{
"cells": [
{
"cell_type": "markdown",
"id": "58ff55b9-7d22-47fe-8d5b-1d29d2194498",
"metadata": {},
"source": [
"## An initial concentration pulse (near the left edge of the system, and halfway vertically) moving towards equilibrium\n",
"\n",
"The system starts out with a \"concentration pulse\" in just one bin - i.e. that bin is initially the only one with a non-zero concentration of the only chemical species.\n",
"Then the system is left undisturbed, and followed to equilibrium.\n",
"\n",
"(Note: this is the 2D counterpart of the 1D experiment by the same name)\n",
"\n",
"LAST REVISED: Nov. 28, 2022"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7bc27a42-13c8-469a-b99a-af09f545bb53",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Added 'D:\\Docs\\- MY CODE\\BioSimulations\\life123-Win7' to sys.path\n"
]
}
],
"source": [
"# Extend the sys.path variable, to contain the project's root directory\n",
"import set_path\n",
"set_path.add_ancestor_dir_to_syspath(3) # The number of levels to go up \n",
" # to reach the project's home, from the folder containing this notebook"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d9fd4e5f-c784-449f-9cc6-234dbe9eb20e",
"metadata": {},
"outputs": [],
"source": [
"from experiments.get_notebook_info import get_notebook_basename\n",
"\n",
"from src.life_2D.bio_sim_2d import BioSim2D\n",
"\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"\n",
"from src.modules.reactions.reaction_data import ReactionData as chem"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "008a6f6e-0bae-4d70-b486-43dda6214bf4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"Species `A`:\n",
" 0 1 2 3 4 5 6 7\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"2 0.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n"
]
}
],
"source": [
"# Prepare the initial system, with a single non-zero bin, near the left edge of the system, positioned halfway vertically\n",
"chem_data = chem(names=[\"A\"], diffusion_rates=[0.02])\n",
"bio = BioSim2D(n_bins=(5, 8), chem_data=chem_data)\n",
"\n",
"bio.inject_conc_to_bin(bin_address=(2, 1), species_index=0, delta_conc=10.)\n",
"\n",
"bio.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "51dac46c-74c3-42f2-bc4c-667db8d32554",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 3 | \n",
" 4 | \n",
" 5 | \n",
" 6 | \n",
" 7 | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.0 | \n",
" 10.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
y (row number): %{y}
Concentration: %{z}",
"name": "0",
"texttemplate": "%{z}",
"type": "heatmap",
"x": [
0,
1,
2,
3,
4,
5,
6,
7
],
"xaxis": "x",
"xgap": 2,
"y": [
0,
1,
2,
3,
4
],
"yaxis": "y",
"ygap": 2,
"z": [
[
0,
0,
0,
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0,
0,
0,
0
],
[
0,
10,
0,
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0,
0,
0,
0
]
]
}
],
"layout": {
"autosize": true,
"coloraxis": {
"colorbar": {
"title": {
"text": "Concentration"
}
},
"colorscale": [
[
0,
"rgb(254, 254, 253)"
],
[
0.09090909090909091,
"rgb(224, 224, 223)"
],
[
0.18181818181818182,
"rgb(197, 197, 195)"
],
[
0.2727272727272727,
"rgb(171, 171, 170)"
],
[
0.36363636363636365,
"rgb(146, 146, 145)"
],
[
0.45454545454545453,
"rgb(124, 123, 122)"
],
[
0.5454545454545454,
"rgb(102, 101, 101)"
],
[
0.6363636363636364,
"rgb(81, 80, 80)"
],
[
0.7272727272727273,
"rgb(59, 59, 59)"
],
[
0.8181818181818182,
"rgb(38, 38, 38)"
],
[
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"
],
[
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": "Diffusion. System snapshot as a heatmap at time t=0"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"constrain": "domain",
"domain": [
0.25551782682512736,
0.7444821731748726
],
"range": [
-0.5,
7.5
],
"scaleanchor": "y",
"title": {
"text": "x (col. number)"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"constrain": "domain",
"domain": [
0,
1
],
"range": [
4.5,
-0.5
],
"title": {
"text": "y (row number)"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.imshow(bio.system_snapshot(), \n",
" title= f\"Diffusion. System snapshot as a heatmap at time t={bio.system_time}\", \n",
" labels=dict(x=\"x (col. number)\", y=\"y (row number)\", color=\"Concentration\"),\n",
" text_auto=True, color_continuous_scale=\"gray_r\") # text_auto=’.2f’\n",
"\n",
"fig.data[0].xgap=2\n",
"fig.data[0].ygap=2\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "1206f461-ee37-4d82-89d2-2599e8115a5f",
"metadata": {},
"source": [
"# Initial Diffusion Step"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2345e05a-8c43-466a-b0ac-c562b68c4113",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" {'steps': 100}\n",
"SYSTEM STATE at Time t = 9.99999999999998:\n",
"Species `A`:\n",
" 0 1 2 3 4 5 6 \\\n",
"0 0.021467 0.101184 0.019573 0.001899 0.000122 0.000006 2.203662e-07 \n",
"1 0.205745 0.959044 0.187423 0.018382 0.001194 0.000058 2.203531e-06 \n",
"2 1.052394 4.856162 0.957855 0.094854 0.006221 0.000303 1.171495e-05 \n",
"3 0.205745 0.959044 0.187423 0.018382 0.001194 0.000058 2.203531e-06 \n",
"4 0.021467 0.101184 0.019573 0.001899 0.000122 0.000006 2.203662e-07 \n",
"\n",
" 7 \n",
"0 7.055683e-09 \n",
"1 7.136569e-08 \n",
"2 3.834040e-07 \n",
"3 7.136569e-08 \n",
"4 7.055683e-09 \n"
]
}
],
"source": [
"delta_time = 10.\n",
"\n",
"status = bio.diffuse(total_duration=delta_time, time_step=0.1)\n",
"print(\"\\n\", status)\n",
"\n",
"bio.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2ed82456-a52c-4d03-8810-6158d948f219",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"hovertemplate": "x (col. number): %{x}
y (row number): %{y}
Concentration: %{z}",
"name": "0",
"texttemplate": "%{z:.2f}",
"type": "heatmap",
"x": [
0,
1,
2,
3,
4,
5,
6,
7
],
"xaxis": "x",
"xgap": 2,
"y": [
0,
1,
2,
3,
4
],
"yaxis": "y",
"ygap": 2,
"z": [
[
0.02146688826037897,
0.10118402793934769,
0.019573452351388746,
0.0018994826745392488,
0.00012203283980530516,
5.8265896185901106e-06,
2.203661958757689e-07,
7.055683044197343e-09
],
[
0.20574512258142752,
0.9590443647520057,
0.18742296767102817,
0.01838197901956071,
0.001193751252362995,
5.762252535921028e-05,
2.203530544757651e-06,
7.136569063222217e-08
],
[
1.0523935699845237,
4.856161777459187,
0.957854716581625,
0.09485383370505904,
0.006220686444302897,
0.000303275917618468,
1.1714953819992749e-05,
3.8340398679157607e-07
],
[
0.20574512258142752,
0.9590443647520057,
0.1874229676710282,
0.01838197901956071,
0.0011937512523629951,
5.762252535921028e-05,
2.2035305447576506e-06,
7.136569063222215e-08
],
[
0.02146688826037897,
0.10118402793934769,
0.019573452351388753,
0.0018994826745392488,
0.00012203283980530517,
5.826589618590111e-06,
2.203661958757689e-07,
7.055683044197342e-09
]
]
}
],
"layout": {
"autosize": true,
"coloraxis": {
"colorbar": {
"title": {
"text": "Concentration"
}
},
"colorscale": [
[
0,
"rgb(254, 254, 253)"
],
[
0.09090909090909091,
"rgb(224, 224, 223)"
],
[
0.18181818181818182,
"rgb(197, 197, 195)"
],
[
0.2727272727272727,
"rgb(171, 171, 170)"
],
[
0.36363636363636365,
"rgb(146, 146, 145)"
],
[
0.45454545454545453,
"rgb(124, 123, 122)"
],
[
0.5454545454545454,
"rgb(102, 101, 101)"
],
[
0.6363636363636364,
"rgb(81, 80, 80)"
],
[
0.7272727272727273,
"rgb(59, 59, 59)"
],
[
0.8181818181818182,
"rgb(38, 38, 38)"
],
[
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"
],
[
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": "Diffusion. System snapshot as a heatmap at time t=9.99999999999998"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"constrain": "domain",
"domain": [
0.25551782682512736,
0.7444821731748726
],
"range": [
-0.5,
7.5
],
"scaleanchor": "y",
"title": {
"text": "x (col. number)"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"constrain": "domain",
"domain": [
0,
1
],
"range": [
4.5,
-0.5
],
"title": {
"text": "y (row number)"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.imshow(bio.system_snapshot(), \n",
" title= f\"Diffusion. System snapshot as a heatmap at time t={bio.system_time}\", \n",
" labels=dict(x=\"x (col. number)\", y=\"y (row number)\", color=\"Concentration\"),\n",
" text_auto='.2f', color_continuous_scale=\"gray_r\")\n",
"\n",
"fig.data[0].xgap=2\n",
"fig.data[0].ygap=2\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "fbf9545e-9730-41e4-937e-814504f1aa0e",
"metadata": {},
"source": [
"## This is still an early stage in the diffusion process; let's advance it more... (Visualization from results shown at selected times)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "570a5152-42aa-4ea3-a377-134296f2dc09",
"metadata": {
"lines_to_next_cell": 2,
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 20.000000000000014:\n",
"Species `A`:\n",
" 0 1 2 3 4 5 6 \\\n",
"0 0.099450 0.227366 0.083545 0.016125 0.002093 0.000204 0.000016 \n",
"1 0.454281 1.033156 0.381327 0.073972 0.009652 0.000945 0.000074 \n",
"2 1.220265 2.763366 1.023643 0.199392 0.026127 0.002569 0.000202 \n",
"3 0.454281 1.033156 0.381327 0.073972 0.009652 0.000945 0.000074 \n",
"4 0.099450 0.227366 0.083545 0.016125 0.002093 0.000204 0.000016 \n",
"\n",
" 7 \n",
"0 0.000001 \n",
"1 0.000005 \n",
"2 0.000014 \n",
"3 0.000005 \n",
"4 0.000001 \n"
]
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"hovertemplate": "x (col. number): %{x}
y (row number): %{y}
Concentration: %{z}",
"name": "0",
"texttemplate": "%{z:.2f}",
"type": "heatmap",
"x": [
0,
1,
2,
3,
4,
5,
6,
7
],
"xaxis": "x",
"xgap": 2,
"y": [
0,
1,
2,
3,
4
],
"yaxis": "y",
"ygap": 2,
"z": [
[
0.09944981844130271,
0.227365851946506,
0.08354456962266177,
0.016124852683422404,
0.0020929577991235225,
0.00020382190967127906,
1.5843775969025154e-05,
1.0785846188668353e-06
],
[
0.4542811511240858,
1.0331555188632073,
0.3813273829432773,
0.07397225493675172,
0.009651671725056777,
0.0009449299290877872,
7.384826901834985e-05,
5.05603325149345e-06
],
[
1.2202654222872393,
2.7633660374216458,
1.0236431420866778,
0.19939222675103754,
0.026127332759658072,
0.002569085367515645,
0.00020166450834604714,
1.3871643851413668e-05
],
[
0.4542811511240858,
1.0331555188632073,
0.38132738294327734,
0.07397225493675173,
0.00965167172505678,
0.0009449299290877875,
7.384826901834985e-05,
5.05603325149345e-06
],
[
0.09944981844130271,
0.22736585194650596,
0.08354456962266178,
0.016124852683422407,
0.0020929577991235233,
0.0002038219096712791,
1.5843775969025157e-05,
1.0785846188668358e-06
]
]
}
],
"layout": {
"autosize": true,
"coloraxis": {
"colorbar": {
"title": {
"text": "Concentration"
}
},
"colorscale": [
[
0,
"rgb(254, 254, 253)"
],
[
0.09090909090909091,
"rgb(224, 224, 223)"
],
[
0.18181818181818182,
"rgb(197, 197, 195)"
],
[
0.2727272727272727,
"rgb(171, 171, 170)"
],
[
0.36363636363636365,
"rgb(146, 146, 145)"
],
[
0.45454545454545453,
"rgb(124, 123, 122)"
],
[
0.5454545454545454,
"rgb(102, 101, 101)"
],
[
0.6363636363636364,
"rgb(81, 80, 80)"
],
[
0.7272727272727273,
"rgb(59, 59, 59)"
],
[
0.8181818181818182,
"rgb(38, 38, 38)"
],
[
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"
],
[
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": "Diffusion. System snapshot as a heatmap at time t=20.000000000000014"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"constrain": "domain",
"domain": [
0.25551782682512736,
0.7444821731748726
],
"range": [
-0.5,
7.5
],
"scaleanchor": "y",
"title": {
"text": "x (col. number)"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"constrain": "domain",
"domain": [
0,
1
],
"range": [
4.5,
-0.5
],
"title": {
"text": "y (row number)"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 30.000000000000156:\n",
"Species `A`:\n",
" 0 1 2 3 4 5 6 \\\n",
"0 0.201446 0.315009 0.158323 0.044518 0.008576 0.001250 0.000146 \n",
"1 0.600798 0.936757 0.471904 0.133094 0.025724 0.003763 0.000442 \n",
"2 1.161444 1.807104 0.911874 0.257745 0.049932 0.007322 0.000862 \n",
"3 0.600798 0.936757 0.471904 0.133094 0.025724 0.003763 0.000442 \n",
"4 0.201446 0.315009 0.158323 0.044518 0.008576 0.001250 0.000146 \n",
"\n",
" 7 \n",
"0 0.000015 \n",
"1 0.000047 \n",
"2 0.000092 \n",
"3 0.000047 \n",
"4 0.000015 \n"
]
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"hovertemplate": "x (col. number): %{x}
y (row number): %{y}
Concentration: %{z}",
"name": "0",
"texttemplate": "%{z:.2f}",
"type": "heatmap",
"x": [
0,
1,
2,
3,
4,
5,
6,
7
],
"xaxis": "x",
"xgap": 2,
"y": [
0,
1,
2,
3,
4
],
"yaxis": "y",
"ygap": 2,
"z": [
[
0.20144563014109335,
0.3150091558019576,
0.15832305486470175,
0.044517875505316946,
0.008576282080643632,
0.0012503453629968896,
0.0001463229769449903,
1.544393550885811e-05
],
[
0.6007981753518251,
0.93675666766923,
0.47190442165441787,
0.13309427038960028,
0.025723655160520616,
0.0037628287450430747,
0.0004418497268626793,
4.680849114143566e-05
],
[
1.1614436672503397,
1.8071041380264994,
0.9118743713849364,
0.257744750041132,
0.049932331352319205,
0.0073217380377940695,
0.0008618776757019313,
9.155051566175024e-05
],
[
0.6007981753518251,
0.93675666766923,
0.47190442165441787,
0.13309427038960028,
0.025723655160520616,
0.003762828745043075,
0.0004418497268626793,
4.680849114143566e-05
],
[
0.20144563014109335,
0.3150091558019576,
0.15832305486470175,
0.044517875505316946,
0.008576282080643633,
0.0012503453629968899,
0.0001463229769449903,
1.544393550885811e-05
]
]
}
],
"layout": {
"autosize": true,
"coloraxis": {
"colorbar": {
"title": {
"text": "Concentration"
}
},
"colorscale": [
[
0,
"rgb(254, 254, 253)"
],
[
0.09090909090909091,
"rgb(224, 224, 223)"
],
[
0.18181818181818182,
"rgb(197, 197, 195)"
],
[
0.2727272727272727,
"rgb(171, 171, 170)"
],
[
0.36363636363636365,
"rgb(146, 146, 145)"
],
[
0.45454545454545453,
"rgb(124, 123, 122)"
],
[
0.5454545454545454,
"rgb(102, 101, 101)"
],
[
0.6363636363636364,
"rgb(81, 80, 80)"
],
[
0.7272727272727273,
"rgb(59, 59, 59)"
],
[
0.8181818181818182,
"rgb(38, 38, 38)"
],
[
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"
],
[
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": "Diffusion. System snapshot as a heatmap at time t=30.000000000000156"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"constrain": "domain",
"domain": [
0.25551782682512736,
0.7444821731748726
],
"range": [
-0.5,
7.5
],
"scaleanchor": "y",
"title": {
"text": "x (col. number)"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"constrain": "domain",
"domain": [
0,
1
],
"range": [
4.5,
-0.5
],
"title": {
"text": "y (row number)"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 79.99999999999973:\n",
"Species `A`:\n",
" 0 1 2 3 4 5 6 \\\n",
"0 0.506124 0.470504 0.348500 0.196510 0.086253 0.030518 0.009056 \n",
"1 0.651796 0.605845 0.448797 0.253152 0.111169 0.039355 0.011686 \n",
"2 0.751013 0.698018 0.517108 0.291737 0.128146 0.045379 0.013480 \n",
"3 0.651796 0.605845 0.448797 0.253152 0.111169 0.039355 0.011686 \n",
"4 0.506124 0.470504 0.348500 0.196510 0.086253 0.030518 0.009056 \n",
"\n",
" 7 \n",
"0 0.002731 \n",
"1 0.003527 \n",
"2 0.004070 \n",
"3 0.003527 \n",
"4 0.002731 \n"
]
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"hovertemplate": "x (col. number): %{x}
y (row number): %{y}
Concentration: %{z}",
"name": "0",
"texttemplate": "%{z:.2f}",
"type": "heatmap",
"x": [
0,
1,
2,
3,
4,
5,
6,
7
],
"xaxis": "x",
"xgap": 2,
"y": [
0,
1,
2,
3,
4
],
"yaxis": "y",
"ygap": 2,
"z": [
[
0.5061243023322185,
0.4705037261188663,
0.3485002220995404,
0.19650997445187074,
0.08625343723869962,
0.03051764282116726,
0.009056324567610907,
0.0027313500604127516
],
[
0.6517964137622168,
0.6058447132428576,
0.44879711707127073,
0.2531516571997063,
0.11116869147443902,
0.039355254422294594,
0.011686329660149843,
0.0035271680864909253
],
[
0.7510127307304977,
0.6980175198248431,
0.5171079823401196,
0.29173740181544683,
0.12814646708287405,
0.045379416204342236,
0.013479749590577163,
0.004070083191665973
],
[
0.6517964137622168,
0.6058447132428577,
0.44879711707127073,
0.25315165719970634,
0.11116869147443902,
0.039355254422294594,
0.011686329660149845,
0.003527168086490926
],
[
0.5061243023322186,
0.47050372611886626,
0.3485002220995404,
0.19650997445187074,
0.0862534372386996,
0.03051764282116726,
0.009056324567610909,
0.002731350060412752
]
]
}
],
"layout": {
"autosize": true,
"coloraxis": {
"colorbar": {
"title": {
"text": "Concentration"
}
},
"colorscale": [
[
0,
"rgb(254, 254, 253)"
],
[
0.09090909090909091,
"rgb(224, 224, 223)"
],
[
0.18181818181818182,
"rgb(197, 197, 195)"
],
[
0.2727272727272727,
"rgb(171, 171, 170)"
],
[
0.36363636363636365,
"rgb(146, 146, 145)"
],
[
0.45454545454545453,
"rgb(124, 123, 122)"
],
[
0.5454545454545454,
"rgb(102, 101, 101)"
],
[
0.6363636363636364,
"rgb(81, 80, 80)"
],
[
0.7272727272727273,
"rgb(59, 59, 59)"
],
[
0.8181818181818182,
"rgb(38, 38, 38)"
],
[
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"
],
[
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": "Diffusion. System snapshot as a heatmap at time t=79.99999999999973"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"constrain": "domain",
"domain": [
0.25551782682512736,
0.7444821731748726
],
"range": [
-0.5,
7.5
],
"scaleanchor": "y",
"title": {
"text": "x (col. number)"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"constrain": "domain",
"domain": [
0,
1
],
"range": [
4.5,
-0.5
],
"title": {
"text": "y (row number)"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 2009.9999999992674:\n",
"Species `A`:\n",
" 0 1 2 3 4 5 6 \\\n",
"0 0.250896 0.250759 0.250507 0.250178 0.249822 0.249493 0.249241 \n",
"1 0.250896 0.250759 0.250507 0.250178 0.249822 0.249493 0.249241 \n",
"2 0.250896 0.250759 0.250507 0.250178 0.249822 0.249493 0.249241 \n",
"3 0.250896 0.250759 0.250507 0.250178 0.249822 0.249493 0.249241 \n",
"4 0.250896 0.250759 0.250507 0.250178 0.249822 0.249493 0.249241 \n",
"\n",
" 7 \n",
"0 0.249104 \n",
"1 0.249104 \n",
"2 0.249104 \n",
"3 0.249104 \n",
"4 0.249104 \n"
]
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"hovertemplate": "x (col. number): %{x}
y (row number): %{y}
Concentration: %{z}",
"name": "0",
"texttemplate": "%{z:.2f}",
"type": "heatmap",
"x": [
0,
1,
2,
3,
4,
5,
6,
7
],
"xaxis": "x",
"xgap": 2,
"y": [
0,
1,
2,
3,
4
],
"yaxis": "y",
"ygap": 2,
"z": [
[
0.250895501268314,
0.250759169313471,
0.25050726071068236,
0.25017812626487884,
0.24982187371444772,
0.24949273928075472,
0.2492408306950936,
0.24910449875236104
],
[
0.250895501268314,
0.250759169313471,
0.25050726071068236,
0.25017812626487884,
0.24982187371444772,
0.24949273928075472,
0.2492408306950936,
0.24910449875236104
],
[
0.250895501268314,
0.250759169313471,
0.25050726071068236,
0.25017812626487884,
0.24982187371444772,
0.24949273928075472,
0.2492408306950936,
0.24910449875236104
],
[
0.250895501268314,
0.250759169313471,
0.25050726071068236,
0.25017812626487884,
0.24982187371444772,
0.24949273928075472,
0.2492408306950936,
0.24910449875236104
],
[
0.250895501268314,
0.250759169313471,
0.25050726071068236,
0.25017812626487884,
0.24982187371444772,
0.24949273928075472,
0.2492408306950936,
0.24910449875236104
]
]
}
],
"layout": {
"autosize": true,
"coloraxis": {
"colorbar": {
"title": {
"text": "Concentration"
}
},
"colorscale": [
[
0,
"rgb(254, 254, 253)"
],
[
0.09090909090909091,
"rgb(224, 224, 223)"
],
[
0.18181818181818182,
"rgb(197, 197, 195)"
],
[
0.2727272727272727,
"rgb(171, 171, 170)"
],
[
0.36363636363636365,
"rgb(146, 146, 145)"
],
[
0.45454545454545453,
"rgb(124, 123, 122)"
],
[
0.5454545454545454,
"rgb(102, 101, 101)"
],
[
0.6363636363636364,
"rgb(81, 80, 80)"
],
[
0.7272727272727273,
"rgb(59, 59, 59)"
],
[
0.8181818181818182,
"rgb(38, 38, 38)"
],
[
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"
],
[
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": "Diffusion. System snapshot as a heatmap at time t=2009.9999999992674"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"constrain": "domain",
"domain": [
0.25551782682512736,
0.7444821731748726
],
"range": [
-0.5,
7.5
],
"scaleanchor": "y",
"title": {
"text": "x (col. number)"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"constrain": "domain",
"domain": [
0,
1
],
"range": [
4.5,
-0.5
],
"title": {
"text": "y (row number)"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in range(200):\n",
" status = bio.diffuse(total_duration=delta_time, time_step=0.1)\n",
"\n",
" if i<2 or i==6 or i>=199:\n",
" bio.describe_state()\n",
" fig = px.imshow(bio.system_snapshot(), \n",
" title= f\"Diffusion. System snapshot as a heatmap at time t={bio.system_time}\", \n",
" labels=dict(x=\"x (col. number)\", y=\"y (row number)\", color=\"Concentration\"),\n",
" text_auto='.2f', color_continuous_scale=\"gray_r\")\n",
"\n",
" fig.data[0].xgap=2\n",
" fig.data[0].ygap=2\n",
"\n",
" fig.show()"
]
},
{
"cell_type": "markdown",
"id": "f52f7bd3-202a-44be-af53-5b2b63772eae",
"metadata": {},
"source": [
"## All bins now have essentially uniform concentration\n",
"\n",
"Notice the continued symmetry across the mid-row.\n",
"\n",
"**Mass conservations**: the \"10. units of concentration\" are now uniformly spread across the 40 bins, leading to a near-constant concentration of 10./40"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "84026abc-687d-409a-a58e-2f71d5e5ecd3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.25"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10./40"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ffea62e-d31b-4230-ace8-c001a815cdf8",
"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
}