{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "## `U` (\"Up-regulator\") up-regulates `X` , by sharing a reaction product `D` (\"Drain\") across 2 separate reactions: \n", "### `U <-> 2 D` and `X <-> D` (both mostly forward)\n", "\n", "1st-order kinetics throughout. \n", "\n", "Invoking [Le Chatelier's principle](https://www.chemguide.co.uk/physical/equilibria/lechatelier.html), it can be seen that, starting from equilibrium, when [U] goes up, so does [D]; and when [D] goes up, so does [X]. \n", "Conversely, when [U] goes down, so does [D]; and when [D] goes down, so does [X]. \n", "\n", "LAST REVISED: Feb. 11, 2023" ] }, { "cell_type": "code", "execution_count": 1, "id": "d9efa3fd-e95d-4e1c-878a-81ae932b2709", "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(2) # 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": "01bae555-3dcf-42c1-bddc-9477a37f49f8", "metadata": { "tags": [] }, "outputs": [], "source": [ "from experiments.get_notebook_info import get_notebook_basename\n", "\n", "from src.modules.reactions.reaction_data import ReactionData as chem\n", "from src.modules.reactions.reaction_dynamics import ReactionDynamics\n", "\n", "import numpy as np\n", "import plotly.express as px\n", "from src.modules.visualization.graphic_log import GraphicLog" ] }, { "cell_type": "code", "execution_count": 3, "id": "cc53849f-351d-49e0-bfa8-22f8d8e22f8e", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-> Output will be LOGGED into the file 'up_regulate_2.log.htm'\n" ] } ], "source": [ "# Initialize the HTML logging\n", "log_file = get_notebook_basename() + \".log.htm\" # Use the notebook base filename for the log file\n", "\n", "# Set up the use of some specified graphic (Vue) components\n", "GraphicLog.config(filename=log_file,\n", " components=[\"vue_cytoscape_1\"],\n", " extra_js=\"https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.21.2/cytoscape.umd.js\")" ] }, { "cell_type": "markdown", "id": "d6d3ca49-589d-49b7-8424-37c7b01bcacf", "metadata": {}, "source": [ "### Initialize the system" ] }, { "cell_type": "code", "execution_count": 4, "id": "23c15e66-52e4-495b-aa3d-ecddd8d16942", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 2 (at temp. 25 C)\n", "0: U <-> 2 D (kF = 8 / kR = 2 / Delta_G = -3,436.56 / K = 4) | 1st order in all reactants & products\n", "1: X <-> D (kF = 6 / kR = 3 / Delta_G = -1,718.28 / K = 2) | 1st order in all reactants & products\n", "[GRAPHIC ELEMENT SENT TO LOG FILE `up_regulate_2.log.htm`]\n" ] } ], "source": [ "# Initialize the system\n", "chem_data = chem(names=[\"U\", \"X\", \"D\"])\n", "\n", "# Reaction U <-> 2D , with 1st-order kinetics for all species\n", "chem_data.add_reaction(reactants=\"U\", products=[(2, \"D\")],\n", " forward_rate=8., reverse_rate=2.)\n", "\n", "# Reaction X <-> D , with 1st-order kinetics for all species\n", "chem_data.add_reaction(reactants=\"X\", products=\"D\",\n", " forward_rate=6., reverse_rate=3.)\n", "\n", "chem_data.describe_reactions()\n", "\n", "# Send the plot of the reaction network to the HTML log file\n", "graph_data = chem_data.prepare_graph_network()\n", "GraphicLog.export_plot(graph_data, \"vue_cytoscape_1\")" ] }, { "cell_type": "markdown", "id": "d1d0eabb-b5b1-4e15-846d-5e483a5a24a7", "metadata": {}, "source": [ "### Set the initial concentrations of all the chemicals" ] }, { "cell_type": "code", "execution_count": 5, "id": "e80645d6-eb5b-4c78-8b46-ae126d2cb2cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "3 species:\n", " Species 0 (U). Conc: 50.0\n", " Species 1 (X). Conc: 100.0\n", " Species 2 (D). Conc: 0.0\n" ] } ], "source": [ "dynamics = ReactionDynamics(reaction_data=chem_data)\n", "dynamics.set_conc(conc={\"U\": 50., \"X\": 100., \"D\": 0.})\n", "dynamics.describe_state()" ] }, { "cell_type": "markdown", "id": "0b46b395-3f68-4dbd-b0c5-d67a0e623726", "metadata": { "tags": [] }, "source": [ "# 1. Take the initial system to equilibrium" ] }, { "cell_type": "code", "execution_count": 6, "id": "bcf652b8-e0dc-438e-bdbe-02216c1d52a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "single_compartment_react(): setting abs_fast_threshold to 16.666666666666668\n", "17 total step(s) taken\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEUXDcaption
00.01544.00000091.00000021.000000Interm. step, due to the fast rxns: [0, 1]
10.03039.35000083.75500037.545000
20.04535.75435077.90657550.584725Interm. step, due to the fast rxns: [0, 1]
30.06032.98137073.17129660.865965
40.07530.84958469.32484868.975984Interm. step, due to the fast rxns: [0, 1]
50.09029.21691466.18953175.376642
60.10527.97218363.62442280.431212Interm. step, due to the fast rxns: [0, 1]
70.12027.02845861.51762884.425456
80.13526.31780659.78018787.584200Interm. step, due to the fast rxns: [0, 1]
90.15025.78719658.34125990.084349
100.16525.39526357.14434292.065133Interm. step, due to the fast rxns: [0, 1]
110.18025.10978556.14428293.636148
120.19524.90569555.30492394.883686Interm. step, due to the fast rxns: [0, 1]
130.21024.76352254.59724695.875709
140.22524.66817153.99790196.665757Interm. step, due to the fast rxns: [0, 1]
150.24024.60796353.48804997.296025
160.25524.57388853.05244697.799778Interm. step, due to the fast rxns: [1]
170.27024.53981452.67871598.241657
180.28524.54228652.35850698.556923Interm. step, due to the fast rxns: [1]
190.30024.54475852.08130298.829183
200.31524.56426251.84129899.030178Interm. step, due to the fast rxns: [1]
210.33024.58376751.63193999.200527
220.36024.63569451.26623799.462374
230.39024.69087050.98992899.628331
240.42024.74276150.77829199.736187
250.45024.78867050.61445599.808205
260.48024.82788150.48659299.857645
270.51024.86064950.38619399.892510
\n", "
" ], "text/plain": [ " SYSTEM TIME U X D \\\n", "0 0.015 44.000000 91.000000 21.000000 \n", "1 0.030 39.350000 83.755000 37.545000 \n", "2 0.045 35.754350 77.906575 50.584725 \n", "3 0.060 32.981370 73.171296 60.865965 \n", "4 0.075 30.849584 69.324848 68.975984 \n", "5 0.090 29.216914 66.189531 75.376642 \n", "6 0.105 27.972183 63.624422 80.431212 \n", "7 0.120 27.028458 61.517628 84.425456 \n", "8 0.135 26.317806 59.780187 87.584200 \n", "9 0.150 25.787196 58.341259 90.084349 \n", "10 0.165 25.395263 57.144342 92.065133 \n", "11 0.180 25.109785 56.144282 93.636148 \n", "12 0.195 24.905695 55.304923 94.883686 \n", "13 0.210 24.763522 54.597246 95.875709 \n", "14 0.225 24.668171 53.997901 96.665757 \n", "15 0.240 24.607963 53.488049 97.296025 \n", "16 0.255 24.573888 53.052446 97.799778 \n", "17 0.270 24.539814 52.678715 98.241657 \n", "18 0.285 24.542286 52.358506 98.556923 \n", "19 0.300 24.544758 52.081302 98.829183 \n", "20 0.315 24.564262 51.841298 99.030178 \n", "21 0.330 24.583767 51.631939 99.200527 \n", "22 0.360 24.635694 51.266237 99.462374 \n", "23 0.390 24.690870 50.989928 99.628331 \n", "24 0.420 24.742761 50.778291 99.736187 \n", "25 0.450 24.788670 50.614455 99.808205 \n", "26 0.480 24.827881 50.486592 99.857645 \n", "27 0.510 24.860649 50.386193 99.892510 \n", "\n", " caption \n", "0 Interm. step, due to the fast rxns: [0, 1] \n", "1 \n", "2 Interm. step, due to the fast rxns: [0, 1] \n", "3 \n", "4 Interm. step, due to the fast rxns: [0, 1] \n", "5 \n", "6 Interm. step, due to the fast rxns: [0, 1] \n", "7 \n", "8 Interm. step, due to the fast rxns: [0, 1] \n", "9 \n", "10 Interm. step, due to the fast rxns: [0, 1] \n", "11 \n", "12 Interm. step, due to the fast rxns: [0, 1] \n", "13 \n", "14 Interm. step, due to the fast rxns: [0, 1] \n", "15 \n", "16 Interm. step, due to the fast rxns: [1] \n", "17 \n", "18 Interm. step, due to the fast rxns: [1] \n", "19 \n", "20 Interm. step, due to the fast rxns: [1] \n", "21 \n", "22 \n", "23 \n", "24 \n", "25 \n", "26 \n", "27 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n", "\n", "dynamics.single_compartment_react(time_step=0.03, stop_time=0.5,\n", " dynamic_substeps=2, rel_fast_threshold=50)\n", "\n", "df = dynamics.get_history()\n", "df" ] }, { "cell_type": "code", "execution_count": 7, "id": "b56d1612-a68c-4da3-be37-a7245b6c1a80", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From time 0 to 0.33, in 22 substeps of 0.015 (each 1/2 of full step)\n", "From time 0.33 to 0.51, in 6 FULL steps of 0.03\n", "(for a grand total of 17 full steps)\n" ] } ], "source": [ "dynamics.explain_time_advance()" ] }, { "cell_type": "markdown", "id": "cbf6c9c7-8cec-400f-9e70-49ff1a9f485c", "metadata": { "tags": [] }, "source": [ "## Plots of changes of concentration with time" ] }, { "cell_type": "code", "execution_count": 8, "id": "c388dae7-c4a6-4644-a390-958e3862d102", "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=U
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "U", "line": { "color": "red", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "U", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002 ], "xaxis": "x", "y": [ 44, 39.35, 35.75435, 32.98136975, 30.84958431875, 29.21691371174375, 27.97218332465947, 27.028457673333072, 26.317806442361846, 25.78719566340256, 25.395262660660435, 25.109785127452582, 24.905695343734937, 24.763522483135375, 24.668171054200002, 24.607963239743544, 24.573888392070153, 24.53981354439676, 24.54228564342021, 24.54475774244366, 24.564262298931922, 24.583766855420187, 24.635694451117335, 24.690870205216974, 24.742761233657305, 24.78866972844948, 24.827881300934596, 24.860648518216053 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002 ], "xaxis": "x", "y": [ 91, 83.755, 77.90657499999999, 73.171295875, 69.32484765437499, 66.18953063234687, 63.624421762923106, 61.51762832570914, 59.78018731113843, 58.341259444322176, 57.14434180963245, 56.144282025872634, 55.304923290909095, 54.597246065700226, 53.99790082334851, 53.488048817318465, 53.05244553540355, 52.678715432837755, 52.35850563040895, 52.08130166239592, 51.841297741152545, 51.63193893919308, 51.266237391635315, 50.98992829469266, 50.778291018186586, 50.6144554212179, 50.48659190636816, 50.38619345748053 ], "yaxis": "y" }, { "hovertemplate": "Chemical=D
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "D", "line": { "color": "gray", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "D", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002 ], "xaxis": "x", "y": [ 21, 37.545, 50.584725000000006, 60.865964625000004, 68.975983708125, 75.37664194416563, 80.43121158775796, 84.42545632762472, 87.58419980413788, 90.0843492288727, 92.06513286904668, 93.6361477192222, 94.88368602162102, 95.87570896802902, 96.66575706825148, 97.29602470319443, 97.79977768045613, 98.24165747836871, 98.55692308275061, 98.82918285271674, 99.0301776609836, 99.20052734996653, 99.46237370613001, 99.62833129487339, 99.73618651449881, 99.80820512188315, 99.85764549176265, 99.89250950608736 ], "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": "Changes in concentrations for 2 reactions" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0.015, 0.5100000000000002 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ 16.61708280521737, 104.27542670087 ], "title": { "text": "concentration" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_curves(colors=['red', 'green', 'gray'])" ] }, { "cell_type": "markdown", "id": "962acf15-3b50-40e4-9daa-3dcca7d3291a", "metadata": {}, "source": [ "### Equilibrium" ] }, { "cell_type": "code", "execution_count": 9, "id": "c3afbcc8-bdae-4938-a3f1-ce00d62816f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "U <-> 2 D\n", "Final concentrations: [D] = 99.89 ; [U] = 24.86\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 4.0181\n", " Formula used: [D] / [U]\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.4524 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n", "X <-> D\n", "Final concentrations: [D] = 99.89 ; [X] = 50.39\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 1.98254\n", " Formula used: [D] / [X]\n", "2. Ratio of forward/reverse reaction rates: 2.0\n", "Discrepancy between the two values: 0.8731 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "dynamics.is_in_equilibrium()" ] }, { "cell_type": "markdown", "id": "448ec7fa-6529-438b-84ba-47888c2cd080", "metadata": { "tags": [] }, "source": [ "# 2. Now, let's suddenly increase [U]" ] }, { "cell_type": "code", "execution_count": 10, "id": "7245be7a-c9db-45f5-b033-d6c521237a9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.51:\n", "3 species:\n", " Species 0 (U). Conc: 70.0\n", " Species 1 (X). Conc: 50.38619345748053\n", " Species 2 (D). Conc: 99.89250950608736\n" ] } ], "source": [ "dynamics.set_chem_conc(species_name=\"U\", conc=70., snapshot=True)\n", "dynamics.describe_state()" ] }, { "cell_type": "code", "execution_count": 11, "id": "e5ce5d59", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEUXDcaption
260.4824.82788150.48659299.857645
270.5124.86064950.38619399.892510
280.5170.00000050.38619399.892510Set concentration of `U`
\n", "
" ], "text/plain": [ " SYSTEM TIME U X D caption\n", "26 0.48 24.827881 50.486592 99.857645 \n", "27 0.51 24.860649 50.386193 99.892510 \n", "28 0.51 70.000000 50.386193 99.892510 Set concentration of `U`" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_history(tail=3)" ] }, { "cell_type": "markdown", "id": "24455d58-a0ea-43fa-b6ad-95c42a8b34b2", "metadata": {}, "source": [ "### Again, take the system to equilibrium" ] }, { "cell_type": "code", "execution_count": 12, "id": "c06fd8d8-d550-4e35-a239-7b91bee32be9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "single_compartment_react(): setting abs_fast_threshold to 16.666666666666668\n", "17 total step(s) taken\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEUXDcaption
00.01544.00000091.00000021.000000Interm. step, due to the fast rxns: [0, 1]
10.03039.35000083.75500037.545000
20.04535.75435077.90657550.584725Interm. step, due to the fast rxns: [0, 1]
30.06032.98137073.17129660.865965
40.07530.84958469.32484868.975984Interm. step, due to the fast rxns: [0, 1]
50.09029.21691466.18953175.376642
60.10527.97218363.62442280.431212Interm. step, due to the fast rxns: [0, 1]
70.12027.02845861.51762884.425456
80.13526.31780659.78018787.584200Interm. step, due to the fast rxns: [0, 1]
90.15025.78719658.34125990.084349
100.16525.39526357.14434292.065133Interm. step, due to the fast rxns: [0, 1]
110.18025.10978556.14428293.636148
120.19524.90569555.30492394.883686Interm. step, due to the fast rxns: [0, 1]
130.21024.76352254.59724695.875709
140.22524.66817153.99790196.665757Interm. step, due to the fast rxns: [0, 1]
150.24024.60796353.48804997.296025
160.25524.57388853.05244697.799778Interm. step, due to the fast rxns: [1]
170.27024.53981452.67871598.241657
180.28524.54228652.35850698.556923Interm. step, due to the fast rxns: [1]
190.30024.54475852.08130298.829183
200.31524.56426251.84129899.030178Interm. step, due to the fast rxns: [1]
210.33024.58376751.63193999.200527
220.36024.63569451.26623799.462374
230.39024.69087050.98992899.628331
240.42024.74276150.77829199.736187
250.45024.78867050.61445599.808205
260.48024.82788150.48659299.857645
270.51024.86064950.38619399.892510
280.51070.00000050.38619399.892510Set concentration of `U`
290.52564.59677550.346599110.738553Interm. step, due to the fast rxns: [0, 1]
300.54060.16731950.798640119.145425
310.55556.52160351.588307125.647190Interm. step, due to the fast rxns: [0, 1]
320.57053.50842752.599482130.662367
330.58551.00728653.745336134.518794Interm. step, due to the fast rxns: [0, 1]
340.60048.92197654.961601137.473150
350.61547.17553356.201349139.726288Interm. step, due to the fast rxns: [0, 1]
360.63045.70625857.430910141.435277
370.64544.46456558.626716142.722857Interm. step, due to the fast rxns: [0, 1]
380.66043.41050359.772840143.684857
390.67542.51178860.859103144.396023Interm. step, due to the fast rxns: [0, 1]
400.69041.74225561.879605144.914589
410.70541.08062262.831597145.285863Interm. step, due to the fast rxns: [0, 1]
420.72040.50952363.714617145.545040
430.73540.01473164.529828145.719412Interm. step, due to the fast rxns: [0, 1]
440.75039.58454665.279517145.830094
450.76539.20930365.966715145.893382Interm. step, due to the fast rxns: [0, 1]
460.78038.88098866.594913145.921814
470.79538.59292467.167852145.925003Interm. step, due to the fast rxns: [0, 1]
480.81038.33952367.689371145.910286
490.82538.11608968.163290145.883235Interm. step, due to the fast rxns: [0, 1]
500.84037.91865568.593340145.848053
510.85537.74385868.983101145.807885Interm. step, due to the fast rxns: [0, 1]
520.87037.58883269.335977145.765062
530.88537.45112469.655167145.721288Interm. step, due to the fast rxns: [0, 1]
540.90037.32862869.943660145.677788
550.91537.21952670.204231145.635420Interm. step, due to the fast rxns: [1]
560.93037.11042470.439444145.618410
570.96036.94102770.866001145.530648
580.99036.80701971.207879145.456785
591.02036.70074271.481572145.395648
\n", "
" ], "text/plain": [ " SYSTEM TIME U X D \\\n", "0 0.015 44.000000 91.000000 21.000000 \n", "1 0.030 39.350000 83.755000 37.545000 \n", "2 0.045 35.754350 77.906575 50.584725 \n", "3 0.060 32.981370 73.171296 60.865965 \n", "4 0.075 30.849584 69.324848 68.975984 \n", "5 0.090 29.216914 66.189531 75.376642 \n", "6 0.105 27.972183 63.624422 80.431212 \n", "7 0.120 27.028458 61.517628 84.425456 \n", "8 0.135 26.317806 59.780187 87.584200 \n", "9 0.150 25.787196 58.341259 90.084349 \n", "10 0.165 25.395263 57.144342 92.065133 \n", "11 0.180 25.109785 56.144282 93.636148 \n", "12 0.195 24.905695 55.304923 94.883686 \n", "13 0.210 24.763522 54.597246 95.875709 \n", "14 0.225 24.668171 53.997901 96.665757 \n", "15 0.240 24.607963 53.488049 97.296025 \n", "16 0.255 24.573888 53.052446 97.799778 \n", "17 0.270 24.539814 52.678715 98.241657 \n", "18 0.285 24.542286 52.358506 98.556923 \n", "19 0.300 24.544758 52.081302 98.829183 \n", "20 0.315 24.564262 51.841298 99.030178 \n", "21 0.330 24.583767 51.631939 99.200527 \n", "22 0.360 24.635694 51.266237 99.462374 \n", "23 0.390 24.690870 50.989928 99.628331 \n", "24 0.420 24.742761 50.778291 99.736187 \n", "25 0.450 24.788670 50.614455 99.808205 \n", "26 0.480 24.827881 50.486592 99.857645 \n", "27 0.510 24.860649 50.386193 99.892510 \n", "28 0.510 70.000000 50.386193 99.892510 \n", "29 0.525 64.596775 50.346599 110.738553 \n", "30 0.540 60.167319 50.798640 119.145425 \n", "31 0.555 56.521603 51.588307 125.647190 \n", "32 0.570 53.508427 52.599482 130.662367 \n", "33 0.585 51.007286 53.745336 134.518794 \n", "34 0.600 48.921976 54.961601 137.473150 \n", "35 0.615 47.175533 56.201349 139.726288 \n", "36 0.630 45.706258 57.430910 141.435277 \n", "37 0.645 44.464565 58.626716 142.722857 \n", "38 0.660 43.410503 59.772840 143.684857 \n", "39 0.675 42.511788 60.859103 144.396023 \n", "40 0.690 41.742255 61.879605 144.914589 \n", "41 0.705 41.080622 62.831597 145.285863 \n", "42 0.720 40.509523 63.714617 145.545040 \n", "43 0.735 40.014731 64.529828 145.719412 \n", "44 0.750 39.584546 65.279517 145.830094 \n", "45 0.765 39.209303 65.966715 145.893382 \n", "46 0.780 38.880988 66.594913 145.921814 \n", "47 0.795 38.592924 67.167852 145.925003 \n", "48 0.810 38.339523 67.689371 145.910286 \n", "49 0.825 38.116089 68.163290 145.883235 \n", "50 0.840 37.918655 68.593340 145.848053 \n", "51 0.855 37.743858 68.983101 145.807885 \n", "52 0.870 37.588832 69.335977 145.765062 \n", "53 0.885 37.451124 69.655167 145.721288 \n", "54 0.900 37.328628 69.943660 145.677788 \n", "55 0.915 37.219526 70.204231 145.635420 \n", "56 0.930 37.110424 70.439444 145.618410 \n", "57 0.960 36.941027 70.866001 145.530648 \n", "58 0.990 36.807019 71.207879 145.456785 \n", "59 1.020 36.700742 71.481572 145.395648 \n", "\n", " caption \n", "0 Interm. step, due to the fast rxns: [0, 1] \n", "1 \n", "2 Interm. step, due to the fast rxns: [0, 1] \n", "3 \n", "4 Interm. step, due to the fast rxns: [0, 1] \n", "5 \n", "6 Interm. step, due to the fast rxns: [0, 1] \n", "7 \n", "8 Interm. step, due to the fast rxns: [0, 1] \n", "9 \n", "10 Interm. step, due to the fast rxns: [0, 1] \n", "11 \n", "12 Interm. step, due to the fast rxns: [0, 1] \n", "13 \n", "14 Interm. step, due to the fast rxns: [0, 1] \n", "15 \n", "16 Interm. step, due to the fast rxns: [1] \n", "17 \n", "18 Interm. step, due to the fast rxns: [1] \n", "19 \n", "20 Interm. step, due to the fast rxns: [1] \n", "21 \n", "22 \n", "23 \n", "24 \n", "25 \n", "26 \n", "27 \n", "28 Set concentration of `U` \n", "29 Interm. step, due to the fast rxns: [0, 1] \n", "30 \n", "31 Interm. step, due to the fast rxns: [0, 1] \n", "32 \n", "33 Interm. step, due to the fast rxns: [0, 1] \n", "34 \n", "35 Interm. step, due to the fast rxns: [0, 1] \n", "36 \n", "37 Interm. step, due to the fast rxns: [0, 1] \n", "38 \n", "39 Interm. step, due to the fast rxns: [0, 1] \n", "40 \n", "41 Interm. step, due to the fast rxns: [0, 1] \n", "42 \n", "43 Interm. step, due to the fast rxns: [0, 1] \n", "44 \n", "45 Interm. step, due to the fast rxns: [0, 1] \n", "46 \n", "47 Interm. step, due to the fast rxns: [0, 1] \n", "48 \n", "49 Interm. step, due to the fast rxns: [0, 1] \n", "50 \n", "51 Interm. step, due to the fast rxns: [0, 1] \n", "52 \n", "53 Interm. step, due to the fast rxns: [0, 1] \n", "54 \n", "55 Interm. step, due to the fast rxns: [1] \n", "56 \n", "57 \n", "58 \n", "59 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.single_compartment_react(time_step=0.03, stop_time=1,\n", " dynamic_substeps=2, rel_fast_threshold=50)\n", "\n", "df = dynamics.get_history()\n", "df" ] }, { "cell_type": "code", "execution_count": 13, "id": "6de58fe9-ff1e-40dd-9ac7-83eee458f818", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From time 0 to 0.33, in 22 substeps of 0.015 (each 1/2 of full step)\n", "From time 0.33 to 0.51, in 6 FULL steps of 0.03\n", "From time 0.51 to 0.93, in 28 substeps of 0.015 (each 1/2 of full step)\n", "From time 0.93 to 1.02, in 3 FULL steps of 0.03\n", "(for a grand total of 34 full steps)\n" ] } ], "source": [ "dynamics.explain_time_advance()" ] }, { "cell_type": "code", "execution_count": 14, "id": "db4e74d0-3f9d-49dc-9553-bf3cdfe785f2", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=U
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "U", "line": { "color": "red", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "U", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002, 0.5100000000000002, 0.5250000000000002, 0.5400000000000003, 0.5550000000000003, 0.5700000000000003, 0.5850000000000003, 0.6000000000000003, 0.6150000000000003, 0.6300000000000003, 0.6450000000000004, 0.6600000000000004, 0.6750000000000004, 0.6900000000000004, 0.7050000000000004, 0.7200000000000004, 0.7350000000000004, 0.7500000000000004, 0.7650000000000005, 0.7800000000000005, 0.7950000000000005, 0.8100000000000005, 0.8250000000000005, 0.8400000000000005, 0.8550000000000005, 0.8700000000000006, 0.8850000000000006, 0.9000000000000006, 0.9150000000000006, 0.9300000000000006, 0.9600000000000006, 0.9900000000000007, 1.0200000000000007 ], "xaxis": "x", "y": [ 44, 39.35, 35.75435, 32.98136975, 30.84958431875, 29.21691371174375, 27.97218332465947, 27.028457673333072, 26.317806442361846, 25.78719566340256, 25.395262660660435, 25.109785127452582, 24.905695343734937, 24.763522483135375, 24.668171054200002, 24.607963239743544, 24.573888392070153, 24.53981354439676, 24.54228564342021, 24.54475774244366, 24.564262298931922, 24.583766855420187, 24.635694451117335, 24.690870205216974, 24.742761233657305, 24.78866972844948, 24.827881300934596, 24.860648518216053, 70, 64.59677528518262, 60.167318853534354, 56.521603349696974, 53.50842664033367, 51.007286460067974, 48.92197591915641, 47.175533309096146, 45.706257939352724, 44.464565289603634, 43.41050315103712, 42.51178847386525, 41.742254549941755, 41.080621679020304, 40.509522962162, 40.01473141084886, 39.58454600021829, 39.20930329346365, 38.88098834373364, 38.5929241500314, 38.33952332712236, 38.116089099790614, 37.91865544820416, 37.743858370313596, 37.588831912278984, 37.451123945728135, 37.32862771733721, 37.219526021663334, 37.110424325989456, 36.941027104842505, 36.807019463259174, 36.70074188602148 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002, 0.5100000000000002, 0.5250000000000002, 0.5400000000000003, 0.5550000000000003, 0.5700000000000003, 0.5850000000000003, 0.6000000000000003, 0.6150000000000003, 0.6300000000000003, 0.6450000000000004, 0.6600000000000004, 0.6750000000000004, 0.6900000000000004, 0.7050000000000004, 0.7200000000000004, 0.7350000000000004, 0.7500000000000004, 0.7650000000000005, 0.7800000000000005, 0.7950000000000005, 0.8100000000000005, 0.8250000000000005, 0.8400000000000005, 0.8550000000000005, 0.8700000000000006, 0.8850000000000006, 0.9000000000000006, 0.9150000000000006, 0.9300000000000006, 0.9600000000000006, 0.9900000000000007, 1.0200000000000007 ], "xaxis": "x", "y": [ 91, 83.755, 77.90657499999999, 73.171295875, 69.32484765437499, 66.18953063234687, 63.624421762923106, 61.51762832570914, 59.78018731113843, 58.341259444322176, 57.14434180963245, 56.144282025872634, 55.304923290909095, 54.597246065700226, 53.99790082334851, 53.488048817318465, 53.05244553540355, 52.678715432837755, 52.35850563040895, 52.08130166239592, 51.841297741152545, 51.63193893919308, 51.266237391635315, 50.98992829469266, 50.778291018186586, 50.6144554212179, 50.48659190636816, 50.38619345748053, 50.38619345748053, 50.34659897408122, 50.79863997027437, 51.588306510829796, 52.5994824637556, 53.74533556687912, 54.96160111730488, 56.2013487671052, 57.4309103190879, 58.626715844829846, 59.77283996307405, 60.85910291782627, 61.879604694632405, 62.83159678472283, 63.71461690103398, 64.52982818616037, 65.27951718741288, 65.96671486045305, 66.59491269124072, 67.16785216034775, 67.68937057855854, 68.16329008437268, 68.59333953736177, 68.98310134284011, 69.33597704158903, 69.65516690222996, 69.94365984867395, 70.20423090790317, 70.4394440267471, 70.86600102756839, 71.20787913797439, 71.48157153405576 ], "yaxis": "y" }, { "hovertemplate": "Chemical=D
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "D", "line": { "color": "gray", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "D", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002, 0.5100000000000002, 0.5250000000000002, 0.5400000000000003, 0.5550000000000003, 0.5700000000000003, 0.5850000000000003, 0.6000000000000003, 0.6150000000000003, 0.6300000000000003, 0.6450000000000004, 0.6600000000000004, 0.6750000000000004, 0.6900000000000004, 0.7050000000000004, 0.7200000000000004, 0.7350000000000004, 0.7500000000000004, 0.7650000000000005, 0.7800000000000005, 0.7950000000000005, 0.8100000000000005, 0.8250000000000005, 0.8400000000000005, 0.8550000000000005, 0.8700000000000006, 0.8850000000000006, 0.9000000000000006, 0.9150000000000006, 0.9300000000000006, 0.9600000000000006, 0.9900000000000007, 1.0200000000000007 ], "xaxis": "x", "y": [ 21, 37.545, 50.584725000000006, 60.865964625000004, 68.975983708125, 75.37664194416563, 80.43121158775796, 84.42545632762472, 87.58419980413788, 90.0843492288727, 92.06513286904668, 93.6361477192222, 94.88368602162102, 95.87570896802902, 96.66575706825148, 97.29602470319443, 97.79977768045613, 98.24165747836871, 98.55692308275061, 98.82918285271674, 99.0301776609836, 99.20052734996653, 99.46237370613001, 99.62833129487339, 99.73618651449881, 99.80820512188315, 99.85764549176265, 99.89250950608736, 99.89250950608736, 110.73855341912144, 119.14542528622482, 125.64718975334415, 130.66236721914498, 134.51879447655284, 137.47315000795024, 139.72628757827044, 141.43527676577457, 142.7228565395308, 143.68485669841962, 144.39602309801114, 144.914589169052, 145.28586282080448, 145.54504013820994, 145.71941195570983, 145.83009377571847, 145.8933815161876, 145.92181358485993, 145.92500250315737, 145.91028573076468, 145.88323467961402, 145.84805252979785, 145.80788488010063, 145.76506209742095, 145.72128816988172, 145.67778768021958, 145.6354200123381, 145.61841028484193, 145.53064772631456, 145.45678489907522, 145.39564765746923 ], "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": "Changes in concentrations for 2 reactions" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0.015, 1.0200000000000007 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ 14.059722083157924, 152.86528041999944 ], "title": { "text": "concentration" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_curves(colors=['red', 'green', 'gray'])" ] }, { "cell_type": "markdown", "id": "158e3787-f2d5-4a01-aaa9-6066e93e584c", "metadata": {}, "source": [ "### The (transiently) high value of [U] led to an increase in [X]" ] }, { "cell_type": "code", "execution_count": 15, "id": "2783a665-fca0-44e5-8d42-af2a96eae392", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "U <-> 2 D\n", "Final concentrations: [D] = 145.4 ; [U] = 36.7\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 3.96165\n", " Formula used: [D] / [U]\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.9586 %\n", "Reaction IS in equilibrium (within 2% tolerance)\n", "\n", "X <-> D\n", "Final concentrations: [D] = 145.4 ; [X] = 71.48\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 2.03403\n", " Formula used: [D] / [X]\n", "2. Ratio of forward/reverse reaction rates: 2.0\n", "Discrepancy between the two values: 1.701 %\n", "Reaction IS in equilibrium (within 2% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "dynamics.is_in_equilibrium(tolerance=2)" ] }, { "cell_type": "markdown", "id": "f6619731-c5ea-484c-af3e-cea50d685361", "metadata": { "tags": [] }, "source": [ "# 3. Let's again suddenly increase [U]" ] }, { "cell_type": "code", "execution_count": 16, "id": "d3618eba-a673-4ff5-85d0-08f5ea592361", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 1.02:\n", "3 species:\n", " Species 0 (U). Conc: 100.0\n", " Species 1 (X). Conc: 71.48157153405576\n", " Species 2 (D). Conc: 145.39564765746923\n" ] } ], "source": [ "dynamics.set_chem_conc(species_name=\"U\", conc=100., snapshot=True)\n", "dynamics.describe_state()" ] }, { "cell_type": "code", "execution_count": 17, "id": "61eead55-fcef-41cd-b29e-f2d5ad5c6078", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEUXDcaption
580.9936.80701971.207879145.456785
591.0236.70074271.481572145.395648
601.02100.00000071.481572145.395648Set concentration of `U`
\n", "
" ], "text/plain": [ " SYSTEM TIME U X D caption\n", "58 0.99 36.807019 71.207879 145.456785 \n", "59 1.02 36.700742 71.481572 145.395648 \n", "60 1.02 100.000000 71.481572 145.395648 Set concentration of `U`" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_history(tail=3)" ] }, { "cell_type": "markdown", "id": "0974480d-ca45-46fe-addd-c8d394780fdb", "metadata": {}, "source": [ "### Yet again, take the system to equilibrium" ] }, { "cell_type": "code", "execution_count": 18, "id": "8fe20f9c-05c4-45a4-b485-a51005440200", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "single_compartment_react(): setting abs_fast_threshold to 40.0\n", "20 total step(s) taken\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEUXDcaption
00.01544.00000091.00000021.000000Interm. step, due to the fast rxns: [0, 1]
10.03039.35000083.75500037.545000
20.04535.75435077.90657550.584725Interm. step, due to the fast rxns: [0, 1]
30.06032.98137073.17129660.865965
40.07530.84958469.32484868.975984Interm. step, due to the fast rxns: [0, 1]
..................
881.50052.843169102.307495208.883386
891.53052.693812102.691651208.797945
901.56052.575174102.998969208.727903
911.59052.480806103.244666208.670941
921.62052.405669103.441011208.624870
\n", "

93 rows × 5 columns

\n", "
" ], "text/plain": [ " SYSTEM TIME U X D \\\n", "0 0.015 44.000000 91.000000 21.000000 \n", "1 0.030 39.350000 83.755000 37.545000 \n", "2 0.045 35.754350 77.906575 50.584725 \n", "3 0.060 32.981370 73.171296 60.865965 \n", "4 0.075 30.849584 69.324848 68.975984 \n", ".. ... ... ... ... \n", "88 1.500 52.843169 102.307495 208.883386 \n", "89 1.530 52.693812 102.691651 208.797945 \n", "90 1.560 52.575174 102.998969 208.727903 \n", "91 1.590 52.480806 103.244666 208.670941 \n", "92 1.620 52.405669 103.441011 208.624870 \n", "\n", " caption \n", "0 Interm. step, due to the fast rxns: [0, 1] \n", "1 \n", "2 Interm. step, due to the fast rxns: [0, 1] \n", "3 \n", "4 Interm. step, due to the fast rxns: [0, 1] \n", ".. ... \n", "88 \n", "89 \n", "90 \n", "91 \n", "92 \n", "\n", "[93 rows x 5 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.single_compartment_react(time_step=0.03, stop_time=1.6,\n", " dynamic_substeps=2, rel_fast_threshold=120)\n", "\n", "df = dynamics.history.get()\n", "df" ] }, { "cell_type": "code", "execution_count": 19, "id": "35850ec7-e78e-4b57-976c-bc0ad6c824d5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From time 0 to 0.33, in 22 substeps of 0.015 (each 1/2 of full step)\n", "From time 0.33 to 0.51, in 6 FULL steps of 0.03\n", "From time 0.51 to 0.93, in 28 substeps of 0.015 (each 1/2 of full step)\n", "From time 0.93 to 1.02, in 3 FULL steps of 0.03\n", "From time 1.02 to 1.38, in 24 substeps of 0.015 (each 1/2 of full step)\n", "From time 1.38 to 1.62, in 8 FULL steps of 0.03\n", "(for a grand total of 54 full steps)\n" ] } ], "source": [ "dynamics.explain_time_advance()" ] }, { "cell_type": "code", "execution_count": 20, "id": "5af5d869-16ff-4f1d-ab83-4865b42e6376", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=U
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "U", "line": { "color": "red", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "U", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002, 0.5100000000000002, 0.5250000000000002, 0.5400000000000003, 0.5550000000000003, 0.5700000000000003, 0.5850000000000003, 0.6000000000000003, 0.6150000000000003, 0.6300000000000003, 0.6450000000000004, 0.6600000000000004, 0.6750000000000004, 0.6900000000000004, 0.7050000000000004, 0.7200000000000004, 0.7350000000000004, 0.7500000000000004, 0.7650000000000005, 0.7800000000000005, 0.7950000000000005, 0.8100000000000005, 0.8250000000000005, 0.8400000000000005, 0.8550000000000005, 0.8700000000000006, 0.8850000000000006, 0.9000000000000006, 0.9150000000000006, 0.9300000000000006, 0.9600000000000006, 0.9900000000000007, 1.0200000000000007, 1.0200000000000007, 1.0350000000000006, 1.0500000000000007, 1.0650000000000006, 1.0800000000000007, 1.0950000000000006, 1.1100000000000008, 1.1250000000000007, 1.1400000000000008, 1.1550000000000007, 1.1700000000000008, 1.1850000000000007, 1.2000000000000008, 1.2150000000000007, 1.2300000000000009, 1.2450000000000008, 1.260000000000001, 1.2750000000000008, 1.290000000000001, 1.3050000000000008, 1.320000000000001, 1.3350000000000009, 1.350000000000001, 1.3650000000000009, 1.380000000000001, 1.410000000000001, 1.440000000000001, 1.470000000000001, 1.500000000000001, 1.5300000000000011, 1.5600000000000012, 1.5900000000000012, 1.6200000000000012 ], "xaxis": "x", "y": [ 44, 39.35, 35.75435, 32.98136975, 30.84958431875, 29.21691371174375, 27.97218332465947, 27.028457673333072, 26.317806442361846, 25.78719566340256, 25.395262660660435, 25.109785127452582, 24.905695343734937, 24.763522483135375, 24.668171054200002, 24.607963239743544, 24.573888392070153, 24.53981354439676, 24.54228564342021, 24.54475774244366, 24.564262298931922, 24.583766855420187, 24.635694451117335, 24.690870205216974, 24.742761233657305, 24.78866972844948, 24.827881300934596, 24.860648518216053, 70, 64.59677528518262, 60.167318853534354, 56.521603349696974, 53.50842664033367, 51.007286460067974, 48.92197591915641, 47.175533309096146, 45.706257939352724, 44.464565289603634, 43.41050315103712, 42.51178847386525, 41.742254549941755, 41.080621679020304, 40.509522962162, 40.01473141084886, 39.58454600021829, 39.20930329346365, 38.88098834373364, 38.5929241500314, 38.33952332712236, 38.116089099790614, 37.91865544820416, 37.743858370313596, 37.588831912278984, 37.451123945728135, 37.32862771733721, 37.219526021663334, 37.110424325989456, 36.941027104842505, 36.807019463259174, 36.70074188602148, 100, 92.36186942972408, 86.0953184809022, 80.93328319309427, 76.66319863357549, 73.1156002851514, 70.15513881781278, 67.67349423834368, 65.5837851947597, 63.816155516173566, 62.31428791795238, 61.03264814583863, 59.93430477473006, 58.98920285738769, 58.17279555269112, 57.46495825907151, 56.84912582082107, 56.31160599404069, 55.841032286783516, 55.427927099658184, 55.06435224089993, 54.743628729115024, 54.46011160695604, 54.20900848954537, 53.957905372134704, 53.57220860231578, 53.26981174059882, 53.03156004579193, 52.84316915159411, 52.69381170005297, 52.57517359555349, 52.48080613327257, 52.40566914084128 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002, 0.5100000000000002, 0.5250000000000002, 0.5400000000000003, 0.5550000000000003, 0.5700000000000003, 0.5850000000000003, 0.6000000000000003, 0.6150000000000003, 0.6300000000000003, 0.6450000000000004, 0.6600000000000004, 0.6750000000000004, 0.6900000000000004, 0.7050000000000004, 0.7200000000000004, 0.7350000000000004, 0.7500000000000004, 0.7650000000000005, 0.7800000000000005, 0.7950000000000005, 0.8100000000000005, 0.8250000000000005, 0.8400000000000005, 0.8550000000000005, 0.8700000000000006, 0.8850000000000006, 0.9000000000000006, 0.9150000000000006, 0.9300000000000006, 0.9600000000000006, 0.9900000000000007, 1.0200000000000007, 1.0200000000000007, 1.0350000000000006, 1.0500000000000007, 1.0650000000000006, 1.0800000000000007, 1.0950000000000006, 1.1100000000000008, 1.1250000000000007, 1.1400000000000008, 1.1550000000000007, 1.1700000000000008, 1.1850000000000007, 1.2000000000000008, 1.2150000000000007, 1.2300000000000009, 1.2450000000000008, 1.260000000000001, 1.2750000000000008, 1.290000000000001, 1.3050000000000008, 1.320000000000001, 1.3350000000000009, 1.350000000000001, 1.3650000000000009, 1.380000000000001, 1.410000000000001, 1.440000000000001, 1.470000000000001, 1.500000000000001, 1.5300000000000011, 1.5600000000000012, 1.5900000000000012, 1.6200000000000012 ], "xaxis": "x", "y": [ 91, 83.755, 77.90657499999999, 73.171295875, 69.32484765437499, 66.18953063234687, 63.624421762923106, 61.51762832570914, 59.78018731113843, 58.341259444322176, 57.14434180963245, 56.144282025872634, 55.304923290909095, 54.597246065700226, 53.99790082334851, 53.488048817318465, 53.05244553540355, 52.678715432837755, 52.35850563040895, 52.08130166239592, 51.841297741152545, 51.63193893919308, 51.266237391635315, 50.98992829469266, 50.778291018186586, 50.6144554212179, 50.48659190636816, 50.38619345748053, 50.38619345748053, 50.34659897408122, 50.79863997027437, 51.588306510829796, 52.5994824637556, 53.74533556687912, 54.96160111730488, 56.2013487671052, 57.4309103190879, 58.626715844829846, 59.77283996307405, 60.85910291782627, 61.879604694632405, 62.83159678472283, 63.71461690103398, 64.52982818616037, 65.27951718741288, 65.96671486045305, 66.59491269124072, 67.16785216034775, 67.68937057855854, 68.16329008437268, 68.59333953736177, 68.98310134284011, 69.33597704158903, 69.65516690222996, 69.94365984867395, 70.20423090790317, 70.4394440267471, 70.86600102756839, 71.20787913797439, 71.48157153405576, 71.48157153405576, 71.59103424057686, 72.37315123304244, 73.61367201691914, 75.15130567087519, 76.86566639190387, 78.66787226695185, 80.49322188092883, 82.29549730917113, 84.04253936852328, 85.71281742093564, 87.29277602011223, 88.77478778789023, 90.15557887041797, 91.43502232936528, 92.61521757877739, 93.69979182594463, 94.69337346918684, 95.60119837500157, 96.42881855218447, 97.18188947228896, 97.86601755546758, 98.48665346347774, 99.04902006490083, 99.55806645569876, 100.4839152729131, 101.22921032804697, 101.82770715340376, 102.30749514097946, 102.69165073286531, 102.9989686562194, 103.24466559907779, 103.44101051057498 ], "yaxis": "y" }, { "hovertemplate": "Chemical=D
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "D", "line": { "color": "gray", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "D", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002, 0.5100000000000002, 0.5250000000000002, 0.5400000000000003, 0.5550000000000003, 0.5700000000000003, 0.5850000000000003, 0.6000000000000003, 0.6150000000000003, 0.6300000000000003, 0.6450000000000004, 0.6600000000000004, 0.6750000000000004, 0.6900000000000004, 0.7050000000000004, 0.7200000000000004, 0.7350000000000004, 0.7500000000000004, 0.7650000000000005, 0.7800000000000005, 0.7950000000000005, 0.8100000000000005, 0.8250000000000005, 0.8400000000000005, 0.8550000000000005, 0.8700000000000006, 0.8850000000000006, 0.9000000000000006, 0.9150000000000006, 0.9300000000000006, 0.9600000000000006, 0.9900000000000007, 1.0200000000000007, 1.0200000000000007, 1.0350000000000006, 1.0500000000000007, 1.0650000000000006, 1.0800000000000007, 1.0950000000000006, 1.1100000000000008, 1.1250000000000007, 1.1400000000000008, 1.1550000000000007, 1.1700000000000008, 1.1850000000000007, 1.2000000000000008, 1.2150000000000007, 1.2300000000000009, 1.2450000000000008, 1.260000000000001, 1.2750000000000008, 1.290000000000001, 1.3050000000000008, 1.320000000000001, 1.3350000000000009, 1.350000000000001, 1.3650000000000009, 1.380000000000001, 1.410000000000001, 1.440000000000001, 1.470000000000001, 1.500000000000001, 1.5300000000000011, 1.5600000000000012, 1.5900000000000012, 1.6200000000000012 ], "xaxis": "x", "y": [ 21, 37.545, 50.584725000000006, 60.865964625000004, 68.975983708125, 75.37664194416563, 80.43121158775796, 84.42545632762472, 87.58419980413788, 90.0843492288727, 92.06513286904668, 93.6361477192222, 94.88368602162102, 95.87570896802902, 96.66575706825148, 97.29602470319443, 97.79977768045613, 98.24165747836871, 98.55692308275061, 98.82918285271674, 99.0301776609836, 99.20052734996653, 99.46237370613001, 99.62833129487339, 99.73618651449881, 99.80820512188315, 99.85764549176265, 99.89250950608736, 99.89250950608736, 110.73855341912144, 119.14542528622482, 125.64718975334415, 130.66236721914498, 134.51879447655284, 137.47315000795024, 139.72628757827044, 141.43527676577457, 142.7228565395308, 143.68485669841962, 144.39602309801114, 144.914589169052, 145.28586282080448, 145.54504013820994, 145.71941195570983, 145.83009377571847, 145.8933815161876, 145.92181358485993, 145.92500250315737, 145.91028573076468, 145.88323467961402, 145.84805252979785, 145.80788488010063, 145.76506209742095, 145.72128816988172, 145.67778768021958, 145.6354200123381, 145.61841028484193, 145.53064772631456, 145.45678489907522, 145.39564765746923, 145.39564765746923, 160.56244609149996, 172.31343099667816, 181.3969807884173, 188.39951625349883, 193.78035222931834, 197.8990692889476, 201.03700883390883, 203.4141514928345, 205.2023687906546, 206.53582593468462, 207.51914687973553, 208.23382185417466, 208.74323460633167, 209.0966057567775, 209.33208509460462, 209.47917572393825, 209.5606337342568, 209.59395624295644, 209.5925464400242, 209.56662523743623, 209.52394417782742, 209.4703425141352, 209.41018214753345, 209.40334199155686, 209.24888671398037, 209.10838538228043, 208.98639194653742, 208.88338574735738, 208.7979450585538, 208.7279033441987, 208.67094132590213, 208.62487039926754 ], "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": "Changes in concentrations for 2 reactions" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0.015, 1.6200000000000012 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ 10.52255798650242, 220.07139825645402 ], "title": { "text": "concentration" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_curves(colors=['red', 'green', 'gray'])" ] }, { "cell_type": "markdown", "id": "ffbf3294-7a8d-4679-9c4b-5b9a975bf8fc", "metadata": {}, "source": [ "### The (transiently) high value of [U] again led to an increase in [X]" ] }, { "cell_type": "code", "execution_count": 21, "id": "aff608b1-5c78-4070-845a-118afe7c2108", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "dynamics.is_in_equilibrium(explain=False)" ] }, { "cell_type": "markdown", "id": "64ebc51b-0dc7-4cff-b231-4c35843a7113", "metadata": { "tags": [] }, "source": [ "# 4. Now, instead, let's DECREASE [U]" ] }, { "cell_type": "code", "execution_count": 22, "id": "52f4843c-0671-4cd9-9c51-74a44feb4fe4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 1.62:\n", "3 species:\n", " Species 0 (U). Conc: 5.0\n", " Species 1 (X). Conc: 103.44101051057498\n", " Species 2 (D). Conc: 208.62487039926754\n" ] } ], "source": [ "dynamics.set_chem_conc(species_name=\"U\", conc=5., snapshot=True)\n", "dynamics.describe_state()" ] }, { "cell_type": "code", "execution_count": 23, "id": "e8fe3554-d5ab-4306-b890-4e36289b5b4b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEUXDcaption
911.5952.480806103.244666208.670941
921.6252.405669103.441011208.624870
931.625.000000103.441011208.624870Set concentration of `U`
\n", "
" ], "text/plain": [ " SYSTEM TIME U X D caption\n", "91 1.59 52.480806 103.244666 208.670941 \n", "92 1.62 52.405669 103.441011 208.624870 \n", "93 1.62 5.000000 103.441011 208.624870 Set concentration of `U`" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_history(tail=3)" ] }, { "cell_type": "markdown", "id": "da46e3d8-58d2-4b48-8b32-887613967fce", "metadata": {}, "source": [ "### Take the system to equilibrium" ] }, { "cell_type": "code", "execution_count": 24, "id": "c392f375-c7b4-476b-809e-5cc4f5c14fa4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "single_compartment_react(): setting abs_fast_threshold to 26.666666666666668\n", "23 total step(s) taken\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEUXDcaption
00.01544.00000091.00000021.000000Interm. step, due to the fast rxns: [0, 1]
10.03039.35000083.75500037.545000
20.04535.75435077.90657550.584725Interm. step, due to the fast rxns: [0, 1]
30.06032.98137073.17129660.865965
40.07530.84958469.32484868.975984Interm. step, due to the fast rxns: [0, 1]
..................
1242.19039.98839581.223853160.865238
1252.22040.04309481.081431160.898261
1262.25040.08664780.967617160.924969
1272.28040.12135080.876693160.946487
1282.31040.14901580.804072160.963778
\n", "

129 rows × 5 columns

\n", "
" ], "text/plain": [ " SYSTEM TIME U X D \\\n", "0 0.015 44.000000 91.000000 21.000000 \n", "1 0.030 39.350000 83.755000 37.545000 \n", "2 0.045 35.754350 77.906575 50.584725 \n", "3 0.060 32.981370 73.171296 60.865965 \n", "4 0.075 30.849584 69.324848 68.975984 \n", ".. ... ... ... ... \n", "124 2.190 39.988395 81.223853 160.865238 \n", "125 2.220 40.043094 81.081431 160.898261 \n", "126 2.250 40.086647 80.967617 160.924969 \n", "127 2.280 40.121350 80.876693 160.946487 \n", "128 2.310 40.149015 80.804072 160.963778 \n", "\n", " caption \n", "0 Interm. step, due to the fast rxns: [0, 1] \n", "1 \n", "2 Interm. step, due to the fast rxns: [0, 1] \n", "3 \n", "4 Interm. step, due to the fast rxns: [0, 1] \n", ".. ... \n", "124 \n", "125 \n", "126 \n", "127 \n", "128 \n", "\n", "[129 rows x 5 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.single_compartment_react(time_step=0.03, stop_time=2.3,\n", " dynamic_substeps=2, rel_fast_threshold=80)\n", "\n", "df = dynamics.history.get()\n", "df" ] }, { "cell_type": "code", "execution_count": 25, "id": "ad01c472-3ebe-4d0d-8913-1bcd85ea7a6c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From time 0 to 0.33, in 22 substeps of 0.015 (each 1/2 of full step)\n", "From time 0.33 to 0.51, in 6 FULL steps of 0.03\n", "From time 0.51 to 0.93, in 28 substeps of 0.015 (each 1/2 of full step)\n", "From time 0.93 to 1.02, in 3 FULL steps of 0.03\n", "From time 1.02 to 1.38, in 24 substeps of 0.015 (each 1/2 of full step)\n", "From time 1.38 to 1.62, in 8 FULL steps of 0.03\n", "From time 1.62 to 1.98, in 24 substeps of 0.015 (each 1/2 of full step)\n", "From time 1.98 to 2.31, in 11 FULL steps of 0.03\n", "(for a grand total of 77 full steps)\n" ] } ], "source": [ "dynamics.explain_time_advance()" ] }, { "cell_type": "code", "execution_count": 26, "id": "54346a72-bac9-4cc7-ba01-0533ed60371f", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=U
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "U", "line": { "color": "red", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "U", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002, 0.5100000000000002, 0.5250000000000002, 0.5400000000000003, 0.5550000000000003, 0.5700000000000003, 0.5850000000000003, 0.6000000000000003, 0.6150000000000003, 0.6300000000000003, 0.6450000000000004, 0.6600000000000004, 0.6750000000000004, 0.6900000000000004, 0.7050000000000004, 0.7200000000000004, 0.7350000000000004, 0.7500000000000004, 0.7650000000000005, 0.7800000000000005, 0.7950000000000005, 0.8100000000000005, 0.8250000000000005, 0.8400000000000005, 0.8550000000000005, 0.8700000000000006, 0.8850000000000006, 0.9000000000000006, 0.9150000000000006, 0.9300000000000006, 0.9600000000000006, 0.9900000000000007, 1.0200000000000007, 1.0200000000000007, 1.0350000000000006, 1.0500000000000007, 1.0650000000000006, 1.0800000000000007, 1.0950000000000006, 1.1100000000000008, 1.1250000000000007, 1.1400000000000008, 1.1550000000000007, 1.1700000000000008, 1.1850000000000007, 1.2000000000000008, 1.2150000000000007, 1.2300000000000009, 1.2450000000000008, 1.260000000000001, 1.2750000000000008, 1.290000000000001, 1.3050000000000008, 1.320000000000001, 1.3350000000000009, 1.350000000000001, 1.3650000000000009, 1.380000000000001, 1.410000000000001, 1.440000000000001, 1.470000000000001, 1.500000000000001, 1.5300000000000011, 1.5600000000000012, 1.5900000000000012, 1.6200000000000012, 1.6200000000000012, 1.6350000000000011, 1.6500000000000012, 1.6650000000000011, 1.6800000000000013, 1.6950000000000012, 1.7100000000000013, 1.7250000000000012, 1.7400000000000013, 1.7550000000000012, 1.7700000000000014, 1.7850000000000013, 1.8000000000000014, 1.8150000000000013, 1.8300000000000014, 1.8450000000000013, 1.8600000000000014, 1.8750000000000013, 1.8900000000000015, 1.9050000000000014, 1.9200000000000015, 1.9350000000000014, 1.9500000000000015, 1.9650000000000014, 1.9800000000000015, 2.0100000000000016, 2.0400000000000014, 2.070000000000001, 2.100000000000001, 2.130000000000001, 2.1600000000000006, 2.1900000000000004, 2.22, 2.25, 2.28, 2.3099999999999996 ], "xaxis": "x", "y": [ 44, 39.35, 35.75435, 32.98136975, 30.84958431875, 29.21691371174375, 27.97218332465947, 27.028457673333072, 26.317806442361846, 25.78719566340256, 25.395262660660435, 25.109785127452582, 24.905695343734937, 24.763522483135375, 24.668171054200002, 24.607963239743544, 24.573888392070153, 24.53981354439676, 24.54228564342021, 24.54475774244366, 24.564262298931922, 24.583766855420187, 24.635694451117335, 24.690870205216974, 24.742761233657305, 24.78866972844948, 24.827881300934596, 24.860648518216053, 70, 64.59677528518262, 60.167318853534354, 56.521603349696974, 53.50842664033367, 51.007286460067974, 48.92197591915641, 47.175533309096146, 45.706257939352724, 44.464565289603634, 43.41050315103712, 42.51178847386525, 41.742254549941755, 41.080621679020304, 40.509522962162, 40.01473141084886, 39.58454600021829, 39.20930329346365, 38.88098834373364, 38.5929241500314, 38.33952332712236, 38.116089099790614, 37.91865544820416, 37.743858370313596, 37.588831912278984, 37.451123945728135, 37.32862771733721, 37.219526021663334, 37.110424325989456, 36.941027104842505, 36.807019463259174, 36.70074188602148, 100, 92.36186942972408, 86.0953184809022, 80.93328319309427, 76.66319863357549, 73.1156002851514, 70.15513881781278, 67.67349423834368, 65.5837851947597, 63.816155516173566, 62.31428791795238, 61.03264814583863, 59.93430477473006, 58.98920285738769, 58.17279555269112, 57.46495825907151, 56.84912582082107, 56.31160599404069, 55.841032286783516, 55.427927099658184, 55.06435224089993, 54.743628729115024, 54.46011160695604, 54.20900848954537, 53.957905372134704, 53.57220860231578, 53.26981174059882, 53.03156004579193, 52.84316915159411, 52.69381170005297, 52.57517359555349, 52.48080613327257, 52.40566914084128, 5, 10.658746111978026, 15.296565077139547, 19.11282003071304, 22.266126746701243, 24.882882812946335, 27.06399025905205, 28.8901564693314, 30.426076012233807, 31.72373139784097, 32.8249999735742, 33.763714228214674, 34.56729137167077, 35.25802336606533, 35.854099167054095, 36.37041566448096, 36.81922180166657, 37.2106309057425, 37.55302882957509, 37.85339965829295, 38.11758613170244, 38.35049831182538, 38.5562811732214, 38.73844954819882, 38.89999709017334, 39.18697195910941, 39.41050442585107, 39.58576857510474, 39.72386024176136, 39.83305526736156, 39.91962741577175, 39.988394649409784, 40.043094230959476, 40.086647292250156, 40.121350097199105, 40.1490153228061 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002, 0.5100000000000002, 0.5250000000000002, 0.5400000000000003, 0.5550000000000003, 0.5700000000000003, 0.5850000000000003, 0.6000000000000003, 0.6150000000000003, 0.6300000000000003, 0.6450000000000004, 0.6600000000000004, 0.6750000000000004, 0.6900000000000004, 0.7050000000000004, 0.7200000000000004, 0.7350000000000004, 0.7500000000000004, 0.7650000000000005, 0.7800000000000005, 0.7950000000000005, 0.8100000000000005, 0.8250000000000005, 0.8400000000000005, 0.8550000000000005, 0.8700000000000006, 0.8850000000000006, 0.9000000000000006, 0.9150000000000006, 0.9300000000000006, 0.9600000000000006, 0.9900000000000007, 1.0200000000000007, 1.0200000000000007, 1.0350000000000006, 1.0500000000000007, 1.0650000000000006, 1.0800000000000007, 1.0950000000000006, 1.1100000000000008, 1.1250000000000007, 1.1400000000000008, 1.1550000000000007, 1.1700000000000008, 1.1850000000000007, 1.2000000000000008, 1.2150000000000007, 1.2300000000000009, 1.2450000000000008, 1.260000000000001, 1.2750000000000008, 1.290000000000001, 1.3050000000000008, 1.320000000000001, 1.3350000000000009, 1.350000000000001, 1.3650000000000009, 1.380000000000001, 1.410000000000001, 1.440000000000001, 1.470000000000001, 1.500000000000001, 1.5300000000000011, 1.5600000000000012, 1.5900000000000012, 1.6200000000000012, 1.6200000000000012, 1.6350000000000011, 1.6500000000000012, 1.6650000000000011, 1.6800000000000013, 1.6950000000000012, 1.7100000000000013, 1.7250000000000012, 1.7400000000000013, 1.7550000000000012, 1.7700000000000014, 1.7850000000000013, 1.8000000000000014, 1.8150000000000013, 1.8300000000000014, 1.8450000000000013, 1.8600000000000014, 1.8750000000000013, 1.8900000000000015, 1.9050000000000014, 1.9200000000000015, 1.9350000000000014, 1.9500000000000015, 1.9650000000000014, 1.9800000000000015, 2.0100000000000016, 2.0400000000000014, 2.070000000000001, 2.100000000000001, 2.130000000000001, 2.1600000000000006, 2.1900000000000004, 2.22, 2.25, 2.28, 2.3099999999999996 ], "xaxis": "x", "y": [ 91, 83.755, 77.90657499999999, 73.171295875, 69.32484765437499, 66.18953063234687, 63.624421762923106, 61.51762832570914, 59.78018731113843, 58.341259444322176, 57.14434180963245, 56.144282025872634, 55.304923290909095, 54.597246065700226, 53.99790082334851, 53.488048817318465, 53.05244553540355, 52.678715432837755, 52.35850563040895, 52.08130166239592, 51.841297741152545, 51.63193893919308, 51.266237391635315, 50.98992829469266, 50.778291018186586, 50.6144554212179, 50.48659190636816, 50.38619345748053, 50.38619345748053, 50.34659897408122, 50.79863997027437, 51.588306510829796, 52.5994824637556, 53.74533556687912, 54.96160111730488, 56.2013487671052, 57.4309103190879, 58.626715844829846, 59.77283996307405, 60.85910291782627, 61.879604694632405, 62.83159678472283, 63.71461690103398, 64.52982818616037, 65.27951718741288, 65.96671486045305, 66.59491269124072, 67.16785216034775, 67.68937057855854, 68.16329008437268, 68.59333953736177, 68.98310134284011, 69.33597704158903, 69.65516690222996, 69.94365984867395, 70.20423090790317, 70.4394440267471, 70.86600102756839, 71.20787913797439, 71.48157153405576, 71.48157153405576, 71.59103424057686, 72.37315123304244, 73.61367201691914, 75.15130567087519, 76.86566639190387, 78.66787226695185, 80.49322188092883, 82.29549730917113, 84.04253936852328, 85.71281742093564, 87.29277602011223, 88.77478778789023, 90.15557887041797, 91.43502232936528, 92.61521757877739, 93.69979182594463, 94.69337346918684, 95.60119837500157, 96.42881855218447, 97.18188947228896, 97.86601755546758, 98.48665346347774, 99.04902006490083, 99.55806645569876, 100.4839152729131, 101.22921032804697, 101.82770715340376, 102.30749514097946, 102.69165073286531, 102.9989686562194, 103.24466559907779, 103.44101051057498, 103.44101051057498, 103.51943873259027, 103.07799199455548, 102.27873685929085, 101.24391822146532, 100.06500249530731, 98.80973234621857, 97.52762399710728, 96.2542453162009, 95.01453999835564, 93.82540591371486, 92.69769075868459, 91.63773286666576, 90.64854734715843, 89.73073599328907, 88.8831823501031, 88.1035799639788, 87.38883134763459, 86.73534697513, 86.13926717976861, 85.59662478219639, 85.10346232568956, 84.65591470460011, 84.25026555483208, 83.88298388653477, 83.21850804282501, 82.68178520050839, 82.24974168160375, 81.90280236593772, 81.62468016550332, 81.40199585457817, 81.22385332088898, 81.08143116924103, 80.96761707385907, 80.87669323319793, 80.80407232462447 ], "yaxis": "y" }, { "hovertemplate": "Chemical=D
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "D", "line": { "color": "gray", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "D", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0.015, 0.03, 0.045, 0.06, 0.075, 0.09, 0.105, 0.12, 0.135, 0.15, 0.16499999999999998, 0.18, 0.195, 0.21, 0.22499999999999998, 0.24, 0.255, 0.27, 0.28500000000000003, 0.30000000000000004, 0.31500000000000006, 0.33000000000000007, 0.3600000000000001, 0.3900000000000001, 0.42000000000000015, 0.4500000000000002, 0.4800000000000002, 0.5100000000000002, 0.5100000000000002, 0.5250000000000002, 0.5400000000000003, 0.5550000000000003, 0.5700000000000003, 0.5850000000000003, 0.6000000000000003, 0.6150000000000003, 0.6300000000000003, 0.6450000000000004, 0.6600000000000004, 0.6750000000000004, 0.6900000000000004, 0.7050000000000004, 0.7200000000000004, 0.7350000000000004, 0.7500000000000004, 0.7650000000000005, 0.7800000000000005, 0.7950000000000005, 0.8100000000000005, 0.8250000000000005, 0.8400000000000005, 0.8550000000000005, 0.8700000000000006, 0.8850000000000006, 0.9000000000000006, 0.9150000000000006, 0.9300000000000006, 0.9600000000000006, 0.9900000000000007, 1.0200000000000007, 1.0200000000000007, 1.0350000000000006, 1.0500000000000007, 1.0650000000000006, 1.0800000000000007, 1.0950000000000006, 1.1100000000000008, 1.1250000000000007, 1.1400000000000008, 1.1550000000000007, 1.1700000000000008, 1.1850000000000007, 1.2000000000000008, 1.2150000000000007, 1.2300000000000009, 1.2450000000000008, 1.260000000000001, 1.2750000000000008, 1.290000000000001, 1.3050000000000008, 1.320000000000001, 1.3350000000000009, 1.350000000000001, 1.3650000000000009, 1.380000000000001, 1.410000000000001, 1.440000000000001, 1.470000000000001, 1.500000000000001, 1.5300000000000011, 1.5600000000000012, 1.5900000000000012, 1.6200000000000012, 1.6200000000000012, 1.6350000000000011, 1.6500000000000012, 1.6650000000000011, 1.6800000000000013, 1.6950000000000012, 1.7100000000000013, 1.7250000000000012, 1.7400000000000013, 1.7550000000000012, 1.7700000000000014, 1.7850000000000013, 1.8000000000000014, 1.8150000000000013, 1.8300000000000014, 1.8450000000000013, 1.8600000000000014, 1.8750000000000013, 1.8900000000000015, 1.9050000000000014, 1.9200000000000015, 1.9350000000000014, 1.9500000000000015, 1.9650000000000014, 1.9800000000000015, 2.0100000000000016, 2.0400000000000014, 2.070000000000001, 2.100000000000001, 2.130000000000001, 2.1600000000000006, 2.1900000000000004, 2.22, 2.25, 2.28, 2.3099999999999996 ], "xaxis": "x", "y": [ 21, 37.545, 50.584725000000006, 60.865964625000004, 68.975983708125, 75.37664194416563, 80.43121158775796, 84.42545632762472, 87.58419980413788, 90.0843492288727, 92.06513286904668, 93.6361477192222, 94.88368602162102, 95.87570896802902, 96.66575706825148, 97.29602470319443, 97.79977768045613, 98.24165747836871, 98.55692308275061, 98.82918285271674, 99.0301776609836, 99.20052734996653, 99.46237370613001, 99.62833129487339, 99.73618651449881, 99.80820512188315, 99.85764549176265, 99.89250950608736, 99.89250950608736, 110.73855341912144, 119.14542528622482, 125.64718975334415, 130.66236721914498, 134.51879447655284, 137.47315000795024, 139.72628757827044, 141.43527676577457, 142.7228565395308, 143.68485669841962, 144.39602309801114, 144.914589169052, 145.28586282080448, 145.54504013820994, 145.71941195570983, 145.83009377571847, 145.8933815161876, 145.92181358485993, 145.92500250315737, 145.91028573076468, 145.88323467961402, 145.84805252979785, 145.80788488010063, 145.76506209742095, 145.72128816988172, 145.67778768021958, 145.6354200123381, 145.61841028484193, 145.53064772631456, 145.45678489907522, 145.39564765746923, 145.39564765746923, 160.56244609149996, 172.31343099667816, 181.3969807884173, 188.39951625349883, 193.78035222931834, 197.8990692889476, 201.03700883390883, 203.4141514928345, 205.2023687906546, 206.53582593468462, 207.51914687973553, 208.23382185417466, 208.74323460633167, 209.0966057567775, 209.33208509460462, 209.47917572393825, 209.5606337342568, 209.59395624295644, 209.5925464400242, 209.56662523743623, 209.52394417782742, 209.4703425141352, 209.41018214753345, 209.40334199155686, 209.24888671398037, 209.10838538228043, 208.98639194653742, 208.88338574735738, 208.7979450585538, 208.7279033441987, 208.67094132590213, 208.62487039926754, 208.62487039926754, 197.2289499532962, 188.39475876100795, 181.5615039891256, 176.2897091949747, 172.23511278864254, 169.12816804551986, 166.75794397407245, 164.959483569174, 163.60387811580495, 162.59047504897924, 161.84076169472857, 161.2935652998352, 160.9012868305534, 160.62694658244524, 160.4418672307775, 160.32385734253057, 160.25578775072293, 160.22447627556232, 160.219814413488, 160.23408386424126, 160.2614219605022, 160.2974038587996, 160.3387162586128, 160.38290284296107, 160.4734289487987, 160.563086857632, 160.6446020780293, 160.7153580603821, 160.7750902096161, 160.82463022372087, 160.86523829013402, 160.89826127868258, 160.92496925148316, 160.9464874822464, 160.96377793960585 ], "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": "Changes in concentrations for 2 reactions" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0.015, 2.3099999999999996 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -6.366330902386469, 220.9602871453429 ], "title": { "text": "concentration" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_curves(colors=['red', 'green', 'gray'])" ] }, { "cell_type": "markdown", "id": "a1629b91-2753-4df7-b6a0-dedf86ac3dc1", "metadata": {}, "source": [ "### The (transiently) LOW value of [U] led to an DECREASE in [X]" ] }, { "cell_type": "code", "execution_count": 27, "id": "31c9c18f-3a7f-4690-8e2f-70fdb02ef5c7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "dynamics.is_in_equilibrium(explain=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "7ddbe0ec-53c3-4d25-825a-cbe3bdf8e50a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }