme\")"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "1fde8290-ac8a-4d02-80c3-14a5ee801b5a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.002465868124424963, 10.0)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_crossover = dynamics.curve_intersect(\"S\", \"P\", t_start=0, t_end=0.5)\n",
"crossover_points.store(par=dynamics.get_chem_conc(\"E\"), \n",
" data_snapshot = {\"crossover time\": new_crossover[0]})\n",
"new_crossover"
]
},
{
"cell_type": "markdown",
"id": "d69fe48c-8f70-43ad-9cdf-4c2530bd6c49",
"metadata": {},
"source": [
"## Yet more speedup of the reaction, over the previous run"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "b49cc4ba-b349-43c1-9e0d-9b5969653d88",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium(explain=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50b3aa69-81a6-4cfb-8de6-a88d4e64470a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "e998b386-e0ae-4342-a18b-493fc80695b9",
"metadata": {},
"source": [
"# 7. Finally, re-start all the reactions from the same initial concentrations - except for now having a huge amount of enzyme (far more than the initial [S])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "7979df61-ab06-4a86-ae67-62a12561e4bd",
"metadata": {},
"outputs": [],
"source": [
"dynamics = ReactionDynamics(chem_data=chem_data) # A brand-new simulation "
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "9fa2865e-ccf0-4f3f-8f63-3c54692c541f",
"metadata": {},
"outputs": [],
"source": [
"dynamics.set_conc(conc={\"S\": 20., \"E\": 100.},\n",
" snapshot=True) # A lavish amount of enzyme `E`"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "f430f845-821d-4e35-b015-cb3e09012d7d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (S). Conc: 20.0\n",
" Species 1 (P). Conc: 0.0\n",
" Species 2 (E). Conc: 100.0\n",
"Set of chemicals involved in reactions (not counting enzymes): {'P', 'S'}\n",
"Set of enzymes involved in reactions: {'E'}\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "2ffda9ba-42c0-40fa-bd3f-fabb689a43f6",
"metadata": {
"tags": []
},
"source": [
"### Re-take the new system to equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "9bdb8454-4e1d-4a04-ae00-c178a0a799b7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* INFO: the tentative time step (0.0005) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 0.0002) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (0.0002) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 8e-05) [Step started at t=0, and will rewind there]\n",
"40 total step(s) taken\n"
]
}
],
"source": [
"dynamics.single_compartment_react(duration=0.02, \n",
" initial_step=0.0005, variable_steps=True, explain_variable_steps=False)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "a30fa6b8-d788-43bf-8203-0fe90ab853cf",
"metadata": {},
"outputs": [],
"source": [
"#dynamics.explain_time_advance()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "5bcb8653-ebf6-41da-9b68-298931d91359",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "S",
"line": {
"color": "cyan",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "S",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
8e-05,
0.00012000000000000002,
0.00016,
0.0002,
0.00024,
0.00028000000000000003,
0.00032,
0.00036,
0.0004,
0.00044,
0.00048800000000000004,
0.0005456,
0.0006032,
0.00067232,
0.000755264,
0.0008547968,
0.00097423616,
0.00109367552,
0.00121311488,
0.0013325542399999998,
0.0014758814719999997,
0.0016192087039999996,
0.0017912013823999995,
0.0019631940607999993,
0.0021695852748799993,
0.0024172547317759994,
0.0027144580800511995,
0.0030711020979814394,
0.003499074919497727,
0.004012642305317273,
0.004628923168300727,
0.005368460203880872,
0.006255904646577046,
0.0073208379778124544,
0.008598757975294946,
0.010132261972273935,
0.011972466768648722,
0.014180712524298466,
0.016830607431078157,
0.020010481319213787
],
"xaxis": "x",
"y": [
20,
18.3984,
17.674553663486474,
16.985486689732678,
16.329528000205837,
15.705086808340633,
15.110648761671413,
14.544772269327206,
14.006085005983225,
13.493280583790483,
13.005115384212498,
12.447463573660574,
11.816864832361555,
11.229896655497727,
10.574269008780037,
9.852837533308353,
9.073373228847784,
8.249846223810035,
7.5444710782866045,
6.940296481851367,
6.422803137643994,
5.8909051805719725,
5.450581244483579,
5.013162376188607,
4.666113182959234,
4.3356935748870775,
4.037490043862578,
3.7861046461860783,
3.5921358543920516,
3.459088948365278,
3.3815089032589043,
3.345843529090064,
3.3347277637470483,
3.3332382298503873,
3.3333561975033694,
3.3333166739470466,
3.333342049836217,
3.3333164086771676,
3.3333536535976886,
3.333279794938443,
3.3334732797983446
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=P
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "P",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "P",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
8e-05,
0.00012000000000000002,
0.00016,
0.0002,
0.00024,
0.00028000000000000003,
0.00032,
0.00036,
0.0004,
0.00044,
0.00048800000000000004,
0.0005456,
0.0006032,
0.00067232,
0.000755264,
0.0008547968,
0.00097423616,
0.00109367552,
0.00121311488,
0.0013325542399999998,
0.0014758814719999997,
0.0016192087039999996,
0.0017912013823999995,
0.0019631940607999993,
0.0021695852748799993,
0.0024172547317759994,
0.0027144580800511995,
0.0030711020979814394,
0.003499074919497727,
0.004012642305317273,
0.004628923168300727,
0.005368460203880872,
0.006255904646577046,
0.0073208379778124544,
0.008598757975294946,
0.010132261972273935,
0.011972466768648722,
0.014180712524298466,
0.016830607431078157,
0.020010481319213787
],
"xaxis": "x",
"y": [
0,
1.6016000000000001,
2.325446336513525,
3.0145133102673225,
3.670471999794164,
4.294913191659369,
4.889351238328588,
5.455227730672796,
5.993914994016777,
6.506719416209519,
6.9948846157875035,
7.552536426339428,
8.183135167638447,
8.770103344502274,
9.425730991219964,
10.147162466691649,
10.926626771152218,
11.750153776189967,
12.455528921713398,
13.059703518148636,
13.577196862356008,
14.10909481942803,
14.549418755516424,
14.986837623811395,
15.333886817040769,
15.664306425112924,
15.962509956137424,
16.213895353813925,
16.407864145607952,
16.540911051634726,
16.6184910967411,
16.65415647090994,
16.665272236252953,
16.666761770149613,
16.66664380249663,
16.66668332605295,
16.66665795016378,
16.66668359132283,
16.66664634640231,
16.666720205061555,
16.666526720201652
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=E
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "E",
"line": {
"color": "violet",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "E",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
8e-05,
0.00012000000000000002,
0.00016,
0.0002,
0.00024,
0.00028000000000000003,
0.00032,
0.00036,
0.0004,
0.00044,
0.00048800000000000004,
0.0005456,
0.0006032,
0.00067232,
0.000755264,
0.0008547968,
0.00097423616,
0.00109367552,
0.00121311488,
0.0013325542399999998,
0.0014758814719999997,
0.0016192087039999996,
0.0017912013823999995,
0.0019631940607999993,
0.0021695852748799993,
0.0024172547317759994,
0.0027144580800511995,
0.0030711020979814394,
0.003499074919497727,
0.004012642305317273,
0.004628923168300727,
0.005368460203880872,
0.006255904646577046,
0.0073208379778124544,
0.008598757975294946,
0.010132261972273935,
0.011972466768648722,
0.014180712524298466,
0.016830607431078157,
0.020010481319213787
],
"xaxis": "x",
"y": [
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100,
100
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"shapes": [
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0,
"x1": 0,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 8e-05,
"x1": 8e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00012000000000000002,
"x1": 0.00012000000000000002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00016,
"x1": 0.00016,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0002,
"x1": 0.0002,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00024,
"x1": 0.00024,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00028000000000000003,
"x1": 0.00028000000000000003,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00032,
"x1": 0.00032,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00036,
"x1": 0.00036,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0004,
"x1": 0.0004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00044,
"x1": 0.00044,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00048800000000000004,
"x1": 0.00048800000000000004,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0005456,
"x1": 0.0005456,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0006032,
"x1": 0.0006032,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00067232,
"x1": 0.00067232,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.000755264,
"x1": 0.000755264,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0008547968,
"x1": 0.0008547968,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00097423616,
"x1": 0.00097423616,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00109367552,
"x1": 0.00109367552,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00121311488,
"x1": 0.00121311488,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0013325542399999998,
"x1": 0.0013325542399999998,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0014758814719999997,
"x1": 0.0014758814719999997,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0016192087039999996,
"x1": 0.0016192087039999996,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0017912013823999995,
"x1": 0.0017912013823999995,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0019631940607999993,
"x1": 0.0019631940607999993,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0021695852748799993,
"x1": 0.0021695852748799993,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0024172547317759994,
"x1": 0.0024172547317759994,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0027144580800511995,
"x1": 0.0027144580800511995,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0030711020979814394,
"x1": 0.0030711020979814394,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.003499074919497727,
"x1": 0.003499074919497727,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004012642305317273,
"x1": 0.004012642305317273,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004628923168300727,
"x1": 0.004628923168300727,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.005368460203880872,
"x1": 0.005368460203880872,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.006255904646577046,
"x1": 0.006255904646577046,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0073208379778124544,
"x1": 0.0073208379778124544,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.008598757975294946,
"x1": 0.008598757975294946,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.010132261972273935,
"x1": 0.010132261972273935,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.011972466768648722,
"x1": 0.011972466768648722,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.014180712524298466,
"x1": 0.014180712524298466,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.016830607431078157,
"x1": 0.016830607431078157,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.020010481319213787,
"x1": 0.020010481319213787,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
}
],
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "With a huge amount of enzyme
Changes in concentration for `S <-> P` and `S + E <-> P + E` (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.423220577468975e-05,
0.020024713524988476
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-5.555555555555555,
105.55555555555556
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(colors=['cyan', 'green', 'violet'], show_intervals=True, title_prefix=\"With a huge amount of enzyme\")"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "05d1e5d7-b21e-4fba-bd2a-fd020dac6711",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0007383445224719487, 10.0)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_crossover = dynamics.curve_intersect(\"S\", \"P\", t_start=0, t_end=0.5)\n",
"crossover_points.store(par=dynamics.get_chem_conc(\"E\"), \n",
" data_snapshot = {\"crossover time\": new_crossover[0]})\n",
"new_crossover"
]
},
{
"cell_type": "markdown",
"id": "122ce249-db1a-439b-b7ac-51999895bb5c",
"metadata": {},
"source": [
"## Yet more speedup of the reaction, over the previous run"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "d73a6b89-2b43-4ca2-b8fd-56d04c39de5c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium(explain=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf097a1e-4741-4301-b723-f73ff7297972",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "eff43c80-0f3a-4c40-8993-f619978a7a16",
"metadata": {},
"source": [
"# 8. Finally, re-start all the reactions from the same initial concentrations - except for now having a LAVISH amount of enzyme (two orders of magnitude more than the starting substrate concentration [S])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "ba4e3803-839e-48b6-ac06-0b29b0863101",
"metadata": {},
"outputs": [],
"source": [
"dynamics = ReactionDynamics(chem_data=chem_data) # A brand-new simulation "
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "19d037f7-7fc2-4f1f-b9fe-3e9b22bf5f8c",
"metadata": {},
"outputs": [],
"source": [
"dynamics.set_conc(conc={\"S\": 20., \"E\": 2000.},\n",
" snapshot=True) # A lavish amount of enzyme `E`"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "932e7c3e-0a39-40ca-a66d-d94277b09c1f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 species:\n",
" Species 0 (S). Conc: 20.0\n",
" Species 1 (P). Conc: 0.0\n",
" Species 2 (E). Conc: 2000.0\n",
"Set of chemicals involved in reactions (not counting enzymes): {'P', 'S'}\n",
"Set of enzymes involved in reactions: {'E'}\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "eb327388-65a3-4e61-ba7e-11e5206a203d",
"metadata": {
"tags": []
},
"source": [
"### Re-take the new system (now with a lavish amount of enzyme) to equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "1e470552-7588-4a8a-a3e2-b4ff6f4b24c1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* INFO: the tentative time step (5e-06) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.4 (set to 2e-06) [Step started at t=0, and will rewind there]\n",
"48 total step(s) taken\n"
]
}
],
"source": [
"dynamics.single_compartment_react(duration=0.0015, \n",
" initial_step=0.000005, variable_steps=True, explain_variable_steps=False)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "66abe729-adb3-4528-abd9-9d7fe0554185",
"metadata": {},
"outputs": [],
"source": [
"#dynamics.explain_time_advance()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "832c1130-a9d5-48bc-8327-414d3bcc9dab",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=S
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "S",
"line": {
"color": "cyan",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "S",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
2.0000000000000003e-06,
4.4e-06,
5.600000000000001e-06,
6.800000000000001e-06,
8.000000000000001e-06,
9.200000000000002e-06,
1.0400000000000002e-05,
1.1600000000000002e-05,
1.2800000000000003e-05,
1.4000000000000003e-05,
1.5440000000000005e-05,
1.6880000000000004e-05,
1.8608000000000005e-05,
2.0336000000000007e-05,
2.2409600000000006e-05,
2.4897920000000006e-05,
2.7386240000000005e-05,
3.0372224000000006e-05,
3.395540480000001e-05,
3.825522176000001e-05,
4.341500211200001e-05,
4.857478246400001e-05,
5.476651888640001e-05,
6.095825530880001e-05,
6.714999173120001e-05,
7.458007543808001e-05,
8.201015914496001e-05,
9.092625959321601e-05,
9.984236004147202e-05,
0.00011054168057937921,
0.00012338086522486785,
0.0001387878867994542,
0.00015727631268895785,
0.00017946242375636222,
0.00020608575703724745,
0.00023803375697430975,
0.0002763713568987845,
0.0003223764768081542,
0.0003775826206993978,
0.00044382999336889015,
0.000523326840572281,
0.00061872305721635,
0.0007331985171892327,
0.0008705690691566921,
0.0010354137315176431,
0.0012332273263507844,
0.001470603640150554,
0.0017554552167102777
],
"xaxis": "x",
"y": [
20,
19.19996,
18.28599660014224,
17.855338357682417,
17.4370836906074,
17.030875358146762,
16.636366408576915,
16.253219882881787,
15.881108526948514,
15.519714512052296,
15.1687291633917,
14.759677403052686,
14.364763175946537,
13.907244802306217,
13.468701576084774,
12.964275509692332,
12.389089894286228,
11.848255891324753,
11.238015139279211,
10.558207322375072,
9.812594617947482,
9.010196720076873,
8.307168499040953,
7.568011009893149,
6.938699034082024,
6.402908531092059,
5.855507927013171,
5.405725762152446,
4.96223923111242,
4.613657519654578,
4.28487402507243,
3.9916503675076744,
3.748212258153332,
3.5641108078275807,
3.4412218372609265,
3.3722801215309617,
3.3424138948843507,
3.3340557789156424,
3.3332548866324556,
3.333355008957094,
3.3333159596319253,
3.333343607311661,
3.3333134817562144,
3.3333601024973247,
3.3332623361215226,
3.3335318261081825,
3.3325757395502067,
3.3368755623483497,
3.312638413746954
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=P
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "P",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "P",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
2.0000000000000003e-06,
4.4e-06,
5.600000000000001e-06,
6.800000000000001e-06,
8.000000000000001e-06,
9.200000000000002e-06,
1.0400000000000002e-05,
1.1600000000000002e-05,
1.2800000000000003e-05,
1.4000000000000003e-05,
1.5440000000000005e-05,
1.6880000000000004e-05,
1.8608000000000005e-05,
2.0336000000000007e-05,
2.2409600000000006e-05,
2.4897920000000006e-05,
2.7386240000000005e-05,
3.0372224000000006e-05,
3.395540480000001e-05,
3.825522176000001e-05,
4.341500211200001e-05,
4.857478246400001e-05,
5.476651888640001e-05,
6.095825530880001e-05,
6.714999173120001e-05,
7.458007543808001e-05,
8.201015914496001e-05,
9.092625959321601e-05,
9.984236004147202e-05,
0.00011054168057937921,
0.00012338086522486785,
0.0001387878867994542,
0.00015727631268895785,
0.00017946242375636222,
0.00020608575703724745,
0.00023803375697430975,
0.0002763713568987845,
0.0003223764768081542,
0.0003775826206993978,
0.00044382999336889015,
0.000523326840572281,
0.00061872305721635,
0.0007331985171892327,
0.0008705690691566921,
0.0010354137315176431,
0.0012332273263507844,
0.001470603640150554,
0.0017554552167102777
],
"xaxis": "x",
"y": [
0,
0.8000400000000002,
1.7140033998577602,
2.144661642317584,
2.5629163093926017,
2.969124641853239,
3.3636335914230866,
3.7467801171182145,
4.118891473051487,
4.480285487947705,
4.831270836608303,
5.240322596947316,
5.6352368240534645,
6.092755197693785,
6.531298423915229,
7.035724490307671,
7.610910105713775,
8.15174410867525,
8.76198486072079,
9.44179267762493,
10.18740538205252,
10.989803279923128,
11.692831500959048,
12.431988990106852,
13.061300965917978,
13.597091468907943,
14.14449207298683,
14.594274237847555,
15.037760768887582,
15.386342480345425,
15.715125974927572,
16.008349632492326,
16.25178774184667,
16.43588919217242,
16.558778162739078,
16.627719878469044,
16.657586105115655,
16.665944221084363,
16.66674511336755,
16.66664499104291,
16.66668404036808,
16.666656392688342,
16.66668651824379,
16.66663989750268,
16.666737663878482,
16.666468173891822,
16.6674242604498,
16.663124437651657,
16.687361586253054
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"shapes": [
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0,
"x1": 0,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 2.0000000000000003e-06,
"x1": 2.0000000000000003e-06,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 4.4e-06,
"x1": 4.4e-06,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 5.600000000000001e-06,
"x1": 5.600000000000001e-06,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 6.800000000000001e-06,
"x1": 6.800000000000001e-06,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 8.000000000000001e-06,
"x1": 8.000000000000001e-06,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 9.200000000000002e-06,
"x1": 9.200000000000002e-06,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.0400000000000002e-05,
"x1": 1.0400000000000002e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.1600000000000002e-05,
"x1": 1.1600000000000002e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.2800000000000003e-05,
"x1": 1.2800000000000003e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.4000000000000003e-05,
"x1": 1.4000000000000003e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.5440000000000005e-05,
"x1": 1.5440000000000005e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.6880000000000004e-05,
"x1": 1.6880000000000004e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.8608000000000005e-05,
"x1": 1.8608000000000005e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 2.0336000000000007e-05,
"x1": 2.0336000000000007e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 2.2409600000000006e-05,
"x1": 2.2409600000000006e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 2.4897920000000006e-05,
"x1": 2.4897920000000006e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 2.7386240000000005e-05,
"x1": 2.7386240000000005e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 3.0372224000000006e-05,
"x1": 3.0372224000000006e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 3.395540480000001e-05,
"x1": 3.395540480000001e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 3.825522176000001e-05,
"x1": 3.825522176000001e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 4.341500211200001e-05,
"x1": 4.341500211200001e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 4.857478246400001e-05,
"x1": 4.857478246400001e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 5.476651888640001e-05,
"x1": 5.476651888640001e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 6.095825530880001e-05,
"x1": 6.095825530880001e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 6.714999173120001e-05,
"x1": 6.714999173120001e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 7.458007543808001e-05,
"x1": 7.458007543808001e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 8.201015914496001e-05,
"x1": 8.201015914496001e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 9.092625959321601e-05,
"x1": 9.092625959321601e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 9.984236004147202e-05,
"x1": 9.984236004147202e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00011054168057937921,
"x1": 0.00011054168057937921,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00012338086522486785,
"x1": 0.00012338086522486785,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0001387878867994542,
"x1": 0.0001387878867994542,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00015727631268895785,
"x1": 0.00015727631268895785,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00017946242375636222,
"x1": 0.00017946242375636222,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00020608575703724745,
"x1": 0.00020608575703724745,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00023803375697430975,
"x1": 0.00023803375697430975,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0002763713568987845,
"x1": 0.0002763713568987845,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0003223764768081542,
"x1": 0.0003223764768081542,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0003775826206993978,
"x1": 0.0003775826206993978,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00044382999336889015,
"x1": 0.00044382999336889015,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.000523326840572281,
"x1": 0.000523326840572281,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00061872305721635,
"x1": 0.00061872305721635,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0007331985171892327,
"x1": 0.0007331985171892327,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0008705690691566921,
"x1": 0.0008705690691566921,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0010354137315176431,
"x1": 0.0010354137315176431,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0012332273263507844,
"x1": 0.0012332273263507844,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.001470603640150554,
"x1": 0.001470603640150554,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0017554552167102777,
"x1": 0.0017554552167102777,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
}
],
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "With a LAVISH amount of enzyme (NOT shown)
Changes in concentration for `S <-> P` and `S + E <-> P + E` (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.2485456733359017e-06,
0.0017567037623836136
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.1111111111111112,
21.11111111111111
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(chemicals=['S', 'P'],\n",
" colors=['cyan', 'green', 'violet'], show_intervals=True, title_prefix=\"With a LAVISH amount of enzyme (NOT shown)\")"
]
},
{
"cell_type": "markdown",
"id": "7faa3d9f-f2b3-4031-9bb5-ebb18dfb5345",
"metadata": {},
"source": [
"_Note: NOT showing the enzyme (concentration 2,000) in the graph, to avoid squishing down the other curves!_"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "fb5f2c87-acbe-42e7-867f-15a42e0c726e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3.7174487975074336e-05, 10.0)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_crossover = dynamics.curve_intersect(\"S\", \"P\", t_start=0, t_end=0.5)\n",
"crossover_points.store(par=dynamics.get_chem_conc(\"E\"), \n",
" data_snapshot = {\"crossover time\": new_crossover[0]})\n",
"new_crossover"
]
},
{
"cell_type": "markdown",
"id": "7cf81e1a-dd35-4075-95d0-a7f014dc02e5",
"metadata": {},
"source": [
"## Yet more speedup of the reaction, over the previous run"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "859e65f6-873e-4126-88f3-24ef0cfb8030",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0: S <-> P\n",
"Final concentrations: [S] = 3.313 ; [P] = 16.69\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 5.03748\n",
" Formula used: [P] / [S]\n",
"2. Ratio of forward/reverse reaction rates: 5.00001\n",
"Discrepancy between the two values: 0.7496 %\n",
"Reaction IS in equilibrium (within 1% tolerance)\n",
"\n",
"1: S + E <-> P + E\n",
"Final concentrations: [S] = 3.313 ; [E] = 2,000 ; [P] = 16.69\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 5.03748\n",
" Formula used: ([P][E]) / ([S][E])\n",
"2. Ratio of forward/reverse reaction rates: 5.00001\n",
"Discrepancy between the two values: 0.7496 %\n",
"Reaction IS in equilibrium (within 1% tolerance)\n",
"\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5de0f238-e027-42ca-bbed-a4a7b3e54e79",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "d7ba66b0-45c6-4bc0-8a4c-6fbd0cc1c2a1",
"metadata": {},
"source": [
"## Now, let's look at the time of the crossover points (for the [S] and [P] curves to intersect), as a function of the Enzyme concentration"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "fbeb3b6a-4c93-49ef-ac7a-ed1c9c916735",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Enzyme concentration | \n",
" crossover time | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 0.743526 | \n",
" | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.2 | \n",
" 0.247107 | \n",
" | \n",
"
\n",
" \n",
" | 2 | \n",
" 1.0 | \n",
" 0.067698 | \n",
" | \n",
"
\n",
" \n",
" | 3 | \n",
" 5.0 | \n",
" 0.014494 | \n",
" | \n",
"
\n",
" \n",
" | 4 | \n",
" 20.0 | \n",
" 0.003688 | \n",
" | \n",
"
\n",
" \n",
" | 5 | \n",
" 30.0 | \n",
" 0.002466 | \n",
" | \n",
"
\n",
" \n",
" | 6 | \n",
" 100.0 | \n",
" 0.000738 | \n",
" | \n",
"
\n",
" \n",
" | 7 | \n",
" 2000.0 | \n",
" 0.000037 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
Enzyme concentration=%{x}
Crossover time when [S]=[P]=%{y}",
"legendgroup": "crossover time",
"line": {
"color": "#636efa",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "crossover time",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.2,
1,
5
],
"xaxis": "x",
"y": [
0.7435259497128707,
0.24710707675961718,
0.06769827987210066,
0.014494267066104415
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "variable"
},
"tracegroupgap": 0
},
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Time of crossover of [S] and [P] , as a function of Enzyme concentration (first 4 points)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
0,
5
],
"title": {
"text": "Enzyme concentration"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-0.026007493080938155,
0.7840277098599132
],
"title": {
"text": "Crossover time when [S]=[P]"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Let's plot just the first 4 data points, to avoid squishing the graph\n",
"px.line(data_frame=df.loc[0:3],\n",
" x=\"Enzyme concentration\", y=[\"crossover time\"],\n",
" title=\"Time of crossover of [S] and [P] , as a function of Enzyme concentration (first 4 points)\",\n",
" labels={\"value\":\"Crossover time when [S]=[P]\"})"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "42894206-49cb-4880-857c-e831fd8800bb",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "variable=crossover time
Enzyme concentration=%{x}
Crossover time when [S]=[P]=%{y}",
"legendgroup": "crossover time",
"line": {
"color": "#636efa",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "crossover time",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.2,
1,
5,
20,
30,
100,
2000
],
"xaxis": "x",
"y": [
0.7435259497128707,
0.24710707675961718,
0.06769827987210066,
0.014494267066104415,
0.003687634618431487,
0.002465868124424963,
0.0007383445224719487,
3.7174487975074336e-05
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "variable"
},
"tracegroupgap": 0
},
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Time of crossover of [S] and [P] , as a function of Enzyme conc - log scales on both axes
([E]=0 value skipped)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-0.6989700043360187,
3.301029995663981
],
"title": {
"text": "Enzyme concentration"
},
"type": "log"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-4.6687022898183725,
0.11024341565758267
],
"title": {
"text": "Crossover time when [S]=[P]"
},
"type": "log"
}
}
},
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA3AAAAFoCAYAAAAW8BkIAAAgAElEQVR4Xu29C6xm1X3Yu85rXswZZs68Z8AQTFJNHHJT3BsqC1S7sVwSyRghxaaqaLmGWJCUXNluBZiIpihjjFpj3dAERIFya1XFREIYSzfEcooruNa177WTW0K4jV2CMcz7PcO855y7/vs765z17dnPb+1vr/0/329Lwzmcs9dea/3Wf+9v/856jc3Zw3BAAAIQgAAEIAABCEAAAhCAQOcJjCFwnW8jCggBCEAAAhCAAAQgAAEIQCAhgMARCBCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOAUNNSvffoLZs++Q0lJf+WXrjb/6d/9noJSU8QPffT2BQi//g+vM//2wbszobzxN2+bT3/u9/t+97V//TvmE//gf64N8T/+yZ+ZR/7oP/ele+O7z9a+zjAT/IuHHjd/+l++b6qU6988/px59hsvLxRny6YZ8+fPP1qreE3yrZVxh09Ox8mg8dbhKrZeNJi2jnxJZPjt//p/m8//qz8y9/7OPzb/9Df/0ZKoU1El/sk//4Pkfabuc1wLGPd5c/tnbjT/8u5bh15s9xn5/JO/bz70C1cOPT8y6A4BdQLnvxQXYZSb5zd+7e8nL8aapUcednIgbd25aaqURKT77/7Sz+dKm38N98Av+gDPkhC5RpEYugd7FVGqUqemzhlE4LLqkMdEyplXZ/eyNMrC0jUGRfEgz/tBpL2pWK16nbpM5bn+l3/1k9zLt/XyV7V+nDc8Agjc8NjGuPIwBK7omghcjFbuRp7qBC6NzX34Z/31wQW9VoGr8mLfjTCiFD6Bui9zZe3sHtDplzr3F/+8+B4VgUtLrHs5zpK0um2zFCNbnpl/8Vc/7sxfwJeCwNVl6mK0a39cWYrx3vU6IXBdb6F65UPg6vHi7MEJLGmBGxxLN1LystmNdqhbCidWVYc0FAmc+11RT5u8PGYNzxxVgZP2yuu54Z4ypmu9+nV6ZOvei22dX5cpAtdWy3Q/HwSu+21Up4QIXB1anBtCYMkLnLzI+S+/7uVa/jovc4Xc3DKB6F6408M0s/5K6h66PvyqL+ySpix91lwmv4xFjZ41PMf1RvgvS/7cOr+3wv+55JM1nCdr+FpaMrLqmHWtdHnT15H2yOtlkrLK4Y+nd3V0jNJp/Qfs5g3rFuaMVempLZtjlp6z5cpQ9Jf2KgI3yJCqOgKXF2/pYZ3+/fO/2/l2/jCwrB6vrDiRIXFy31XpfSiqQ5Hc5r0gD1vgsoZ458VV+twq8efkNH3/V02bvrflOu5+k99J26SHa6fnrPj3j6T35yjmxWneM+nb//X/SeZDpg8XS0Vl8mMv6w8cLu0/s/OKZI5R3vOg7AO07HlSxDTv2lUFrg7roukFwlMO4VD0PHccu/I5mff88J/3Zc9kqbc/zOx3f+8PMz/3/baqkm9Z3FT9fZ7A1Zm7m76/5Hkg90fVeXXpGM4atpz1ee4/d7Lug6zr5M2BqzLfucp7RxH3sntZ0oY+N4ru2bz2SN+7/nl5UwUc+7qxncUnzcWd47/Tuvssr00l3tLPlqJ6uTyy8q7zLl31PluK542swElj+kHiPwDTP09PuHU3jH+TuZ9VmVuTdW7Wzwb5y1yW7Mi15ZAJtf7Nkr5Jsl6Is37myuXfrO7mdkzS/+9/iPqMssqb/lneGO+sl3H3IeLLQVry/AdiUc9W+obPGq6b9TP/5afKQiRlQyjdQ7DuQ62uwP2ZnUzvv7xntaH/suS3Y1YvSlacCNOqL69+zBTNgctqw5gCl26ndDxXvafyPnDkemV5FH1Y5b1A1RU4ySPrGZB+BlZ9JmW1cVaZqjwz3IuYPLv9l8y6fx3P+gNR1s/qLsxQ9R7wn1VVWGdJiP/ClZdv+v717/NYn5NZzx/Xrn/4B7+bLNhQ9Znsy0HZ53uVfJt8Gcz6nC969qYlICses95Rip4nWfeI/1zNe7+Re9Hdt1IOKZv/mVf1XnFxmRVr7vpV3juK2qXqvezeIQZ9bpTds377ZdUp62dV5sBlvdNWXSxG7qPNG9f1LbqSdW+5smXFRla9/POy6pX1PMpqpybvt6V0rZEVuPRLRp4kpH9edCNVHUaT7hV0AZV+CagrcFWGIhWdk/e7tJDksRI2P/x//3uyklbetaROcshDvkzM3AMhr6clPe+kqFyymE36enV6tYraPf2S2bTAZf3lUxiW/XW1jsDlPdTSi7Hk1S0rVvOEoEqcuvIM0gNX1DM37B64LI7pOhTFh5w7yMplddq6KYHLun/Sz7YqbV10TjqGqj4z3It+lR7FvNjPa6esZ8GgApeXt2Nb9NzJ+xxx13Qvov5Lcd5nilyriiB24XPS1a/OMzmv3FltXMa16RfAkGdnnfshq9xF7xjuWVT3jx5+PlnTCdL3StHz0I/LKu8dTdzLVf+QlZdX2TuiL1VlvZHu3q0icOk/6tV9D0nXJ+8Py/4fB+T5mtWrn8cw/TmVd68N+jnY9L3Z9eshcPMtlBfs6Z8X3RRVJrKXPTBlOJK7EesKXN5N4wdhnZelvA9K/6+zecPg3I1atIJc0UtP2Ytg3Zco/4NgkA+ksnb3l8Wv++As64Hz2y9rmEqeiNZ5qXd5lA0BrCpwRYyrvNSHCFzWi6u7XhsCVzYcxRfyur2qrh5leRR98LQpcE0/k+o8M0JfxOo8KwcVuLJhxIMKnCt71oiQvD84ZQ2XSqfvwueki+06z+SqAlf3M9e/z/L+0FbWxuk8i9o8Xee8uKtaD7+3KG/0UJ3PM7+n02fjXztd5rL7zK3mXOW9I++5V5aH/4ee0OdGHdnKE5g6MVE1tstkJC9+s94v/M+foikq6T9GpoXefVbX+WN6WT1G6fcIXE2By3tA+UFT9MAu++u7CJx72FV9CPsv32VDAoseZHkPk6weDf9h6vJP38hZrPzyFb3cpYdJpVlkyUmW3Pjt4vIeRODyHpJy/fRQgzofeJK+jsClH05FslJH4Fwd0vFTtXcx3T5Fsdu0wKWZFM0HG6bA+R+A/jMgK3ayPiyrLJdfJ4+8D7I2Ba5Kb0YTL1dS1/QzI/RFrEjK0sN8uiRw7vmT91KUfj6le9mFZVVRy5OpYX5Opv+4k/VHkPQzuepLbt1ndxMvi3WenemhlXkxXufdwZe4rPeYos8+d75/jbI/BKTvlaz5sX45/Od5lfeOrDapcy+HPjeqCpyUU0YGZd2n6eGGVa/p7wNXJ5bde0TW3Lu854hLk37fzRPBPKHPem+rMhWpiXtP+zUQuJoClzUkoE4QLIUeuKz6ug/Mor+kuAe1e0jU+Wu65Ok/WLNeOqoOYR1E4Or8tbfOgzNU4NyHa9YDr47A5b1oaxC4sj9a+PE6TIHLE5EqL0DunLK6hOThOLQpcPTAZX861J0DV2W4quTk4rtsURv3/PzEP/h7yRCoQXva8gRumJ+TeXn6pNP3SVWBqyM+dT73i86t09uSJd9Zw4RD6uHycDFU5fOsKl/hkNUDN+i2JlXeOyTPJv5IVPWPNHVGn3ShBy4vVqrUQ9jm9cCVTfHIuif8PwSU9Vw3df9pvg4CV1PgBnn5TwdIjDlwbkxxE3Pg5BpZy9b79co6J82u7vh9/y/Lfk+l41vlRdkXpjrd9nXmW1T5wPNjoqgHTh6uMmZe5hUWiXPWw66qwDVRt6wPgbw4b7oHrkx62hK4qvMZpF1+49f+frIIg39UkZ2qeRR9KLUpcEVt7Z5JRXG61OfAlb2k1B1C6YZAl13X/4PB7n0HL1p5NLQHbpifk1K2D/9PfycJ8bwejKpxk1XPvOeWyzd934a+AGY9O/OeBen7Ke/+qipwUicRwPRiW/4zoqz3x18cLR13WXzrzIFLS3nZe0deW+TFc1bdhtkDl752ned5ep5q2XtP1feQOmwkT/+PxvJuIqu5l43cyZO1/+PP/6+L5nxXfY8Lve+WQnoErqbA+QGc/guDPDRlafX0MtzpQHEBWraKZdWHsLu+exil/yLi3/hFL1RZ6bOGT2bNr0gP7XB/YfaHU2T1FknZ0kPHslaZc3V0LyhF2wpkLVEvZf7lHVclIjToy0VWvdNDdaScVR+c6XbL+otV0Xwpx7OJOXDpIWFSNje0IWsIS/ov9lmxmtXe/tCqspdM/16ruwpl3sO5bg+ca8sqwxvL6uvuhaz7Pz1kJq/8VfMYROCyPjjdfezXv45UVHkmFT3nsl6mqj4zQl/E/HugaFVbOa/qX+dduwyjBy7rmVsUB+5ZmtV7Hypww/qcTPfsVX0m1+khyro/Q3sUi9ohK/6zhsGmP2Plmlmfz/Jz17ZlvSBZ+VT9zPf/8JJVNn+4Y9EcOHf/yDL06eGwkoccviT618rKN4911pC/vM+9kMWP8tok613BtX3Zao1Fz5g6sZ3FJqu8fk+Y/36R9Tnl6lW2CqX/DifP07zPhqw2Kbp/Rvl3CNx869f9wMob51v2wHTBlpU+/fCqK3Du2ukxxf7DoUrvRzp9lb3bJO/0i0DW2Oas+QrpMfBlm1bLgiFFnP0Jtv7N7V7CBhU4uZb/oeSuXeWvjkUPmSpz4LIWF0k+wO2DMO+o2gOXFzfSVrJnkv9hlnef5MWqL2ySj8TS3v2Hk72/NAhc1gtiUVtm1VfO9xcnkv/PitGqz46qeeSVs0g20uWSe1r+KOWvnFZH4Ko8k+ScdJ2a3AeubF+7sheANJOsPx4NKnB5edddhTJvHpO7flZsFZW57udh2XMhXc+qsZ71OZk33DPrWe9+Vvclt0q+ZXFT9fdlQ9j861Tda1POyxoam1Wm9L3nntN5C1D418ia7+Z+L5/j8kdT6aEpE7isZ4C7Tt42Fu73deZLVbmXQ//w4+5Ft99pUVy636U/36vsF5e1D9ygc+DSzw8pu2zV4fdwp4fW+vVy73pl+8BJmixZ9a9VNvy76n01CuepF7hRaCTquLQJVBG4QQjUFbhB8hh2mqbrULcHrsqwxmEz4PoQaJrAoH8cbLocXG84BIbZYzicEnNVCECgLgEEri4xzodAwwQQuHygMQWuruw1HBZcDgJDI1B1COfQCsCFGyMgf2RyG5u7i1ZZ/bWxAnAhCEAgCgEELgp2MoXAIoGs4U91hoX4LKsM8dTEPj28p8pctHT9BuUreb/8yg/Mnz//qCZklBUChQSqzrcEow4CWVMV6izQpaOWlBICEEgTQOCICQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQUC9wbgldf+d6t0yy37zPP/n7xt+lvkrTN7GEeZV8unBO1/eNcW0xSDt2gW+6DOn6+EvdL5U6dpE7ZYIABCAAAQhAAALaCagWuH/x0OPmT//L9016z6yyDXhFVoqOrD24JK+/+KsfN7onVHr/lph7tywVgcuLia7dqHlCKjEhB3uPda3FKA8EIAABCEAAAhDoBgG1Aud6LLKkp4rA/covXW3+07/7vcqt0LTAiTD5ZSiqT+VCBpyIwAXAGyBpnsCxye4AMEkCAQhAAAIQgAAERoiAWoFzPS1vfPfZi5qr6wKX9/Lufp5Vp2HH5FIRuGFzaur6RUNCi2K7qfy5DgQgAAEIQAACEICATgJqBa5IOLoucP/kn/+B2bPv0EXD5Fy5/fl8WWEl6f/yr35iskTPvfy7eVRZw0Wzeh/TPPPKUtRTGDIkND3fUOrtD2XNEx5XP8cs6zwp15ZNM+af/eY/Mp//V3+0gDTNwa/zf3vzrWR4rjvy2iTNt+p5Uh6Jgaz5blXjQOcjh1JDAAIQgAAEIAABCIQQUClwZYJW9vv08MUqALOGUPoLTxRdQ17W/TlNTijSQzirDqMsesGXuvnDSuX/05KQVf8QgXPlzhoS+uv/8Drzbx+8uxBxWjrl5LSkZomZkydfZPMETmSprHz+4je+iLlr+kLpzvXrlzX80bFJc8iqsw+p6z2iVe4ZzoEABCAAAQhAAAIQaJ6ASoH7j3/yZ+aRP/rPmb0XgqiKwBWhHPYiJnkCWVXgpOxZi12UcXF1zhqqGSJweT2KVcuTJ7RSzn95961JsdNiJuVNi3HWeY6VnJsW5vQ1i8Q4XcaiMj/7jZcXekfz2JStqinp5KgzT7P5xwNXhAAEIAABCEAAAhDoGgGVAlc2V6yKwMVcxKQJgXNy5MtmnlS43p508Pk9cyECl9dbVNYOrjyut62ot84Xnk9/7vcz5a2uwKWFrUjg/HlpRaKdltY8Nghc1x6FlAcCEIAABCAAAQjoIIDAVWynLg2hdEX25SBLlvwhgWXDDAcVuCrDSMvm9El9nMT5zZE1jNH9Pm/LhaI5cOnerEEFLmufwXQYiVhv37rRiGxmlRWBq3jjcRoEIAABCEAAAhCAQB8BlQJXNjSvrOenqTlwg8ZS6CImLl+/VyhLMPNWM8ybT5Y1nystX1m9T+l5d4Ny8dO5+W2ul9CVWcq4eeM6I8MUq4pRXs9kuhezqAfObzPHoExMi3rqELgmooRrQAACEIAABCAAgdEjoFLgygSt7PexBa6pbQR8QcgSmjrzrwbtgXO9Z1mrala9nUQ00wudpOUqzcz9f1ri6vTApQW3bHEYX3DzpDBd57K5clmrUMo1WMSkavRwHgQgAAEIQAACEBgtAioFruwFt+sC58rfxEbe/tDD9LYCWasn+sv1F82BkzKm5cMfLumLU9YqlO42kmv84R/8rvnQL1yZe2dlLUhSZRVKJ2BZQy39uhUt+OKnzdtEO2vbhqxVKF0FpT6uLZyI+vm4n8n5bCMwWg9cagsBCEAAAhCAAARCCagVuNCNvIvADXsVSl9upOfKHXnzuorK6mQgbwGQ9P5qkocc0mNXJnBONF3+spKjyFjevK70PnCSrspiMVnz6NIrTOb1Wjq5cvnk9cBJWXzW8v/pdnZS5vZo8+vtbwPht0fWPnvptvCFTdL6w0CzBI6NvEMfa6SHAAQgAAEIQAACS5eAWoErml9U1gM3SHNmzTEb5DqkaZ9A1eGOXdhAO68XsH1q5AgBCEAAAhCAAAQg0EUCagVOYGYNEZSfI3BdDLV4ZdIkcFnDPeORI2cIQAACEIAABCAAga4RUC1wAtMN28uay+TDzlssoqhB0sMPszaO7lqDUp6LCWgQOH8Y6SCxSrtDAAIQgAAEIAABCIwGAfUCNxrNRC0hAAEIQAACEIAABCAAAQgYg8ARBRCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQggMARAxCAAAQgAAEIQAACEIAABJQQQOCUNBTFhAAEIAABCEAAAhCAAAQgkClwH/ro7bXJXH3ldvPNZ3fWTkcCCEAAAhCAAAQgAAEIQAACEKhGIFfg3vjus9WuYM967OkXzHde/SECV5kYJ0IAAhCAAAQgAAEIQAACEKhPAIGrz4wUEIAABCAAAQhAAAIQgAAEohBA4KJgJ1MIQAACEIAABCAAAQhAAAL1CbCISX1mpIAABCAAAQhAAAIQgAAEIBCFQKHAffYLj5jv/+jNpGDXXbvDPPPovVEKSaYQgAAEIAABCEAAAhCAAAQgYEyuwN3/5SfNaz943bz64mMJp0/d/oBZP7MGiSNqIAABCEAAAhCAAAQgAAEIRCKQK3A33HyP+fQnP2buueOWpGivv/mWufXuh0yd1Skj1YlsIQABCEAAAhCAAAQgAAEILEkCuQIne8HtvO9Oc/ON1y9UPOtnS5IKlYIABCAAAQhAAAIQgAAEINBBAghcBxuFIkEAAhCAAAQgAAEIQAACEMgigMARFxCAAAQgAAEIQAACEIAABJQQKBS4KnUYlTlxbg7gc48/aK7ZcVUVNJwDAQhAAAIQgAAEIAABCECgUQLsA1cBpyzocujI8eTMtMDtOniqwhWW3inTKyeTSh0/dV5l5TavW2EOHD1jLszOqSv/8qlxs3rllDl47Iy6socWWHvd165eZs6eu2BOnrkQiiJK+m3rV5pRfeZprvuq5RNm2dSEOXLibJS4Cc10/Zrl5sSpc+bMudnQS6lLr7nuE+NjZsOly83ew6fVcXcFlvueAwJdJJApcLJYSZ2etceefsF859Ufmm8+u7OLdWykTHk9cKP6MoPANRJWA11Eu8QMVOn5RNrrjsCFtH7ctAhcPP6aJSaUmua6I3ChrU96COQTQOAqRgcC1w8KgasYOEM4TbvEhCDRXncELqT146ZF4OLx1ywxodQ01x2BC2190kMAgQuOgTyB0zokJRTIimUTySVOn9U5FGzNJVPmxMnzZnZO3xDKyYlxI/xlSNGoHdrrvmrFpDl/YdYOo9Q5FEwEdFSfeZrrvswOu5Z75+RpnUPeZci4fNbIvTNqh+a6j4+NmdWrJs2x9/V+Vsl9zwGBLhLI7YGrW9irr9w+kkMotc5lqdu+6fOnJsaSH527oE+ApNwrrQCdtnORFPqbse9hycvYKM4H0V73ZZPjZtbOuzyvcO6l3Dcyl2pUn3ma6z5p5yKN239nz+sUIOl5F3kbQX8zmutu/c2ssHMvTyn9Q6975oW+L5EeAsMgwCImFakyhLIfFEMoKwbOEE7TPowwBIn2ujOEMqT146ZlCGU8/pqHEYZS01x3hlCGtj7pIZBPAIGrGB0IHAJXMVSGfpp2iQkBpL3uCFxI68dNi8DF469ZYkKpaa47Ahfa+qSHAAIXFAP+NgJyoZm10+bVFx9LrskqlDrnVLCNQNAtES0xAhcNfZKxZokJJae57mwjENr68dIjcPHYu2de3BKE5/6p2x8w62fWmGcevTfzYuxzHM44xhUq9cDd/+UnzV//zU+TOW6f/cIjSTnzAiFGJWLmicAhcG3Hn3aJCeGlve70wIW0fty0CFw8/polJpSa5rrTAxfa+tnpX3z5NfPAV54yO++709x84/WlmSBwpYhUnoDABTYbAofABYZQ7eTaJaZ2hb0E2uuOwIW0fty0CFw8/polJpSa5rojcKGt30x6BK4Zjl27CgIX2CIIHAIXGEK1k2uXmNoVRuBCkDWaVrPEhILQXHeGUIa2frz0CFw89pKz3PeDHjJ67aVvf8+88d1n+y4h03Ku/9VrzMNf+lwyqu37P3qz7/f++U6+5AR3nvS8SQ/cc48/aK7ZcdVA1/CnAmUNoUyXy+U1KAvSNU8AgQtkisAhcIEhVDs5AjdlDh47U5tbFxLQA9eFVhisDAjcYNyaSKVZYkLrr7nu9MAZ86GP3m7uuu0mc88dtySh4IY/OkkTUfr8b/1mImJyiLDJIVOW3P//5O33+q6RFq5BruHnk3W9g4eOLZThsadfME98/aWLRDQ0tkkfRgCBC+PHIianELjAEKqdHIFD4GoHTUMJNEtMKALNdacHLrT146VH4OKxl5xDeuAkvciVL0Nl60iILD3/rVcWFsrLGv5YtuhIlWs4kZSeNTluvfuhpEfP/95JpfxMeg0//cmPLYho3FYhdyGAwAXGAT1wCFxgCNVOjsAhcLWDpqEEmiUmFIHmuiNwoa0fLz0CF499EwKX7nGTHrn04iPplc4lX9dDV1XgBr2GlOWDV2xbELj/8dNdyfDMrMPvSYzbKuSOwDUQAwgcAtdAGNW6BAKHwNUKmAZP1iwxoRg01x2BC239eOkRuHjsmxA4uYbrvbp8+ybz1Se+sdC7Jr8Tobvu2h0LK7unhytWEbiQa+QJXHreXtxWIPcsAvTABcYFAofABYZQ7TJK19IAACAASURBVOQIHAJXO2gaSqBZYkIRaK47Ahfa+vHSI3Dx2DclcG4rLtmLTQ63DVe6d05+V1fgXI+ZL1xVruHn7Q/JlDLIcMqqWxTEbZ3Rzr2SwI02ouLaI3AIXNv3BwKHwLUdcy4/zRITykxz3RG40NaPlx6Bi8e+KYFzgiTX88XI/dz/mfSmyVF1CGWWcFW5hpxz0yc+kqyEmbWIiax46UuhSOh11/5ipX3n4rbY6OSOwAW2NQKHwAWGUO3kCBwCVztoGkqgWWJCEWiuOwIX2vrx0iNw8dg3JXByHRkKeejIsb7hk/Jzt9WAq6XMM/NXfKwyhLLKNWQlS/9w8iY/q7KNgC+VcVuE3B0BBC4wFhA4BC4whGonR+AQuNpB01ACzRITikBz3RG40NaPlx6Bi8e+SYGLWwtyX4oEMgXOdb/WqfDVV25f2DOiTjrt5yJwCFzbMYzAIXBtx5zLT7PEhDLTXHcELrT146VH4OKxR+Disif3YgK5AldnBRqZMPmdV3+IwI1QtE2vnExqe5x94FpvdQQOgWs96OYz1Cwxocw01x2BC239eOkRuHjsEbi47MkdgRtqDNADRw/cUAMs4+IIHALXdszRA9fb0Ffr8x6Bi3XHhOeLwIUzDLlC6EbeIXmTFgJFBOiBC4wPrR/ogdU29MCFEhw8PQKHwA0ePWEpNUtMWM0RuFB+Iek1S0xIvSWt5rpPjI+ZDZcuN3sPnw7FEC09AhcNPRmXEGARk8AQQeDogQsModrJETgErnbQNJQAgTvVEMl2L0MPXLu8m8wNgWuSZv1rIXD1mZGiHQIIXCBnBA6BCwyh2skROASudtA0lACBQ+AaCqVal9EsMbUqmnGy5rrTAxfa+qSHQD6BXIFz+0r4Gwy63d3lcv4eEqMMGIFD4NqOfwQOgWs75lx+CBwCFyP2NEtMKC/NdUfgQluf9BAYQODSmwemd4yX33/8hg+be+64ZaT5PrDzrNmwwZiNG+fMxg3yz36/fs5M9BZpXLIHc+DiNS0Ch8DFij4EDoGLEXuaJSaUl+a6I3ChrU96CAwgcDfcfI/59Cc/tiBo0vv2/LdeWdhFfpS3DvBx3vm/nruI7ooVc2b7tjGzZfOc/TdrtmwxZvOmuSUVhwhcvOZE4BC4WNGHwCFwMWJPs8SE8tJcdwQutPVJ3yYB11H13OMPmmt2XNVm1gPllTuEUjbz9odPfvYLjyQZPPPovcnXF19+zTzwladMnf3iBiphxxN97y9OmwP7x8z+A8b+GzMH7L8jRy8u9PT0fO+c9NJttL109usG+296dccrmFM8BC5euyFwCFys6EPgELgYsadZYkJ5aa47Ahfa+qQfFgFtspbFobLAyZDJX/yFK8zDX/ocAueRTM+BO2s75PbPC53I3P79Inbj5sDBi/GvWOmkzphNG2YTsdtgh1+uWzeskG3uughccyzrXgmBQ+DqxkxT5yNwCFxTsVTnOpolpk49s87VXHcELrT1ST8sAkta4NJz3MqGVA4LctevW2URkzk7enKflboDB6WHblHwpMfufGoNkMkJfz6d7aWb762TuXVjY92hgcDFawsEDoGLFX0IHAIXI/Y0S0woL811R+BCW3946Z3AuBzuuu0m89GP/Iq59e6HjHz/xNdfSn7lhhOKAxw6cjz52dVXbjfffHbnQuHciDz3A//3Rb+T8/OuKwsp7j1weGHUn5wrIwE3b1i30JEk///9H725UA5XVle3rHq4k/18XZ3+4N47kvrLdeSQ76+7dsdCHrJ4oxwvfft7ydf0Yo555RlWK+b2wPlz3LKGS4rgyeE34rAK2eXrVhG4vPIfPmwSqZNeun1JL13v+1MZ7wgy3HJhkZSFYZizZmoqjtUhcPGiEoFD4GJFHwKHwMWIPc0SE8pLc90RuF7rHz02Z/bsbX8dhDVrxszWzdnviDJNSgTHLUQo8vH53/rNRFrSgpZ+308vcpieciW/d25Q9Lui6zoJ86dpybWcpIngvfaD1xfW5RBP+eoT30j+36VN18O/F7N64PyfOYFzkuY8KP3/rnxF5Ql9BuSlL9wHzrfJrO0E/MYfVgG7ft0Qgcuq24n3+3vp9s0Pwzx+/OKbcO2lMp9O5tLJnDr5Z4dh2q+rVg2fGgI3fMZ5OSBwCFys6EPgELgYsadZYkJ5aa47Atdr/b/4b7Pmj56+EBoKtdP/3V8eN79zhx3WlTrSixK6XxdJje8A6U6dtAz62eX9Lr2yvaRJX9cfCZheODEthpJezv9fbv1188Erti30pOUtRlJV4NK9enn/X1Sem2+8vnbbVUnARt5VKBWc07TAZWV1+sycOWh76HoLpdivyby6MXPw0MVnX7LKSt2mMTuXbtbOq5szmzb15E4WUWnyQOCapFnvWggcAlcvYpo7G4FD4JqLpupX0iwx1WuZfabmuiNwvTb9qzdnzZ9+ZzY0FGqn/9COMfMbH88WuO+8+sOLRtBlSY2TKn9lxvR56WGS/tDCvN9Vua4vbelpXSJMWYeIZiyByytPqwInYOqsLjnKWwq0IXBZQXHBPgsWV7+03y8I3pi5kPpDz4rlVuiSYZfeMEz7/cy6waUOgav9LG0sAQKHwDUWTDUvhMAhcDVDppHTNUtMKADNdUfgQlt/OOmb7oHzS5nVs+Z+7//OSVZRz56kcz1b6VXv/eGUaUpVFigZRg9c29sPZPbAIXDVb5pYApdXQplX1xt2KVsb9LY3kNUwT5/uTzE+MdYbcimLpNiVLzclm5HbVTDtz8bHy+uPwJUzGtYZCBwCN6zYKrsuAofAlcXIMH6vWWJCeWiuOwIX2vrDS180By4tImVz4Pw5b2kxKvud1NDNl0vPrZPfualcspiI28bM/fzgoWN9vYiStzvcYiRF+7mlhz1mzYGrOoRSyplXnmHtKYfABd4fXRO4rOqcODG/T52Vun3eKpjy8/Sxfsatgtnbqy7Zr8721i1f1n8mAhcYOAHJETgELiB8gpIicAhcUAANmFizxAxY5YVkmuuOwIW2/vDSF61CmdWTVLQKpYjXT95+b6Gw/voYRb+TBEXXld+7oZZ+T53LSBYOcStCup/5K0iW9YhJT6RbbVMWPMlahbKqwEn+eeVpXeDqhk3Rai91rzXs8/2AqlruvC5ZDQKXxVN65GQu3YGDMrfOrYQ5ZqQHL32sWTO/SMrCEMw583OXT5jVlxhz/FRqH4RhN15D19+8boU5cPSMuTA7+DDShopS+zIIHAJXO2gaSoDAIXANhVKty2iWmFoVzThZc90RuNDWJz0E8gmM3CIm6W7OrC7bNC7/LwRpo9cqcFkhIXPn3LDLhc3IZe8623Mnc+7845JVY2bzJjuPbqa38mVvJcw5s3atDiFC4HQ+FrXL69rVy8zZcxfMyTPtr0jWRIsjcAhcE3FU9xqaJaZuXdPna647Ahfa+qSHAAK3QEBk7It3fca4VWH8vSOKAmWp9cDVuSlktUt/9ctkXp2VujNn+68yNeWGXVqhs711ybw6O6dO5K5rBwLXtRapVh4ErhqnYZ2FwCFww4qtoutqlphQXprrjsCFtj7pIYDAJQTKVp0pGqc6ygKXFT4Xzk4m8+l++u5sb3sDK3QH7FfZx84/xuz/9vaok7l0i3vVyf9PTsa7NRG4eOxDckbgQuiFp0XgELjwKKp/Bc0SU7+2/Sk01x2BC2190kMAgRuawGkdChV6U0zZVSzlOHdhccjkyZNzZs++MbN335z9Z+z3Jvl68NDFwyrXz8gQTJP827LZ/rN7122xgreyhU3Ipdwrl02Y03Yo25yOEZ99zTVhVwmdtP85c679fWVC4yY0vfa6L5scN7N23uV5hXMvpe1WLZ9QO/wzNPY0131yfMyuLjxmzp7X+cyQP9yct+P400P5Q9tUQ3rNdZc/4K6YmjCnzuocMu6eeRrihDKOHoGRmgM3jB64IydS4whHJIZWWAGS43TJg/n8OStxdrGUffafyJx83SMbke8ft/LUb0/T01boZOjlxp7cbbJCJ/8vi6g0fay5ZMqcOHnezCo0OJE34X/ilIU7Yof2uq9aMZm8iJ5VKt8yh29Un3ma677MCpDcOydP61x0avXKqeSzRu6dUTs0133cGtzqVZPm2Pt6P6vkvueAQBcJjJTASQNkzYFLbxCY1VAMoeynErqNwAG7OIpb/VL2qXPDMM+mnvPLV8hWBovDMGUz8k12+OW6dWG3E0Mow/jFSs0Qyljke/kyhJIhlDEiUPMwwlBemuvOEMrQ1ic9BPIJjJzAla1Cmd6w0KFD4JoVuKyQPHpUeujmNx+fX/1SJO/9k/1nT9jOv94edfNz6pINyHs/q7IJuVwNgdP5WETg4rYbAofAxYhAzRITyktz3RG40NYnPQQQuD4CRfvAZQmcv42AXGhm7bR59cXHkmsupW0E6twooT1wVfM6eXK+d26hl663d92RIxdfYf36/kVSZNEU6a1bljECAoGr2gLdOg+Bi9seCBwCFyMCNUtMKC/NdUfgQluf9BAIFLi0wPiXe+O7z440XwSu/TkV5+wwS7dfnWxnIKthyjYHsgpm+rh0je2pS1a/7PXYyfYG8v1V29nIW+ONi8DFbTUEDoGLEYGaJSaUl+a6I3ChrU96CAQIXJWNrkcZMALXvsDlxdvxE7ZHdPeY2bPbmN37xs2ePWPmkN3DLn3IMMttW8fM9q2zZtu2WXPFB2RDcj1RrF1iQkhrrzsbeYe0fty0muVVVtBcZlcD1LoAjWaJCY1azXVH4EJbn/QQCBC4D330drPzvjsXNr4GZj8BBK47ApcVm6dPy7YGxry3S1bBHDe7rdzJPLvZ1GJm06uNueyyOStzs+YDlxmzdduckSXru3hol5gQptrrjsCFtH7ctAhcPP6aJSaUmua6I3ChrU96CCBwQ4sBBK7bApfV8Odtkc+fWm7+8q/Pmbd/NmZ+9s6Ykd47/5BNxi/bPpfI3OVW6q643O5nY1fE7MKhXWJCGGqvOwIX0vpx0yJw8fhrlphQaprrjsCFtj7pIRAgcDKE8uM3fNjcc8ctcMwggMDpEzhpRn8RE9kKbvfe3tDLPfbrbjv0UoZfnvG2+Fs2ZTcb3zKX/NsqG4/LV/uv6qqXTd482iUmhIX2uiNwIa0fNy0CF4+/ZokJpaa57ghcaOuTHgIBAvfiy6+Zrz7xjYVVF4HZTwCB0y9wWTF9+LCdTzcvciJ18k+2OfCPzZus0FmZ27pVvtp/9v9XXTL8O0S7xIQQ0l53BC6k9eOmReDi8dcsMaHUNNcdgQttfdJDIEDgZA5c0cEqlDpXJQu9KdraRiC0nHnp624jINsZ7Nk730MnC6XY7/fuG+u7/Bq74qWI3NYtvR66bfZf6IbjWeXXLjEhbaq97ghcSOvHTYvAxeOvWWJCqWmuOwIX2vqkh0CAwAGvmAA9cEuzB64s7i/YRVBkmKX82y0yt9cukGK/P3t2cZ7ccrv/nBtqmYjdVjt00/bShQ671C4xZWyLfq+97ghcSOvHTYvAxeOvWWJCqWmuOwIX2vqkhwACN7QYQOBGU+CyAurQYZlHN2Z2SU/dHjuvzgrd8eP9vXSbrMBtkx66pKdO5tQZs3JlvcVRtEtMyM2ove4IXEjrx02LwMXjr1liQqlprjsCF9r6pIdAoMDJQiY/efu95CpuSwEZWnndtTvMM4/eO9J8ETgELu8GeP/kosglvXV2yOW+1LDLSy+1c+hk/pztnZOvmzfPmpl1xbeUdokJeWBorzsCF9L6cdMicPH4a5aYUGqa647AhbY+6SEQIHD+Rt433HyP+eJdn0n2hHvs6RfM8996ZeQXN0HgELiqD5jZC73VLt2Kl8lXK3bnzi1eYfnyeaGbX+VSeuikt27M68jTLjFVeWWdp73uCFxI68dNi8DF469ZYkKpaa47Ahfa+qSHQIDASU/bc48/aK7ZcZXxBU5Wp3zgK08ZFjFhERONN1jdRUyGVceDB3tDLXvbF8giKeN22GV/bskKl1bkpIdOVry8fLsxm2aWmYPHzgyrWJ29LgIXt2k0S0woOc11X7V8wiybmjBHTnh7o4QCaTG9ZokJxaS57ghcaOuTHgIBAifS9scPf/4igaMHrgeVHjh64Jp8wLxvV7vcndqPbv+B/hzWXWol7rIxs37DbG/7Alntcm2TpejutRC4uG2jWWJCyWmuOwIX2vrx0iNw8dhLznLfc0CgiwTG5uxRVLD7v/ykee0HrydDJV0P3Aev2GZuvfshc9MnPmIe/tLnuliv1sqEwCFwwwy2CzLscn61y122h27vvvH5YZeLt+2KFb396LbZeXQidJvnF0gZZrliXRuBi0W+l69miQklp7nuCFxo68dLj8DFY4/AxWVP7sUESgVOkrvhkv6l7rrtJnPPHbeMPF8EDoFr+yY4dnTCHDo0aX781rne3nSy2uWJ/tUu/f3okj3prOAtt6Kn/UDg4ragZokJJae57ghcaOvHS4/AxWOPwMVlT+4NCBwQ8wkgcAhc2/dHWmKOn+hJnOxHJ19lT7qLhl3alS2T1S7nV7zcsmnWrFU47BKBazva+vPTLDGh5DTXHYELbf146RG4eOwRuLjsyR2BG2oMIHAI3FADLOPiZRJz3g67lP3okhUvZWEUkTv7T4ZjukOGXW6VFS5ltctkyKXdZNx+7fpRVveul59VKLveQvnlQ+DitZ1miQmlprnuLGIS2vqkh0A+gUpDKGXu26EjqaXx5q/JKpSsQqnxBuvKKpSDsBtEYqRHzs2lkxUvRepOvL+Yu2xT4PajS4Zf2tUuN9teuhXL+4dmDlLeJtMMUvcm8w+9FgIXSjBeegQuHnvNEhNKTXPdEbjQ1ic9BAIEzt8HDpAXE6AHjh64tu+LJiTmhJ0ztytZHEW2Luj10MmWBv4xs1Y2GO8Nu3S9dZeuabu2/fk1UfeYNUDgYtIPyxuBC+MXklqzxITUW9JqrjsCF9r6pIdAgMDJPnA777sz2bybA4FzBKZXTibfHj+FwLV9XwxDYmQzcbcf3R67jUGyybhd8fLC+cVhlatWicyN9YZd2n+y8qX00rV5DKPubZYfgWuTdrN5IXDN8qxzNc0SU6eeWedqrjsCF9r6pIcAAje0GKAHDoEbWnDlXLgtidm3v7fBuPTS9YZfGiP71LljfLy3bUGyyXgidD2xW7ZseETaqvuwaoDADYvs8K+LwA2fcV4OmiUmlJrmuiNwoa1PeggECJwMofz4DR9my4AchggcAtf2AyaWxByz02Bl7pyI3C4RO7tQyqHD/XPkZmZ6C6Ns88Ruerq5xVFi1b2pNkbgmiLZ/nUQuPaZuxw1S0woNc11R+BCW5/0EAgQONkD7qtPfCPZyJvjYgIIHALX9n3RFYk5a4dd9oRucQsD6amb9UZV9oZdzvfQJSteGrNp0+BC15W6D9rmCNyg5OKnQ+DitYFmiQmlprnuCFxo65MeAjUFTua9VT1YhZJVKKvGSpfOG7VVKNtiv3efDLu0Qy5t71wy/NL+/0lvtcvxid5ql8mQSxl6ab/fvHnWDrusttolAtdWS2bno1liQslprjv7wIW2frz0CFw89pKz3PccEOgigUrbCHSx4F0pEz1w9MC1HYuaJObYsZ7Iuf3oZOXLw4f7ic3MOKGzQy9lbzorddPT2VQ11T2rBvTAtX23NJcfAtccy7pX0iwxdeuaPl9z3emBC2190kMgnwACFxgdCBwCFxhCtZNrlpgzZxc3Fk/Ezq54Kb12/rDLSy4RobOrXc7vRydfN27oDbvUXHcpPwJXO9w7kwCBi9cUmiUmlJrmuiNwoa1PeggECJwbTslWAtkQETgEru0HjHaJSfPaZ7cr2LVnbmE/Olkk5dQpb7VLGXaZzJ+bM5dvHzMfvHLCrJw+a6Z6O1moOhA4Vc3VV1gELl7baZaYUGqa647AhbY+6SEQIHCS9LNfeMR8/0dv9l1lZu00C5tYIggcAtf2A2apCVya39Gj8/vQuf3obE/d4dRqlxvW26GWdpNxkTpZJEW+n7Y9d10/ELiut1B++RC4eG2nWWJCqWmuOwIX2vqkh0CgwKWT3//lJ81L3/5e8mMWMWERE403GIuY6Gm1M6et0MniKHb+3D77de/ecfPurjkz5/naaitvvQ3Ge9sYiNht2NC9OiJw3WuTqiVC4KqSav48zRITSkNz3RG40NYnPQQCBe71N98yt9790MJVbvrER8zDX/pcZ7nK3nU/efu9pHxXX7ndfPPZnYVlrXK+Y/Dc4w+aa3ZctXA9euDogWv7RljqPXBFPF3d3/jx2d4G48k8ut7Kl6dPLw67nLTDK53Ibd3aWxhFhmFO2OGYMQ8ELib9sLwRuDB+Iak1S0xIvSWt5rojcKGtT3oIBAicmwOnpadNhnsePHRsQdpEztbPrDHPPHpvJoUq599w8z3m0BG7i7E9ELgexumVvQlIx08hcG0/YBC4KXPw2Jk+7EeOzu9HZ6Uu2b5AVrs80t8yG+xCKMmQS6+nbvUl7bYeAtcu7yZzQ+CapFnvWpolpl5NLz5bc90RuNDWJz0EAgTOJfX3hrvu2h25QhQbtsjWF+/6jLn5xuuTopRtRF71fHrg+lsWgYsX6QjcxQKXbg3pjZNeuV1uP7p5sfPPW73a7vFj584lc+jmV7xcb7c0GOaBwA2T7nCvjcANl2/R1TVLTCg1zXVH4EJbn/QQaEDg/Es89vQL5omvv5T8qEs9c1mSlSdeUvY65yNwCFxXHiQIXLnApdtqzk6Y27tvwu5HZ1e7tL1z8m+XFbwzqWGXvQ3Ge3PpXG9dk8MuEbiu3EX1y4HA1WfWVArNEhPKQHPdEbjQ1ic9BAIELj3/zV2qytyytsHXEbKmBK7tOpIfBCDQDIEDB+fMO+/NmZ/Jv3d73x9KbTK+ze5HJ1sXfOCy3lf5N2177jggAAEIQAACEIBALAKlG3lr2gcuhsCxiAlz4Nq+eemBq98DV6WNZNjlbhlqaXvnksVR7Pd77Vf/mJ6WIZdzZtv8apfSW7d+psrVe+fQA1edVdfOpAcuXoto7oUKpaa57vTAhbY+6SGQT6BU4LTBy5rT9sBXnsod6ln1fIZQ9kcCc+Di3RkI3HAELt2isk2BiNweux+dLIwiq13utZuOnz6zOE9u2dTF+9HJ0Mvx8ez4QODi3TehOSNwoQQHT69ZYgavdS+l5rojcKGtT3oIjJDAla0qKatSyuG2Fig736FD4BC4rjxIELh2BC6rvQ8d6c2fS3rp5nvrjh7rP3PTxtmF/eh62xeMmVWretKHwHXlLqpfDgSuPrOmUmiWmFAGmuuOwIW2PukhMEICJ1Ut2tctLXBl58vv/W0E5P9n1k6bV198LKHKEEqGULb9gEHg4glcuq1PnuytdrkgdPZ76aXzjzVr5hdEsfvRffCKCbN506xZeYnO+0azxITep5rrvmr5hFk2NWGOnDgbiiFKes0SEwpMc90RuNDWJz0ERkzg2mxwBE7ni+jmdSvMgaNnzIXZ4S4bP4xYROC6I3Dp9r1wQVa7HF/YZLwndmPmrPfevGyZFTpZ7dLfk65g2OUwYmjQa2qWmEHr7NJprjsCF9r68dIjcPHYS85y33NAoIsEltwcuLYhI3AIXNsxh8B1V+CyYkFWttxj58/tsvPoDh4YT1a9PHasf3GUzTLU0tuPTvamW7mye39c0Cwxofep5rojcKGtHy89AhePPQIXlz25FxNA4AIjBIFD4AJDqHZyBE6XwPkNLHPgDh05b97+2VzSM+eGXu7b3y90l67p7Ue3xe1HZzccn1lbO1QaT6BZYkJhaK47Ahfa+vHSI3Dx2CNwcdmTOwI31BhA4BC4oQZYxsURON0Cd/bcBXPyzIWFlp213ybbFsyveNn7OmbOebfWiuV2tUsZcmlFrrfBuP1/K3j92jf8SNQsMaF0NNcdgQtt/XjpEbh47BG4uOzJvQGB8xcF2XnfnebmG683sj/cddfuMM88eu9IM0bgELi2bwAEbmkJXFb8HDwoWxfIXDo79FJWu7RSd/x4/5ky7FKGWm5Leup6QzBXDHnYpWaJCb1PNdcdgQtt/XjpEbh47BG4uOzJPVDgRN7Wz6xJRM3fM+2xp18wz3/rlYXVGEcVNAKHwLUd+wjc0he4dEy9b1e73L2wH11P6PYf6O9/W2uHWPa2Lej1zklP3dq1zc6j0ywxofep5rojcKGtHy89AhePPQIXlz25Bwqc9LQ99/iD5podV/UJ3Isvv2aKNsgeFfAIHALXdqwjcKMncOkYk9Uu9+wZ7w29nN+PTnrr/GGXy5f3ZM6teLnVbmMgghdyaJaYkHq7Fzmtz3sELrT146VH4OKxR+Disif3QIGTXrc/fvjzFwkcPXA9sFo/0ENvjOmVk8kljp9C4EJZ1k2PwCFwWTFzQIZd2rlzyRw62ZvOfn/8/f5euqSHbl7kROxE8ET0qh4I3KmqqDp1HgLXqeaoVRgErhauxk9mG4HGkXLBhgiUrkJ5/5efNK/94PVkqKQbQvnBK7aZW+9+yNz0iY+Yh7/0uYaKovMyCBwC13bkInAIXJWYO37Cbipu59G5HjoRuwMH+lPKEMveoijzQy+TYZf5V0fgELgqsdf0OZolJpSF5rqzkXdo65MeAvkESgVOkrrhkv5l7rrtJnPPHbeMPFsEDoFr+yZA4BC4QWLuvL1Vk20LZNilm09n///C4oKYySIoyX50VuiSxVHs97JYijsQOARukNgLTaNZYka57ghcaOuTHgKBAgfAfAIIHALX9v2BwCFwTcXcyZPG/PSdMfPOz8bNT3/WEzwRPf9YZ3vkLts+Zz5w+az5e7+83EysON1U9qquo1leGUKpKtT6CqtZXhE4vXFHybtPoFIPXPerEa+ECBwC13b0IXAI3LBiTuTtvV1jicy99964+du3jTl9un8e3YoVc+byy4y54opZ8wErdpfZ7yd7U2KX9IHAlUqkHAAAIABJREFUxWtezRITSk1z3RG40NYnPQQCeuBef/OtZL5b3vHGd58dab4IHALX9g2AwCFwbcbcfjtv7p13xs07744lUrdvf/+iJyJvMuTyqiuM2b591vzclXY/Oit5S+1A4OK1qGaJCaWmue4IXGjrkx4CAQInC5dc/6vXjPxiJXkIETgEru0HDAKHwLUdcy4/kZi//P9OJwuj7JZtDOxcOvne76VbNmW3LkhWupztbWEwv5XBWH9HXqwqDJwvAjcwuuCEmiUmtPKa647AhbY+6SEQIHCyD9zO++40N994PRwzCCBwCFzbNwYCh8C1HXO+wKWfeUeOzO9FJyte7p6zQjdmjh7rt7VkpUsrdT2hE7Gz2xcsi1WLwfJF4Abj1kQqzRITWn/NdUfgQluf9BAIEDjpgfv0Jz/GipM5DBE4BK7tBwwCh8C1HXNFApcuy8mTInR2o/FE6Hpyt/9Av9CtW9fbj06ETla7FKFbvbrbwy4RuFhRZ4xmiQmlprnuCFxo65MeAgEC5+8DB8iLCSBwCFzb9wUCh8C1HXN1BC5dNtmmIBG5+e0LpIdOvp/1ti9YfYkInYjcWG/opf1+/UysWmbni8DFaw/NEhNKTXPdEbjQ1ic9BAIELmsPOP9yLGKic1+g0JtiemVv2bnjpxC4UJZ10yNwCFzdmGnq/KYkRjYZf8duX/Czd8fNW39r7MIoVuhm+0u5eZNsXTBnrrzSrnRpV7uU7QxiHk3VPUYd2EYgBvVm8kTgmuE46FXkvueAQBcJlG4jwCImxc1GDxwC1/aNjcAhcG3HnMtvWBJz9mxvP7p33xuzWxeMmV22x05+5h8icCJ0H/iAlTq7hcHGDe1SGFbd26gFAtcG5eHkgcANh2vVqyJwVUlxXtsESgWORUwQuCwC9MC1fasu5ofAIXCxoq8tiblge+N22/3o3nlXxC5/P7qfu9LuR/cBux+d3Ytu67Y5MzE+PDJt1X0YNUDghkG1nWsicO1wzssFgYvLn9zzCZQKHIuYIHAIXLceIQgcAhcrImNJzDnb0d9bEGXM7JE5dHb7gl32q39cukbm0C0ujiLfX3ppc6Ri1b2JGiBwTVCMcw0ELg53lysCF5c/uQcI3GNPv2C+8+oPzTef3QnHDAIMoWQIZds3BgKHwLUdc/7LTFeeeXv8RVESqbPDLs8tklm1qre65Ra7hYEsiiKrXW4IGHaJwMWKOlahPHHqnDlzLjVJNF5zVM6ZRUwqo+JECNQmUNoDJ0Moiw4WMWERk9pR14EEm9etMAeOnjEXZru9dHkWKgQOgYt1C3VZYg4e9Fa7nO+le99uaeCOSbvuUm+lS6+nzspd1Q3Gu1z3snigB66MUHd/Tw9c3LahBy4uf3IP6IEDXjGBrvw1uu12Yg5c28QX80PgELhY0adJYo4ds0I3L3LJV/vvyNH+YZe93rnefnS94ZdzZtlUNl1NdU/XAIGLdceE54vAhTMMuQICF0KPtMMkUNoDN8zMl8K1ETiGULYdxwgcAtd2zLn8NEvMKTtYQjYV70mdnUtnNxrft7+f5Pr1nszNz6e7xA7FlENz3RG4WHdMeL4IXDjDkCsgcCH0SDtMArkCJ0Mn77rtJvPE118qzJ8hlAyhHGaADuvaDKEcFtnhXle7vK5dvczO07pgTp7xdrEeLrJGr65ZYtIgzp+fswuijPeEzomd/d7fj27Nmnmhs710H/r5KbN81Rmzdq2+YdcIXKO3QasXQ+BaxX1RZghcXP7knk+AHrjA6KAHjh64wBCqnVy7xNSusJdAe90RuJDWH37avbZXzu+l220XSjlzZlHYVto9fWVhFDfcUr5u3NB9oUPghh87w8oBgRsW2WrXReCqceKs9gmUClzePnCyOuXz33rFvPriY+2XukM5InAIXNvhqF1iQnhprzsCF9L67ac9dLg33FJ66Q4dnDBvvzNrTry/WI6FhVFkLp2I3fwiKVUXRmmrRghcW6SbzweBa55pnSsicHVocW6bBAYWuBdffs088JWnTIwhlJ+6/QHzk7ffSzhdfeX20i0Oys4v+73k8/qbb5lb737IPPf4g+aaHVcttBECh8C1ecNKXtolJoSX9rojcCGtHzetvMj997dPXzTk8vDh/nL5C6Ns2TJrtlq5W768f/GUtmuCwLVNvLn8ELjmWA5yJQRuEGqkaYPAwAJ3/5efNK/94PXWe+A++4VHzMFDxxakTeRr/cwa88yj92byKju/7PdyUdnM/NCR48n1EbgeZlahbOP2zM5Du8SEkNNedwQupPXjps2a/3f69Pz8ufleOhl+uW9/v6zNrOutbtlb6bLXS7f6knbrgsC1y7vJ3BC4JmnWvxYCV58ZKdohkClwrnetrAg777vT3Hzj9WWnNfp7kakv3vWZhXylrF994hu5Ill2ftnvXeHpgetvRgSu0bCudTHtElOrsqmTtdcdgQtp/bhpqyzgcsGuTeO2LNi9e/F7f2GU6WkrcqntC9atG27dELjh8h3m1RG4YdItvzYCV86IM+IQGLgHLkZxsyQqT6ykfGXnyznpYZF510PgELgYMZ+Vp3aJCeGove4IXEjrx01bReCySrjXLoSye6+3fYGdT3f6zOKZK1b0z5/butmYTZuaXRgFgYsbOyG5I3Ah9MLTInDhDLnCcAiUCtxwsh3sqmVC5s9Na0vgjpw4O1hllKdasWwiqcHpszqXQ19zyZQ5cfK8mZ1r9kWpjWadnBg3wv/EqXNtZNepPLTXfdWKSXP+wqzdSmC2U1yrFkYEdFSfeU3V/eAhY97bNf/P9tK9Z/8dP7447HLSPlq3220Ltm2zX7fNmW32++32e3vbD3wsmxo3cu+cPK1zzvLqlVPJZ43cO6N2aK77uF3NZ/WqSXPsfb2fVXLfc0CgiwQQuNTCJHV74LTu5xQajFMTvReOcxf0CZCUe6UVoNN2Py6F/pa8yMnL2BmlEhASe9rrvmxy3O4zNmfO238aD+nJGdVn3rDqfuz4nHnXrsn17nv2qxU7+f6AlTz/2Gol7rKtY+ay7fbr9jFzuRW6FSuqR9Dk+JgZt//OntcpQNLzLvI2gv6WLFqlte6yGuuKqQlzSukfeuUOk/ueAwJdJKBK4ARg1py1otUwy84v+71rNIZQ9ocvc+Di3c7ahxGGkNNed4ZQhrR+3LSDDqGsW+ozp2W4pZs/19uXbu++/oVRZM5c38IodoGU6dX5fxRgCGXdVujO+QyhjNsWDKGMy5/c8wmoE7iyVSNlVUo5vvnszuRr2fllv0fgsoMHgYv3WNEuMSHktNcdgQtp/bhp2xK4dC1nvYVRdrmFUey8Ovm5O1ZbeettLr64yfjM2sXfI3BxYyckdwQuhF54WgQunCFXGA4BdQInGIr2bUsLXNn5VX7vbyMg58+snV5Y9ZJ94HTOqdi8boU5cPSMHZKjbyibdokJeZRprzsCF9L6cdPGErisWkuPnOul27On10t32vbcuWO5LIxiF0NxvXRXfWDcfOCyCbXzFzVLTGjUaq77hB22u+HS5WbvYS84Q4G0nB6Baxk42VUmoFLgKteuhRMROASuhTDry0K7xITw0l53BC6k9eOm7ZLApUkcPiwrXYrU2X/zvXTHe1uXJseEncYj8+c2bpxNFkVJtjGwPXbjSqb3aJaY0KjVXHcELrT1SQ+BfAKlAvehj95uYuz3pqXREDgEru1Y1S4xIby01x2BC2n9uGm7LHBpMifeF5Fb7KWTrQwOHOw/a7PdqiDZWDwZetn7fvnybo5I0CwxoVGrue4IXGjrkx4CCNzQYgCBQ+CGFlw5F9YuMSG8tNcdgQtp/bhpNQlcmtTE2IRdCGXC/Pit8/MbjduFUazU+ceMLIwiMufNpZO5dV04NEtMKD/NdUfgQluf9BAIEDiZU/bxGz5s7rnjFjhmEEDgELi2bwztEhPCS3vdEbiQ1o+bVrPApRcxmbW7CSTDLWXYpVsYxfbYXfAWRple7ebQGbNlkx16abcuWLcujtBplpjQqNVcdwQutPVJD4EAgZPl83/7/q8tLNoBzH4CCBwC1/Y9oV1iQnhprzsCF9L6cdMuJYHLIrlv/7zUydDLZD6dXRjl1GIv3YrlY2bL5tm+7Qs227l0bRyaJSaUj+a6I3ChrU96CAQInMyBKzre+O6zI80XgUPg2r4BtEtMCC/tdUfgQlo/btqlLnBpuoePuL3o/IVRFoVufHxxMRR/+wJZMKXpQ7PEhLLQXHcELrT1SQ+BAIEDXjEBBA6Ba/se0S4xIby01x2BC2n9uGlHTeDStN+XhVFk2KXbZNz21B081H/Wpo0yj8720s3PpdtiF0ZZuTK83TRLTGjtNdcdgQttfdJDAIEbWgwgcAjc0IIr58LaJSaEl/a6I3AhrR837agLXJr+2bPekMt5sduTWhhl7dr5RVG8hVHWTNdvR80SU7+2/Sk01x2BC2190kMgUOD8jbPdlgIytPK6a3eYZx69d6T5InAIXNs3gHaJCeGlve4IXEjrx02LwBXzn7PT4Xr70PX30p33Fka5ZNXilgVu+4L1M+XtqlliymtXfIbmuiNwoa1PeggECJzI2/qZNYmo3XDzPeaLd33G3Hzj9eaxp18wz3/rlZFf3ASBQ+DafsBol5gQXtrrjsCFtH7ctAhcff77D1zcS3fKWxhl2TJvHzrZXNxuMr4lY2EUzRJTnxo9cKHMmkwv9z0HBLpIoNJG3s89/qC5ZsdVfQL34suvmQe+8pRhEZNTXWzXoZdpeuVkksfxUwjc0GGnMtAuMSG8tNcdgQtp/bhpEbhw/kf8hVHm59IdO7a4MMqY/dbfWNzNpds8s9ycOHXOnDln9z8YsUOzvNIDN2LBSnVbJVAqcNLr9scPf/4igaMHrtdO9MAhcK3esTYz7RITwkt73RG4kNaPmxaBa57/+yeN2ZPMn3NDL8fMgYP9+WzcYMyVl4+ZTXYvuo3239ZkYZR2ti9ovsb1r4jA1WfWZAp64JqkybWaJFAqcPd/+Unz2g9eT4ZKuiGUH7xim7n17ofMTZ/4iHn4S59rsjzqroXAIXBtB612iQnhpb3uCFxI68dNi8ANn/+5c97WBYnU9f7fP9aune+lmx9yKb10a9YsXaFD4IYfd0U5IHBx+ZN7PoFSgZOkbrikf5m7brvJ3HPHLSPPFoFD4Nq+CbRLTAgv7XVH4EJaP25aBC4OfxG440emzFvvXDDv7rJCZxdJEdFzxyWrrNDZuXNuuKV8Xb9+6QgdAhcn7lyuCFxc/uQeKHAAzCeAwCFwbd8f2iUmhJf2uiNwIa0fNy0CF4+/LzEyxLI35HJ8YV+6k3YopjuWTY1ZoevfvmDLZr1z5xC4eHEnOSNwcfmTOwI3tBhA4BC4oQVXzoW1S0wIL+11R+BCWj9uWgQuHv8iiTlqF0HpCd3i8MujRxfLKgujiMDJ3LmFXjoreFNW9DQcCFzcVkLg4vIn9wCBY7+34vBB4BC4th8w2iUmhJf2uiNwIa0fNy0CF49/HYk5edLbh25+gZQDdjsD/9hgh1gurnZpv99szKpL4tWvKOc6de9aDViFsmstQnmWEoHSOXDp+W8za6dHfu83PwAQOASu7QeCdokJ4aW97ghcSOvHTYvAxeMfIjHn7EeUzJtbXO3S/v/eMSMbj7vjUrsISk/oFnvpLr00Xn39nEPqHrsGCFzsFiD/pUygVODSlZdVKV/69vcWfsw+cOwDp/EG2bxuhTlw9Iy5MKtvsrt2iQmJF+11R+BCWj9uWgQuHv+mJWaPFTh/yKUI3llvYZRVq+bMFhlymax02ZM72c4gxtF03dusAwLXJm3yGjUCtQXus194xHz/R28icPME6IGjB67th4Z2iQnhpb3uCFxI68dNi8DF4z9siTkoC6NYiZOeOTef7n07FNMdk5Oy0qW3MMr8fDqZXzfsY9h1H2b5Ebhh0uXao06gVODSPW4MoewPGQQOgWv7IaJdYkJ4aa87AhfS+nHTInDx+LctMceOuZUuFxdGOXK039a2ePvQufl0y6aaZ9R23ZusAQLXJE2uBYF+AqUCxyImxSGDwCFwbT9UtEtMCC/tdUfgQlo/bloELh7/2BJzys6U2L3HSV2vl27/gX6hW7++fy86mU93iR2KGXrErntI+RG4EHqkhUAxgVKBAyACl0VgeqUdU2KP46cQuLbvEe0SE8JLe90RuJDWj5sWgYvHv2sSc/78XGrIZU/q/IVR1qyZFzq3MIrtsVu7tr7Qda3udaIAgatDi3MhUI8AAleP10Vn0wOHwAWGUO3k2iWmdoW9BNrrjsCFtH7ctAhcPP4aJGbvXtlY3Ouls/PpzpxZFLaVK0Xo5J/dk67Gwiga6p4XGQhcvHuGnJc+gUoCl95KQLDsvO9Oc/ON1y99QiU1ROAQuLZvAu0SE8JLe90RuJDWj5sWgYvHX6PEHDosK13OL4oiYme/P/H+IsOFhVFkLp2I3bzUpRdG0Vh3V0sELt49Q85Ln0CpwD329Avmia+/ZJ57/EFzzY6rEiKvv/mWufXuh8xdt91k7rnjlqVPqaCGCBwC1/YNoF1iQnhprzsCF9L6cdMicPH4a5YYR+34cU/m5jcYP3y4n+lFC6PI3nQblpsTp86ZM+dm4zXAgDkjcAOCIxkEKhAoFbgbbr7HfPqTH7tI1ETsnv/WKyO/qTcCh8BVuM8aPUW7xITA0F53BC6k9eOmReDi8V8KApemd/q0CN389gUidHuN2bcvtTDKjDFXXj5mNm2eNRs39vaka2JhlLZaEoFrizT5jCKBUoGTVSizhku6YZVs5M1G3hpvHDby1thqxiBwcdtNs8SEktNc91XLJ8yyqQlz5MTZUAxR0i9FgUuDvHBhccsCf5PxWa/jbXraSpy3fcE2u0DKIAujtNWICFxbpMlnFAmUChw9cMVhQQ8cPXBtPzi0S0wIL+11pwcupPXjpkXg4vEfBYHLorvXLoRy/NiU+dufXjDvvtcTvNNnFs9csaJ//tyWzcZs3lR/pcthtSwCNyyyXBcCxpQKnJY5cG5enj9XzzXwp25/wPzkbfv0s8fVV24333x2Z1/bl/2+KFAQOASu7QeJdokJ4aW97ghcSOvHTYvAxeM/qgInxP26y5y5voVRrNAdP7E47HJ8wphtMm/O/tviti+w30+Mx2k7BC4Od3IdDQKlAicYur4KpfQSHjpyPGmxtMB99guPmIOHji1Im8ja+pk15plH703OL/t9WRggcAhcWYw0/XvtEhPCQ3vdEbiQ1o+bFoGLxx+By17E5MQJt22B+zpu34X622mT7ZHrbV/Qm0MnX1csb6ctEbh2OJPLaBKoJHAa0OT1wIncffGuzyxseSAy+tUnvrGw+ErR7+Xc//Dcn/b12Ek+v33/1xbSI3AIXNv3h3aJCeGlve4IXEjrx02LwMXjj8BVW4XyjCyMYhdD8Xvp9qYWRlm3zm1ZYPejs0MuReqmVw+nbRG44XDlqhAQAkta4LKkzv+ZAJDtELK2SJCf/Y+f7kLgcu6T6ZWTyW+On0Lg2n6UaJeYEF7a647AhbR+3LQIXDz+CFw1gUu30Ky3MMqu3Vbs7Jw6mUcnP3fH6tW9XrmtbsilFbqZtc20NQLXDEeuAoEsAghcoMBp3JuliVthcrw37v78bHcmTNep17LJcXPuwqyZU1h8QT8xMWbOnVdY+DqNlHGu9rpP2nabs0FnQ0/lIQI9qs88zXWXOVBjdofo8xd0PjOmJsfMBVt2pR83Qfd603XftWcuWRDlZ+/NJf/k+1OnF4u4cqUxl20z5vLtY8m/y7bLvLr+7Q2qVkg2JZ+ywXf2vNIHnq2o3PccEOgigVKBkzliP37r3b4hh3nzzWJWsKy3Lb0JufSwyRHaA3fwmLckVEwALee90i5LLcepM96f8louQ0h266aXmaMnztkXAn0vNFNWPlcumzTHTupcEjyk3bTXffXKKSves1aCdN430hMyqs88zXVfbrcQkHtHNoTWeKxZtcycOns+uXdG7Rh23Q8dHjO77H5071mRe8/20klPnWw67o4JWRjF9s5tsz1z2+e/yv/Lz8uOcWtwl66eMoeP6/2skvueAwJdJFAqcP4cMX/zbvn+O6/+8KIVHWNVss4cuAe+8pRx+9dlzYFzv2cOXH5rMoQyVqTr3wsthBxDKEPohafVPIwwtPaa684+cKGtHy9928NHT7xvh1nunt9kXIZc2u8PHeqvv2xV0L8wiv1cWn7xH0MZQhkvbsh56RMoFTh/I2/pjZNDVnDs2kbeeQJXtspk0e9dHf05cvd/+Unz2g9eZxET5sBFezpol5gQcNrrzhy4kNaPmxaBi8e/bYmJV9OLc45d9zO280zmzfUtjGLFzj9kzpxb4dLNpZO5dQhclyKJsiw1AqUCJ8vuf/yGD5t77rjFiMzdddtNyfd+b1xsKP42AlKWmbXTC4Il/1+2z1ve710P3KEjxxa2Kbju2h19Q0pZhZJFTNqOf+0SE8JLe90RuJDWj5sWgYvHP7bExKt5/z5wMcvh8p61o1jdYii7ZWGUebm74I0Kl1UtRehkqOXfuWrSrJo+a9at0zddQeos9z0HBLpIoFTgXM+WFN7fBFtkTmTG7afWxcqFlilrCGX6mggcAhcaZ3XTa5eYuvX1z9dedwQupPXjpkXg4vFH4AZbhbKtFtu33+ulm1/p8vSpxdxXLB/r24dOhl9u2qRjPiMC11YUkU9dAqUCV/eCS+l8BC6/NZkDFy/StUtMCDntdUfgQlo/bloELh5/BK7bApeOjMN2M/Hde8bNXitzB/dPmLd/NmuOHV88a9wu7Lh1s9u+oPd1i+2xk1V6u3YgcF1rEcrjCCBwgbFADxw9cIEhVDu5dompXWEvgfa6I3AhrR83LQIXjz8Cp0vgXKS4OXBvvXumN9TSrnbphlweTC2MsmnjvNCJzMm+dLaXTrY0iH0gcLFbgPzzCCBwgbGBwCFwgSFUO7l2ialdYQQuBFmjaTVLTCgIzXVnFcrQ1o+XXrO85i1ictZfGCVZ6dKYPamFUdauXRQ6tzDKmun22wGBa585OVYjUCpwWvaBq1bd5s9C4BC45qOq+IoI3JTavcjogWv7bmkuPwSuOZZ1r6RZYurWNX2+5rpXXYVStmNdXOly8fvz3sIol6yyQmeHWfrbF6yfCaVbnh6BK2fEGXEIlAqcln3g4uCzm14e9GbqxipEhHyZAxcB+nyWCBwCFyv6NEtMKDPNdacHLrT146UfBYHLorv/wMXbF5w6tThHbtkyO49OhlqK1Ml8Orvi5Rb7tekDgWuaKNdrikCpwGnZB64pIHWvg8DRA1c3ZkLPR+AQuNAYGjS9ZokZtM4unea6I3ChrR8v/agKXJr4kWRhFJlHtziX7tixRaEbs98uCJ2dP5d8b/9NToa1HQIXxo/UwyNQKnAa9oEbHp7yKyNwCFx5lDR7BgKHwDUbUdWvplliqtcy+0zNdUfgQls/XnoELpv9+yet0Ll96Ob3ojt4qH8Vy40bfKlzC6PU66VD4OLFPjkXEygVuFHeB65K8CBwCFyVOGnyHAQOgWsynupcS7PE1Kln1rma647AhbZ+vPQIXDX2Z8/OeT10bmEUu1+Bd6xdOy9080MupYduzZpioUPgqvHnrPYJlApc+0XSlSMCh8C1HbEIHALXdsy5/DRLTCgzzXVH4EJbP156BG5w9r2FUWxP3d7xhUVSzp1bFLZLVlmhs3PnesMtZ5Ov69f354fADc6flMMlgMAF8kXgELjAEKqdHIFD4GoHTUMJNEtMKALNdUfgQls/XnoErjn2Bw7Oz6HbPf91rzEn31+8/rIpEbr+7Quu/cUVzRWAK0GgQQKVBO7Fl18zD3zlqb5sd953p7n5xusbLIrOSyFwCFzbkYvAIXBtxxw9cMYgcLGizhjNEhNKTXPdq24jEMpo0PRH7SIoC7108wukHD3af7Wn/jdrdRwQ6CCBUoF77OkXzBNff8k89/iD5podVyVVcPPi7rrtJnPPHbd0sFrtFQmBQ+Dai7ZeTggcAtd2zCFwCFysmJN8NUtMKDfNde+6wKXb5sKsHW65a8y8864xP31n3OyxUvdv/jUCFxrDpB8OgVKBk33gPv3Jj10kaiJ2z3/rFfPqi48Np2RKrorAIXBthyoCh8C1HXMIHAIXK+YQuOXmxKlz5sw5axfKDm0Cl4WXOXDKgm6EilsqcP4+cD4XN6zyje8+O0K4Lq4qAofAtX0DIHAIXNsxh8AhcLFiDoFD4GLGHgIXkz55FxEoFTh64IoDCIFD4Np+xCBwCFzbMYfAIXCxYg6BQ+Bixh4CF5M+eQcJHHPgELgsAtMrJ5MfHz+FwLX9iEHgELi2Yw6BQ+BixRwCh8DFjD0ELiZ98g4SOEnMKpT5COmBQ+DafsQgcAhc2zGHwCFwsWIOgUPgYsYeAheTPnkHCxwIEbg0AXrg4t0VCBwCFyv6NC+lH8pMc93ZBy609eOlZxXKeOwlZwQuLn9yzydQOgcubxEToPYI0ANHD1zb9wICh8C1HXP0wNEDFyvm6IGjBy5m7CFwMemTd1APHAJXHEAIHALX9iMGgUPg2o45BA6BixVzCBwCFzP2ELiY9Mk7SOA+dfsD5uM3fHjkN+wmjCAAAQhAAAIQgAAEIAABCMQmUDqE8vU33zK/ff/XRn7D7tgNRf4QgAAEIAABCEAAAhCAAARKBU6GUBYdo76RNyEEAQhAAAIQgAAEIAABCECgLQKlAtdWQbTlIxucHzpyfKHYzz3+oLlmx1XaqlG7vOl6ywVGpe61YQ0hwajGnaAk9oYQUBUvSdwtPut55lUMmgZOI+6IuwbCaKBLjHLsDQSMRK0TQOAGQC7DSr/27//EPPPovUlq2ez8+W+9MhLDTOWh9scPf169rEob3nr3Q6rkc5TjzgmcttgrijOZX/yTt99LniFXX7ndfPPZnQM8jYafhLjr/jPfE23qAAALrElEQVQvJM66GofEXffibphx1qU4HPXYG/6nCjk0QSBX4ERKnvj6S+au2266aAGTot81USht15CNzv/Dc3/a2RewJnmm/yq18747zc03Xt9kFkO/ll8Hzb2HoxR3WT1wXY+9ojj77BceMQcPHVt4ZsjLy/qZNQt/FBr6TRCQAXHXrWdeSJxpikPiLm7cDTPOuh6HoxZ7AR8PJG2RQK7Alb1QpG+4FsvcuaxknqBmERgUqPtrnMZ5kBp74NLtNKpxJxy0xF5enMnL0Bfv+szCHz/kBeGrT3xDRS8+cfeQ6dozb9A40xSHxF38uBtWnHU9Dkc59gZ9PyTd8AnkClzZ/m/ywvHAV57q3AfZ8JEt5uB6Irv2Yd4mA61DKjULHHHXi3ANsZcVZ1V/1uZ9XCUv4q67cVc1pvzzpDbpYeRdfC4Sd92Ju2HEWZfjkNir8snAObEIIHADki/roRzwsp1OJtL+0rf/z4VhXpp6DdJgu/iiUqXxRzHuhIvW2BvkhaeLiyERd735zl195g0SZ11+cXbPQuKuW3E3jDjrahyOauxVeQ/hnG4QyBW4dJd2urhd/SBrA6v7q0w6r6z5gm2Up8080ttKaO191Chwoxx3EuMaY2+QF56uCRxxd3vfI7aLz7xB4qyrL84ONnHXvbgbRpx1MQ5HPfbafKckr8EJ5Arc/V9+0vz13/w0d2EO/joxOHRSxiegUeDiU6MEdQnUmTMy6kPS67Ll/EUCg8ZZ1twj4pDIyiMwrDgjDok5CNQnULiNgNxUcrz64mN9V3arEXXxL5H1EZBiFAkgcKPY6u3XOS/Our7qWvukyDGEwKBxRhyGUB+9tMOKM+Jw9GKJGocTKN0HTnriXvr29/pyuu7aHSqWuw7HwxWWIoH0Vggza6dVrP63FNtiKdepLM66tO/RUm6HpV630DgjDpd6hDRTv2HHGXHYTDtxldEhUCpwo4OCmkIAAhCAAAQgAAEIQAACEOg2AQSu2+1D6SAAAQhAAAIQgAAEIAABCCwQQOAIBghAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACEIAABCAAAQhAAAIQQOCIAQhAAAIQgAAEIAABCEAAAkoIIHBKGopiQgACzRP47BceMd//0ZuZF955353m5huvbz5TrhhEQNrs4KFj5pvP7gy6jkucdb0XX37NPPCVpwwx0AhiLgIBCEAAAg0TQOAaBsrlIAABPQSalgE9Nddb0qbbrOnr6SVLySEAAQhAQAsBBE5LS1FOCECgcQJVXt4/dfsDZv3MmiRv11s3s3bavPriY8nPXG9NVuHuuu0ms2vvAfPSt79n3vjus32nfOijt5ubPvER8/CXPmduuPkec/2vXmNe+8Hr5tCR48l5kvby7ZuSniB3PPf4g+aaHVct/H+6BzH9+6wypdNIPvfccUty6v1ffjIpqztc+dz/l7Fw5xXlUVTmsuunyyf5XXftDvPMo/earLTC42v//k8u6mV1bZF3vc//1m+aW+9+yPg8m2LTeBBzQQhAAAIQGDkCCNzINTkVhgAEfNEoG44nYvCTt99LhMqJjgjXz191WSIOWYf8Xg4neSJrfvrHnn7BPPH1lxakTs4XcXPC4H7vi6KUQw43dDAtn+lr5snbj996d6Fcr7/5ViI4Ug8nKL5o+pIp16vCQsqVl0dZmateP6vNstJKmSVPETInvmUcJY1w8QWuKTbceRCAAAQgAIEmCCBwTVDkGhCAgEoCVebAuZ4dX9bkhf6v/+anmfOwnEj4vTdpcUlf0/XASW9clkDIzyRP6aETKUwLhoMv1/n0Jz+2IJp+o7g0efO60pIpadNSWMaiKI8qZS67vhOyPIGTntI8qXYspE7Pf+uVBYnN6oVNl7UJNipvEAoNAQhAAAKdJIDAdbJZKBQEINAGgTpDKKsInOupSUuSvyjGB6/YlvTu+OfkCZx/ji8eZcM2XU+hz9ClSQ/lzBPGrJ+XCVZRHlXKXHb9QQXO9XD6PByHMoGTNOnhlIOwaSOeyQMCEIAABEaDAAI3Gu1MLSEAgQwCTQqc663yh0r6Wbphl5s3rFvoSXO/H1TgsmQsr6G7InBFZR6GwEnvmZsnJ2zSvYoIHI8GCEAAAhDQRgCB09ZilBcCEGiMQFMC54bc+aKQLqTrnZN5bbJgiRsuKefVFbiy4ZBZgMrSNDFMsMoQyqKl+ZsWuCxprStwMneuCTaNBS0XggAEIACBkSeAwI18CAAAAqNLoCmBkxf8q6/cXrg3mZMboZ1eLbKuwMk13Pw9v0dLJPG6a38xd/86EaRDR44FLWKSnmeWng9YlEdZmasIXN5iLVlps4RS2koOxy3reoMuYlLGZnTvNGoOAQhAAAJNEkDgmqTJtSAAAVUEmljExAlAVsXzluFPL7QxiMD5EufnXTas0i2y4tL4PWJVl8ovmw9YlEcWc1fmKgIn5favn95GIM02XScZ4uqvAJp1vZBtBMrYqLpBKCwEIAABCHSSAALXyWahUBCAwFIkIL0/RUMIl2KdqRMEIAABCEAAAs0SQOCa5cnVIAABCGQSSC9fDyYIQAACEIAABCAwCAEEbhBqpIEABCBQk0B6mGTN5JwOAQhAAAIQgAAEEgIIHIEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACQEETklDUUwIQAACEIAABCAAAQhAAAIIHDEAAQhAAAIQgAAEIAABCEBACYH/H1mF4GkpkXgWAAAAAElFTkSuQmCC",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Same plot, but with log scales on both axes (all data points used this time, \n",
"# but the value E = 0 is automatically dropped by the graphic function because of the log scale)\n",
"px.line(data_frame=df,\n",
" x=\"Enzyme concentration\", y=[\"crossover time\"],\n",
" log_x=True, log_y=True,\n",
" title=\"Time of crossover of [S] and [P] , as a function of Enzyme conc - log scales on both axes
([E]=0 value skipped)\",\n",
" labels={\"value\":\"Crossover time when [S]=[P]\"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1bb12fdd-1c93-4cf3-a302-13da5339e82a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"formats": "ipynb,py:percent"
},
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}