{
"cells": [
{
"cell_type": "markdown",
"id": "3bbe8002-bdf3-490c-bde0-80dd3713a3d0",
"metadata": {},
"source": [
"## Association/Dissociation reaction `2A <-> C`\n",
"#### with 2nd-order kinetics for `A`, \n",
"#### and 1-st order kinetics for `C`\n",
"\n",
"Taken to equilibrium. (Adaptive variable time teps are used)\n",
"\n",
"_See also the experiment \"1D/reactions/reaction_7\"_ \n",
"\n",
"LAST REVISED: Dec. 3, 2023"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e6cefd48-5909-4cc0-a48f-ef016a2ee1cf",
"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": "b708df90",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from experiments.get_notebook_info import get_notebook_basename\n",
"\n",
"from src.modules.reactions.reaction_dynamics import ReactionDynamics\n",
"from src.modules.visualization.graphic_log import GraphicLog"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "83c3cc5f-de21-4f66-9988-2806fbf0666d",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-> Output will be LOGGED into the file 'react_4.log.htm'\n"
]
}
],
"source": [
"# Initialize the HTML logging (for the graphics)\n",
"log_file = get_notebook_basename() + \".log.htm\" # Use the notebook base filename for the log file\n",
"\n",
"# Set up the use of some specified graphic (Vue) components\n",
"GraphicLog.config(filename=log_file,\n",
" components=[\"vue_cytoscape_1\"],\n",
" extra_js=\"https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.21.2/cytoscape.umd.js\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "429c1b46-8a5e-45a1-a88e-5af3f278f5d8",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "475aa75c-9a1d-42dc-adfe-88614e8ad7fc",
"metadata": {},
"source": [
"# PART 1 - The Simulation"
]
},
{
"cell_type": "markdown",
"id": "9329208b-070f-4902-8f37-0f11ddf75ed6",
"metadata": {},
"source": [
"# Initialize the System\n",
"Specify the chemicals, the reactions, and the initial state"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "72b4245c-de4e-480d-a501-3495b7ed8bc4",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of reactions: 1 (at temp. 25 C)\n",
"0: 2 A <-> C (kF = 3 / kR = 2 / delta_G = -1,005.1 / K = 1.5) | 2-th order in reactant A\n",
"Set of chemicals involved in the above reactions: {'C', 'A'}\n"
]
}
],
"source": [
"# Instantiate the simulator and specify the chemicals\n",
"dynamics = ReactionDynamics(names=[\"A\", \"C\"])\n",
"\n",
"# Reaction 2A <-> C , with 2nd-order kinetics for A, and 1st-order kinetics for C\n",
"dynamics.add_reaction(reactants=[(2, \"A\")], products=\"C\",\n",
" forward_rate=3., reverse_rate=2.) \n",
"# Note: the reaction order for a chemical defaults to its stoichiometry coefficient; \n",
"# to specify it explicitly, pass it as 3rd term in tuple: (2, \"A\", 2)\n",
"\n",
"dynamics.describe_reactions()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cb582868-431c-4022-aa0e-a2f554f80d6c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[GRAPHIC ELEMENT SENT TO LOG FILE `react_4.log.htm`]\n"
]
}
],
"source": [
"# Send a plot of the network of reactions to the HTML log file\n",
"graph_data = dynamics.prepare_graph_network()\n",
"GraphicLog.export_plot(graph_data, \"vue_cytoscape_1\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ae304704-c8d9-4cef-9e0b-2587bb3909ef",
"metadata": {},
"outputs": [],
"source": [
"# Initial concentrations of all the chemicals, in index order\n",
"dynamics.set_conc([200., 40.], snapshot=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a605dacf-2c67-403e-9aa9-5be25fc9f481",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"2 species:\n",
" Species 0 (A). Conc: 200.0\n",
" Species 1 (C). Conc: 40.0\n",
"Set of chemicals involved in reactions: {'C', 'A'}\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "0ff2c242-a15b-456d-ad56-0ba1041c0b4c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" A | \n",
" C | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 200.0 | \n",
" 40.0 | \n",
" Initial state | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SYSTEM TIME A C caption\n",
"0 0.0 200.0 40.0 Initial state"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dynamics.get_history()"
]
},
{
"cell_type": "markdown",
"id": "fc516ca2-e62d-4784-b826-5372ff7f4c75",
"metadata": {
"tags": []
},
"source": [
"## Run the reaction"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2502cd11-0df9-4303-8895-98401a1df7b8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"*** CAUTION: negative concentration in chemical `A` in step starting at t=0. It will be AUTOMATICALLY CORRECTED with a reduction in time step size, as follows:\n",
" INFO: the tentative time step (0.002) leads to a NEGATIVE concentration of `A` from reaction 2 A <-> C (rxn # 0): \n",
" Baseline value: 200 ; delta conc: -479.68\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.001) [Step started at t=0, and will rewind there]\n",
"\n",
"*** CAUTION: negative concentration in chemical `A` in step starting at t=0. It will be AUTOMATICALLY CORRECTED with a reduction in time step size, as follows:\n",
" INFO: the tentative time step (0.001) leads to a NEGATIVE concentration of `A` from reaction 2 A <-> C (rxn # 0): \n",
" Baseline value: 200 ; delta conc: -239.84\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.0005) [Step started at t=0, and will rewind there]\n",
"* 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 0.6 (set to 0.0003) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (0.0003) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.6 (set to 0.00018) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (0.00018) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.6 (set to 0.000108) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (0.000108) leads to a least one norm value > its ABORT threshold:\n",
" -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.6 (set to 6.48e-05) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (6.48e-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 0.6 (set to 3.888e-05) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (3.888e-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 0.6 (set to 2.3328e-05) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (2.3328e-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 0.6 (set to 1.3997e-05) [Step started at t=0, and will rewind there]\n",
"* INFO: the tentative time step (1.3997e-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 0.6 (set to 8.3981e-06) [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",
"110 total step(s) taken\n"
]
}
],
"source": [
"dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n",
"\n",
"# For repeatibility, we avoid the defaults, and instead specify a particular group of preset parameters \n",
"# applicable to the adaptive time steps.\n",
"# Here we use a \"fast\" heuristic: advance quickly thru time\n",
"dynamics.use_adaptive_preset(preset=\"fast\")\n",
"\n",
"dynamics.single_compartment_react(initial_step=0.002, reaction_duration=0.03,\n",
" snapshots={\"initial_caption\": \"1st reaction step\",\n",
" \"final_caption\": \"last reaction step\"},\n",
" variable_steps=True, explain_variable_steps=False)"
]
},
{
"cell_type": "markdown",
"id": "99a9a4b2-a588-4ba5-85c9-0a5a5d1dbaad",
"metadata": {},
"source": [
"### Note how the (tentative) original time step that we provide, 0.002, turned out to be so large that the simulation backtracks several times, because of \"hard\" aborts (negative concentrations) or \"soft\" aborts (concentration changes surpassing the thresholds we provided)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "80fbaee3-bd6f-4197-9270-23374d46a4a7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" A | \n",
" C | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.000000 | \n",
" 200.000000 | \n",
" 40.000000 | \n",
" Initial state | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.000008 | \n",
" 197.985804 | \n",
" 41.007098 | \n",
" 1st reaction step | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.000015 | \n",
" 196.406789 | \n",
" 41.796605 | \n",
" | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.000025 | \n",
" 194.075953 | \n",
" 42.962023 | \n",
" | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.000033 | \n",
" 192.255349 | \n",
" 43.872326 | \n",
" | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 106 | \n",
" 0.013829 | \n",
" 13.032172 | \n",
" 133.483914 | \n",
" | \n",
"
\n",
" \n",
" | 107 | \n",
" 0.016762 | \n",
" 11.608977 | \n",
" 134.195511 | \n",
" | \n",
"
\n",
" \n",
" | 108 | \n",
" 0.021163 | \n",
" 10.412711 | \n",
" 134.793645 | \n",
" | \n",
"
\n",
" \n",
" | 109 | \n",
" 0.027765 | \n",
" 9.677514 | \n",
" 135.161243 | \n",
" | \n",
"
\n",
" \n",
" | 110 | \n",
" 0.037666 | \n",
" 9.466796 | \n",
" 135.266602 | \n",
" last reaction step | \n",
"
\n",
" \n",
"
\n",
"
111 rows × 4 columns
\n",
"
"
],
"text/plain": [
" SYSTEM TIME A C caption\n",
"0 0.000000 200.000000 40.000000 Initial state\n",
"1 0.000008 197.985804 41.007098 1st reaction step\n",
"2 0.000015 196.406789 41.796605 \n",
"3 0.000025 194.075953 42.962023 \n",
"4 0.000033 192.255349 43.872326 \n",
".. ... ... ... ...\n",
"106 0.013829 13.032172 133.483914 \n",
"107 0.016762 11.608977 134.195511 \n",
"108 0.021163 10.412711 134.793645 \n",
"109 0.027765 9.677514 135.161243 \n",
"110 0.037666 9.466796 135.266602 last reaction step\n",
"\n",
"[111 rows x 4 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dynamics.get_history()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "9af1fff4-8551-4c15-94fa-44c59cf9afe0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=A
SYSTEM TIME=%{x}
concentration=%{y}",
"legendgroup": "A",
"line": {
"color": "red",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "A",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
8.398079999999997e-06,
1.5116543999999995e-05,
2.5194239999999993e-05,
3.325639679999999e-05,
4.131855359999999e-05,
4.9380710399999987e-05,
5.7442867199999985e-05,
6.550502399999998e-05,
7.356718079999997e-05,
8.162933759999996e-05,
8.969149439999995e-05,
9.775365119999994e-05,
0.00010984688639999994,
0.00011952147455999994,
0.00012919606271999994,
0.00013887065087999994,
0.00014854523903999993,
0.00015821982719999993,
0.00016789441535999992,
0.00017756900351999992,
0.0001872435916799999,
0.00020175547391999992,
0.0002133649797119999,
0.0002249744855039999,
0.00023658399129599988,
0.0002481934970879999,
0.0002598030028799999,
0.00027141250867199993,
0.00028302201446399995,
0.00030043627315199994,
0.00031436768010239993,
0.0003282990870527999,
0.0003422304940031999,
0.0003561619009535999,
0.00037009330790399987,
0.00038402471485439986,
0.00040492182527999986,
0.0004216395136204799,
0.0004383572019609599,
0.0004550748903014399,
0.00047179257864191993,
0.0004885102669823999,
0.0005052279553228799,
0.0005303044878335998,
0.0005503657138421758,
0.0005704269398507519,
0.0005904881658593279,
0.000610549391867904,
0.00063061061787648,
0.000660702456889344,
0.0006847759280996351,
0.0007088493993099263,
0.0007329228705202174,
0.0007569963417305086,
0.0007810698129407998,
0.0008171800197562366,
0.000846068185208586,
0.0008749563506609355,
0.0009038445161132849,
0.0009327326815656344,
0.0009760649297441585,
0.0010107307282869779,
0.001045396526829797,
0.0010800623253726163,
0.0011147281239154356,
0.0011667268217296647,
0.001208325779981048,
0.0012499247382324313,
0.0012915236964838146,
0.0013539221338608894,
0.0014038408837625492,
0.001453759633664209,
0.0015036783835658688,
0.0015785565084183587,
0.0016384590083003505,
0.0016983615081823424,
0.00178821525800533,
0.0018600982578637203,
0.0019319812577221105,
0.002039805757509696,
0.0021260653573397644,
0.002212324957169833,
0.002341714356914935,
0.002445225876711017,
0.0025487373965070986,
0.0027040046762012216,
0.0028282184999565197,
0.0030145392355894673,
0.0031635958240958254,
0.0033871807068553625,
0.003566048613062992,
0.0038343504723744363,
0.004048991959823592,
0.004370954190997325,
0.004628523975936312,
0.005014878653344792,
0.005401233330753272,
0.005787588008161752,
0.006367120024274472,
0.006946652040387191,
0.007526184056499911,
0.00839548208066899,
0.009264780104838069,
0.010568727141091689,
0.011872674177345308,
0.013828594731725739,
0.016762475563296384,
0.02116329681065235,
0.027764528681686298,
0.03766637648823722
],
"xaxis": "x",
"y": [
200,
197.9858044928,
196.4067891314626,
194.07595334497333,
192.25534898661533,
190.46879740430532,
188.71534993819054,
186.99409292105136,
185.30414607674047,
183.64466100591383,
182.01481975354585,
180.413833453115,
178.8409410427091,
176.52264155159475,
174.7158686464458,
172.94596814009458,
171.21182008364218,
169.51234954024133,
167.84652434187538,
166.21335297917756,
164.61188261516287,
163.04119721445474,
160.7300250631269,
158.93326473964117,
157.1765542323314,
155.45856600172303,
153.77803069527553,
152.133733987297,
150.52451362294565,
148.94925665108212,
146.6357169224966,
144.84211135489045,
143.09225548651855,
141.38456387891168,
139.71752700678886,
138.08970675790684,
136.4997322498795,
134.16957778233262,
132.36879532470405,
130.61621777514426,
128.90993008249367,
127.24811762984149,
125.62905971933556,
124.05112355869569,
121.74357627793043,
119.96590134278878,
118.24001728379018,
116.56368739309407,
114.93480237154793,
113.351371359806,
111.04158489742949,
109.2687305903879,
107.55237706353577,
105.88985862493844,
104.27867529774959,
102.7164801024925,
100.44336119220299,
98.7050418479391,
97.02682657844456,
95.40564341405201,
93.83862706351552,
91.56533911676678,
89.83453130782063,
88.16914727211656,
86.56553760884185,
85.02032056253428,
82.78537648667351,
81.09121389710347,
79.46648786673279,
77.9070066041123,
75.65986265590448,
73.96572967479426,
72.34768795251657,
70.80071472427237,
68.57997565365879,
66.91489951075998,
65.3311102130008,
63.06862999277249,
61.38426396067837,
59.79055059290303,
57.52526128383666,
55.85096377632318,
54.2752007633405,
52.0466837978649,
50.41148777357389,
48.880683692615165,
46.726553773792986,
45.157276312252364,
42.9651397869594,
41.38484933300463,
39.193940122296404,
37.63146143283778,
35.48182042695287,
33.965438176115484,
31.895273567682466,
30.450914577527918,
28.494240237134733,
26.806442549712752,
25.336314685381662,
23.399376307880704,
21.792923050674183,
20.440773545233533,
18.712754345180954,
17.34062951686442,
15.673056180321085,
14.440546857084291,
13.032171680702598,
11.6089773772302,
10.412710546541897,
9.677513749893356,
9.466795875764356
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=C
SYSTEM TIME=%{x}
concentration=%{y}",
"legendgroup": "C",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "C",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
8.398079999999997e-06,
1.5116543999999995e-05,
2.5194239999999993e-05,
3.325639679999999e-05,
4.131855359999999e-05,
4.9380710399999987e-05,
5.7442867199999985e-05,
6.550502399999998e-05,
7.356718079999997e-05,
8.162933759999996e-05,
8.969149439999995e-05,
9.775365119999994e-05,
0.00010984688639999994,
0.00011952147455999994,
0.00012919606271999994,
0.00013887065087999994,
0.00014854523903999993,
0.00015821982719999993,
0.00016789441535999992,
0.00017756900351999992,
0.0001872435916799999,
0.00020175547391999992,
0.0002133649797119999,
0.0002249744855039999,
0.00023658399129599988,
0.0002481934970879999,
0.0002598030028799999,
0.00027141250867199993,
0.00028302201446399995,
0.00030043627315199994,
0.00031436768010239993,
0.0003282990870527999,
0.0003422304940031999,
0.0003561619009535999,
0.00037009330790399987,
0.00038402471485439986,
0.00040492182527999986,
0.0004216395136204799,
0.0004383572019609599,
0.0004550748903014399,
0.00047179257864191993,
0.0004885102669823999,
0.0005052279553228799,
0.0005303044878335998,
0.0005503657138421758,
0.0005704269398507519,
0.0005904881658593279,
0.000610549391867904,
0.00063061061787648,
0.000660702456889344,
0.0006847759280996351,
0.0007088493993099263,
0.0007329228705202174,
0.0007569963417305086,
0.0007810698129407998,
0.0008171800197562366,
0.000846068185208586,
0.0008749563506609355,
0.0009038445161132849,
0.0009327326815656344,
0.0009760649297441585,
0.0010107307282869779,
0.001045396526829797,
0.0010800623253726163,
0.0011147281239154356,
0.0011667268217296647,
0.001208325779981048,
0.0012499247382324313,
0.0012915236964838146,
0.0013539221338608894,
0.0014038408837625492,
0.001453759633664209,
0.0015036783835658688,
0.0015785565084183587,
0.0016384590083003505,
0.0016983615081823424,
0.00178821525800533,
0.0018600982578637203,
0.0019319812577221105,
0.002039805757509696,
0.0021260653573397644,
0.002212324957169833,
0.002341714356914935,
0.002445225876711017,
0.0025487373965070986,
0.0027040046762012216,
0.0028282184999565197,
0.0030145392355894673,
0.0031635958240958254,
0.0033871807068553625,
0.003566048613062992,
0.0038343504723744363,
0.004048991959823592,
0.004370954190997325,
0.004628523975936312,
0.005014878653344792,
0.005401233330753272,
0.005787588008161752,
0.006367120024274472,
0.006946652040387191,
0.007526184056499911,
0.00839548208066899,
0.009264780104838069,
0.010568727141091689,
0.011872674177345308,
0.013828594731725739,
0.016762475563296384,
0.02116329681065235,
0.027764528681686298,
0.03766637648823722
],
"xaxis": "x",
"y": [
40,
41.0070977536,
41.796605434268706,
42.962023327513336,
43.87232550669233,
44.76560129784734,
45.642325030904736,
46.502953539474326,
47.34792696162977,
48.17766949704308,
48.99259012322707,
49.79308327344249,
50.57952947864544,
51.73867922420261,
52.64206567677707,
53.52701592995269,
54.39408995817889,
55.243825229879306,
56.076737829062274,
56.893323510411186,
57.69405869241852,
58.47940139277259,
59.634987468436506,
60.53336763017938,
61.41172288383427,
62.27071699913845,
63.1109846523622,
63.933133006351454,
64.73774318852713,
65.5253716744589,
66.68214153875165,
67.57894432255473,
68.45387225674068,
69.30771806054412,
70.14123649660553,
70.95514662104654,
71.75013387506021,
72.91521110883365,
73.81560233764793,
74.69189111242783,
75.54503495875312,
76.37594118507921,
77.18547014033219,
77.97443822065212,
79.12821186103476,
80.01704932860558,
80.87999135810489,
81.71815630345294,
82.53259881422602,
83.32431432009699,
84.47920755128524,
85.36563470480604,
86.22381146823211,
87.05507068753077,
87.8606623511252,
88.64175994875374,
89.77831940389851,
90.64747907603045,
91.48658671077771,
92.29717829297398,
93.08068646824222,
94.2173304416166,
95.08273434608968,
95.91542636394172,
96.71723119557907,
97.48983971873285,
98.60731175666324,
99.45439305144825,
100.26675606663359,
101.04649669794384,
102.17006867204775,
103.01713516260286,
103.8261560237417,
104.5996426378638,
105.71001217317058,
106.54255024461999,
107.33444489349958,
108.46568500361373,
109.30786801966079,
110.10472470354846,
111.23736935808164,
112.07451811183837,
112.86239961832972,
113.97665810106751,
114.79425611321302,
115.55965815369238,
116.63672311310347,
117.42136184387378,
118.51743010652027,
119.30757533349765,
120.40302993885176,
121.18426928358107,
122.25908978652353,
123.01728091194222,
124.05236321615872,
124.774542711236,
125.75287988143259,
126.59677872514358,
127.33184265730912,
128.3003118460596,
129.10353847466286,
129.77961322738318,
130.64362282740947,
131.32968524156774,
132.16347190983942,
132.77972657145781,
133.48391415964866,
134.19551131138485,
134.793644726729,
135.1612431250533,
135.26660206211778
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"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": "Reaction 2A <-> C (2nd order in A). Changes in concentrations with time"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
0,
0.03766637648823722
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.1183821311376239,
210.58517800690197
],
"title": {
"text": "concentration"
},
"type": "linear"
}
}
},
"image/png": "iVBORw0KGgoAAAANSUhEUgAABb8AAAFoCAYAAAB38YZnAAAAAXNSR0IArs4c6QAAIABJREFUeF7snQmYXEW5sL+eLTOTTCb7piFI2DWsBoIXEVkUAgGMEFC8F8Qb8we8ymIwARERJTExgKhgjEZAEQgaL0SiKChElLAJghcikAAuZF8nyewz/1Nn5nS6e053nz5dVd3nnLd5eGYyfer7vnq/6p6Zt2uqE93d3d3CDQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAQIQIJ5HeEuslUIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAwCGA/GYhQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOQIIL8j11ImBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAshv1gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhEjgDyO3ItZUIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCA/GYNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOQIIL8j11ImBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAshv1gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhEjgDyO3ItZUIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCA/GYNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOQIIL8j11ImBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAshv1gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhEjgDyO3ItZUIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCA/GYNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOQIIL8j11ImBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAshv1gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhEjgDyO3ItZUIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCA/GYNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOQIIL8j11ImBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAshv1gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhEjgDyO3ItZUIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCA/GYNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOQIIL8j11ImBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAshv1gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhEjgDyO3ItZUIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCA/GYNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOQIIL8j11ImBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAshv1gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhEjgDyO3ItZUIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCA/GYNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOQIIL8j11ImBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAshv1gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhEjgDyO3ItZUIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCA/GYNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOQIIL8j11ImBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAshv1gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhEjgDyO3ItZUIQ6Etg2YqVct38JfKZT0yWK2dMs45o244mmTn7Fpk25USZOvkE6/lTE968aKms37hVbph1idTV1pS0FhvJm1va5PoFS+Thx1bJnbfOlolHHGwjbd4c7po45oiDS7Im8xbIBRCAQEEE1rz9jsy4eqFcetHZJX+eL6hwLoYABCAAAQhAAAIQgAAEIk0A+e2zvakCKXNIqYRiZh25fvFUwm/F75+WRfOvkvHjxvictb7LvPhNOGQ/uWPeFTK4scEzkTufdRu2yI1XXxLpX6affXG1XHz5vD4cdMjKXOvCK68J1nGW36Yfe+4LG6NHDs35+DZdR+ri9bOekd/pD3eb/dH3zF5ekRTDZ15cnfP7SjEVZ4vvPsdOPunYyL2Qk7kucz1udchv9Xx2+10Pej6X8RgpZvUyFgIQgAAEIAABCEAAAvElgPz22XtX3qrLU3eMur/sHfW+A0q+k7Rc5bfL7h/vbEyTEvl+kXV/CR42eKDs866RWvkGlRWu1NP1gkeuXbk6cmVbt2odu/FTZXdQLvkeRshvMy88JR9b/94gm7ftzLnj0oZsLmQ926gn37osp/vzPR+WU63lWgvyW39nkN/6mRIRAhCAAAQgAAEIQAACELBLAPntk3cuiejuvNSxS9dnOZ6X6dh1VUz+bGMVu5WrXpSPnnhM2iW55JfLe9SIIbLv2FFZd4IVWm/qbvKgAltHDLfufMJLMVrx2Cq5cOqphU7Vud4V3F5r829/f1Pqavv1+UuAXDvvAhUhInGW30GZ+RnnrsWvXPFf8qvfPeUMyXWci4neptZZyHpGfvvpMNcUQqBU8ruQGsN+bSl3foedHfVDAAIQgAAEIAABCEAAAqUhgPz2yT2I/PY66iPzSAk/xwOkluiK9tSvKbE5ZPBA56xNdURI6s0VvNmkQGa8M06e1EeeuWNvmjNdrpm7WF5+da2Twutanzidy3L9Ep0qbd25FXOOqE5h7c6x2GNZTO2yzpSRhR4D4CVIC10DmetqwsHvybszOXPtqJw/undF8suZL1akrp+zTzs+uf5T12Vqj1QgdTRItr8kyPdY8JPPa/1nPvZSX6RS16uz2N1boS+gpcb+w59eyPsikckXIQpdz6k8P3jsYWnH/mQ+T7rXus89Lq9cayJfTDdG5jpz1+rcOdPTzkf3qsGrX33i5TneKbWO1MdqIXxyPe/6OXLKz9wKXbd+8vr5Huk+H936tc/J3Usfcc6uV7fMY7MyubuPd3XU15BBDc57Dqjz5TOfK2b/z6dk3nd+mozrssz8/pYrvhrjdda1Ca5B1pfXC+PZfqbJfP5PfY5R81QcMx+HLqt3NmxOcgjy3Ob1842K4z7Osj2XqsfqH59+Ke37hRrzvoP3S77fQa7nWD99KuRnG66FAAQgAAEIQAACEIAABMqLAPLbZz9yyW8vsewlgrxkr5K86pe21Dch9Nqtm+3oEJXnsT8+L5/91BTJd+xJpgTNrDvX8SRKQqbKBh27Nr2O3fASQbX9+jm/wKpboW9SaEJ6Zy6ZoBLc9F8MBOmRjjXgtfu3kL9K8HqsuXPZZ8yI5BpIFRZeL8R4rS+v2Lnm7CUjlfgp5IWfbMIm8yz7QndlZ/bXD+Ncz2M+nwqzXlboek7tX6rE9nr+U9fOve0emfP5C5PvEeC1vguJma3vXvlzHRGU+qJctudUP89dmWMLmUu2pmR7QUL1auyYEY7c91o3uebrZ90Wkjf1nGyvnnqdaZ9tHWd7kTfXc4WKteCO++TCqack/wqm0PheDE1wDbq+Uv+Syv1Zw61PrZ3U9wJROVLfEDjbuvR6o9ps3wsLeW7Ld+Z36nNyar7UF6JSXyDI/Hrmz0F+H9vFPj8yHgIQgAAEIAABCEAAAhAoHQHkt0/22X4ZziZwM3+BdNP4+SXQ6xdVP+MKkd/ZjsPwkg+5do0vXf54oDcXc+f4l7+93ueNrbK9SKDeENLvztjUHYVBjzfxuTSSl2X7RTxbHD89LbSG1Ov9yNDM+NnOGfe7Btyc2XbNTptyYt43Ls3GJXM+fo/NSX1hSc0387Gp5jxn7uI+67CQfLn6lGvn99TJJySHFror2+sx7OfYB8U36OM21zwLXc/Z+uf1/JfrMZQ6l0Ji5uu7u4Zz1eO1K9ZLCj7y+DNywqQjpK62JitCv5LRLx8/L3Tk+4um1DcezPZ8krlu/eT1ehxm+x6ZbV159S+f/PbqTbaGFBI/k40Jrrme7/ysr8zHvfr3b594zpn+Rz70fud52es5yO+6VHH8rpGgzyO5dn6rF3LcW7bvQ5lf9/vYzvam3MV8f2YsBCAAAQhAAAIQgAAEIGCPAPLbJ2uvP89WQ712gOaSWF6/lPn5k/5sMt2v7Mz8pTGbAPP6pT2X+EyVIz5ROpe5O7MyjzdQ92Xb+en+2XqmzPTKG0R+Z+uxV41eOctVfmeK6Gx9cusfNqSxzwsaftdANlFViNjNttYz5U8uGZTvhaDMnY2p/3b5ZIqRIDvp3bXutVsx8xifQuN79STXGe/uvMIiv1W9mX/pkXnkg7rGz1+kFPK8lvkcnU2kuc9V7gsnY0YOSx6x4Pc5I/Wx6Fcy+pXLfh5zua7JXEv5xKYrlovNm8k7l/zOfEG0GPntdeSGOiYpc1e01zFS2V4o83qxLyjX1O9PQdZXqsx31+qkow91luCq519xHmvusSWp3zP8rksVx+8ayfWzQpCd35nf4/zKb7+P7fHjxhTy4w3XQgACEIAABCAAAQhAAAJlRgD57bMhXsLB/WU58xfRVAmaLby7g9mNkbo7OVO6+ZUd+YRf6i/tuXaIZt7nV3z6RJlTfKeKca94mee85suZ2gtTO8Cz/al3vtoKPSYiX7zM+3P9Yp/tWi/x7fbES/rkOh82dbecHyGm8uRa65n3tbS2Js/xzXxBJJcETpXrKqc6Usc9R9iLsbtuCpXTbqxCd3772Z2a7QUzN2eutW5Sfqvzy/3+dUa+XdpqLq78zvbCTOZc/MZ0+56aw2WX+bjJ9r4M7vWpgtTrBbRMgZrtcexXMur4fpA5V6/3U8j8fuJXbPr5i5NCv0d6vchayF8+5Hrsuvdt3rojTXQXs/M7F4OgXFOfH1Ofr/yur9Tn4MPfu79cO3exfGPOdGcpuJ9v3bZTFi5amvbip991qeL4XSO5vpfZlN+FPLYL/f7L9RCAAAQgAAEIQAACEIBA+RBAfvvsRa4zQNV52KkC3K/oyydqRo0YkjwLPCo7v3Pt+M71y7O6z8+u1mztNCHBg0rvTPGUeuatz+Xo6zI/EiqVeTbxra7x+wJINrHq9zHh5vLaiR1k57fXrvfMx5Kfx5aqq5zkdyGCKHOxmJLf7nrzu57zPf+pul357ffYnUJjeq2zQnZ+53sg5vprisyxfiWjX/nt5zFXjju/vdarSfmt41gVGzu/vdZaIesr9UX1fceOSu72VnHVC4BqF/hb/1yfdt6313N/kL+4KeS5s5DnNr87vDO/57rfFwp5gTjfY537IQABCEAAAhCAAAQgAIHyJYD89tmbbMIhdaefu+PRr5zIJicLOfNbXbty1Yvy0ROP8Tyv051epljRdea332NPvDh5oc/1i6/7C3Tqmx76bF/yMh0SXEeM1L6s+P3Tfc6bdu9P7W+hc/UjHNx1kO8NHP3K73zrytaZ37nmnim7/Z5V7YenV4907/zO9/yS641k840tdI15CVy/69mvqHblnPqYeQxK0J3f6uztfGfLu4LMb9/VderNUE+YdHgaFr/rS7f8ztVr9RzW3NIq48e9K+ubCWfW7XdXb7F5i5Hf+V588/qrimzPbV47v7PFz2Sj88zv1ONkillfiqvb02GDB8q0sz6cfP8Fr/O/s/3skOucbL9rJNfzTLaz+NUY3Wd++31sF/u8yHgIQAACEIAABCAAAQhAoLQEkN8++ef6Zdbrz6bdXwKPet8BacJGxVlwx31y4dRTxD13M1XqZDur2v36P97ZmPyT5EKOR/H6BV99LVVUeeXw+oXTReZX6rhxvd7cMhW/HzHnN2e+tha6S9WNl+0NIfPly3Z/rhcFdOTKdbxNIfH9ym+vHqbO0c9ZtV7r0OuFj3ziwmutuEfNpMr+XOtTXa9uqW8G5+dYktR+65bf+Xb0+3muynwRopC1kGutF7Ke/crvbKLa7WXQM7+98qceJ5N6fEs2Pur6ubfdI3M+f6GDJfN9Cfw8p2WTjIXwydYTr+e5bDvbU3fse72AUojYLCRvru+R6qzlQs78zvfim9dj12uM+7XMI0Wyxfdi48WgGK5e66GQ9aXWiFuT+jz1LPNsX8/2/T/b94NC1ki+Net1FI9u+a1q8PPY5g0vg/6EwzgIQAACEIAABCAAAQiUBwHkt88+5PslM3U3cOYO8MzzhL3O93avUb9s3/q1z8ndSx+R1GNPUgWJOmbFvWXu2E2VN+oaN1cueanO6c0WL9svv+rrfkV0Khsv3K5gUOeNqjcwyyVI84k/n+0su8uynT3q9/zkbBPKd/Z16lrKjJG6Tv3KbxUjVYCqf6v+ql206ixZPzu/s631zHOs88lvd42mrm+1trz+tN/rWvU1P2I132LSLb8zX7Tyyp/tsZlPJOo6F9/Pei5U7rqy252v6qW6LV3+ePIFwUJjZj5fqn5fNWOaqDexzDwyJ/Pa1DrUiyPq5vVc55ep7p3fbn1edWc+x3pdk/ncU6jY9JM387nCrTmVWb41m1ln6jpxv7cMGdSQ9f0BVM7M9aryf/DYw5x1kCqJM58n3Pjq6zOuXijZ3sRW7dh2b8VwLWZ9qfzZ/noq2wvf2b7/Z/bN/TnEfcPMYt/MN7MfLjMT8juVS2qf1Nf9vFib7/mf+yEAAQhAAAIQgAAEIACB0hNAfpe+B1QAAWME8r1oYywxgcuOgJ8XC8qu6BIUlOvYhRKUQ0oIQAACEIAABCAAAQhAAAIQgAAEiiCA/C4CHkMhEAYCUd0tHwb25VRjriNwyqlOW7WkHkGljtdQt1znGduqizwQgAAEIAABCEAAAhCAAAQgAAEI6COA/NbHkkgQKFsC7lEAfo9gKNuJUFjBBHKdw11wsAgNyHbkBkcdRKjJTAUCEIAABCAAAQhAAAIQgAAEYk8A+R37JQAACEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALRI4D8jl5PmREEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgdgTQH7HfgkAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA9Aggv6PXU2YEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCD2BJDfsV8CAIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgED0CyO/o9ZQZQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIPQHkd+yXAAAgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCESPAPI7ej1lRhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEYk8A+R37JQAACEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALRI4D8jl5PmREEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgdgTQH7HfgkAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA9Aggv6PXU2YEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCD2BJDfsV8CAIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgED0CyO/o9ZQZQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIPQHkd+yXAAAgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCESPAPI7ej1lRhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEYk8A+R37JQAACEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALRI4D8jl5PmREEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgdgTQH7HfgkAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA9Aggv6PXU2YEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCD2BJDfsV8CAIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgED0CyO/o9ZQZQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIPQHkd+yXAAAgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCESPAPI7ej1lRhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEYk8A+R37JQAACEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALRI4D8jl5PmREEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgdgTQH7HfgkAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA9Aggv6PXU2YEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCD2BJDfsV8CAIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgED0CyO/o9ZQZQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIPQHkd+yXAAAgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCESPAPI7ej1lRhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEYk8A+R37JQAACEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALRI4D8jl5PmREEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgdgTQH7HfgkAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA9Aggv6PXU2YEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCD2BJDfsV8CAIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgED0CyO/o9ZQZQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIPQHkd+yXAAAgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCESPAPI7ej1lRhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEYk8A+R37JQAACEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALRI4D8jl5PmREEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgdgTQH7HfgkAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA9Aggv6PXU2YEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCD2BJDfsV8CAIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgED0CyO/o9ZQZQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIPQHkd+yXAAAgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCESPAPJbQ0/f2dKsIQohIGCOwKghdbJxW7N0dZvLQWQIFEugrl+l1FZXyrZdbcWGYjwEjBIY1thPduxul/aOLqN5CA6BYghUVyZk0IAa2bSjtZgwjIWAcQJqnba1d8qe1k7juUgAgaAEEiIyamidrON3/6AIGWeJwIC6KqlIJGTnnnZLGeOdZszQungDCMnskd8aGoX81gCREEYJIL+N4iW4JgLIb00gCWOcAPLbOGISaCCA/NYAkRBWCCC/rWAmSZEEkN9FAmS4NQLIb2uonUTIb7u8g2ZDfgcllzIO+a0BIiGMEkB+G8VLcE0EkN+aQBLGOAHkt3HEJNBAAPmtASIhrBBAflvBTJIiCSC/iwTIcGsEkN/WUCO/7aIuKhvyuyh8PYOR3xogEsIoAeS3UbwE10QA+a0JJGGME0B+G0dMAg0EkN8aIBLCCgHktxXMJCmSAPK7SIAMt0YA+W0NNfLbLuqissVCft+8aKn86N4VSVA3Xn2JTJ18QvLf23Y0yczZt8jLr651vnbnrbNl4hEHJ+9ftmKlXDd/ifPvM06eJDfMukTqamuS9yO/i1qDDLZAAPltATIpiiaA/C4aIQEsEUB+WwJNmqIIIL+LwsdgiwSQ3xZhkyowAeR3YHQMtEwA+W0XOMee2OUdNFvk5XdzS5vccdf/yqcvOF0GNzbImrffkRlXL5S5c6Y7glvdf/2CJTLp6EMdIa7uv3buYvnGnOkyftwYefbF1bJw0VK5Y94Vzngl0tXtyhnTkN9BVx3jrBNAfltHTsIABJDfAaAxpCQEkN8lwU7SAgkgvwsExuUlI4D8Lhl6EhdAAPldACwuLSkB5Ldd/Mhvu7yDZou8/M4E4yW7F9x+n8y9ZrojtzPvV7J737GjkjvFM2W4is/O76DLj3G2CCC/bZEmTzEEkN/F0GOsTQLIb5u0yRWUAPI7KDnG2SaA/LZNnHxBCCC/g1BjTCkIIL/tUkd+2+UdNFvs5Ld7xMlVM6Y5O7+9ZLa7u3vmReek7QpXkDN3hiO/gy49xtkkgPy2SZtcQQkgv4OSY5xtAshv28TJF4QA8jsINcaUggDyuxTUyVkoAeR3ocS4vlQEkN92yZer/PZynXbJ6MmW6XCDRo2d/M48tkQtiAeWP552jnem/D5vyonJM8D7yO8335SmUWOD8mccBKwQUN8Adzd3SLeVbCSJE4Hu7m5JJNSvA8XflKipqqyQ5rbO4oMRAQIGCdT3q5TW9i7p7OJZ1SDmUITW+Ryoe8KVCZHamkrZ3cpzqm62xNNLQK3Tzs4uae/kOVUvWfPRurtFNP0YaL7YIjOon3bV71RNzR1FRmI4BMwSqKmqcB6X6mdVbuYJNNRVmU/ikcE9teLhx1al3eu+x2Ep5bd638Slyx9PHh9dDCDkdwB6Smqv37g1TXQXvfM7kZBdr7wm3eP2DVARQyBgh8CAumrZ3dIu6gdUbhDQSUAtKT3qW6SqqkKqKhLSgvzW2SJiGSBQX1vlrNMu5LcBuuEK2S0JSZTpS8sVFQlHfu9pQdSEa1XFr1pHfnd1S3sHoiZs3e9OJCQRo18wBtRXy6497WFrE/XGjEBNdYXz00lrOy9+22h9Q321jTRpOdz3Mpx80rFp70eoRPGcmxbLrEsvkK3bdqa9f6H1IjUlRH4XCNJLfKsQatEUdeZ3IiFb77xXWs48u8CKuBwC9ghw7Ik91mQKToBjT4KzY6RdAhx7Ypc32YIR4NiTYNwYZZ8Ax57YZ07Gwglw7EnhzBhRGgIce2KXu+1jT9wd36NGDEkT35mzdjf6qiOf58wpNlpDAAAgAElEQVRdLOs2bHEuufPW2cmTLVwnOuPqhcn7P/OJycm4rnj+9PmnyY/v/428/OpaJ4baXX74e/eX1HGpcdXO71XPv5K28dgV9m4dEw7Zz9kZrm4zZ9+SjK3+7VWDe3R10O7G4tiTzKNOUmF5vQHmtXMXyzfmTJfx48b0ORO8T6xEQpqu/JI0XXN90B4wDgLGCSC/jSMmgQYCyG8NEAlhhQDy2wpmkhRJAPldJECGWyOA/LaGmkRFEEB+FwGPoVYJIL+t4hbb8tuVyHPnTE+T2JmzVvL74svnyRknT0pK6MzjSDKPdc4U6678VrGVqB7c2OA4UhXXldfu1xYuWpq8JlN+e9X8yOPPyP7vebcMGdQgP77v16Lec7GutkbcnNOmnChTJ5+Q/DfyO8+6dsG5r1C4l6cugMxrMl8JUY27bv4SZ2jqOOcLiYS0nnqabLl3md1HGNkgUAAB5HcBsLi0ZASQ3yVDT+ICCSC/CwTG5SUhgPwuCXaSBiCA/A4AjSHWCSC/rSMnYUACyO+A4AIOsy2/lXxWO7kXzb/K2bCb7eZ1xHOm7Fabe/cdO8qRzO4tdZz6mtqVnSqevY4hyfxapvzOtSHZq341/q1/rnd2oHPsScCFqX1YIiFdw0fI+lff0h6agBDQRQD5rYskcUwSQH6bpEtsnQSQ3zppEssUAeS3KbLE1U0A+a2bKPFMEEB+m6BKTBMEkN8mqGaPGVb5PWbkMLl+wRLJfMNMNdPMI0mKkd8qnspz3pQTs+5UzzwSRY1xNx63tLb2EfBBOhyLY0+CgPE9prFRZOdO2fDKm9I5YqTvYVwIAZsEkN82aZMrKAHkd1ByjLNNAPltmzj5ghBAfgehxphSEEB+l4I6OQslgPwulBjXl4oA8tsuedvyu5BjT1KPIlFUUnd+u/J70tGHpu38TqXnZ5e3uj7Xzu988ts9aSPbmeHIb7vrOXu2D31IZOVK2fKzX0jrR04vl6qoAwJpBJDfLIgwEEB+h6FL1KgIIL9ZB2EggPwOQ5eoURFAfrMOwkAA+R2GLlGjIoD8trsObMvvfG946Z6lvXXbTsklv9WRKfmOI9Ehv9U53rnyeB29knpsCvLb7nrOnu3yy0W+/W1pmn2dNH1xTrlURR0QQH6zBkJHAPkdupbFtmDkd2xbH6qJI79D1a5YF4v8jnX7QzN55HdoWhX7QpHfdpeAbfmtZufu/p580rHOudjuTYnkFb9/2jkP3I/8dt+88sarL0nu/lbC230DSi/x7EeIZ5757eZJ3d3tSvoHf/OkrN+4NfmmnO7cjnrfAc7XkN9213P2bHffLXLRRdIyeYpsvfv+cqmKOiCA/GYNhI4A8jt0LYttwcjv2LY+VBNHfoeqXbEuFvkd6/aHZvLI79C0KvaFIr/tLoFSyG81Q3cHeOqZ3e5Z3YMbG8TPG16mivR1G7Ykwbky3I/oVoPyveGlusYV4G4St9bafv3Szh5XZ30fduh4eemVNchvu0s5T7a//U1kwgTpHLuPbHhhdVmVRjEQcAlw7AlrIQwEkN9h6BI1KgLIb9ZBGAggv8PQJWpUBJDfrIMwEEB+h6FL1KgIIL/troNSyW+7swx/Nt7wstgednVJd22tJNrbZf2addKl3gCTGwTKjADyu8waQjmeBJDfLIywEEB+h6VT8a4T+R3v/odp9sjvMHUrvrUiv+Pb+7DNHPltt2PIb7u8g2ZDfgcllzKu/eiJUv2X52TLL34lrR86SUNEQkBALwHkt16eRDNDAPlthitR9RNAfutnSkT9BJDf+pkS0QwB5LcZrkTVSwD5rZcn0cwRQH6bY+sVGfltl3fQbMjvoORSxu3+9HTpf+cPZef1X5dd/3OlhoiEgIBeAshvvTyJZoYA8tsMV6LqJ4D81s+UiPoJIL/1MyWiGQLIbzNciaqXAPJbL0+imSOA/DbHFvltl63ObMhvDTS33/I9GXTl56T5Y+fKtsV3a4hICAjoJYD81suTaGYIIL/NcCWqfgLIb/1MiaifAPJbP1MimiGA/DbDlah6CSC/9fIkmjkCyG9zbJHfdtnqzIb81kBz02N/kuGnHC8d4/eXjU+/pCEiISCglwDyWy9PopkhgPw2w5Wo+gkgv/UzJaJ+Ashv/UyJaIYA8tsMV6LqJYD81suTaOYIIL/NsUV+22WrMxvyWwPNdet3yuh3DRbp6pJ1/9ws3XX1GqISAgL6CCC/9bEkkjkCyG9zbImslwDyWy9PopkhgPw2w5Wo+gkgv/UzJaJ+Ashv/UyJaIYA8tsM12xROfPbLu+g2ZDfQcmljHtnS7MMP3GSVP/tJdm8/HfSdtx/aIhKCAjoI4D81seSSOYIIL/NsSWyXgLIb708iWaGAPLbDFei6ieA/NbPlIj6CSC/9TMlohkCyG8zXJHfdrnqzob81kBUye9B/zND6u/9iez4xgLZPeMyDVEJAQF9BJDf+lgSyRwB5Lc5tkTWSwD5rZcn0cwQQH6b4UpU/QSQ3/qZElE/AeS3fqZENEMA+W2GK/LbLlfd2ZDfGogq+d1/8R3SOOcq2XP+hbL9e4s1RCUEBPQRQH7rY0kkcwSQ3+bYElkvAeS3Xp5EM0MA+W2GK1H1E0B+62dKRP0EkN/6mRLRDAHktxmuyG+7XHVnQ35rIKrkd83TT8mwM06WjkPeKxv/+KyGqISAgD4CyG99LIlkjgDy2xxbIuslgPzWy5NoZgggv81wJap+Ashv/UyJqJ8A8ls/UyKaIYD8NsMV+e2P67YdTTJz9i2yz5gRcsOsS6SutsbfQMNXIb81AFbyO9G8R0aPHSZSUSHr/r1NuqurNUQmBAT0EEB+6+FIFLMEkN9m+RJdHwHktz6WRDJHAPltji2R9RJAfuvlSTQzBJDfZrgSVT8B5Ld+prki8oaX6XSefXG1PLD8cdm5a4/MuvQCGT9ujN2GZMmG/NbQBiW/1W3EpCOk6o3XZNMjT0j70RM1RCYEBPQQQH7r4UgUswSQ32b5El0fAeS3PpZEMkcA+W2OLZH1EkB+6+VJNDMEkN9muBJVPwHkt36myG//TG9etFQ+eOxh8senX5J9x46SqZNP8D/Y4JXIbw1wXfk9+LMXS92ypbL9W7fJnov/W0NkQkBADwHktx6ORDFLAPltli/R9RFAfutjSSRzBJDf5tgSWS8B5LdenkQzQwD5bYYrUfUTQH7rZ1qW8nvLFpGXX7Y7WZVt6FCRCRM886ojT+bedo/M+fyF8sab/3Z2gJfL0SfIbw1LxZXfA753qwy8/hrZ81+XyPabv6shMiEgoIcA8lsPR6KYJYD8NsuX6PoIIL/1sSSSOQLIb3NsiayXAPJbL0+imSGA/DbDlaj6CSC/9TMtS/m9fLnIWWfZnazKNmWKyEMPeeZVR56oHd9Xzpgm7tnfV82YJhOPONh+nRkZkd8aWuDK734r/yBDp54h7YcfKZse+5OGyISAgB4CyG89HIlilgDy2yxfousjgPzWx5JI5gggv82xJbJeAshvvTyJZoYA8tsMV6LqJ4D81s+0LOX3n/8scu21diersh13nMhNN/XJ29zSJtcvWCLnTTkxKbvVESjqpmR4qW/Ibw0dcOV3xY4dMmr8aOkcu49seGG1hsiEgIAeAshvPRyJYpYA8tssX6LrI4D81seSSOYIIL/NsSWyXgLIb708iWaGAPLbDFei6ieA/NbPtCzlt91p5s225u13ZMbVC2Xdhi1p1044ZD+5Y94VMrixIW8MkxcgvzXQdeW3CjXiuCOk6vXXZPOvfidtk/5DQ3RCQKB4Asjv4hkSwTwB5Ld5xmTQQwD5rYcjUcwSQH6b5Ut0fQSQ3/pYEskcAeS3ObZE1ksA+a2XZ75oY4bW5bskFvcvW7FSVj3/StoZ3167wUsFA/mtgXyq/G6cfZXU33OX7LxxnuzmTS810CWEDgLIbx0UiWGaAPLbNGHi6yKA/NZFkjgmCSC/TdIltk4CyG+dNIlligDy2xRZ4uomgPzWTTR3POS3iCu5Jx19qEydfEIaMCXF3/rn+pIffYL81vC4SJXftY+skCEXnittxx4nmx9+TEN0QkCgeALI7+IZEsE8AeS3ecZk0EMA+a2HI1HMEkB+m+VLdH0EkN/6WBLJHAHktzm2RNZLAPmtl2e+aMjvfITK437kt4Y+pMrvxJ7dMnq/0U7UdWvXSXd9fw0ZCAGB4gggv4vjx2g7BJDfdjiTpXgCyO/iGRLBPAHkt3nGZNBDAPmthyNRzBJAfpvlS3R9BJDf+lj6iYT89kOp9NcgvzX0IFV+q3DDppwqNU/9Sbb+9AFpOe0MDRkIAYHiCCC/i+PHaDsEkN92OJOleALI7+IZEsE8AeS3ecZk0EMA+a2HI1HMEkB+m+VLdH0EkN/6WPqJhPz2Q6n01yC/NfQgU343fGuuNMy7UXZPnyk75i7UkIEQECiOAPK7OH6MtkMA+W2HM1mKJ4D8Lp4hEcwTQH6bZ0wGPQSQ33o4EsUsAeS3Wb5E10cA+a2PpZ9IyG8/lEp/DfJbQw8y5Xf1c8/I8NNOlI4DDpKNT72gIQMhIFAcAeR3cfwYbYcA8tsOZ7IUTwD5XTxDIpgngPw2z5gMegggv/VwJIpZAshvs3yJro8A8lsfSz+RkN9+KJX+GuS3hh5kym/p7pbR40aIOv97/eq3pWvYcA1ZCAGB4ASQ38HZMdIeAeS3PdZkKo4A8rs4foy2QwD5bYczWYongPwuniERzBNAfptnTAY9BJDfejj6jYL89kuqtNcZld/bdjTJzNm3yMuvru0zywmH7Cd3zLtCBjc2lJaAhux95LeIDLnoAql9+CHZftv3Zc8n/0tDFkJAIDgB5Hdwdoy0RwD5bY81mYojgPwujh+j7RBAftvhTJbiCSC/i2dIBPMEkN/mGZNBDwHktx6OfqMgv/2SKu11RuX3zYuWOrO7csa00s7ScHYv+d3/x4ulcdYXpHnqNNn2gzsNV0B4COQmgPxmhYSBAPI7DF2iRkUA+c06CAMB5HcYukSNigDym3UQBgLI7zB0iRoVAeS33XWA/LbLO2g2Y/Jb7fqec9NimXXpBTJ+3Jig9YVinJf8rly7RkYeM0G6Ghtl/Zp1oZgHRUaXAPI7ur2N0syQ31HqZrTngvyOdn+jMjvkd1Q6Gf15IL+j3+MozBD5HYUuxmMOyG+7fUZ+2+UdNBvyOyi5lHFe8lvdPfLwA6Xy3/+STX94StonHK4hEyEgEIwA8jsYN0bZJYD8tsubbMEJIL+Ds2OkPQLIb3usyVQcAeR3cfwYbYcA8tsOZ7IUTwD5XTzDQiIgvwuhVbprjclvNSV17Mm+Y0fJ1MknlG6GKZmXrVgpb/1zfZ9jWFSdP7p3RVqNN159SbJuNe66+Uuc+884eZLcMOsSqautSV6fTX4PuvJzUn/3Etl5w02y67LLy4IBRcSTAPI7nn0P26yR32HrWHzrRX7Ht/dhmjnyO0zdinetyO949z8ss0d+h6VT1In8trsGkN/pvJ99cbVcfPm85BdHjxwqi+ZfVfITQYzK7zVvvyP3LHtUZs28IE0W212KIqnwP/OJyZ7yW9XkdTa5Grtw0dLkm3N6nWOeTX7XPrhMhnzmU9L64VNkywMP2Z42+SCQJID8ZjGEgQDyOwxdokZFAPnNOggDAeR3GLpEjYoA8pt1EAYCyO8wdIkaFQHkt911gPzey1ttHL79rgfTZLfywo/98Xn57Kem2G1MRjZj8lud+T1z9i3y8qtrPSc44ZD9kkLZFoFcO7+zye/M3euZMlyNyya/K3bskFH7j5Hu6hpZ/9Z66a7pZ2uq5IFAGgHkNwsiDASQ32HoEjUiv1kDYSGA/A5Lp6gT+c0aCAMB5HcYukSNyG/7awD53cPcdcBXzZgmE4842H4j8mQ0Jr/LbqYi4vfYE/fIk+aWNrl+wRKZdPShySNQ1KsW185dLN+YMz25bT+b/FYMhp9yvFS/+BfZsuxhaT3hw+WIhZpiQAD5HYMmR2CKyO8INDEmU2Dnd0waHfJpIr9D3sAYlY/8jlGzQzxV5HeImxez0tn5bbfhpZLfW5q3yMsbXrY7WREZWj9UJoyY0Cev10Zh68XlSIj8zoCj5PaMqxfK3DnT5X0H7+fI7/OmnJh85cJLfuds6DXXiMydK/KlL4nM23vuTTktAmqBAAQgEJRAe0eXVFdVBB3OOAhAAAKhJtDW0SU1PAeGuocUDwEIBCfQ3tEt1VVKC3ODAAQgAAGbBJa/tlzOuvcsmymdXFMOnCIPfaLvsc5Kfj+w/PE+75FovcAsCY3L78zDzlUdd946uyTb4LPt/M5k4x51cvpJk4re+V3z5BMy7JzTpf3wI2XTY38ql75TR8wIsPM7Zg0P6XTZ+R3SxsWwbHZ+x7DpIZwyO79D2LSYlszO75g2PmTTZud3yBoW43LZ+W23+aXa+f3nf/5Zrv39tXYnKyLHvfs4uenkm/rkjfXOb6/JuzurL73o7ORRIra6Vaj8njr5BCnmzG9nXh0dMnqf4ZJob5P1b7wjXY2NtqZLHggkCSC/WQxhIID8DkOXqFERQH6zDsJAAPkdhi5RoyKA/GYdhIEA8jsMXaJGRQD5bXcdlEp+251l/myxPfPbPS879cgQF1eptsN7yW/VoBWPrZILp57qlJd5rEmmwFcyXN2unDEt2f1cZ36ri4Ze8DHp9+gjsu2HP5Hmcz6ef9VwBQQ0E0B+awZKOCMEkN9GsBLUAAHktwGohNROAPmtHSkBDRFAfhsCS1itBJDfWnESzCAB5LdBuB6hkd97oSjnevtdD8qi+Vcl3yNROdbH/vi8fPZTU+w2JiObsWNPlFSec9NimXXpBclJu7nV5Bfcfp/MvWa6DG5sMA4g19ErrqR/+LFVyToyj2VRDbxu/hLn/jNOntTnDJt88nvAou/KwGuvlpbTzpCtP33A+HxJAIFMAshv1kQYCCC/w9AlalQEkN+sgzAQQH6HoUvUqAggv1kHYSCA/A5Dl6hREUB+210HyO903pn+dfTIoWky3G539mYzJr/Lcee3Kcj55HfVG6/LiEmHS3f/AbLu7Y2myiAuBLISQH6zOMJAAPkdhi5RI/KbNRAWAsjvsHSKOpHfrIEwEEB+h6FL1Ij8tr8GkN/2mQfJaEx+q2LUjumlyx+XO+ZdkdzhXcozv4MA8jMmn/xWMUZ84Eipeu3vsuWB5dL64ZP9hOUaCGgjgPzWhpJABgkgvw3CJbRWAuz81oqTYIYIIL8NgSWsdgLIb+1ICWiAAPLbAFRCGiHAzm8jWLMGRX7b5R00m1H5rYrKdeRI0KLLbZwf+T3gtoUy8GvXcfRJuTUvJvUgv2PS6JBPE/kd8gbGqHzkd4yaHeKpIr9D3LyYlY78jlnDQzpd5HdIGxfDspHfdpuO/LbLO2g24/I7aGFhGudHflds2SyjDh4nUlEh6195U7qGDgvTFKk15ASQ3yFvYEzKR37HpNERmCbyOwJNjMEUkN8xaHJEpoj8jkgjIz4N5HfEGxyh6SG/7TYT+W2Xd9BsyO+g5FLG+ZHf6vKh550l/f7wqOz4xgLZPeMyDZkJAQF/BJDf/jhxVWkJIL9Ly5/s/gkgv/2z4srSEUB+l449mQsjgPwujBdXl4YA8rs03MlaOAHkd+HMihmB/C6Gnr2xyG8NrP3K77pfLJXBMy6W9sOOkE2//7OGzISAgD8CyG9/nLiqtASQ36XlT3b/BJDf/llxZekIIL9Lx57MhRFAfhfGi6tLQwD5XRruZC2cAPK7cGbFjEB+F0PP3ljt8nvbjiaZOfsW+fT5p8mP7/+NvPzqWs/ZTDhkv7Q3wrQ3Zf2Z/MrvRGuLjDpgrCT27JaNT70oHQccqL8YIkLAgwDym2URBgLI7zB0iRoVAeQ36yAMBJDfYegSNSoCyG/WQRgIIL/D0CVqVASQ33bXAfLbLu+g2bTLb7cQJcHn3LRYZl16gYwfNyatPvUmmA8sf1xumHWJ1NXWBK29bMb5ld/OD3dXXCb1P/mx7Lr0C7Lza3PLZg4UEm0CyO9o9zcqs0N+R6WT0Z8H8jt/j1s7W5yLuqW752N378c+/+6J5d6vRmRe32ese02W2JI1V75aMmrsje8/XvpcMufeZ24Jn/V0q0jp13pxyuRWVZmQ/rVVsn13W9Y+9J1b7xzy9i2j9t65+GeVe+6p80vOPU/f+64xd5365Gx6XbnrMgCr3qH+10GhrLq6kg/qru6ez7M+drPEzr4m86+pfjUV0tnZJe2d3erJoCd/jsdH/ueVnhhZ12MBjz23+pRwPp7XvJ9Lcj3P5X6e9FjDPjh59zHzObe31+4iS3l6dzlnPuPvncfee7yuzTrefW5NzVVkfnf9pdbqVWcqk3zXetVfXVUhbe2dmUiSayJfzNS1nf/a3nWckc03fw+mPfPve/MbM3WdptXvFTVbfvc5UGP/Pddf1vn3JVDI/N3nlXzzL2j9eT0mPDuV+vNS9sdfQhLOz1Xej0G/8/dYKGk/r+V5/BfA3/2Z0Ttj+X+1+3rvx2r5Vx6vCksiv9e8/Y4suP0+mXvNdBnc2BB64oXI75pVf5ZhZ57ivOGleuNLqawM/fyZQPkTQH6Xf4+oUAT5bWcVNHfskc7uLunq6pTO7k7pUv/1ft6pPkpn77+7RP27u7vL+Zr6vKu752PqNUqUOPf1XuPE7L2mZ0x3z7je/1NzqTE947vSruns7HC+7vzY3vvDu6qz5xcJFU993vMLu/qae636ohImPff3/NCf+rm63/l3In3c3rh78zlxVU63hpR6qqsTzi+/qu7eLMl8e+vp+QWlZ3xKPb3z8IqbNmenxr3jeuK68Xrv651H5jwdeZVybdo80uqRND5h/+XDziOILBCAAAQgAAEIQAACEOghgPwOx0ooifxetmKlrHr+lVju/FbLYuRRh0rlP96SLfcuk9ZTTwvHSqHKUBNAfoe6fbEpPpf8VlKuvatDOrs6pKO7Qzo626VD/Vt9rr7mfl19VPd1q2t7hWsWMeslXVPFrFKfjrD1ELOO4FXS1vnYc03y815B3Fca98brHdNXGveIzo6u9mQs95pMIe2K5mQNabK5R0a3dDbHZu0w0fAQ6FdZ6xSrdiU5HxPqs72f99zXc1P39X6W9u++1/eOd+P0idcbpzde1vF+7+8uNF76fDLzOzTc3L2xk//OMpds97v0eriKVFdWOLtp+1yfh1UiUZFcVHvH5p5HH64Fc8q+JlLnlbZ+8s4jY224S6pAroksfRG/ayZfvhRW/ten++hIf/xoY1XRswZ815OlF1nrcR9GkpC6flXS1dUtbR1dTr7M9ZrkXOTjw51L3r75XFd75+bdi0Kfq9KeC/KtmXzPcz5ZVaQ81lO/kySfflO+mOSX78KUdZN+afIBuPf5JfmMn/59zCuX59e8Ci0g/97vOBn5PeJWSEKGNvaTzTta+zw/Zn4X9lurJ9O07397I7vPQ/mYpn7fzHdtQfkzJ5mlztTnjaD53cdo2niPtZK1fq9rPXpa2Pw91m8B68/vnNJ//knpfwHzd489adrTkXOtFjJ/r8fK3p/TMh+/fReL17WF5Pe61v2Z0mNpWv0Sx55YxR04mXb5rXZ1z7h6oazbsCVrUaNHDpVF86/qcxxK4FmUeGAhO79VqQ3fmisN826U5nM+Ltt++JMSV0/6OBBAfsehy+lz3N2+S9o726W9u13aO9ukvatN2js7nI9tnW2OYO35XEnkdudr6t9KIrc516r/e65x46jduK1drT0CuqvTGecI6K4OJ4/79XYlb5NCWl2j5LDKnfr1vdJaCWyVo8sR2Z3OdSouu1DNrFv1g2JlolIqEgmpqKh0Pu/5d6VU9v5bfa5+Ia5U/1dUSoWk3OdeIxW943uuUf8l41VUipJnzlfV+N44qde4X9sbuzeeVEpVRZUzeRXPlaPq+h5N2iNGVPyez3vkjHN/r0hNSMrnap7Ja1PH9cSqyLg2GSN1XDJvT9zG/jXS0qpeZOjJnaynu/fzjHoUa1Woe62Ts3dcj3jwHqfiqmvd+3vmsVcYO2N742bOw51z8v4Ufi6rvXH31lZbWWdm4RHVOgHO/LaOnIQBCXDmd0BwDLNKgDO/reImWREEOPO7CHgBhiK/A0ArwRDt8tudQ64zv0swT6MpC5XflevXycgJ+0t3VZVsWP0P6WpsNFofwSGA/A6+Btwdx3sFcoo07hXLShZ3dHakCOT2XoGsrk2Rz87nvfd19MhmRz53p8hnFceVz72CWu0qdurobHPuU2I4KaVTvqaOs4jyra6q3pGizv+JKqmqrHY+r0xUSXXvR+c+9XV1f0W1I3h1i1lH5vaRxkoU935dSWBXGveK5Z77eq7pEc0VfWSzc19F79c9rtkrqHviOHLZjedRj8qByDTziODMbzNciaqXAPJbL0+imSOA/DbHlsj6CCC/9bEkklkCyG+zfDOjI7/t8g6azZj8DlpQGMcVKr/VHIedc7rUPPmEbF/4Hdlz0WfCOG1qDhGBsMvvprYm5wgHJXdb2tXHZmnp2NP7seff6r6e+1ucj3va9yTHNLer63uuae1o9bUbOuy7jl1RXFNRI9WV6v9qqXY+r5bqRLXzNXVfVaUSxzVSU1njyOKe65VUrna+5oyp6Lmmukp9Xu3I5qqKyhTJXCWV6ppEtSOaewR1r5iuqJJ+Ff16JbSKo65xBba6Rgnfnq8PqO0n/fvVyK493U4MNQduEChHAsjvcuwKNWUSQH6zJsJCAPkdlk7Fu07kd7z7H6bZI7/tdgv5bZd30GzI76DkUsYFkd/19/1UBn3us9I28VjZ/Os/aKiCEBDITsCm/FZvvLarbZfsbm9K+7irvUl2t+0S52P7btnVulN2te+S3W1NzsddberrPR9Tv15KCa0OAlC7iF0h7ErjHomcLo17BHK6NFYCN5t8rqnq5wjfpJR2Pu+VzamxE0pC94hrp28vAqkAACAASURBVJZMIe38uyeOI7MrqkP7UOANL0PbutgVjvyOXctDOWHkdyjbFsuikd+xbHvoJo38Dl3LYlsw8ttu65HfdnkHzWZUfuc6/3vCIfvJHfOukMGNDUFrL5txQeR3Ys9uGXXAWEm0tsiGv7winfvsWzbzoZDoEcgnv5V0ThfPPZI6TWJ7SGpHYrelS2wTR2/UV/WX2qpaqauul7qqOucoB7UruK6652NtlfpY59xfW1kr9TUDeq+pk1rnmt7rKuuk1pHO+XdDq5zc7BJAftvlTbbgBJDfwdkx0h4B5Lc91mQqjgDyuzh+jLZDAPlthzNZiieA/C6eYSERkN+F0Crdtcbkd3NLm1y/YIlMOvpQOfy9+8s9yx6VWTMvkLraGrl50VL54LGHycQjDi7dzDVmDiK/VfpBl02X+vvvkaYrvyRN11yvsSJCRZ2A2g3tZ9e0u7u6U5ply+4dfYV27y7sru4ubcjUbun66v7Sv3qADKhpkAHVA6R/78cBNQOkf3WDqI8Dagb2fKxukP4ZH93r+lf35+gLbZ0p/0DI7/LvERX2EEB+sxLCQAD5HYYuUaPzO9GAGmlr75Q9rZ0AgUDZEkB+l21rKCyDAPLb7pJAftvlHTSbMfmd+oaXqrgFt98nc6+Z7uz0fvbF1fLA8sflhlmXODI87Leg8lud+a3O/u4cPUY2vPS6SEJ9S+UWJwI7W3fIhj3rZePu9bJxzwbZuGed7GjZLttatkpT286e/1t7P7b3fNzesk07on6VtRmSuqGPvG6oUZK6R2Yrqe3KbS/Jrb1AAsaCAPI7Fm2OxCSR35FoY+QngfyOfIsjM0Hkd2RaGemJIL8j3d5ITQ75bbedyG+7vINmsyK/hwxqkLm33SNzPn+hI7/VcSipMjxo8eUyLqj8VvWPPPJgSTTtlK1LH5K2o95fLlOijiIJbFRCW8ns3esdub15z0ZZv+udFNGthPcGKeY860G1g9N2Taftqq5qkIH9Gnt2YNcMkHcNHiJd7f2kvqpn13Vy93V1z3XcIFAOBJDf5dAFavBDAPnthxLXlJoA8rvUHSC/XwLIb7+kuK6UBJDfpaRP7kIIIL8LoVX8tcjv4hnaiGBMfqceezJ18gnOUSf7jh0l6vNlK1bKqudfif3Ob9XgAbcukIFfv15aJk+RrXffb6Pn5CiCwD+a3urZpe3u1FZye/c62bB7vWzas8GR2+qj35s6r3pE/SgZUT9SRvQfJSPrR8mw/iOkod9AaageKA01A6Wx3yBHYA90jgkZKMPqhvsNn7wu35nfBQdkAAQMEEB+G4BKSCMEkN9GsBJUMwHkt2aghDNGAPltDC2BNRJAfmuESSijBJDfRvH2CY78tss7aDZj8juzIHUMyszZt8jLr66V0SOHyqL5V8n4cWOC1l1W44rZ+V2xfbuM2r+Hw+Zf/17aJk4qq7nFoRj36BFHXveK7B65vc7Zva2+pj6q6/ze1K5sJbVH9R8tw+pHOFJ75IDRztdG9h8lwx3hPUrUUSI2bshvG5TJUSwB5HexBBlviwDy2xZp8hRDAPldDD3G2iSA/LZJm1xBCSC/g5JjnG0CyG+7xJHfdnkHzWZNfgctMAzjipHfan6NX75a+n//u9J2/Idk8//+OgxTDlWNa7e/Lm/tWCurt/yfrGv6t6xXUrt397baqV3I0SMj+4+WUf3HyPD6EUmRrXZs75XaI+XdDfuUHR/kd9m1hII8CCC/WRZhIYD8Dkun4l0n8jve/Q/T7JHfYepWfGtFfse392GbOfLbbseQ33Z5B81mTH6nvuFlVHZ4Z4NcrPyu2LpVRk7YXxKtLbJl+W+l9bjjg/YztuN2t++SV7f8TdZuf0Pe3PaG8/mbO96QN7a9lpeJerNHZ2d2/9HJo0fULu3h/Uf2fE0dSaKOIwlw3Eje5JYuQH5bAk2aogggv4vCx2CLBJDfFmGTKjAB5HdgdAy0TAD5bRk46QIRQH4HwsagEhBAftuFjvy2yztoNuR3UHIp44qV3yrUwK9dJwNuW+i86eXm367UUFU0Q7y9c60jtNdse01e3/p3R3a/sW21bG7elHXCw+tHyv6DD5L9Bx8o4xr3c0T3qAFq9/ZI5/M4vOEj8juaj4eozQr5HbWORnc+yO/o9jZKM0N+R6mb0Z4L8jva/Y3K7JDfUelk9OeB/LbbY+S3Xd5BsxmT36og9SaXHzz2MJl4xMFB6wvFOB3yu2LnThn5vvGS2LNbtix9UFpPOjUUczdV5MubXpTXtr4qr29dLWu2vy5v71gr/7f5pZzpDhxysIxXknvQgY7sHj/4ADlo6KFSX9XfVJmhiYv8Dk2rYl0o8jvW7Q/V5JHfoWpXbItFfse29aGbOPI7dC2LZcHI71i2PZSTRn7bbRvy2y7voNmMyu81b78j9yx7VGbNvEDqamuC1lj243TIbzXJhm9+XRoW3CTt750gm554uuznratA9YaSz65/Sp5752n5y4Zn5G+b/pr1HO6htcNk/yEHy36D9pcDhhwk4x3RfaDs2zheVzmRjIP8jmRbIzcp5HfkWhrZCSG/I9vaSE0M+R2pdkZ6MsjvSLc3MpNDfkemlZGfCPLbbouR33Z5B81mTH6rM79nzr5FXn51rWdtEw7ZT+6Yd4UMbmwIWnvZjNMlv9Wub7X7W+0C33rPz6Xlo5PLZo46C3lp0wvy3Lqn5Ol//9mR3e/s+lef8KMHvEsOHTpBDhn2Ptlv8AGO7D5k6AQZUD1AZymxiYX8jk2rQz1R5Heo2xer4pHfsWp3aCeL/A5t62JXOPI7di0P5YSR36FsWyyLRn7bbTvy2y7voNmMye+gBYVxnC75reY+4NYFMvDr10dm93dT2055dt1T8ty6VfLMuqfkhfXPSktnc582HzP6AzJxzHGiPh45aqKoHd7c9BFAfutjSSRzBJDf5tgSWS8B5LdenkQzQwD5bYYrUfUTQH7rZ0pE/QSQ3/qZEtEMAeS3Ga7ZoiK/7fIOms2Y/FY7v+fctFhmXXqBjB83Jq2+Z19cLQ8sf1xumHVJJI5D0Sm/nd3fRxwkFVu3ytYf/0xappwTtLclGbd2++vy3Pqn5dl3npLn1q9yzuzulu60WkbUj5L3j54kE53/j5MJw4+QqorqktQbl6TI77h0OtzzRH6Hu39xqh75Haduh3euyO/w9i5ulSO/49bxcM4X+R3OvsWxauS33a4jv+3yDpqtJPJbnQW+4Pb7ZO410zn2xKNzA26/TQZ+ZbZ0HHCgbHzqxaC9tTJOvRnlo2+tkKf+9UdHdm9r2don7yFD3yfHjPmAvH/UJHn/6GNln4HvsVIbSfYSQH6zGsJAAPkdhi5RoyKA/GYdhIEA8jsMXaJGRQD5zToIAwHkdxi6RI2KAPLb7jpAftvlHTRbSeT3shUrZdXzr7DzO0vXEq0tMuLIQ6Ry4wbZ9oO7pHnqeUH7a2TcCxuelRVrHpRfr3lI3tzxRlqO/tUD5KhREx3RPXH0B+To0cdyTreRLhQWFPldGC+uLg0B5HdpuJO1cALI78KZMcI+AeS3feZkDEYA+R2MG6PsEkB+2+VNtuAEkN/B2QUZifwOQs3+GO3yW+3qnnH1Qlm3YUvW2YweOVQWzb+qz3Eo9qevJ6POY0/civr/aJE0fukK6Rj3Htn47MsiFRV6ig0Y5el3npQVax6SFWv+N+0NKg8ccrC8d9jhzjEmR4861jnChFv5EUB+l19PqKgvAeQ3qyIsBJDfYelUvOtEfse7/2GaPfI7TN2Kb63I7/j2PmwzR37b7Rjy2y7voNm0y2+3kFxnfgcttlzHmZDfibY2GTHxfVL573/Jtu8tlubzL7Q6/c7uTvnTv56Qh9/4pTyydrlsat6YzH/UqGPk7APOlXMOPF+G1Q23WhfJghFAfgfjxii7BJDfdnmTLTgB5Hdwdoy0RwD5bY81mYojgPwujh+j7RBAftvhTJbiCSC/i2dYSATkdyG0SnetMflduinZz2xCfqtZ1N9zlwz6wkxpO/Jo2fy7PxqfWHtnmzz+z0cd4f3bNx+WHa3bkznfO+wwOefAafKxA8+X0QPeZbwWEuglgPzWy5NoZgggv81wJap+Ashv/UyJqJ8A8ls/UyKaIYD8NsOVqHoJIL/18iSaOQLIb3NsvSIjv+3yDpotVvJbnTX+1j/Xy5UzpqXxUrvUZ86+RV5+da3z9TtvnS0Tjzg4eY0ad938Jc6/zzh5Up+zyk3Jb5VvxMQJUvXmGtl5w1zZddkXgvY55zh1lMlDr/9CHnvrN7KnY3fy2gMGHyRnHXiefOzAafKexv2N5CaoHQLIbzucyVIcAeR3cfwYbY8A8tseazIFJ4D8Ds6OkXYJIL/t8iZbMALI72DcGGWfAPLbLnPkt13eQbMZld+ZUjm1yAmH7Cd3zLtCBjc2BK3d97hnX1wtF18+z7n+M5+YnCa/m1va5PoFS2TS0YfK1MkniDqz/Nq5i+Ubc6Y7Z5KrsQsXLU3WevOipU6cVIFuUn7X/vbXMuSTH3dyblr5jLQf+j7f8851odrVvfjF78jdLy+WLS2bk5fu07Cvs8P7rAPPlUOG6smlpWCCFEUA+V0UPgZbIoD8tgSaNEUTQH4XjZAAFgggvy1AJoUWAshvLRgJYpgA8tswYMJrI4D81obSVyDkty9MJb/IqPz2EsWlnLHXzm8luxfcfp/MvWa6I+IzZbiaw75jRzliXN0yZbj6mkn5reIPufBcqX1khXQceJBsXPmsSFVVYIzbW7bJ7S/cLHe+tEh2t+9y4gypHSrTDvmUnLn/VDly5MTAsRlYvgSQ3+XbGyrbSwD5zWoICwHkd1g6Fe86kd/x7n+YZo/8DlO34lsr8ju+vQ/bzJHfdjuG/LbLO2g2Y/K7HN/w0kt+e8lsV9rPvOictF3hCnLmznAb8rti+3YZ/h9HSeWG9dJ05Zek6ZrrC+63kt7f+8tC+fFL35fmjj3O+LMPOE8+NO4UOf/g/yw4HgPCRQD5Ha5+xbVa5HdcOx++eSO/w9ezOFaM/I5j18M5Z+R3OPsWt6qR33HreHjni/y22zvkt13eQbMhv19cLQ8sfzztHO9M+X3elBOTZ4B7ye+de9qD8vc9rurJlVL/0VNEKipk9+NPSufR7/c1dlvLVrl51Xz54YuLZE/7bqlIVMjHDjpX5hz/FTlg8IG+YnBR+AkMqKuW3c3t0h3+qTCDMiSQSKhfB4q/KVFTVVkhzW2dxQcjAgQMEqjvVymt7V3S2cWzqkHMoQjd3d0tup4DdU+4MiFSW1Mpu1t5TtXNlnh6Cah12tnZJe2dPKfqJWs+Wrf6Pqjnx0DzxRaZQU1zQH21NFn43b/IUhkecwI1VRWifj1TP6tyM09gYH21+SRkKJqAMfmtKss8MqToaosMYGrnt61vgP1mXSk1t39Xut6zn+z+y0siNTVZiWxu3iS3rFogP/zrD6S5fY8jvacedJ58+YNflf0GjS+SJMPDRsCR3y3t0s3vFGFrXdnXq5aUrt95qqoqpKoiIS3I77Lve9wLrK+tctZpF/I77ktBuiUhiTJ9abmiIuHI7z0tHbHvEwDKm4Ajv7u6pb0DUVPenepbnepYRdiKLqJeJb93Ib+LIMhQGwRqqiucn05a23nx2wbvBuS3DcxF5zAqv9Uu6XuWPSqzZl4gdbXZRW3Rs/AZIKxnfrvTS7S2yPAPHStVb7wuu2b+j+y88Zt9Zr6peaN897lvyU//9iNp6Wx2pPfZB5wrXzz2Otm3Eentc6lE7jKOPYlcSyM5IY49iWRbIzkpjj2JZFsjNymOPYlcSyM7IY49iWxrIzUxjj2JVDsjPRmOPbHbXo49scs7aDZj8lud+T1z9i3y8qtrPWubcMh+cse8K5w3mbR185LfmW9wmXmsSeaZ4F5v4mn6DS9T+VS/+n8y/MPHiXR2yuaHH5W2Y45L3v2d5xfIrc/Mc6S3up21/7kya9J1st+gA2whJk+ZEkB+l2ljKCuNAPKbBREWAsjvsHQq3nUiv+Pd/zDNHvkdpm7Ft1bkd3x7H7aZI7/tdgz5bZd30GzG5HfQgkyMUwL74svnpYW+89bZyXO8M0V96n1qkJLm181f4ow/4+RJaeeDq6/ZlN8q34Bb5svAb3xVOsfuIxv/9Ly83vpv+Z/fflpe2vSCU+PHDpwmVx5zLdLbxGIKaUzkd0gbF7Oykd8xa3iIp4v8DnHzYlQ68jtGzQ75VJHfIW9gTMpHfsek0RGYJvLbbhOR33Z5B80WC/kdFI7fcbblt3R1ybDJJ0nNc8/ITV84Vq4d/LRT6nHvOkFuOWWRjG0Y57d0rosJAeR3TBod8mkiv0PewBiVj/yOUbNDPFXkd4ibF7PSkd8xa3hIp4v8DmnjYlg28ttu05HfdnkHzWZUfrtHijz82CoZPXKoLJp/lYwZOUyuX7BEJh19qEydfELQustqnHX5LSLr3nhGrlx8sqwc2yn9EjUy5/gb5b8P/5zzxgbcIJBJAPnNmggDAeR3GLpEjYoA8pt1EAYCyO8wdIkaFQHkN+sgDASQ32HoEjUqAshvu+sA+W2Xd9BsRuW3Oh9737Gj5PSTJsmCO+6TC6eeIuPHjRF1DMkDyx/vc3xI0EmUepxt+b109U/lKyuvkqa2JpmwQeTex4fL8Lt/Lx3v4Q0tS70WyjU/8rtcO0NdqQSQ36yHsBBAfoelU/GuE/kd7/6HafbI7zB1K761Ir/j2/uwzRz5bbdjyG+7vINmMya/1Tnac25aLLMuvcDZ7Z0qv9WbSi64/T6Ze810q294GRRSvnG25Pfm5k3yxcdmyu/eWiGViUqZedQV8o0frJaBy38lHQcdLJse+5N019blK5f7Y0gA+R3DpodwysjvEDYtpiUjv2Pa+JBNG/kdsobFuFzkd4ybH6KpI79D1KyYl4r8trsAkN92eQfNVhL5zc7vwtulhPcVj35WtrVslX0Gvke+85EfyftHTZLErl0y/CMflKrX/i57LrxItn/7jsKDMyLyBJDfkW9xJCaI/I5EG2MxCeR3LNoc+kkiv0PfwthMAPkdm1aHeqLI71C3L1bFI7/tthv5bZd30GzG5LcqaNmKlbLq+VdkzucvlO8s+aVz7MmQQQ0yc/YtMm3KiZz57aNrTW075fJHPyu/WfuQc/UnD/20fO2EBVJXVZ8cXfX2mzLslOOlYts2BLgPpnG8BPkdx66Hb87I7/D1LK4VI7/j2vlwzRv5Ha5+xbla5Hecux+euSO/w9OruFeK/La7ApDfdnkHzWZUfqui1C7viy+fl1bfnbfOlolHHBy05rIbZ+rYk+0t2+T8/50sf9v8VxlWN1y+dfIdcuq+kz3nX/3yX2XYlFOdneC7PnuZ7LxpQdlxoqDSEUB+l449mf0TQH77Z8WVpSWA/C4tf7L7I4D89seJq0pPAPld+h5QQX4CyO/8jLiiPAggv+32Afltl3fQbMbld9DCwjTOhPxW53uf8/OT5c0db8i7GsbKL6c+6nzMdat5+s8y9GOTJdHWJju/fIPsunxWmDBSq0ECyG+DcAmtjQDyWxtKAhkmgPw2DJjwWgggv7VgJIgFAshvC5BJUTQB5HfRCAlgiQDy2xLo3jTIb7u8g2YzKr9vXrRU1m/cKjfMukTqamucGptb2uT6BUtk0tGHcuxJlq79u+mfcu6yj8o/mt6S9zTuL7+Y+oiM7D/aV49rf/OwDPmv80W6umT7d38gey74lK9xXBRtAsjvaPc3KrNDfkelk9GfB/I7+j2OwgyR31HoYjzmgPyOR5/DPkvkd9g7GJ/6kd92e438tss7aDZj8tuV3OdNObHPESe84WX2dqmd3h9f9lHZsHudHDjkYFk29VEZXDukoP7W33OXDPrCTJGKCtn2o59K85RzChrPxdEjgPyOXk+jOCPkdxS7Gs05Ib+j2deozQr5HbWORnc+yO/o9jZKM0N+R6mb0Z4L8ttuf5HfdnkHzWZMfm/b0SRzblossy69QMaPG5NW35q335EFt98nc6+ZLoMbG4LWXjbjdB178trW1TJ12SmyrWWrHDb8SLn/nBUysF9joHk2zP+GqP+lqkq23PtLaf3wyYHiMCgaBJDf0ehj1GeB/I56h6MzP+R3dHoZ5Zkgv6Pc3WjNDfkdrX5GdTbI76h2NnrzQn7b7Sny2y7voNmMyW92fhfWkpc2veC8ueXO1h1y1Khj5P5zHpb6qv6FBcm4Wu3+VrvAu/vVypb/XSFtEycVFY/B4SWA/A5v7+JUOfI7Tt0O91yR3+HuX1yqR37HpdPhnyfyO/w9jMMMkN9x6HI05oj8tttH5Ldd3kGzGZPfqiB1vMmcuYtl0fyrkru/1a7vGVcvlEsvOpszv3u7pnZ8n/nACbK7fZecMPYk+fGZD0htZV3QnqaNG3zJp6TuoWXS3dAgG1c+K51j99ESlyDhIoD8Dle/4lot8juunQ/fvJHf4etZHCtGfsex6+GcM/I7nH2LW9XI77h1PLzzRX7b7R3y2y7voNmMym9VlCu7123Ykqzxzltn9zkHPOgEymFcMceeNHfskdPv/w95fdvf5eRxp8ndU5Zpn9LQqZOl38rHpauxUbb+7BfSduwHtOcgYHkTQH6Xd3+orocA8puVEBYCyO+wdCredSK/493/MM0e+R2mbsW3VuR3fHsftpkjv+12DPltl3fQbMbld9DCwjSuGPn92V9fKA+v+aXz5pYrpj0pdVX12qee2L1Lhlz8Sen3h0ela/gI2TH/Fmme8jHteQhYvgSQ3+XbGyrbSwD5zWoICwHkd1g6Fe86kd/x7n+YZo/8DlO34lsr8ju+vQ/bzJHfdjuG/LbLO2g25HdQcinjgsrvH7z4HbnhyS9J/+oB8tsLnpJ9G8drqCZ7iMarL5f+S37gXNB0xdXSdO1XjeYjePkQQH6XTy+oJDsB5DerIywEkN9h6VS860R+x7v/YZo98jtM3Ypvrcjv+PY+bDNHftvtGPLbLu+g2YzK7207mmTm7Fvk5VfX9qlvwiH7yR3zrpDBjQ1Bay+bcUHk91/WPyPn/OJk6ezulDvP/Lmcuu9kK/Opv/cnMuiKy0Q6OqTllI/Kth/9RLr7D7CSmySlI4D8Lh17MvsngPz2z4orS0sA+V1a/mT3RwD57Y8TV5WeAPK79D2ggvwEkN/5GXFFeRBAftvtA/LbLu+g2YzK75sXLXXqunLGtKD1hWJcofJ7c/MmOfln7xf1ccYRX5CvHD/X6jxrnl0lQ/7zfKnYvEk6DjhQtix9UDrHjrNaA8nsEkB+2+VNtmAEkN/BuDHKPgHkt33mZCycAPK7cGaMKA0B5HdpuJO1MALI78J4cXXpCCC/7bJHftvlHTSbMfmtdn3PuWmxzLr0Ahk/bkzQ+kIxrhD53dHVLh9bdqqond9HjzpWlk39nVRVVFmfZ+X6dTLkE1Ol+uW/StegQbLt7vul9QMftF4HCe0QQH7b4UyW4gggv4vjx2h7BJDf9liTKTgB5Hdwdoy0SwD5bZc32YIRQH4H48Yo+wSQ33aZI7/t8g6aDfkdlFzKuELk97VPXCF3vrxIhteNkN998hnnY6luidYWafzCTKn/+f0iVVWy4+vflN3/PbNU5ZDXIAHkt0G4hNZGAPmtDSWBDBNAfhsGTHgtBJDfWjASxAIB5LcFyKQomgDyu2iEBLBEAPltCXRvGuS3Xd5BsxmT36ogdezJvmNHydTJJwStLxTj/MrvX762VD7324udnd5qx7fa+V0OtwHf+7YM/NqXRTo7Zc8n/lO23/I9R4Zziw4B5Hd0ehnlmSC/o9zdaM0N+R2tfkZ1NsjvqHY2evNCfkevp1GcEfI7il2N5pyQ33b7ivy2yztoNqPye83b78g9yx6VWTMvkLramqA1lv04P/L7ta2r5cM/O8qZy40nfEsuOezSsppXvyf+IEMuvkASTU3SNnGSbP3J/dI1bHhZ1UgxwQkgv4OzY6Q9Ashve6zJVBwB5Hdx/BhthwDy2w5nshRPAPldPEMimCeA/DbPmAx6CCC/9XD0GwX57ZdUaa8zJr/Vmd8zZ98iL7+61nOGEw7ZT+6Yd4UMbmwoLQEN2f3I78lLPyh/3fi8TB5/jiw+/WcasuoPUfXmGhly4blS9drfpePAg6Tp6mul+Zxz9ScionUCyG/ryEkYgADyOwA0hpSEAPK7JNhJWiAB5HeBwLi8ZASQ3yVDT+ICCCC/C4DFpSUlgPy2ix/5bZd30GzG5HfQgsI4Lp/8Xv7GL+T//eY/ZUT9KPntJ1aV9JzvfHwTu3bJ4Es/I7UrljuXKvm98/obpXPsuHxDub+MCSC/y7g5lJYkgPxmMYSFAPI7LJ2Kd53I73j3P0yzR36HqVvxrRX5Hd/eh23myG+7HUN+2+UdNBvyOyi5lHG55HdHV7sc/9PD5J8735ZvnXS7fOLQizVkNB+i/09+LAPnfFESLc1OsqYvfVmaZl1jPjEZjBBAfhvBSlDNBJDfmoESzhgB5LcxtATWSAD5rREmoYwSQH4bxUtwTQSQ35pAEsY4AeS3ccRpCZDfdnkHzWZcfj/74mq5+PJ5afXdeetsmXjEwUFrLrtxueT3D164TW7402zZb9AB8sSFL0hFoqLs6s9WUOW//yUDr/mi1D38kHNJ5z7jZPvC70jrh08JzRwotIcA8puVEAYCyO8wdIkaFQHkN+sgDASQ32HoEjUqAshv1kEYCCC/w9AlalQEkN921wHy2y7voNmMym8lvhcuWpp2trd6E8wZVy+USy86W6ZOPiFo3WU1Lpv8bmprkol37i/q411n/kJO2ff0sqrbbzH9/viENF5xmVS91XN+e8tHJ8uOeQs5CsUvwDK4DvldBk2ghLwEkN95EXFBmRBAfpdJIygjJwHkNwskLASQ32HpVLzrRH7Hu/9hmj3y2263kN92eQfNZkx+N7e0yfULlsh5U07ss8tbSfEHlj8uN8y6NSDS2AAAIABJREFUROpqa4LWXjbjsslvteNb7fx+/6hJ8uC5vy+beoMUkmhrkwF3fEcGLJwniT27pbtfrey6/Iuy6wtXSXdNvyAhGWORAPLbImxSBSaA/A6MjoGWCSC/LQMnXSACyO9A2BhUAgLI7xJAJ2XBBJDfBSNjQIkIIL/tgkd+2+UdNJsx+b1tR5PMuWmxzLr0Ahk/bkxafWr394Lb75O510yXwY0NQWsvm3Fe8vufTW/L8T85TNSZ3785/88yYfgRZVNvMYVUrl8nA6+ZJXUPLXPCcBRKMTTtjUV+22NNpuAEkN/B2THSLgHkt13eZAtGAPkdjBuj7BNAfttnTsbCCSC/C2fGiNIQQH7b5Y78tss7aDZj8jvuO78v/e1F8uBrD8jZB54nt3/krqD9Kdtx/Z5cKY2zPi9Vr7/m1Lhn2idk11WzpWP8AWVbc5wLQ37HufvhmTvyOzy9inulyO+4r4BwzB/5HY4+USVnfrMGwkEA+R2OPlElZ37bXgPIb9vEg+UzJr9VOctWrJSlyx+P3ZnfL296UU67/wNSVVEtT/7nSzK2YVyw7oRg1IDbvy0N877uHIWibrsv/m9pmvMV6Ro6LATVx6dE5Hd8eh3mmSK/w9y9eNWO/I5Xv8M6W+R3WDsXv7rZ+R2/nodxxsjvMHYtnjWz89tu35HfdnkHzWZUfqui1PneF18+L62+O2+d3ecc8KATKIdxmceenP3zk+S59atk+hGfk68eP78cSjRaQ8XmTdLwrbnS/4ffd/J01/d3doGr3eCdo9OPvDFaCMGzEkB+szjCQAD5HYYuUaMigPxmHYSBAPI7DF2iRkUA+c06CAMB5HcYukSNigDy2+46QH7b5R00m3H5HbSwMI1Lld9P/OMx+eRDU6ShpkGeueg1GdivMUxTKarWyn+8JQO//lWpW7Y0Gaf57I/L7v93mbRNnFRUbAYXRwD5XRw/RtshgPy2w5ksxRNAfhfPkAjmCSC/zTMmgx4CyG89HIlilgDy2yxfousjgPzWx9JPJOS3H0qlv8ao/L550VJZv3Gr3DDrEqmrrXFm654FPunoQ2Xq5BNKT0BDBany+8KHzpbH//E7+doJC+Qzh12mIXr4QlStfUP6L/qe1N93jyR273Im0Hbk0bJnxuek+eyp0l1dHb5Jhbxi5HfIGxiT8pHfMWl0BKaJ/I5AE2MwBeR3DJockSkivyPSyIhPA/kd8QZHaHrIb7vNRH7b5R00mzH5HaY3vFSS/kf3rkhjeOPVlyTlvDq7/Lr5S5z7zzh5UprMV19z5fe/mv4hk+46RKorauSl/35bGmoGBu1LJMYlmpqk/09/LP0Xf1/UrnB16xw5SvZc8lnZ/enp0jVkaCTmGYZJIL/D0CVqRH6zBsJCAPkdlk7Fu07kd7z7H6bZI7/D1K341or8jm/vwzZz5LfdjiG/7fIOms2Y/N62o0nm3LRYZl16gYwfl37u85q335EFt98nc6+ZLoMbG4LWrm2ckt/qduWMaX1iqjPLFy5amnzTTq9rXfn9zVU3yG3PfVM+ftAn5LZTf6StvtAH6uqS2kdWSP9F35V+T650ptNd00+aP36e7PrcldJx0MGhn2K5TwD5Xe4doj5FAPnNOggLAeR3WDoV7zqR3/Huf5hmj/wOU7fiWyvyO769D9vMkd92O4b8tss7aDZj8jtsO7+zyW8lu/cdOyq5CzxThqtxSn53dXfJ0T/eXzbuWS8//9gjcty7Phi0J5EeV/X31dL/jtuk/uf3S6Kl2Zlr63HHy54Zl0nz5CkiFRWRnn+pJof8LhV58hZCAPldCC2uLSUB5Hcp6ZPbLwHkt19SXFdqAsjvUneA/H4IIL/9UOKaciCA/LbbBeS3Xd5BsxmT36ogJYrnzF0si+Zfldz9rXZ9z7h6oVx60dllc+Z35rEn7pEnXueTq/qvnbtYvjFnenJOSn7/Zu1y+cyK8+XdDfvI0xetDtqP2Iyr2LpV+t/1Q6lf8gOpXPeOM+/2Q94rzRd8SlrOPEs6xr0nNixsTBT5bYMyOYolgPwuliDjbRFAftsiTZ5iCCC/i6HHWJsEkN82aZMrKAHkd1ByjLNNAPltlzjy2y7voNmMym9VlCu7123Ykqzxzltny8QjyvOoC7feuXOmy/sO3k+uX7BEzptyYrJeL/nd3S1yxs8my6/f+LV885T5MusDs4L2I5bjEg8sFfn2t0X+/Oe98z/uOJGrrpLuY44Vefe7Y8lF56QTCRG1TrlBQDeB9s4uqa7U9BcbCRH1iwVrVXeXiKebgPOcqoLyvKobbejiaX0ONDB7vv8bgEpI7QR4TtWO1FrAjs4uqdL1c6C1qoMn4jk1ODtG2iOg1qnzYyo/p1qB7vK2kowkgQkYl9+BKyvhQPeok9NPmuTI70lHH5rcpe4lv//6rzflyB+Nl8qKKnnxkrUypI43cgzSvuq/vST1P/y+1P/kzrTh7UccJS1TzpGW08/kfPAgYEVk5OA62bRdHc8TMADDIJCNgFpTvT9gFQuprqZS+lVXyvbdbcWGYjwEjBIYOrCf7NzTLu0dXUbzELz8CahfLMv1lx6187uxf41s3tla/iCpMNYE1Dptb++UPW2dseYQyslr/Dmw3OevftwdOaRO1m/tObqTGwTKlUD/2iqpSCSkqbm9XEuMVF2jh9RFaj5RnQzy26Ozqed8+znz+4oV18itz86VM8afIz84/WdRXSvW5lWxY4fU33On1D70S6l57pm0vB3vGS/NU86W1slnSdv7j7FWU9gTcexJ2DsYj/o59iQefY7CLDn2JApdjP4cOPYk+j2Oygw59iQqnYz2PDj2JNr9jdLsOPbEbjc59sQu76DZYi+/t+1okhWPrZILp57qMMzc2Z35BpdKhqvblTOmOR/VG12O/ta7nDe6vOesB+XEfXricNNDoHLjBqn99a+k38MPSb8nn5BE294doZ2jRju7wVsmnyWtH/yQSFWVnqQRjIL8jmBTIzgl5HcEmxrRKSG/I9rYiE0L+R2xhkZ4OsjvCDc3QlNDfkeomRGfCvLbboOR33Z5B80We/ntvqnlw4+tSjLMPJN82YqVct38Jc79Z5w8SW6YdYnU1dY4/37o7w/J2fedLSPqR8lfLlkjCV1//x+0oxEel2hqktpHfyO1Dy93PiZ27UrOtmvgQGk99TRpOfNsaTn5I9Jd3z/CJAqfGvK7cGaMsE8A+W2fORmDEUB+B+PGKLsEkN92eZMtOAHkd3B2jLRHAPltjzWZiiOA/C6OX6Gjkd+FEivN9bGX38ViP/NnZ8rDrz8sXzzmy3LFMdcUG47xBRCoffQRZ0d43YrlUrFlc9rIllNPk92fvUzaDztcuoYOKyBqNC9Ffkezr1GbFfI7ah2N7nyQ39HtbZRmhvyOUjejPRfkd7T7G5XZIb+j0snozwP5bbfHyG+7vINmQ34HJdc7LnFDzzu9/eXTa2Rk/9FFRmN4UAI1z66S2l89KLW/ekiq3n4zLUzbkUdL62lnSMspH5X2w48MmiLU45DfoW5fbIpHfsem1aGfKPI79C2MxQSQ37FocyQmifyORBsjPwnkd+RbHJkJIr/tthL5bZd30GzI76DkUuT3yfueJnefuazISAzXRaB69atS+5tfSc3KP0jN06sk0dqSDN129ETpGjxE2o+ZJG1HHyNtE4+JxREpyG9dq4s4Jgkgv03SJbZOAshvnTSJZYoA8tsUWeLqJoD81k2UeCYIIL9NUCWmCQLIbxNUs8dEftvlHTQb8jsouRT5veSMpf+/vXuNkeus7zj+n8tefKsdO3YSg+MbCBMITcmFlEuUNiBaQ6gUCRfEGxrkuvCKECWKQVEUIeooUYBXpGnUFKRK0CChVhHpG1JFqAgTJ4QQAQHFt1yc2PElvsS73t2ZqZ5nzpk5c/bMnHOeeZ7n7Oz5TmTt7Mxz/TwnZ3Z/c/YZ+eTmTw/ZEtVdCYzv+5VM/PIXMrb3FzLxy/8TtXd49DZ7xftl9qoPyuy1H5KZD14js++70tVQCmuX8LswejrOIUD4nQOLooUKEH4Xyk/nGQUIvzNCUaxwAcLvwpeAAWQQIPzOgESRBSFA+O13GQi//Xqb9kb4bSoX1PvZgZ/JFSs/MmQrVPcpUD+4X8af2Sdjz/xKxp95WsZ+94LI3FxnCK0VK0RtlaKuDNeB+HV/Kc1Vq3wO0XpfhN/WSWnQgQDhtwNUmnQiQPjthJVGLQsQflsGpTlnAoTfzmhp2KIA4bdFTJpyKkD47ZR3XuOE3369TXsj/DaVi9Q7cmLKQis0UZRAZeaCjD33bDcQf3af1I681jOcuU1bZOaaa2X2mg/JzDXXyez7PyBSrxc15Nz9En7nJqNCAQKE3wWg06WRAOG3ERuVPAsQfnsGpztjAcJvYzoqehQg/PaITVdDCRB+D8WXuzLhd26yQioQfltgJ/y2gLjAmqgef7O9VYq+OnyfjD3/nFSmu29ytCYm9Ydn6iD82uv0VeKN9e9YYLPoDofwe8EuDQOLCBB+cziMigDh96isVLnHSfhd7vUfpdkTfo/SapV3rITf5V37UZs54bffFSP89utt2hvht6lcpB7htwXEEWhiXF0dvm+v1NXX55+T+p/+2DPqCx/+mMhYXWa3vU8a79kms9veK7NXXCmt5csLnx3hd+FLwAAyCBB+Z0CiyIIQIPxeEMvAIFIECL85REZFgPB7VFaq3OMk/C73+o/S7Am//a4W4bdfb9PeCL9N5Qi/LciNdhPVM2dk/Olfytg+dXX4r2T818/M+zBNNUN1Rfj09s9I86KLpLV8hcy9693SuHyjzG3eIq3JJV4QCL+9MNPJkAKE30MCUt2bAOG3N2o6GkKA8HsIPKp6FSD89spNZ4YChN+GcFTzLkD47Zec8Nuvt2lvhN+mcoTfFuQWXxP1Qwdk7IXfytizT8vYc7+W8ed/LZVz5/pOtLHuEmls3ipzmzZJY8u7pLFxs8xt2qyD8eaai60BEX5bo6QhhwKE3w5xadqqAOG3VU4acyRA+O0IlmatCxB+WyelQQcChN8OUGnSiQDhtxPWvo0Sfvv1Nu2N8NtUjvDbglw5mqj/8UUZ+9OLUjt8SGoHXhIVkNcPHZTay4cHArSWLZe5re+SuY2bZG7LVmnqYHyLzG3eLI0NG3PhEX7n4qJwQQKE3wXB021uAcLv3GRUKECA8LsAdLo0EiD8NmKjkmcBwm/P4HRnLED4bUxnVJHw24jNeyXCbwvk7PltAbGMTczNSf2Vw1I7dLAdhh9UofiB4OtBqUyd76vSGh/XAbi6SryxeYv+p4PxTer+ZmmNT/TUJfwu4wE2enMm/B69NSvriAm/y7ryozVvwu/RWq8yj5bwu8yrPzpzJ/wenbUq+0gJv/0eAYTffr1NeyP8NpWL1CP8toBIE/MEakffkJq6SvygCsb3t4Pxlw9Lff9LUj15YqBY49LLZG7zVmmo7VQ2b5Xl73+PHPvox6WxtPgP32SpEegnQPjNsTEqAoTfo7JS5R4n4Xe513+UZk/4PUqrVd6xEn6Xd+1HbeaE335XjPDbr7dpb4TfpnKE3xbkaMJUoPL2OR2C1w8flNqhYDuVw8EV5Cnbqag+Gxsul7l3Xi7Ny9bL3OXq6zuksXGTNC65VBrveKc0V68xHRr1EDAWIPw2pqOiZwHCb8/gdGckQPhtxEalAgQIvwtAp8vcAoTfucmoUJAA4bdfeMJvv96mvRF+m8oRfluQowlXAvUDL+k9xusH9uuvy14+IHMqJH/tVameOpXabWtyiTTWv0Mu3HCjNNeua5ev16W56iJpXnyxNC9eJ83Vq/WHcqoP7eSGgA0Bwm8birThQ4Dw24cyfQwrQPg9rCD1fQkQfvuSpp9hBAi/h9Gjrk8Bwm+f2iKE3369TXsj/DaVI/y2IEcTvgSie35XLkxL7eWXpfb6Eam9+rLUjrwmtVfU11d1OF577TVRV5bnuTVXrpTmmrXSvHhtNxRfu05aOiBfK801a9pBuQrOV68R9WGe3BCICxB+c0yMigDh96isVLnHSfhd7vUfpdkTfo/SapV3rITf5V37UZs54bffFSP89utt2hvht6kc4bcFOZrwJZD3Ay+rp0/rMLx69KjU3jwm1RNvSuXECakef1NqJ45LVd0/8abee7x68mTuabQmJjuBuL6SfPUaaaxZK621KjyPBuXqKvP289wWvwDh9+Jf48UyQ8LvxbKSi3sehN+Le30X0+wIvxfTai7euRB+L961XWwzI/z2u6KE3369TXsj/DaVI/y2IEcTvgTyht+5xtVsSvXUSR2I11Qgfvx4OxQ/cVwqx4+3H+sE5u3gXF19nutWq3W2XGmsDgLxNcFV5GvXSkPfjzx+8VppjY3l6oLCxQsQfhe/BowgmwDhdzYnShUrQPhdrD+9Zxcg/M5uRcniBAi/i7On53wChN/5vIYtTfg9rKCf+oTfFpyPnJiy0ApNIOBOwGn4bTDsyrlzQSjeDsnDcLxy6oTUjh5th+enTkk1uOq8euZM7l7U1ipquxUdjOt/6orytXLh458UqdU67bUmJqS5cpW0Vq5k//LcynYrEH7b9aQ1dwKE3+5sadmeAOG3PUtacitA+O3Wl9btCBB+23GkFfcChN/ujaM9EH779TbtjfDbVC5Sj/DbAiJNOBVYaOG3yWRrR99oh+QnT0r1+LH2FebqinMVkOurzdshek3df/OYSRedOupDPFUYrj/gc8WfSWvlKtH7mq+6SN/Xz+nvV0tr6VJpLVsmrSVLpLV0mTSXLpPWihVD9V/WyoTfZV350Zs34fforVkZR0z4XcZVH805E36P5rqVbdSE32Vb8dGdL+G337Uj/Pbrbdob4bepHOG3BTma8CWwGMLvvFbVt97qBOLhViwqGFdXl1dOnxa1r3n1zFuiyrW/V+VP5O1mYPnmqlU6ENcBuf7aDsmbKiyPPrZ0qYgOzoMgXX1dEtZpf20ubYfrYT2rA10gjRF+L5CFYBipAoTfqUQUWAAChN8LYBEYQiYBwu9MTBQqWIDwu+AFoPvMAoTfmamsFCT8tsLovBHCbwvEXPltAZEmnAqUMfw2Ba2cf1sH4iocr5w93b2vwvHTb0lFP/eWVM60A/PK22+LrnP+fPv+1JT+3vVNh+PqavMgSNdXnAfhuv66rDc87w3h1XNL2yF8NGRX91W9iUnXw09sn/C7EHY6NRAg/DZAo4p3AcJv7+R0aChA+G0IRzWvAoTfXrnpbAgBwu8h8AyqEn4boBVQhfDbAjrhtwVEmnAqQPjtlDex8crUeamoQDz4Vz2vgvEwIA8e18G5ut8O0Lv3u0G6rhc+p+ufz/+BoQbTby1f3t7CRYfjka1dJibbofvkZDskn1wirSXBfR3It++31OOTE8HX3vsSfzwI2wm/DRaKKoUIEH4Xwk6nOQUIv3OCUbwwAcLvwujpOIcA4XcOLIoWKkD47Zef8Nuvt2lvhN+mcpF6hN8WEGnCqQDht1PeQhpXHxraCdTDq86np6Vy7mw7ZD9/vv382+dF3j7XuSK9c5W6em76Qrt8GMBPnddXvBdx00H6kiX6X2N8QofoOliPhug6eI+E6z1lVPkgdJ9Q9YO6Y3WR+pi0xsfbX8fGRMbq0qqrr+r78e5jBV3xXoQ3fQ4nQPg9nB+1/QgQfvtxppfhBQi/hzekBfcChN/ujenBjgDhtx3HrK0QfmeVKrYc4bcFf8JvC4g04VSA8Nsp76JsXG/7El6tHobjKixXwfr0lFSmpvUV6Pr+9AWR8HF1Xz82JZULqrz6qspNJ98/e3ZB+rWWLZdWGJzrwDwMyoMwXT0WhurRMD0M1cfHugF75DEZG9dttYP4ehC8BwF88Fi33bCNdlivg/vx8aDdaICvxqfaqOsr9bm5FyD8dm9MD8MLEH4Pb0gLfgQIv/0408twAoTfw/lR258A4bc/a9UT4bdfb9PeCL9N5SL1CL8tINKEUwHCb6e8NG5BQAXtS5qzMtmYkTMnzrTDcvVPBedTUyL6+0igHtwXFcxHyunAXYfzUyIzM1KZuSCV2VmR2VmpzKmvc+2v6jn1+NysVNRjHvZpt8CUqwm9ZU08wFdBfDSYV6H++IRIvRa5Gr4bzrdD9SD81yG+CuzHdQg/70r6IJjvubq+E9a3w/5unSCs77wREAnwly3PNc8iChN+F6FOn3kFCL/zilG+KAHC76Lk6TePAOF3Hi3KFilA+O1Xn/Dbr7dpb4TfpnKE3xbkaMKXAOG3L2n6GUZgQez53Wh0Q3IVmM/O9AbnYWA+M9MJzrvBeru8CtPboXoQuus2gtBdPaaCdx3Ah4F8UDYI4kW3kfxYYojf6SvoY25umGVYOHWrVWnVaiLVmkhNhfPtr+p7/bj6V69LK3heakH5Wr1dPiwTlg/qd8urNuvd9mvVdpu1ereuake1r8qF7VdrsnTZhFxoVaVRqUbGU+1przuGsL2gfTUe3V4wxuj4g/F0xq7LtMtG+9fzHjQ/1Q630gsQfpf+EBgZAMLvkVmqUg+U8LvUyz9Skyf89rtchN9+vU17I/w2lSP8tiBHE74ECL99SdPPMAILIvweZgILrG5F7fXeE8SHoXoknA+D82gQHwbznavlIyH9TPtqeWk0RNRV+SrUD98giFxJr8N/9QZB7Ip7/YZAGP5H3hDQZcOr8KfOLzDJ0R+O3g5Hhfc9YX/wBkIn7G+H892wvxvO66v5KxUR9Zt/VQX+Vf19S4f/7fvh463w+9hzrWpQJiyrnldtRNrrtBE81+2r3e+8tsO60TYqQTk9jj7jjYy506Z+rDuX4car5hrMTbsFTmoOEauOWzhm/VzXqT22Xl/tEDPssQnqq89LIPwe/f93yzIDwu+yrPRoz5Pwe7TXr0yjJ/z2u9qE3369TXsj/DaVI/y2IEcTvgQIv31J088wAoTfw+gt3rpqGxuZa0hFBe7qXzO4r65wD+/r55pBmTldrqf8XFBXPd5siKi6YRn1vb7fFGnE6urH24912m82ZGmtJdNTM9Kcmwv6aeo2ddvBONv9h+2ptoOxB33rPjvlm1IJ5tMZV8/82vW7YwnmF449mJ+24oZADgEVknffvIgE9pHgvfMGRfSNDhXY9wT50TcoYoF9/E2O4M0A/dcL4U21pW7qS+d++Fjka/icLijtMeh66k2GhPIpzyXWD9ruaa9v27ExhP316zeh7Z4xpNVPmm+SRarJfOehLdTxkdqvXuBuuaD85ERNGs2WzDSCAyJ6PGRqM1yH/MdA9NhJXIvYMZd4nAVluvUj8xh0XCYda9aO64wWnfnFThxB9Z5HE8p25hw/7yS1m/WxzskgPqaEQfUdf7ayecZfqVRkzcoJOX76wvxzl+35J84r25x6zo3RceXxTywbn2TkPGJ5/onrktUkeh61Ov/s/p3zRKT/7HPq45pj/suW1qVaqcjZ85G/Bh1y/fONP+OxMuz/vxOTCR35f4jw27+5SY+E3yZqsTrs+W0BkSacChB+O+WlcUsChN+WIGnGucAo7fmtA/Ew3J/3hkFyuK+D/PibDerNgVZLpKm+NqWivwbfB/f1Y+qfRB5vtqQSLRc8N69+2EYraKNTp6X7aZePPxe8qaDKBs91+2rXU+Pp1O03XlW3p6y05xHOMZxv0pwTxtszt3jb0TaC5zpurW6/lUi9zjg6c+nOV89Pjz1cm5b+3ANuCCCAAAIIIIAAAh4E1M9h3Ba8AOG3hSUi/LaASBNOBQi/nfLSuCUBwm9LkDTjXGCUwm/nGHSwYAXStj3RIbkO+SNvMEQC/+4bB71vcsx/8yN4kyHyJkT0jZGeNz96Av/gDYrwl0ZdP/gFMv5VvWmiLoYbVCblOV1X3fq0rR/vV0Y/166e2E6ONjPVH2SS2yI4RGNjTLQMJ5lmod/kSrBMqhdtM6gTN5ioV/WV33NzwRtRKW136mcdb9hekkH8uYTx9jsuuvPoY5Gh7b7HZdoaxC1zjFsXjYY16g3GpPCmX6CTuWyfQChr/Rz9Oxl/Qv9j9arMzoZ/ohA5/WedU/t/nPmvGwn1E+cU/X8j2kqe/pOWJVf9Icefcf49x6jJXHMcP4l99amf+VjL039Wk4zrX1F/VdJqifovfss+/j4/3mQ9VnLMv3JhesH+LJVpYP3mmqkyhXwJEH5bkCb8toBIE04FCL+d8tK4JQHCb0uQNONcgPDbOTEdWBBIC78tdEETCFgRYM9vK4w04liAPb8dA9O8NQH2/LZGmakhtj3JxFR4IcJvC0tA+G0BkSacChB+O+WlcUsChN+WIGnGuQDht3NiOrAgQPhtAZEmvAgQfnthppMhBQi/hwSkujcBwm9v1Lojwm+/3qa9EX5nkPvJEz+Xu+9/VJf81E3Xy7133CpLJsc7NQm/MyBSpFABwu9C+ek8owDhd0YoihUuQPhd+BIwgAwChN8ZkCiyIAQIvxfEMjCIFAHCbw6RUREg/Pa7UoTffr1NeyP8TpHb95sX5cGHH5OH7rtNLlq5Qr798GO6xtd27SD8Nj3qqOddgPDbOzkdGggQfhugUaUQAcLvQtjpNKcA4XdOMIoXJkD4XRg9HecQIPzOgUXRQgUIv/3yE3779TbtjfA7RU6F3Zs2XCq3bL9Bl4yH4eoxrvw2Pfyo50uA8NuXNP0MI0D4PYwedX0KEH771KYvUwHCb1M56vkWIPz2LU5/JgKE3yZq1ClCgPDbrzrht19v094IvwfITU3PyD0PPCrXX31FJ/zef/iIfGPPI/Kt3Ttl68b1ujbht+nhRz1fAoTfvqTpZxgBwu9h9KjrU4Dw26c2fZkKEH6bylHPtwDht29x+jMRIPw2UaNOEQKE337VCb/9epv2RvidIfz+7M03yrVXbdMlk8JvU3zqIYAAAggggAACCCCAAAIIIIAAAggggAACCLgRIPzOEH6nXfntZmloFQEEEEAAAQQQQAABBBBAAAH7ADb0AAASv0lEQVQEEEAAAQQQQMBUgPA7RS7Lnt+m+NRDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcCNA+J3iGv+ASxWGq9vXdu1wsyK0igACCCCAAAIIIIAAAggggAACCCCAAAIIIDC0AOF3BsKfPPFzufv+R3XJT910vdx7x62yZHI8Q02KIIAAAggggAACCCCAAAIIIIAAAggggAACCBQhQPhdhDp9IoAAAggggAACCCCAAAIIIIAAAggggAACCDgVIPw25OVqcEM4qhkL5D3mBpU/dfqsfPmu78gLfzigx/P9794l1161Td+PP6ceu+ySNfLw/bfL1o3rjcdPxXIKqK2jvvjV+/Tkr3zvFnnovtvkopUr+mJkKb//8BF54Hs/kj1f3zmwrXKKM+usAjbPqWGfqs1Dr7zRszUa59SsK0K5LAKDXr+T6g8qHz3fqrr8dWOWFaBMP4Esr9/RuoPKq20m/+2HT3SKf/POW+WW7Td0vo8/r56Il2GlEMgiYPOcGm8ry8+9WcZImXIK2Dynqt+ddt35oLx+9ITGjL/ec04t5zFWtlkTfhusOPuAG6BRZSiBvMfcoPJT0zNyzwOPyvVXX6F/kVAvht/Y84h8a/dOHW6HP7jdvmtHJxAfavBULq1A/NhSweDeZ3/fd+uotPLRXyr4haK0h5WVids8p6oBRX9B+dLntyeG35xTrSxdqRtJe/2O46SVV+fkDevX6df6sOyl61bzuTalPsrMJp/2+h1vdVB5VfahH/yX/MPn/la/wR2GNnt27+z8XMpnMJmtE7V6BdLOkXnPqepngVeOHOu8UZP2cy/rgUA/AZvnVLVdb/T1XvUZP4dyTuVYLIMA4bfBKquTw6YNl3Ze2OK/RBs0SRUEBgrkPeYGlT/51tmeq2bjP/gRfnMw2hKIXwUb/0Eu3k/W8lz5bWuFytuOzXNq9C8ZBl35Tfhd3uPN1szj577463dSwBj9K5m08gQ1tlaqfO1kff0OZfKUTzpuCWrKd4y5mLHrcyoZgYtVK0ebec6RSsSkfPSCJM6p5Tiuyj5Lwu+cR0DSD2BpgU7OLiiOQI9A3mMurfzJU2fkwYcf69l+IvqCF/+TPbY84YA0FYj/IJX2xkrW8oTfpitCPSWQdo6Mb++Up3yWbU84p3IcmgokBSmDfmHNW55ffk1XhnpZX79DqTzlk352SNsWhRVBIItA3nNk3vLqOH3j2Mm+f/GYZYyUKadAnnOkEspTPukvvTinlvM4K9usCb9zrnh4svjszTd2/vSO8DsnIsVzCeQ95tLKq/D7x48/1fOD2KBfeFWY89jjT6Xu1ZxrUhQuhUD86tos4Xf0r2r6lSf8LsXh42ySaefIfuF3ltf9pPA7PhHOqc6WdtE3rIKXPK/fecpzheKiP3ycTtDV631SqBOfSNK2KE4nS+OLRiDPOVJNOmv58DNF2KJv0Rwq3ifi6pwahtyDPuODc6r35aZDTwKE3zmh81wBlrNpiiOQKJD3mEsrn3bld3wQKoDc/c+PyB1f+RwfeMkxmksgz1UISb/gEn7n4qZwRoG0c6TtK785p2ZcGIqlCuS96jBreVVu955H+GDr1BWgQD8BV6/3Wa+cjQdFrBQCWQSyniPDtmyUzzIuyiDg6pwayqZtc8Y5lWNwMQoQfhusat69Qg26oAoCPQJ5j7lh9vwmqOHgsyVgsv/coVfe6HzYWr+/quHKb1srVN52bJ5T0/b85pxa3uPM9sxd7E9L8G17lcrZnovX+6zBtxInqCnncTfsrF2cU6Nj4gKiYVeovPVdnFOjmmm/S3FOLe+xt5hnTvhtsLrxd33ZI9EAkSq5BNKOufgvCIPKp32yuaqrbtdetU1/TXtnONdEKFwqgbRPKo//WV1a+RAv7Qe2UiEzWSOBtHNqfGuStPLhIJK2PeGcarREVEoQSHv9jp9T08qz1QmHmS2BtNfv+Dk1rfyg361UoPjEk3vlC7d8Qg+f7SdtrWL52kk7R4Z/gbjj5hvllu03zPvMkKTjeMP6dT2/Q7F1ZPmOKxszTjtH5v0d6l//43G56WNXd/6KO5odTF+4wDnVxqLRxoIXIPw2XKJwLy9VfdCeSYbNUw2BeQKDjrmkq2MGlY9/qOX3v3tXzx72u+58UF4/ekKPgf3qOBiHEVDhyhe/el/isZS0p9yg8vHjVjX6pc9v71wpPsw4qVs+gUHnyKR9uQeVjx63oWR4Xg2Pc86p5TvGXMw4y+v3nt07O6/pg8rHP+BKjZcPZHWxauVoc9Drd9I5tV/5pNf66O9b6v49DzwqP31ybwc2+nNsObSZpS2BQefIePit+sxyDub13tbqlLsdm79DxX9OjeZX4ZtAnFPLfbyVYfaE32VYZeaIAAIIIIAAAggggAACCCCAAAIIIIAAAgiUTIDwu2QLznQRQAABBBBAAAEEEEAAAQQQQAABBBBAAIEyCBB+l2GVmSMCCCCAAAIIIIAAAggggAACCCCAAAIIIFAyAcLvki0400UAAQQQQAABBBBAAAEEEEAAAQQQQAABBMogQPhdhlVmjggggAACCCCAAAIIIIAAAggggAACCCCAQMkECL9LtuBMFwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKIMA4XcZVpk5IoAAAggggAACCCCAAAIIIIAAAggggAACJRMg/C7ZgjNdBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgDAKE32VYZeaIAAIIIIAAAggggAACCCCAAAIIIIAAAgiUTIDwu2QLznQRQAABBBBAAAEEEEAAAQQQQAABBBBAAIEyCBB+l2GVmSMCCCCAAAIIIIAAAggggAACCCCAAAIIIFAyAcLvki0400UAAQQQQAABBBBAAAEEEEAAAQQQQAABBMogQPhdhlVmjggggAACCCCAAAIIIIAAAggggAACCCCAQMkECL9LtuBMFwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKIMA4XcZVpk5IoAAAggggAACCCCAAAIIIIAAAggggAACJRMg/C7ZgjNdBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgDAKE32VYZeaIAAIIIIAAAggggAACCCCAAAIIIIAAAgiUTIDwu2QLznQRQAABBBBAAAEEEEAAAQQQQAABBBBAAIEyCBB+l2GVmSMCCCCAAAIIILCABX7yxM/l7vsf7Rnhle/dIg/dd5u8dPA1+eJX75Pvf/cuufaqbT1lvv3wY/L0b17U5S5auUIGtXPyrbOy684H5fWjJ/pKfPPOW2XD+nW6v6RbOIZ9v3lRl/nUTdfLvXfcKksmxzvFBz2nCp06fVa+fNd35IU/HOg7ji99frts2nBpj4ka2y3bb5D9h4/oeVy8emVn3mFDSc+F4xk0nwV8aDA0BBBAAAEEEEAAAQSGEiD8HoqPyggggAACCCCAAALDCMQD7LAt9fjHPvQBHXirUPuxx5/qCXtV0PuNPY/It3bvlK0b10uWdqLjVG3uffb3ieH17j2PyMP3367bTbqFgfJll6zpKTc1PSP3PPCo/PTJvYnBeL+2Hnz4sXlBtiqr+omPJQy4VYgff0NAGfzbD5+Q8I0D9YZAUhvDrBd1EUAAAQQQQAABBBAYJQHC71FaLcaKAAIIIIAAAggsIoHwKugdN9+or2rudwtD5UvXrZav7doh4ffXX32Frpe1HZvhtwqs//ojfyHn3p7SYwrD6h8//pQsX75Uzp07Py9YtxV+q9D/05/4sPz29/s7fahQ/IHv/Ug+eOW75X9/8VwnTCf8XkT/wzAVBBBAAAEEEEAAgdwChN+5yaiAAAIIIIAAAgggYEMgHmoPajO84nnP7p3yypFjPVeC52kn7GPYK79V+H37rh3y4L/8Z8/V52q7kkOvvCFvHDvpNPy+/Z/+XsIxhFfHq35V/9Gr5Am/bRyptIEAAggggAACCCAwqgKE36O6cowbAQQQQAABBBBYBALxPamjW3bEpxdu66Eej2/5kacdVX9Q+J1lz+9wq5J//9H/6GH+3d98VF95vefrO0U95jr8Vtu9PP+7l/TWLbf942fl3m//QO74yuf0Y/HwO20+i+AwYgoIIIAAAggggAACCCQKEH5zYCCAAAIIIIAAAggULhDdL1sNJr6ftnos3N7kuqu2dbYaiQ88Sztp4XeWPb/D8Ft9kKbahkTddnzmr/Q2LCqk9xF+r161Qn945soVy+Q9Wzdok/j+6Fz5XfihzQAQQAABBBBAAAEEChQg/C4Qn64RQAABBBBAAAEE5gv028Ykvtd3mt2g7VBsbHvy0H23ifpQyfiHbfoKv9UHcqp5fO8H/9354E3C77SjgucRQAABBBBAAAEEyiRA+F2m1WauCCCAAAIIIIDAAhJQV3K/8IcDcsP1fz5vVCpAVrfwwyTV/X7hd952VFs2w2+1H/lLB1+VT954nR6zz/Bbzf2JJ/fKF275hO6b8HsBHeAMBQEEEEAAAQQQQKBwAcLvwpeAASCAAAIIIIAAAuUUCLcxuXz9up4Phwz3747v6z0o/Fbbf2Rtx3b4HV89n+F3vG/C73L+v8SsEUAAAQQQQAABBJIFCL85MhBAAAEEEEAAAQQKEwgDcHUFeHhL2u9bPTdo25M87aSF32kfEKnC+XDPb7XtiYvwW4XYd9//aKfpb955q95PXF1lrvYYVx94qbY9yRJ+p82nsMWnYwQQQAABBBBAAAEEHAsQfjsGpnkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB/wKE3/7N6REBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAsQDht2NgmkcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwL0D47d+cHhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQcCxB+OwameQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAH/AoTf/s3pEQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMCxAOG3Y2CaRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPAvQPjt35weEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBwLEH47BqZ5BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAf8ChN/+zekRAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwLEA4bdjYJpHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8C9A+O3fnB4RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEHAsQfjsGpnkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB/wKE3/7N6REBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAsQDht2NgmkcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwL0D47d+cHhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQcCxB+OwameQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAH/AoTf/s3pEQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMCxAOG3Y2CaRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPAvQPjt35weEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBwLEH47BqZ5BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAf8ChN/+zekRAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwLEA4bdjYJpHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8C9A+O3fnB4RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEHAsQfjsGpnkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB/wKE3/7N6REBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAsQDht2NgmkcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwL0D47d+cHhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQcCxB+OwameQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAH/AoTf/s3pEQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMCxAOG3Y2CaRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPAvQPjt35weEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBwLEH47BqZ5BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAf8ChN/+zekRAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwLEA4bdjYJpHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8C9A+O3fnB4RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEHAsQfjsGpnkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB/wKE3/7N6REBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAsQDht2NgmkcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwL/D/jTsnD8TuY8wAAAAASUVORK5CYII=",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(colors=['red', 'green'],\n",
" title=\"Reaction 2A <-> C (2nd order in A). Changes in concentrations with time\")"
]
},
{
"cell_type": "markdown",
"id": "b1366038-2dea-4d69-a655-ae464ca22922",
"metadata": {},
"source": [
"### Note: \"A\" (now largely depleted) is the limiting reagent"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fce512b2-bcc8-4721-85c9-2241ab3ae55e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "82daf161-82de-44a5-9310-da2333ba6faa",
"metadata": {},
"source": [
"# PART 2 - Analysis and Validation"
]
},
{
"cell_type": "markdown",
"id": "39cb26e8-c061-41ab-a91a-77df6f431efa",
"metadata": {},
"source": [
"#### Let's take a look at time t=0.002, which in our simulation run had proposed as the first step:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "232349ed-fa23-4ebf-b4ba-d5bddcc8902c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" search_value | \n",
" SYSTEM TIME | \n",
" A | \n",
" C | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 80 | \n",
" 0.002 | \n",
" 0.00204 | \n",
" 57.525261 | \n",
" 111.237369 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" search_value SYSTEM TIME A C caption\n",
"80 0.002 0.00204 57.525261 111.237369 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Locate the value closest to the original time step we had requested\n",
"dynamics.get_history(t=0.002)"
]
},
{
"cell_type": "markdown",
"id": "b03c9994-cade-48cf-8b14-4b894cc755cf",
"metadata": {},
"source": [
"### Because of the very large changes happening between t=0 and 0.002, the simulation automatically slowed down and opted to actually take 80 steps in lieu of the 1 step we had (optimistically!) proposed \n",
"The number of variable steps actually taken can be modulated by changing the preset passed to our earlier call to `use_adaptive_preset()`. For finer control, advanced users may tweak internal parameters such as \"norm thresholds\" and \"step factors\""
]
},
{
"cell_type": "markdown",
"id": "ce3cd198-de57-4e0f-80f2-8e07f2cd7f96",
"metadata": {},
"source": [
"### Notice how, late in the simulation, the step sizes get BIGGER than the 0.002 we had originally proposed:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "071a9544-639a-40f7-92bc-3a20050c9c00",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From time 0 to 8.398e-06, in 1 step of 8.4e-06\n",
"From time 8.398e-06 to 1.512e-05, in 1 step of 6.72e-06\n",
"From time 1.512e-05 to 2.519e-05, in 1 step of 1.01e-05\n",
"From time 2.519e-05 to 9.775e-05, in 9 steps of 8.06e-06\n",
"From time 9.775e-05 to 0.0001098, in 1 step of 1.21e-05\n",
"From time 0.0001098 to 0.0001872, in 8 steps of 9.67e-06\n",
"From time 0.0001872 to 0.0002018, in 1 step of 1.45e-05\n",
"From time 0.0002018 to 0.000283, in 7 steps of 1.16e-05\n",
"From time 0.000283 to 0.0003004, in 1 step of 1.74e-05\n",
"From time 0.0003004 to 0.000384, in 6 steps of 1.39e-05\n",
"From time 0.000384 to 0.0004049, in 1 step of 2.09e-05\n",
"From time 0.0004049 to 0.0005052, in 6 steps of 1.67e-05\n",
"From time 0.0005052 to 0.0005303, in 1 step of 2.51e-05\n",
"From time 0.0005303 to 0.0006306, in 5 steps of 2.01e-05\n",
"From time 0.0006306 to 0.0006607, in 1 step of 3.01e-05\n",
"From time 0.0006607 to 0.0007811, in 5 steps of 2.41e-05\n",
"From time 0.0007811 to 0.0008172, in 1 step of 3.61e-05\n",
"From time 0.0008172 to 0.0009327, in 4 steps of 2.89e-05\n",
"From time 0.0009327 to 0.0009761, in 1 step of 4.33e-05\n",
"From time 0.0009761 to 0.001115, in 4 steps of 3.47e-05\n",
"From time 0.001115 to 0.001167, in 1 step of 5.2e-05\n",
"From time 0.001167 to 0.001292, in 3 steps of 4.16e-05\n",
"From time 0.001292 to 0.001354, in 1 step of 6.24e-05\n",
"From time 0.001354 to 0.001504, in 3 steps of 4.99e-05\n",
"From time 0.001504 to 0.001579, in 1 step of 7.49e-05\n",
"From time 0.001579 to 0.001698, in 2 steps of 5.99e-05\n",
"From time 0.001698 to 0.001788, in 1 step of 8.99e-05\n",
"From time 0.001788 to 0.001932, in 2 steps of 7.19e-05\n",
"From time 0.001932 to 0.00204, in 1 step of 0.000108\n",
"From time 0.00204 to 0.002212, in 2 steps of 8.63e-05\n",
"From time 0.002212 to 0.002342, in 1 step of 0.000129\n",
"From time 0.002342 to 0.002549, in 2 steps of 0.000104\n",
"From time 0.002549 to 0.002704, in 1 step of 0.000155\n",
"From time 0.002704 to 0.002828, in 1 step of 0.000124\n",
"From time 0.002828 to 0.003015, in 1 step of 0.000186\n",
"From time 0.003015 to 0.003164, in 1 step of 0.000149\n",
"From time 0.003164 to 0.003387, in 1 step of 0.000224\n",
"From time 0.003387 to 0.003566, in 1 step of 0.000179\n",
"From time 0.003566 to 0.003834, in 1 step of 0.000268\n",
"From time 0.003834 to 0.004049, in 1 step of 0.000215\n",
"From time 0.004049 to 0.004371, in 1 step of 0.000322\n",
"From time 0.004371 to 0.004629, in 1 step of 0.000258\n",
"From time 0.004629 to 0.005788, in 3 steps of 0.000386\n",
"From time 0.005788 to 0.007526, in 3 steps of 0.00058\n",
"From time 0.007526 to 0.009265, in 2 steps of 0.000869\n",
"From time 0.009265 to 0.01187, in 2 steps of 0.0013\n",
"From time 0.01187 to 0.01383, in 1 step of 0.00196\n",
"From time 0.01383 to 0.01676, in 1 step of 0.00293\n",
"From time 0.01676 to 0.02116, in 1 step of 0.0044\n",
"From time 0.02116 to 0.02776, in 1 step of 0.0066\n",
"From time 0.02776 to 0.03767, in 1 step of 0.0099\n",
"(110 steps total)\n"
]
}
],
"source": [
"dynamics.explain_time_advance()"
]
},
{
"cell_type": "markdown",
"id": "9fb5f6b8-dde3-415d-9e90-b8d102bfd748",
"metadata": {},
"source": [
"### Notice how the reaction proceeds in far-smaller steps in the early times, when the concentrations are changing much more rapidly"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "12118fdd-5e81-42e5-b271-818f8d686b79",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([200., 40.], dtype=float32),\n",
" array([197.98581, 41.0071 ], dtype=float32))"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's look at the first two arrays of concentrations, from the run's history\n",
"arr0 = dynamics.get_historical_concentrations(0) # The initial concentrations\n",
"arr1 = dynamics.get_historical_concentrations(1) # After the first actual simulation step\n",
"arr0, arr1"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "a2450ae8-e342-4adf-9330-ce86a1dfcbeb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's verify that the reaction's stoichiometry is being respected\n",
"dynamics.stoichiometry_checker(rxn_index=0, \n",
" conc_arr_before = arr0, \n",
" conc_arr_after = arr1)"
]
},
{
"cell_type": "markdown",
"id": "bf6dc3ed-5999-4379-8ae1-05f73e2a670d",
"metadata": {},
"source": [
"#### Indeed, it can be easy checked that the drop in [A] is twice the increase in [C], as dictated by the stoichiometry.\n",
"The diagnostic data, enabled by our earlier call to `set_diagnostics()`, makes it convenient to check"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "4ccfa79c-0bd4-40f0-be82-2da19523cd40",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reaction: 2 A <-> C\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" START_TIME | \n",
" Delta A | \n",
" Delta C | \n",
" time_step | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.000000 | \n",
" NaN | \n",
" NaN | \n",
" 0.002000 | \n",
" aborted: neg. conc. in `A` | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.000000 | \n",
" NaN | \n",
" NaN | \n",
" 0.001000 | \n",
" aborted: neg. conc. in `A` | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.000000 | \n",
" -119.920000 | \n",
" 59.960000 | \n",
" 0.000500 | \n",
" aborted: excessive norm value(s) | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.000000 | \n",
" -71.952000 | \n",
" 35.976000 | \n",
" 0.000300 | \n",
" aborted: excessive norm value(s) | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.000000 | \n",
" -43.171200 | \n",
" 21.585600 | \n",
" 0.000180 | \n",
" aborted: excessive norm value(s) | \n",
"
\n",
" \n",
" | 5 | \n",
" 0.000000 | \n",
" -25.902720 | \n",
" 12.951360 | \n",
" 0.000108 | \n",
" aborted: excessive norm value(s) | \n",
"
\n",
" \n",
" | 6 | \n",
" 0.000000 | \n",
" -15.541632 | \n",
" 7.770816 | \n",
" 0.000065 | \n",
" aborted: excessive norm value(s) | \n",
"
\n",
" \n",
" | 7 | \n",
" 0.000000 | \n",
" -9.324979 | \n",
" 4.662490 | \n",
" 0.000039 | \n",
" aborted: excessive norm value(s) | \n",
"
\n",
" \n",
" | 8 | \n",
" 0.000000 | \n",
" -5.594988 | \n",
" 2.797494 | \n",
" 0.000023 | \n",
" aborted: excessive norm value(s) | \n",
"
\n",
" \n",
" | 9 | \n",
" 0.000000 | \n",
" -3.356993 | \n",
" 1.678496 | \n",
" 0.000014 | \n",
" aborted: excessive norm value(s) | \n",
"
\n",
" \n",
" | 10 | \n",
" 0.000000 | \n",
" -2.014196 | \n",
" 1.007098 | \n",
" 0.000008 | \n",
" | \n",
"
\n",
" \n",
" | 11 | \n",
" 0.000008 | \n",
" -1.579015 | \n",
" 0.789508 | \n",
" 0.000007 | \n",
" | \n",
"
\n",
" \n",
" | 12 | \n",
" 0.000015 | \n",
" -2.330836 | \n",
" 1.165418 | \n",
" 0.000010 | \n",
" | \n",
"
\n",
" \n",
" | 13 | \n",
" 0.000025 | \n",
" -1.820604 | \n",
" 0.910302 | \n",
" 0.000008 | \n",
" | \n",
"
\n",
" \n",
" | 14 | \n",
" 0.000033 | \n",
" -1.786552 | \n",
" 0.893276 | \n",
" 0.000008 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" START_TIME Delta A Delta C time_step \\\n",
"0 0.000000 NaN NaN 0.002000 \n",
"1 0.000000 NaN NaN 0.001000 \n",
"2 0.000000 -119.920000 59.960000 0.000500 \n",
"3 0.000000 -71.952000 35.976000 0.000300 \n",
"4 0.000000 -43.171200 21.585600 0.000180 \n",
"5 0.000000 -25.902720 12.951360 0.000108 \n",
"6 0.000000 -15.541632 7.770816 0.000065 \n",
"7 0.000000 -9.324979 4.662490 0.000039 \n",
"8 0.000000 -5.594988 2.797494 0.000023 \n",
"9 0.000000 -3.356993 1.678496 0.000014 \n",
"10 0.000000 -2.014196 1.007098 0.000008 \n",
"11 0.000008 -1.579015 0.789508 0.000007 \n",
"12 0.000015 -2.330836 1.165418 0.000010 \n",
"13 0.000025 -1.820604 0.910302 0.000008 \n",
"14 0.000033 -1.786552 0.893276 0.000008 \n",
"\n",
" caption \n",
"0 aborted: neg. conc. in `A` \n",
"1 aborted: neg. conc. in `A` \n",
"2 aborted: excessive norm value(s) \n",
"3 aborted: excessive norm value(s) \n",
"4 aborted: excessive norm value(s) \n",
"5 aborted: excessive norm value(s) \n",
"6 aborted: excessive norm value(s) \n",
"7 aborted: excessive norm value(s) \n",
"8 aborted: excessive norm value(s) \n",
"9 aborted: excessive norm value(s) \n",
"10 \n",
"11 \n",
"12 \n",
"13 \n",
"14 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dynamics.get_diagnostic_rxn_data(rxn_index=0, head=15)"
]
},
{
"cell_type": "markdown",
"id": "a2f4d0e3-b259-4bd1-a94e-5751264775f7",
"metadata": {},
"source": [
"### From the diagnostic data, it can be seen that the first step had several false starts - and the time was automatically repeatedly shrunk - but finally happened. `Delta A` indeed equals - 2 * `Delta C`, satisfying the stoichiometry"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "014c9870-1e91-4979-a24b-6e9f1c216640",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dynamics.stoichiometry_checker_entire_run()"
]
},
{
"cell_type": "markdown",
"id": "21ba52bf-0754-496f-bca7-e120b54ff403",
"metadata": {},
"source": [
"### Check the final equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "820a5564-bd4e-49db-8289-c912a106a5d9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0: 2 A <-> C\n",
"Final concentrations: [A] = 9.467 ; [C] = 135.3\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 1.50933\n",
" Formula used: [C] / [A]^2 \n",
"2. Ratio of forward/reverse reaction rates: 1.5\n",
"Discrepancy between the two values: 0.6221 %\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": "markdown",
"id": "6ac3dd4e-9dd0-4d3a-aa83-76102bd79524",
"metadata": {
"tags": []
},
"source": [
"## Display the variable time steps"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "21e4814e-5603-4d38-acc8-549b1d59ec93",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=A
SYSTEM TIME=%{x}
concentration=%{y}",
"legendgroup": "A",
"line": {
"color": "red",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "A",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
8.398079999999997e-06,
1.5116543999999995e-05,
2.5194239999999993e-05,
3.325639679999999e-05,
4.131855359999999e-05,
4.9380710399999987e-05,
5.7442867199999985e-05,
6.550502399999998e-05,
7.356718079999997e-05,
8.162933759999996e-05,
8.969149439999995e-05,
9.775365119999994e-05,
0.00010984688639999994,
0.00011952147455999994,
0.00012919606271999994,
0.00013887065087999994,
0.00014854523903999993,
0.00015821982719999993,
0.00016789441535999992,
0.00017756900351999992,
0.0001872435916799999,
0.00020175547391999992,
0.0002133649797119999,
0.0002249744855039999,
0.00023658399129599988,
0.0002481934970879999,
0.0002598030028799999,
0.00027141250867199993,
0.00028302201446399995,
0.00030043627315199994,
0.00031436768010239993,
0.0003282990870527999,
0.0003422304940031999,
0.0003561619009535999,
0.00037009330790399987,
0.00038402471485439986,
0.00040492182527999986,
0.0004216395136204799,
0.0004383572019609599,
0.0004550748903014399,
0.00047179257864191993,
0.0004885102669823999,
0.0005052279553228799,
0.0005303044878335998,
0.0005503657138421758,
0.0005704269398507519,
0.0005904881658593279,
0.000610549391867904,
0.00063061061787648,
0.000660702456889344,
0.0006847759280996351,
0.0007088493993099263,
0.0007329228705202174,
0.0007569963417305086,
0.0007810698129407998,
0.0008171800197562366,
0.000846068185208586,
0.0008749563506609355,
0.0009038445161132849,
0.0009327326815656344,
0.0009760649297441585,
0.0010107307282869779,
0.001045396526829797,
0.0010800623253726163,
0.0011147281239154356,
0.0011667268217296647,
0.001208325779981048,
0.0012499247382324313,
0.0012915236964838146,
0.0013539221338608894,
0.0014038408837625492,
0.001453759633664209,
0.0015036783835658688,
0.0015785565084183587,
0.0016384590083003505,
0.0016983615081823424,
0.00178821525800533,
0.0018600982578637203,
0.0019319812577221105,
0.002039805757509696,
0.0021260653573397644,
0.002212324957169833,
0.002341714356914935,
0.002445225876711017,
0.0025487373965070986,
0.0027040046762012216,
0.0028282184999565197,
0.0030145392355894673,
0.0031635958240958254,
0.0033871807068553625,
0.003566048613062992,
0.0038343504723744363,
0.004048991959823592,
0.004370954190997325,
0.004628523975936312,
0.005014878653344792,
0.005401233330753272,
0.005787588008161752,
0.006367120024274472,
0.006946652040387191,
0.007526184056499911,
0.00839548208066899,
0.009264780104838069,
0.010568727141091689,
0.011872674177345308,
0.013828594731725739,
0.016762475563296384,
0.02116329681065235,
0.027764528681686298,
0.03766637648823722
],
"xaxis": "x",
"y": [
200,
197.9858044928,
196.4067891314626,
194.07595334497333,
192.25534898661533,
190.46879740430532,
188.71534993819054,
186.99409292105136,
185.30414607674047,
183.64466100591383,
182.01481975354585,
180.413833453115,
178.8409410427091,
176.52264155159475,
174.7158686464458,
172.94596814009458,
171.21182008364218,
169.51234954024133,
167.84652434187538,
166.21335297917756,
164.61188261516287,
163.04119721445474,
160.7300250631269,
158.93326473964117,
157.1765542323314,
155.45856600172303,
153.77803069527553,
152.133733987297,
150.52451362294565,
148.94925665108212,
146.6357169224966,
144.84211135489045,
143.09225548651855,
141.38456387891168,
139.71752700678886,
138.08970675790684,
136.4997322498795,
134.16957778233262,
132.36879532470405,
130.61621777514426,
128.90993008249367,
127.24811762984149,
125.62905971933556,
124.05112355869569,
121.74357627793043,
119.96590134278878,
118.24001728379018,
116.56368739309407,
114.93480237154793,
113.351371359806,
111.04158489742949,
109.2687305903879,
107.55237706353577,
105.88985862493844,
104.27867529774959,
102.7164801024925,
100.44336119220299,
98.7050418479391,
97.02682657844456,
95.40564341405201,
93.83862706351552,
91.56533911676678,
89.83453130782063,
88.16914727211656,
86.56553760884185,
85.02032056253428,
82.78537648667351,
81.09121389710347,
79.46648786673279,
77.9070066041123,
75.65986265590448,
73.96572967479426,
72.34768795251657,
70.80071472427237,
68.57997565365879,
66.91489951075998,
65.3311102130008,
63.06862999277249,
61.38426396067837,
59.79055059290303,
57.52526128383666,
55.85096377632318,
54.2752007633405,
52.0466837978649,
50.41148777357389,
48.880683692615165,
46.726553773792986,
45.157276312252364,
42.9651397869594,
41.38484933300463,
39.193940122296404,
37.63146143283778,
35.48182042695287,
33.965438176115484,
31.895273567682466,
30.450914577527918,
28.494240237134733,
26.806442549712752,
25.336314685381662,
23.399376307880704,
21.792923050674183,
20.440773545233533,
18.712754345180954,
17.34062951686442,
15.673056180321085,
14.440546857084291,
13.032171680702598,
11.6089773772302,
10.412710546541897,
9.677513749893356,
9.466795875764356
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=C
SYSTEM TIME=%{x}
concentration=%{y}",
"legendgroup": "C",
"line": {
"color": "green",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "C",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
8.398079999999997e-06,
1.5116543999999995e-05,
2.5194239999999993e-05,
3.325639679999999e-05,
4.131855359999999e-05,
4.9380710399999987e-05,
5.7442867199999985e-05,
6.550502399999998e-05,
7.356718079999997e-05,
8.162933759999996e-05,
8.969149439999995e-05,
9.775365119999994e-05,
0.00010984688639999994,
0.00011952147455999994,
0.00012919606271999994,
0.00013887065087999994,
0.00014854523903999993,
0.00015821982719999993,
0.00016789441535999992,
0.00017756900351999992,
0.0001872435916799999,
0.00020175547391999992,
0.0002133649797119999,
0.0002249744855039999,
0.00023658399129599988,
0.0002481934970879999,
0.0002598030028799999,
0.00027141250867199993,
0.00028302201446399995,
0.00030043627315199994,
0.00031436768010239993,
0.0003282990870527999,
0.0003422304940031999,
0.0003561619009535999,
0.00037009330790399987,
0.00038402471485439986,
0.00040492182527999986,
0.0004216395136204799,
0.0004383572019609599,
0.0004550748903014399,
0.00047179257864191993,
0.0004885102669823999,
0.0005052279553228799,
0.0005303044878335998,
0.0005503657138421758,
0.0005704269398507519,
0.0005904881658593279,
0.000610549391867904,
0.00063061061787648,
0.000660702456889344,
0.0006847759280996351,
0.0007088493993099263,
0.0007329228705202174,
0.0007569963417305086,
0.0007810698129407998,
0.0008171800197562366,
0.000846068185208586,
0.0008749563506609355,
0.0009038445161132849,
0.0009327326815656344,
0.0009760649297441585,
0.0010107307282869779,
0.001045396526829797,
0.0010800623253726163,
0.0011147281239154356,
0.0011667268217296647,
0.001208325779981048,
0.0012499247382324313,
0.0012915236964838146,
0.0013539221338608894,
0.0014038408837625492,
0.001453759633664209,
0.0015036783835658688,
0.0015785565084183587,
0.0016384590083003505,
0.0016983615081823424,
0.00178821525800533,
0.0018600982578637203,
0.0019319812577221105,
0.002039805757509696,
0.0021260653573397644,
0.002212324957169833,
0.002341714356914935,
0.002445225876711017,
0.0025487373965070986,
0.0027040046762012216,
0.0028282184999565197,
0.0030145392355894673,
0.0031635958240958254,
0.0033871807068553625,
0.003566048613062992,
0.0038343504723744363,
0.004048991959823592,
0.004370954190997325,
0.004628523975936312,
0.005014878653344792,
0.005401233330753272,
0.005787588008161752,
0.006367120024274472,
0.006946652040387191,
0.007526184056499911,
0.00839548208066899,
0.009264780104838069,
0.010568727141091689,
0.011872674177345308,
0.013828594731725739,
0.016762475563296384,
0.02116329681065235,
0.027764528681686298,
0.03766637648823722
],
"xaxis": "x",
"y": [
40,
41.0070977536,
41.796605434268706,
42.962023327513336,
43.87232550669233,
44.76560129784734,
45.642325030904736,
46.502953539474326,
47.34792696162977,
48.17766949704308,
48.99259012322707,
49.79308327344249,
50.57952947864544,
51.73867922420261,
52.64206567677707,
53.52701592995269,
54.39408995817889,
55.243825229879306,
56.076737829062274,
56.893323510411186,
57.69405869241852,
58.47940139277259,
59.634987468436506,
60.53336763017938,
61.41172288383427,
62.27071699913845,
63.1109846523622,
63.933133006351454,
64.73774318852713,
65.5253716744589,
66.68214153875165,
67.57894432255473,
68.45387225674068,
69.30771806054412,
70.14123649660553,
70.95514662104654,
71.75013387506021,
72.91521110883365,
73.81560233764793,
74.69189111242783,
75.54503495875312,
76.37594118507921,
77.18547014033219,
77.97443822065212,
79.12821186103476,
80.01704932860558,
80.87999135810489,
81.71815630345294,
82.53259881422602,
83.32431432009699,
84.47920755128524,
85.36563470480604,
86.22381146823211,
87.05507068753077,
87.8606623511252,
88.64175994875374,
89.77831940389851,
90.64747907603045,
91.48658671077771,
92.29717829297398,
93.08068646824222,
94.2173304416166,
95.08273434608968,
95.91542636394172,
96.71723119557907,
97.48983971873285,
98.60731175666324,
99.45439305144825,
100.26675606663359,
101.04649669794384,
102.17006867204775,
103.01713516260286,
103.8261560237417,
104.5996426378638,
105.71001217317058,
106.54255024461999,
107.33444489349958,
108.46568500361373,
109.30786801966079,
110.10472470354846,
111.23736935808164,
112.07451811183837,
112.86239961832972,
113.97665810106751,
114.79425611321302,
115.55965815369238,
116.63672311310347,
117.42136184387378,
118.51743010652027,
119.30757533349765,
120.40302993885176,
121.18426928358107,
122.25908978652353,
123.01728091194222,
124.05236321615872,
124.774542711236,
125.75287988143259,
126.59677872514358,
127.33184265730912,
128.3003118460596,
129.10353847466286,
129.77961322738318,
130.64362282740947,
131.32968524156774,
132.16347190983942,
132.77972657145781,
133.48391415964866,
134.19551131138485,
134.793644726729,
135.1612431250533,
135.26660206211778
],
"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": 8.398079999999997e-06,
"x1": 8.398079999999997e-06,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 1.5116543999999995e-05,
"x1": 1.5116543999999995e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 2.5194239999999993e-05,
"x1": 2.5194239999999993e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 3.325639679999999e-05,
"x1": 3.325639679999999e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 4.131855359999999e-05,
"x1": 4.131855359999999e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 4.9380710399999987e-05,
"x1": 4.9380710399999987e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 5.7442867199999985e-05,
"x1": 5.7442867199999985e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 6.550502399999998e-05,
"x1": 6.550502399999998e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 7.356718079999997e-05,
"x1": 7.356718079999997e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 8.162933759999996e-05,
"x1": 8.162933759999996e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 8.969149439999995e-05,
"x1": 8.969149439999995e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 9.775365119999994e-05,
"x1": 9.775365119999994e-05,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00010984688639999994,
"x1": 0.00010984688639999994,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00011952147455999994,
"x1": 0.00011952147455999994,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00012919606271999994,
"x1": 0.00012919606271999994,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00013887065087999994,
"x1": 0.00013887065087999994,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00014854523903999993,
"x1": 0.00014854523903999993,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00015821982719999993,
"x1": 0.00015821982719999993,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00016789441535999992,
"x1": 0.00016789441535999992,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00017756900351999992,
"x1": 0.00017756900351999992,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0001872435916799999,
"x1": 0.0001872435916799999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00020175547391999992,
"x1": 0.00020175547391999992,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0002133649797119999,
"x1": 0.0002133649797119999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0002249744855039999,
"x1": 0.0002249744855039999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00023658399129599988,
"x1": 0.00023658399129599988,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0002481934970879999,
"x1": 0.0002481934970879999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0002598030028799999,
"x1": 0.0002598030028799999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00027141250867199993,
"x1": 0.00027141250867199993,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00028302201446399995,
"x1": 0.00028302201446399995,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00030043627315199994,
"x1": 0.00030043627315199994,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00031436768010239993,
"x1": 0.00031436768010239993,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0003282990870527999,
"x1": 0.0003282990870527999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0003422304940031999,
"x1": 0.0003422304940031999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0003561619009535999,
"x1": 0.0003561619009535999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00037009330790399987,
"x1": 0.00037009330790399987,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00038402471485439986,
"x1": 0.00038402471485439986,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00040492182527999986,
"x1": 0.00040492182527999986,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0004216395136204799,
"x1": 0.0004216395136204799,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0004383572019609599,
"x1": 0.0004383572019609599,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0004550748903014399,
"x1": 0.0004550748903014399,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00047179257864191993,
"x1": 0.00047179257864191993,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0004885102669823999,
"x1": 0.0004885102669823999,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0005052279553228799,
"x1": 0.0005052279553228799,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0005303044878335998,
"x1": 0.0005303044878335998,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0005503657138421758,
"x1": 0.0005503657138421758,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0005704269398507519,
"x1": 0.0005704269398507519,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0005904881658593279,
"x1": 0.0005904881658593279,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.000610549391867904,
"x1": 0.000610549391867904,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00063061061787648,
"x1": 0.00063061061787648,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.000660702456889344,
"x1": 0.000660702456889344,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0006847759280996351,
"x1": 0.0006847759280996351,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0007088493993099263,
"x1": 0.0007088493993099263,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0007329228705202174,
"x1": 0.0007329228705202174,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0007569963417305086,
"x1": 0.0007569963417305086,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0007810698129407998,
"x1": 0.0007810698129407998,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0008171800197562366,
"x1": 0.0008171800197562366,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.000846068185208586,
"x1": 0.000846068185208586,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0008749563506609355,
"x1": 0.0008749563506609355,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0009038445161132849,
"x1": 0.0009038445161132849,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0009327326815656344,
"x1": 0.0009327326815656344,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0009760649297441585,
"x1": 0.0009760649297441585,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0010107307282869779,
"x1": 0.0010107307282869779,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.001045396526829797,
"x1": 0.001045396526829797,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0010800623253726163,
"x1": 0.0010800623253726163,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0011147281239154356,
"x1": 0.0011147281239154356,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0011667268217296647,
"x1": 0.0011667268217296647,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.001208325779981048,
"x1": 0.001208325779981048,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0012499247382324313,
"x1": 0.0012499247382324313,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0012915236964838146,
"x1": 0.0012915236964838146,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0013539221338608894,
"x1": 0.0013539221338608894,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0014038408837625492,
"x1": 0.0014038408837625492,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.001453759633664209,
"x1": 0.001453759633664209,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0015036783835658688,
"x1": 0.0015036783835658688,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0015785565084183587,
"x1": 0.0015785565084183587,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0016384590083003505,
"x1": 0.0016384590083003505,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0016983615081823424,
"x1": 0.0016983615081823424,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00178821525800533,
"x1": 0.00178821525800533,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0018600982578637203,
"x1": 0.0018600982578637203,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0019319812577221105,
"x1": 0.0019319812577221105,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.002039805757509696,
"x1": 0.002039805757509696,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0021260653573397644,
"x1": 0.0021260653573397644,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.002212324957169833,
"x1": 0.002212324957169833,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.002341714356914935,
"x1": 0.002341714356914935,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.002445225876711017,
"x1": 0.002445225876711017,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0025487373965070986,
"x1": 0.0025487373965070986,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0027040046762012216,
"x1": 0.0027040046762012216,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0028282184999565197,
"x1": 0.0028282184999565197,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0030145392355894673,
"x1": 0.0030145392355894673,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0031635958240958254,
"x1": 0.0031635958240958254,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0033871807068553625,
"x1": 0.0033871807068553625,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.003566048613062992,
"x1": 0.003566048613062992,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.0038343504723744363,
"x1": 0.0038343504723744363,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004048991959823592,
"x1": 0.004048991959823592,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004370954190997325,
"x1": 0.004370954190997325,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.004628523975936312,
"x1": 0.004628523975936312,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.005014878653344792,
"x1": 0.005014878653344792,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.005401233330753272,
"x1": 0.005401233330753272,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.005787588008161752,
"x1": 0.005787588008161752,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.006367120024274472,
"x1": 0.006367120024274472,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.006946652040387191,
"x1": 0.006946652040387191,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.007526184056499911,
"x1": 0.007526184056499911,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.00839548208066899,
"x1": 0.00839548208066899,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.009264780104838069,
"x1": 0.009264780104838069,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.010568727141091689,
"x1": 0.010568727141091689,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.011872674177345308,
"x1": 0.011872674177345308,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.013828594731725739,
"x1": 0.013828594731725739,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.016762475563296384,
"x1": 0.016762475563296384,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.02116329681065235,
"x1": 0.02116329681065235,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.027764528681686298,
"x1": 0.027764528681686298,
"xref": "x",
"y0": 0,
"y1": 1,
"yref": "y domain"
},
{
"line": {
"color": "gray",
"dash": "dot",
"width": 1
},
"type": "line",
"x0": 0.03766637648823722,
"x1": 0.03766637648823722,
"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": "Reaction 2A <-> C (2nd order in A). Concentrations changes (time steps shown in dashed lines)"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.4667592090435056e-05,
0.03768104408032766
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.1183821311376239,
210.58517800690197
],
"title": {
"text": "concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_history(colors=['red', 'green'], show_intervals=True,\n",
" title=\"Reaction 2A <-> C (2nd order in A). Concentrations changes\")"
]
},
{
"cell_type": "markdown",
"id": "fde6184c-b365-4ef3-aac7-ad7561671f2d",
"metadata": {},
"source": [
"### The intersection of the two lines may be found as follows:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "e5370c40-4812-4bcd-aac0-949757513454",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Min abs distance found at data row: 60\n"
]
},
{
"data": {
"text/plain": [
"(0.0009423643313311743, 93.33333333333331)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dynamics.curve_intersection('A', 'C', t_start=0, t_end=0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f288907f-4305-43c9-80f2-38f35d19fc56",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "5c3f8b4f-3a75-4a21-8579-13550bcebb3c",
"metadata": {},
"source": [
"#### For additional diagnostic insight:\n",
"`norm_A` and `norm_B` are computed quantities that are used to guide the adaptive time steps"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "c75e9ff2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" START_TIME | \n",
" action | \n",
" step_factor | \n",
" caption | \n",
" time_step | \n",
" Delta A | \n",
" Delta C | \n",
" norm_A | \n",
" norm_B | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.000000 | \n",
" ABORT | \n",
" 0.5 | \n",
" | \n",
" 0.002000 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.000000 | \n",
" ABORT | \n",
" 0.5 | \n",
" | \n",
" 0.001000 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.000000 | \n",
" ABORT | \n",
" 0.6 | \n",
" excessive norm value(s) | \n",
" 0.000500 | \n",
" -119.920000 | \n",
" 59.960000 | \n",
" 4494.002000 | \n",
" NaN | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.000000 | \n",
" ABORT | \n",
" 0.6 | \n",
" excessive norm value(s) | \n",
" 0.000300 | \n",
" -71.952000 | \n",
" 35.976000 | \n",
" 1617.840720 | \n",
" NaN | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.000000 | \n",
" ABORT | \n",
" 0.6 | \n",
" excessive norm value(s) | \n",
" 0.000180 | \n",
" -43.171200 | \n",
" 21.585600 | \n",
" 582.422659 | \n",
" NaN | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 115 | \n",
" 0.011873 | \n",
" OK (low) | \n",
" 1.5 | \n",
" | \n",
" 0.001956 | \n",
" -1.408375 | \n",
" 0.704188 | \n",
" 0.619850 | \n",
" 0.097529 | \n",
"
\n",
" \n",
" | 116 | \n",
" 0.013829 | \n",
" OK (low) | \n",
" 1.5 | \n",
" | \n",
" 0.002934 | \n",
" -1.423194 | \n",
" 0.711597 | \n",
" 0.632963 | \n",
" 0.109206 | \n",
"
\n",
" \n",
" | 117 | \n",
" 0.016762 | \n",
" OK (low) | \n",
" 1.5 | \n",
" | \n",
" 0.004401 | \n",
" -1.196267 | \n",
" 0.598133 | \n",
" 0.447204 | \n",
" 0.103047 | \n",
"
\n",
" \n",
" | 118 | \n",
" 0.021163 | \n",
" OK (low) | \n",
" 1.5 | \n",
" | \n",
" 0.006601 | \n",
" -0.735197 | \n",
" 0.367598 | \n",
" 0.168911 | \n",
" 0.070606 | \n",
"
\n",
" \n",
" | 119 | \n",
" 0.027765 | \n",
" OK (low) | \n",
" 1.5 | \n",
" | \n",
" 0.009902 | \n",
" -0.210718 | \n",
" 0.105359 | \n",
" 0.013876 | \n",
" 0.021774 | \n",
"
\n",
" \n",
"
\n",
"
120 rows × 9 columns
\n",
"
"
],
"text/plain": [
" START_TIME action step_factor caption time_step \\\n",
"0 0.000000 ABORT 0.5 0.002000 \n",
"1 0.000000 ABORT 0.5 0.001000 \n",
"2 0.000000 ABORT 0.6 excessive norm value(s) 0.000500 \n",
"3 0.000000 ABORT 0.6 excessive norm value(s) 0.000300 \n",
"4 0.000000 ABORT 0.6 excessive norm value(s) 0.000180 \n",
".. ... ... ... ... ... \n",
"115 0.011873 OK (low) 1.5 0.001956 \n",
"116 0.013829 OK (low) 1.5 0.002934 \n",
"117 0.016762 OK (low) 1.5 0.004401 \n",
"118 0.021163 OK (low) 1.5 0.006601 \n",
"119 0.027765 OK (low) 1.5 0.009902 \n",
"\n",
" Delta A Delta C norm_A norm_B \n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -119.920000 59.960000 4494.002000 NaN \n",
"3 -71.952000 35.976000 1617.840720 NaN \n",
"4 -43.171200 21.585600 582.422659 NaN \n",
".. ... ... ... ... \n",
"115 -1.408375 0.704188 0.619850 0.097529 \n",
"116 -1.423194 0.711597 0.632963 0.109206 \n",
"117 -1.196267 0.598133 0.447204 0.103047 \n",
"118 -0.735197 0.367598 0.168911 0.070606 \n",
"119 -0.210718 0.105359 0.013876 0.021774 \n",
"\n",
"[120 rows x 9 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dynamics.get_diagnostic_decisions_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4226f78-aaad-44dd-9af7-e02df71acef9",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}