{
"cells": [
{
"cell_type": "markdown",
"id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f",
"metadata": {},
"source": [
"### A simple `A <-> B` reaction between 2 species with initial uniform concentrations across 3 bins,\n",
"with 1st-order kinetics in both directions, taken to equilibrium\n",
"\n",
"Diffusion NOT taken into account\n",
"\n",
"See also the experiment `reactions_single_compartment/react_1`"
]
},
{
"cell_type": "markdown",
"id": "a8445ac5-5fca-43f7-9858-cdd35c84772f",
"metadata": {},
"source": [
"### TAGS : \"reactions 1D\", \"quick-start\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e3fee905-f0fe-4537-8352-2f07345d25cd",
"metadata": {},
"outputs": [],
"source": [
"LAST_REVISED = \"May 4, 2025\"\n",
"LIFE123_VERSION = \"1.0.0rc3\" # Library version this experiment is based on"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7245be7a-c9db-45f5-b033-d6c521237a9c",
"metadata": {},
"outputs": [],
"source": [
"#import set_path # Using MyBinder? Uncomment this before running the next cell!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3cddd49a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#import sys, os\n",
"#os.getcwd()\n",
"#sys.path.append(\"C:/some_path/my_env_or_install\") # CHANGE to the folder containing your venv or libraries installation!\n",
"# NOTE: If any of the imports below can't find a module, uncomment the lines above, or try: import set_path\n",
" \n",
"from life123 import ChemData, BioSim1D, check_version"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a14ca0a0-a287-4bba-a20f-ccd09b5e82de",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OK\n"
]
}
],
"source": [
"check_version(LIFE123_VERSION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e049ec5d-7502-48f2-a79d-c647fe732e00",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "8af818bc-9bac-4ec7-9672-b76223876595",
"metadata": {},
"source": [
"# Initialize the System"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "de47e1df-67cf-4200-a580-fbcf50ebd1c0",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 0:\n",
"3 bins and 2 chemical species:\n"
]
},
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Species | \n",
" Diff rate | \n",
" Bin 0 | \n",
" Bin 1 | \n",
" Bin 2 | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" A | \n",
" None | \n",
" 10.0 | \n",
" 10.0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | 1 | \n",
" B | \n",
" None | \n",
" 50.0 | \n",
" 50.0 | \n",
" 50.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Species Diff rate Bin 0 Bin 1 Bin 2\n",
"0 A None 10.0 10.0 10.0\n",
"1 B None 50.0 50.0 50.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Initialize the system\n",
"chem_data = ChemData(names=[\"A\", \"B\"]) # Diffusion NOT taken into account\n",
"bio = BioSim1D(n_bins=3, chem_data=chem_data) # We'll specify the reactions later\n",
"\n",
"bio.set_uniform_concentration(chem_label=\"A\", conc=10.) # Same across all bins\n",
"bio.set_uniform_concentration(chem_label=\"B\", conc=50.) # Same across all bins\n",
"\n",
"bio.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b653cda-902a-40d4-b1d4-33005a8b4274",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "e648c35b-0ec5-47b3-b759-67d14f7865b2",
"metadata": {},
"source": [
"## Enable History"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "db8a132d-febf-45b4-8ca2-4e3cb5eb2dc4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"History enabled for bins None and chemicals None (None means 'all')\n"
]
}
],
"source": [
"# Let's enable history - by default for all chemicals and all bins\n",
"bio.enable_history(take_snapshot=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "64b4eaff-5e55-4a15-a148-d271ccdd9d11",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" A | \n",
" B | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 10.0 | \n",
" 50.0 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SYSTEM TIME A B caption\n",
"0 0.0 10.0 50.0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.get_bin_history(bin_address=0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e07f8c5e-270d-4703-b30d-1e019f611b46",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" A | \n",
" B | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 10.0 | \n",
" 50.0 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SYSTEM TIME A B caption\n",
"0 0.0 10.0 50.0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.get_bin_history(bin_address=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d7c855b6-6a52-4700-b8e9-04a55c4b3b0c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" A | \n",
" B | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 10.0 | \n",
" 50.0 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SYSTEM TIME A B caption\n",
"0 0.0 10.0 50.0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.get_bin_history(bin_address=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c2ccfa6-4fc4-4340-8d38-09ce125acdaa",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"id": "25155c63-e53c-41e1-be1e-41577158a4ca",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of reactions: 1 (at temp. 25 C)\n",
"0: A <-> B (kF = 3 / kR = 2 / delta_G = -1,005.1 / K = 1.5) | 1st order in all reactants & products\n",
"Set of chemicals involved in the above reactions: {'A', 'B'}\n"
]
}
],
"source": [
"# Specify the reaction\n",
"reactions = bio.get_reactions()\n",
"\n",
"# Reaction A <-> B , with 1st-order kinetics in both directions\n",
"reactions.add_reaction(reactants=\"A\", products=\"B\", forward_rate=3., reverse_rate=2.)\n",
"\n",
"reactions.describe_reactions()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3210ae02-198a-4d4a-9232-184265d9147c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "0b46b395-3f68-4dbd-b0c5-d67a0e623726",
"metadata": {
"tags": []
},
"source": [
"### First Reaction Step"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "bcf652b8-e0dc-438e-bdbe-02216c1d52a0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System Time is now: 0.1\n",
"SYSTEM STATE at Time t = 0.1:\n",
"3 bins and 2 chemical species:\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Species | \n",
" Diff rate | \n",
" Bin 0 | \n",
" Bin 1 | \n",
" Bin 2 | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" A | \n",
" None | \n",
" 17.0 | \n",
" 17.0 | \n",
" 17.0 | \n",
"
\n",
" \n",
" | 1 | \n",
" B | \n",
" None | \n",
" 43.0 | \n",
" 43.0 | \n",
" 43.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Species Diff rate Bin 0 Bin 1 Bin 2\n",
"0 A None 17.0 17.0 17.0\n",
"1 B None 43.0 43.0 43.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First step of reaction\n",
"bio.react(time_step=0.1, n_steps=1)\n",
"bio.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "7dc56592-179d-4e4c-b75a-8eb81dcafe71",
"metadata": {},
"source": [
"NOTE: the concentration of the chemical species `A` is increasing, while that of `B` is decreasing.\n",
"All bins have identical concentrations; so, there's no diffusion (and we're not attempting to compute it; didn't specify diffusion rates) "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "499da263-4575-466f-841a-05490c75bb3f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" A | \n",
" B | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 10.0 | \n",
" 50.0 | \n",
" | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.1 | \n",
" 17.0 | \n",
" 43.0 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SYSTEM TIME A B caption\n",
"0 0.0 10.0 50.0 \n",
"1 0.1 17.0 43.0 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.get_bin_history(bin_address=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a753a5f-25be-46e5-9fa0-ae1e05777aa3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "82a62165-425d-4e8d-abac-01d579dfd1ae",
"metadata": {},
"source": [
"### Several more reaction steps"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "cf6a7337-8e2e-4c02-9bb3-85052f37144f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System Time is now: 1.1\n",
"SYSTEM STATE at Time t = 1.1:\n",
"3 bins and 2 chemical species:\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Species | \n",
" Diff rate | \n",
" Bin 0 | \n",
" Bin 1 | \n",
" Bin 2 | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" A | \n",
" None | \n",
" 23.993164 | \n",
" 23.993164 | \n",
" 23.993164 | \n",
"
\n",
" \n",
" | 1 | \n",
" B | \n",
" None | \n",
" 36.006836 | \n",
" 36.006836 | \n",
" 36.006836 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Species Diff rate Bin 0 Bin 1 Bin 2\n",
"0 A None 23.993164 23.993164 23.993164\n",
"1 B None 36.006836 36.006836 36.006836"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Several more steps\n",
"bio.react(time_step=0.1, n_steps=10)\n",
"\n",
"bio.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "363053b6-e9cf-45d5-a77c-b0f4560cb51e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" A | \n",
" B | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 10.000000 | \n",
" 50.000000 | \n",
" | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.1 | \n",
" 17.000000 | \n",
" 43.000000 | \n",
" | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.2 | \n",
" 20.500000 | \n",
" 39.500000 | \n",
" | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.3 | \n",
" 22.250000 | \n",
" 37.750000 | \n",
" | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.4 | \n",
" 23.125000 | \n",
" 36.875000 | \n",
" | \n",
"
\n",
" \n",
" | 5 | \n",
" 0.5 | \n",
" 23.562500 | \n",
" 36.437500 | \n",
" | \n",
"
\n",
" \n",
" | 6 | \n",
" 0.6 | \n",
" 23.781250 | \n",
" 36.218750 | \n",
" | \n",
"
\n",
" \n",
" | 7 | \n",
" 0.7 | \n",
" 23.890625 | \n",
" 36.109375 | \n",
" | \n",
"
\n",
" \n",
" | 8 | \n",
" 0.8 | \n",
" 23.945312 | \n",
" 36.054688 | \n",
" | \n",
"
\n",
" \n",
" | 9 | \n",
" 0.9 | \n",
" 23.972656 | \n",
" 36.027344 | \n",
" | \n",
"
\n",
" \n",
" | 10 | \n",
" 1.0 | \n",
" 23.986328 | \n",
" 36.013672 | \n",
" | \n",
"
\n",
" \n",
" | 11 | \n",
" 1.1 | \n",
" 23.993164 | \n",
" 36.006836 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SYSTEM TIME A B caption\n",
"0 0.0 10.000000 50.000000 \n",
"1 0.1 17.000000 43.000000 \n",
"2 0.2 20.500000 39.500000 \n",
"3 0.3 22.250000 37.750000 \n",
"4 0.4 23.125000 36.875000 \n",
"5 0.5 23.562500 36.437500 \n",
"6 0.6 23.781250 36.218750 \n",
"7 0.7 23.890625 36.109375 \n",
"8 0.8 23.945312 36.054688 \n",
"9 0.9 23.972656 36.027344 \n",
"10 1.0 23.986328 36.013672 \n",
"11 1.1 23.993164 36.006836 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.get_bin_history(bin_address=0)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "0e059c0b-e956-4b5f-bcbe-c60025df3717",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" A | \n",
" B | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.0 | \n",
" 10.000000 | \n",
" 50.000000 | \n",
" | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.1 | \n",
" 17.000000 | \n",
" 43.000000 | \n",
" | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.2 | \n",
" 20.500000 | \n",
" 39.500000 | \n",
" | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.3 | \n",
" 22.250000 | \n",
" 37.750000 | \n",
" | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.4 | \n",
" 23.125000 | \n",
" 36.875000 | \n",
" | \n",
"
\n",
" \n",
" | 5 | \n",
" 0.5 | \n",
" 23.562500 | \n",
" 36.437500 | \n",
" | \n",
"
\n",
" \n",
" | 6 | \n",
" 0.6 | \n",
" 23.781250 | \n",
" 36.218750 | \n",
" | \n",
"
\n",
" \n",
" | 7 | \n",
" 0.7 | \n",
" 23.890625 | \n",
" 36.109375 | \n",
" | \n",
"
\n",
" \n",
" | 8 | \n",
" 0.8 | \n",
" 23.945312 | \n",
" 36.054688 | \n",
" | \n",
"
\n",
" \n",
" | 9 | \n",
" 0.9 | \n",
" 23.972656 | \n",
" 36.027344 | \n",
" | \n",
"
\n",
" \n",
" | 10 | \n",
" 1.0 | \n",
" 23.986328 | \n",
" 36.013672 | \n",
" | \n",
"
\n",
" \n",
" | 11 | \n",
" 1.1 | \n",
" 23.993164 | \n",
" 36.006836 | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SYSTEM TIME A B caption\n",
"0 0.0 10.000000 50.000000 \n",
"1 0.1 17.000000 43.000000 \n",
"2 0.2 20.500000 39.500000 \n",
"3 0.3 22.250000 37.750000 \n",
"4 0.4 23.125000 36.875000 \n",
"5 0.5 23.562500 36.437500 \n",
"6 0.6 23.781250 36.218750 \n",
"7 0.7 23.890625 36.109375 \n",
"8 0.8 23.945312 36.054688 \n",
"9 0.9 23.972656 36.027344 \n",
"10 1.0 23.986328 36.013672 \n",
"11 1.1 23.993164 36.006836 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.get_bin_history(bin_address=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7c73b92-2d91-4644-8aeb-66f833a2fc25",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "962acf15-3b50-40e4-9daa-3dcca7d3291a",
"metadata": {},
"source": [
"### Equilibrium"
]
},
{
"cell_type": "markdown",
"id": "809b4afa-fb2f-4ac3-92c9-083fc487c81b",
"metadata": {},
"source": [
"NOTE: Consistent with the 3/2 ratio of forward/reverse rates (and the 1st order reactions),\n",
" the systems settles in the following equilibrium:\n",
"\n",
"[A] = 23.99316406\n",
" \n",
"[B] = 36.00683594\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "490fdc0f-fa2a-48d8-ae04-7c80d298842a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'A': 23.9931640625, 'B': 36.0068359375}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.bin_snapshot(bin_address=0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "f22258b2-5181-4ff9-b379-f6a12ad5c8fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0: A <-> B\n",
"Current concentrations: [A] = 23.99 ; [B] = 36.01\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 1.50071\n",
" Formula used: [B] / [A]\n",
"2. Ratio of forward/reverse reaction rates: 1.5\n",
"Discrepancy between the two values: 0.04749 %\n",
"Reaction IS in equilibrium (within 1% tolerance)\n",
"\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"bio.reaction_dynamics.is_in_equilibrium(conc=bio.bin_snapshot(bin_address=0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77741c9d-a432-4911-979e-9b36856a3ce5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "cbf6c9c7-8cec-400f-9e70-49ff1a9f485c",
"metadata": {
"tags": []
},
"source": [
"## Plots of changes of concentration with time"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "71c3c2e2-0411-4c3e-a607-31c8196d1c7f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=A
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "A",
"line": {
"color": "darkturquoise",
"dash": "solid",
"shape": "linear"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "A",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.1,
0.2,
0.30000000000000004,
0.4,
0.5,
0.6,
0.7,
0.7999999999999999,
0.8999999999999999,
0.9999999999999999,
1.0999999999999999
],
"xaxis": "x",
"y": [
10,
17,
20.5,
22.25,
23.125,
23.5625,
23.78125,
23.890625,
23.9453125,
23.97265625,
23.986328125,
23.9931640625
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=B
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "B",
"line": {
"color": "green",
"dash": "solid",
"shape": "linear"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "B",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.1,
0.2,
0.30000000000000004,
0.4,
0.5,
0.6,
0.7,
0.7999999999999999,
0.8999999999999999,
0.9999999999999999,
1.0999999999999999
],
"xaxis": "x",
"y": [
50,
43,
39.5,
37.75,
36.875,
36.4375,
36.21875,
36.109375,
36.0546875,
36.02734375,
36.013671875,
36.0068359375
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Reaction A <-> B . Concentrations at bin 0"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
0,
1.0999999999999999
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
7.777777777777778,
52.22222222222222
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bio.plot_history_single_bin(bin_address=0, \n",
" title=\"Reaction A <-> B . Concentrations at bin 0\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "46c41c61-aeed-42f4-a4b1-6f49a5970399",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Chemical=A
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "A",
"line": {
"color": "darkturquoise",
"dash": "solid",
"shape": "spline"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "A",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.1,
0.2,
0.30000000000000004,
0.4,
0.5,
0.6,
0.7,
0.7999999999999999,
0.8999999999999999,
0.9999999999999999,
1.0999999999999999
],
"xaxis": "x",
"y": [
10,
17,
20.5,
22.25,
23.125,
23.5625,
23.78125,
23.890625,
23.9453125,
23.97265625,
23.986328125,
23.9931640625
],
"yaxis": "y"
},
{
"hovertemplate": "Chemical=B
SYSTEM TIME=%{x}
Concentration=%{y}",
"legendgroup": "B",
"line": {
"color": "green",
"dash": "solid",
"shape": "spline"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "B",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
0,
0.1,
0.2,
0.30000000000000004,
0.4,
0.5,
0.6,
0.7,
0.7999999999999999,
0.8999999999999999,
0.9999999999999999,
1.0999999999999999
],
"xaxis": "x",
"y": [
50,
43,
39.5,
37.75,
36.875,
36.4375,
36.21875,
36.109375,
36.0546875,
36.02734375,
36.013671875,
36.0068359375
],
"yaxis": "y"
}
],
"layout": {
"autosize": true,
"legend": {
"title": {
"text": "Chemical"
},
"tracegroupgap": 0
},
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Reaction A <-> B . Concentrations at bin 0"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
0,
1.0999999999999999
],
"title": {
"text": "SYSTEM TIME"
},
"type": "linear"
},
"yaxis": {
"anchor": "x",
"autorange": true,
"domain": [
0,
1
],
"range": [
7.777777777777778,
52.22222222222222
],
"title": {
"text": "Concentration"
},
"type": "linear"
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Same plot, but with a smoothed line\n",
"bio.plot_history_single_bin(bin_address=0, \n",
" title=\"Reaction A <-> B . Concentrations at bin 0\",\n",
" smoothed=True)"
]
},
{
"cell_type": "markdown",
"id": "37879680-50e8-4564-a872-a1f94cedfd22",
"metadata": {},
"source": [
"## For more in-depth analysis of this reaction, see the experiment `reactions_single_compartment/react_1`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da56d751",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"formats": "ipynb,py:percent"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}