{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Network Plotting Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Installation:\n",
    "\n",
    "The plotting package allows you to make an interactive CRN plot. Plotting requires the [Bokeh](https://docs.bokeh.org/en/latest/docs/installation.html) and [ForceAtlas2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098679) libraries to be installed on your machine. Bokeh is used for plotting, ForceAtlas2 is used for graph layout. To install, type the following into the consol:\n",
    "\n",
    "    Conda install bokeh\n",
    "    pip install fa2\n",
    "\n",
    "Alternatively, follow the installation instructions in the links above.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Example\n",
    "The CRN plot has a default representation:\n",
    "* species are circles\n",
    "    - orange circles are RNA species\n",
    "    - blue circles are complexes\n",
    "    - green circles are proteins\n",
    "    - grey circles are DNA\n",
    "    -there's always a purple circle that represents nothing. If your CRN doesn't have reactions that go to nothing, then it won't have any connections.\n",
    "    \n",
    "* reactions are squares\n",
    "\n",
    "Click on a node (either reaction or species) and all arrows including that node will be highlighted\n",
    "Mouse over a node (either reaction or species) and a tooltip will tell you what it is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a BioCRNpyler Model\n",
    "Here we model a DNA assembly with a regulated promoter in a TxTl mixture."
   ]
  },
  {
   "cell_type": "code",
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     "text": [
      "Species(N = 13) = {\n",
      "dna[mydna] (@ 10),  \n",
      "   found_key=(mech=initial concentration, partid=None, name=mydna).\n",
      "   search_key=(mech=initial concentration, partid=mixture1, name=mydna).\n",
      "complex[protein[Ribo]:rna[mydna]] (@ 0),  complex[protein[RNAase]:rna[mydna]] (@ 0),  rna[mydna] (@ 0),  protein[laci] (@ 0),  complex[dna[mydna]:2x_protein[laci]] (@ 0),  complex[dna[mydna]:protein[RNAP]] (@ 0),  complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]] (@ 0),  complex[complex[dna[mydna]:2x_protein[laci]]:protein[RNAP]] (@ 0),  protein[Ribo] (@ 0),  protein[RNAase] (@ 0),  protein[RNAP] (@ 0),  protein[GFP] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (12) = [\n",
      "0. dna[mydna]+protein[RNAP] <--> complex[dna[mydna]:protein[RNAP]]\n",
      " Kf=k_forward * dna_mydna * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_mydna_protein_RNAP_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=transcription_mm, partid=plac_leak, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=transcription_mm, partid=plac_leak, name=ku).\n",
      "\n",
      "1. complex[dna[mydna]:protein[RNAP]] --> dna[mydna]+rna[mydna]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_mydna_protein_RNAP_\n",
      "  k_forward=0.05\n",
      "  found_key=(mech=None, partid=None, name=ktx).\n",
      "  search_key=(mech=transcription_mm, partid=plac_leak, name=ktx).\n",
      "\n",
      "2. 2protein[laci]+dna[mydna] <--> complex[dna[mydna]:2x_protein[laci]]\n",
      " Kf=k_forward * protein_laci^2 * dna_mydna\n",
      " Kr=k_reverse * complex_dna_mydna_protein_laci_2x_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=one_step_cooperative_binding, partid=plac_laci, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=one_step_cooperative_binding, partid=plac_laci, name=ku).\n",
      "\n",
      "3. complex[dna[mydna]:2x_protein[laci]]+protein[RNAP] <--> complex[complex[dna[mydna]:2x_protein[laci]]:protein[RNAP]]\n",
      " Kf=k_forward * complex_dna_mydna_protein_laci_2x_ * protein_RNAP\n",
      " Kr=k_reverse * complex_complex_dna_mydna_protein_laci_2x__protein_RNAP_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=transcription_mm, partid=plac_laci, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=transcription_mm, partid=plac_laci, name=ku).\n",
      "\n",
      "4. complex[complex[dna[mydna]:2x_protein[laci]]:protein[RNAP]] --> complex[dna[mydna]:2x_protein[laci]]+rna[mydna]+protein[RNAP]\n",
      " Kf=k_forward * complex_complex_dna_mydna_protein_laci_2x__protein_RNAP_\n",
      "  k_forward=0.05\n",
      "  found_key=(mech=None, partid=None, name=ktx).\n",
      "  search_key=(mech=transcription_mm, partid=plac_laci, name=ktx).\n",
      "\n",
      "5. rna[mydna]+protein[Ribo] <--> complex[protein[Ribo]:rna[mydna]]\n",
      " Kf=k_forward * rna_mydna * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_mydna_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=translation_mm, partid=UTR1, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=translation_mm, partid=UTR1, name=ku).\n",
      "\n",
      "6. complex[protein[Ribo]:rna[mydna]] --> rna[mydna]+protein[GFP]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_mydna_\n",
      "  k_forward=0.05\n",
      "  found_key=(mech=None, partid=None, name=ktl).\n",
      "  search_key=(mech=translation_mm, partid=UTR1, name=ktl).\n",
      "\n",
      "7. rna[mydna]+protein[RNAase] <--> complex[protein[RNAase]:rna[mydna]]\n",
      " Kf=k_forward * rna_mydna * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_mydna_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_mydna, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_mydna, name=ku).\n",
      "\n",
      "8. complex[protein[RNAase]:rna[mydna]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_mydna_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_mydna, name=kdeg).\n",
      "\n",
      "9. complex[protein[Ribo]:rna[mydna]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_mydna_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_mydna__protein_RNAase_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_rna_mydna_, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_rna_mydna_, name=ku).\n",
      "\n",
      "10. complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_mydna__protein_RNAase_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_rna_mydna_, name=kdeg).\n",
      "\n",
      "11. rna[mydna] --> \n",
      " Kf=k_forward * rna_mydna\n",
      "  k_forward=0.1\n",
      "\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\documents\\github\\biocrnpyler\\biocrnpyler\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler import *\r\n",
    "\r\n",
    "\r\n",
    "txtl = TxTlExtract(\"mixture1\", parameter_file = 'default_parameters.txt')\r\n",
    "dna = DNAassembly(\"mydna\",promoter=RegulatedPromoter(\"plac\",[\"laci\"]),rbs=\"UTR1\",protein=\"GFP\", initial_concentration = 10)\r\n",
    "txtl.add_component(dna)\r\n",
    "crn1 = txtl.compile_crn()\r\n",
    "crn1.add_reactions([Reaction.from_massaction([Species(\"mydna\",material_type=\"rna\")],[],k_forward=0.1)])\r\n",
    "print(crn1.pretty_print())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a reaction graph\n",
    "First we import bokeh in order to plot an interactive graph then use the graphPlot function and the BioCRNpyler.plotting.generate_networkx_graph(crn) function to produce a graph. Mouseover the circles to see which species they identify and the squares to see reactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
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   "outputs": [
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       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\bokeh\\plotting\\graph.py:120: UserWarning: Node keys in 'layout_function' don't match node keys in the graph. These nodes may not be displayed correctly.\n",
      "  warn(\"Node keys in 'layout_function' don't match node keys in the graph. \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"042fae27-c7be-400c-81cf-28cca7d04fbb\" data-root-id=\"1004\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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0x00000210F5297BC8>\"]},\"selected\":{\"id\":\"1167\"},\"selection_policy\":{\"id\":\"1166\"}},\"id\":\"1009\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data\":{\"color\":[\"purple\",\"green\",\"green\",\"green\",\"grey\",\"orange\",\"cyan\",\"green\",\"cyan\",\"cyan\",\"green\",\"cyan\",\"cyan\",\"cyan\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\"],\"image\":[\"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\",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25],\"k\":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,\"100.0\",\"0.05\",\"100.0\",\"100.0\",\"0.05\",\"100.0\",\"0.05\",\"100.0\",\"0.001\",\"100.0\",\"0.001\",\"0.1\"],\"k_r\":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,\"10.0\",\"None\",\"10.0\",\"10.0\",\"None\",\"10.0\",\"None\",\"10.0\",\"None\",\"10.0\",\"None\",\"None\"],\"species\":[\"nothing\",\"protein_RNAP\",\"protein_Ribo\",\"protein_RNAase\",\"dna_mydna\",\"rna_mydna\",\"complex_dna_mydna_protein_RNAP_\",\"protein_laci\",\"complex_dna_mydna_protein_laci_2x_\",\"complex_complex_dna_mydna_protein_laci_2x__protein_RNAP_\",\"protein_GFP\",\"complex_protein_Ribo_rna_mydna_\",\"complex_protein_RNAase_rna_mydna_\",\"complex_complex_protein_Ribo_rna_mydna__protein_RNAase_\",\"dna[mydna]+protein[RNAP] <--> complex[dna[mydna]:protein[RNAP]]\",\"complex[dna[mydna]:protein[RNAP]] --> dna[mydna]+rna[mydna]+protein[RNAP]\",\"2protein[laci]+dna[mydna] <--> complex[dna[mydna]:2x_protein[laci]]\",\"complex[dna[mydna]:2x_protein[laci]]+protein[RNAP] <--> complex[complex[dna[mydna]:2x_protein[laci]]:protein[RNAP]]\",\"complex[complex[dna[mydna]:2x_protein[laci]]:protein[RNAP]] --> complex[dna[mydna]:2x_protein[laci]]+rna[mydna]+protein[RNAP]\",\"rna[mydna]+protein[Ribo] <--> complex[protein[Ribo]:rna[mydna]]\",\"complex[protein[Ribo]:rna[mydna]] --> rna[mydna]+protein[GFP]+protein[Ribo]\",\"rna[mydna]+protein[RNAase] <--> complex[protein[RNAase]:rna[mydna]]\",\"complex[protein[RNAase]:rna[mydna]] --> protein[RNAase]\",\"complex[protein[Ribo]:rna[mydna]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]]\",\"complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\",\"rna[mydna] --> \"],\"type\":[\"nothing\",\"protein\",\"protein\",\"protein\",\"dna\",\"rna\",\"complex\",\"protein\",\"complex\",\"complex\",\"protein\",\"complex\",\"complex\",\"complex\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F5281808>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F5281D08>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F5286248>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F52867C8>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F5286BC8>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F528F148>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F528F648>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F528FC48>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F528FF48>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F5297548>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F52978C8>\",\"<biocrnpyler.propensities.MassAction object at 0x00000210F5297BC8>\"]},\"selected\":{\"id\":\"1163\"},\"selection_policy\":{\"id\":\"1162\"}},\"id\":\"1037\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1163\",\"type\":\"Selection\"},{\"attributes\":{\"fill_color\":{\"value\":\"#abdda4\"},\"size\":{\"units\":\"screen\",\"value\":15}},\"id\":\"1104\",\"type\":\"Circle\"},{\"attributes\":{\"source\":{\"id\":\"1027\"}},\"id\":\"1029\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1165\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1129\",\"type\":\"LinearScale\"},{\"attributes\":{\"edge_renderer\":{\"id\":\"1014\"},\"inspection_policy\":{\"id\":\"1140\"},\"layout_provider\":{\"id\":\"1020\"},\"node_renderer\":{\"id\":\"1010\"},\"selection_policy\":{\"id\":\"1139\"}},\"id\":\"1007\",\"type\":\"GraphRenderer\"},{\"attributes\":{},\"id\":\"1161\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1164\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1109\"},{\"id\":\"1110\"},{\"id\":\"1111\"},{\"id\":\"1112\"},{\"id\":\"1113\"},{\"id\":\"1114\"},{\"id\":\"1115\"}]},\"id\":\"1116\",\"type\":\"Toolbar\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1159\",\"type\":\"BoxAnnotation\"}],\"root_ids\":[\"1004\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.3\"}};\n",
       "  var render_items = [{\"docid\":\"1556dcf8-4a81-4b37-8a5e-f47354d14ede\",\"root_ids\":[\"1004\"],\"roots\":{\"1004\":\"042fae27-c7be-400c-81cf-28cca7d04fbb\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1004"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\r\n",
    "    from bokeh.models import (Plot , Range1d)\r\n",
    "    import bokeh.plotting\r\n",
    "    import bokeh.io\r\n",
    "    bokeh.io.output_notebook() #this makes the graph appear in line with the notebook\r\n",
    "    plot = render_network_bokeh(crn1)\r\n",
    "    bokeh.io.show(plot) #if you don't type this the plot won't show\r\n",
    "except ModuleNotFoundError:\r\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')\r\n",
    "\r\n",
    "\r\n",
    "#mouse over nodes to get a tooltip telling you what they are\r\n",
    "#mouse over single lines to outline them\r\n",
    "#click on nodes to highlight all edges that touch them\r\n",
    "#use the magnifying glass symbol to zoom in and out\r\n",
    "#click and drag to move around\r\n",
    "\r\n",
    "#NOTE: this function is not deterministic in how it displays the network, \r\n",
    "# because it uses randomness to push the nodes around. However, it does always get the same seed\r\n",
    "# so if you don't change the network it will end up always looking the same. To set the seed use rseed=<some number>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advanced options for generate_networkx_graph\n",
    "The following options are useful changing how plotting works.\n",
    "\n",
    "To get better control over the way reactions and species text is display using the following keywords (default values shown below):\n",
    "\n",
    "    use_pretty_print=False\n",
    "    pp_show_rates=True\n",
    "    pp_show_attributes=True\n",
    "    pp_show_material=True\n",
    "\n",
    "To get better control over the colors of different nodes, use the keywords to set a species' color.\n",
    "The higher keywords will take precedence.\n",
    "\n",
    "    repr(species): \"color\"\n",
    "    species.name: \"color\"\n",
    "    (species.material_type, tuple(specie.attributes)): \"color\"\n",
    "    species.material_type: \"color\"\n",
    "    tuple(species.attributes): \"color\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1441\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1441\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1441\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"7fe4e685-ea5e-4147-ac7d-795053d596d2\" data-root-id=\"1444\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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default.]\"],\"type\":[\"nothing\",\"protein\",\"protein\",\"protein\",\"dna\",\"rna\",\"complex\",\"protein\",\"complex\",\"complex\",\"protein\",\"complex\",\"complex\",\"complex\"]},\"selected\":{\"id\":\"1656\"},\"selection_policy\":{\"id\":\"1655\"}},\"id\":\"1463\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1650\",\"type\":\"Selection\"},{\"attributes\":{\"callback\":null},\"id\":\"1552\",\"type\":\"TapTool\"},{\"attributes\":{\"line_color\":{\"value\":\"#fdae61\"},\"line_join\":\"round\",\"line_width\":{\"value\":5}},\"id\":\"1499\",\"type\":\"MultiLine\"},{\"attributes\":{},\"id\":\"1648\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"1467\"}},\"id\":\"1469\",\"type\":\"CDSView\"},{\"attributes\":{\"graph_layout\":{\"0\":[49.99999914850508,9.508453879737703e-07],\"1\":[45.04844167361564,21.694188586078216],\"10\":[-11.126050684059937,-48.74639374516552],\"11\":[11.126051322681127,-48.74639374516552],\"12\":[31.17448320410521,-39.0915798224777],\"13\":[45.04844167361564,-21.69418668438744],\"14\":[34.99999975080412,7.765237228957124e-07],\"15\":[30.31088844326708,17.500000388261864],\"16\":[17.499999095984716,30.310890778625485],\"17\":[-1.002570718997825e-06,35.0],\"18\":[-17.500001170572826,30.310888692462964],\"19\":[-30.310887388611405,17.500002474424385],\"2\":[31.17448916456964,39.09157576370405],\"20\":[-34.999998696148445,-2.283273389290083e-06],\"21\":[-30.310887388611405,-17.499998835214416],\"22\":[-17.499995955166522,-30.310889225578038],\"23\":[9.446986501827269e-07,-34.99999844695255],\"24\":[17.499997009822195,-30.310889225578038],\"25\":[30.310886357104557,-17.50000509370198],\"3\":[11.126048342448913,48.7463956468563],\"4\":[-11.126047703827725,48.7463956468563],\"5\":[-31.17449001606456,39.09157576370405],\"6\":[-45.048442525110566,21.69419156631043],\"7\":[-50.0,-3.4202934034799975e-06],\"8\":[-45.048442525110566,-21.69418668438744],\"9\":[-31.174481075367922,-39.0915798224777]}},\"id\":\"1460\",\"type\":\"StaticLayoutProvider\"},{\"attributes\":{},\"id\":\"1651\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1652\",\"type\":\"Selection\"},{\"attributes\":{\"data\":{\"end\":[],\"start\":[]},\"selected\":{\"id\":\"1654\"},\"selection_policy\":{\"id\":\"1653\"}},\"id\":\"1467\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"1444\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.3\"}};\n",
       "  var render_items = [{\"docid\":\"affbe61a-3421-4b24-9cca-f52cb50064a0\",\"root_ids\":[\"1444\"],\"roots\":{\"1444\":\"7fe4e685-ea5e-4147-ac7d-795053d596d2\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1444"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\r\n",
    "    from bokeh.models import (Plot , Range1d)\r\n",
    "    import bokeh.plotting\r\n",
    "    import bokeh.io\r\n",
    "    bokeh.io.output_notebook() #this makes the graph appear in line with the notebook\r\n",
    "\r\n",
    "    #this demonstrates the \"circle\" layout. reactions are in the middle with species on the outside.\r\n",
    "    #also, the pretty_print text display style\r\n",
    "\r\n",
    "    colordict = {\r\n",
    "        \"G1\":\"red\", #will only effect the species dna_G1 and rna_G1\r\n",
    "        \"protein_GFP\": \"lightgreen\", #will only effect the species protein_GFP\r\n",
    "        \"protein\": \"blue\", #All protein species, protein_Ribo, protein_RNAase, and protein_RNAP will be blue\r\n",
    "        #All other species will be grey by default. This will include all complexes.\r\n",
    "    }\r\n",
    "\r\n",
    "    plot = render_network_bokeh(crn1,\r\n",
    "                            colordict = colordict,\r\n",
    "                            use_pretty_print=True, #uses pretty print\r\n",
    "                            pp_show_rates=False, #this would put the reaction rates in the reaction name. It's already listed seperately in the tool tip\r\n",
    "                            pp_show_attributes=False,\r\n",
    "                            pp_show_material = True, #this lists the material of the species being displayed\r\n",
    "                            layout=\"circle\",\r\n",
    "    )\r\n",
    "    bokeh.io.show(plot)\r\n",
    "except ModuleNotFoundError:\r\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\documents\\github\\biocrnpyler\\biocrnpyler\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1926\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1926\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1926\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": ""
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\documents\\github\\biocrnpyler\\biocrnpyler\\plotting.py:554: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout(pad=0.0001)\n",
      "d:\\documents\\github\\biocrnpyler\\biocrnpyler\\plotting.py:552: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  fig = plt.figure(figsize=figsize)\n",
      "D:\\anaconda3\\lib\\site-packages\\bokeh\\plotting\\graph.py:120: UserWarning: Node keys in 'layout_function' don't match node keys in the graph. These nodes may not be displayed correctly.\n",
      "  warn(\"Node keys in 'layout_function' don't match node keys in the graph. \"\n"
     ]
    },
    {
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       "  function embed_document(root) {\n",
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\",\"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\",\"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\",\"iVBORw0KGgoAAAANSUhEUgAAAEAAAAA5CAYAAACGRC3XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAHtklEQVRoge2abUyU2RXHf3dmEOVtBghvuooo4pqwrto1NYovTVclbt249JtRgiAm1UQbQzTB4AdtyKLVD/1iQO1oaDQbNw2abqMxETXdNqshotUVV4o7QlkRBmScRYHhOf3wyAwjIiIXp4n8E5L79vzvef7PuXfuORclIsJ7DEuoDQg1xgUItQGhxrgAoTYg1LC9y8lEDPj5MXS6EI8Luj0gBlhsqIhEcEwHeypqQvQ7s+mdCCA9Xmj6FnFdhq7Hrx7jLykkIROVugIS56LU2DqpGstzgIgBDy4i96rA6Bk5QWQK6uM8VGy6fuNeYMwEkK5WpPYYdNQHd9gmQexM09UjEkFZoa8b8TRCpws6f2SgP4CCGatQs3NQFv0OOyYCiKcRuXbYXOP9iJqMSlsJk3+JsoUP/WxXq7lUXNXQ1x3oSPwYteB3KGuYVlu1CyDeR8i/voSepy9msKLSP4P0z0b0BaWrDfn3SWj7PtCYNA+1YCvKYtVmr1YBxPAh3/4BPI1mg20SauF2VFzG2/GJIPf+Cv/5u79Nzc4xBdUEvVts/TeBl7eEoRb+/q1fHkAphZqdAzNW+9vkh7PI06bRWuqHNgGkqxWp/8ZfV7O/QMWNfvc2RfgtONJeTNSH3P7LqHn7oU8A12WQPrPimAFpK3VRoyxW1Nx88xcDoP0+0vlQC7cWAaSvFxr/4a+r9N9oP8Co6MmQ8ovAnA8va+HVY2X7Pej1muVJ8ZD4kRbal6FSfxWo/FSjhVOPAJ2uQDnho7E7vsammwcpCAg+SuhZAgMEUPZUHZSvhFIW0Myv51M97wiUo1K0UA4Jzfx6BDB8gbLmo+ogWPTy6xFg4BG3r1cL5ZDoe4uo8jXQI8DE2EDZ26yFckh4f9JKp0UA5ZjuLw/cEHVDxACPXn49HmCfHig/voUYfVpoB8H9A/iem2VNaTM9AsTOggkxZvl5Bzy+qYX2ZYjrUqCS8okWTj1LwBoGU5f661L/N+1eIJ5GaLkRmDN1hRZebUc2lbo88GvQ6YKGC7qozTzDzT+bGWSA+A9R0R9o4dYnwKR41KzP/XW5fxZx142aV0SQ778Cz4vozxKGytw4at5+6D20z8gG+4u43fAh1/+EDExpjRAiBnL3Kxiw9tXsL1BRyaO1NMCnPSfY1Yr880voftI/BcxYjcpYN6KEpvzcgtx0Qsf9QGPKQtT8LVqDrbHJCnsfId/9MThGiEhETf81fLAYFRYx9LNPmxFXtZlfGHiXkPIJal6h9tT42N0LPGs3Ny733eAOy4TAFVhEIlis4Htu5vme/Dj4JKmskL4GNevzMQmzx/hmSODhFaTua/A9GzlBzDTU3LyxDbHfxb/IiO8Z/Pc707WHy+gqCyTNN7M/8R+ilBpT296JAP/PeO//P2BcgOEGrFixgvDwcKKiorDb7cybN48zZ84E9SuluHr1atBz6enpnDhxIqhNRMjIyCAmJgavV09Sc7R4Iw8oKSnB6/XidrvJy8tj/fr11NcHrr3j4+MpKipiuO2kurqahoYGLBYLp0+fHp3lmjCiJWCz2SgsLMTn81FbW+tvLywspKmpadiXKi8vJzs7m40bN1JeXh7U19TURHZ2NgkJCdjtdpYuXUpNTSD3f+PGDbKysrDb7cTFxbF48WI6OsyDls/no7S0lIyMDBwOB0uWLAl69nUYkQA9PT0cOXIEgIyMwKVnZGQk+/bto7i4mO7u7lc+29raSlVVFfn5+RQUFFBTUxNkpGEYbN26FZfLxaNHj1iwYAE5OTn09po5xm3btrFq1Sra29tpaWnh8OHDTJgwAYC9e/dy9uxZzp8/j9vtJj8/n9WrV/sFei1kGCxfvlwmTpwodrtdLBaLhIeHy7Fjx4L69+/fLz6fTzIzM6WsrExERGbOnClOp9M/rqysTBISEqSnp0dERObPny9btmwZcl6PxyOA3Llzxz9PQUGBPHjwIGicYRgSFRUlV65cCWrPzMyUysrK4V5P3sgD9uzZw5MnT2hra2PNmjVcunRp0Bir1cqBAwcoLS3F7Xa/LDJHjx5lw4YNhIWZAVFBQQGnTp3yb4ZtbW3k5uYybdo0YmJimDp1qt9zAJxOJ4ZhkJWVRVpaGiUlJfh8Ptra2vB6vaxduxaHw+H/a2hooKnpDa7Rh1Oo/wv3w+12i8PhkKqqqlf2r1y5UrZv3x7kARcvXhRA7Ha7JCUlSVJSksTHxwsg5eXlIiKyefNm+fTTT6W5uTnIA6qrqwfZdOvWLUlOTpbjx4+LYRgSGRkp165dG/Zrv7UHDERcXBw7d+6kuLgYwzAG9R88eJCKigr/lwOoqKhg2bJl1NXVUVtbS21tLbdv32bTpk3+zdDj8RAREUFsbCxer5fdu3cH8Z48eZLmZjNQcjgc2Gw2bDYbSil27NhBUVER9++bobPX6+XChQv+8Vo9QESks7NTYmNjxel0vrI/Ly9PAHE6ndLS0iJhYWFy7ty5Qdx1dXWilJLr16/L3bt3ZdGiRRIRESFpaWlSWVkpVqvV7wG5ubmSnJwskZGRMmXKFNm1a5f4fD4REent7ZVDhw7JnDlzJDo6WpKTk2XdunXS2Ng4rAeMxwKhNiDUGBcg1AaEGuMChNqAUGNcgFAbEGq89wL8D7HpwPaY9mtoAAAAAElFTkSuQmCC\",\"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\",\"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\",\"iVBORw0KGgoAAAANSUhEUgAAADsAAABWCAYAAABxT+43AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAJMElEQVR4nO2bf1DUZR7HX8uyKrCIQSsgqZhsIehkJnGQ1x05SacU1YnEaUq25KV2eOeV14xN102p1V3WlKUzRDErMhk356XkjwbrUByDhJEmCEdZCGxJpORHBsuyn/tjdRdEEfe7WLPsa+Y78/zY5/N83vs83x/PL5WICCMEn5/bgeuJV6yn4hXrqXjFeiq+w11Bezvs2QPl5XDsGJw6Bd3doNFAaCjccYf9mj8fIiOH2RkZJqqrRZ58UkSrFYGrXyqVSEqKyN69Ijbb8PjkdrHd3SLPPSeiVg9N5OWu1FQRs9ndnomoRNz3uVhXBw89BFVV/dNjdM08HFPFrPAmpoeaCdBY6LH5cLJVR4X5Jj45dSufnLq1X5kbbgCjERYscJd34DaxtbWQlARmszPtrkl1/OOevfx6ch0q1eDlT7aG8K8jSeQcS3CkqdWQnw/p6e7w0E1izWaIj4fGRnt8tLqHDfcWsfrOw/j4XJv5T+uiMPw3nW/aggHw9YWiIpg3T6mXbhArAg88YH/iAvhruvnoD+/ymymnXLZp7ggkOe+P1JwNAyAsDL76CoKDlXjqhves0egUCrBzUZ4ioQDhgR3sW7qNMG07AM3NkJ2tyCSgUKzFAuvWOeMr7zxMsr5WqU8ATBjbztsphY749u1QUaHMpiKxu3bZ/3WAcG0bL80tUubNJdwf/RUPTzvuiL/9tjJ7isS+844znDX7KNrRFmXeXIbshBJHeMcOOHfOdVsui/3pJzh0yBm/d+pedJu2MPe9nczeamTzkS8AiNqcQ/bHBx2/S84rpP6HNkc8++ODxG/L72db/3oOc9/bSfy2fHbV5BE73uyo88gRVz1WILaqCnp77eFbQs4Qpu1kVngoxY8tomT5I7z0v8/p7Lbg66PiaKOZ0+0dA2xYe2183mQmctxYalpaHelqlYrixxZRashg/8l6om/8yJF37JirHisQW1npDM8Kb+qX12GxYBPBduGt9te7ZvPyobIBNvafrOe+qEiW3R7LjqqaAfm+ah9mhN5IiH/1Zeu9VlwW2+psCCaN+wGACvN3/Db3A/Svv8vf70lk7JjRACyMvYXPm5ppauvfugVf1rB0ZizJUZF8Zmrk0lf+j5YevjjdzOwJWkfa99+76rGCId7FLgygVtkAmBUeyv5lC/nidDPPHyzlT7+aBYBKpeLpOXG8fNjZuh3dFg41NPFUUTEA33Wep/Sb08yZfBO9Isx9bye9IqyMvx19SLijnNXqqscKxPr7O8Pnuvz65c2OCGO8NoCPT9Q50n4fo+fVw+Wc7+kB4N/VJ/hLYhzZCfY/pPx0M7kVXzJn8k2Oe/Yiu792Vta33mvF5W58a59ByvHmiAH5f064g0197lOVSsUzc+L4+qy9H+YfryE5KtKRP3tCKCX1TXRfpumON09whKOjXfVYwbfxt99CxAWN/ppuzv5tPRq1zXVPBuHBHcspOhELwPvvw7JlrtlxuWUnTICJE+3h8z2j2V0b66qpQTnTqe031o2Pd92Woi+ovv/wO2V3KTF1Rd6vvBNLr/3REh+vrBsrEvvEE+BzwcJn9Xr21MYoMTcAc0cg/yxNcsRXrlRmT5HYiRNh8WJnfOXuhXx/3u/KBa4BEVi1ZyE/dNkfv5GRsGjR4GWuhuLx7ObN9ilRAHNnEGkfZHLeolFkUwSeP3gfu2unO9Jyc2HMGEVmlYsNCYFt25zxkoYoFmzPorkj0CV7Fquap/c/wMZD9zrSVq2yz28pxW0Tbq++Cs8844wH+/3I67/7D4/MqLzqZNtFKs0RGHalU/Wd8709f7593KxR1lkAN4oFeO01WLu2f1qszsyKuCM8HFNFqLZzQJnO7lF8atKztTyRA6f6P2offBAKCpR334u4VSxAcTE8/jg0NAzMiwg8x/RQM9pRFiy9ak623sjXZ8cjl9xNfn6wYQM89ZR9OtVtuH/eXaS9XWTNmqEvffRdArn/fpETJ4bDq2FY/uhLW5vIli0iiYkifn6XF+jjIxIbK7JunYjJNJzeuHn5YzCsVqipsS+RdHXBqFH2V9Ztt0FAwPXwYBju2V8y3sXowdhjUviB6mZSpgx9MnlEteyIEqtoT8W1dCF34uqtNKJa1ivWU/GK9VS8Yj0Vr1hPxSvWU/GK9VQUjXp+aQP5qzGiWnZEifXOLnoqXrGeilfs9cRgMJCZmXld6vrZxV5PvGL7EhkZyYsvvkhSUhJarZYZM2ZQVVVFQUEBUVFRBAUFYTAYsFqtpKenk33JMY3c3Fz0er1je21ubi5Tp05l7NixPProo3R1dTl+W19fj0qlwmg0EhMTQ2BgIPPmzcPc52TUG2+8QXR0NIGBgUyaNIlnn32W3r5bZAfjagu4kydPlqioKKmurhaLxSKLFy+Wm2++WbKysqSzs1MaGhpEp9NJfn6+FBcXS3BwsHR1dTnKJyQkyMaNG0VEpKSkRMaMGSMHDhyQnp4eycvLE19fX1m2bJmIiJhMJgFkwYIF0tLSIm1tbZKYmCgGg8Fhr7CwUOrq6sRms0lFRYWMHz9etm7dOqTF6CGJfeWVVxzxoqIiAeTMmTOOtLS0NFmzZo3YbDbR6/VSUFAgIiLV1dWi0WjEfOEUocFgkCVLlvSzn5iYOEBsWVmZI/+tt96SmTNnXtG/tWvXSlpa2hCkigzpng0Pd25u9vf3R61Wo9Pp+qV1dHSgUqnIysoiJycHgJycHFJSUggLs5+8ampqIvKSQ7JTpkwZtL6AgAA6Opw70AsKCoiLiyMkJISgoCC2bNlCS0vLUGS4/wGVmZlJaWkptbW1GI1GsrKyHHkRERHU19f3+73JZBqy7cbGRpYsWcL69esxm820tbWxatWqAdvtr4Tbxep0OlJTU8nIyMDPz4/k5GRH3tKlSyksLKS4uBir1cr27dspKxt4UOJKdHZ2YrPZ0Ol0aDQajh49itFoHHL5YXn1rFixgsrKSpYvX46Pj7OKu+++mzfffBODwUBwcDD79u0j/RrOi06bNo0XXniB1NRUxo0bx6ZNm8jIyBhy+WEZ4plMJvR6PSaTiYkXd2D/AnC7WKvVyurVq2ltbeXDDz90p2nlDOmZPUTKy8vF399fpk+fLvX19e407Ra8MxWeilesp+IV66mMKLH/B0tTRx3NgCecAAAAAElFTkSuQmCC\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],\"k\":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,\"100.0\",\"0.05\",\"100.0\",\"100.0\",\"0.05\",\"100.0\",\"0.05\",\"100.0\",\"0.05\",\"100.0\",\"100.0\",\"0.05\",\"100.0\",\"0.05\",\"100.0\",\"0.001\",\"100.0\",\"0.001\",\"100.0\",\"0.001\",\"0.1\"],\"k_r\":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,\"10.0\",\"None\",\"10.0\",\"10.0\",\"None\",\"10.0\",\"None\",\"10.0\",\"None\",\"10.0\",\"10.0\",\"None\",\"10.0\",\"None\",\"10.0\",\"None\",\"10.0\",\"None\",\"10.0\",\"None\",\"None\"],\"species\":[\"nothing\",\"protein[RNAP in default.]\",\"protein[Ribo in default.]\",\"protein[RNAase in default.]\",\"dna[mydna in default.]\",\"rna[mydna in default.]\",\"complex[dna[mydna]:protein[RNAP] in default.]\",\"protein[laci in default.]\",\"complex[dna[mydna]:2x_protein[laci] in default.]\",\"complex[complex[dna[mydna]:2x_protein[laci]]:protein[RNAP] in default.]\",\"protein[GFP in default.]\",\"complex[protein[Ribo]:rna[mydna] in default.]\",\"dna[part[ptet-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward] in default.]\",\"rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward] in default.]\",\"ordered_polymer[complex[part[ptet]:protein[RNAP]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward] in default.]\",\"protein[tetR in default.]\",\"ordered_polymer[complex[part[ptet]:2x_protein[tetR]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward] in default.]\",\"ordered_polymer[complex[complex[part[ptet]:2x_protein[tetR]]:protein[RNAP]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward] in default.]\",\"ordered_polymer[complex[part[utr1]:protein[Ribo]-forward]:part[GFP-forward]:part[t16-forward] in default.]\",\"complex[protein[RNAase]:rna[mydna] in default.]\",\"complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase] in default.]\",\"complex[protein[RNAase]:rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]] in default.]\",\"dna[mydna]+protein[RNAP] <--> complex[dna[mydna]:protein[RNAP]]\",\"complex[dna[mydna]:protein[RNAP]] --> dna[mydna]+rna[mydna]+protein[RNAP]\",\"2protein[laci]+dna[mydna] <--> complex[dna[mydna]:2x_protein[laci]]\",\"complex[dna[mydna]:2x_protein[laci]]+protein[RNAP] <--> complex[complex[dna[mydna]:2x_protein[laci]]:protein[RNAP]]\",\"complex[complex[dna[mydna]:2x_protein[laci]]:protein[RNAP]] --> complex[dna[mydna]:2x_protein[laci]]+rna[mydna]+protein[RNAP]\",\"rna[mydna]+protein[Ribo] <--> complex[protein[Ribo]:rna[mydna]]\",\"complex[protein[Ribo]:rna[mydna]] --> rna[mydna]+protein[GFP]+protein[Ribo]\",\"dna[part[ptet-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[RNAP] <--> ordered_polymer[complex[part[ptet]:protein[RNAP]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]]\",\"ordered_polymer[complex[part[ptet]:protein[RNAP]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]] --> dna[part[ptet-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[RNAP]\",\"2protein[tetR]+dna[part[ptet-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]] <--> ordered_polymer[complex[part[ptet]:2x_protein[tetR]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]]\",\"ordered_polymer[complex[part[ptet]:2x_protein[tetR]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[RNAP] <--> ordered_polymer[complex[complex[part[ptet]:2x_protein[tetR]]:protein[RNAP]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]]\",\"ordered_polymer[complex[complex[part[ptet]:2x_protein[tetR]]:protein[RNAP]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]] --> ordered_polymer[complex[part[ptet]:2x_protein[tetR]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[RNAP]\",\"rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[Ribo] <--> ordered_polymer[complex[part[utr1]:protein[Ribo]-forward]:part[GFP-forward]:part[t16-forward]]\",\"ordered_polymer[complex[part[utr1]:protein[Ribo]-forward]:part[GFP-forward]:part[t16-forward]] --> rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[GFP]+protein[Ribo]\",\"rna[mydna]+protein[RNAase] <--> complex[protein[RNAase]:rna[mydna]]\",\"complex[protein[RNAase]:rna[mydna]] --> protein[RNAase]\",\"complex[protein[Ribo]:rna[mydna]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]]\",\"complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\",\"rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[RNAase] <--> complex[protein[RNAase]:rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]]\",\"complex[protein[RNAase]:rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]] --> protein[RNAase]\",\"rna[mydna] --> 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ordered_polymer[complex[part[ptet]:2x_protein[tetR]-forward]:part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[RNAP]\",\"rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[Ribo] <--> ordered_polymer[complex[part[utr1]:protein[Ribo]-forward]:part[GFP-forward]:part[t16-forward]]\",\"ordered_polymer[complex[part[utr1]:protein[Ribo]-forward]:part[GFP-forward]:part[t16-forward]] --> rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[GFP]+protein[Ribo]\",\"rna[mydna]+protein[RNAase] <--> complex[protein[RNAase]:rna[mydna]]\",\"complex[protein[RNAase]:rna[mydna]] --> protein[RNAase]\",\"complex[protein[Ribo]:rna[mydna]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]]\",\"complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\",\"rna[part[utr1-forward]:part[GFP-forward]:part[t16-forward]]+protein[RNAase] <--> 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vhFIW0Myv51M97wiUo1K0UA4Jzfx6BDB8gbLmo+ogWPTy6xFg4BG3r1cL5ZDoe4uo8jXQI8DE2EDZ26yFckh4f9JKp0UA5ZjuLw/cEHVDxACPXn49HmCfHig/voUYfVpoB8H9A/iem2VNaTM9AsTOggkxZvl5Bzy+qYX2ZYjrUqCS8okWTj1LwBoGU5f661L/N+1eIJ5GaLkRmDN1hRZebUc2lbo88GvQ6YKGC7qozTzDzT+bGWSA+A9R0R9o4dYnwKR41KzP/XW5fxZx142aV0SQ778Cz4vozxKGytw4at5+6D20z8gG+4u43fAh1/+EDExpjRAiBnL3Kxiw9tXsL1BRyaO1NMCnPSfY1Yr880voftI/BcxYjcpYN6KEpvzcgtx0Qsf9QGPKQtT8LVqDrbHJCnsfId/9MThGiEhETf81fLAYFRYx9LNPmxFXtZlfGHiXkPIJal6h9tT42N0LPGs3Ny733eAOy4TAFVhEIlis4Htu5vme/Dj4JKmskL4GNevzMQmzx/hmSODhFaTua/A9GzlBzDTU3LyxDbHfxb/IiO8Z/Pc707WHy+gqCyTNN7M/8R+ilBpT296JAP/PeO//P2BcgOEGrFixgvDwcKKiorDb7cybN48zZ84E9SuluHr1atBz6enpnDhxIqhNRMjIyCAmJgavV09Sc7R4Iw8oKSnB6/XidrvJy8tj/fr11NcHrr3j4+MpKipiuO2kurqahoYGLBYLp0+fHp3lmjCiJWCz2SgsLMTn81FbW+tvLywspKmpadiXKi8vJzs7m40bN1JeXh7U19TURHZ2NgkJCdjtdpYuXUpNTSD3f+PGDbKysrDb7cTFxbF48WI6OsyDls/no7S0lIyMDBwOB0uWLAl69nUYkQA9PT0cOXIEgIyMwKVnZGQk+/bto7i4mO7u7lc+29raSlVVFfn5+RQUFFBTUxNkpGEYbN26FZfLxaNHj1iwYAE5OTn09po5xm3btrFq1Sra29tpaWnh8OHDTJgwAYC9e/dy9uxZzp8/j9vtJj8/n9WrV/sFei1kGCxfvlwmTpwodrtdLBaLhIeHy7Fjx4L69+/fLz6fTzIzM6WsrExERGbOnClOp9M/rqysTBISEqSnp0dERObPny9btmwZcl6PxyOA3Llzxz9PQUGBPHjwIGicYRgSFRUlV65cCWrPzMyUysrK4V5P3sgD9uzZw5MnT2hra2PNmjVcunRp0Bir1cqBAwcoLS3F7Xa/LDJHjx5lw4YNhIWZAVFBQQGnTp3yb4ZtbW3k5uYybdo0YmJimDp1qt9zAJxOJ4ZhkJWVRVpaGiUlJfh8Ptra2vB6vaxduxaHw+H/a2hooKnpDa7Rh1Oo/wv3w+12i8PhkKqqqlf2r1y5UrZv3x7kARcvXhRA7Ha7JCUlSVJSksTHxwsg5eXlIiKyefNm+fTTT6W5uTnIA6qrqwfZdOvWLUlOTpbjx4+LYRgSGRkp165dG/Zrv7UHDERcXBw7d+6kuLgYwzAG9R88eJCKigr/lwOoqKhg2bJl1NXVUVtbS21tLbdv32bTpk3+zdDj8RAREUFsbCxer5fdu3cH8Z48eZLmZjNQcjgc2Gw2bDYbSil27NhBUVER9++bobPX6+XChQv+8Vo9QESks7NTYmNjxel0vrI/Ly9PAHE6ndLS0iJhYWFy7ty5Qdx1dXWilJLr16/L3bt3ZdGiRRIRESFpaWlSWVkpVqvV7wG5ubmSnJwskZGRMmXKFNm1a5f4fD4REent7ZVDhw7JnDlzJDo6WpKTk2XdunXS2Ng4rAeMxwKhNiDUGBcg1AaEGuMChNqAUGNcgFAbEGq89wL8D7HpwPaY9mtoAAAAAElFTkSuQmCC\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"iVBORw0KGgoAAAANSUhEUgAAAN4AAAB+CAYAAABLe69ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAahklEQVR4nO2deVgUx9bG35mBGWCQJQKCKBAQZFFUxBgFcYtxjSjEJajBqNclxuxxvx8aNRpNjOs1mERcERe8agwaRdxQAb0oghgVBAUVBA3KgCwzU98fHXpmFJSBHmahfs/Tj13dXdWnkJeqrlN1ikcIIaBQKE0KX9sGUCjNESo8CkULUOFRKFqACo9C0QJUeBSKFqDCo1C0ABUehaIFqPAoFC1AhUehaAEjbRvQVDx+DGRnA5WVgIkJ0K4dYG2tbasozRWDFR4hQHIyEBkJnD4N5Oa+/IybG9CvHzB9OuDn19QWUpozPEOcq3n5MjBjBvNvfQkIAH7+GejQQXN2USg1GJTwZDJg0SJg+XLmXBmRoBqeto8gNq5CaZUIfxXZoVqu2uAbGwOLFwNz5wI8XtPZTWl+GIzwZDLgww+B6GjFNROjaoztmIpJfsnwb50HY4GcvVcpFSAl3xm/pnbHvozOKiKcNg3YtImKj6I5DEZ4H3/MiKWGXs7Z+CV4D9zeePzavJmPWmHKoTG4dN+ZvTZvHvDdd5qwlEIxEOH98QcwbJgiPdX/AtYPOQA+v/5Vk8r4mHxoDKKv+QNgWrszZ4Bevbi2lkIxAOFJJED79sCDB0w61DsN0e/vUEt0NUhlfARHT8bxbE8AjMvh+nVAKOTSYgrFABzoO3cqRGcnLsWGobENEh0AGAnk2By8B5ai5wCArCzgwAGuLKVQFOi18AgB/vMfAMgFYI0Wwh4YtONn/HSB8SO0++lXfBaXwD4/cNt+5P79lE1/FpeA7pG7VMrss2U1rEy7A/AH8DU2btR0LSjNEb0W3t27QHo6c87ndUHS1GCcnTQWy84kQ1JZBSM+D0l5D3H/WelLeaUyOZLzH8LFygI3ihQDMAIeD+cmh4CP8wCOIjExGU+eNFGFKM0GvRbe//6nOLcUVcDKtAKlVVWQEwL5P5+uXwf44/tzKS/l/TMrF4PauSC8iw+ir91QuefQohSdHR4B8AWQi9RUDVaC0izRa+HVtHYAUF6djj5b9sB9zW9Y1K8nLExEAID3fTyQnF+A/Keqrd7u9Bv4sLMPBrZzwemcPLw4xtTB7jaASwC8ce2ahitCaXbo9VzNUiUttbV0w+lJ/XH5fgEiEs7j07eZyZc8Hg/fBHbD94mKVq+0sgrn7uZj1h8nAQCFknKcv3cfgc5tICME/aP24s7fxwHMAtBR5T0UChfotfAEAsV5TYPl72gPO3Mx4m7dYe+FertjVeIllFdXAwBiM2/hy57d8FkPRpyX7hdgS2o6Ap3bQMDj4eRHozH/xFCsOt8PAGCk1z8lii6i113N1q0V52XVCmfbFz26YoXSdx2Px8PswG74q5gZJdmVdgMD27mw9/1bt8LZ3HxUSqXstVuPbdlzBwcNGE9p1ui1A/3cOSAoiDn3ti1A2sxVnJRLCPDm6n/jfqkVACAtDfD15aRoCgWAnrd4XbowKwoAILPIHqkPHDkp92yuGys6c3PAy4uTYikUFr0Wnrk5MGqUIr02KYiTctdc7M2ef/ihQtwUClfotfAAZlVCDdHX/HEiy6NR5cVe98WRWz5sesaMRhVHodSK3guvZ08gNFSRnnxwLO48eaNBZWU+aoWPj7yvKGsyXZFO0Qx6PbhSQ2Eh4OPDBDQCAMcWJdg/Ngr+jvn1LiPx7psYvSccReUtAABt2gAZGYClpSYspjR3DEJ4ABPQaNAgJooYAAh4MnwVcBpf9DgDG3FZnfkKJeb4/lx/bEgOBPmnA2BuDiQkAN26NYHhlGaJwQgPAE6eBEaMYNbo1SASVGOEVwZ6OuXAt9UDmBlXQVIlQlpBa5y/54rDf/mohH2wsmIW1vbsqYUKUJoNBiU8gImdOWkScPas+nkHDgR++QVo25Z7uygUZQxOeACQmcm4GTIz65+nSxdg3z4m1iaFomn0flRTGUKAH39kgtPWJjpjoRxCkRzGQvlL965cATp1YgImGd6fIoquYTAtnlwOzJzJBKWtQSAgCBr0BCPGF6LTW6Uwt1AE23xWIsCVJAsc3NEKiSesQYgilt/s2cCKFTS8H0VzGIzwZs8GVilN1fT0lWDR+iy08y5/bd7rV8yxeFY73Llpxl5btgyYP18TllIoBiK8hASgf39FelBoERatz4KRcf2rVlnBw/ypHjhztCUAgM8HkpKoS4GiGfReeM+fA97eik1Jevb/Gz/tuqGyVq++VFXyMPN9b1xJYrzmPj7A1at0PR6Fe/R+cCUmRiE6C6tq/N+arAaJDgCEIoKI9VkwNWO+Ba9fBw4f5sZOCkUZvRceE96P4YNpN5B05tVKidm8jz2/fD4VA32GY2rwJwjrOxHHYo+jjUslPpj2oNbyKRSu0Gvh5eUptuISiuTo9e4dxO099so8Mb/sU0kHDuiBzYc2YPXO7/HjwrUAgJDwQjYo7smTQEkJ97ZTmjd6/fWiHN6vQ9dS/L57F26k3cTU4E8QPG4YThw8iYrnFRAKhYhYvwCXzl1GUUERpgZ/gq49O6NroGI3ytKSUvD+8R/YO1ahnXcZbmWYA2B8fH37NmnVKAaOXrd4ymH3vDqVYdyMsfDq1B6bD21A4okLmPxlOH7+73qMnhyKrWu3Y1Dou7C1t8XmQxswbc4UAEDiiYv4aPBUTBz0L8xd+bVKeTWkpTVZlSjNBL1u8Z49U5y3tKtSuZd9IxvrlzDedLlMBgen2iMWBQ7ogX+vmYfTcWdx9s/z6Desz0vlKb+HQuECvRYeX6m9lst5MBYaQSplRiRd27ti4mcT4OnLrEivrmJC+wmMBJDL5eDzVRv7PkOCEB25F1k37qCdlyvkMsW0lYaOklIodaHXXc1WrRTneXdM0NKuJUxMRPhm4nz0GhiAyJW/YkbIp5g+chb+PHACAND/vb74POwb7PzP7pfKGzdjDKLWbAcA3Ltjyl63s9NsPSjND712oJ8+rRj0cPcpw+7T3HyMEQIM7+qHh3kmAJhBHD+/12SiUNRAr1s8Pz9FN/D2dTGy/zJ9dYZ6cu1SC1Z0pqY07gqFe/RaeBYWwMiRivTuyNZ1P6wGMZsVAzFhYXRHWAr36LXwANXwfod22eHKxRaNKi/xuDVOHLKptXwKhSv0Xnh9+gCDBzPnhPDwfzPdUVTQsAi0+bkiLPlcsQR97Fj6bUfRDHo9uFJDfj6zkqDG39b2zef4addfcHF/Xu8ybqab4csJXii8z+yrZ2fHTJK2sXlNRgqlAeh9iwcwMTCjoxXLd/JyTBHWtxO2rnPE87JXV1FSKkDkyrb48F1fVnQmJsDevVR0FM1hEC1eDYcOAaNHA1VKk1jMLaQYGFKMzt2fwd27HCZmMpRLBLh1XYyrSRb484ANnpcrPORmZsDBg8CAAVqoAKXZYFDCA5j5mxMnMhOb1aVHDyAqCmjfnnOzKBQVDE54ABNbMzRUvcnNb70FxMYy3VYKRdMYxDeeMr/9xsTIrE10PD4BX0DA47/8tyYlhdl8cvfLM8koFM4xmBaPEGDePOD771WvvxVUguB/wvu1al0FHo959mGeCFeSLPDf7a1wNdlCJQ+NMEbRNAYjvKVLgX//W5F2cnuOiHVZ6PRW6WvzXjpngSWft8ODeybstXXrgFmzNGEphaIjwpMROQS8hvd6L14EAgIUEaCDBj3Bd5G3YGL2csTouiiT8PFNuCdSzjJbMBsbM5OjO3ZssFkUSp3ohPDSnzzCndK/0cOuDexMxWrlraxkQq/fvMmk/Xo+xcZ9mTAWql+tinI+pgZ3QOZVJuSDnx/z7UfX41G4RicGVwgIskv/xs7sdBy6exOPnte9n92L7N+vEJ2ZWIbFG7IaJDoAMDGTY/HG2xCKmJYyNRU49urYSRRKg9AJ4SmjrgCVw++N+Vcm/nfh4Cuff114vzc9nmPMlIe1lk+hcIXOCa+G+gjw4UPgwgXm3MhYjj5DuAnvFzqxADwe02oePQqUvn58hkJRC52PuZJd+jeyS/+GWwvrl74Ba2JqAkAHPwni9u7kJLxfG5dKuHqWI/uGGIQws2CCgpqsypRmgM62eC9SWwuo7CT36iThNryfr6KVvXq1aepo6OTn5yMnJ0fbZugEnLd4qzOSuC5SBeUW8GGxKwBm7Z2tA7fh/exaV7LP0EjS3FBSUoLQ0FCcP38eNhpe+nHqxG3Oy+w7wJ2zsnS+q1kbQr4AtiZmECqN8xMCTsP7EbkivB9fb/oFus+tW7cwfPhwxMfHw8zM7PUZDBS9+pUS8gV429YRU9p3Qc9WbeHQSmF+fi634f3ycxWzWGxtNVyxZsbFixcRFhYGmUz2+ocNFM4d6MpdzS87vF2vPNeeFCL+Qd19fyFfAL+W9vCzcYCJQNFIx8cr1s15+kqw8+S1OkpQn+Bufrj/j/hSUugGlVyQkZGBjkpTgWbMmIGNGzeyg1pNjXJ3lMtuZH3Q6a5mXYKroWtXphsolwN/XTPH3WwTOLtVNPq9mVfFrOhEIjptTFNs2rQJbdq0wfxmOCNdJ7uaL3YpaxMdAFhbA0OHKtJ7fql9AEVdYpTKGT2aCQVB0QwLFizAtm3btG1Gk6NTwquv4JRRDr8Xu9UemVfVm+v5IpcTLRC3VxGznYb30zxTpkzBn3/+qW0zmhSdEJ5IYKS24Gp4912gd2/mXCbjYeF0D5Q8blgP+tFDIRbNUvT133sP6N69QUVRXoN1W2u09mECEEulUoSGhiI1NVXLVjUdOiG89pYt1RZcDXw+sGULE6QIAO5lm2J6iA8e5onUKudetgmmj/BBQT6Tz9oaiIwEtPTdb/CIxCJM2zsNVo7MMqyysjIMGTKk2TjYdUJ4jcXVlRFfjUiyMsUYG9QJsdtaQVr9auVUVfIQ/bMDPujbid0hyMgI2LkTcODmk5FSB5YOlpi+bzpMLZmfe2FhIQYNGoTi4mItW6Z5dHpUUx3GjAGqq5kIYzIZUCYxwvKv3bB5ZVsMG/MInd8uhbt3GUzFcpSVCnDruhmuJlngyB47lDxWRJ4WCoGYGGDIkMbZI5fLUVFRAblcDlNTUwgasajv2bNn0IFlk42mtJbZ5vae9pgSPQWbQjZBWiltNg52gxEeAIwfD7i5MeK7dYu59viRENvWt8G29a/P37EjsHVrw8O2p6enY8eOHUhOTsaVK1fYXzQjIyP4+PjA398fISEhGDhwoFpC9PDwQGFhYcOM0gPcerhh/M/jsW3SNhBCWAd7bGxso/5g6TIG0dVUpkcPZlLzt9/Wv6vYti0TJOny5YaJ7vz58wgKCoKvry9WrVqFs2fPqvx1l0qlSEtLw2+//YahQ4fC3d0dkZGRkMvrH5rC0Okc3BkjvhvBpg8dOoRZs2YZREtfGwbV4tVgasoEPpo7F/j9d+DMGSZ+yu3bTJRpkQjw8AD8/ZmNLQcPVoR/V4eKigrMnz8fa9asqfUXxMRECD6fh/LySpXrOTk5mD59Ovbs2YMtW7bAxcWl3u8UmYu0NtODS0Tilwe/ek/rjacPniJhfQIAw3awG6TwajA2BkJCmINrJBIJhg0bhjNnzrDXjIwECAnpgbFjg9CtmzscHVuCx+Ph6dMyXLlyB0eOpCAq6iSePGFaw1OnTuHtt9/GiRMnVKZSvYoFlxbAopXF6x/UU4ZFDEPJgxKkxjKuhQULFsDR0RHh4eFatoxbDK6r2RRUV1cjODhYRXTvvtsFWVmbsWfPHIwc2QNt2tiwLZOlpRh9+nTEDz9MRn5+FObMCWVXRxQWFuKdd95Bdna2Vuqia/D5fIRtCIN7kMKfOmXKFBw/flyLVnEPFV4DWL58ORISEpTSH+LYscVwdrZ7RS4GU1MRVqyYiFOnlsHCghm1e/ToESZMmNCsZ+srYyQywqTtkwzawW6QwrtQmMceXOe5du0alixZwqYXLQrD3Lmj1P7uCgrqgLi4CBgZMaN2Fy9exNq1a9Uqw5AxtTDF1D1TWQe7RCIxKAe7QQovqeg+e3CdZ/ny5ZBKpQCAnj29sHDh6AbbGRDgjYULx6iUXVlZ+YoczQur1lYG62A3SOFpioKCAsTGxrLpjRunN9rPNH/+KDg5MStti4uLsX///kaVZ2jUONiNRMw4YI2Dvby8XMuWNQ4qPDU4cOAAqquZEBIBAV7o3Nm10WUaGxth2rRBbDomJqbRZRoaNQ72mu68Iaxgp8JTg8tK8QRHjuzBWbnKZSm/g6LA0BzsBu3H45qrSnH+9u8/j+LiZ5yUK5MpZrAUFBSgoKAA9vb2nJRtSPSe1hsl90twasMpAPrtYG+08F4czfsx/WKd95oKZRsaQn3sTkq6iaSkm416T1040GURdfLeovfw9OFTFQf7ggULGlRWwvFb7HlDf1cb2uLSriZFryBygqryqtc/qONQ4VH0BkIIYmfHIuNohrZNaTSN7mq+qqn9Uksfvo2NZq1rH+z29vYGvSyovpxYfQIXtl5g09988w1WrlzJSdlN/X9OWzyKXpAcnYy4ZXFsOiwsDCtWrNCiRY2DCo+i89yIv4E9n+1h0/3790dUVNRLYfj1Cf21nNIsyLuah6iPoiD/x+Xi6+uL2NhYCIVCLVvWOAzej6fp3YsomqM4txiRYyJRVcaMYjo5OeHo0aOwtLTUsmWNh7Z4FJ1E8liCyFGRkBRJAABWVlY4evQoWrdurWXLuIEKj6JzVJVX4ZcPfkFRdhEAQCQS4fDhw/D29tayZdxhkF3N+u5SRNE9ZFIZtv9rO+5evguAmVGya9cu9OrVS8uWcQvn23RRuMcQ/Xj2nvaYe2GuyjVCCPZ9tU/FV7du3TrMmjWrqc3TOLSrSdEZXnSQz5492yBFB1DhUXSEFx3k48aNw/Lly7VokWZpUuEtWrQI77zzjlp5cnNzwePxkJ+fryGruEOd+q1btw7du3eHmZkZ2rVr98pnHz58CLlcrvHj0qVLCAkJga2tLcRiMZydnRESEoL4+HjI5XKEh4fDyMgIYrGYPQYMGAC5XM4Gf6q57uDggIkTJ6K4uJgt/9q12nfsrc1BvmXLlkY5yPv06YOlS5ey6bS0NAwePBgODg7g8XhITEx8KU9ZWRk+/fRTODg4wNzcHF5eXrhy5UqDbXgVtMVrYmpWsLdu3RqzZ8+u15IWHo+n8SM+Ph6BgYFwc3PD5cuXUVpaivT0dISFheHgwYPsspnw8HBIJBL2OH78OFuGQCBgrycmJuLixYv44osvVN7zIk3lIBcKhQgJCcHhw4drvU8IwYgRI5Cbm4uUlBRIJBL88ccfmnNfEI5xdnYmixcvJgEBAUQsFpOuXbuSlJQUEhMTQ4yNjYlAICBisZiIxWKSnZ1NCCHk7NmzJCAggFhbWxNXV1fyww8/ELlcTgghxMLCggAgZmZmRCwWk2+//ZZrk9XC2dmZ7Nixg03n5OQQAGTr1q211i8qKoq4ubmRlStXEkdHR+Lt7a1SXs19bePm5kYmT578ymfCw8PrfObUqVNEIBCoXPvqq69Ihw4d2HR6ejoBQAAQe097sjB1ITG3NWevOTk5kfv37ze6LjNnziR8Pp8IhUIiFouJh4eHyn0A5Ny5cyrXjh07RkxNTcnjx48b/f76oBHhOTg4kMuXL5PKykqyfPlyYmNjQ54+fUoiIiJI//79VZ7PyMgg5ubm5ODBg0QqlZIbN24QFxcXsm3bNkKI4hc7Ly+Pa1MbRF3Cy8vLq7V+UVFRRCAQkM8//5yUl5eTsrKyl+5rW3g3b94kAEh8fPwrn1NHeLdv3yYeHh7ko48+Yq8pC8/K0YrYutmyaWtra5KZmclNhQghvXv3JkuWLKn1Xm3CmzNnDunQoQOZPXs2sbW1Ja6urmTevHmkqqqKM5uU0UhXc/LkyejatSuEQiHmzJkDU1NTHDlypNZnN23ahFGjRiE4OBgCgQCenp745JNPsH37dk2YphWMjY2xYsUKmJqa6uTWU0VFjKPa0dGRvXb48GFYWVnB0tISJkqbwO/YsQNWVlbssXfvXvaeTCaDlZUVrK2tMWDAAPTt2xerV6+u9Z0l90tecpB7eXlponr1ori4GBkZGZDJZLh37x6OHTuGffv2cbbs6EU04kBX3oSDx+PBycmpzsGRnJwcJCQk4MCBA+w1uVyOtm3basI0reDg4ACRSL0dapsSGxsbAEB+fj48PT0BAMOHD0dJSQkSExNVnNcTJkzAr7/+Wms5AoEAJSUlar27xkEeGBjYQOu5oUWLFhAIBFi2bBlEIhHc3d0xc+ZMREdHNzi0xKvQSIuXm5vLnhNCcO/ePbRp06bWUSpnZ2dMmjQJJSUl7PHs2TNcv36dMVDHln6Ym5ujrKyMTT948IA9r8tWXavDi3h4eMDV1VUroQXXrl2L0NBQzstV92feuXPnWq9rKm6QRn4jtmzZgtTUVFRXV2PVqlUoLy/H0KFDYW9vj3v37qGqShEz4+OPP0ZMTAx+//13VFdXQyqVIjMzk90QxNbWFnw+H7dv39aEqWrj7++P3bt3QyKRoKioSCWce231qwupVIqKigpUV1eDEIKKigpUVFRo0vQ64fF42LhxI3bs2IE5c+YgLy8PhBCUl5cjOTlZY+/VpIPc3t4eWVlZbPrFn3FVVRUqKirY2Jw1bpSIiAhUVVUhJycHmzZtQogmtpr6xyBOcXZ2JosWLWJHNf38/EhSUhIhhJAnT56Qfv36EWtra2JpaUnu3LlDCCHkwoULpF+/fqRly5bE2tqadOvWjezbt48tc9myZaRVq1bE0tKSLF26lGuT1SIvL4/069ePmJubE29vb7J161Z2cKW2+tU1eBIREcEOLCgf2iQlJYWMGDGC2NjYEDMzM+Li4kJGjhxJTp48SQhRf1TzRZQHV8aNG0dkMhnndaghJSWF+Pj4EEtLS+Lt7c0Ogr14REVFsXnS0tJIYGAgMTMzI05OTiQiIoJIpVKN2Mf5XE0XFxcsXboU48eP57JYigGQkZGBjh07on///oiLi9P7xayNQbc/PigGR6dOnXDgwIFmLTqACo/ShLzxxhuIi4uDhYXh7mhbX+iyIApFC9AWj0LRAlR4FIoWoMKjULQAFR6FogWo8CgULUCFR6FoASo8CkUL/D9TukQxE58zWgAAAABJRU5ErkJggg==\",\"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\",\"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\",\"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\",\"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   "source": [
    "#this is an example of how to use the new image plotting. It is designed to combine dnaplotlib and the network representation to show constructs and where proteins are bound to them.\r\n",
    "\r\n",
    "#to use this to its full potential you must make dna_constructs:\r\n",
    "txtl = TxTlExtract(\"mixture1\", parameter_file = 'default_parameters.txt')\r\n",
    "\r\n",
    "ptet = RegulatedPromoter(\"ptet\",[\"tetR\"])\r\n",
    "utr1 = RBS(\"utr1\")\r\n",
    "gfp = CDS(\"GFP\")\r\n",
    "t16 = Terminator(\"t16\")\r\n",
    "\r\n",
    "construct = DNA_construct([ptet,utr1,gfp,t16], initial_concentration=5)\r\n",
    "\r\n",
    "\r\n",
    "dna = DNAassembly(\"mydna\",promoter=RegulatedPromoter(\"plac\",[\"laci\"]),rbs=utr1,protein=\"GFP\", initial_concentration = 10)\r\n",
    "txtl.add_component(dna)\r\n",
    "txtl.add_component(construct)\r\n",
    "crn1 = txtl.compile_crn()\r\n",
    "crn1.add_reactions([Reaction.from_massaction([Species(\"mydna\",material_type=\"rna\")],[],k_forward=0.1)])\r\n",
    "try:\r\n",
    "    #dnaplotlib is a cool library for plotting DNAs.\r\n",
    "    #please use my fork located at https://github.com/dr3y/dnaplotlib\r\n",
    "    #to install it type: pip install git+git://github.com/dr3y/dnaplotlib.git@master\r\n",
    "    import dnaplotlib as dpl\r\n",
    "    %matplotlib inline\r\n",
    "    dpl_enabled = True\r\n",
    "except (ModuleNotFoundError,ImportError) as e:\r\n",
    "    dpl_enabled = False\r\n",
    "\r\n",
    "try:\r\n",
    "    from bokeh.models import (Plot , Range1d)\r\n",
    "    import bokeh.plotting\r\n",
    "    import bokeh.io\r\n",
    "    bokeh.io.output_notebook() #this makes the graph appear in line with the notebook\r\n",
    "\r\n",
    "    #this demonstrates the \"circle\" layout. reactions are in the middle with species on the outside.\r\n",
    "    #also, the pretty_print text display style\r\n",
    "\r\n",
    "    colordict = {\r\n",
    "        \"G1\":\"red\", #will only effect the species dna_G1 and rna_G1\r\n",
    "        \"protein_GFP\": \"green\", #will only effect the species protein_GFP\r\n",
    "        \"GFP\":\"lightgreen\", #this will affect how the dnaplotlib is drawn\r\n",
    "        \"protein\": \"blue\", #All protein species, protein_Ribo, protein_RNAase, and protein_RNAP will be blue\r\n",
    "        \"transcription_mm\":\"green\"\r\n",
    "        #All other species will be grey by default. This will include all complexes.\r\n",
    "    }\r\n",
    "    reactioncolordict = {\r\n",
    "        \"kint\":\"yellow\", #reactions containing \"kint\" will be yellow\r\n",
    "        \"kb\":\"cornflowerblue\", #reactions containing \"kb\" will be cornflowerblue\r\n",
    "        \"ktx\":\"orange\" #transcription reactions will be orange\r\n",
    "        #all other reactions will be grey by default\r\n",
    "        }\r\n",
    "    if(dpl_enabled):\r\n",
    "        images =render_mixture(txtl,crn1,colordict)\r\n",
    "    else:\r\n",
    "        images = None\r\n",
    "    plot = render_network_bokeh(crn1,\r\n",
    "                                                colordict = colordict,\r\n",
    "                                                reactioncolordict = reactioncolordict,\r\n",
    "                                                use_pretty_print=True, #uses pretty print\r\n",
    "                                                pp_show_rates=False, #this would put the reaction rates in the reaction name. It's already listed seperately in the tool tip\r\n",
    "                                                pp_show_attributes=False,\r\n",
    "                                                pp_show_material = True, #this lists the material of the species being displayed\r\n",
    "                                                imagedict =images, #this is a dictionary of which images to show for each species\r\n",
    "                                                layout=\"circle\",\r\n",
    "    )\r\n",
    "    bokeh.io.show(plot)\r\n",
    "except ModuleNotFoundError:\r\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')"
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