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"# Exploring the change of delta_x (spatial resolution) in diffusion accuracy.\n",
"#### From the same initial setup, diffusion is carried out over a fixed time span,\n",
"#### at different spatial resolutions - and then the respective results are compared\n",
"\n",
"LAST REVISED: June 23, 2024 (using v. 1.0 beta34.1)"
]
},
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"id": "b3ae84bc-72b9-4043-b5b9-70532eafdb44",
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"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"
]
},
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"id": "52bd1bf4",
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"outputs": [],
"source": [
"from experiments.get_notebook_info import get_notebook_basename\n",
"\n",
"from life123 import BioSim1D\n",
"from life123 import ChemData as chem\n",
"from life123 import Numerical as num\n",
"\n",
"import plotly.express as px\n",
"from life123 import HtmlLog as log\n",
"from life123 import GraphicLog"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7a4dd331-d083-4929-b7f1-089f44776f85",
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"-> Output will be LOGGED into the file 'spatial_resolution_1.log.htm'\n"
]
}
],
"source": [
"# Initialize the HTML logging\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_heatmap_11\", \"vue_curves_3\"])"
]
},
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"execution_count": 4,
"id": "14b4a1c2-9854-41c6-b407-6008bb4738be",
"metadata": {},
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"# Set the heatmap parameters (for the log file)\n",
"heatmap_pars = {\"range\": [0, 150],\n",
" \"outer_width\": 850, \"outer_height\": 150,\n",
" \"margins\": {\"top\": 30, \"right\": 30, \"bottom\": 30, \"left\": 55}\n",
" }\n",
"\n",
"# Set the parameters of the line plots\n",
"lineplot_pars = {\"range\": [0, 150],\n",
" \"outer_width\": 850, \"outer_height\": 250,\n",
" \"margins\": {\"top\": 30, \"right\": 30, \"bottom\": 30, \"left\": 55}\n",
" }"
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{
"cell_type": "markdown",
"id": "28c89c3d-a7f7-41d9-a99b-b0185620b5c1",
"metadata": {},
"source": [
"## Prepare the initial system"
]
},
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"execution_count": 5,
"id": "01b3a969-5122-4c25-900b-ad6fba315553",
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"SYSTEM STATE at Time t = 0:\n",
"60 bins and 1 species:\n",
" Species 0 (A). Diff rate: 0.1. Conc: [ 10. 13. 17. 21. 25. 28. 30. 38. 42. 55. 65. 47. 35. 32.\n",
" 27. 23. 20. 17. 14. 8. 3. 10. 16. 18. 20. 25. 30. 35.\n",
" 40. 65. 85. 115. 150. 92. 73. 69. 65. 50. 42. 36. 20. 45.\n",
" 50. 55. 69. 82. 95. 77. 60. 43. 37. 31. 25. 22. 20. 18.\n",
" 15. 11. 9. 8.]\n"
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"chem_data = chem(names=[\"A\"], diffusion_rates=[0.1])\n",
"\n",
"conc_list=[10,13,17,21,25,28,30,38,42,55,65,47,35,32,27,23,20,17,14,8,3,10,16,18,\n",
" 20,25,30,35,40,65,85,115,150,92,73,69,65,50,42,36,20,45,50,55,69,82,95,\n",
" 77,60,43,37,31,25,22,20,18,15,11,9, 8]\n",
"\n",
"bio = BioSim1D(n_bins=len(conc_list), chem_data=chem_data)\n",
"\n",
"bio.set_species_conc(species_name=\"A\", conc_list=conc_list)\n",
"\n",
"bio.describe_state()"
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"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Line plot\n",
"fig = px.line(data_frame=bio.system_snapshot(), y=[\"A\"], \n",
" title= f\"Diffusion. System snapshot at time t={bio.system_time}\",\n",
" color_discrete_sequence = ['red'],\n",
" labels={\"value\":\"concentration\", \"variable\":\"Chemical\", \"index\":\"Bin number\"})\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "94126c1b-fe6c-4709-98de-8f37a4b36402",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"hovertemplate": "Bin number: %{x}
Chem. species: %{y}
Concentration: %{z}",
"name": "0",
"type": "heatmap",
"x": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
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50,
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57,
58,
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],
"xaxis": "x",
"xgap": 2,
"y": [
"A"
],
"yaxis": "y",
"ygap": 4,
"z": [
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]
]
}
],
"layout": {
"autosize": true,
"coloraxis": {
"colorbar": {
"title": {
"text": "Concentration"
}
},
"colorscale": [
[
0,
"rgb(254, 254, 253)"
],
[
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"rgb(224, 224, 223)"
],
[
0.18181818181818182,
"rgb(197, 197, 195)"
],
[
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"rgb(171, 171, 170)"
],
[
0.36363636363636365,
"rgb(146, 146, 145)"
],
[
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"rgb(124, 123, 122)"
],
[
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"rgb(102, 101, 101)"
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[
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"rgb(59, 59, 59)"
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[
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],
[
0.9090909090909091,
"rgb(16, 16, 16)"
],
[
1,
"rgb(0, 0, 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"
],
[
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"#46039f"
],
[
0.2222222222222222,
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],
[
0.3333333333333333,
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],
[
0.4444444444444444,
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],
[
0.5555555555555556,
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],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
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],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
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"#0d0887"
],
[
0.1111111111111111,
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],
[
0.2222222222222222,
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[
0.3333333333333333,
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[
0.4444444444444444,
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[
0.5555555555555556,
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[
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[
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[
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],
[
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],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
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[
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[
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[
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],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
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"ticks": ""
},
"colorscale": [
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],
[
0.1111111111111111,
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[
0.4444444444444444,
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show as a heatmap\n",
"fig = px.imshow(bio.system_snapshot().T, \n",
" title= f\"Diffusion. System snapshot as a heatmap at time t={bio.system_time}\", \n",
" labels=dict(x=\"Bin number\", y=\"Chem. species\", color=\"Concentration\"),\n",
" text_auto=False, color_continuous_scale=\"gray_r\")\n",
"fig.data[0].xgap=2\n",
"fig.data[0].ygap=4\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d3097f06-8871-4379-8434-84b207d24b1a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1-D diffusion of a single species, with Diffusion rate 0.1\n",
"\n",
"\n",
"Initial system state at time t=0:\n",
"[GRAPHIC ELEMENT SENT TO LOG FILE `spatial_resolution_1.log.htm`]\n",
"[GRAPHIC ELEMENT SENT TO LOG FILE `spatial_resolution_1.log.htm`]\n"
]
}
],
"source": [
"log.write(\"1-D diffusion of a single species, with Diffusion rate 0.1\",\n",
" style=log.h2)\n",
"log.write(\"Initial system state at time t=0:\", blanks_before=2, style=log.bold)\n",
"\n",
"# Output a heatmap to the log file\n",
"bio.single_species_heatmap(species_index=0, heatmap_pars=heatmap_pars, graphic_component=\"vue_heatmap_11\")\n",
"\n",
"# Output a line plot the log file\n",
"bio.single_species_line_plot(species_index=0, plot_pars=lineplot_pars, graphic_component=\"vue_curves_3\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1e42793c-0991-4324-8cae-4fe7b608f3eb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"[[ 10. 13. 17. 21. 25. 28. 30. 38. 42. 55. 65. 47. 35. 32.\n",
" 27. 23. 20. 17. 14. 8. 3. 10. 16. 18. 20. 25. 30. 35.\n",
" 40. 65. 85. 115. 150. 92. 73. 69. 65. 50. 42. 36. 20. 45.\n",
" 50. 55. 69. 82. 95. 77. 60. 43. 37. 31. 25. 22. 20. 18.\n",
" 15. 11. 9. 8.]]\n"
]
}
],
"source": [
"bio.describe_state(concise=True)"
]
},
{
"cell_type": "markdown",
"id": "5b7faf57-7059-4bfc-adf8-82fa83fc822a",
"metadata": {},
"source": [
"### Populate the data set with more bins, using interpolated concentration values\n",
"### IMPORTANT: we're **NOT** changing spacial resolution here; we're just creating a less ragged dataset, as *our initial system state*"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cd81720a-dfe7-4051-a839-1d77f4493873",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"119 bins and 1 species:\n",
" Species 0 (A). Diff rate: 0.1. Conc: [ 10. 11.5 13. 15. 17. 19. 21. 23. 25. 26.5 28. 29.\n",
" 30. 34. 38. 40. 42. 48.5 55. 60. 65. 56. 47. 41.\n",
" 35. 33.5 32. 29.5 27. 25. 23. 21.5 20. 18.5 17. 15.5\n",
" 14. 11. 8. 5.5 3. 6.5 10. 13. 16. 17. 18. 19.\n",
" 20. 22.5 25. 27.5 30. 32.5 35. 37.5 40. 52.5 65. 75.\n",
" 85. 100. 115. 132.5 150. 121. 92. 82.5 73. 71. 69. 67.\n",
" 65. 57.5 50. 46. 42. 39. 36. 28. 20. 32.5 45. 47.5\n",
" 50. 52.5 55. 62. 69. 75.5 82. 88.5 95. 86. 77. 68.5\n",
" 60. 51.5 43. 40. 37. 34. 31. 28. 25. 23.5 22. 21.\n",
" 20. 19. 18. 16.5 15. 13. 11. 10. 9. 8.5 8. ]\n"
]
}
],
"source": [
"bio.smooth_spatial_resolution()\n",
"bio.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2a9d184f-152c-4a07-8f90-43261dc3a39b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"119"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.n_bins"
]
},
{
"cell_type": "markdown",
"id": "7efb504b-d93d-412d-b211-424d77c2fd53",
"metadata": {},
"source": [
"# The STARTING POINT\n",
"### This system setup will be our starting point in exploring diffusion using different spacial resolutions"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "19a33c8b-abef-41eb-9671-ceea867f2cb8",
"metadata": {},
"outputs": [],
"source": [
"original_state = bio.save_system() # SAVE a copy of the system state, to do multiple runs starting from it"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "98c65a70-2509-4e3c-be0c-e9da89e7e847",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=A
Bin number=%{x}
concentration=%{y}",
"legendgroup": "A",
"line": {
"color": "red",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "A",
"orientation": "v",
"showlegend": true,
"type": "scatter",
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BBBBAAAEEEEAgVgHC71jleM5SAXZ9G8dP+G2cLS0jgAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPhtEjTdIIAAAggggAACCCCAAAIIIIAAAggggAACCJgnQPhtnjU9IYAAAggggAACCCCAAAIIIIAAAggggAACCJgkQPitA/RvBZU6tEITCCAQTiA/O0UqquqkqrYBIAQQMEigW366bCuqlAa/QR3QLAIeF0hM8EmnnFTZWlTlcQmmj4BxAmkpiZKRmiiFpTXGdULLCHhcoENGsjT4/VJWWedxCaaPQKNA947pUDhAgPBbhyIRfuuASBMItCJA+M3SQMB4AcJv443pwdsChN/erj+zN0eA8NscZ3rxtgDht7frz+xbChB+O2NVEH7rUCfCbx0QaQIBwm/WAAKWCRB+W0ZPxx4RIPz2SKGZpqUChN+W8tO5RwQIvz1SaKYZsQDhd8RUlt5I+K0DP+G3Dog0gQDhN2sAAcsECL8to6djjwgQfnuk0EzTUgHCb0v56dwjAoTfHik004xYgPA7YipLb/RU+L1y9Vr58ZctMmns8GbodyxcLoufWt3sY7dOHS1DB/fXPqaeu2HOEu3fTxvUT2ZOGS3paSlN9xN+W7qG6dzlAhx74vICMz1bCBB+26IMDMLFAoTfLi4uU7ONAOG3bUrBQFwsQPjt4uIytZgECL9jYjP9IU+E3xs2fSUXTpit4V48cnDY8Fv9WWgorj6mnp23cLksmD1R8nKyRQXlofcSfpu+bunQQwKE3x4qNlO1TIDw2zJ6OvaIAOG3RwrNNC0VIPy2lJ/OPSJA+O2RQjPNiAUIvyOmsvRGT4TfAeG2dn63Fn6rsHuv3bs17QIPDcPVc4Tflq5hOne5AOG3ywvM9GwhQPhtizIwCBcLEH67uLhMzTYChN+2KQUDcbEA4beLi8vUYhIg/I6JzfSHCL9FtN3cwceeBI48qayqkZvmLpF+hx3QFH5/99Nvcv2sRXLb9DHSc8/uWsEIv01ft3ToIQHCbw8Vm6laJkD4bRk9HXtEgPDbI4VmmpYKEH5byk/nHhEg/PZIoZlmxAJ2Db/DbdyNeFI2urGopFTGTZsv14wdLn377B/zyAi/Q+hUuD126jyZNX2MHLh/Dy38HjZkQBNyuPC7tLIu5gLwIAIItC2QnpIotXUNUtfgh8qGAn6/X3w+nw1HxpCiEchKT5LyyjrhsywaNe5FQCTSr4EJPpGM1CQpq3LA94xVVZKycIE0/PGPUnfiSZQZAccIJCX6JDkxQSpr6h0zZqcP1O8X4dtAp1cxuvGnJieIqntNXUN0D3I3Ai4VyE5PsmRmgc26L725vln/gc28Vobf6tSN5avWNB0fHQ8Q4XcMeq0dexLaVOCok1NP6BfRzu/SitoYRsMjCCAQiUB66v+H3/XEcpF4mX2PqgrRt9nq+veXlZ4s5VW12l9muBBAIHIBv/jEF8GPjdQPCTPSErUfMtn9Sp0zW1Jm3qgNs/zzL6Sh5z52HzLjQ0ATSEpMkOQkn1RWE36btST8Pp/4+ObBLG5b9JOanCh+8UtNLeG3LQrCICwXyM5INn0MgU27g084stm7C1VQPP32RTJl/AgpLNrZ7P2Fpg9Spw4Jv2OAjDb8Hjq4v3YkCmd+x4DNIwjoJMCxJzpB0gwCbQhw7AnLAwFjBZxy7ElCwQ7p2md/8VVWaCCl026Q0snTjcWhdQR0EuDYE50gaQaBNgQ49oTlgUBzAbOPPQns+O7WJb9Z8B1al8DOb3VcyPRZi+T3rQXaLUvvnNbs+JBAkB7484tHDm5qNxA8X3TOKfLwP1+RzV9+r7Whdpcf/Md9tFMzwrWrstf1G7+QmVNGS3paivZMaD+9e/XQdoarSx1rEmhb/Xe4MXDsSRSfeeHCb1XM1W+ul1FD/9RUkOAzvUN/VUCF4eqaNHZ4U8+c+R1FEbgVgSgFCL+jBON2BGIQIPyOAY1HEIhCwCnhd+6VYyXjqcekrue+kvTdt1K33/6y7f1PopgptyJgnQDht3X29OwdAcJv79SamUYmYHb4HXxUc1tnYKss88IJs+W0Qf2aQujQ40hCj3UODdYD4beSUEF1Xk62BNoNhNeBj81buLzpntDwO9yYX13zkeyz926Sn5stDy97WcZdcKYWlAf6HD5kgPbuRXZ+R7YOtbsCxQl+JPDTjnDn5IT+JEQV7oY5S7THgxdOoD3C7yiKwa0IRClA+B0lGLcjEIMA4XcMaDyCQBQCTgi/k77+Srocc6g2q62ffimdTzpeErZv08JvFYJzIWB3AcJvu1eI8blBgPDbDVVkDnoKmB1+q3xT7eReOOca6bln91anEu7M79CwO/Ski0B+Ggiy1X+HvmwyXBgd+rHQ8DvcJuK2ahC8cZnwW8/VGmdbhN9xAvI4Am0IEH6zPBAwXoDw23hjevC2gBPC746nnyyp696VsnFXys5b/y4dbpwmWfffrR17oo4/4ULA7gKE33avEONzgwDhtxuqyBz0FHBq+N29ayftHYehL8xUNqFHkgQfORJt+K3aU/0MGzKg2XErwTUIPRJF/Vlg43FVdXWLAD6W+vn86jX1XHEJEH7HxcfDCLQpQPjNAkHAeAHCb+ON6cHbAnYPv9NWr5L888+Rhtxc2frp1+LPzpbkTz6Wzif1l7q9e8q2DZu9XUBm7wgBwm9HlIlBOlyA8NvhBWT4uguYHX5Hc+xJ8FEkauLBO78D4Xe/ww7QjhcJd0USdKvn2tr53V74HThpI/gEjuCd44Tfui/Z2Bsk/I7djicRaE+A8Ls9If4cgfgFCL/jN6QFBNoSsHP47autlS5H9JbEX36WklnzpHzMuKapdD34D5L4639l+1vrpPagPhQZAVsLEH7bujwMziUChN8uKSTT0E3A7PC7vRdeBs7SLizaKW2F3+rIlPaOI9Ej/FbneLfVT7ijVwi/dVue+jZE+K2vJ60hECxA+M16QMB4AcJv443pwdsCdg6/1dEm6ogTbYf3+k0iiYlNxepwyw2Sdfc8KbvqGtl5463eLiKzt70A4bftS8QAXSBA+O2CIjIFXQXMDr/V4AO7vwefcKRMGju8aT4qSF791ofaeeCRhN+B9yPeOnV00+5vFXgHXkAZbtd1JIF46JnfgX6Cd3cHQvrnX3lPtmwrbHopZ2Buhx64r/Yxdn7rulzja4zwOz4/nkagLQHCb9YHAsYLEH4bb0wP3hawa/idUFwsXQ/ZT3ylpVLw5DNSfdKpzQqV/Pkm6XzC0VK/626y9bNvvF1EZm97AcJv25eIAbpAgPDbBUVkCroKWBF+qwkEdoAHn9kdOKs7LydbInnhZXCQ/vvWgiaXQBgeSdCtHmrvhZfqnkAAHugkMNa01NRmZ4+rs74POqCnfP7Fd4Tfuq5UHRoj/NYBkSYQaEWA8JulgYDxAoTfxhvTg7cF7Bp+51w7UTIXL5Tqo4+TghdeDVukLn17S9IP38n219ZK7aGHe7uQzN7WAoTfti4Pg3OJAOG3SwrJNHQTsCr81m0CHmmIF17qUGjCbx0QaQIBwm/WAAKWCRB+W0ZPxx4RsGP4rQLtLv36iDQ0yLb3NkrdfvuHrUb27Fsl+x+zpGz8VbLzltkeqRjTdKIA4bcTq8aYnSZA+O20ijFeowUIv40W1qd9wm8dHAm/dUCkCQQIv1kDCFgmQPhtGT0de0TAjuF3x5FDJfX1V6TigouleN49rVYi6asvpMuxh0tD5y6y5YsfRHw+j1SNaTpNgPDbaRVjvE4UIPx2YtUYs5EChN9G6urXNuG3DpaE3zog0gQChN+sAQQsEyD8toyejj0iYLfwO3Xdu9Lx9JPFn50tWz/+tzR07NRmJQJHn+x46U2pOfIoj1SNaTpNgPDbaRVjvE4UIPx2YtUYs5EChN9G6urXNuG3DpaE3zog0gQChN+sAQQsEyD8toyejj0iYKvwu75euvTvK0lffyU7b7xVyq66pt0qqGNP1PEn5WPGScmsee3ezw0IWCFA+G2FOn16TYDw22sVZ77tCRASM71qAAAgAElEQVR+tydkjz8n/NahDoTfOiDSBAKE36wBBCwTIPy2jJ6OPSJgp/A7Y+lDkjv5KqnffQ/Z9tFm8Scnt1sF7Xzwvr05+qRdKW6wUoDw20p9+vaKAOG3VyrNPCMVIPyOVMra+wi/dfAn/NYBkSYQIPxmDSBgmQDht2X0dOwRAbuE377SUul6yH6SUFwshYsfl6ozhkZcgc4Dj5LkzZ/Jjudelppjj4/4OW5EwCwBwm+zpOnHywKE316uPnMPJ0D47Yx1QfitQ50Iv3VApAkECL9ZAwhYJkD4bRk9HXtEwC7hd4ebr5ese+dLzeFHyI5X1kSln3XXP6TDrTe2+4LMqBrlZgR0FCD81hGTphBoRYDwm6WBQHMBwm9nrAjCbx3qRPitAyJNIED4zRpAwDIBwm/L6OnYIwJ2CL8Tf/lZuhzRW3y1tbL97Q+ktvfBUekn/vpf6XrwH6QhN1e2fP2LSGJiVM9zMwJGCxB+Gy1M+wiIEH6zChAg/HbiGiD81qFqhN86INIEAoTfrAEELBMg/LaMno49ImCH8Dtv9F8l/YWVUjF8pBTfvzgm+U4nHy8pGzdIwdMvSvWAE2Jqg4cQMEqA8NsoWdpF4H8ChN+sBgQIv524Bgi/daga4bcOiDSBAOE3awABywQIvy2jp2OPCFgdfqd89IF0GjxI/Gnpsm3jv6W+a7eY5LMeuEc6zLhWKkZdIMV3LYipDR5CwCgBwm+jZGkXAcJv1gACrQlw7ElzmaKSUhk3bb7s0b2LzJwyWtLTUmyxeAi/dSgD4bcOiDSBAOE3awABywQIvy2jp2OPCFgdfgdeVlk65TopvXZGzOoJ27dJt157iT8rS7Z8+6v4k5NjbosHEdBbgPBbb1HaQ6ClADu/WRUINBcg/G7usWHTV7Ji1RrZWVYhU8aPkJ57drfFkiH81qEMhN86INIEAoTfrAEELBMg/LaMno49ImBl+J3+zyck7/IxUt9tF9m2YbP40zPiUu902iBJ+fADKXjyGak+6dS42uJhBPQUIPzWU5O2EAgvQPjNykCA8LutNXDHwuVy3JEHybsffi577d5Nhg7ub4slQ/itQxkIv3VApAkECL9ZAwhYJkD4bRk9HXtEwKrw21dTLV0OPUASt/wuxfc+KBUj/hq3eOaiBZIz/RqpHDZSihbEdnZ43IOgAQTCCBB+sywQMF6A8Nt4Y3pwloBlO78LCkQ2bzYfq2NHkd69w/arjjyZdfcTMv2qUfKfH37VdoDb5egTwm8dlgrhtw6INIEA4TdrAAHLBAi/LaOnY48IWBV+Z8+5TdT/ansfLNvf/kAXbe3okwP2Fn9mJkef6CJKI3oJEH7rJUk7CLQuQPjN6kCguYBl4feqVSKnn25+OYYMEXnhhbD9qiNP1I7vSWOHS+Ds72vGDpe+ffY3f5whPRJ+61ACwm8dEGkCAcJv1gAClgkQfltGT8ceEbAi/Fa7vdWub7X7e8cra6Tm8CN00+50ximS8v5aKXxkmVSdZsFfvHSbCQ25SYDw203VZC52FSD8tmtlGJdVApaF3+vWiVx/vfnTPuookdtvb9FvZVWN3DR3iQwbMqAp7FZHoKhLheFWX4TfOlSA8FsHRJpAgPCbNYCAZQKE35bR07FHBKwIv3MvHyMZ/3xCqoacKYUPP6mrdMbShyR38lVSedbZUrToUV3bpjEEYhUg/I5VjucQiFyA8DtyK+70hoBl4bfNeL/76TcZO3We/L61oNnIevfqIQtmT5S8nGxLR0z4rQM/4bcOiDSBAOE3awABywQIvy2jp2OPCJgdfidv/kw6DzxK/MnJsu2jzVK/+x66SicUF0u3/XYXf3KKbPnPf8Wflq5r+zSGQCwChN+xqPEMAtEJEH5H58Xd7hcg/G6s8crVa2X9xi+anfEdbje4VSuC8FsHecJvHRBpAgHCb9YAApYJEH5bRk/HHhEwO/zudMoASfn4Iym7cpLsvOlvhih3HHa6pL79hhQ99JhUnvkXQ/qgUQSiESD8jkaLexGITYDwOzY3nnKvAOG3SCDk7nfYATJ0cP9mxVah+I+/bLH86BPCbx0+Bwm/dUCkCQQIv1kDCFgmQPhtGT0de0TAzPA77YVnJX/0KGnIzZWtn34t/mxjfs0044lHJPfqcVL15zOkcOlTHqkk07SzAOG3navD2NwiQPjtlkoyD70ECL/1kjS2HcJvHXwJv3VApAkECL9ZAwhYJkD4bRk9HXtEwKzw21dbK12O6C2Jv/wsxfPukYoLLjZM2FdWJrv03EX8CQmy5dtfxZ+VZVhfNIxAJAKE35EocQ8C8QkQfsfnx9PuEyD8dkZNCb91qBPhtw6INIEA4TdrAAHLBAi/LaOnY48ImBV+Z907XzrcfL3U7be/bHtvo4jPZ6hw/rl/kbTXXpaiBYulcthIQ/uicQTaEyD8bk+IP0cgfgHC7/gNacFdAoTfzqgn4bcOdSL81gGRJhAg/GYNIGCZAOG3ZfR07BEBM8LvhMIC6XpIL/GVl0nBMy9J9fEDDdfNWLFMcseNlqpTTpPCx1cY3h8dINCWAOE36wMB4wUIv403pgdnCRB+O6NehN861InwWwdEmkCA8Js1gIBlAoTfltHTsUcEzAi/c6ZcLZkPL5Kqk06VwiefMUVWO/pkry5aX1u++10acnJM6ZdOEAgnQPjNukDAeAHCb+ON6cFZAoTfzqgX4bcOdSL81gGRJhAg/GYNIGCZAOG3ZfR07BEBo8PvpK+/ki79+2qa29Zvkrq9e5omm3/+OZK2epUU37VAKkZdYFq/dIRAqADhN2sCAeMFCL+NN6YHZwkQfjujXoaG30UlpTJu2nzZ/OX3LTR69+ohC2ZPlLwcY95AbyY/4beZ2vTlNYH87BSpqKqTqtoGr02d+SJgmgDht2nUdORRAaPD746nnyyp696V8jHjpGTWPFOV01eukLxLL5DqAYOk4OlVpvZNZwgECxB+sx4QMF6A8Nt4Y3pwlgDhtzPqZWj4fcfC5ZrCpLHDnaER4ygJv2OE4zEEIhAg/I4AiVsQiFOA8DtOQB5HoB0BI8Pv1DdelY4jzhJ/drZs/fRracjNNbUevqpK6bbPbuKrrZEtX/9iev+mTpbObC1A+G3r8jA4lwgQfrukkExDNwHCb90oDW3IsPBb7fqefvsimTJ+hPTcs7uhk7C6ccJvqytA/24WIPx2c3WZm10ECL/tUgnG4VYBw8Lv+nrp0q+PJP3wnZT8bY6UX3aFJYR5l5wn6c89IyVz7pTy0ZdaMgY6RYDwmzWAgPEChN/GG9ODswQIv51RL8JvHepE+K0DIk0g0IoA4TdLAwHjBQi/jTemB28LGBV+Zy5eKDnXTtTO+N6+7hPxJydbAp324vOSf+FIqTn6WNnxwmuWjIFOESD8Zg0gYLwA4bfxxvTgLAHCb2fUy7DwW01fHXuy1+7dZOjg/s7QiHGUhN8xwvEYAhEIEH5HgMQtCMQpQPgdJyCPI9COgBHhd0JRkXQ6ZYAkffetFD6+QqpOOc2yOviqq6TbH/YQX3mZbP3Xd1LfbRfLxkLH3hUg/PZu7Zm5eQKE3+ZZ05MzBAi/nVEnQ8Pv7376TZ5Y+YZMGTdC0tNSnCESwygJv2NA4xEEIhQg/I4QitsQiEOA8DsOPB5FIAIBI8LvvDHnS/qzT0vVSadK4ZPPRDAKY2/Ju2y0pD+9TEpu/buUj7vS2M5oHYEwAoTfLAsEjBcg/DbemB6cJUD47Yx6GRZ+qzO/x02bL5u//D6sRO9ePWTB7ImSl5PtDKk2Rkn47fgSMgEbCxB+27g4DM01AoTfriklE7GpgN7hd9rrr0j+yKHiz8ySrR//Sxo6d7F85mmvrpb8UWdL7aGHy/bX1lo+HgbgPQHCb+/VnBmbL0D4bb45PdpbgPDb3vUJjM6w8NsZ09dnlITf+jjSCgLhBAi/WRcIGC9A+G28MT14W0DP8Dth507pckRvSdixXYrn3ycV511kC1xfba1023dX8ZWVydbPvpH6XXezxbgYhHcECL+9U2tmap0A4bd19vRsTwHCb3vWJXRUhN861InwWwdEmkCgFQHCb5YGAsYLEH4bb0wP3hbQM/zOG3expK94SqqPOlYKVtnr5ZK5V46VjKcek5033CJlV0/2dtGZvekChN+mk9OhBwUIvz1YdKbcpgDhtzMWiOHh94ZNX8mFE2Y301h65zTp22d/ZwhFMErC7wiQuAWBGAUIv2OE4zEEohAg/I4Ci1sRiEFAr/C76biTtHTZ9sEnUr/7njGMxrhHUt98TTqec6bUHniQbF+z3riOaBmBMAKE3ywLBIwXIPw23pgenCVA+O2Mehkafqvge97C5c3O9lYvwRw7dZ6Mv+AMGTq4vzOU2hkl4bcrysgkbCpA+G3TwjAsVwkQfruqnEzGhgJ6hN/Bx52U3DZXysdebr+Z1tdLt/12l4TiYtm2YbPU7d3TfmNkRK4VIPx2bWmZmI0ECL9tVAyGYgsBwm9blKHdQRgWfldW1chNc5fIsCEDWuzyVqH4ilVrZOaU0ZKeltLuIO1+A+G33SvE+JwsQPjt5OoxdqcIEH47pVKM06kCeoTfuVddJhlPPio1h/WVHa+sEfH5bMmRO+kKyXh0iZROvV77HxcCZgkQfpslTT9eFiD89nL1mXs4AcJvZ6wLw8LvopJSmX77IpkyfoT03LN7Mw21+3vu/ctk1nVjJC8n2xlSbYyS8NvxJWQCNhYg/LZxcRiaawQIv11TSiZiU4F4w+/UtWuk49DB4k9Jke3vfSx1Pfax6UxFUte+LR2Hnqbt+la7v7kQMEuA8NssafrxsgDht5erz9wJv527BgwLv9n57dxFwcgRsJMA4bedqsFY3CpA+O3WyjIvuwjEE377ysukS78+kvj7b7LzupukbNK1dplW+HH4/dJt310bjz5Zs17qDjzI3uNldK4RIPx2TSmZiI0FCL9tXByGZokAO78tYY+6U8PCbzWSlavXyvJVazjzO+qy8AACCAQECL9ZCwgYL0D4bbwxPXhbIJ7wO3CMSO3+vWT7Ox+JJCbaHjNn6gTJXPKglF09WXbecIvtx8sA3SFA+O2OOjILewsQftu7PozOfAHCb/PNY+nR0PBbDUid733hhNnNxrb0zmktzgGPZfB2eYZjT+xSCcbhRgHCbzdWlTnZTYDw224VYTxuE4g1/E75cJ10Ou1ELfDe/sZ7Utv7YEfQpKx7TzqdfpLU77qbbP3sG0eMmUE6X4Dw2/k1ZAb2FyD8tn+NGKG5AoTf5nrH2pvh4XesAzPiObUT/cdftsikscObNa/OJx83bb5s/vJ77eOh4bx67oY5S7Q/O21QvxYv6iT8NqJatIlAowDhNysBAeMFCL+NN6YHbwvEEn77qiqly1GHSOIvP0vZhCmyc8ZM5yCqo08O2FsStm+T7a++I7WH9XXO2BmpYwUIvx1bOgbuIAHCbwcVi6GaIkD4bQpz3J14IvwO3n1+8cjBzcLvwNnk/Q47QIYO7i/qZZzXz1okt00fo72oUz07b+HypqNb7li4XEMPDtAJv+NehzSAQKsChN8sDgSMFyD8Nt6YHrwtEEv4nXPtRMlcvFB7uaV6yaV62aWTrpwZUyXzgXul/LIrpORvc5w0dMbqUAHCb4cWjmE7SoDw21HlYrAmCBB+m4CsQxeeCL8DTuF2fquwe+79y2TWdWMkLydbQsNwFXbvtXs3LRhXV2gYrj5G+K3DSqQJBFoRIPxmaSBgvADht/HG9OBtgWjD76bjTnw+2fHKGqlx4M7p5I8/ks6nDJCGzl1kyxc/iPh83l4EzN5wAcJvw4npAAEh/GYRINBcgPDbGStC9/A7cITIReecIg//85Wmo0RCOXr36tHsRZhmcIULv8OF2YHd3eMuOFNumrtEArvC1RhDd4YTfptROfrwsgDht5erz9zNEiD8NkuafrwqEE34HXzcSfml46Xk9n84lq3rwX+QxF//KzteeE1qjj7WsfNg4M4QIPx2Rp0YpbMFCL+dXT9Gr78A4bf+pka0qHv4HRikCsGn375IpowfoR0fEnypwHnFqjUtzs42YoLBbbYWfoeOJTT8HjZkQNMLOsOF3yXltUYPnfYR8KxAZlqS1NTWS22937MGdp64389mPjvXJ9KxdchMltKKWlH15EIAgcgFGvwiCRFsaE7w+SQrPUl2VrT/PWPatMmSeu/d0rDHHlK2cbP409MjH5DN7kybMU1S77xDqi8ZK1V33mOz0TEctwkkJyVISlKClFfVuW1qtp0P3wfatjSGDUz9kMnv90t1bYNhfdAwAk4SyMlMdtJwPTtWS8Lv0KNGzNI3auc332CZVUH68aJAanKC1NX7pV4lDFy2E1BViSD3sd24GVBzgYzUJKmsrhM+y1gZCEQnEGnwo078UIFBZXV9mx0kfvShpB3fuEO66uXXpH7AwOgGZLO7EzZ9KulHHSH+3Dyp+H2bzUbHcNwmoH7DIinRRyhnYmH5PtBEbJt0pX7ApOpeW0f4bZOSMAyLBdRmPS77C1gSfqsQev3GL2yx85szv+2/SBmhtwU49sTb9Wf25ghw7Ik5zvTiXYFIjj3x1dRI52MOk6QfvpOKURdI8V0LXAEWOPqkYMULUj3wRFfMiUnYU4BjT+xZF0blLgGOPXFXPZlN/AIcexK/oRkt6B5+qzB57NR58vvWglbHv0vXjrJwzjUtjkMxesLhdn6HvuAy9FiT0DPBA0eiTBo7vGm4vPDS6MrRvpcFCL+9XH3mbpYA4bdZ0vTjVYFIwu8OM2dI1j13SP0u3WX7+59IQ4cOruDqcNvNkjV/jlSce74U3/2AK+bEJOwpQPhtz7owKncJEH67q57MJn4Bwu/4Dc1oQffwOzDots78NmNiwX2oAPvCCbObdbv0zmlN53gHXtK5+cvvtXuC/0z9twrNb5izRPuz0wb1a7FjnfDb7IrSn5cECL+9VG3mapUA4bdV8vTrFYH2wu/kf30unQcdI1JfLwUrV0t1/wGuoUn692bpcvyR4s/Kki3f/ir+ZM7GdE1xbTYRwm+bFYThuFKA8NuVZWVScQgQfseBZ+KjhoXfJs7B8q4Ivy0vAQNwsQDht4uLy9RsI0D4bZtSMBCXCrQXfnc55lBJ+vorqRh5nhTfs9B1Cl2O6iNJ334jhU8+I1Unneq6+TEhewgQftujDnYYRda9d0rtgQdJ9YAT7DAcV42B8NtV5WQyOggQfuuAaEIThN86IBN+64BIEwi0IkD4zdJAwHgBwm/jjenB2wJthd/Zt90s2fPnSH23XWT7uk9dc9xJcMWz//43yZ57u1ScfY4UP/CwtxcDszdMgPDbMFrHNJz0n28l9/JLJGXjBm3MRYsfk8oz/uKY8TthoITfTqgSYzRTgPDbTO3Y+zI0/G7r/O/evXrIgtkTJS8nO/bR2+RJwm+bFIJhuFKA8NuVZWVSNhMg/LZZQRiO6wRaC7+TN38mnQcepc23cNmzUnXiya6bu5qQCqS69DtY/JlZsuWbn8WfmubKeTIpawUIv631t7r37Dv/Idl/u7HFMErm3iXlF42xeniu6Z/w2zWlZCI6CRB+6wRpcDOGhd/BL5I8+I/7yBMr35Ap40ZIelqKqJdGHnfkQU1nbhs8R8ObJ/w2nJgOPCxA+O3h4jN10wQIv02jpiOPCoQNv+vqpMvxRzQed3LOKCm+b5Grdbr0P0KSvviXFC5+XKrOGOrquTI5awQIv61xt7rX5K++lNyxF0ryvzdrQ6npd4wULVgsaa+ulpxpk7SPlU65TkqvnWH1UF3RP+G3K8rIJHQUIPzWEdPApgwLv4NfeKnGP/f+ZTLrujHaTm/1AsoVq9a0eHGkgfM0tGnCb0N5adzjAoTfHl8ATN8UAcJvU5jpxMMC4cLv7H/MkuzZt0p9126Nx53k5LhaKLArs/q446Xg2ZddPVcmZ40A4bc17lb2qr6Gqq+l6vJnZ8vOmbOk/PzRTUPKWPa45F5xqfbf5aMvlZI5d1o5XFf0TfjtijIyCR0FCL91xDSwKVPC7/zcbJl19xMy/apRWvitjkMJDsMNnJ8pTRN+m8JMJx4VIPz2aOGZtqkChN+mctOZBwVCw+/kL/8tnY/rq0kUPrVSqv50iutVEn/9r3Q+7nBJ2LlTqk45TQofX+H6OTNBcwUIv831trK35E2fSN7ll2i/OaOuqhP+JCX3LNR+mBh6qR3g+aPO1j6szv9W54BzxS5A+B27HU+6U4Dw2xl1NSz8Dj72ZOjg/tpRJ3vt3k3Uv69cvVbWb/yCnd/OWCOMEgFLBQi/LeWnc48IEH57pNBM0zKBZuF3fb10HnSMJP/rc6k8e4QUPbDEsnGZ3bEKqjqdcbIk7Ngu1f0HaOec+1NSzR4G/blUgPDbpYUNmpavpkayZt8i2ffdJVJfLw15ebLztn9IxfCRbU4+5aMPpOPwM8RXVibVx/aXwiefEX9GpvvBDJgh4bcBqDTpaAHCb2eUz7DwO3T66hiUcdPmy+Yvv5ddunaUhXOukZ57dneGUjujZOe3K8rIJGwqQPht08IwLFcJEH67qpxMxoYCweF31vw50uG2m6WhU2fZ9tFmaejQwYYjNm5IiT//KJ0GD5LELb9rZ/MWPP2C+NPSjeuQlj0jQPjt7lKnbNwguWq393++1SZaOeRMKZl3jzTkd4xo4uo3bjqeNVj74Vtt74Ol4JmXpCE/P6Jnuel/AoTfrAYEmgsQfjtjRZgWfjuDI7ZREn7H5sZTCEQiQPgdiRL3IBCfAOF3fH48jUB7AoHwu2D9Juk8sJ+o3YteOe4knE3if3+RTqefLCoIrzn8CClYsUo7r5cLgXgECL/j0bPvs76qSsm+9SbJWnS/SEODFnarFwTHclxU4i8/SaczTpHEn3+Sur17SsFzL0v9rrvZd/I2HBnhtw2LwpAsFSD8tpQ/4s4NC7+DX3jplh3erakSfke83rgRgagFCL+jJuMBBKIWIPyOmowHEIhKQAu/s5Kk4dDDGo87GTpcih5cGlUbbrtZ7b5UR6Coo1BqDzhQCla97vqXfrqthnabD+G33SoS/3jUcSV5l43Wwmp1qeNNdt4+Txpyc2NuXPvaM+RPkvTtN9oZ4QUvvq4F4VyRCRB+R+bEXd4RIPx2Rq0Jv3WoE+G3Dog0gUArAoTfLA0EjBcg/DbemB68LaDC7y4P3Su+qVO0XYvbPvxcO6vW61dCUZF0HDpYkjd/JrW9/igFz78S8REGXrdj/i0FCL/dsyrU2dwdbrhWMh97WJtU/S7dpfjuhVI9cJAuk0woKZH8EWdJyob12tfiwmXPSc1hjS8h5mpbgPCbFYJAcwHCb2esCMPCbzV99ZLL4448SPr22d8ZGjGOkvA7RjgeQyACAcLvCJC4BYE4BQi/4wTkcQTaEUj56XvpdNShIh4/7iQck6+0VDoOGyIpH38kdT32kR2r39TOQ+dCIFoBwu9oxex5f+o7b0vuVWMl8df/ivh8Un7eRbLzltniz8rSdcDqOJX884ZL6ttvij81TYoeXSZVg07StQ83Nkb47caqMqd4BAi/49Ez71lDw+/vfvpNnlj5hkwZN0LS01LMm5XJPRF+mwxOd54SIPz2VLmZrEUChN8WwdOtJwR85WXS8bxzJGXt21J51jApWvSIJ+YdzSR9lRWNIdSatxrP4X3+Fanvvms0TXAvAkL47exFkLBzp3SYMVUynnxUm0j9HntJ8b0Lpfro4wydWO6Vl0rGU49rfZTcca+Unz/a0P6c3jjht9MryPj1FiD81lvUmPYMC7/Vmd/jps2XzV9+H3bkvXv1kAWzJ0pejvNfbkP4bczipFUElADhN+sAAeMFCL+NN6YHbwqoULfj2adLyofrxH/AAbL1+dc57qSNpZB//jmStnqVFnwXvPCq1O3Vw5sLh1nHJED4HRObLR5Ke+1lyZl4uSRu3aKNp+zyq6V0+o3iT0s3ZXzZs26R7Hmztb5KJ0yR0hkzTenXiZ0QfjuxaozZSAHCbyN19WvbsPBbvyHavyXCb/vXiBE6V4Dw27m1Y+TOESD8dk6tGKlzBIKD77p99xPfu2tla5LzN30YXYHcyy6SjKf/2fgiulWvaUehcCEQiQDhdyRK9rpHnfvfYfok7XNeXXX77CvF9z1kyfnbGU88IrlXj9PGUTFshBQvWGIvLJuMhvDbJoVgGLYRIPy2TSnaHIhh4bfa+T399kUyZfwI6bln92aD2LDpK1mxao3MnDLaFcehEH47Y7EzSmcKEH47s26M2lkChN/Oqhejtb9A8+D7D1L00puS33NX2VpUZf/BWz1Cv18LoNTRBw35+bJj1RtSt5+73x9kNblb+if8dlYl0155SftcTyjYIZKUJKVXTJSyqdeLP8W641LT3nxN8i48V9TXcHXcSuGTz+h+1rizqtRytITfTq8g49dbgPBbb1Fj2rMk/FZngc+9f5nMum4Mx54YU1daRcA1AoTfriklE7GxAOG3jYvD0BwnoF6iph11sv59qdv3D7Jj1evi69JFOuWkEn5HUc0OM66VrAfukYbcXCl47hWpPfCgKJ7mVi8KEH47o+pqt3fO5Ksk/flntAHX9jpA22Vtl8/x5M2fScezh2ihfG2vP0rh06u030ThahQg/GYlINBcgPDbGSvCkvB75eq1sn7jF+z8dsYaYZQIWCpA+G0pP517RIDw2yOFZpqGC/iqqyR/xFmS+u47jcH3i29IQ8dOkpjgI/yOQb/DTdMl6767xJ+dLTtUAH7wITG0wiNeESD8tn+lg3d7+5OTpWzCFCmbdK2of7fTlfjLz9LxrMGS9OP3je8gWLlaO5KFi/CbNYBAqADhtzPWhO7ht9rVPXbqPPl9a0GrArt07SgL51zT4jgUZ5C1HCXHnji1cozbCQKE306oEmN0ugDht9MryPjtINBa8K3GRvgde4Wy/zFLsmffKv6MTClY+T+HargAACAASURBVJLUHH5E7I3xpKsFCL/tW15tt/eUqyX9uae1Qdbu30uKFy6V2j/2tu2g1Zg7DhsiyZs+kYYOHaTwn89LTd8jbTteswbGzm+zpOnHKQKE386olO7hd2DabZ357QyayEdJ+B25FXciEK0A4Xe0YtyPQPQChN/Rm/EEAsECvpoayT/njMYd33v3lB2vvK3t+A5chN/xrRe1+1vtAvenpUvB0y9ITb9j4muQp10pQPhtz7I6Zbd3OD31Q82880eIOgvcn5IqxQ8slsrTh9oT2qRREX6bBE03jhEg/HZGqQwLv50xfX1GSfitjyOtIBBOgPCbdYGA8QKE38Yb04N7BVTwnffXYZL21uta8F3w0htS36VrswkTfsdf/8yHHpCcaZO0AKrosX9K1aCT4m+UFlwlQPhtr3K2ONvbAbu9wwo2NEjOtRMl8+FFIj6f7Jx5u5SNv9pe2CaOhvDbRGy6coQA4bcjyiSE3zrUifBbB0SaQKAVAcJvlgYCxgsQfhtvTA/uFIgk+FYzJ/zWp/6Zjz0sOZOuEElMlMKHn5SqU/+sT8O04goBwm/7lDH9hZWSM2WC9tJIO5/tHY1Y1j3zpcMtM0T8fim/aIyUzLlTC8O9dhF+e63izLc9AcLv9oTs8eeGht/q6JNx0+bL5i+/bzHb3r16yILZEyUvJ9seEnGMgvA7DjweRaAdAcJvlggCxgsQfhtvTA/uFMgfdbakvbpa6nruKwWrXmux4zswa8Jv/eqfsfwpyR1/sdZg0YNLpXLocP0apyVHCxB+W1++hJISyZl4uajwW111++0vReps7wMPsn5wOowg/dkVkjfmAq2lytNOl6JHlunQqrOaIPx2Vr0YrfEChN/GG+vRg6Hh9x0Ll2tjnDTW3d+UEn7rsRRpA4HwAoTfrAwEjBcg/DbemB5cJlBXJ/kXjhR1lm1rR50Ez5jwW9/6awHUZaNFGhqkeMESqTj7HH07oDVHChB+W1s2dfRTzhWXSuK2ra7Z7R1ONHXdu5J/7l/EV1amvQBTvQhTvRDTKxfht1cqzTwjFSD8jlTK2vsMC7954aW1haV3BNwiQPjtlkoyDzsLEH7buTqMzY4C+ecNl7SXX4wo+FbjJ/zWv4ppq1dJ/vmNoXfJvHuk/ILG3eBc3hUg/Lam9r7KCukw41rJfGSxNgC37fYOp5r85ReSP/x0Sfz9N6n7w35S8M/npX73PawpgMm9En6bDE53thcg/LZ9ibQBEn7rUCd2fuuASBMItCJA+M3SQMB4AcJv443pwT0CeRedK+mrnpO6vXpIwQuvSn33XdudHOF3u0Qx3ZD22svaDkx11fQ7Rhry8qS2z6FSe1Afqd3/AM+EUTHhufAhwm/zi5qyYb3kjR0tiT//qHVeOmGylM64xfyBWNBj4tYtkn/2EEn+8t/S0KmzFDy9yjXHu7TFSfhtwWKjS1sLEH7bujxNgzMs/FY9qGNP9tq9mwwd3N8ZGjGOkvA7RjgeQyACAcLvCJC4BYE4BQi/4wTkcc8IBILv+j32lB0vvhFR8K1wCL+NWyKpa9dIzrSJkvTN1y068WdlSc1Bh0jdAQdKbe+DtH/WHHKYcYOhZUsFCL/N5c++ZYZk332H1mldj320M/jVD5+8dPlKS0W9+0EdheLPzJLCR5ZJ9YATXE1A+O3q8jK5GAQIv2NAs+ARQ8Pv7376TZ5Y+YZMGTdC0tNSLJieOV0SfpvjTC/eFCD89mbdmbW5AoTf5nrTmwMF6usl/4IR2hnf0QbfhN/m1NtXXiYpn30qyZs+keRNjf9M+uE7Eb+/+QCSkqR2v16NO8QPPVwLw+t6/VE7o5jL2QKE3+bUT+10zr30Qm3Hs/h8Unbp5VJ64y3iT00zZwA268VXWyu5Yy9qfMlnYqIU3/eQq99DQPhtswXIcCwXIPy2vAQRDcCw8Fud+T1u2nzZ/OX3YQfSu1cPWTB7ouTlZEc0UDvfRPht5+owNqcLEH47vYKM3wkChN9OqBJjtEygvl7yLjlf0lc9q+303vHKmoh3fAfGzM5va6qnXkiXooXh//tf0o8t/27iT0mV2gN7NwXitQcfKrX77S+SkGDNwOk1JgHC75jYIn+ooUGy75kvWX+/VXw1NVK/625StOgRqTniqMjbcPGdwTvhS6fdIKWTp7tytoTfriwrk4pDgPA7DjwTHzUs/DZxDpZ3RfhteQkYgIsFCL9dXFymZhsBwm/blIKB2E0gNPh+6Q2p333PqEdJ+B01mWEPqGMKUj75WFI+3ShJnzb+M/G3X1v058/IlNreB2s7w2u1/x2qHe3AZV8Bwm/japP00w+Se9nFos74Vlf5hZfIzltmifo84fqfQObDiyTn2okiDQ1Sce75Unzn/a77IRrhNysegeYChN/OWBGE3zrUifBbB0SaQKAVAcJvlgYCxgsQfhtvTA8OFNAp+FYzJ/y2d/0TCnZIyscfSfInH0vypxu13eIJhQUtBt2QkyO1Bx/SeFRKn8O0f9bvtru9J+eh0RF+G1PszEcWS4cbpomvolzqu+0ixQuXSvUxxxnTmQtaTXvzNck7f4T4qquk+tj+UvjUSvGnZ7hgZo1TIPx2TSmZiE4ChN86QRrcjKHhd2VVjdw0d4m89OZ62aVrR1k45xrp3rWT9rF+hx3gmhdhEn4bvEpp3tMChN+eLj+TN0mA8NskaLpxlEDexedJ+vPPSEPnLrJj9ZtSt3fPmMdP+B0znWUPJv78o7YrXDsyZePH2nni6lzx0Kshv2PQ7vDDpObwI6ShYyfLxu3ljgm/9a1+4ratknPFpZL21utawxWjLpCS2+aKepEsV9sCKRs3SP7w0yWhpERqDzxICla8oP1/iRsuwm83VJE56ClA+K2npnFtGRp+37Fwuey1ezc59YR+MnfBMhk19ETpuWd32bDpK1mxao3MnDLaFS/CJPw2boHSMgKE36wBBIwXIPw23pgeHCQQvOO7S1cpeOmNuIJvNXPCbwfVv7Wh+v2S9O03jYH4px83huKbP9d2d4Ze6mz4phdqqvPDD+srDR06uADB3lMg/NavPukvPi85V1+mhbfqhznF9z8kVYNO0q8DD7SkjorJH/pnUf+s36W7FDz9otSpdwk4/CL8dngBGb7uAoTfupMa0qBh4bd64eX02xfJlPEjtN3eweH3dz/9JnPvXyazrhvDCy8NKSuNIuAegc4fvC3lPfeTii7d3TMpZoKAzQQIv21WEIZjqUDuuNGSsWKZ1Kvge9VrUtdz37jHQ/gdN6FtG0j+fFNjIK6OTPl8kyRv/izsWOv23LsxED/kMKk+4USpPeBA287JqQMj/I6/cgnFxZIz+SpJf+5prbHKs4ZJyZw7pSEvL/7GPdiC8swfOVQ7K12dj66OQHH6kTGE3x5cyEy5TQHCb2csEEvCb3Z+O2NxMEoErBRIXfOWZM++RTuDs6FbNylY8aLU9jrAyiHRNwKuFSD8dm1pmViUAtm3zJDsu++Qhk6dG4860ekFh4TfURbCwberneDJn336/y/U3Cgpn2+SpG++bjGj0hm3SOmEyQ6eqf2GTvgdX01S335Tcq+8VBK3/K6F3SV33CuVQ86Kr1Ge1gTyRv9V0l9Yqf178YIlUjFshGNlCL8dWzoGbpAA4bdBsDo3a1j4rca5cvVaWb/xC5l+1Si5Z8mz2rEn+bnZMm7afBk+ZABnfutcTJpDwA0CqW+/Idl//5sWegdf/uxsKVj2rNQcebQbpskcELCVAOG3rcrBYCwSSF/1rORdNErrffub72svNtTrIvzWS9KZ7fjKyrQwPEXtDv/oA0l7dbU2kao/nSLFDzws6kWaXPELEH7HZuirrJAON18vmYsXNq7LkwdL8V0LtB8Ccukn0OHm6yTr3ju1Bkuvv1lKJ07Vr3ETWyL8NhGbrhwhQPjtiDKJoeG3IlC7vC+cMLuZxtI7p0nfPs4/7yowKc78dsZiZ5T2FlC7TbLn3Kb9WqC66vfYS0onT5O0kcMlcdjZkrzmLe3jhY8tl6pT/2zvyTA6BBwmQPjtsIIxXN0F1FnOnU84WlQIVDzvHqm44GJd+yD81pXT8Y2lvfma5F52kSQUFUn9brtr39vU9j7Y8fOyegKE39FXQP1QJveS87VzqdW59Dtn3SEV55wbfUM8EZFA5mMPS841V4o0NGjOxXcvFElMjOhZu9xE+G2XSjAOuwgQftulEm2Pw/Dw2xkM8Y2S8Ds+P572tkDaay9L1rzZot6KroXeu+4mZZOnS/l5F2n/HXjhZdpll0jGU49rHyuZe5eUXzTG23DMHgEdBQi/dcSkKccJJOzcKZ1OOFqSfvxeKs49X4rvfkD3ORB+607q+AYTf/2v5I0e1fT9T8k/7pbyCy9x/LysnADhd3T62bfdLNnz52gPVQ8cJMX3PCj13XaJrhHujlog7fVXtN8y8lVVau6FS58Sf2ZW1O1Y9QDht1Xy9GtXAcJvu1am+bgMDb/vWLhctmwrlJlTRkt6WorWc2VVjdw0d4n0O+wAjj1xxhphlAgYIqCd6T1r5v9C7+67StmU65pC70CngfC7qrZBsv92k2TfOVf7I/WrgupXBrkQQCB+AcLv+A1pwbkC+eecKWonrnoZ4fY33jNkIoTfhrC6otEO102RrAfv0+aidoKWzLtH/Gnprpib2ZMg/I5MXJ1Bn3fpBZL8r8+1lzDu/Nvfpfz80ZE9zF26CKgX43Ycdrok7NiuvdOo8OkXpb5rN13aNroRwm+jhWnfaQKE386omGHhdyDkHjZkQIsjTnjhpTMWB6NEwAiBtLdelyx1vMn/n+mtdpiUXTOt1Z3cweG3Gk/mw4skZ8rVjX9JHPlXbZcKFwIIxCdA+B2fH087VyB71i2SPW+2NOTmyva1G6S++66GTIbw2xBW1zSa/tILkjv+EvGVl2lBWNEjy3R72aprkCKYCOF3+0hZ990lHW6art1Yc+jhUvzgUqnbq0f7D3KH7gKJv/wsHYefLurYLfX/PYXLnpXaAw7UvR+9GyT81luU9pwuQPjtjAoaFn4XlZTK9NsXyZTxI6Tnnt2baXz3028y9/5lMuu6MZKXk+0MqTZGybEnji8hEzBBIHXtGsmefaukfPSB1psWek+YIhXnjxZ/SuNvhoS7QsNvdU/ayy9K3sXnia+mWqoGnSRFjy4Tf2qaCbOgCwTcKUD47c66Mqu2BdSvnuePHKqdt1rw3MtSfdSxhpERfhtG65qGE3/+UfJHDZPkL/+t7cYtenCpVJ1ymmvmZ8ZECL9bV1ZBq/oBS+oH72lf80onXSulk6c77rxpM9aRmX2oY7fU/w+lfLhOO/qkcOmTUj3wRDOHEHVfhN9Rk/GAywUIv51RYMPCb3Z+O2MBMEoEjBZIffedxtD7w3VaV/Vdukr5hMnauZb+lNR2uw8XfquHUjZ8KPnnnCHqm0b1q+oFK1drL+rhQgCB6AUIv6M34wlnC2gvuDzxWG2nbcmtf5fycVcaOiHCb0N5XdO4+qF+h+mTJfORxdqcysZeITtn3i6SlOSaORo5EcLv8LoZTz4qOddNFl9ZmdTtubcUP/So1BxymJGloO0oBHy1tdoPJtKfXSGSkKAdfRR491EUzZh2K+G3adR05BABwm9nFMqw8FtNXx1vMn3WIlk455qm3d9q1/fYqfNk/AVncOa3M9YIo0QgJoHUd96WbHW8yf+H3g0dO0nZxCmNoXcUZ1m2Fn6rQSV9/ZV0PPvPkvj7b1L3h/2kQJ2XZ9CvrMeEwEMIOESA8NshhWKYugiowLvzoGMk6T/fSuWZZ0vRQ4/q0m5bjRB+G07sqg5UCJZ71TjxVVZIzeFHSNHDT0r9Ls1/k9ZVE9ZpMoTfzSHVedK5V4+TtFdXa39QfsHFsvPW2dpvFnDZT0C910i930hdZeOvlp23zLLfIEWE8NuWZWFQFgoQfluIH0XXhobfahyBsPv3rQVNw1p657QW54BHMWbb3cqxJ7YrCQOyUEDb6f33v0nK+ve1UcQaegem0Fb4re5RwXfHoYMbz8vr2k17YYw6L5MLAQQiFyD8jtyKO50vkP/XYZL2yktSt9/+2gsu/ekZhk+K8NtwYtd1oL6vyb9whPaD/oa8PCleuFSqTviT6+ap54QIv/+nmbZ6leROGC8JhQXSkN9Riu9bJFV/OkVPbtoyQCD9+We0ox3VVXXqn6Vo0SNRbRoyYEgtmiT8NkOZPpwkQPjtjGoZHn47gyG+URJ+x+fH0+4QUMeQZN96o6Sue1ebkPpGu+zqyVI+ekxcwUJ74bfqSzsvb8RZ2nni/uxsKfjnc1JzxFHugGUWCJggQPhtAjJd2EJAvdxSveSyISdHtr/9gdTvsZcp4yL8NoXZdZ34qiol59pJkvHEI9rcSidMltIZt7hunnpNiPC7UTJ3/MWSsfwp7d/V+dFF9z8kDZ276MVMOwYLpHzysfb3GvWDC+1ox2XPSkOnzgb3GnnzhN+RW3GnNwQIv51RZ8JvHepE+K0DIk04VkD9xazDzTMk86EF2hzU7qTyKyZJ2SVjtRe3xHtFEn4H+si7cKSkv/i8tkOi6OEn2OESLz7Pe0aA8Nszpfb0RNPefE3yzzlTMyhY8YKpLxUj/Pb00ot78hkrlknONVeKr6Jcqo8+TjuzWb1Dhau5gBfDb3WOd+r69yXlnbcl9f21kvz5piaUkjl3SvnoS1kmDhRI/OUn6TjiLO03P+p330MK/vm8dsSjHS7CbztUgTHYSYDw207VaH0shobfRSWlMm7afNn85fctRtC7Vw9ZMHui5OVkWy51x8LlsvipxrPQAtetU0c3nUm+cvVauWHOEu2PThvUT2ZOGS3paSlN9xJ+W15CBmCRQOr770rulWMl8ecftRGUXn2NlE26VpfQOzClaMJv9UzOxMsl87GHtceL718sFcNHWqRDtwg4R4Dw2zm1YqSxCST98J10PuFo8ZWWSumMmVI6YUpsDcX4FOF3jHA81iSgjkHJG32uJH/5hbYLtGjRo1J93PEIBQl4IfxW7yxIXb9OUt57R1LeWyspn25stgbqd91NqgcMkrIJk6Vu756sDwcLqN9szbvgHFFHSqrfbC18dLktPucJvx28qBi6IQKE34aw6t6ooeG3CpXVNWnscN0HrmeDbY1TvbRz3sLlTUF9uHsJv/WsBm05QUB9491hxrVNIXNdj32k+P6HtJcy6X1FG36r/rNvnynZd/xdG8rO2+ZI2dgr9B4W7SHgKgHCb1eVk8mECKiXBmovuPzma+0M1cLHGr8/NfMi/DZT2719tTgG5doZUjrlOvdOOMqZuTH8Vl+/UlTY/e4aSV33nqR8/FEzFfVuner+A6Wm/wCpPrY/gXeUa8YJt6uXlgaOPiq+Z6FUjGw8E9yqi/DbKnn6tasA4bddK9N8XIaF32rX9/TbF8mU8SOk5572fjt5W+G3+rO9du/WtAs8NAxXnITfzljsjFIfAW239/iLJfHX/4okJkrZuCuldPqN4k9N06eDkFZiCb9VE5kPPSA5068R8fs5I9OQytComwQIv91UTeYSKhA4Eqturx6y/Z31uv52UqTahN+RSnFfJALpzywXFYipMLz6+IFS9OAj2gvGvX65IfxWNU356MPGsPu9tZK8aaP4amubSqveV1BzTH+p7j9Aao4dILX79/J62T0x/+y775DsW2/4/7/XTNF+g8mqi/DbKnn6tasA4bddK0P43aIyoceeBI48qayqkZvmLpF+hx3QFH5/99Nvcv2sRXLb9DFNoT7htzMWO6OMT0CdM6nt9n608Qigun32laKFS6X24EPia7idp2MNv1Wz6c+ukLxxF4vU1UnFiFFSfPdCkYQEQ8dL4wg4UYDw24lVY8yRCGTdd5d0uGm6Fnhvf+M9qdv3D5E8pvs9hN+6k3q+QfWbDPkXjtB+o0Gd/130yFNS07efp12cGH77qqskZcNH2jEmWtj9yQbx1dQ01dGfkSk1Rx6l7e5Wx9zUHtSH72U9usrTXntZ8i4aJWrNVP75DCl+8BHxp/zvKFazWAi/zZKmH6cIEH47o1KG7fxW0w/dNe0EEhVuj506T2ZNHyMH7t9DC7+HDRkgffvsrw0/XPhdWPq/b1CcMEfGiEC0AslvvSGZ4y+VhN9+1R6tnDBZKm+5LdpmYro/Oz1JqmvrpabOH9PzyW+/KVkjh2kviao5Y6iUPfZUTO3wUCsCqiw+dJwukJedIiVlNdIQ26eZ06fP+F0qkPzeWske/CdtdmVPPS01pw2xbKYJCT5RgUFxGd8zWlYEF3bsq6yUjMlXS+pjj2izq/7LcKkef6XU9dX/GDon8KUkJUhqcoKUVtbZerjJ770rSe+9I0nvviPJ765tMdbaY46VuuMHSl3/gVJ79DG2nguDM1cg6dONknX2GZKwfbvUHXGklC5/Tvz5+aYOIiM1Sfx+v1TW1JvaL50hYFcBtVmPy/4ChobfKih+YuUbMmXciGYviLQ7SyC0P/WEfhHt/K7iC7/dS8r4YhUoLZXkyZMkcWnjCyT9++4rtQ8/Kg19+8baYtTPJSclSH29Xxr8sadyCR9/LMlDBouvsFAaBgyUmpXPiWRmRj0WHmgpUN/gF7WjkcvZAqnJidoPmbgQcIuA75efJeWIvuIrLJC6adOlbuathkwt0q+B6qtkSnKCVNc2GDIOGvW2QOKTj0vyuMtEqqo0iIaDD5aGy8ZL3chzRdLTPYOjfsikvieprbPX51nCuvclYc0aSXhnjSSs/6CpToHCNBx2mDScMEgaBp4gDSrsdlDNIv0a6JlFaMJEff/9RZKHnCYJX3wh/r32kpoXX9b+jmbWlZToE/W3MvX3My4EEBBRv3XEZX8Bw8Jvdeb3uGnzZfOX34dV6N2rR9NLJO3GFLxjnTO/7VYdxmOWQOo7b0vu5ZdI4pbfG8/2Hn+VlE6/yfRfr4vn2JNgq6QfvpOOZ56qnVVe2/tgKXjmJWkweaeEWbWjHwSiFeDYk2jFuN/OAr6aaul0ykBJ/nyTdh5ywdMvivis/SEdx57YecW4Y2wJhQWS+dhSyXhksST+/GNjCN6hg1Sc81epuHisdlyd2y9bHHtSVycpn26U1PfXakeZpHy4XtRLK5sun087p7v62AFSc/xAqTn6OK1OXAhEI+ArL5P8UWdrR+U05OZK4RNPS82RR0fTRMz3cuxJzHQ86FIBjj1xRmENC7+dMX0RFdKvfnO9jBra+GuxoceahL7gMtzLMTnz2ynVZpyRCCTs3Ckdrp8sGU89rt2uzkctWrBEavscGsnjut+jV/itBpb426/S8S+nSdK330hdj31k+1vrxJ+VpfuYaRABpwkQfjutYoy3LYHcK8ZIxrInpH6PvWT72x+IekGc1Rfht9UV8Fb/aW+9LhmLF0raq6ubJl59bH8tBK8ccpZrMawKv1M/eE9SPnhfUtQ/172vnckcfKnvOauPPlZqBg6S6mOP5+Wkrl2B5k8s55orJfORxVrHxQ88LBVnn2P4IAi/DSemA4cJEH47o2CeD78DL7V86c31TRVbeue0pjO+1QdXrl4rN8xpfMnfaYP6ycwpo5sd40L47YzFzijbF1B/Wcq56rLG3d4iUjpxqpRef3P7Dxp4h57htxpmQnGx5J9zhqRs3KDtlCi9fqaUXzTGwBnQNAL2FyD8tn+NGGFkAplLH5KcyVeJPz1Ddry2Vmp7HRDZgwbfRfhtMDDNhxVQP/TPWPKgZDz5qCRu26rdU9+1m1T89UKpuPASqd+lu6vkzAq/1W5bbVf3B+9rO7xDr4bOXaRK7eoeMEh7UWV9911d5cxk7CWQteBu6XDDtMa/u027QUonTzd0gITfhvLSuAMFCL+dUTTDw2+1c/rCCbObaYSGy86gan2UhN9OryDjD93tXfvH3lJ8/0Oi/mn1pXf4reajfv20w83XS+bihdr0ag88SEr+cbfUHO7NF0RZXWP6t16A8Nv6GjCC+AVSNqyXTqeeoDVU9NCjUnnm2fE3qlMLhN86QdJMzALpq57VdoOr4DZwVQ06SSouuFiqBlv3MtiYJxTmQaPC75RPPpbU1S9I6gfrJOXDdS16bujYSaqPGyA1x/aX6mP6a781yYWAmQJpr78ieaP/qv0dp2L4SCm+v3E3uBEX4bcRqrTpZAHCb2dUz9DwO/TIEEWijhUZO3WejL/gDBk6uL8zlNoZJeG3K8ro2Ulou72vHCuJW7doBmbsGIgG24jwO9B/8hf/kpyrx2lnM6qr4tzzZedNf+PXUaMpEPe6QoDw2xVl9PQkErZvk84D+mn/X1Y27irZeWvzjRdW4xB+W10B+g8IJH3/H8lY/KBkPPWoqM0P6lLvQFHfA1WcP1o7Fs6plxHht/pNwU4nH9+MpCG/oxZy1xx3fGPYvd/+TiVj3C4SUO+5yB9xlvZbHtVHHStFj68w5Ngvwm8XLRqmoosA4bcujIY3Ylj4HThOZNiQAc2OEFEzUqH4ilVrWhwfYvhsDeqA8NsgWJo1VEDb7T1jqvarsOqqOayvFN+7yHa7VYwMvwPAmQ8vkuxbb9D+EqheOlR63U1Sfsk4Q/1pHAE7CRB+26kajCUWgU6nnajtyKw+7ngpePblWJow9BnCb0N5aTwGAXUudfqzT0vmQw9I8qZPmlqo6XeMVJx3kVSecZb409JjaNm6R/QOv7Ufqg08SjsOUH1tqR48RAu7aw840LpJ0jMCbQioo45UAK42+KjfQChY/rzU776nrmaE37py0pgLBAi/nVFEw8Jv9SLJ6bcvkinjR0jPPZufJ6d2f8+9f5nMum6M5OVkO0OqjVESfju+hJ6bQOjZ3jtnzpKyy6+2pYMZ4beauPoLTs6MqZL+zHLNQZ0TWzLvHqk54ihbujAoBPQUIPzWU5O2zBbImTZJC/Dqd9tdtr/1gbaL1W4X4bfdKsJ4ggWSP/tU+xzKeOqxpg/7M7O04xPU+eC1T98bxgAAIABJREFUBx/iCDC9w++mH6od218KnnvFEQYMEgFfeZl2BEram69JQ16e1Bx5tFQPOkmqTv2z1HfbJW4gwu+4CWnAZQKE384oqGHhNzu/nbEAGKX7BXylpaLOKkz5ZIMkb9wgKRs+lISCHdrE1e6e4nsekLq9e9oWwqzwOwCQ+u47ot6crn4tWF0Vw0bIzltmi3p5ERcCbhUg/HZrZd0/r4wVyyR33Gjxp6bJjpffktqD+thy0oTftiwLgwoRSCgpkYxlj0nGkkWS9N23TX+qHfHR9wipPnmw1PTtZ1s3PcPvph+q7bqbbH9b/VCto23nzcAQCCfQYca1kvXAPc3+SP0gq+rEkxs/lw89PCY4wu+Y2HjIxQKE384ormHht5r+ytVrZfmqNbJg9sSmHd6c+e2MhcEonSngq6rUfnU1ZdMnkvzRh6LOfkv68fsWk/GnZ2hnW5dfcpntJ2p2+B0Ayb7j75J9+0ztP/3Z2dpZ6GVjr7C9FwNEIBYBwu9Y1HjGaoHkzZ9Jp1MGijq+QR3bVTFilNVDarV/wm/bloaBtSKQuuYtyXh0iaS/sLLZHQ25uVJ18mCpPulULURTO8TtcukVfmcsf0pyx1+sTWv7W+ts+0M1u7gzDvsKqBdgqs/ltJeel7SXXxT1A67ApX6go3aDa5/PAweJ+vthJBfhdyRK3OMlAcJvZ1Tb0PBbEajzvS+c0PylQ0vvnNbiHHBncIUfJceeOLl6zh672tGtwu7kTzeK+pVVdb5buEud+VbT51CpO/gQ7af8amecU85xtCr8Vo5JP/2g7QJX3zSqq/bAg6T4rgWO+fVfZ69uRm+mAOG3mdr0Fa9Ayob1kvLhesn8v/buBEyq6s77+L+7tm4QkEWWRvYdlR0B2UVEQaIxCWMymUmCMU6c5R31MaOTyevj5E10TEwyk3njyzgxZhsjcVdwZQdBEAURaJAd2aERWbpr636f/ymqra6u7q7lVtW9Vd/7PDwNTd17z/mcW91Vvzr3f379/8R18ICcv/1OOfNvP8/0sFndn/A7q7wcPIsCpZ9+KmVvvS6+1xeJb9lb9YtkRk/pnzLNBOHVs+dKuGfvLLak5UNbEX43/FDtv+TCbV9v+cQ8AgGHCPhWLIsE4YteNgtEx24agNfMmiM1c2+RcJeuTfaI8Nshg00zcyZA+J0z6oxOlPXwO6PWOWRnwm+HDJTDm+nZukW8H2wU94ebTBmT2MWJYrumJUyCI0ZF/owcZULvulatHdv7fIbfUbTyl56Tdvffa+qC66aLYX72g4dsNdvJsQNMw20hQPhti2GgEU0IeDduEN+aVeJdtUy869aKzmSLbrouw8nFS2xvR/ht+yGigUkKaHk431uvS9kbixuURtHdg4OHSM31N4p/9hckMObqJI9o3cMyDb9LT5+Wy6aPF9cnB+X8N78tZ376H9Y1jiMhYDMB87v1tVek/KUXxL13d4PWmfIoenfHDXMaTfoh/LbZQNKcvAsQfud9CJJqQFbD758tWChHj1fJQ/fNl/Iyr2lQtBb4+NFD5dbZU5JqpN0fRPht9xFyZvt0ZlvZSy+IvjDRvyfawt0vl8DI0RIaOcbM6Na/111in9tPrZC3Q/it/Sg5d07aPPojueRX/266pTMiPvvhI1J96zwruskxEMirAOF3Xvk5eZyAftDrNWH3cvGtXSMlF843eIR/0hQJTJ4m/slTHbMoMeE3l3khCmhgpiG4mRW+emWDLoa7VUjN7LlSc+Nc8U+7NifdzzT87njzDeJbs1ICo8fKyTdW5KTNnAQBOwi4d+6Q8sWviO/N18S7fm3C57J/5g2m1BHhtx1GjDbYSYDw206j0XRbshZ+s+ClMy4AWmkvAX2D3+qZ/5FWv3miUQmT2o6dTLgdHD1WgiNHS2DUWKnt0MFeHchCa+wSfke75tm+Tdrd83f1H0joi8Azj/1S9IMINgScKkD47dSRK4x26/oUGjh5dUbp2tWiCzXHboFx10SCbi2vcM1kR3aa8NuRw0ajUxDQ523Z8iWRWeGLXxYtlxLddO2U6htvMjPCa2bMTLq2cAqnNw/NJPxu++ADcsn//XfR19snVrwr4a7dUj09j0egIAT0DojIh1qvStnStxt8AK1lM8PXXSehG2bLp9ffxEKwBTHidCJTAcLvTAVzs3/Wwu/TZ87KAz9+Qu676zbp16uiQW900cuf/OpP8vA/31G/EGZuupudszDzOzuuxXRUz/at0urJ/xJdYKfk/DnTda3LrQuQhK4cJoFhIyTco2cxkdT31W7hd7Rhrf/wlLT513+R0qoq862z//v/yNl/uKcox4hOO1+A8Nv5Y+ikHmgZLxN2r1gm3rWrG9UQ1nIJ/klTJaB/xk9wzBoVzY0B4beTrlDaaoWA3rXoe2OxlL31huhzPnbTUgo1N91i+SK16Ybfuqhn+/mR2t4nF70t+oEbGwIIRAR8y942YXjZ64tMSaDYLTB2nPi1TvisGyU45ArIEChKAcJvZwx71sJvZn474wKglfkVaPXsM9Lq1wsalDW5MO+rcmH+nXmplZhfjcRnt2v4ra3VmRE6U6jV//zOND40aLCc/ad/kcD4iRLu3MWOnLQJgYQChN9cGNkU0MWYNez2rF5pvsbOCNXz6l1NgWsmSWDKdPFPmOjodSqaciT8zuYVxrHtLqAL05rwTGeTLnu7vrm6WObp3zwtte3aWdKFdMJv945Kuey6SWYtgTOP/EzOf/tvLGkLB0GgEAV0Qdh2S18X12uLxfXehgZdDPXqYyZu+fUuj8lTC7H79AmBhAKE3864MLIWfmv3N2yqlAcefkIWPHpv/exvnfV95/cek7u+cTM1v51xjdBKiwVcB/ZJ618vkFZP/0FKq05FQtNefeTC7d+RC1/9a6lt397iMzr7cHYOv6Oy3vfWS7u77xItiRLdatu2ldCgoRIaOMiE4qFBQyQ4YKCEe/Z29oDQ+oIUIPwuyGHNW6c8ldvFqzO7Nexevdx8UBi7BYdeKf4p0yWgpUwmTBL9eVnoG+F3oY8w/UtWQEPmsrffkHb/67vmrg99XXTq6efNa6VMt1TD79IzZ6TTtdeIe/9eufCV2+TTx5/MtAnsj0DBC0Rrfl84cFjK9QOtRS9L2VuvN+i3rkGlQXjNjTdJzYxZoqWP2BAoVAHCb2eMbFbDbyWIht1HjkVCPt2e+sX9MnZE5i9w7EJM2RO7jIS921H+0nPS6ndPim/FsvqG6guCC9+6Q2qunWnvxuexdU4Iv6M8lyz4Tyn/w29Fy9g0tWmtvFD/AfWBuH7wEe7TV0K9+1A3L4/XWbGfmvC72K+AzPrv/nin+FYtN4tUmpndJ080OKCGWv7J0yUwcbIpZ1KMH/ISfmd2jbF34Qm49+2R9l//iuiHZXWtWsvpJ35rwrJMtlTD7w7zbpaypW9J8KrhcmJZw0X+MmkH+yJQyAKJFrwsqakW37IlpjRK2Wuv1k/wijroQtV+LXd0401m0hcbAoUkQPjtjNHMevjtDIbMWkn4nZlfIe+tK2e3fnKBlD/7p/rbvGsv6yzn//IbJvRmkcSWR99J4Xdsb9x7dokGQnoNuHdWinvXTnHv2N6otm3sPjpLItR/oIR69pZQnz5S26efhPRPr94SvrxHy1g8AoE0BQi/04Qr0t3cuz8W35pVkZndq5ZL6YnjDcPu/gPMwpSBqdPFP3GK1Ha6rEilPu824XfRXwIAJBDQdW7af+ebphyKbmf/+UE5e88/pW2VSvjd5scPSZuf/ZvUXnqpnFixntfkaauzY7EJJAq/4w28G969WPP/9UY1/7U2eM3MWaY8SmDs+GLjo78FKED47YxBJfy2YJwIvy1ALKBD6O2crZ5bKOW//414N35eC00DgAvfmC/Vt84roN5mvytODb+bknEdP3YxFK8U1949oiGSzn5y7dsnJf6aJkE1GPePu0YCk6eZ2ZNaI5cNAasECL+tkizM4+jPKJ3V7V21XHyrV4rr6JEGHQ337CV+87NpimgN33DXboUJkUGvCL8zwGPXghfQEFrDaN2q535RPv3VE1JX3irlficbfmvZlQ63fdEc/9Rzi8Q/dXrK52IHBIpVIJnwO9bGdeSwlC16ycwIj70DWh9T26GDVM/+ggSumyU1M2am9bwv1nGg3/YRIPy2z1g01xLCbwvGifDbAsQCOITWfS7/429N8F1y4bzpkdYxvXDbX8mF+d8xpS7YUhcotPC7OQHX4UOm7qSG4iYY37NbtEa8Z9dOKTl7tsGuda0vEb+G4Hob4cQpEhw+MnVc9kDgogDhN5dCgzeqBw9EypisXmFmeLsOfdIAKNylq1nMyixQOXmqhHv0ArAFAcJvLhEEmhfQQLr9HX9tXu8ErxwmVX98NuXZ2MmE3/q66rLpE0TrfX/24I/k3N/fzdAggEAKAqmG37GH1rs9ypa+Lb7XXjF3fOjzMHarmXG9+GfeIDVzviDhbhUptIqHIpA/AcLv/NmncmbC71S0mngs4bcFiA45hOvgfnEdPSquI4fMzLeSi4t4mTqn76yq70Vg9FhT1qT6i1+WOl+ZQ3pnz2YWU/jd3AjU19TVMgNaU/fUyQYP14Vk/BMmmQXkzMKpl15qzwGlVbYUIPy25bDkrFH6wZspY7JyWSTsPrCvwblrO3aKzOzWD9smT5VQPz7MTXVwCL9TFePxxSigd8N1uO1Wce/dbdZBqfr9MxIYd03SFC2F33qHXafrp5oyDDWz50rV755J+tg8EAEEIgKZhN/xht533zEzwjUI1/c6sZtO7DELZl43S4IjRsGPgG0FCL9tOzQNGkb4bcE4EX5bgJjnQ2gpitKjR0S/6gy30sORcNt878jhyN+rPl+0NVFz9fbMC1+aJxe+/TdmxgqbNQKE34kddYEo75qVkZq7q5dL6cUPYqKP1sWjAmOvlsCYcRIYP1ECY8eJlk5hQyCRAOF3cV0X+jvNfGi7cpn5qmVNYjddkFIXpgzoH72zZPCQ4gLKQm8Jv7OAyiELUkBnfrf/9l9J2ZI3Tf/OPPZLOf+N25Pqa0vh96V/d4e0+tMfzXoqusAlr4uSYuVBCDQQsDL8jj2wfuhVtvhVUys8dlKZPiZc0V1qrr/RzAj3T7+OEUHAVgKE37YajiYbQ/htwTgRfluAmMVDaAkS966PI+Uk9u0T1ycHIqH2saOis930T7KbLtqltUzNn24VUlvRXWq7dDW3ZWo9Zl5EJyuZ/OMIv5Oz8mz7yMwI92gYvnZNwg9rgkOvlMA1kyQwboLo3Qnhnr2TOziPKngBwu/CHuLSkyculjGJ3DmivxNjN3PniC5QqTO7p0yX4BVXFTZIHnpH+J0HdE7paIE2j/xQ2vz0YdOH8/O/I2ce/UWL/Wku/G79+99Iu7v/1tQUPvH2agkNGtzi8XgAAgg0FshW+B17ptLPPjOzwX2LX5ayJW/VlxTVx9SVlYt/+gwThNfMmiP6gT0bAvkUIPzOp37y5yb8Tt6qyUcSfluAmOEhzIztfXvEvVcDbq2XvFvc+/aKe++eRuUhEp1Ka3PXXgy064Ptrt2ktmvF52H35T0ybCW7pyNA+J2Omoh7zy7xrn9XPBvWiW/dGnHvqGx0IK3bG7h6ggTHTzCrrQdGjUnvZOzleAHCb8cPYYMOaFkkU7NbZ3evWt447NY1A8ZHFtD1T5rC7cQ5GH7C7xwgc4qCE9ByCO2/803RxeT1dUrVHxaKlmFqamsq/PZs/kAumzHR7Hb6v38n1bd8ueCs6BACuRLIRfgd3xff8qXie/M1KX/1xUYT1/TuVv+Nc83McO5Uy9VVwHliBQi/nXE9EH5bME6E3xYgpnAIDfG8H24S95bN4vnwA/Fu/qDRYoDxhwv17S+h3n0k3KevhHv1jgTaOmu7cxcza5u63CkMQI4fSvhtDbjOoPCuXyued9eKV/+8/56U1FQ3Orgunqkzw4Njx5k6m/rBEFvhCxB+O3uMS6uqxLdmhXjXrBbfqmWNPuzSWVKBceMlMHl6ZKHcseOc3WEHtp7w24GDRpNtIaCv+zvedou4Dh4wr99P/36hBEaOTti2ROG3/ny87NoJ4vrkoJy78+/ksx89aot+0QgEnCqQj/A71srz0YeiC+T6XntVvBs3NGAM9epj6oT7Z802a5SwIZALAcLvXChnfg7C78wNhfDbAsQmDuHZ9L54t2y+GHRvEs+WD0UXq4nf9I19qP8ACWmw3befhHv3NfX8Qr17U9ohe8OTkyMTfmeP2fvBRvGuW2MCcd+7a6X0xPFGJwsOGRqZHT52nFy47evZawxHzqsA4Xde+VM+udb413qYpszR6hXi2b610THMB1kXF6jUuv9s+RUg/M6vP2d3tkDpmTPSfv7XxLdimenIp//5XwlfkyQKvzt+Za74li0x65+cfPUtZ0PQegRsIJDv8DuWQNfkKnt9kZQtfsU8z2NzAi3pprPBq+d9TWpmXG8DOZpQqAKE384YWcJvC8aJ8DtzxJLz50Q/xdVZ3O4PN4nnoy3m34k2vd0xMGKUBK8aLiH9euVVEurdN/NGcARbChB+525YXAf3i/e99ZGZ4e+uFc+WzY1OrnXttTyKzroKjhlnnodhSgLlbpCydCbC7yzBWnRYc+fGO6siC9xqbf8Ez00tCaCznAJTppvFbrmjySJ8iw5D+G0RJIcpaoG2P7hfLnn8P4zBuTvuks8e/mkDj/jwu83D/yptHntEtMzbieXrpPayzkXtR+cRsELATuF3fH/MjHANw19fJLq4d+ym9f5DAwdJcMBACQ8aIsFBQyQ0YJCEBgy0goVjFLEA4bczBp/w24JxIvxODVFvP/R8uOnzoFtndu/ZlfAg4Z69JFAfcg+LBG1du6V2Qh7taAHC7/wNn34opXXDtVyK+bNxg5ScO9eoQboQrAnDR4+VoAnGx7D4TP6GLa0zE36nxZa1nfR5prX6vSuWRWZ2Jwq7R42JhN2Tpkpg/DVmETc2+woQftt3bGiZswRaLXxaLr3rdtNo/4RJcvq3T0tth47m37Hhd9mSN6XDX9xivn9y8RJzFxsbAghkLmDn8Du2d3oHuVk0c/kS8VRua7ZMqgnFBw6WwNRrJTRwsNS5Sk2J1HDP3pmDcYSCFyD8dsYQE35bME6E300jan09ncGtv3z0zbuG3q4jhxPuoKuuB68cJsFhIyU4fKQEh42g3rAF16fTD0H4ba8R1A+qzHN543umbrg+t+Nrh+uM08DosSIet7krI9y3vwSHXmFq7nOXhr3GM9oawu/8jot+0ORb9454V68ws7u1JFH8pr8TTSmTqdPFP2Gi1LW+JL+N5uwpCRB+p8TFgxFoVkBfe3T4yy+L69hRE1BV/eHP5n1DNPw+s3WnXDZ9gmi5FJ0drrPE2RBAwBoBp4Tf8b3VmeDuXR+Le8d2cX+8U9w7tol75w7zc6S5Te9w1Vri4R49JGy+9pJwz54S6tmbu1+tuaQcfxTCb2cMIeG3BeNE+B1B1F8ini1al3uz6Krq+lXrkiba9AWqzuI2Ibd+vWqYaN1uNgTiBQi/7X9NaL1hzwcbI3d0vLfeBOLNbeaDrv4DzcyKcL8Bpl5/cPBQ0ZIqbPkRIPzOrXtJ9QXxXgy7fRp2xy3YpK3Revv+SdMkqHW7J02V2nbtcttIzmapAOG3pZwcDAEpPXlCOnztS+aDeN1OL3hK6r76VWnlc4lr5AjxbPtIqm/5spz+79+hhQACFgo4NfxuiqDk7FnxaCC+a6e4du0U9759oqUg3Qf2J1wPKf44Ojs81KtXJBTvHQnHQz17Rv5d0d1CeQ5lVwHCb7uOTMN2EX5bME7FGH6bgNvM5I7M5ta/65v5+K2uVWszmzswfKSEhg2PzOy+argF6hyiWAQIv5050u7dH4t7315xb98mrj27TGkjnW0RX38vtndaz19vOTSL1vYbYGaLh3S2eL8BzkRwUKsJv7M7WHp3hCkhtHKZ+NasEu+GdY1OqLfcmpndk6ea0Lu2Q4fsNoqj51SA8Dun3JysiAQu/fvvSKun/2B6XPO3/yCeqlPievqPEhw8RE6+tYqSUEV0LdDV3AgUWvjdnJouoOnav1/cB/eL64D+2Wf+7frkgHmfowtutrSF+vQT//QZou9zdNM793T9AS3lWtupk4Q7d2E9gpYQbf7/hN82H6CLzSP8tmCcCjn81kDbLD65+f1I0L1V//5BQrXa9u3NLYcm6NYZ3VcMMzM62RDIRIDwOxM9++2rIaC7cru49+4W99494tpZab56KreLln5oatOfJfri0ZRP0dniAwaaUDxa59N+PXVWiwi/Mx8v18ED5sMd19HD5mtJVZU5qN5SW/7Sc41OUB9268zuiVNEa+ezFa4A4Xfhji09y7/AJQv+U9p+/3v1DdE7yU4sW2teN7AhgIC1AsUUfrckp1mJ+2IY7tq318wY15BcZ41rUN7UXfCJjmsCcf3TrcKUU6nVGeVaakVnkF/ew3yfzZ4ChN/2HJf4VhF+WzBOhRJ+l376af0sblO2ZOuH4t5RmVBIb+Ex5UpGjDKBd/CKq6h5ZcG1xCEaCxB+F89VUXri+OczxLW2uNbj0xnkTfwcUpm6Nm0kOORKCfXrJ+EBWk5Fy6hoSZVBxQNnQU8Jv5tGdB0/JqUm1D5i1qzQv5ceOWz+Xv+9ZGb+9B8g/snTJEDYbcEV67xDEH47b8xosbMEfCuXS4dvfU1KznwqVf/znNRcf6OzOkBrEXCIAOF38gOlE3vc+/dJ6bFj4jpySFzHj0vJsaPiOnFcSo8fE9eJY+arrk+QzGbWUtISK917REqsVFweqT/eI1JmhS0/AoTf+XFP9ayE36mKJXi8E8NvfdNuFqLUkFvLluhClJ8cTKgR0sXqrhxmZnPrrG6zECW3Y1tw5XCIZAQIv5NRKvzHuPfvFdeePeLZsU1ce/dEQvKPdzb5c0tFdMZXqG8/CQ0Zahbd1NniWmtcZ1awNRQoxvBb32iYUFvfhBw+ZILs0iOHGn7v8KGkLxWdua23sJoZO3ora0V3qdV/V3Q3vzuZ2Z00ZUE+kPC7IIeVTtlMoNWRg3LJkjfk+Ne/bbOW0RwECkeA8Ds7Y6lZjAnF9bXpoYNmooWZVa6TLT45IK5Dn7R4YrM4py7E2b27aC3y2h49I4t1VlRQRrJFvfQfQPidvl0u9yT8tkDb7uG3/iD1frBR3JvfF68JuzdL6amTCXseHHqlBIcNjwTdZiHK4aYuFRsC+RIg/M6XvHPOqwtumtXbd+8S164d5qvOGC+9WHYivie1bdtGZof3HyDhgUPMrHH9d3DIFc7ptMUtLbTwWz8sMbOzL87YLtWA+9ChBt9LtE5FIla9uyA21NYwW0Pt2i6fB936ZoMNgeYECL+5PhDIvkCZ12UWvKw6G8j+yTgDAkUqQPidv4HXAFyD8cjXQ/WhuPn3JwdbrEGu9cXDF2eJh3r3llozc1zLqvSUUJ8+Uucry1/nHHxmwm9nDB7htwXjZKfwW+tKeTeuF88HG80f73vrE/4Q1B9sGnJruZL6oHvEKAs0OAQC1goQflvrWUxHM6u3V24V9+7dkdXbNRTft8cs0NvUprMkoqVTwlpXfOBgM3u80OvsOSn81hf3OoamBElswK21tvV7n36a9GVuXvB36y7hLl3NGNfqHzNbuyLyve6XS11ZedLH44EINCVA+M21gUD2BQi/s2/MGRAg/Lb3NaATgFxHjkSCcQ3EPzko7vrQ/FCzayzp3f2mtEr3HiYMr9W7aPVPr94mNGdLLED47Ywrg/DbgnHKZ/itP9zKX31Z3O9vMLO6E5Uu0TfuWps7MGqMhC7O5tYV0NkQcIIA4bcTRsl5bdSflbrQpvvjHZEyKju2R8Lx/XsTdsb8HNXFNvv0ldCgIZFyKroA5/CRzut8ghbbPfz2blgnvjcWS9lbb5iFl1vazMyW7pdLrX7VQLviYsCts7Y11O5aQfmulhD5f0sFCL8t5eRgCCQUIPzmwkAg+wKE39k3zuYZdJKI6/AnkXIqB/ZLqZb+04U6Dx8WtwbmB/Y3efpQvwES6tNXwhqQ9+5df+esvuYu5o3w2xmjT/htwThlO/wu/ewzcW/bIp7K7eLauUM8+vft25osXRIYOVqCo8eaUMb8GXqlBb3kEAjkR4DwOz/uxXxW984dkRni+jNXZ0/s3SOej3eILsiZaDP19fr1l+Dgi+F4dLa4g0ph2C381t97vqVvRgLvN19rtBCQf+IUs8CPCbEv1tmOfi30WfrF/Nx0ct8Jv508erTdKQKE304ZKdrpZAHCbyePXnJtj66t5NlRKa6d2806S/q+SBfwTLTVduhoJloGx1wtwVFjJDByjNS2b5/cyQrgUYTfzhhEwm8Lxsmq8Ft/mHi2bRXPju3i2lFpQm63hi/HjjbZSq3JrYtRBoePkODoq0WDbzYECkmA8LuQRtPZfSm5cD4SiOtMcS2jsutjcV0MyRPVj9ZbB2uuv1H8s+ZIzbXX2Xr9BDuE3/qhru/tN6TsjcXiXbemwcXiJEtnX+W0PlsChN/ZkuW4CHwuQPjN1YBA9gUIv7NvbNcz6ALxJgjfsc1MynTvqDRry5XUVDdqspZJCQwbIcEx4yQ4crQERo6y9XuhTMwJvzPRy92+hN8WWKcafusPB32Tb26z1z9bP4oE3s2s4Gtqc+tswqFXSHDwUAkNHMSKvRaMHYewvwDht/3HiBaK+ZAyOkvCtXe3+bnue2d1gxeD/inTxH/9jVIza7YpmWKnLV/ht2fbR1L2wrNS/vILZpHS2E1/79XMnCX+WbMlMHa8nbhoCwIpCxB+p0zGDgikLED4nTIZOyCQsgDhd8pkBb+Dvp73bP5APJs3iWfjehOIJ9pCgwabQDxkwvAxEhg7riBsCL+dMYyE3xaMU1Phd0nAL3r7vJkpWLk1EnhXbhf3gX0idXWNzlzn8Zhb50ODr5DQkKFLju0sAAAaMklEQVSmrqwJuvv0FXG5LGgph0DAeQKE384bM1ocESjx14hvyVtS9soLjUp3aM28yKzw2eIff42I251XtlyG3/ohQfmf/yTlLz1nfkdGtzqvT/yTpoj/hjlSc+NNBb/IaF4HnJPnXIDwO+fknLAIBQi/i3DQ6XLOBQi/c07uuBOWBIPi3vaRCcE9778nnk3vm8meEg437IvbbfIurV4QMuVSRktwyBWOy74Iv51xiRJ+WzBOGn7rAlzunZUm3NZPvvQWEH2D39QWGjBQgjqTe8gVErpyWGSxABahtGA0OEShCRB+F9qIFm9/ypa8Kb5XXpTyxS9LaVXV56HvJZfI+W/cLuE+/aRm5g1mocZcb9kOv83izC88K2UvPS+e7VsbdE9nwtfc/CWpnjO3YG+HzPV4cj77CRB+229MaFHhCRB+F96Y0iP7CRB+229MnNAiU/1g8wfijYbhWzY3mAQT2wedBKOlfc3m80n4ss4S1kXr23eQWv17Ht4rNWdM+O2EK1CE8DvTcRo2TGTLlqZDbl0Rd+Ags+hkaPAQU7pEb+VmQwCB5AQIv5Nz4lHOEvCtXS2+V1+W8ldfbFTySm8JrJk+UwLTZ4h/4mSpKyvPeueyEX679+6OBN4vvyCejz4k8M76KHICOwsQftt5dGhboQgQfhfKSNIPOwsQftt5dJzVtpJz58zscO/GDeJ5f4MJx5srBRzbu9pLL5Xazl3Ef/UEqe1WEfkvl0tqL20vtR07Sm2nzqJrBtV27CThrt2yCkP4nVVeyw5O+J0pZZs2IufOSah3X9HAQkPusJYr0b9fNTzTo7M/AkUvQPhd9JdAwQPorYBmVvjSt8X77juN+uufPFUC02dKzbRrJThsRFY8rAq/3fv3fj7De8tmAu+sjBYHdaIA4bcTR402O02A8NtpI0Z7nShA+O3EUXNOm/XuWK2q4DpyWEpPHJeSqpOiC226ThyX0mPHIl9Pnki5Q3Vt2ki442Vm5nht+0hAHu7cRerad4yE5R07SW0HDc31ayepbds26XMQfidNldcHEn5nyr91qxzu2jfTo7A/Agg0IUD4zaVRTAIl58+Jb9UK8S19S8reWNxo9oO+KNNFIAPTrpOaGdebGQ1WbJmE3/qCtNWzz0jZy8+bmn6xm9Y1NyVNbvoCJU2sGCiO4VgBwm/HDh0Nd5AA4beDBoumOlaA8NuxQ1dQDXcdP2bC8dKTJ00Y7jp1QkpOnTJ/Lz11UkpPnTLf06/671S34IhRUtuqdcPdSkqkrqzMvKepa93afNWQvM1jj6R6eB6fBwHCbwvQm1rw0oJDcwgEil6A8LvoL4GiBtDSIb7lS8WnM8NXrRANx2M3vcOoZsZME4brYpHpbqmG3xp4lz//Zyl/8dnGgffMG6Rm7i1SPfeLorMs2BBAQITwm6sAgewLEH5n35gzIED4zTXgRAGdUR4JxTUojwTipVWnpOTECSnV2eUaoldd/P7Jk6I1ypPe6uqSfigPzJ8A4bcF9oTfFiByCASaECD85tJA4HMBrRXuXb7UlEmJn2VdV97KBOD+GTPFP+06CfUfkDRdMuG36+gRKX/hz1L24nOmNl/spgt1Engnzc0Di1CA8LsIB50u51yA8Dvn5JywCAUIv4tw0Iu0y6WnT0vJhfNScv68lJ4/Z75G/n1OSvXvOikpGJK2Dz5QpELO6jbhtwXjRfhtASKHQIDwm2sAgZQESs+cEd+KJeJdtkTKX19kbv2L3UJ9+0v1l+ZJuG9/0brhzS320lT4rbcUlj+/UMpeflG869c2DLyvmyU1X/yKVM+eywzvlEaOBxejAOF3MY46fc61AOF3rsU5XzEKEH4X46jT5+YEqPntjOuD8NuCcSL8tgCRQyBA+M01gEBGAp5tH4lv+RLxLVsi3ndWS4m/pmEY3n+A+CdOkcDEyeKfMl1qO11W//+x4bfWymulM7xfekG869Y0Drxv+bJUz/kCgXdGo8XOxSZA+F1sI05/8yFA+J0Pdc5ZbAKE38U24vS3JQHC75aE7PH/hN8WjAPhtwWIHAIBwm+uAQQsFdAa4d7VK0wQruVS4rfQwEHinzRVApOmSLtxo+Tc629L2Z+fIfC2dBQ4GAIRAcJvrgQEsi9A+J19Y86AAOE31wACDQUIv51xRRB+JzFOzy9eKT949EnzyDkzxstD982X8jJv/Z6E30kg8hAE0hSg5neacOyGQIxAScAv3vfWmyDcu2aleDesb3Ihlzqv18wMr2GGN9cQApYJEH5bRsmBEGhSgPCbiwOB7AsQfmffmDM4S4Dw2xnjRfjdwjht2FQpjy1YKI8/cre0b9dGfrZgodnjnjvnEX474xqnlQ4XIPx2+ADSfFsKlASD4vngPfGZMHyV+DZtFP+Yq6X65i9J9ZybKWliy1GjUU4WIPx28ujRdqcIEH47ZaRop5MFCL+dPHq0PRsChN/ZULX+mITfLZhq2N27R1e5dfYU88j4MFy/x8xv6y9MjohAVIDwm2sBgewLNLXgZfbPzBkQKA4Bwu/iGGd6mV8Bwu/8+nP24hAg/C6OcaaXyQsQfidvlc9HEn43o19dE5AHf/KkjB89tD783r3/sHz/4SfkRw/cIf16VZi9/cHafI4h50agoAU8rhIJ19ZJbV1Bd5POIZBXAa+nVILBWuFpltdh4OQFLFBSIuJxlUogxGvGAh5mupZngdJSEVdJiQTD/DbL81Bw+gIWcLtKpK5OzPszNgQQEPF5SmFwgADhdxLh91fmTpOxIwabRyYKvx0wzjQRAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoKgHC7yTC75Zmfp/6LFBUFw2dRSCXAm1aucUfCEsgxOyCXLpzruIS6NDWK6fPBsxMHjYEELBeQGektmsdeZ6xIYBAdgT0LiadgXf2Qig7J+CoCCAgrcpc5vVitT+MBgIIiEjHtl4cHCBA+N3CIFHz2wFXMU0saAFqfhf08NI5mwhQ89smA0EzClaAmt8FO7R0zEYC1Py20WDQlIIVoOZ3wQ4tHUtTgJrfacLleDfC7xbA4xe41DBct3vunFe/Jwte5viq5XRFJUD4XVTDTWfzJED4nSd4Tls0AoTfRTPUdDSPAoTfecTn1EUjQPhdNENNR5MUIPxOEirPDyP8TmIAnl+8Un7w6JPmkXNmjJeH7psv5WWf39pA+J0EIg9BIE0Bwu804dgNgRQECL9TwOKhCKQhQPidBhq7IJCiAOF3imA8HIE0BAi/00Bjl4IWIPx2xvASflswToTfFiByCASaECD85tJAIPsChN/ZN+YMxS1A+F3c40/vcyNA+J0bZ85S3AKE38U9/vS+sQDhtzOuCsJvZ4wTrUQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIQYDwOwUsHooAAggggAACCCCAAAIIIIAAAggggAACCCDgDAHCb2eME61EAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSEGA8DsFLB6KAAIIIIAAAggggAACCCCAAAIIIIAAAggg4AwBwu80x+n5xSvlB48+afaeM2O8PHTffCkv86Z5NHZDAIENmyrlm//4SD1E/PPq9Jmz8t37fy5btu8xj3nqF/fL2BGDgUMAgTQFfrZgoazfVCmPP3K3tG/Xxhwl9nl41ZC+Df4vzdOwGwJFKdDcc4nnWVFeEnTaYoHY92LdunSUBY/eK/16VdSfhfdqFoNzuKIR0PdcD/z4CbnvrtsaPKcUoLnnFe/ViuYSoaMIOFKA8DuNYdM3LY8tWFgfCmiAoNs9d85L42jsggAC0RdTPSo6m0C7uiYgD/7kSenauYN5XkX/PX70ULl19hTZvf+wfP/hJ+RHD9zR6EUZmggg0LKA/t769dOLJTbgjn9e6RucdRu38eFuy5w8AoEGAvGvE2P/k+cZFwsCmQvEP8da+jfv1TI35wiFLxB9v7VoyTpJ9IFScxkI79UK//qghwg4XYDwO40R1BdQvXt0NSGcbs29yUnj8OyCAAIXZxZEg7fDx07KT371J3n4n+8wM1TjX2ABhgACyQtoqL3v4FGZPG5Ygw9yo9+PfpDLh0zJm/JIBKICzc2Y08fwPONaQSBzgfgPZ+N/X/FeLXNjjlC8Ak39HmvueVX16VneqxXvJUPPEXCEAOF3isOUKHQjIEgRkYcjkIRA7CydRB8wMYsnCUQegkCcQGxg8FHlnmbvYorevnrvnfMoMcSVhECSAvqa8M7vPSZHjp2q3+P2r86uvzsw/ncXz7MkYXkYAjEC0edNz4rO5u6k15auMx/qJrpbUHfjvRqXDwLJCyQKv1vKQKpOf9bgNaWejfdqyZvzSAQQyL4A4XeKxtEf/F+ZO60+DOAFVYqIPByBFgQS3b7651eWNyi/wAsqLiMEUhPQ51Xs8yjR7auxdzURyqXmy6MRUIH451n0eTRv7jRzx2D8zDmeZ1w3CKQnoM+lHbsPyur1WxqUaOC9Wnqe7IVAVKC58LupDETDb96rcQ0hgICdBQi/Uxydlj71jF1oJcVD83AEELgYHDzw8BMNFi5i5jeXBgKZC8QuUhR7tGjd79/86TXz7WjZE0K5zM05QvEJxIffKhB7x8Xjv32R51nxXRb02GKB+PJB+ryLvnas6NLJrBsTXSdGT81EJYsHgMMVtAAzvwt6eOkcAkUrQPidxtBTRy4NNHZBIAmB2DcvsR8k6ZsWan4nAchDEEhBIP5DJWoRp4DHQxFoQiD+91U0/I6WZOB5xqWDQOYCLd1BwXu1zI05QvEKUPO7eMeeniNQyAKE32mMbnMrHadxOHZBAIEWFo5lBXEuEQSsF4j/XRY/My5+QTHrW8ARESg8gejvq66dO5i7KOLvoOB5VnhjTo9yL6C/nxa+slwef+RusxB6/OQJ3qvlfkw4Y+EINBV+N/e84r1a4Yw/PUGgUAUIv9Mc2djbx+fMGN+gFnGah2Q3BIpaQGfp/PrpxQ0MunXpWF/+JBogbNm+xzzmqV/czyJ8RX3F0PlMBRKVE9LvffMfHzGHjpZD0WCBDQEEkheI/331w+/NN/W+oxvPs+QteSQCTQnEvm6Mfb0YfTzv1bh2EEhNIBpgL1qyrn7H+JyjuecV79VS8+bRCCCQWwHC79x6czYEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBHAgQfucAmVMggAACCCCAAAIIIIAAAggggAACCCCAAAII5FaA8Du33pwNAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIAcChN85QOYUCCCAAAIIIIAAAggggAACCCCAAAIIIIAAArkVIPzOrTdnQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMiBAOF3DpA5BQIIIIAAAggggAACCCCAAAIIIIAAAggggEBuBQi/c+vN2RBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRyIED4nQNkToEAAggggAACCCCAAAIIIIAAAggggAACCCCQWwHC79x6czYEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBHAgQfucAmVMggAACCCCAAAIIIIAAAggggAACCCCAAAII5FaA8Du33pwNAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIAcChN85QOYUCCCAAAIIIIAAAggggAACCCCAAAIIIIAAArkVIPzOrTdnQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMiBAOF3DpA5BQIIIIAAAggggAACCCCAAAIIIIAAAggggEBuBQi/c+vN2RBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRyIED4nQNkToEAAggggAACCCCAAAIIIIAAAggggAACCCCQWwHC79x6czYEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBHAgQfucAmVMggAACCCCAAAIIINCcwPOLV8q6jdvkofvmS3mZFywEEEAAAQQQQAABBBCwQIDw2wJEDoEAAggggAACCCBgvUB1TUAe/MmTsmjJugYH79aloyx49F7p16vCfF+D44WvLJfHH7lb2rdrY31DcnBEwu8cIHMKBBBAAAEEEEAAgaITIPwuuiGnwwgggAACCCCAgDMEouF3184d5J4759U3+mcLFsr6TZWODrvjR4Dw2xnXJK1EAAEEEEAAAQQQcJYA4bezxovWIoAAAggggAACRSPQVPgdP9M7PjiO/vummRPku/f/3HjFzxa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"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Line plot\n",
"fig = px.line(data_frame=bio.system_snapshot(), y=[\"A\"], \n",
" title= f\"Diffusion. System snapshot at time t={bio.system_time}\",\n",
" color_discrete_sequence = ['red'],\n",
" labels={\"value\":\"concentration\", \"variable\":\"Chemical\", \"index\":\"Bin number\"})\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "1583e159-8fca-4f6f-99e0-23b5ccb44f4b",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"hovertemplate": "Bin number: %{x}
Chem. species: %{y}
Concentration: %{z}",
"name": "0",
"type": "heatmap",
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"xaxis": "x",
"xgap": 2,
"y": [
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"yaxis": "y",
"ygap": 4,
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"coloraxis": {
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"text": "Concentration"
}
},
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0,
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[
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[
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[
0.7272727272727273,
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[
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],
[
0.9090909090909091,
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],
[
1,
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]
]
},
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
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"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
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"marker": {
"line": {
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"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": [
{
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"ticks": ""
},
"colorscale": [
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[
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],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
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"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
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},
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"type": "heatmap"
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"heatmapgl": [
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show as a heatmap\n",
"fig = px.imshow(bio.system_snapshot().T, \n",
" title= f\"Diffusion. System snapshot as a heatmap at time t={bio.system_time}\", \n",
" labels=dict(x=\"Bin number\", y=\"Chem. species\", color=\"Concentration\"),\n",
" text_auto=False, color_continuous_scale=\"gray_r\")\n",
"fig.data[0].xgap=2\n",
"fig.data[0].ygap=4\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "42c21d56-3f59-4b7e-a118-f9d2b206d909",
"metadata": {},
"source": [
"# Initial Diffusions with delta_x = 1"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "be02747a-1a34-40ff-864e-9d4cc524ef24",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"[[ 10. 11.5 13. 15. 17. 19. 21. 23. 25. 26.5 28. 29.\n",
" 30. 34. 38. 40. 42. 48.5 55. 60. 65. 56. 47. 41.\n",
" 35. 33.5 32. 29.5 27. 25. 23. 21.5 20. 18.5 17. 15.5\n",
" 14. 11. 8. 5.5 3. 6.5 10. 13. 16. 17. 18. 19.\n",
" 20. 22.5 25. 27.5 30. 32.5 35. 37.5 40. 52.5 65. 75.\n",
" 85. 100. 115. 132.5 150. 121. 92. 82.5 73. 71. 69. 67.\n",
" 65. 57.5 50. 46. 42. 39. 36. 28. 20. 32.5 45. 47.5\n",
" 50. 52.5 55. 62. 69. 75.5 82. 88.5 95. 86. 77. 68.5\n",
" 60. 51.5 43. 40. 37. 34. 31. 28. 25. 23.5 22. 21.\n",
" 20. 19. 18. 16.5 15. 13. 11. 10. 9. 8.5 8. ]]\n"
]
}
],
"source": [
"bio.describe_state(concise=True) # Our initial state"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "40c45d26-e2b7-4a8c-8977-da4420bf3f10",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 7:\n",
"[[ 10.81374413 11.75949885 13.25000185 15.062428 17.01188631\n",
" 19.00006197 20.98868984 22.94206218 24.77953224 26.39811453\n",
" 27.84518887 29.27277252 31.20853868 34.12698244 37.32677234\n",
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"source": [
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},
{
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"execution_count": 17,
"id": "fc2dfb7d-b303-45e4-be54-27c471792d9f",
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"source": [
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"diffuse_dx_1 = bio.system"
]
},
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},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
-1.4416053228239898,
137.8898110284388
],
"title": {
"text": "concentration"
},
"type": "linear"
}
}
},
"image/png": 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"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Line plot\n",
"fig = px.line(data_frame=bio.system_snapshot(), y=[\"A\"], \n",
" title= f\"Diffusion. System snapshot at time t={bio.system_time}\",\n",
" color_discrete_sequence = ['red'],\n",
" labels={\"value\":\"concentration\", \"variable\":\"Chemical\", \"index\":\"Bin number\"})\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "444efd67-8989-4265-83d0-2d431e5416b3",
"metadata": {},
"source": [
"### Enough time has proceeded to result in some smoothing, and non-puny changes in most values - but still nowhere near equilibrium"
]
},
{
"cell_type": "markdown",
"id": "9f2c62f4-a88e-4ed4-902b-86f27d23c9ca",
"metadata": {},
"source": [
"# Now restore the system to its initial (pre-diffusion) state\n",
"### and then perform a diffusion over the same time span, but with DOUBLE the spacial resolution\n",
"#### delta_x will be be 1/2 instead of the original default 1"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "a3cc74ba-fa61-47ff-bc0a-f3360446aa2e",
"metadata": {},
"outputs": [],
"source": [
"bio.restore_system(original_state)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "58217aca-d556-45cd-899f-bed201d4c52b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"119 bins and 1 species:\n",
" Species 0 (A). Diff rate: 0.1. Conc: [ 10. 11.5 13. 15. 17. 19. 21. 23. 25. 26.5 28. 29.\n",
" 30. 34. 38. 40. 42. 48.5 55. 60. 65. 56. 47. 41.\n",
" 35. 33.5 32. 29.5 27. 25. 23. 21.5 20. 18.5 17. 15.5\n",
" 14. 11. 8. 5.5 3. 6.5 10. 13. 16. 17. 18. 19.\n",
" 20. 22.5 25. 27.5 30. 32.5 35. 37.5 40. 52.5 65. 75.\n",
" 85. 100. 115. 132.5 150. 121. 92. 82.5 73. 71. 69. 67.\n",
" 65. 57.5 50. 46. 42. 39. 36. 28. 20. 32.5 45. 47.5\n",
" 50. 52.5 55. 62. 69. 75.5 82. 88.5 95. 86. 77. 68.5\n",
" 60. 51.5 43. 40. 37. 34. 31. 28. 25. 23.5 22. 21.\n",
" 20. 19. 18. 16.5 15. 13. 11. 10. 9. 8.5 8. ]\n"
]
}
],
"source": [
"bio.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "1c16e9ce-4a92-40a6-952a-1586b957ee7c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"238 bins and 1 species:\n",
" Species 0 (A). Diff rate: 0.1. Conc: [ 10. 10. 11.5 11.5 13. 13. 15. 15. 17. 17. 19. 19.\n",
" 21. 21. 23. 23. 25. 25. 26.5 26.5 28. 28. 29. 29.\n",
" 30. 30. 34. 34. 38. 38. 40. 40. 42. 42. 48.5 48.5\n",
" 55. 55. 60. 60. 65. 65. 56. 56. 47. 47. 41. 41.\n",
" 35. 35. 33.5 33.5 32. 32. 29.5 29.5 27. 27. 25. 25.\n",
" 23. 23. 21.5 21.5 20. 20. 18.5 18.5 17. 17. 15.5 15.5\n",
" 14. 14. 11. 11. 8. 8. 5.5 5.5 3. 3. 6.5 6.5\n",
" 10. 10. 13. 13. 16. 16. 17. 17. 18. 18. 19. 19.\n",
" 20. 20. 22.5 22.5 25. 25. 27.5 27.5 30. 30. 32.5 32.5\n",
" 35. 35. 37.5 37.5 40. 40. 52.5 52.5 65. 65. 75. 75.\n",
" 85. 85. 100. 100. 115. 115. 132.5 132.5 150. 150. 121. 121.\n",
" 92. 92. 82.5 82.5 73. 73. 71. 71. 69. 69. 67. 67.\n",
" 65. 65. 57.5 57.5 50. 50. 46. 46. 42. 42. 39. 39.\n",
" 36. 36. 28. 28. 20. 20. 32.5 32.5 45. 45. 47.5 47.5\n",
" 50. 50. 52.5 52.5 55. 55. 62. 62. 69. 69. 75.5 75.5\n",
" 82. 82. 88.5 88.5 95. 95. 86. 86. 77. 77. 68.5 68.5\n",
" 60. 60. 51.5 51.5 43. 43. 40. 40. 37. 37. 34. 34.\n",
" 31. 31. 28. 28. 25. 25. 23.5 23.5 22. 22. 21. 21.\n",
" 20. 20. 19. 19. 18. 18. 16.5 16.5 15. 15. 13. 13.\n",
" 11. 11. 10. 10. 9. 9. 8.5 8.5 8. 8. ]\n"
]
}
],
"source": [
"# Double the spacial resolution\n",
"bio.increase_spatial_resolution(2)\n",
"bio.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "9fe5455a-d6d4-4b70-8066-4ae93dd8afda",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'steps': 14000}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now repeat the idential diffusion process as before, but with half the delta_x\n",
"bio.diffuse(total_duration=7, time_step=0.0005, delta_x=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "04f50181-dcad-493a-bd5d-2a945ac4321d",
"metadata": {},
"outputs": [],
"source": [
"# Finally, halve the resolution, to return to the original number of bins\n",
"bio.decrease_spatial_resolution(2)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "535fe84a-d96e-463a-9e3f-325f9dee3684",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 7:\n",
"[[ 10.89266176 11.7848539 13.26762024 15.06704663 17.01129224\n",
" 19.00001264 20.98893325 22.93586947 24.75899763 26.38192007\n",
" 27.82438658 29.30805339 31.32877441 34.1360355 37.24341866\n",
" 40.31811624 43.99520185 48.83623707 54.09824855 58.06985342\n",
" 58.57966756 54.62426556 48.17247393 41.90469993 37.11644548\n",
" 33.94904366 31.65030415 29.45000857 27.22040768 25.12325332\n",
" 23.24141451 21.56414243 20.01086312 18.49744005 16.96700181\n",
" 15.31270487 13.32065434 10.88965328 8.33006728 6.31235026\n",
" 5.7658755 7.18787019 9.85810569 12.70102493 15.06827539\n",
" 16.75109893 17.98883114 19.18350838 20.69123723 22.68863443\n",
" 25.03321123 27.50397596 30.00236415 32.52570687 35.22087023\n",
" 38.75134897 44.55667676 53.45617766 64.17981427 75.34602862\n",
" 87.29799843 100.81761735 115.23752191 127.02910436 129.04849394\n",
" 117.62993512 100.12856257 85.77621563 76.87924473 71.97878724\n",
" 69.04898398 66.33678631 62.54319331 57.25105821 51.51321158\n",
" 46.53921156 42.43097232 38.55845361 34.16913353 29.92635975\n",
" 29.12067234 33.80781119 40.84273642 46.30651206 49.8823516\n",
" 53.03921126 57.06182315 62.50278661 68.86587891 75.40882931\n",
" 81.6468186 86.55058818 87.86393314 84.11348004 76.88853987\n",
" 68.53716736 60.12964051 52.19284837 45.53614153 40.69176909\n",
" 37.12206867 34.01800894 31.03444131 28.19000887 25.70272045\n",
" 23.75153806 22.26365369 21.06545194 20.0002062 18.9371436\n",
" 17.75869087 16.37681178 14.7806228 13.06289215 11.46110516\n",
" 10.1887551 9.26494555 8.63951029 8.30600783]]\n"
]
}
],
"source": [
"bio.describe_state(concise=True)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "426a1290-ebe1-41b0-b593-ee4d904ac8a7",
"metadata": {},
"outputs": [],
"source": [
"# SAVE the above system data: this is the result of diffusion with delta_x of 1/2\n",
"diffuse_dx_1_2 = bio.system"
]
},
{
"cell_type": "markdown",
"id": "30c10874-86f5-44fe-a7f7-95a85b483a0a",
"metadata": {},
"source": [
"### Compare the last 2 runs (with dx=1 and dx=1/2)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "279f7108-0945-41e7-9d96-a7459f684dfc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Max of unsigned absolute differences: 1.8747462722119792\n",
"L2 norm of differences (vectors) / Frobenius norm (matrices): 2.7316311840631684\n",
"Relative differences: [[-7.29790045e-03 -2.15613319e-03 -1.32968992e-03 -3.06632150e-04\n",
" 3.49208820e-05 2.59663515e-06 -1.15973861e-05 2.69928224e-04\n",
" 8.28692275e-04 6.13470206e-04 7.47069769e-04 -1.20524538e-03\n",
" -3.85265494e-03 -2.65275678e-04 2.23308031e-03 -7.91437615e-04\n",
" -4.21776329e-03 -1.14564947e-03 1.02054299e-03 3.63125894e-03\n",
" 9.52150784e-03 2.69913226e-03 -2.61544918e-03 -2.73428998e-03\n",
" -4.94289119e-03 -1.26519730e-03 1.37316033e-03 4.04243991e-04\n",
" -6.99793078e-04 -5.57578381e-04 -8.60763159e-04 -2.85258123e-04\n",
" -3.42447190e-07 -5.41183447e-05 -2.91538906e-05 1.33712864e-03\n",
" 4.62348860e-03 1.77707875e-03 -2.13643382e-03 -1.40717463e-02\n",
" -4.36038933e-02 -1.03891427e-02 2.25192283e-03 3.07702233e-03\n",
" 5.39592866e-03 1.64346643e-03 -2.06718026e-05 -1.13869947e-03\n",
" -2.93410303e-03 -8.73056317e-04 3.31929918e-05 7.18400209e-05\n",
" 1.02952955e-04 3.19751640e-04 1.32020109e-04 -3.46532734e-03\n",
" -9.05658439e-03 -1.74764791e-03 1.68597072e-03 -2.88180506e-04\n",
" -2.40816367e-03 -1.47528453e-03 -1.01888209e-03 4.73261129e-03\n",
" 1.43194308e-02 3.09737859e-03 -8.05273331e-03 -4.72644216e-03\n",
" -3.96504687e-03 -1.19222672e-03 1.02094122e-04 1.27352632e-03\n",
" 3.57160065e-03 4.79195110e-04 -2.79506797e-03 -1.49043883e-03\n",
" -8.67812292e-04 1.99277808e-03 6.09613948e-03 -7.16694099e-03\n",
" -2.92480890e-02 -4.24727283e-03 9.95470117e-03 3.38541861e-03\n",
" 4.92498985e-05 -1.30768881e-03 -3.21582770e-03 -8.58429642e-04\n",
" 2.63595933e-04 -6.49752035e-05 -8.72520217e-05 2.35016149e-03\n",
" 7.00792303e-03 2.34058050e-03 -3.47674716e-04 -2.48301846e-04\n",
" -1.66695826e-05 -1.38900549e-03 -4.85254083e-03 -1.77757860e-03\n",
" 8.94397998e-05 2.15443001e-04 7.77095929e-05 -6.73194980e-04\n",
" -2.32826908e-03 -1.10862093e-03 -8.69432472e-04 -2.63384091e-04\n",
" 1.37117998e-05 3.52307297e-04 1.13315268e-03 8.68045408e-04\n",
" 1.37839117e-03 -5.01506606e-04 -3.49996806e-03 -1.92703159e-03\n",
" -2.03295683e-03 -1.35774126e-03 -3.07625876e-03]]\n",
"Max of unsigned relative differences: 0.04360389332348327\n",
"Mean of relative differences: -0.0009733870307526427\n",
"Median of relative differences: -0.00026338409050621263\n",
"Standard deviation of relative differences: 0.005897685168763968\n",
"np.allclose with lax tolerance? (rtol=1e-01, atol=1e-01) : True\n",
"np.allclose with mid tolerance? (rtol=1e-02, atol=1e-03) : False\n",
"np.allclose with tight tolerance? (rtol=1e-03, atol=1e-05) : False\n",
"np.allclose with extra-tight tolerance? (rtol=1e-05, atol=1e-08) : False\n"
]
}
],
"source": [
"num.compare_states(diffuse_dx_1 , diffuse_dx_1_2, verbose=True)"
]
},
{
"cell_type": "markdown",
"id": "029954ed-7283-4deb-b1b8-e8abcaaa7b11",
"metadata": {},
"source": [
"# Again, restore the system to its initial (pre-diffusion) state\n",
"### and then perform a diffusion over the same time span, but with QUADRUPLE the spacial resolution\n",
"### delta_x will be be 1/4 instead of the original default 1"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "5165aa24-ef43-42ef-87a9-546822f5a1b5",
"metadata": {},
"outputs": [],
"source": [
"bio.restore_system(original_state)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "99eeec00-5118-4360-b228-3e0a1042d13e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"119 bins and 1 species:\n",
" Species 0 (A). Diff rate: 0.1. Conc: [ 10. 11.5 13. 15. 17. 19. 21. 23. 25. 26.5 28. 29.\n",
" 30. 34. 38. 40. 42. 48.5 55. 60. 65. 56. 47. 41.\n",
" 35. 33.5 32. 29.5 27. 25. 23. 21.5 20. 18.5 17. 15.5\n",
" 14. 11. 8. 5.5 3. 6.5 10. 13. 16. 17. 18. 19.\n",
" 20. 22.5 25. 27.5 30. 32.5 35. 37.5 40. 52.5 65. 75.\n",
" 85. 100. 115. 132.5 150. 121. 92. 82.5 73. 71. 69. 67.\n",
" 65. 57.5 50. 46. 42. 39. 36. 28. 20. 32.5 45. 47.5\n",
" 50. 52.5 55. 62. 69. 75.5 82. 88.5 95. 86. 77. 68.5\n",
" 60. 51.5 43. 40. 37. 34. 31. 28. 25. 23.5 22. 21.\n",
" 20. 19. 18. 16.5 15. 13. 11. 10. 9. 8.5 8. ]\n"
]
}
],
"source": [
"bio.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "53417fe4-e623-45c9-af24-250eb4300796",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"476 bins and 1 species:\n",
" Species 0 (A). Diff rate: 0.1. Conc: [ 10. 10. 10. 10. 11.5 11.5 11.5 11.5 13. 13. 13. 13.\n",
" 15. 15. 15. 15. 17. 17. 17. 17. 19. 19. 19. 19.\n",
" 21. 21. 21. 21. 23. 23. 23. 23. 25. 25. 25. 25.\n",
" 26.5 26.5 26.5 26.5 28. 28. 28. 28. 29. 29. 29. 29.\n",
" 30. 30. 30. 30. 34. 34. 34. 34. 38. 38. 38. 38.\n",
" 40. 40. 40. 40. 42. 42. 42. 42. 48.5 48.5 48.5 48.5\n",
" 55. 55. 55. 55. 60. 60. 60. 60. 65. 65. 65. 65.\n",
" 56. 56. 56. 56. 47. 47. 47. 47. 41. 41. 41. 41.\n",
" 35. 35. 35. 35. 33.5 33.5 33.5 33.5 32. 32. 32. 32.\n",
" 29.5 29.5 29.5 29.5 27. 27. 27. 27. 25. 25. 25. 25.\n",
" 23. 23. 23. 23. 21.5 21.5 21.5 21.5 20. 20. 20. 20.\n",
" 18.5 18.5 18.5 18.5 17. 17. 17. 17. 15.5 15.5 15.5 15.5\n",
" 14. 14. 14. 14. 11. 11. 11. 11. 8. 8. 8. 8.\n",
" 5.5 5.5 5.5 5.5 3. 3. 3. 3. 6.5 6.5 6.5 6.5\n",
" 10. 10. 10. 10. 13. 13. 13. 13. 16. 16. 16. 16.\n",
" 17. 17. 17. 17. 18. 18. 18. 18. 19. 19. 19. 19.\n",
" 20. 20. 20. 20. 22.5 22.5 22.5 22.5 25. 25. 25. 25.\n",
" 27.5 27.5 27.5 27.5 30. 30. 30. 30. 32.5 32.5 32.5 32.5\n",
" 35. 35. 35. 35. 37.5 37.5 37.5 37.5 40. 40. 40. 40.\n",
" 52.5 52.5 52.5 52.5 65. 65. 65. 65. 75. 75. 75. 75.\n",
" 85. 85. 85. 85. 100. 100. 100. 100. 115. 115. 115. 115.\n",
" 132.5 132.5 132.5 132.5 150. 150. 150. 150. 121. 121. 121. 121.\n",
" 92. 92. 92. 92. 82.5 82.5 82.5 82.5 73. 73. 73. 73.\n",
" 71. 71. 71. 71. 69. 69. 69. 69. 67. 67. 67. 67.\n",
" 65. 65. 65. 65. 57.5 57.5 57.5 57.5 50. 50. 50. 50.\n",
" 46. 46. 46. 46. 42. 42. 42. 42. 39. 39. 39. 39.\n",
" 36. 36. 36. 36. 28. 28. 28. 28. 20. 20. 20. 20.\n",
" 32.5 32.5 32.5 32.5 45. 45. 45. 45. 47.5 47.5 47.5 47.5\n",
" 50. 50. 50. 50. 52.5 52.5 52.5 52.5 55. 55. 55. 55.\n",
" 62. 62. 62. 62. 69. 69. 69. 69. 75.5 75.5 75.5 75.5\n",
" 82. 82. 82. 82. 88.5 88.5 88.5 88.5 95. 95. 95. 95.\n",
" 86. 86. 86. 86. 77. 77. 77. 77. 68.5 68.5 68.5 68.5\n",
" 60. 60. 60. 60. 51.5 51.5 51.5 51.5 43. 43. 43. 43.\n",
" 40. 40. 40. 40. 37. 37. 37. 37. 34. 34. 34. 34.\n",
" 31. 31. 31. 31. 28. 28. 28. 28. 25. 25. 25. 25.\n",
" 23.5 23.5 23.5 23.5 22. 22. 22. 22. 21. 21. 21. 21.\n",
" 20. 20. 20. 20. 19. 19. 19. 19. 18. 18. 18. 18.\n",
" 16.5 16.5 16.5 16.5 15. 15. 15. 15. 13. 13. 13. 13.\n",
" 11. 11. 11. 11. 10. 10. 10. 10. 9. 9. 9. 9.\n",
" 8.5 8.5 8.5 8.5 8. 8. 8. 8. ]\n"
]
}
],
"source": [
"# Quadruple the spacial resolution\n",
"bio.increase_spatial_resolution(4)\n",
"bio.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "391a6de8-4e23-46cf-b9a4-fe0b5ca47d81",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'steps': 14000}"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now repeat the idential diffusion process as before, but with 1/4 the delta_x\n",
"bio.diffuse(total_duration=7, time_step=0.0005, delta_x=0.25)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "e2af3fdf-98cb-4d7b-bdec-cb33297d560c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 7:\n",
"[[ 10.9110498 11.79298317 13.27125046 15.06849169 17.01120689\n",
" 19.00000505 20.9889355 22.93396624 24.75464755 26.37685365\n",
" 27.82036504 29.31906987 31.35336039 34.13881577 37.22693938\n",
" 40.32794219 44.03328497 48.85375011 54.08582706 58.00398505\n",
" 58.46305352 54.57827613 48.19757613 41.94032917 37.15466295\n",
" 33.96229007 31.64173724 29.44629083 27.22423232 25.12764114\n",
" 23.24562187 21.56603866 20.01093031 18.49776361 16.96694036\n",
" 15.30636174 13.30779924 10.88358481 8.33420858 6.33960051\n",
" 5.81582285 7.21086316 9.85389609 12.68880637 15.05118007\n",
" 16.74254972 17.98886835 19.19030566 20.7038042 22.6947809\n",
" 25.03319586 27.50341077 30.00154792 32.52240795 35.22096403\n",
" 38.7930256 44.63917184 53.48517183 64.1588254 75.35244\n",
" 87.34200001 100.86427456 115.25735194 126.84107661 128.66226344\n",
" 117.51620543 100.29022264 85.90199754 76.94490243 72.00512119\n",
" 69.04735136 66.31052176 62.496998 57.24253268 51.5425832\n",
" 46.56093864 42.43820564 38.53427331 34.12800735 29.99268233\n",
" 29.29052368 33.85235505 40.75977408 46.25740082 49.88097703\n",
" 53.06093861 57.09980983 62.51931722 68.86280603 75.4104181\n",
" 81.64659333 86.48718521 87.7355725 84.05211787 76.89241675\n",
" 68.54251739 60.13069322 52.21527733 45.58166864 40.71424215\n",
" 37.12191698 34.01569142 31.03400274 28.19593816 25.71513906\n",
" 23.75971844 22.26781878 21.06716906 20.00013474 18.93508199\n",
" 17.754446 16.37238573 14.77644125 13.06493272 11.46938478\n",
" 10.19485558 9.26894187 8.64323169 8.31194648]]\n"
]
}
],
"source": [
"# Finally, reduce the resolution by a factor 4, to return to the original number of bins\n",
"bio.decrease_spatial_resolution(4)\n",
"bio.describe_state(concise=True)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "4add12b8-1fb5-4315-a292-2f7094ec7990",
"metadata": {},
"outputs": [],
"source": [
"# SAVE the above system data: this is the result of diffusion with delta_x of 1/4\n",
"diffuse_dx_1_4 = bio.system"
]
},
{
"cell_type": "markdown",
"id": "15d35fe4-d252-4b3a-8792-7bb0f859e05c",
"metadata": {},
"source": [
"### Compare the latest 2 runs (with dx=1/2 and dx=1/4)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "9466d672-5fbd-4d09-bd55-84d2a44d7f10",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Max of unsigned absolute differences: 0.3862305022112764\n",
"L2 norm of differences (vectors) / Frobenius norm (matrices): 0.6099407690744825\n",
"Max of unsigned relative differences: 0.008662578999936136\n",
"Mean of relative differences: -0.00023057453135932627\n",
"Median of relative differences: -7.806036263243106e-05\n",
"Standard deviation of relative differences: 0.001284581809105773\n",
"np.allclose with lax tolerance? (rtol=1e-01, atol=1e-01) : True\n",
"np.allclose with mid tolerance? (rtol=1e-02, atol=1e-03) : True\n",
"np.allclose with tight tolerance? (rtol=1e-03, atol=1e-05) : False\n",
"np.allclose with extra-tight tolerance? (rtol=1e-05, atol=1e-08) : False\n"
]
}
],
"source": [
"num.compare_states(diffuse_dx_1_2 , diffuse_dx_1_4)"
]
},
{
"cell_type": "markdown",
"id": "dcbe0848-a86c-41b0-8a78-ed02541b2b0f",
"metadata": {},
"source": [
"### Notice how the discrepancies have gone down"
]
},
{
"cell_type": "markdown",
"id": "4ec7d406-d535-4217-b458-3c0a4f17cbd1",
"metadata": {},
"source": [
"# One last time, restore the system to its initial (pre-diffusion) state\n",
"### and then perform a diffusion over the same time span, but with 10x the spacial resolution\n",
"### delta_x will be be 1/10 instead of the original default 1"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "c30da69c-664b-4e5e-9200-b3e8f0d1638a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1190"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.restore_system(original_state)\n",
"\n",
"# Increase by a factor 10 the spacial resolution\n",
"bio.increase_spatial_resolution(10)\n",
"bio.n_bins"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "4f5bb9ea-9488-4300-a894-f8ad341e1a4d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'steps': 14000}"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now repeat the idential diffusion process as before, but with 1/10 the delta_x\n",
"bio.diffuse(total_duration=7, time_step=0.0005, delta_x=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "417e3555-761e-4732-9713-6f5e69cfed65",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 7:\n",
"[[ 10.9161213 11.79534703 13.27224519 15.06889944 17.01119333\n",
" 19.00000338 20.98892613 22.93342377 24.75346019 26.37538504\n",
" 27.8193336 29.32224482 31.35993783 34.13959443 37.22264801\n",
" 40.33074926 44.04350095 48.85885219 54.08228117 57.9850481\n",
" 58.43161184 54.5650653 48.20415782 41.95062292 37.16503013\n",
" 33.96606998 31.63952326 29.44520209 27.22524894 25.12890686\n",
" 23.24676849 21.56657954 20.01095977 18.49786149 16.96689233\n",
" 15.30454171 13.30433429 10.88181544 8.33541818 6.34742941\n",
" 5.82930238 7.21746817 9.85284142 12.68527302 15.04654903\n",
" 16.74009407 17.98886906 19.19226541 20.70719698 22.69654408\n",
" 25.03322271 27.50324578 30.00131432 32.52141484 35.22119659\n",
" 38.80499992 44.66137184 53.49347076 64.15346368 75.35420342\n",
" 87.35391443 100.87790208 115.26183635 126.78700415 128.55849521\n",
" 117.48352229 100.33303879 85.9383081 76.96307468 72.01259667\n",
" 69.04692294 66.30294186 62.4845971 57.24008251 51.55042279\n",
" 46.56723079 42.44010875 38.52721713 34.11735772 30.01175902\n",
" 29.33604167 33.86516393 40.73781027 46.24318925 49.88047063\n",
" 53.06723078 57.11005998 62.52405086 68.86208623 75.4109248\n",
" 81.64620029 86.46899111 87.70095286 84.03450966 76.89314005\n",
" 68.54409875 60.13111756 52.22170551 45.59395032 40.72069246\n",
" 37.1219866 34.01499435 31.0339081 28.19763978 25.71849996\n",
" 23.76206593 22.26897366 21.06765706 20.00011435 18.93449137\n",
" 17.75328892 16.37111133 14.77532277 13.06551858 11.47161872\n",
" 10.19660621 9.27004622 8.64430525 8.31358802]]\n"
]
}
],
"source": [
"# Finally, reduce the resolution by a factor 10, to return to the original number of bins\n",
"bio.decrease_spatial_resolution(10)\n",
"bio.describe_state(concise=True)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "8163bc82-24ab-435f-89a7-0eb7401c1046",
"metadata": {},
"outputs": [],
"source": [
"# SAVE the above system data: this is the result of diffusion with delta_x of 1/10\n",
"diffuse_dx_1_10 = bio.system"
]
},
{
"cell_type": "markdown",
"id": "e09667d0-7903-4967-bf8c-50c49c82ec02",
"metadata": {},
"source": [
"### Again, compare the latest 2 runs (with dx=1/4 and dx=1/10)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "0214630a-0762-49f8-a075-f81bbed36503",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Max of unsigned absolute differences: 0.1037682236950559\n",
"L2 norm of differences (vectors) / Frobenius norm (matrices): 0.16693544887735995\n",
"Max of unsigned relative differences: 0.002317733310755785\n",
"Mean of relative differences: -6.382496227915533e-05\n",
"Median of relative differences: -2.2808505444596986e-05\n",
"Standard deviation of relative differences: 0.0003503290914748486\n",
"np.allclose with lax tolerance? (rtol=1e-01, atol=1e-01) : True\n",
"np.allclose with mid tolerance? (rtol=1e-02, atol=1e-03) : True\n",
"np.allclose with tight tolerance? (rtol=1e-03, atol=1e-05) : False\n",
"np.allclose with extra-tight tolerance? (rtol=1e-05, atol=1e-08) : False\n"
]
}
],
"source": [
"num.compare_states(diffuse_dx_1_4 , diffuse_dx_1_10)"
]
},
{
"cell_type": "markdown",
"id": "850eea0e-ac7d-43c6-a5ba-7934fb008c1a",
"metadata": {},
"source": [
"### Notice how the discrepancies have gone down even more\n",
"### This matches expectations that we're getting closer and closer to a \"true\" (very high precision) value, as we keep increasing the spacial resolution"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf46c5b5",
"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"
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