From 8bac636a0c7a3dc6a0c05dd92b1d315306c65dc0 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 27 Aug 2024 03:30:14 -0700 Subject: [PATCH] Update render_network_bokeh to use Scatter instead of Square + other small fixes (#281) * change some docstrings using math to use raw strings * ignore files created by Emacs * update networkx plots to not use Square + unit test * update specialized tutorials to run with latest versions --- .gitignore | 4 +- Tests/Unit/test_plotting.py | 51 +- biocrnpyler/chemical_reaction_network.py | 2 +- biocrnpyler/plotting.py | 33 +- biocrnpyler/reaction.py | 2 +- .../1. CombinatorialPromoter Details.ipynb | 550 +++++++++++------- .../5. TxTl Toolbox.ipynb | 48 +- 7 files changed, 440 insertions(+), 250 deletions(-) diff --git a/.gitignore b/.gitignore index f88ddb25..8bf0c402 100644 --- a/.gitignore +++ b/.gitignore @@ -24,7 +24,6 @@ Tests/bioscrape_test.xml Tests/dcas9_repression_test.xml Tests/sbml_test_file.xml - # Sphinx documentation docs/build/ @@ -74,3 +73,6 @@ docs/build/ ### BioCRNPlyer # Ignore xml files generated by this toolbox *.xml + +### Emacs backup files +*~ diff --git a/Tests/Unit/test_plotting.py b/Tests/Unit/test_plotting.py index 6171447c..fefaa4ca 100644 --- a/Tests/Unit/test_plotting.py +++ b/Tests/Unit/test_plotting.py @@ -1,13 +1,16 @@ - # Copyright (c) 2020, Build-A-Cell. All rights reserved. # See LICENSE file in the project root directory for details. -from biocrnpyler.dna_construct import RNA_construct +import copy +import re +import warnings + import pytest -#from unittest import TestCase + +import biocrnpyler as bcp from biocrnpyler import CRNPlotter, DNA_construct, Promoter,IntegraseSite,\ - RBS,CDS,Terminator, Complex, Species, DNA_part, RegulatedPromoter -import copy + RBS,CDS,Terminator, Complex, Species, DNA_part, RegulatedPromoter +from biocrnpyler.dna_construct import RNA_construct def test_CRNPlotter(): class dummy_renderer: @@ -25,7 +28,7 @@ def renderDNA(self,ax,designs,renderers,**keywords): ax.plot([0],[1]) return(0,100) - + d_r = dummy_renderer() test_plotter = CRNPlotter(dna_renderer=dummy_renderer(),rna_renderer=dummy_renderer(),cmap=[0,1,2,3,4,5,6]) @@ -77,7 +80,7 @@ def renderDNA(self,ax,designs,renderers,**keywords): boundspec = test_plotter.make_dpl_from_species(Species("ooga",material_type="booga")) #make a species to be bound to the promoter revpart = test_plotter.part_dpl_dict[RegulatedPromoter("p1",regulators=["r1","r2"])].get_directed("reverse",bound=[boundspec]) #return reverse promoter with things bound assert(revpart.get_dpl()[0]["type"]=="Operator") #reverse promoter has operators on the left - + test_plotter.colordict = {"Promoter":"purple"} prom_dpl = test_plotter.make_dpl_from_part(Promoter("pconst")) #make a promoter part @@ -94,7 +97,7 @@ def renderDNA(self,ax,designs,renderers,**keywords): test_construct4 = DNA_construct([RegulatedPromoter("p1",regulators=["r1","r2"]),DNA_part("test")]) test_plotter.make_dpl_from_species(test_construct4.get_species()) assert(test_construct4.get_species() in test_plotter.species_dpl_dict) #construct is in the dictionary - + rna_construct = RNA_construct([RBS("ooga"),CDS("booga")]) condpl1 = test_plotter.make_dpls_from_construct(rna_construct) assert(condpl1.material_type == "rna") #material type is set correctly @@ -103,7 +106,7 @@ def renderDNA(self,ax,designs,renderers,**keywords): condpl2 = test_plotter.make_dpls_from_construct(rna_construct2) assert(condpl2==condpl1) #this construct is already in the dictionary! Just return it - + bound_construct = Complex([test_construct4.get_species()[1],Species("integrase",material_type="protein")]).parent #making a construct that is bound to something @@ -118,5 +121,33 @@ def renderDNA(self,ax,designs,renderers,**keywords): #make sure the part inside the construct made it into the species dict assert(Species("mydna",material_type="dna") in test_plotter.species_dpl_dict) - +def test_render_network_bokeh(): + # Create a simple reaction + parameters = { + (None, None, "ktx"): 0.05, + (None, None, "kb"): 100, + (None, None, "ku"): 10, + (None, None, "ktl"): 0.05, + ("rna_degredation_mm", None, "kdeg"): 0.001, + (None, None, "cooperativity"): 2} + txtl = bcp.TxTlExtract('mixture1', parameters=parameters) + dna = bcp.DNAassembly( + 'mydna', promoter=bcp.RegulatedPromoter('plac', ['laci']), + rbs='UTR1', protein='GFP', initial_concentration=10) + txtl.add_component(dna) + crn1 = txtl.compile_crn() + crn1.add_reactions([ + bcp.Reaction.from_massaction( + [bcp.Species('mydna', material_type='rna')], [], k_forward=0.1)]) + + # Generate a graph and make sure it is created + with warnings.catch_warnings(record=True) as records: + plot = bcp.render_network_bokeh(crn1) + + for w in records: + if re.search("plotting disabled", w.message.args[0]): + pytest.fail("plotting library import error") + else: + warnings.showwarning( + w.message.args[0], w.category, w.filename, w.lineno) diff --git a/biocrnpyler/chemical_reaction_network.py b/biocrnpyler/chemical_reaction_network.py index a6d1e411..725d9b0c 100644 --- a/biocrnpyler/chemical_reaction_network.py +++ b/biocrnpyler/chemical_reaction_network.py @@ -18,7 +18,7 @@ class ChemicalReactionNetwork(object): - """A chemical reaction network is a container of species and reactions + r"""A chemical reaction network is a container of species and reactions chemical reaction networks can be compiled into SBML. reaction types: diff --git a/biocrnpyler/plotting.py b/biocrnpyler/plotting.py index 23549482..3303ccc8 100644 --- a/biocrnpyler/plotting.py +++ b/biocrnpyler/plotting.py @@ -47,15 +47,14 @@ PLOT_NETWORK = False try: import networkx as nx - from bokeh.models import (BoxSelectTool, Circle, EdgesAndLinkedNodes, + from bokeh.models import (BoxSelectTool, EdgesAndLinkedNodes, HoverTool, MultiLine, NodesAndLinkedEdges, - PanTool, Plot, Range1d, TapTool, + PanTool, Plot, Range1d, Scatter, TapTool, WheelZoomTool) - from bokeh.models.markers import Square from bokeh.plotting import from_networkx from bokeh.palettes import Spectral4 from bokeh.io import export_svgs, output_notebook - from fa2 import ForceAtlas2 + from fa2_modified import ForceAtlas2 PLOT_NETWORK = True except ModuleNotFoundError: pass @@ -184,8 +183,9 @@ def graphPlot(DG,DGspecies,DGreactions,plot,layout="force",positions=None,plot_s ybounds[1] += max_glyph # edges - edges_renderer.node_renderer.glyph = Circle( - size=species_glyph_size, line_alpha=0, fill_alpha=0, fill_color="color") + edges_renderer.node_renderer.glyph = Scatter( + marker="circle", size=species_glyph_size, line_alpha=0, + fill_alpha=0, fill_color="color") edges_renderer.edge_renderer.glyph = MultiLine( line_alpha=0.2, line_width=4, line_join="round", line_color="color") edges_renderer.edge_renderer.selection_glyph = MultiLine( @@ -217,17 +217,20 @@ def graphPlot(DG,DGspecies,DGreactions,plot,layout="force",positions=None,plot_s plot.y_range = Range1d(ylim[0], ylim[1]) # reactions - reaction_renderer.node_renderer.glyph = Square( - size=reaction_glyph_size, fill_color="color") - reaction_renderer.node_renderer.selection_glyph = Square( - size=reaction_glyph_size, fill_color=Spectral4[2]) - reaction_renderer.node_renderer.hover_glyph = Square( - size=reaction_glyph_size, fill_color=Spectral4[1]) + reaction_renderer.node_renderer.glyph = Scatter( + marker="square", size=reaction_glyph_size, fill_color="color") + reaction_renderer.node_renderer.selection_glyph = Scatter( + marker="square", size=reaction_glyph_size, fill_color=Spectral4[2]) + reaction_renderer.node_renderer.hover_glyph = Scatter( + marker="square", size=reaction_glyph_size, fill_color=Spectral4[1]) # nodes - species_renderer.node_renderer.glyph = Circle(size=12, fill_color="color") - species_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color=Spectral4[2]) - species_renderer.node_renderer.hover_glyph = Circle(size=15, fill_color=Spectral4[1]) + species_renderer.node_renderer.glyph = Scatter( + marker="circle", size=12, fill_color="color") + species_renderer.node_renderer.selection_glyph = Scatter( + marker="circle", size=15, fill_color=Spectral4[2]) + species_renderer.node_renderer.hover_glyph = Scatter( + marker="circle", size=15, fill_color=Spectral4[1]) #this part adds the interactive elements that make it so that the lines are highlighted #when you mouse over and click diff --git a/biocrnpyler/reaction.py b/biocrnpyler/reaction.py index fb2ee3d9..1003f3a4 100644 --- a/biocrnpyler/reaction.py +++ b/biocrnpyler/reaction.py @@ -15,7 +15,7 @@ class Reaction(object): - """An abstract representation of a chemical reaction in a CRN. + r"""An abstract representation of a chemical reaction in a CRN. A reaction has the form: .. math:: diff --git a/examples/Specialized Tutorials/1. CombinatorialPromoter Details.ipynb b/examples/Specialized Tutorials/1. CombinatorialPromoter Details.ipynb index afdc7199..d576b087 100644 --- a/examples/Specialized Tutorials/1. CombinatorialPromoter Details.ipynb +++ b/examples/Specialized Tutorials/1. CombinatorialPromoter Details.ipynb @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 19, "metadata": { "slideshow": { "slide_type": "skip" @@ -80,11 +80,18 @@ { "data": { "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" + " \n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" ] }, "metadata": {}, @@ -93,29 +100,29 @@ { "data": { "application/javascript": [ - "\n", + "'use strict';\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", - " var force = true;\n", + " const 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", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const 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", + " const script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", @@ -123,33 +130,38 @@ " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", + " function drop(id) {\n", + " const view = Bokeh.index.get_by_id(id)\n", + " if (view != null) {\n", + " view.model.document.clear()\n", + " Bokeh.index.delete(view)\n", + " }\n", + " }\n", + "\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\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", + " if (id != null) {\n", + " drop(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", + " const cmd_clean = \"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_clean, {\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", + " const id = msg.content.text.trim()\n", + " drop(id)\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", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", " }\n", " }\n", "\n", @@ -157,15 +169,15 @@ " * 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", + " const output_area = handle.output_area;\n", + " const 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", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " const 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", @@ -173,10 +185,10 @@ " 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", + " const 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", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let 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", @@ -189,14 +201,14 @@ "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", + " const 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", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", @@ -222,21 +234,19 @@ "\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", + " const events = require('base/js/events');\n", + " const 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", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", @@ -252,21 +262,45 @@ " \"\\n\"+\n", " \"

\"}};\n", "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"1001\");\n", + " function display_loaded(error = null) {\n", + " const el = document.getElementById(\"d711ed48-f2a6-4f25-a254-ea25c52548c3\");\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", + " const html = (() => {\n", + " if (typeof root.Bokeh === \"undefined\") {\n", + " if (error == null) {\n", + " return \"BokehJS is loading ...\";\n", + " } else {\n", + " return \"BokehJS failed to load.\";\n", + " }\n", + " } else {\n", + " const prefix = `BokehJS ${root.Bokeh.version}`;\n", + " if (error == null) {\n", + " return `${prefix} successfully loaded.`;\n", + " } else {\n", + " return `${prefix} encountered errors while loading and may not function as expected.`;\n", + " }\n", + " }\n", + " })();\n", + " el.innerHTML = html;\n", + "\n", + " if (error != null) {\n", + " const wrapper = document.createElement(\"div\");\n", + " wrapper.style.overflow = \"auto\";\n", + " wrapper.style.height = \"5em\";\n", + " wrapper.style.resize = \"vertical\";\n", + " const content = document.createElement(\"div\");\n", + " content.style.fontFamily = \"monospace\";\n", + " content.style.whiteSpace = \"pre-wrap\";\n", + " content.style.backgroundColor = \"rgb(255, 221, 221)\";\n", + " content.textContent = error.stack ?? error.toString();\n", + " wrapper.append(content);\n", + " el.append(wrapper);\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", + " setTimeout(() => display_loaded(error), 100);\n", " }\n", " }\n", "\n", - "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", @@ -303,15 +337,15 @@ " }\n", " }\n", "\n", - " function on_error() {\n", + " function on_error(url) {\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", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", - " element.onerror = on_error;\n", + " element.onerror = on_error.bind(null, url);\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", @@ -319,19 +353,13 @@ " document.body.appendChild(element);\n", " }\n", "\n", - " const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.2.min.js\": \"ufR9RFnRs6lniiaFvtJziE0YeidtAgBRH6ux2oUItHw5WTvE1zuk9uzhUU/FJXDp\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\": \"8QM/PGWBT+IssZuRcDcjzwIh1mkOmJSoNMmyYDZbCfXJg3Ap1lEvdVgFuSAwhb/J\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\": \"Jm8cH3Rg0P6UeZhVY5cLy1WzKajUT9KImCY+76hEqrcJt59/d8GPvFHjCkYgnSIn\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.min.js\": \"Ozhzj+SI7ywm74aOI/UajcWz+C0NjsPunEVyVIrxzYkB+jA+2tUw8x5xJCbVtK5I\"};\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", " element.onload = on_load;\n", - " element.onerror = on_error;\n", + " element.onerror = on_error.bind(null, url);\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", @@ -343,29 +371,25 @@ " document.body.appendChild(element);\n", " }\n", "\n", - " \n", - " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.min.js\"];\n", - " var css_urls = [];\n", - " \n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.4.1.min.js\"];\n", + " const css_urls = [];\n", "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", + " const inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", - " function(Bokeh) {\n", - " \n", - " \n", + "function(Bokeh) {\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", + " try {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", - " if (force === true) {\n", + "\n", + " } catch (error) {display_loaded(error);throw error;\n", + " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", @@ -373,10 +397,9 @@ " 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", + " const cell = $(document.getElementById(\"d711ed48-f2a6-4f25-a254-ea25c52548c3\")).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", @@ -390,7 +413,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "\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 \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 \"
\\n\"+\n \"

\\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 \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1001\");\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-2.0.2.min.js\": \"ufR9RFnRs6lniiaFvtJziE0YeidtAgBRH6ux2oUItHw5WTvE1zuk9uzhUU/FJXDp\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\": \"8QM/PGWBT+IssZuRcDcjzwIh1mkOmJSoNMmyYDZbCfXJg3Ap1lEvdVgFuSAwhb/J\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\": \"Jm8cH3Rg0P6UeZhVY5cLy1WzKajUT9KImCY+76hEqrcJt59/d8GPvFHjCkYgnSIn\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.min.js\": \"Ozhzj+SI7ywm74aOI/UajcWz+C0NjsPunEVyVIrxzYkB+jA+2tUw8x5xJCbVtK5I\"};\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-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.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": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const 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\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 const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\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 \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded(error = null) {\n const el = document.getElementById(\"d711ed48-f2a6-4f25-a254-ea25c52548c3\");\n if (el != null) {\n const html = (() => {\n if (typeof root.Bokeh === \"undefined\") {\n if (error == null) {\n return \"BokehJS is loading ...\";\n } else {\n return \"BokehJS failed to load.\";\n }\n } else {\n const prefix = `BokehJS ${root.Bokeh.version}`;\n if (error == null) {\n return `${prefix} successfully loaded.`;\n } else {\n return `${prefix} encountered errors while loading and may not function as expected.`;\n }\n }\n })();\n el.innerHTML = html;\n\n if (error != null) {\n const wrapper = document.createElement(\"div\");\n wrapper.style.overflow = \"auto\";\n wrapper.style.height = \"5em\";\n wrapper.style.resize = \"vertical\";\n const content = document.createElement(\"div\");\n content.style.fontFamily = \"monospace\";\n content.style.whiteSpace = \"pre-wrap\";\n content.style.backgroundColor = \"rgb(255, 221, 221)\";\n content.textContent = error.stack ?? error.toString();\n wrapper.append(content);\n el.append(wrapper);\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(() => display_loaded(error), 100);\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(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\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 const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.4.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n try {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n\n } catch (error) {display_loaded(error);throw error;\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 const cell = $(document.getElementById(\"d711ed48-f2a6-4f25-a254-ea25c52548c3\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\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));" }, "metadata": {}, "output_type": "display_data" @@ -440,60 +463,174 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "mixture.add_component: DNAassembly: mydna rna_mydna\n", - "mixture.update_species: DNAassembly: mydna rna_mydna\n", "The species and reactions in the CRN:\n", - " Species (17) = {0. protein[Ribo], 1. complex[dna[mydna]:protein[hrpR]], 2. protein[GFP], 3. complex[protein[Ribo]:rna[mydna]], 4. complex[dna[mydna]:protein[RNAP]], 5. complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]], 6. protein[RNAase], 7. complex[dna[mydna]:protein[hrpR]:protein[hrpS]], 8. complex[protein[RNAase]:rna[mydna]], 9. complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]], 10. rna[mydna], 11. complex[dna[mydna]:protein[hrpS]], 12. dna[mydna], 13. protein[RNAP], 14. complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]], 15. protein[hrpR], 16. protein[hrpS]}\n", - "Reactions (16) = [\n", - "0. dna[mydna] + protein[RNAP] <--> complex[dna[mydna]:protein[RNAP]] \n", - " massaction: k_f(dna[mydna],protein[RNAP])=100*dna[mydna]*protein[RNAP]\n", - " k_r(complex[dna[mydna]:protein[RNAP]])=100*complex[dna[mydna]:protein[RNAP]]\n", - "1. complex[dna[mydna]:protein[RNAP]] --> dna[mydna] + rna[mydna] + protein[RNAP] \n", - " massaction: k_f(complex[dna[mydna]:protein[RNAP]])=0.01*complex[dna[mydna]:protein[RNAP]]\n", - "2. protein[hrpR] + dna[mydna] <--> complex[dna[mydna]:protein[hrpR]] \n", - " massaction: k_f(protein[hrpR],dna[mydna])=100*protein[hrpR]*dna[mydna]\n", - " k_r(complex[dna[mydna]:protein[hrpR]])=50*complex[dna[mydna]:protein[hrpR]]\n", - "3. protein[hrpS] + dna[mydna] <--> complex[dna[mydna]:protein[hrpS]] \n", - " massaction: k_f(protein[hrpS],dna[mydna])=100*protein[hrpS]*dna[mydna]\n", - " k_r(complex[dna[mydna]:protein[hrpS]])=50*complex[dna[mydna]:protein[hrpS]]\n", - "4. protein[hrpR] + complex[dna[mydna]:protein[hrpS]] <--> complex[dna[mydna]:protein[hrpR]:protein[hrpS]] \n", - " massaction: k_f(protein[hrpR],complex[dna[mydna]:protein[hrpS]])=100*protein[hrpR]*complex[dna[mydna]:protein[hrpS]]\n", - " k_r(complex[dna[mydna]:protein[hrpR]:protein[hrpS]])=50*complex[dna[mydna]:protein[hrpR]:protein[hrpS]]\n", - "5. protein[hrpS] + complex[dna[mydna]:protein[hrpR]] <--> complex[dna[mydna]:protein[hrpR]:protein[hrpS]] \n", - " massaction: k_f(protein[hrpS],complex[dna[mydna]:protein[hrpR]])=100*protein[hrpS]*complex[dna[mydna]:protein[hrpR]]\n", - " k_r(complex[dna[mydna]:protein[hrpR]:protein[hrpS]])=50*complex[dna[mydna]:protein[hrpR]:protein[hrpS]]\n", - "6. complex[dna[mydna]:protein[hrpR]] + protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]] \n", - " massaction: k_f(complex[dna[mydna]:protein[hrpR]],protein[RNAP])=100*complex[dna[mydna]:protein[hrpR]]*protein[RNAP]\n", - " k_r(complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]])=10*complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]]\n", - "7. complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpR]] + rna[mydna] + protein[RNAP] \n", - " massaction: k_f(complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]])=0.05*complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]]\n", - "8. complex[dna[mydna]:protein[hrpS]] + protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]] \n", - " massaction: k_f(complex[dna[mydna]:protein[hrpS]],protein[RNAP])=100*complex[dna[mydna]:protein[hrpS]]*protein[RNAP]\n", - " k_r(complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]])=10*complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]]\n", - "9. complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpS]] + rna[mydna] + protein[RNAP] \n", - " massaction: k_f(complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]])=0.05*complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]]\n", - "10. complex[dna[mydna]:protein[hrpR]:protein[hrpS]] + protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]] \n", - " massaction: k_f(complex[dna[mydna]:protein[hrpR]:protein[hrpS]],protein[RNAP])=100*complex[dna[mydna]:protein[hrpR]:protein[hrpS]]*protein[RNAP]\n", - " k_r(complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]])=10*complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]]\n", - "11. complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpR]:protein[hrpS]] + rna[mydna] + protein[RNAP] \n", - " massaction: k_f(complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]])=0.05*complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]]\n", - "12. rna[mydna] + protein[Ribo] <--> complex[protein[Ribo]:rna[mydna]] \n", - " massaction: k_f(rna[mydna],protein[Ribo])=100*rna[mydna]*protein[Ribo]\n", - " k_r(complex[protein[Ribo]:rna[mydna]])=10.0*complex[protein[Ribo]:rna[mydna]]\n", - "13. complex[protein[Ribo]:rna[mydna]] --> rna[mydna] + protein[GFP] + protein[Ribo] \n", - " massaction: k_f(complex[protein[Ribo]:rna[mydna]])=1.5*complex[protein[Ribo]:rna[mydna]]\n", - "14. rna[mydna] + protein[RNAase] <--> complex[protein[RNAase]:rna[mydna]] \n", - " massaction: k_f(rna[mydna],protein[RNAase])=100*rna[mydna]*protein[RNAase]\n", - " k_r(complex[protein[RNAase]:rna[mydna]])=10*complex[protein[RNAase]:rna[mydna]]\n", - "15. complex[protein[RNAase]:rna[mydna]] --> protein[RNAase] \n", - " massaction: k_f(complex[protein[RNAase]:rna[mydna]])=0.000555556*complex[protein[RNAase]:rna[mydna]]\n", + " Species(N = 18) = {\n", + "protein[RNAP] (@ 3.0), \n", + " found_key=(mech=None, partid=e coli extract 1, name=RNAP).\n", + " search_key=(mech=initial concentration, partid=e coli extract 1, name=RNAP).\n", + "complex[protein[Ribo]:rna[mydna]] (@ 0), complex[protein[RNAase]:rna[mydna]] (@ 0), rna[mydna] (@ 0), dna[mydna] (@ 0), protein[hrpS] (@ 0), protein[hrpR] (@ 0), complex[dna[mydna]:protein[hrpS]] (@ 0), complex[dna[mydna]:protein[hrpR]:protein[hrpS]] (@ 0), complex[dna[mydna]:protein[hrpR]] (@ 0), complex[dna[mydna]:protein[RNAP]] (@ 0), complex[complex[protein[Ribo]:rna[mydna]]:protein[RNAase]] (@ 0), complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]] (@ 0), complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]] (@ 0), complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]] (@ 0), protein[Ribo] (@ 0), protein[RNAase] (@ 0), protein[GFP] (@ 0), \n", + "}\n", + "\n", + "Reactions (18) = [\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\n", + " found_key=(mech=None, partid=None, name=kb).\n", + " search_key=(mech=transcription_mm, partid=phrpL_leak, name=kb).\n", + " k_reverse=100\n", + " found_key=(mech=None, partid=phrpL_leak, name=ku).\n", + " search_key=(mech=transcription_mm, partid=phrpL_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=0.01\n", + " found_key=(mech=None, partid=phrpL_leak, name=ktx).\n", + " search_key=(mech=transcription_mm, partid=phrpL_leak, name=ktx).\n", + "\n", + "2. protein[hrpR]+dna[mydna] <--> complex[dna[mydna]:protein[hrpR]]\n", + " Kf=k_forward * protein_hrpR * dna_mydna\n", + " Kr=k_reverse * complex_dna_mydna_protein_hrpR_\n", + " k_forward=100\n", + " found_key=(mech=None, partid=None, name=kb).\n", + " search_key=(mech=Combinatorial_Cooperative_binding, partid=phrpL_hrpR, name=kb).\n", + " k_reverse=50\n", + " found_key=(mech=None, partid=phrpL_hrpR, name=ku).\n", + " search_key=(mech=Combinatorial_Cooperative_binding, partid=phrpL_hrpR, name=ku).\n", + "\n", + "3. protein[hrpS]+dna[mydna] <--> complex[dna[mydna]:protein[hrpS]]\n", + " Kf=k_forward * protein_hrpS * dna_mydna\n", + " Kr=k_reverse * complex_dna_mydna_protein_hrpS_\n", + " k_forward=100\n", + " found_key=(mech=None, partid=None, name=kb).\n", + " search_key=(mech=Combinatorial_Cooperative_binding, partid=phrpL_hrpS, name=kb).\n", + " k_reverse=50\n", + " found_key=(mech=None, partid=phrpL_hrpS, name=ku).\n", + " search_key=(mech=Combinatorial_Cooperative_binding, partid=phrpL_hrpS, name=ku).\n", + "\n", + "4. protein[hrpR]+complex[dna[mydna]:protein[hrpS]] <--> complex[dna[mydna]:protein[hrpR]:protein[hrpS]]\n", + " Kf=k_forward * protein_hrpR * complex_dna_mydna_protein_hrpS_\n", + " Kr=k_reverse * complex_dna_mydna_protein_hrpR_protein_hrpS_\n", + " k_forward=100\n", + " found_key=(mech=None, partid=None, name=kb).\n", + " search_key=(mech=Combinatorial_Cooperative_binding, partid=phrpL_hrpR, name=kb).\n", + " k_reverse=50\n", + " found_key=(mech=None, partid=phrpL_hrpR, name=ku).\n", + " search_key=(mech=Combinatorial_Cooperative_binding, partid=phrpL_hrpR, name=ku).\n", + "\n", + "5. protein[hrpS]+complex[dna[mydna]:protein[hrpR]] <--> complex[dna[mydna]:protein[hrpR]:protein[hrpS]]\n", + " Kf=k_forward * protein_hrpS * complex_dna_mydna_protein_hrpR_\n", + " Kr=k_reverse * complex_dna_mydna_protein_hrpR_protein_hrpS_\n", + " k_forward=100\n", + " found_key=(mech=None, partid=None, name=kb).\n", + " search_key=(mech=Combinatorial_Cooperative_binding, partid=phrpL_hrpS, name=kb).\n", + " k_reverse=50\n", + " found_key=(mech=None, partid=phrpL_hrpS, name=ku).\n", + " search_key=(mech=Combinatorial_Cooperative_binding, partid=phrpL_hrpS, name=ku).\n", + "\n", + "6. complex[dna[mydna]:protein[hrpR]]+protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]]\n", + " Kf=k_forward * complex_dna_mydna_protein_hrpR_ * protein_RNAP\n", + " Kr=k_reverse * complex_complex_dna_mydna_protein_hrpR__protein_RNAP_\n", + " k_forward=100\n", + " found_key=(mech=None, partid=None, name=kb).\n", + " search_key=(mech=transcription_mm, partid=phrpL_hrpR_RNAP, name=kb).\n", + " k_reverse=10\n", + " found_key=(mech=None, partid=None, name=ku).\n", + " search_key=(mech=transcription_mm, partid=phrpL_hrpR_RNAP, name=ku).\n", + "\n", + "7. complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpR]]+rna[mydna]+protein[RNAP]\n", + " Kf=k_forward * complex_complex_dna_mydna_protein_hrpR__protein_RNAP_\n", + " k_forward=0.05\n", + " found_key=(mech=None, partid=None, name=ktx).\n", + " search_key=(mech=transcription_mm, partid=phrpL_hrpR_RNAP, name=ktx).\n", + "\n", + "8. complex[dna[mydna]:protein[hrpS]]+protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]]\n", + " Kf=k_forward * complex_dna_mydna_protein_hrpS_ * protein_RNAP\n", + " Kr=k_reverse * complex_complex_dna_mydna_protein_hrpS__protein_RNAP_\n", + " k_forward=100\n", + " found_key=(mech=None, partid=None, name=kb).\n", + " search_key=(mech=transcription_mm, partid=phrpL_hrpS_RNAP, name=kb).\n", + " k_reverse=10\n", + " found_key=(mech=None, partid=None, name=ku).\n", + " search_key=(mech=transcription_mm, partid=phrpL_hrpS_RNAP, name=ku).\n", + "\n", + "9. complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpS]]+rna[mydna]+protein[RNAP]\n", + " Kf=k_forward * complex_complex_dna_mydna_protein_hrpS__protein_RNAP_\n", + " k_forward=0.05\n", + " found_key=(mech=None, partid=None, name=ktx).\n", + " search_key=(mech=transcription_mm, partid=phrpL_hrpS_RNAP, name=ktx).\n", + "\n", + "10. complex[dna[mydna]:protein[hrpR]:protein[hrpS]]+protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]]\n", + " Kf=k_forward * complex_dna_mydna_protein_hrpR_protein_hrpS_ * protein_RNAP\n", + " Kr=k_reverse * complex_complex_dna_mydna_protein_hrpR_protein_hrpS__protein_RNAP_\n", + " k_forward=100\n", + " found_key=(mech=None, partid=None, name=kb).\n", + " search_key=(mech=transcription_mm, partid=phrpL_hrpR_hrpS_RNAP, name=kb).\n", + " k_reverse=10\n", + " found_key=(mech=None, partid=None, name=ku).\n", + " search_key=(mech=transcription_mm, partid=phrpL_hrpR_hrpS_RNAP, name=ku).\n", + "\n", + "11. complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpR]:protein[hrpS]]+rna[mydna]+protein[RNAP]\n", + " Kf=k_forward * complex_complex_dna_mydna_protein_hrpR_protein_hrpS__protein_RNAP_\n", + " k_forward=0.05\n", + " found_key=(mech=None, partid=None, name=ktx).\n", + " search_key=(mech=transcription_mm, partid=phrpL_hrpR_hrpS_RNAP, name=ktx).\n", + "\n", + "12. 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\n", + " found_key=(mech=None, partid=None, name=kb).\n", + " search_key=(mech=translation_mm, partid=B0030, name=kb).\n", + " k_reverse=10.0\n", + " found_key=(mech=translation_mm, partid=B0030, name=ku).\n", + " search_key=(mech=translation_mm, partid=B0030, name=ku).\n", + "\n", + "13. complex[protein[Ribo]:rna[mydna]] --> rna[mydna]+protein[GFP]+protein[Ribo]\n", + " Kf=k_forward * complex_protein_Ribo_rna_mydna_\n", + " k_forward=1.5\n", + " found_key=(mech=translation_mm, partid=B0030, name=ktl).\n", + " search_key=(mech=translation_mm, partid=B0030, name=ktl).\n", + "\n", + "14. 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\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\n", + " found_key=(mech=None, partid=None, name=ku).\n", + " search_key=(mech=rna_degredation_mm, partid=rna_mydna, name=ku).\n", + "\n", + "15. complex[protein[RNAase]:rna[mydna]] --> protein[RNAase]\n", + " Kf=k_forward * complex_protein_RNAase_rna_mydna_\n", + " k_forward=0.000555556\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", + "16. 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\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\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", + "17. 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=0.000555556\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", "]\n" ] }, @@ -501,21 +638,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "BokehDeprecationWarning: Importing from_networkx from bokeh.models.graphs is deprecated and will be removed in Bokeh 3.0. Import from bokeh.plotting instead\n", - "C:\\ProgramData\\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" + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/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", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/plotting.py:166: UserWarning: Node keys in 'layout_function' don't match node keys in the graph. These nodes may not be displayed correctly.\n", + " reaction_renderer = from_networkx(DGreactions, positions, center=(0, 0))\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/plotting.py:167: UserWarning: Node keys in 'layout_function' don't match node keys in the graph. These nodes may not be displayed correctly.\n", + " species_renderer = from_networkx(DGspecies, positions, center=(0, 0))\n" ] }, { "data": { "text/html": [ "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -526,17 +661,15 @@ "application/javascript": [ "(function(root) {\n", " function embed_document(root) {\n", - " \n", - " var docs_json = {\"9928c12b-e691-4cdb-9e7f-98f5884c7443\":{\"roots\":{\"references\":[{\"attributes\":{\"plot_height\":500,\"plot_width\":500,\"renderers\":[{\"id\":\"1035\"},{\"id\":\"1007\"},{\"id\":\"1021\"}],\"title\":{\"id\":\"1130\"},\"toolbar\":{\"id\":\"1116\"},\"x_range\":{\"id\":\"1077\"},\"x_scale\":{\"id\":\"1128\"},\"y_range\":{\"id\":\"1078\"},\"y_scale\":{\"id\":\"1127\"}},\"id\":\"1004\",\"type\":\"Plot\"},{\"attributes\":{\"edge_renderer\":{\"id\":\"1028\"},\"inspection_policy\":{\"id\":\"1154\"},\"layout_provider\":{\"id\":\"1034\"},\"node_renderer\":{\"id\":\"1024\"},\"selection_policy\":{\"id\":\"1153\"}},\"id\":\"1021\",\"type\":\"GraphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"1027\"}},\"id\":\"1029\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1127\",\"type\":\"LinearScale\"},{\"attributes\":{\"end\":179.48830883389695,\"start\":-100.85017549486308},\"id\":\"1077\",\"type\":\"Range1d\"},{\"attributes\":{\"data_source\":{\"id\":\"1023\"},\"glyph\":{\"id\":\"1094\"},\"hover_glyph\":{\"id\":\"1104\"},\"muted_glyph\":null,\"selection_glyph\":{\"id\":\"1099\"},\"view\":{\"id\":\"1025\"}},\"id\":\"1024\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1128\",\"type\":\"LinearScale\"},{\"attributes\":{\"graph_layout\":{\"0\":[-128.06534390538394,188.8110737063345],\"1\":[-4.942642958329285,-141.43195492316931],\"10\":[-63.05482954977653,53.96466500077084],\"11\":[-18.111741884048353,-144.97809028556364],\"12\":[-0.03034153180732158,0.01367303858752807],\"13\":[-1.289279313130396,15.122781689567564],\"14\":[-2.279250371726325,34.84819716524268],\"15\":[26.43926448068255,-13.496628475304133],\"16\":[-17.653612018224468,15.029871711418627],\"17\":[-26.542599340112336,0.6402053798270149],\"18\":[15.827931173971795,29.42291883724415],\"19\":[42.58663923851341,34.851882467682195],\"2\":[-42.912667181668425,26.8250862178223],\"20\":[-22.425925393415437,19.18005005376656],\"21\":[-10.831238753170846,-2.6236818957972776],\"22\":[-10.842673172635013,17.64794512343082],\"23\":[-34.61463764843533,17.361354830535326],\"24\":[-43.31085414311069,42.33438781184292],\"25\":[-77.741780780435,62.808082530055444],\"26\":[14.022429470966996,-0.553959854560857],\"27\":[36.38357800981411,-22.635106090529064],\"28\":[-8.968942098927226,57.00149749918813],\"29\":[-2.4043658578715292,89.4222184982506],\"3\":[156.37991411946888,-177.56681419580656],\"30\":[-6.805066829718983,-146.37918054221225],\"31\":[12.5197498143886,-152.90399699369698],\"32\":[-31.65772711420718,-144.4222919267058],\"33\":[-46.06217951791717,-137.07274852058956],\"4\":[3.0894514477121673,-149.86496953886694],\"5\":[30.79277003241157,32.55511799827421],\"6\":[-5.172597935166938,76.14428069155251],\"7\":[-34.21960043849704,-149.60570618132508],\"8\":[-20.756455984906772,37.19976305162342],\"9\":[-40.00747948102076,-140.3922770855331]}},\"id\":\"1020\",\"type\":\"StaticLayoutProvider\"},{\"attributes\":{\"data_source\":{\"id\":\"1009\"},\"glyph\":{\"id\":\"1079\"},\"hover_glyph\":{\"id\":\"1089\"},\"muted_glyph\":null,\"selection_glyph\":{\"id\":\"1084\"},\"view\":{\"id\":\"1011\"}},\"id\":\"1010\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1154\",\"type\":\"NodesOnly\"},{\"attributes\":{\"edge_renderer\":{\"id\":\"1042\"},\"inspection_policy\":{\"id\":\"1125\"},\"layout_provider\":{\"id\":\"1048\"},\"node_renderer\":{\"id\":\"1038\"},\"selection_policy\":{\"id\":\"1123\"}},\"id\":\"1035\",\"type\":\"GraphRenderer\"},{\"attributes\":{},\"id\":\"1123\",\"type\":\"NodesAndLinkedEdges\"},{\"attributes\":{\"source\":{\"id\":\"1037\"}},\"id\":\"1039\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1166\",\"type\":\"Selection\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1130\",\"type\":\"Title\"},{\"attributes\":{\"source\":{\"id\":\"1009\"}},\"id\":\"1011\",\"type\":\"CDSView\"},{\"attributes\":{\"data\":{\"color\":[\"purple\",\"green\",\"cyan\",\"green\",\"cyan\",\"cyan\",\"cyan\",\"green\",\"cyan\",\"cyan\",\"cyan\",\"orange\",\"cyan\",\"grey\",\"green\",\"cyan\",\"green\",\"green\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],\"species\":[\"nothing\",\"protein_Ribo\",\"complex_dna_mydna_protein_hrpR\",\"protein_GFP\",\"complex_protein_Ribo_rna_mydna\",\"complex_dna_mydna_protein_RNAP\",\"complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP\",\"protein_RNAase\",\"complex_dna_mydna_protein_hrpR_protein_hrpS\",\"complex_protein_RNAase_rna_mydna\",\"complex_dna_mydna_protein_hrpR_protein_RNAP\",\"rna_mydna\",\"complex_dna_mydna_protein_hrpS\",\"dna_mydna\",\"protein_RNAP\",\"complex_dna_mydna_protein_hrpS_protein_RNAP\",\"protein_hrpR\",\"protein_hrpS\"],\"type\":[\"nothing\",\"protein\",\"complex\",\"protein\",\"complex\",\"complex\",\"complex\",\"protein\",\"complex\",\"complex\",\"complex\",\"rna\",\"complex\",\"dna\",\"protein\",\"complex\",\"protein\",\"protein\"]},\"selected\":{\"id\":\"1170\"},\"selection_policy\":{\"id\":\"1171\"}},\"id\":\"1023\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1160\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1163\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"graph_layout\":{\"0\":[-128.06534390538394,188.8110737063345],\"1\":[-4.942642958329285,-141.43195492316931],\"10\":[-63.05482954977653,53.96466500077084],\"11\":[-18.111741884048353,-144.97809028556364],\"12\":[-0.03034153180732158,0.01367303858752807],\"13\":[-1.289279313130396,15.122781689567564],\"14\":[-2.279250371726325,34.84819716524268],\"15\":[26.43926448068255,-13.496628475304133],\"16\":[-17.653612018224468,15.029871711418627],\"17\":[-26.542599340112336,0.6402053798270149],\"18\":[15.827931173971795,29.42291883724415],\"19\":[42.58663923851341,34.851882467682195],\"2\":[-42.912667181668425,26.8250862178223],\"20\":[-22.425925393415437,19.18005005376656],\"21\":[-10.831238753170846,-2.6236818957972776],\"22\":[-10.842673172635013,17.64794512343082],\"23\":[-34.61463764843533,17.361354830535326],\"24\":[-43.31085414311069,42.33438781184292],\"25\":[-77.741780780435,62.808082530055444],\"26\":[14.022429470966996,-0.553959854560857],\"27\":[36.38357800981411,-22.635106090529064],\"28\":[-8.968942098927226,57.00149749918813],\"29\":[-2.4043658578715292,89.4222184982506],\"3\":[156.37991411946888,-177.56681419580656],\"30\":[-6.805066829718983,-146.37918054221225],\"31\":[12.5197498143886,-152.90399699369698],\"32\":[-31.65772711420718,-144.4222919267058],\"33\":[-46.06217951791717,-137.07274852058956],\"4\":[3.0894514477121673,-149.86496953886694],\"5\":[30.79277003241157,32.55511799827421],\"6\":[-5.172597935166938,76.14428069155251],\"7\":[-34.21960043849704,-149.60570618132508],\"8\":[-20.756455984906772,37.19976305162342],\"9\":[-40.00747948102076,-140.3922770855331]}},\"id\":\"1048\",\"type\":\"StaticLayoutProvider\"},{\"attributes\":{\"data\":{\"color\":[\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\"],\"index\":[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"k\":[100,0.01,100,100,100,100,100,0.05,100,0.05,100,0.05,100,1.5,100,0.000555556],\"k_r\":[100,0,50,50,50,50,10,0,10,0,10,0,10.0,0,10,0],\"species\":[\"dna_mydna + protein_RNAP <--> complex_dna_mydna_protein_RNAP massaction: k_f(dna_mydna,protein_RNAP)=100*dna_mydna*protein_RNAP k_r(complex_dna_mydna_protein_RNAP)=100*complex_dna_mydna_protein_RNAP\",\"complex_dna_mydna_protein_RNAP --> dna_mydna + rna_mydna + protein_RNAP massaction: k_f(complex_dna_mydna_protein_RNAP)=0.01*complex_dna_mydna_protein_RNAP\",\"protein_hrpR + dna_mydna <--> complex_dna_mydna_protein_hrpR massaction: k_f(protein_hrpR,dna_mydna)=100*protein_hrpR*dna_mydna k_r(complex_dna_mydna_protein_hrpR)=50*complex_dna_mydna_protein_hrpR\",\"protein_hrpS + dna_mydna <--> complex_dna_mydna_protein_hrpS massaction: k_f(protein_hrpS,dna_mydna)=100*protein_hrpS*dna_mydna k_r(complex_dna_mydna_protein_hrpS)=50*complex_dna_mydna_protein_hrpS\",\"protein_hrpR + complex_dna_mydna_protein_hrpS <--> complex_dna_mydna_protein_hrpR_protein_hrpS massaction: k_f(protein_hrpR,complex_dna_mydna_protein_hrpS)=100*protein_hrpR*complex_dna_mydna_protein_hrpS k_r(complex_dna_mydna_protein_hrpR_protein_hrpS)=50*complex_dna_mydna_protein_hrpR_protein_hrpS\",\"protein_hrpS + complex_dna_mydna_protein_hrpR <--> complex_dna_mydna_protein_hrpR_protein_hrpS massaction: k_f(protein_hrpS,complex_dna_mydna_protein_hrpR)=100*protein_hrpS*complex_dna_mydna_protein_hrpR k_r(complex_dna_mydna_protein_hrpR_protein_hrpS)=50*complex_dna_mydna_protein_hrpR_protein_hrpS\",\"complex_dna_mydna_protein_hrpR + protein_RNAP <--> complex_dna_mydna_protein_hrpR_protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpR,protein_RNAP)=100*complex_dna_mydna_protein_hrpR*protein_RNAP k_r(complex_dna_mydna_protein_hrpR_protein_RNAP)=10*complex_dna_mydna_protein_hrpR_protein_RNAP\",\"complex_dna_mydna_protein_hrpR_protein_RNAP --> complex_dna_mydna_protein_hrpR + rna_mydna + protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpR_protein_RNAP)=0.05*complex_dna_mydna_protein_hrpR_protein_RNAP\",\"complex_dna_mydna_protein_hrpS + protein_RNAP <--> complex_dna_mydna_protein_hrpS_protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpS,protein_RNAP)=100*complex_dna_mydna_protein_hrpS*protein_RNAP k_r(complex_dna_mydna_protein_hrpS_protein_RNAP)=10*complex_dna_mydna_protein_hrpS_protein_RNAP\",\"complex_dna_mydna_protein_hrpS_protein_RNAP --> complex_dna_mydna_protein_hrpS + rna_mydna + protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpS_protein_RNAP)=0.05*complex_dna_mydna_protein_hrpS_protein_RNAP\",\"complex_dna_mydna_protein_hrpR_protein_hrpS + protein_RNAP <--> complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpR_protein_hrpS,protein_RNAP)=100*complex_dna_mydna_protein_hrpR_protein_hrpS*protein_RNAP k_r(complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP)=10*complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP\",\"complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP --> complex_dna_mydna_protein_hrpR_protein_hrpS + rna_mydna + protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP)=0.05*complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP\",\"rna_mydna + protein_Ribo <--> complex_protein_Ribo_rna_mydna massaction: k_f(rna_mydna,protein_Ribo)=100*rna_mydna*protein_Ribo k_r(complex_protein_Ribo_rna_mydna)=10.0*complex_protein_Ribo_rna_mydna\",\"complex_protein_Ribo_rna_mydna --> rna_mydna + protein_GFP + protein_Ribo massaction: k_f(complex_protein_Ribo_rna_mydna)=1.5*complex_protein_Ribo_rna_mydna\",\"rna_mydna + protein_RNAase <--> complex_protein_RNAase_rna_mydna massaction: k_f(rna_mydna,protein_RNAase)=100*rna_mydna*protein_RNAase k_r(complex_protein_RNAase_rna_mydna)=10*complex_protein_RNAase_rna_mydna\",\"complex_protein_RNAase_rna_mydna --> protein_RNAase massaction: k_f(complex_protein_RNAase_rna_mydna)=0.000555556*complex_protein_RNAase_rna_mydna\"],\"type\":[\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\"]},\"selected\":{\"id\":\"1166\"},\"selection_policy\":{\"id\":\"1167\"}},\"id\":\"1009\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1164\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1114\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1162\",\"type\":\"Selection\"},{\"attributes\":{\"line_color\":{\"value\":\"#fdae61\"},\"line_width\":{\"value\":5}},\"id\":\"1059\",\"type\":\"MultiLine\"},{\"attributes\":{\"data_source\":{\"id\":\"1027\"},\"glyph\":{\"id\":\"1026\"},\"hover_glyph\":null,\"muted_glyph\":null,\"view\":{\"id\":\"1029\"}},\"id\":\"1028\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"renderers\":[{\"id\":\"1035\"}],\"tooltips\":null},\"id\":\"1109\",\"type\":\"HoverTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0},\"fill_color\":{\"field\":\"color\"},\"line_alpha\":{\"value\":0},\"size\":{\"units\":\"screen\",\"value\":12}},\"id\":\"1049\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1165\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"end\":96.09694431560204,\"start\":-184.241540013158},\"id\":\"1078\",\"type\":\"Range1d\"},{\"attributes\":{},\"id\":\"1143\",\"type\":\"NodesOnly\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"size\":{\"units\":\"screen\",\"value\":12}},\"id\":\"1094\",\"type\":\"Circle\"},{\"attributes\":{\"overlay\":{\"id\":\"1159\"}},\"id\":\"1113\",\"type\":\"BoxSelectTool\"},{\"attributes\":{\"data\":{\"end\":[30,20,23,24,30,31,18,19,28,29,32,22,23,28,32,33,24,25,30,32,21,22,26,18,20,21,18,24,26,28,26,27,20,22,21,23,13,14,5,13,11,14,16,13,2,17,13,12,16,12,8,17,2,8,2,14,10,2,11,14,12,14,15,12,11,14,8,14,6,8,11,14,11,1,4,11,3,1,11,7,9,7],\"start\":[1,2,2,2,4,4,5,5,6,6,7,8,8,8,9,9,10,10,11,11,12,12,12,13,13,13,14,14,14,14,15,15,16,16,17,17,18,18,18,19,19,19,20,20,20,21,21,21,22,22,22,23,23,23,24,24,24,25,25,25,26,26,26,27,27,27,28,28,28,29,29,29,30,30,30,31,31,31,32,32,32,33],\"weight\":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],\"xs\":[[-4.942642958329285,-5.571948087837751,-7.619177362423808,-6.805066829718983,-2.9397787074374366,-5.571948087837751],[-42.912667181668425,-25.70504622139233,-27.35683481803103,-22.425925393415437,-25.608735794818845,-25.70504622139233],[-42.912667181668425,-36.92212162178128,-39.34915001838829,-34.61463764843533,-35.589667392033924,-36.92212162178128],[-42.912667181668425,-43.22102448416216,-40.700542302667614,-43.31085414311069,-45.69889522166027,-43.22102448416216],[3.0894514477121673,-3.503931703604464,-1.8902760771415483,-6.805066829718983,-3.55166649503836,-3.503931703604464],[3.0894514477121673,9.188459711312802,7.631526849050843,12.5197498143886,9.165167475042576,9.188459711312802],[30.79277003241157,19.25369727968878,19.55405598668026,15.827931173971795,20.578379146905025,19.25369727968878],[30.79277003241157,39.1511771961488,38.81423549396341,42.58663923851341,37.85847897613506,39.1511771961488],[-5.172597935166938,-8.288091435191152,-10.578849891458505,-8.968942098927226,-5.674365817884702,-8.288091435191152],[-5.172597935166938,-3.118699137141978,-0.8407464941400313,-2.4043658578715292,-5.735501697651178,-3.118699137141978],[-34.21960043849704,-33.20851054914762,-31.335120064619787,-31.65772711420718,-35.81752802215604,-33.20851054914762],[-20.756455984906772,-12.425506325508966,-15.030664535831551,-10.842673172635013,-10.571178153270077,-12.425506325508966],[-20.756455984906772,-32.61030664492941,-34.18439308608949,-34.61463764843533,-30.08544916327007,-32.61030664492941],[-20.756455984906772,-10.7592216442438,-9.03564299210884,-8.968942098927226,-13.332034251802206,-10.7592216442438],[-40.00747948102076,-34.809790861644174,-36.64407048358997,-31.65772711420718,-34.47071882947701,-34.809790861644174],[-40.00747948102076,-42.99316710610184,-41.063399956364414,-46.06217951791717,-43.46712275615044,-42.99316710610184],[-63.05482954977653,-46.32654372258937,-48.3106634517175,-43.31085414311069,-45.77294136523687,-46.32654372258937],[-63.05482954977653,-74.74337354691369,-72.7426256494153,-77.741780780435,-75.32180209682477,-74.74337354691369],[-18.111741884048353,-10.278500367509423,-11.409767641348935,-6.805066829718983,-10.794885109755823,-10.278500367509423],[-18.111741884048353,-28.160669525947092,-27.22875040515838,-31.65772711420718,-27.433730366312044,-28.160669525947092],[-0.03034153180732158,-7.431134348314944,-7.217725430311383,-10.831238753170846,-6.031675532817221,-7.431134348314944],[-0.03034153180732158,-9.013185785788632,-6.447994169508954,-10.842673172635013,-10.710544881726285,-9.013185785788632],[-0.03034153180732158,10.525281265105615,9.594930534594756,14.022429470966996,9.796730729981729,10.525281265105615],[-1.289279313130396,13.141921156411144,14.107683157586383,15.827931173971795,10.902028018851462,13.141921156411144],[-1.289279313130396,-18.988677711497775,-17.70215266964749,-22.425925393415437,-18.64471579821233,-18.988677711497775],[-1.289279313130396,-9.17375185850481,-10.982524100717992,-10.831238753170846,-6.578736956422074,-9.17375185850481],[-2.279250371726325,12.47518895949925,10.962453079012546,15.827931173971795,12.397523754434706,12.47518895949925],[-2.279250371726325,-39.8676926039124,-38.60233027826227,-43.31085414311069,-39.49976268716127,-39.8676926039124],[-2.279250371726325,12.558523106010098,9.94049547388013,14.022429470966996,14.482134608584126,12.558523106010098],[-2.279250371726325,-7.957162258766869,-5.323926786452475,-8.968942098927226,-10.110451569642466,-7.957162258766869],[26.43926448068255,16.445465445416417,18.824193176667038,14.022429470966996,15.216124931429185,16.445465445416417],[26.43926448068255,33.80649105173634,31.503650395096624,36.38357800981411,34.886869128940404,33.80649105173634],[-17.653612018224468,-19.78489940679811,-17.517979655657776,-22.425925393415437,-20.7990265698432,-19.78489940679811],[-17.653612018224468,-14.109626765743851,-13.987485559201106,-10.842673172635013,-15.78147451354636,-14.109626765743851],[-26.542599340112336,-14.258075242015225,-15.579344419374706,-10.831238753170846,-14.56235438398042,-14.258075242015225],[-26.542599340112336,-33.09305272024596,-30.480772721824636,-34.61463764843533,-34.983556284110264,-33.09305272024596],[15.827931173971795,1.3967307044302553,0.4309687032550147,-1.289279313130396,3.6366238419899366,1.3967307044302553],[15.827931173971795,1.0734918427462188,2.586227723232924,-2.279250371726325,1.151157047810762,1.0734918427462188],[15.827931173971795,27.367003926694586,27.0666452197031,30.79277003241157,26.04232205947834,27.367003926694586],[42.58663923851341,1.9028559171387007,1.6347031207839384,-1.289279313130396,3.6852245436076307,1.9028559171387007],[42.58663923851341,-16.99242118520085,-19.09564934269741,-18.111741884048353,-14.358233967731614,-16.99242118520085],[42.58663923851341,1.2207496164663438,2.050671281570914,-2.279250371726325,2.0510819836052545,1.2207496164663438],[-22.425925393415437,-20.294638004841794,-22.56155775598213,-17.653612018224468,-19.280510841796705,-20.294638004841794],[-22.425925393415437,-4.726526995048056,-6.013052036898341,-1.289279313130396,-5.070488908333502,-4.726526995048056],[-22.425925393415437,-39.633546353691536,-37.98175775705283,-42.912667181668425,-39.72985678026502,-39.633546353691536],[-10.831238753170846,-23.115762851267956,-21.794493673908477,-26.542599340112336,-22.81148370930276,-23.115762851267956],[-10.831238753170846,-2.946766207796431,-1.137993965583249,-1.289279313130396,-5.5417811098791665,-2.946766207796431],[-10.831238753170846,-3.430445936663223,-3.6438548546667846,-0.03034153180732158,-4.829904752160947,-3.430445936663223],[-10.842673172635013,-14.38665842511563,-14.508799631658375,-17.653612018224468,-12.714810677313121,-14.38665842511563],[-10.842673172635013,-1.859828918653703,-4.42502053493338,-0.03034153180732158,-0.16246982271604904,-1.859828918653703],[-10.842673172635013,-19.17362283203282,-16.568464621710234,-20.756455984906772,-21.02795100427171,-19.17362283203282],[-34.61463764843533,-28.0641842683017,-30.676464266723027,-26.542599340112336,-26.173680704437395,-28.0641842683017],[-34.61463764843533,-40.60518320832247,-38.17815481171546,-42.912667181668425,-41.93763743806983,-40.60518320832247],[-34.61463764843533,-22.76078698841269,-21.186700547252606,-20.756455984906772,-25.28564447007203,-22.76078698841269],[-43.31085414311069,-43.002496840616956,-45.5229790221115,-42.912667181668425,-40.52462610311884,-43.002496840616956],[-43.31085414311069,-5.722411910924609,-6.987774236574744,-2.279250371726325,-6.090341827675745,-5.722411910924609],[-43.31085414311069,-60.039139970297846,-58.055020241169714,-63.05482954977653,-60.59274232765035,-60.039139970297846],[-77.741780780435,-45.34688971576819,-47.72057077158106,-42.912667181668425,-44.12790231459846,-45.34688971576819],[-77.741780780435,-19.077195393775,-21.709186979481398,-18.111741884048353,-16.90317469031851,-19.077195393775],[-77.741780780435,-5.56121802742271,-7.208213237800813,-2.279250371726325,-5.47105140345735,-5.56121802742271],[14.022429470966996,3.466806674054059,4.397157404564918,-0.03034153180732158,4.195357209177944,3.466806674054059],[14.022429470966996,-0.8153440067694278,1.8026836253605398,-2.279250371726325,-2.7389555093434566,-0.8153440067694278],[14.022429470966996,24.016228506233126,21.637500774982506,26.43926448068255,25.24556902022036,24.016228506233126],[36.38357800981411,2.941677638275481,4.9669646990981065,-0.03034153180732158,2.3261925282188245,2.941677638275481],[36.38357800981411,-16.68762465462958,-18.633545942097026,-18.111741884048353,-14.066161544160337,-16.68762465462958],[36.38357800981411,-0.3259017745853676,2.2118267010742163,-2.279250371726325,-1.9370431371818257,-0.3259017745853676],[-8.968942098927226,-18.9661764395902,-20.68975509172516,-20.756455984906772,-16.393363832031792,-18.9661764395902],[-8.968942098927226,-3.2910302118866825,-5.924265684201076,-2.279250371726325,-1.137740901011084,-3.2910302118866825],[-8.968942098927226,-5.853448598903011,-3.5626901426356605,-5.172597935166938,-8.467174216209461,-5.853448598903011],[-2.4043658578715292,-19.59604902065719,-21.679423334959353,-20.756455984906772,-16.962226035515567,-19.59604902065719],[-2.4043658578715292,-17.87772855380111,-20.316631228796364,-18.111741884048353,-15.327819714105166,-17.87772855380111],[-2.4043658578715292,-2.287274391947266,-4.789170952332478,-2.279250371726325,0.21081590787464322,-2.287274391947266],[-6.805066829718983,-14.638308346257913,-13.507041072418403,-18.111741884048353,-14.121923604011513,-14.638308346257913],[-6.805066829718983,-6.175761700210516,-4.128532425624459,-4.942642958329285,-8.807931080610832,-6.175761700210516],[-6.805066829718983,-0.21168367840235192,-1.8253393048652677,3.0894514477121673,-0.16394888696845644,-0.21168367840235192],[12.5197498143886,-14.72333415139444,-13.293423483292354,-18.111741884048353,-14.545925499496011,-14.72333415139444],[12.5197498143886,152.9302405196078,151.68962245274597,156.37991411946888,152.53447729406548,152.9302405196078],[12.5197498143886,-2.017424354188008,0.04905006680980861,-4.942642958329285,-2.696297061156375,-2.017424354188008],[-31.65772711420718,-21.60879947230844,-22.540718593097157,-18.111741884048353,-22.33573863194349,-21.60879947230844],[-31.65772711420718,-32.6688170035566,-34.54220748808443,-34.21960043849704,-30.059799530548176,-32.6688170035566],[-31.65772711420718,-36.85541573358376,-35.021136111637965,-40.00747948102076,-37.194487765750935,-36.85541573358376],[-46.06217951791717,-36.62341537291892,-39.010656193650874,-34.21960043849704,-35.37644410964542,-36.62341537291892]],\"ys\":[[-141.43195492316931,-143.1036014837218,-141.4459033312156,-146.37918054221225,-143.20750153390307,-143.1036014837218],[26.8250862178223,20.40371937001509,18.35171905335844,19.18005005376656,23.036177379039714,20.40371937001509],[26.8250862178223,19.992992668983387,18.968959451940204,17.361354830535326,22.265365128148705,19.992992668983387],[26.8250862178223,38.835540768548064,38.069851249021276,42.33438781184292,37.941523164809084,38.835540768548064],[-149.86496953886694,-147.54215383474002,-145.46003318820456,-146.37918054221225,-150.17594051122532,-147.54215383474002],[-149.86496953886694,-151.83044855550276,-153.9553181820807,-152.90399699369698,-149.19633232054383,-151.83044855550276],[32.55511799827421,30.139945049401486,32.7569845054814,29.42291883724415,27.863032925885708,30.139945049401486],[32.55511799827421,34.18285290520235,31.570271584578425,34.851882467682195,36.47807450224215,34.18285290520235],[76.14428069155251,60.43463635068979,61.73522720202786,57.00149749918813,60.76258339669061,60.43463635068979],[76.14428069155251,85.9958898557928,84.67299809008213,89.4222184982506,85.69347420332562,85.9958898557928],[-149.60570618132508,-147.5599774969812,-149.4118735415866,-144.4222919267058,-147.19646863452886,-147.5599774969812],[37.19976305162342,20.76958559122385,20.379378509364148,17.64794512343082,22.640568727755507,20.76958559122385],[37.19976305162342,20.230615576508924,22.342809398889017,17.361354830535326,19.47947939388056,20.230615576508924],[37.19976305162342,53.994023617402775,52.0019424198987,57.00149749918813,54.5594846274938,53.994023617402775],[-140.3922770855331,-142.90094576882674,-144.7915882881992,-144.4222919267058,-140.2886400775749,-142.90094576882674],[-140.3922770855331,-138.75535448043976,-136.96228180613488,-137.07274852058956,-141.34658525158534,-138.75535448043976],[53.96466500077084,44.11079327237937,42.378055621138095,42.33438781184292,46.686183591821916,44.11079327237937],[53.96466500077084,61.00265901686882,62.71616965827157,62.808082530055444,58.43273075324113,61.00265901686882],[-144.97809028556364,-145.94876277009706,-148.32770060507394,-146.37918054221225,-143.36565269394472,-145.94876277009706],[-144.97809028556364,-144.56577789951336,-142.101911503003,-144.4222919267058,-147.09770805766027,-144.56577789951336],[0.01367303858752807,-1.7934469675513647,0.832113449057096,-2.6236818957972776,-4.025178557879905,-1.7934469675513647],[0.01367303858752807,14.664159624878687,15.26324462662143,17.64794512343082,12.649691216840885,14.664159624878687],[0.01367303858752807,-0.4126997177899751,-2.8771587630537736,-0.553959854560857,2.1187672453196256,-0.4126997177899751],[15.122781689567564,27.178960240129705,24.728161467355687,29.42291883724415,28.565318635299473,27.178960240129705],[15.122781689567564,18.520255863771172,20.81894335541434,19.18005005376656,15.9085895241034,18.520255863771172],[15.122781689567564,0.4589691052098659,2.3740288544980555,-2.6236818957972776,0.006190433546576113,0.4589691052098659],[34.84819716524268,30.427468310039664,28.270910630905448,29.42291883724415,33.060542365866226,30.427468310039664],[34.84819716524268,41.70618512561362,44.01658964698427,42.33438781184292,39.09778744812958,41.70618512561362],[34.84819716524268,2.6251875397319404,2.3335676073737392,-0.553959854560857,4.4248624144550215,2.6251875397319404],[34.84819716524268,53.65093015095514,53.57894532693062,57.00149749918813,52.133545555272974,53.65093015095514],[-13.496628475304133,-3.079607626227354,-1.947898631775193,-0.553959854560857,-5.409378595274366,-3.079607626227354],[-13.496628475304133,-20.266852976838415,-21.545929119087905,-22.635106090529064,-17.864376321833944,-20.266852976838415],[15.029871711418627,16.883317213836765,18.225027208371532,19.18005005376656,14.452132941775353,16.883317213836765],[15.029871711418627,16.392152855389142,13.760766834067123,17.64794512343082,18.427843395651173,16.392152855389142],[0.6402053798270149,-1.9117888710212778,-4.190683038924748,-2.6236818957972776,0.704797659424365,-1.9117888710212778],[0.6402053798270149,14.209406336935382,14.548676255131095,17.361354830535326,12.374983500574857,14.209406336935382],[29.42291883724415,17.36674028668201,19.81753905945603,15.122781689567564,15.980381891512241,17.36674028668201],[29.42291883724415,33.84364769244717,36.00020537158139,34.84819716524268,31.21057363662061,33.84364769244717],[29.42291883724415,31.83809178611688,29.221052330036965,32.55511799827421,34.115003909632655,31.83809178611688],[34.851882467682195,16.55814668554415,19.17868192578645,15.122781689567564,14.61848873968774,16.55814668554415],[34.851882467682195,-141.66189952308758,-140.0758534376732,-144.97809028556364,-141.67488300745535,-141.66189952308758],[34.851882467682195,34.84848465666672,37.348552835203996,34.84819716524268,32.34855285207161,34.84848465666672],[19.18005005376656,17.326604551348424,15.984894556813657,15.029871711418627,19.757788823409832,17.326604551348424],[19.18005005376656,15.782575879562952,13.483888387919782,15.122781689567564,18.394242219230726,15.782575879562952],[19.18005005376656,25.60141690157377,27.65341721823042,26.8250862178223,22.968958892549146,25.60141690157377],[-2.6236818957972776,-0.07168764494898461,2.2072065229544857,0.6402053798270149,-2.6882741753946275,-0.07168764494898461],[-2.6236818957972776,12.04013068856042,10.12507093927223,15.122781689567564,12.49290936022371,12.04013068856042],[-2.6236818957972776,-0.8165618896583853,-3.4421223062668456,0.01367303858752807,1.4151697006701547,-0.8165618896583853],[17.64794512343082,16.285663979460303,18.917050000782325,15.029871711418627,14.249973439198271,16.285663979460303],[17.64794512343082,2.9974585371396607,2.398373535396918,0.01367303858752807,5.0119269451774615,2.9974585371396607],[17.64794512343082,34.078122583830385,34.46832966569009,37.19976305162342,32.20713944729874,34.078122583830385],[17.361354830535326,3.792153873426959,3.452883955231246,0.6402053798270149,5.626576709787485,3.792153873426959],[17.361354830535326,24.19344837937424,25.21748159641742,26.8250862178223,21.92107592020892,24.19344837937424],[17.361354830535326,34.33050230564982,32.21830848326973,37.19976305162342,35.08163848827819,34.33050230564982],[42.33438781184292,30.323933261117162,31.08962278064395,26.8250862178223,31.217950864856142,30.323933261117162],[42.33438781184292,35.47639985147198,33.16599533010133,34.84819716524268,38.084797528956024,35.47639985147198],[42.33438781184292,52.1882595402344,53.92099719147567,53.96466500077084,49.61286922079184,52.1882595402344],[62.808082530055444,29.339954137710123,28.197697987444442,26.8250862178223,31.67515875044411,29.339954137710123],[62.808082530055444,-141.61388168314963,-141.50557120205312,-144.97809028556364,-140.12635190244362,-141.61388168314963],[62.808082530055444,36.0642104492831,34.00836083305719,34.84819716524268,38.6968860554806,36.0642104492831],[-0.553959854560857,-0.12758709818335354,2.3368719470804455,0.01367303858752807,-2.6590540612929545,-0.12758709818335354],[-0.553959854560857,31.669049770949883,31.960669703308085,34.84819716524268,29.8693748962268,31.669049770949883],[-0.553959854560857,-10.970980703637636,-12.102689698089797,-13.496628475304133,-8.641209734590625,-10.970980703637636],[-22.635106090529064,-1.8348674810279688,-0.1504319112274803,0.01367303858752807,-4.396173582774341,-1.8348674810279688],[-22.635106090529064,-141.78092120700796,-140.0053927756598,-144.97809028556364,-142.03984596054374,-141.78092120700796],[-22.635106090529064,31.94398827846345,32.65041948627574,34.84819716524268,29.859921490360087,31.94398827846345],[57.00149749918813,40.20723693340878,42.19931813091286,37.19976305162342,39.64177592331775,40.20723693340878],[57.00149749918813,38.19876451347567,38.27074933750019,34.84819716524268,39.71614910915784,38.19876451347567],[57.00149749918813,72.71114184005084,71.41055098871279,76.14428069155251,72.38319479405003,72.71114184005084],[89.4222184982506,40.50180116123407,42.11383786492622,37.19976305162342,40.45611363028396,40.50180116123407],[89.4222184982506,-141.4859222252798,-140.4905004004007,-144.97809028556364,-140.82480515789678,-141.4859222252798],[89.4222184982506,38.348187967387666,39.17258136175548,34.84819716524268,39.184044247785394,38.348187967387666],[-146.37918054221225,-145.40850805767883,-143.02957022270195,-144.97809028556364,-147.99161813383117,-145.40850805767883],[-146.37918054221225,-144.70753398165976,-146.36523213416598,-141.43195492316931,-144.6036339314785,-144.70753398165976],[-146.37918054221225,-148.70199624633918,-150.78411689287464,-149.86496953886694,-146.06820956985388,-148.70199624633918],[-152.90399699369698,-145.8548416969062,-143.6424976122773,-144.97809028556364,-148.48308008749717,-145.8548416969062],[-152.90399699369698,-176.9754158068829,-179.29920101204294,-177.56681419580656,-174.37109586938425,-176.9754158068829],[-152.90399699369698,-143.35369791274564,-141.72005341809376,-141.43195492316931,-145.8989371382956,-143.35369791274564],[-144.4222919267058,-144.83460431275608,-147.29847070926644,-144.97809028556364,-142.30267415460918,-144.83460431275608],[-144.4222919267058,-146.4680206110497,-144.61612456644428,-149.60570618132508,-146.83152947350206,-146.4680206110497],[-144.4222919267058,-141.91362324341216,-140.0229807240397,-140.3922770855331,-144.525928934664,-141.91362324341216],[-137.07274852058956,-147.06175772252126,-148.17539686128018,-149.60570618132508,-144.74137552639178,-147.06175772252126]]},\"selected\":{\"id\":\"1160\"},\"selection_policy\":{\"id\":\"1161\"}},\"id\":\"1041\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1168\",\"type\":\"Selection\"},{\"attributes\":{\"fill_color\":{\"value\":\"#abdda4\"},\"size\":{\"units\":\"screen\",\"value\":8}},\"id\":\"1089\",\"type\":\"Square\"},{\"attributes\":{\"fill_color\":{\"value\":\"#2b83ba\"},\"size\":{\"units\":\"screen\",\"value\":8}},\"id\":\"1079\",\"type\":\"Square\"},{\"attributes\":{},\"id\":\"1167\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"edge_renderer\":{\"id\":\"1014\"},\"inspection_policy\":{\"id\":\"1144\"},\"layout_provider\":{\"id\":\"1020\"},\"node_renderer\":{\"id\":\"1010\"},\"selection_policy\":{\"id\":\"1143\"}},\"id\":\"1007\",\"type\":\"GraphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"1041\"},\"glyph\":{\"id\":\"1054\"},\"hover_glyph\":{\"id\":\"1064\"},\"muted_glyph\":null,\"selection_glyph\":{\"id\":\"1059\"},\"view\":{\"id\":\"1043\"}},\"id\":\"1042\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"1013\"}},\"id\":\"1015\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1170\",\"type\":\"Selection\"},{\"attributes\":{\"fill_color\":{\"value\":\"#fdae61\"},\"size\":{\"units\":\"screen\",\"value\":8}},\"id\":\"1084\",\"type\":\"Square\"},{\"attributes\":{},\"id\":\"1161\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1153\",\"type\":\"NodesOnly\"},{\"attributes\":{},\"id\":\"1169\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data_source\":{\"id\":\"1013\"},\"glyph\":{\"id\":\"1012\"},\"hover_glyph\":null,\"muted_glyph\":null,\"view\":{\"id\":\"1015\"}},\"id\":\"1014\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_color\":{\"value\":\"#abdda4\"},\"line_width\":{\"value\":5}},\"id\":\"1064\",\"type\":\"MultiLine\"},{\"attributes\":{\"line_alpha\":{\"value\":0.2},\"line_width\":{\"value\":4}},\"id\":\"1054\",\"type\":\"MultiLine\"},{\"attributes\":{\"data\":{\"color\":[\"purple\",\"green\",\"cyan\",\"green\",\"cyan\",\"cyan\",\"cyan\",\"green\",\"cyan\",\"cyan\",\"cyan\",\"orange\",\"cyan\",\"grey\",\"green\",\"cyan\",\"green\",\"green\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\",\"blue\"],\"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],\"k\":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,100,0.01,100,100,100,100,100,0.05,100,0.05,100,0.05,100,1.5,100,0.000555556],\"k_r\":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,100,0,50,50,50,50,10,0,10,0,10,0,10.0,0,10,0],\"species\":[\"nothing\",\"protein_Ribo\",\"complex_dna_mydna_protein_hrpR\",\"protein_GFP\",\"complex_protein_Ribo_rna_mydna\",\"complex_dna_mydna_protein_RNAP\",\"complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP\",\"protein_RNAase\",\"complex_dna_mydna_protein_hrpR_protein_hrpS\",\"complex_protein_RNAase_rna_mydna\",\"complex_dna_mydna_protein_hrpR_protein_RNAP\",\"rna_mydna\",\"complex_dna_mydna_protein_hrpS\",\"dna_mydna\",\"protein_RNAP\",\"complex_dna_mydna_protein_hrpS_protein_RNAP\",\"protein_hrpR\",\"protein_hrpS\",\"dna_mydna + protein_RNAP <--> complex_dna_mydna_protein_RNAP massaction: k_f(dna_mydna,protein_RNAP)=100*dna_mydna*protein_RNAP k_r(complex_dna_mydna_protein_RNAP)=100*complex_dna_mydna_protein_RNAP\",\"complex_dna_mydna_protein_RNAP --> dna_mydna + rna_mydna + protein_RNAP massaction: k_f(complex_dna_mydna_protein_RNAP)=0.01*complex_dna_mydna_protein_RNAP\",\"protein_hrpR + dna_mydna <--> complex_dna_mydna_protein_hrpR massaction: k_f(protein_hrpR,dna_mydna)=100*protein_hrpR*dna_mydna k_r(complex_dna_mydna_protein_hrpR)=50*complex_dna_mydna_protein_hrpR\",\"protein_hrpS + dna_mydna <--> complex_dna_mydna_protein_hrpS massaction: k_f(protein_hrpS,dna_mydna)=100*protein_hrpS*dna_mydna k_r(complex_dna_mydna_protein_hrpS)=50*complex_dna_mydna_protein_hrpS\",\"protein_hrpR + complex_dna_mydna_protein_hrpS <--> complex_dna_mydna_protein_hrpR_protein_hrpS massaction: k_f(protein_hrpR,complex_dna_mydna_protein_hrpS)=100*protein_hrpR*complex_dna_mydna_protein_hrpS k_r(complex_dna_mydna_protein_hrpR_protein_hrpS)=50*complex_dna_mydna_protein_hrpR_protein_hrpS\",\"protein_hrpS + complex_dna_mydna_protein_hrpR <--> complex_dna_mydna_protein_hrpR_protein_hrpS massaction: k_f(protein_hrpS,complex_dna_mydna_protein_hrpR)=100*protein_hrpS*complex_dna_mydna_protein_hrpR k_r(complex_dna_mydna_protein_hrpR_protein_hrpS)=50*complex_dna_mydna_protein_hrpR_protein_hrpS\",\"complex_dna_mydna_protein_hrpR + protein_RNAP <--> complex_dna_mydna_protein_hrpR_protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpR,protein_RNAP)=100*complex_dna_mydna_protein_hrpR*protein_RNAP k_r(complex_dna_mydna_protein_hrpR_protein_RNAP)=10*complex_dna_mydna_protein_hrpR_protein_RNAP\",\"complex_dna_mydna_protein_hrpR_protein_RNAP --> complex_dna_mydna_protein_hrpR + rna_mydna + protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpR_protein_RNAP)=0.05*complex_dna_mydna_protein_hrpR_protein_RNAP\",\"complex_dna_mydna_protein_hrpS + protein_RNAP <--> complex_dna_mydna_protein_hrpS_protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpS,protein_RNAP)=100*complex_dna_mydna_protein_hrpS*protein_RNAP k_r(complex_dna_mydna_protein_hrpS_protein_RNAP)=10*complex_dna_mydna_protein_hrpS_protein_RNAP\",\"complex_dna_mydna_protein_hrpS_protein_RNAP --> complex_dna_mydna_protein_hrpS + rna_mydna + protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpS_protein_RNAP)=0.05*complex_dna_mydna_protein_hrpS_protein_RNAP\",\"complex_dna_mydna_protein_hrpR_protein_hrpS + protein_RNAP <--> complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpR_protein_hrpS,protein_RNAP)=100*complex_dna_mydna_protein_hrpR_protein_hrpS*protein_RNAP k_r(complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP)=10*complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP\",\"complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP --> complex_dna_mydna_protein_hrpR_protein_hrpS + rna_mydna + protein_RNAP massaction: k_f(complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP)=0.05*complex_dna_mydna_protein_hrpR_protein_hrpS_protein_RNAP\",\"rna_mydna + protein_Ribo <--> complex_protein_Ribo_rna_mydna massaction: k_f(rna_mydna,protein_Ribo)=100*rna_mydna*protein_Ribo k_r(complex_protein_Ribo_rna_mydna)=10.0*complex_protein_Ribo_rna_mydna\",\"complex_protein_Ribo_rna_mydna --> rna_mydna + protein_GFP + protein_Ribo massaction: k_f(complex_protein_Ribo_rna_mydna)=1.5*complex_protein_Ribo_rna_mydna\",\"rna_mydna + protein_RNAase <--> complex_protein_RNAase_rna_mydna massaction: k_f(rna_mydna,protein_RNAase)=100*rna_mydna*protein_RNAase k_r(complex_protein_RNAase_rna_mydna)=10*complex_protein_RNAase_rna_mydna\",\"complex_protein_RNAase_rna_mydna --> protein_RNAase massaction: k_f(complex_protein_RNAase_rna_mydna)=0.000555556*complex_protein_RNAase_rna_mydna\"],\"type\":[\"nothing\",\"protein\",\"complex\",\"protein\",\"complex\",\"complex\",\"complex\",\"protein\",\"complex\",\"complex\",\"complex\",\"rna\",\"complex\",\"dna\",\"protein\",\"complex\",\"protein\",\"protein\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\",\"massaction\"]},\"selected\":{\"id\":\"1162\"},\"selection_policy\":{\"id\":\"1163\"}},\"id\":\"1037\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"attachment\":\"right\",\"callback\":null,\"renderers\":[{\"id\":\"1021\"}],\"tooltips\":[[\"name\",\"@species\"],[\"type\",\"@type\"]]},\"id\":\"1110\",\"type\":\"HoverTool\"},{\"attributes\":{\"fill_color\":{\"value\":\"#fdae61\"},\"size\":{\"units\":\"screen\",\"value\":15}},\"id\":\"1099\",\"type\":\"Circle\"},{\"attributes\":{\"graph_layout\":{\"0\":[-128.06534390538394,188.8110737063345],\"1\":[-4.942642958329285,-141.43195492316931],\"10\":[-63.05482954977653,53.96466500077084],\"11\":[-18.111741884048353,-144.97809028556364],\"12\":[-0.03034153180732158,0.01367303858752807],\"13\":[-1.289279313130396,15.122781689567564],\"14\":[-2.279250371726325,34.84819716524268],\"15\":[26.43926448068255,-13.496628475304133],\"16\":[-17.653612018224468,15.029871711418627],\"17\":[-26.542599340112336,0.6402053798270149],\"18\":[15.827931173971795,29.42291883724415],\"19\":[42.58663923851341,34.851882467682195],\"2\":[-42.912667181668425,26.8250862178223],\"20\":[-22.425925393415437,19.18005005376656],\"21\":[-10.831238753170846,-2.6236818957972776],\"22\":[-10.842673172635013,17.64794512343082],\"23\":[-34.61463764843533,17.361354830535326],\"24\":[-43.31085414311069,42.33438781184292],\"25\":[-77.741780780435,62.808082530055444],\"26\":[14.022429470966996,-0.553959854560857],\"27\":[36.38357800981411,-22.635106090529064],\"28\":[-8.968942098927226,57.00149749918813],\"29\":[-2.4043658578715292,89.4222184982506],\"3\":[156.37991411946888,-177.56681419580656],\"30\":[-6.805066829718983,-146.37918054221225],\"31\":[12.5197498143886,-152.90399699369698],\"32\":[-31.65772711420718,-144.4222919267058],\"33\":[-46.06217951791717,-137.07274852058956],\"4\":[3.0894514477121673,-149.86496953886694],\"5\":[30.79277003241157,32.55511799827421],\"6\":[-5.172597935166938,76.14428069155251],\"7\":[-34.21960043849704,-149.60570618132508],\"8\":[-20.756455984906772,37.19976305162342],\"9\":[-40.00747948102076,-140.3922770855331]}},\"id\":\"1034\",\"type\":\"StaticLayoutProvider\"},{\"attributes\":{\"data\":{\"end\":[],\"start\":[]},\"selected\":{\"id\":\"1168\"},\"selection_policy\":{\"id\":\"1169\"}},\"id\":\"1027\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1171\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1125\",\"type\":\"EdgesAndLinkedNodes\"},{\"attributes\":{},\"id\":\"1144\",\"type\":\"NodesOnly\"},{\"attributes\":{},\"id\":\"1026\",\"type\":\"MultiLine\"},{\"attributes\":{\"data_source\":{\"id\":\"1037\"},\"glyph\":{\"id\":\"1049\"},\"hover_glyph\":null,\"muted_glyph\":null,\"view\":{\"id\":\"1039\"}},\"id\":\"1038\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null},\"id\":\"1112\",\"type\":\"TapTool\"},{\"attributes\":{},\"id\":\"1012\",\"type\":\"MultiLine\"},{\"attributes\":{\"source\":{\"id\":\"1041\"}},\"id\":\"1043\",\"type\":\"CDSView\"},{\"attributes\":{\"fill_color\":{\"value\":\"#abdda4\"},\"size\":{\"units\":\"screen\",\"value\":15}},\"id\":\"1104\",\"type\":\"Circle\"},{\"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,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1159\",\"type\":\"BoxAnnotation\"},{\"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\":{\"attachment\":\"right\",\"callback\":null,\"renderers\":[{\"id\":\"1007\"}],\"tooltips\":[[\"reaction\",\"@species\"],[\"type\",\"@type\"],[\"k_f\",\"@k\"],[\"k_r\",\"@k_r\"]]},\"id\":\"1111\",\"type\":\"HoverTool\"},{\"attributes\":{},\"id\":\"1115\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"source\":{\"id\":\"1023\"}},\"id\":\"1025\",\"type\":\"CDSView\"},{\"attributes\":{\"data\":{\"end\":[],\"start\":[]},\"selected\":{\"id\":\"1164\"},\"selection_policy\":{\"id\":\"1165\"}},\"id\":\"1013\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"1004\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.2\"}};\n", - " var render_items = [{\"docid\":\"9928c12b-e691-4cdb-9e7f-98f5884c7443\",\"root_ids\":[\"1004\"],\"roots\":{\"1004\":\"dd2c33e0-0319-4473-a862-2301ac5db20f\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", + " const docs_json = {\"75caebd3-1d0e-4069-b4a1-dcc80f75d9af\":{\"version\":\"3.4.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Plot\",\"id\":\"p1270\",\"attributes\":{\"width\":500,\"height\":500,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1335\",\"attributes\":{\"start\":-160.77569608533918,\"end\":204.37319836150976}},\"y_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1336\",\"attributes\":{\"start\":-194.4971030515651,\"end\":170.65179139528385}},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1273\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1274\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1275\"},\"renderers\":[{\"type\":\"object\",\"name\":\"GraphRenderer\",\"id\":\"p1313\",\"attributes\":{\"layout_provider\":{\"type\":\"object\",\"name\":\"StaticLayoutProvider\",\"id\":\"p1330\",\"attributes\":{\"graph_layout\":{\"type\":\"map\",\"entries\":[[0,[100.02473419237913,-173.80308175521154]],[1,[-10.050121930825828,31.615445403933528]],[2,[-7.852400352279124,50.11316908646652]],[3,[-5.3761222612422355,61.32450555222612]],[4,[6.614014612676804,32.22807892511701]],[5,[4.525368568092745,-100.93242929306315]],[6,[-0.4269654237614162,0.1546158049960011]],[7,[-25.861496143302297,57.121836060461]],[8,[-44.47435784202109,37.02317540032585]],[9,[17.04430990997499,43.578692435679386]],[10,[39.64795450620208,19.593547121459324]],[11,[9.158421798360486,71.49622513631088]],[12,[45.436063822589055,85.38178867135393]],[13,[-10.359081730108612,-89.75905998765316]],[14,[75.8705853194636,149.95777009893027]],[15,[-13.017491573866458,-109.88143397319499]],[16,[10.330695600011712,-121.22280863452887]],[17,[26.191914218620354,-109.21028767515988]],[18,[-3.463135170524645,-134.40615279600468]],[19,[-2.409859689072089,14.850853970035446]],[20,[-0.819570506329618,-7.929479836601891]],[21,[-17.53528988985007,44.72025908658326]],[22,[1.5847948543460977,44.89241832481749]],[23,[6.287217924183484,55.618535813157635]],[24,[-10.468331170708783,70.48388414887809]],[25,[-28.539221875354137,40.81374153293126]],[26,[-56.42723191620853,34.52168587387494]],[27,[28.12132123667774,27.379049185158294]],[28,[49.203642341295485,13.481414069572589]],[29,[28.436643030574864,70.13859888336472]],[30,[56.46852400100325,95.343880620008]],[31,[-7.063516451206003,-95.6126894927141]],[32,[-27.352690290235362,-110.7828248799392]],[33,[15.566258886479066,-110.47624459258856]],[34,[33.32791630795836,-107.87126822075649]],[35,[-2.1688831008386487,-123.06787013166935]],[36,[-4.223889690255938,-143.38528382746344]]]}}},\"node_renderer\":{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1318\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1315\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1316\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1317\"},\"data\":{\"type\":\"map\",\"entries\":[[\"species\",[\"nothing\",\"dna_mydna\",\"protein_hrpR\",\"protein_hrpS\",\"protein_RNAP\",\"rna_mydna\",\"complex_dna_mydna_protein_RNAP_\",\"complex_dna_mydna_protein_hrpR_\",\"complex_complex_dna_mydna_protein_hrpR__protein_RNAP_\",\"complex_dna_mydna_protein_hrpS_\",\"complex_complex_dna_mydna_protein_hrpS__protein_RNAP_\",\"complex_dna_mydna_protein_hrpR_protein_hrpS_\",\"complex_complex_dna_mydna_protein_hrpR_protein_hrpS__protein_RNAP_\",\"protein_Ribo\",\"protein_GFP\",\"complex_protein_Ribo_rna_mydna_\",\"protein_RNAase\",\"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]\",\"protein[hrpR]+dna[mydna] <--> complex[dna[mydna]:protein[hrpR]]\",\"protein[hrpS]+dna[mydna] <--> complex[dna[mydna]:protein[hrpS]]\",\"protein[hrpR]+complex[dna[mydna]:protein[hrpS]] <--> complex[dna[mydna]:protein[hrpR]:protein[hrpS]]\",\"protein[hrpS]+complex[dna[mydna]:protein[hrpR]] <--> complex[dna[mydna]:protein[hrpR]:protein[hrpS]]\",\"complex[dna[mydna]:protein[hrpR]]+protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]]\",\"complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpR]]+rna[mydna]+protein[RNAP]\",\"complex[dna[mydna]:protein[hrpS]]+protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]]\",\"complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpS]]+rna[mydna]+protein[RNAP]\",\"complex[dna[mydna]:protein[hrpR]:protein[hrpS]]+protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]]\",\"complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpR]:protein[hrpS]]+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]\"]],[\"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,null,null,null,null,null,null,null,null,null,null,null]],[\"type\",[\"nothing\",\"dna\",\"protein\",\"protein\",\"protein\",\"rna\",\"complex\",\"complex\",\"complex\",\"complex\",\"complex\",\"complex\",\"complex\",\"protein\",\"protein\",\"complex\",\"protein\",\"complex\",\"complex\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\"]],[\"color\",[\"purple\",\"grey\",\"green\",\"green\",\"green\",\"orange\",\"cyan\",\"cyan\",\"cyan\",\"cyan\",\"cyan\",\"cyan\",\"cyan\",\"green\",\"green\",\"cyan\",\"green\",\"cyan\",\"cyan\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\"]],[\"k\",[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,\"100\",\"0.01\",\"100\",\"100\",\"100\",\"100\",\"100\",\"0.05\",\"100\",\"0.05\",\"100\",\"0.05\",\"100\",\"1.5\",\"100\",\"0.000555556\",\"100\",\"0.000555556\"]],[\"k_r\",[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,\"100\",\"None\",\"50\",\"50\",\"50\",\"50\",\"10\",\"None\",\"10\",\"None\",\"10\",\"None\",\"10.0\",\"None\",\"10\",\"None\",\"10\",\"None\"]],[\"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]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1319\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1320\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1331\",\"attributes\":{\"size\":{\"type\":\"value\",\"value\":12},\"line_alpha\":{\"type\":\"value\",\"value\":0},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0}}}}},\"edge_renderer\":{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1325\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1322\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1323\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1324\"},\"data\":{\"type\":\"map\",\"entries\":[[\"weight\",[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]],[\"color\",[\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\",\"gray\"]],[\"start\",[1,1,1,2,2,3,3,4,4,4,4,5,5,6,6,7,7,7,8,8,9,9,9,10,10,11,11,11,12,12,13,15,15,15,16,16,17,17,18,18,19,19,19,20,20,20,21,21,21,22,22,22,23,23,23,24,24,24,25,25,25,26,26,26,27,27,27,28,28,28,29,29,29,30,30,30,31,31,31,32,32,32,33,33,33,34,35,35,35,36,36]],[\"end\",[19,21,22,21,23,22,24,19,25,27,29,31,33,19,20,21,24,25,25,26,22,23,27,27,28,23,24,29,29,30,31,31,32,35,33,35,33,34,35,36,1,4,6,1,5,4,2,1,7,3,1,9,2,9,11,3,7,11,7,4,8,7,5,4,9,4,10,9,5,4,11,4,12,11,5,4,5,13,15,5,14,13,5,16,17,16,15,16,18,13,16]],[\"xs\",[[-10.050121930825828,-3.861317147211354,-6.480465641422178,-2.409859689072089,-1.930679540324834,-3.861317147211354],[-10.050121930825828,-15.799380041674453,-13.216815338877957,-17.53528988985007,-17.55850150596598,-15.799380041674453],[-10.050121930825828,-0.7219430338788946,0.6111552935099334,1.5847948543460977,-3.149261616460344,-0.7219430338788946],[-7.852400352279124,-14.47755459380427,-14.968761729401734,-17.53528988985007,-12.535885351944135,-14.47755459380427],[-7.852400352279124,3.0257181551716394,3.1592223224284814,6.287217924183484,1.3450945133632404,3.0257181551716394],[-5.3761222612422355,0.21957780365646573,-2.4061946850688622,1.5847948543460977,2.1977482578270884,0.21957780365646573],[-5.3761222612422355,-8.767645585475348,-6.179256531417027,-10.468331170708783,-10.549300322394368,-8.767645585475348],[6.614014612676804,-0.7968535666662251,-2.6329657129897424,-2.409859689072089,1.8044014161693065,-0.7968535666662251],[6.614014612676804,-25.13916142448665,-23.739584472252606,-28.539221875354137,-24.92589149315848,-25.13916142448665],[6.614014612676804,24.707023510281203,23.347375885712935,28.12132123667774,24.447070681520117,24.707023510281203],[6.614014612676804,26.690546349655584,28.443079445394005,28.436643030574864,24.109737806784903,26.690546349655584],[4.525368568092745,-3.882639223522167,-2.0852415791432977,-7.063516451206003,-4.17116136471551,-3.882639223522167],[4.525368568092745,12.91838712399735,10.655483533787024,15.566258886479066,13.92525077553499,12.91838712399735],[-0.4269654237614162,-1.9418618560879302,0.6466874902145499,-2.409859689072089,-4.308412547557732,-1.9418618560879302],[-0.4269654237614162,-0.6497926816281449,-3.106581913005142,-0.819570506329618,1.887532069462298,-0.6497926816281449],[-25.861496143302297,-19.486217997049962,-22.024534196393823,-17.53528988985007,-17.873340731284024,-19.486217997049962],[-25.861496143302297,-13.111431596144858,-12.099501865796885,-10.468331170708783,-15.377137941852386,-13.111431596144858],[-25.861496143302297,-27.97212915117445,-30.304592602721915,-28.539221875354137,-25.370660561012272,-27.97212915117445],[-44.47435784202109,-31.94421273219142,-32.17326179654976,-28.539221875354137,-33.330349330372314,-31.94421273219142],[-44.47435784202109,-53.00144893505751,-52.70102863185156,-56.42723191620853,-51.67682065905358,-53.00144893505751],[17.04430990997499,5.072225544244394,6.111054608905236,1.5847948543460977,5.687688160899574,5.072225544244394],[17.04430990997499,8.619145301480557,11.036517577378609,6.287217924183484,7.307942123901748,8.619145301480557],[17.04430990997499,26.14577250699673,23.613532560736843,28.12132123667774,27.740894523139122,26.14577250699673],[39.64795450620208,31.021705180131963,33.10891825652313,28.12132123667774,30.310313290431907,31.021705180131963],[39.64795450620208,46.25520121210295,44.208806616860464,49.203642341295485,46.90297829589163,46.25520121210295],[9.158421798360486,6.910030820675417,4.597648663473814,6.287217924183484,9.517849442511961,6.910030820675417],[9.158421798360486,-6.972977706270603,-6.272730675407066,-10.468331170708783,-6.015174821983611,-6.972977706270603],[9.158421798360486,24.945289780005268,23.941591546978252,28.436643030574864,24.2928356325182,24.945289780005268],[45.436063822589055,31.042458561415273,29.991499717044412,28.436643030574864,33.32950762205911,31.042458561415273],[45.436063822589055,53.870847282707565,54.93019708256001,56.46852400100325,51.57926797983661,53.870847282707565],[-10.359081730108612,-8.780577941144633,-11.366306853823513,-7.063516451206003,-7.0093513918651,-8.780577941144633],[-13.017491573866458,-8.411341465532214,-6.423822880630904,-7.063516451206003,-11.038212028245741,-8.411341465532214],[-13.017491573866458,-23.859589056627932,-23.18798731613859,-27.352690290235362,-22.874209246347004,-23.859589056627932],[-13.017491573866458,-4.392537968739177,-6.850544513473696,-2.1688831008386487,-2.989340559205851,-4.392537968739177],[10.330695600011712,14.033352245187896,15.917248672291473,15.566258886479066,11.422308834986566,14.033352245187896],[10.330695600011712,1.293598709965122,1.7497582139741614,-2.1688831008386487,2.4798961794845695,1.293598709965122],[26.191914218620354,19.04167951623612,19.57021470772622,15.566258886479066,20.161738934392254,19.04167951623612],[26.191914218620354,29.88795266698134,29.533125327358928,33.32791630795836,28.611004713755193,29.88795266698134],[-3.463135170524645,-2.5658264112469538,-0.17610308119214624,-2.1688831008386487,-5.143843093844819,-2.5658264112469538],[-3.463135170524645,-3.9284117245541497,-6.349405680957115,-4.223889690255938,-1.3672553436796306,-3.9284117245541497],[-2.409859689072089,-8.598664472686563,-5.979515978475739,-10.050121930825828,-10.529302079573082,-8.598664472686563],[-2.409859689072089,5.001008490270939,6.837120636594455,6.614014612676804,2.399753507435408,5.001008490270939],[-2.409859689072089,-0.8949632567455754,-3.483512603048056,-0.4269654237614162,1.4715874347242273,-0.8949632567455754],[-0.819570506329618,-9.254540183633546,-6.631288115412355,-10.050121930825828,-11.50040144950866,-9.254540183633546],[-0.819570506329618,4.324552687755222,1.7810417423487888,4.525368568092745,6.772804954249185,4.324552687755222],[-0.819570506329618,5.976950767833309,8.284090392626803,6.614014612676804,3.3676146228209354,5.976950767833309],[-17.53528988985007,-10.910135648324925,-10.418928512727458,-7.852400352279124,-12.851804890185061,-10.910135648324925],[-17.53528988985007,-11.786031779001444,-14.36859648179794,-10.050121930825828,-10.02691031470992,-11.786031779001444],[-17.53528988985007,-23.910568036102404,-21.372251836758544,-25.861496143302297,-25.523445301868342,-23.910568036102404],[1.5847948543460977,-4.010905210552603,-1.3851327218272753,-5.3761222612422355,-5.9890756647232255,-4.010905210552603],[1.5847948543460977,-7.743384042600836,-9.076482369989664,-10.050121930825828,-5.316065460019386,-7.743384042600836],[1.5847948543460977,13.556879220076695,12.518050155415853,17.04430990997499,12.941416603421516,13.556879220076695],[6.287217924183484,-4.59090058326728,-4.724404750524123,-7.852400352279124,-2.910276941458882,-4.59090058326728],[6.287217924183484,14.712382532677916,12.295010256779866,17.04430990997499,16.023585710256725,14.712382532677916],[6.287217924183484,8.535608901868551,10.847991059070157,9.158421798360486,5.927790280032006,8.535608901868551],[-10.468331170708783,-7.0768078464756705,-9.665196900533992,-5.3761222612422355,-5.295153109556652,-7.0768078464756705],[-10.468331170708783,-23.218395717866223,-24.230325448214195,-25.861496143302297,-20.952689372158694,-23.218395717866223],[-10.468331170708783,5.663068333922306,4.962821303058768,9.158421798360486,4.705265449635314,5.663068333922306],[-28.539221875354137,-26.428588867481984,-24.09612541593452,-25.861496143302297,-29.030057457644165,-26.428588867481984],[-28.539221875354137,3.213954161809317,1.8143772095752713,6.614014612676804,3.000684230481147,3.213954161809317],[-28.539221875354137,-41.06936698518381,-40.84031792082547,-44.47435784202109,-39.683230387002915,-41.06936698518381],[-56.42723191620853,-28.67575679208045,-27.85691925942266,-25.861496143302297,-30.829562212075516,-28.67575679208045],[-56.42723191620853,3.0891265154471395,0.468665055133779,4.525368568092745,5.028294642267135,3.0891265154471395],[-56.42723191620853,3.116328784289598,2.195854179529152,6.614014612676804,2.3776470977097723,3.116328784289598],[28.12132123667774,19.019858639656,21.552098585915893,17.04430990997499,17.424736623513613,19.019858639656],[28.12132123667774,10.02831233907334,11.38795996364161,6.614014612676804,10.288265167834428,10.02831233907334],[28.12132123667774,36.74757056274786,34.66035748635669,39.64795450620208,37.45896245244791,36.74757056274786],[49.203642341295485,19.599754688460173,21.914131364782527,17.04430990997499,18.497574444128784,19.599754688460173],[49.203642341295485,5.798483617338775,3.771695655714616,4.525368568092745,8.429184265006127,5.798483617338775],[49.203642341295485,9.817416765500305,11.584368855709378,6.614014612676804,9.570025063595406,9.817416765500305],[28.436643030574864,12.64977504893008,13.653473281957098,9.158421798360486,13.302229196417146,12.64977504893008],[28.436643030574864,8.36011129359608,6.607578197857659,6.614014612676804,10.940919836466763,8.36011129359608],[28.436643030574864,42.830248291748646,43.88120713611951,45.436063822589055,40.54319923110481,42.830248291748646],[56.46852400100325,12.283810903307863,11.899789215680926,9.158421798360486,14.150386842741586,12.283810903307863],[56.46852400100325,5.420793759493189,3.2163692960244665,4.525368568092745,8.04997059142196,5.420793759493189],[56.46852400100325,8.783473111805044,7.33621332583124,6.614014612676804,11.259833535811273,8.783473111805044],[-7.063516451206003,1.3444913404089087,-0.4529063039699608,4.525368568092745,1.6330134816022506,1.3444913404089087],[-7.063516451206003,-8.642020240169982,-6.0562913274911025,-10.359081730108612,-10.413246789449516,-8.642020240169982],[-7.063516451206003,-11.669666559540246,-13.65718514444156,-13.017491573866458,-9.042795996826719,-11.669666559540246],[-27.352690290235362,1.1813757978231072,1.1263230660570023,4.525368568092745,-0.3498221845143892,1.1813757978231072],[-27.352690290235362,74.58227054100642,76.60118461560653,75.8705853194636,71.95223366270683,74.58227054100642],[-27.352690290235362,-12.559270306754586,-11.136838372833797,-10.359081730108612,-15.025379945931254,-12.559270306754586],[15.566258886479066,7.173240330574461,9.436143920784787,4.525368568092745,6.166376679036821,7.173240330574461],[15.566258886479066,11.86360224130288,9.979705814199304,10.330695600011712,14.474645651504211,11.86360224130288],[15.566258886479066,22.7164935888633,22.1879583973732,26.191914218620354,21.59643417070717,22.7164935888633],[33.32791630795836,13.357553185523894,12.82024033387976,10.330695600011712,15.330681045261318,13.357553185523894],[-2.1688831008386487,-10.79383670596593,-8.335830161231412,-13.017491573866458,-12.197034115499257,-10.79383670596593],[-2.1688831008386487,6.868213789207941,6.412054285198901,10.330695600011712,5.681916319688494,6.868213789207941],[-2.1688831008386487,-3.0661918601163394,-5.4559151901711465,-3.463135170524645,-0.48817517751847284,-3.0661918601163394],[-4.223889690255938,-9.961253847182883,-7.383099567373842,-10.359081730108612,-12.350695170136346,-9.961253847182883],[-4.223889690255938,8.409432952969157,10.043416737683962,10.330695600011712,5.864082291822353,8.409432952969157]]],[\"ys\",[[31.615445403933528,18.03570424080359,17.75432898812877,14.850853970035446,19.827839642613434,18.03570424080359],[31.615445403933528,41.68107876962164,42.20018417646527,44.72025908658326,39.72031296478582,41.68107876962164],[31.615445403933528,42.2601264878383,39.988131831801944,44.89241832481749,43.283471672123355,42.2601264878383],[50.11316908646652,46.42327255080358,49.01128847376132,44.72025908658326,44.64309519369589,46.42327255080358],[50.11316908646652,54.34864634681197,51.71781235264401,55.618535813157635,56.377097736946645,54.34864634681197],[61.32450555222612,48.115178384844654,47.90439483473259,44.89241832481749,49.854704907146356,48.115178384844654],[61.32450555222612,67.42485349519396,67.91409062826514,70.48388414887809,65.48453979221738,67.42485349519396],[32.22807892511701,17.95701096044678,19.84587386009511,14.850853970035446,17.541579399515307,17.95701096044678],[32.22807892511701,39.98332661829715,42.214984123901424,40.81374153293126,37.35775490837644,39.98332661829715],[32.22807892511701,28.148835542223324,25.892628724453616,27.379049185158294,30.770196905020093,28.148835542223324],[32.22807892511701,67.10525973633835,65.13860302611,70.13859888336472,67.63302685599469,67.10525973633835],[-100.93242929306315,-97.07283334261464,-95.14709385464491,-95.6126894927141,-99.69120417990753,-97.07283334261464],[-100.93242929306315,-108.18740752336498,-109.53588006997387,-110.47624459258856,-105.75320612357142,-108.18740752336498],[0.1546158049960011,11.382283943594848,10.893895625448675,14.850853970035446,10.22532729261416,11.382283943594848],[0.1546158049960011,-4.433600048874682,-3.483180383603247,-7.929479836601891,-3.7257201331767797,-4.433600048874682],[57.121836060461,47.62609451216012,46.92177800682095,44.72025908658326,49.70881815996365,47.62609451216012],[57.121836060461,68.18953889563925,65.7574391673422,70.48388414887809,69.53329691796516,68.18953889563925],[57.121836060461,44.267493962128015,45.49171825315476,40.81374153293126,44.68158579004091,44.267493962128015],[37.02317540032585,40.00378025925547,37.37953800792629,40.81374153293126,42.243810660550984,40.00378025925547],[37.02317540032585,35.238631454833516,37.85566384075584,34.52168587387494,32.96168815339724,35.238631454833516],[43.578692435679386,44.59606181121353,47.01679414709053,44.89241832481749,42.034750304378676,44.59606181121353],[43.578692435679386,53.00853299572383,54.05515716287464,55.618535813157635,50.723832338164534,53.00853299572383],[43.578692435679386,30.26820255883989,29.542343259725826,27.379049185158294,32.3645557306987,30.26820255883989],[19.593547121459324,25.420025708894435,27.027089006119024,27.379049185158294,22.88368337261299,25.420025708894435],[19.593547121459324,15.367334244894407,13.708605807775491,13.481414069572589,17.92066456376482,15.367334244894407],[71.49622513631088,59.06267635848434,60.32442103416175,55.618535813157635,59.43468832488756,59.06267635848434],[71.49622513631088,70.66417324627452,73.20361510686345,70.48388414887809,68.21025301480891,70.66417324627452],[71.49622513631088,70.38446974324269,67.9489614339645,70.13859888336472,72.9366089347782,70.38446974324269],[85.38178867135393,72.47520441687502,74.89069533488387,70.13859888336472,71.16810171939757,72.47520441687502],[85.38178867135393,92.99823024810162,90.58640752055773,95.343880620008,94.29737426098013,92.99823024810162],[-89.75905998765316,-92.56282066934321,-93.06592787231405,-95.6126894927141,-90.61298288668743,-92.56282066934321],[-109.88143397319499,-98.84276189604448,-100.57159987058591,-95.6126894927141,-98.64613556440563,-98.84276189604448],[-109.88143397319499,-110.56318023108508,-108.01601279062965,-110.7828248799392,-113.00615741006884,-110.56318023108508],[-109.88143397319499,-120.36502736368186,-121.3122943237237,-123.06787013166935,-118.13564451243724,-120.36502736368186],[-121.22280863452887,-113.622702478702,-115.46390999538528,-110.47624459258856,-113.27404336496932,-113.622702478702],[-121.22280863452887,-122.55677355581207,-119.96235081183859,-123.06787013166935,-124.90875339870112,-122.55677355581207],[-109.21028767515988,-110.06217763392233,-107.4815262783725,-110.47624459258856,-112.44641289231114,-110.06217763392233],[-109.21028767515988,-108.5167526502791,-111.12696498390278,-107.87126822075649,-106.21273121107846,-108.5167526502791],[-134.40615279600468,-126.54528814052622,-127.65359011802893,-123.06787013166935,-127.08652824601707,-126.54528814052622],[-134.40615279600468,-139.8977785913692,-138.85955938012094,-143.38528382746344,-139.28167075969492,-139.8977785913692],[14.850853970035446,28.430595133165387,28.711970385840203,31.615445403933528,26.63845973135554,28.430595133165387],[14.850853970035446,29.121921934705675,27.233059035057348,32.22807892511701,29.537353495637152,29.121921934705675],[14.850853970035446,3.6231858314365994,4.111574149582773,0.1546158049960011,4.780142482417286,3.6231858314365994],[-7.929479836601891,28.207066070066112,27.966942239266494,31.615445403933528,26.830396886134665,28.207066070066112],[-7.929479836601891,-97.43819504473288,-96.75287545640761,-100.93242929306315,-96.465995627354,-97.43819504473288],[-7.929479836601891,28.786545886252902,27.515240407914835,32.22807892511701,28.42533161483411,28.786545886252902],[44.72025908658326,48.4101556222462,45.82213969928846,50.11316908646652,50.19033297935389,48.4101556222462],[44.72025908658326,34.65462572089514,34.13552031405152,31.615445403933528,36.61539152573097,34.65462572089514],[44.72025908658326,54.21600063488414,54.92031714022331,57.121836060461,52.13327698708061,54.21600063488414],[44.89241832481749,58.10174549219896,58.31252904231102,61.32450555222612,56.362218969897256,58.10174549219896],[44.89241832481749,34.24773724091272,36.519731896949075,31.615445403933528,33.224392056627664,34.24773724091272],[44.89241832481749,43.87504894928335,41.45431661340635,43.578692435679386,46.436360456118194,43.87504894928335],[55.618535813157635,51.38305855281219,54.013892546980145,50.11316908646652,49.35460716267751,51.38305855281219],[55.618535813157635,46.18869525311319,45.14207108596238,43.578692435679386,48.47339591067249,46.18869525311319],[55.618535813157635,68.05208459098418,66.79033991530676,71.49622513631088,67.68007262458096,68.05208459098418],[70.48388414887809,64.38353620591026,63.89429907283907,61.32450555222612,66.32384990888683,64.38353620591026],[70.48388414887809,59.41618131369985,61.84828104199689,57.121836060461,58.07242329137393,59.41618131369985],[70.48388414887809,71.31593603891446,68.77649417832552,71.49622513631088,73.76985627038006,71.31593603891446],[40.81374153293126,53.668083631264246,52.44385934023751,57.121836060461,53.25399180335135,53.668083631264246],[40.81374153293126,33.05849383975112,30.826836334146847,32.22807892511701,35.68406554967183,33.05849383975112],[40.81374153293126,37.833136674001636,40.457378925330815,37.02317540032585,35.59310627270612,37.833136674001636],[34.52168587387494,55.040985993604,52.53726556938416,57.121836060461,56.557637924781524,55.040985993604],[34.52168587387494,-97.7406885820698,-98.00956141921966,-100.93242929306315,-95.95778705829737,-97.7406885820698],[34.52168587387494,32.35533396784344,29.88716919021296,32.22807892511701,34.88386323076611,32.35533396784344],[27.379049185158294,40.68953906199779,41.415398361111855,43.578692435679386,38.59318589013898,40.68953906199779],[27.379049185158294,31.45829256805198,33.71449938582169,32.22807892511701,28.83693120525521,31.45829256805198],[27.379049185158294,21.552570597723182,19.945507300498594,19.593547121459324,24.08891293400463,21.552570597723182],[13.481414069572589,41.187102591221766,42.4451850478357,43.578692435679386,38.79454964999972,41.187102591221766],[13.481414069572589,-97.67218726655909,-95.98955794726146,-100.93242929306315,-97.80829373189864,-97.67218726655909],[13.481414069572589,30.81803827063723,32.77175042406476,32.22807892511701,28.195461634316903,30.81803827063723],[70.13859888336472,71.25035427643292,73.6858625857111,71.49622513631088,68.6982150848974,71.25035427643292],[70.13859888336472,35.261418072143385,37.22807478237172,32.22807892511701,34.73365095248704,35.261418072143385],[70.13859888336472,83.04518313784364,80.62969221983478,85.38178867135393,84.35228583532108,83.04518313784364],[95.343880620008,73.07164347525334,75.67772064429052,71.49622513631088,71.21287906579427,73.07164347525334],[95.343880620008,-97.54890838628491,-96.10681835705466,-100.93242929306315,-97.38599720191245,-97.54890838628491],[95.343880620008,34.97461307210303,37.17564691542478,32.22807892511701,34.07642048809873,34.97461307210303],[-95.6126894927141,-99.4722854431626,-101.39802493113234,-100.93242929306315,-96.85391460586972,-99.4722854431626],[-95.6126894927141,-92.80892881102405,-92.30582160805321,-89.75905998765316,-94.75876659367982,-92.80892881102405],[-95.6126894927141,-106.6513615698646,-104.92252359532316,-109.88143397319499,-106.84798790150346,-106.6513615698646],[-110.7828248799392,-101.96573096846312,-104.59937484421204,-100.93242929306315,-99.82224231525541,-101.96573096846312],[-110.7828248799392,146.70350443190048,145.01143563101613,149.95777009893027,146.85188531452638,146.70350443190048],[-110.7828248799392,-92.48103908882138,-94.6981990426603,-89.75905998765316,-91.55507250459462,-92.48103908882138],[-110.47624459258856,-103.22126636228673,-101.87279381567784,-100.93242929306315,-105.65546776208029,-103.22126636228673],[-110.47624459258856,-118.07635074841544,-116.23514323173215,-121.22280863452887,-118.42500986214812,-118.07635074841544],[-110.47624459258856,-109.6243546338261,-112.20500598937593,-109.21028767515988,-107.24011937543729,-109.6243546338261],[-107.87126822075649,-119.46550013656179,-116.88666207126906,-121.22280863452887,-121.21074433628647,-119.46550013656179],[-123.06787013166935,-112.58427674118248,-111.63700978114063,-109.88143397319499,-114.8136595924271,-112.58427674118248],[-123.06787013166935,-121.73390521038615,-124.32832795435964,-121.22280863452887,-119.3819253674971,-121.73390521038615],[-123.06787013166935,-130.9287347871478,-129.8204328096451,-134.40615279600468,-130.38749468165696,-130.9287347871478],[-143.38528382746344,-93.23637690958691,-93.77696120185499,-89.75905998765316,-94.34528674889174,-93.23637690958691],[-143.38528382746344,-124.148342746632,-126.21454889772964,-121.22280863452887,-123.46988797338314,-124.148342746632]]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1326\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1327\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"MultiLine\",\"id\":\"p1332\",\"attributes\":{\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"line_width\":{\"type\":\"value\",\"value\":4},\"line_join\":{\"type\":\"value\",\"value\":\"round\"}}},\"selection_glyph\":{\"type\":\"object\",\"name\":\"MultiLine\",\"id\":\"p1333\",\"attributes\":{\"line_color\":{\"type\":\"value\",\"value\":\"#fdae61\"},\"line_width\":{\"type\":\"value\",\"value\":5},\"line_join\":{\"type\":\"value\",\"value\":\"round\"}}},\"hover_glyph\":{\"type\":\"object\",\"name\":\"MultiLine\",\"id\":\"p1334\",\"attributes\":{\"line_color\":{\"type\":\"value\",\"value\":\"#abdda4\"},\"line_width\":{\"type\":\"value\",\"value\":5},\"line_join\":{\"type\":\"value\",\"value\":\"round\"}}}}},\"selection_policy\":{\"type\":\"object\",\"name\":\"NodesAndLinkedEdges\",\"id\":\"p1355\"},\"inspection_policy\":{\"type\":\"object\",\"name\":\"EdgesAndLinkedNodes\",\"id\":\"p1356\"}}},{\"type\":\"object\",\"name\":\"GraphRenderer\",\"id\":\"p1277\",\"attributes\":{\"layout_provider\":{\"type\":\"object\",\"name\":\"StaticLayoutProvider\",\"id\":\"p1294\",\"attributes\":{\"graph_layout\":{\"type\":\"map\",\"entries\":[[0,[100.02473419237913,-173.80308175521154]],[1,[-10.050121930825828,31.615445403933528]],[2,[-7.852400352279124,50.11316908646652]],[3,[-5.3761222612422355,61.32450555222612]],[4,[6.614014612676804,32.22807892511701]],[5,[4.525368568092745,-100.93242929306315]],[6,[-0.4269654237614162,0.1546158049960011]],[7,[-25.861496143302297,57.121836060461]],[8,[-44.47435784202109,37.02317540032585]],[9,[17.04430990997499,43.578692435679386]],[10,[39.64795450620208,19.593547121459324]],[11,[9.158421798360486,71.49622513631088]],[12,[45.436063822589055,85.38178867135393]],[13,[-10.359081730108612,-89.75905998765316]],[14,[75.8705853194636,149.95777009893027]],[15,[-13.017491573866458,-109.88143397319499]],[16,[10.330695600011712,-121.22280863452887]],[17,[26.191914218620354,-109.21028767515988]],[18,[-3.463135170524645,-134.40615279600468]],[19,[-2.409859689072089,14.850853970035446]],[20,[-0.819570506329618,-7.929479836601891]],[21,[-17.53528988985007,44.72025908658326]],[22,[1.5847948543460977,44.89241832481749]],[23,[6.287217924183484,55.618535813157635]],[24,[-10.468331170708783,70.48388414887809]],[25,[-28.539221875354137,40.81374153293126]],[26,[-56.42723191620853,34.52168587387494]],[27,[28.12132123667774,27.379049185158294]],[28,[49.203642341295485,13.481414069572589]],[29,[28.436643030574864,70.13859888336472]],[30,[56.46852400100325,95.343880620008]],[31,[-7.063516451206003,-95.6126894927141]],[32,[-27.352690290235362,-110.7828248799392]],[33,[15.566258886479066,-110.47624459258856]],[34,[33.32791630795836,-107.87126822075649]],[35,[-2.1688831008386487,-123.06787013166935]],[36,[-4.223889690255938,-143.38528382746344]]]}}},\"node_renderer\":{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1282\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1279\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1280\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1281\"},\"data\":{\"type\":\"map\",\"entries\":[[\"species\",[\"dna[mydna]+protein[RNAP] <--> complex[dna[mydna]:protein[RNAP]]\",\"complex[dna[mydna]:protein[RNAP]] --> dna[mydna]+rna[mydna]+protein[RNAP]\",\"protein[hrpR]+dna[mydna] <--> complex[dna[mydna]:protein[hrpR]]\",\"protein[hrpS]+dna[mydna] <--> complex[dna[mydna]:protein[hrpS]]\",\"protein[hrpR]+complex[dna[mydna]:protein[hrpS]] <--> complex[dna[mydna]:protein[hrpR]:protein[hrpS]]\",\"protein[hrpS]+complex[dna[mydna]:protein[hrpR]] <--> complex[dna[mydna]:protein[hrpR]:protein[hrpS]]\",\"complex[dna[mydna]:protein[hrpR]]+protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]]\",\"complex[complex[dna[mydna]:protein[hrpR]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpR]]+rna[mydna]+protein[RNAP]\",\"complex[dna[mydna]:protein[hrpS]]+protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]]\",\"complex[complex[dna[mydna]:protein[hrpS]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpS]]+rna[mydna]+protein[RNAP]\",\"complex[dna[mydna]:protein[hrpR]:protein[hrpS]]+protein[RNAP] <--> complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]]\",\"complex[complex[dna[mydna]:protein[hrpR]:protein[hrpS]]:protein[RNAP]] --> complex[dna[mydna]:protein[hrpR]:protein[hrpS]]+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]\"]],[\"type\",[\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\"]],[\"color\",[\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\",\"cornflowerblue\"]],[\"k\",[\"100\",\"0.01\",\"100\",\"100\",\"100\",\"100\",\"100\",\"0.05\",\"100\",\"0.05\",\"100\",\"0.05\",\"100\",\"1.5\",\"100\",\"0.000555556\",\"100\",\"0.000555556\"]],[\"k_r\",[\"100\",\"None\",\"50\",\"50\",\"50\",\"50\",\"10\",\"None\",\"10\",\"None\",\"10\",\"None\",\"10.0\",\"None\",\"10\",\"None\",\"10\",\"None\"]],[\"index\",[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1283\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1284\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1337\",\"attributes\":{\"size\":{\"type\":\"value\",\"value\":8},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"marker\":{\"type\":\"value\",\"value\":\"square\"}}},\"selection_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1338\",\"attributes\":{\"size\":{\"type\":\"value\",\"value\":8},\"fill_color\":{\"type\":\"value\",\"value\":\"#fdae61\"},\"marker\":{\"type\":\"value\",\"value\":\"square\"}}},\"hover_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1339\",\"attributes\":{\"size\":{\"type\":\"value\",\"value\":8},\"fill_color\":{\"type\":\"value\",\"value\":\"#abdda4\"},\"marker\":{\"type\":\"value\",\"value\":\"square\"}}}}},\"edge_renderer\":{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1289\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1286\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1287\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1288\"},\"data\":{\"type\":\"map\",\"entries\":[[\"start\",[]],[\"end\",[]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1290\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1291\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"MultiLine\",\"id\":\"p1285\"}}},\"selection_policy\":{\"type\":\"object\",\"name\":\"NodesOnly\",\"id\":\"p1292\"},\"inspection_policy\":{\"type\":\"object\",\"name\":\"NodesOnly\",\"id\":\"p1293\"}}},{\"type\":\"object\",\"name\":\"GraphRenderer\",\"id\":\"p1295\",\"attributes\":{\"layout_provider\":{\"type\":\"object\",\"name\":\"StaticLayoutProvider\",\"id\":\"p1312\",\"attributes\":{\"graph_layout\":{\"type\":\"map\",\"entries\":[[0,[100.02473419237913,-173.80308175521154]],[1,[-10.050121930825828,31.615445403933528]],[2,[-7.852400352279124,50.11316908646652]],[3,[-5.3761222612422355,61.32450555222612]],[4,[6.614014612676804,32.22807892511701]],[5,[4.525368568092745,-100.93242929306315]],[6,[-0.4269654237614162,0.1546158049960011]],[7,[-25.861496143302297,57.121836060461]],[8,[-44.47435784202109,37.02317540032585]],[9,[17.04430990997499,43.578692435679386]],[10,[39.64795450620208,19.593547121459324]],[11,[9.158421798360486,71.49622513631088]],[12,[45.436063822589055,85.38178867135393]],[13,[-10.359081730108612,-89.75905998765316]],[14,[75.8705853194636,149.95777009893027]],[15,[-13.017491573866458,-109.88143397319499]],[16,[10.330695600011712,-121.22280863452887]],[17,[26.191914218620354,-109.21028767515988]],[18,[-3.463135170524645,-134.40615279600468]],[19,[-2.409859689072089,14.850853970035446]],[20,[-0.819570506329618,-7.929479836601891]],[21,[-17.53528988985007,44.72025908658326]],[22,[1.5847948543460977,44.89241832481749]],[23,[6.287217924183484,55.618535813157635]],[24,[-10.468331170708783,70.48388414887809]],[25,[-28.539221875354137,40.81374153293126]],[26,[-56.42723191620853,34.52168587387494]],[27,[28.12132123667774,27.379049185158294]],[28,[49.203642341295485,13.481414069572589]],[29,[28.436643030574864,70.13859888336472]],[30,[56.46852400100325,95.343880620008]],[31,[-7.063516451206003,-95.6126894927141]],[32,[-27.352690290235362,-110.7828248799392]],[33,[15.566258886479066,-110.47624459258856]],[34,[33.32791630795836,-107.87126822075649]],[35,[-2.1688831008386487,-123.06787013166935]],[36,[-4.223889690255938,-143.38528382746344]]]}}},\"node_renderer\":{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1300\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1297\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1298\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1299\"},\"data\":{\"type\":\"map\",\"entries\":[[\"color\",[\"purple\",\"grey\",\"green\",\"green\",\"green\",\"orange\",\"cyan\",\"cyan\",\"cyan\",\"cyan\",\"cyan\",\"cyan\",\"cyan\",\"green\",\"green\",\"cyan\",\"green\",\"cyan\",\"cyan\"]],[\"species\",[\"nothing\",\"dna_mydna\",\"protein_hrpR\",\"protein_hrpS\",\"protein_RNAP\",\"rna_mydna\",\"complex_dna_mydna_protein_RNAP_\",\"complex_dna_mydna_protein_hrpR_\",\"complex_complex_dna_mydna_protein_hrpR__protein_RNAP_\",\"complex_dna_mydna_protein_hrpS_\",\"complex_complex_dna_mydna_protein_hrpS__protein_RNAP_\",\"complex_dna_mydna_protein_hrpR_protein_hrpS_\",\"complex_complex_dna_mydna_protein_hrpR_protein_hrpS__protein_RNAP_\",\"protein_Ribo\",\"protein_GFP\",\"complex_protein_Ribo_rna_mydna_\",\"protein_RNAase\",\"complex_protein_RNAase_rna_mydna_\",\"complex_complex_protein_Ribo_rna_mydna__protein_RNAase_\"]],[\"image\",[\"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\",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null]],[\"type\",[\"nothing\",\"dna\",\"protein\",\"protein\",\"protein\",\"rna\",\"complex\",\"complex\",\"complex\",\"complex\",\"complex\",\"complex\",\"complex\",\"protein\",\"protein\",\"complex\",\"protein\",\"complex\",\"complex\"]],[\"index\",[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1301\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1302\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1340\",\"attributes\":{\"size\":{\"type\":\"value\",\"value\":12},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"selection_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1341\",\"attributes\":{\"size\":{\"type\":\"value\",\"value\":15},\"fill_color\":{\"type\":\"value\",\"value\":\"#fdae61\"}}},\"hover_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1342\",\"attributes\":{\"size\":{\"type\":\"value\",\"value\":15},\"fill_color\":{\"type\":\"value\",\"value\":\"#abdda4\"}}}}},\"edge_renderer\":{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1307\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1304\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1305\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1306\"},\"data\":{\"type\":\"map\",\"entries\":[[\"start\",[]],[\"end\",[]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1308\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1309\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"MultiLine\",\"id\":\"p1303\"}}},\"selection_policy\":{\"type\":\"object\",\"name\":\"NodesOnly\",\"id\":\"p1310\"},\"inspection_policy\":{\"type\":\"object\",\"name\":\"NodesOnly\",\"id\":\"p1311\"}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1276\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1343\",\"attributes\":{\"renderers\":[{\"id\":\"p1313\"}],\"tooltips\":null}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1344\",\"attributes\":{\"renderers\":[{\"id\":\"p1295\"}],\"tooltips\":[[\"name\",\"@species\"],[\"type\",\"@type\"]],\"attachment\":\"right\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1345\",\"attributes\":{\"renderers\":[{\"id\":\"p1277\"}],\"tooltips\":[[\"reaction\",\"@species\"],[\"type\",\"@type\"],[\"k_f\",\"@k\"],[\"k_r\",\"@k_r\"]],\"attachment\":\"right\"}},{\"type\":\"object\",\"name\":\"TapTool\",\"id\":\"p1346\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxSelectTool\",\"id\":\"p1347\",\"attributes\":{\"renderers\":\"auto\",\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1348\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"editable\":true,\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1353\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1354\",\"attributes\":{\"renderers\":\"auto\"}}]}}}}]}};\n", + " const render_items = [{\"docid\":\"75caebd3-1d0e-4069-b4a1-dcc80f75d9af\",\"roots\":{\"p1270\":\"a8afe402-2e85-4b5a-b120-eddbd7030fdd\"},\"root_ids\":[\"p1270\"]}];\n", + " void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", @@ -555,7 +688,7 @@ }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "1004" + "id": "p1270" } }, "output_type": "display_data" @@ -569,14 +702,12 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -648,7 +779,7 @@ "\n", " timepoints = np.linspace(0, 200, 1000)\n", " x0 = {\"dna_mydna\":5.0, \"protein_hrpS\":10, \"protein_hrpR\":7, \"protein_RNAP\":10., \"protein_Ribo\":50.,}\n", - " Re1 = CRN_extract_1.simulate_with_bioscrape(timepoints, initial_condition_dict = x0)\n", + " Re1 = CRN_extract_1.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0)\n", " if Re1 is not None:\n", " plt.plot(timepoints,Re1[\"protein_GFP\"], label = \"protein_GFP\")\n", " plt.plot(timepoints,Re1[\"protein_hrpR\"], label = \"protein_hrpR\")\n", @@ -672,35 +803,32 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\integrate\\odepack.py:248: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", - " warnings.warn(warning_msg, ODEintWarning)\n", - "odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500..." + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiQAAAHFCAYAAADCA+LKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABoNElEQVR4nO3deVhUZf8/8PewDUs4sggDLogmiIJmWIpLkChq4doTKYaoZe6KSyqoST4KLqlZ7uaWS9aTYpZGYiJliKJJrrmSojJihiguA8L9+8Mf83XYhmWGA/R+Xde5Lufc99znMzNnxg/3co5MCCFAREREJCEjqQMgIiIiYkJCREREkmNCQkRERJJjQkJERESSY0JCREREkmNCQkRERJJjQkJERESSY0JCREREkmNCQkRERJJjQgIgMjISMpkMf//9d5Ucb9OmTZDJZJrNxMQETk5OGDBgAC5dulThdg8dOgSZTIZvv/1Wj9GWLi0tDaNHj4abmxssLCxga2sLLy8vDB8+HGlpaWVq4+rVqxg7dqymDUtLS7Rs2RIzZ87EzZs3DfwKiir4fI4fP66z7pAhQ9C4cWODxnPr1i1ERkYiJSXFIO0XnP+GfG5BvYLNzMwMrq6umDBhAu7du1ehY0tt+/bt+PTTT6v0mEOGDMELL7xQYvkLL7yAIUOGGDQGKV43/TuYSB3Av9nGjRvRvHlzPHnyBL/99hvmzZuH+Ph4/Pnnn7CxsZE6PJ1u3LiBl19+GXXr1sXkyZPh7u6OrKwsnDt3Dt988w2uXr2Khg0bltrGDz/8gAEDBsDe3h5jx45FmzZtIJPJcPr0aWzYsAF79+7FyZMnq+gVld+sWbMwYcIEgx7j1q1b+Pjjj9G4cWO89NJLem///fffR48ePfTebnFiY2OhUCjw4MED7Nu3D8uWLcOxY8eQmJhY4aRIKtu3b8eZM2cQFhYmdShV6t/6usnwmJBUkhACT548gYWFRbmf6+npibZt2wIA/Pz8kJeXh9mzZ2P37t0YOnSovkPV6dGjR7C0tCxz/XXr1uHvv//GsWPH4Orqqtnft29fREREID8/v9Tnp6amYsCAAXBzc0N8fDwUCoWmrEuXLhg/fjxiYmLK/0KqUNOmTaUOocIKPu8GDRqgQYMGVXJMb29v2NvbAwC6deuGu3fvYsuWLUhMTETHjh1LjfPf4vHjxxX6PSGq6Thk85zbt29j4MCBUCgUcHR0xLBhw5CVlaVVRyaTYezYsVi9ejU8PDwgl8uxefNm/PXXX5DJZFi4cCHmzZuHRo0awdzcHG3btsXPP/9cpuMXJCe3b9+u1OvIzc3FjBkz4OzsjDp16qBr1664cOGCVh0/Pz94enril19+QYcOHWBpaYlhw4YBABo3bozAwEDExMSgVatWMDc3R5MmTfDZZ59ptXH37l0YGRnBwcGh2DiMjEo/vZYsWYKHDx9i5cqVWslIAZlMhv79+2vt27BhA1q3bg1zc3PY2tqiX79+OH/+vFadgm7tP//8E927d4eVlRWcnJwwf/58AEBSUhI6deoEKysruLm5YfPmzcXGl5mZiaFDh8LW1hZWVlbo1asXrl69WuRYhYdsCs6RLVu2wMPDA5aWlmjdujV++OEHrXqXL1/G0KFD0axZM1haWqJ+/fro1asXTp8+ralz6NAhvPLKKwCAoUOHaoY8IiMjNXX27NkDHx8fWFpawtraGt26dcORI0e0jlUwZPL777/jP//5D2xsbDTJVHHDLl9//TUCAgLg5OQECwsLeHh4YPr06Xj48GGx71VFtW/fHgBw7do1AKWfl9evX8e7774LBwcHyOVyeHh4YPHixVqJb8H3cNGiRViwYAEaN24MCwsL+Pn54eLFi8jNzcX06dPh7OwMhUKBfv36ISMjQyum/Px8LFy4EM2bN4dcLoeDgwMGDx6MGzduaOr4+flh7969uHbtmtZQVIGcnBzMnTtX00a9evUwdOhQ3LlzR+tYBd+1Xbt2oU2bNjA3N8fHH3+s1/f4/v37mDJlClxdXWFmZob69esjLCysyGe5YsUKvPbaa3BwcICVlRW8vLywcOFC5Obmlul16+O9L+t5V/AdP3v2LPz9/WFlZYV69eph7NixePTokV7fP6pCgsTs2bMFAOHu7i4++ugjERcXJ5YsWSLkcrkYOnSoVl0Aon79+qJVq1Zi+/bt4uDBg+LMmTMiNTVVABANGzYUnTp1Ejt37hT/+9//xCuvvCJMTU1FYmKipo2NGzcKACI5OVmr7eXLlwsAYufOnRV6HfHx8QKAaNy4sRg0aJDYu3ev+Oqrr0SjRo1Es2bNxNOnTzV1fX19ha2trWjYsKH4/PPPRXx8vEhISBBCCOHi4iLq168vGjVqJDZs2CD27dsnBg0aJACIRYsWadrYunWrACACAgJEbGysyMrKKle8bm5uwtHRscz1o6KiBAAxcOBAsXfvXvHll1+KJk2aCIVCIS5evKipFxoaKszMzISHh4dYtmyZiIuLE0OHDhUARHh4uHBzcxPr168XP/30kwgMDBQAxPHjxzXPL/h8GjZsKIYNGyZ+/PFHsXbtWuHg4CAaNmwoMjMztY7l4uKiFWfBZ/Dqq6+Kb775Ruzbt0/4+fkJExMTceXKFU29hIQEMXnyZPHtt9+KhIQEERMTI/r27SssLCzEn3/+KYQQIisrSxPPzJkzxZEjR8SRI0dEWlqaEEKIbdu2aT6D3bt3i6+//lp4e3sLMzMz8euvv2qOVXCOu7i4iGnTpom4uDixe/durbLn/fe//xVLly4Ve/fuFYcOHRKrV68Wrq6u4vXXX9eqV9xzi1NQ786dO1r7J06cKACI/fv3CyFKPi8zMjJE/fr1Rb169cTq1atFbGysGDt2rAAgRo0apWmv4Hvo4uIievXqJX744QexdetW4ejoKNzc3ERISIjmM129erV44YUXRK9evbRi+uCDDwQAMXbsWBEbGytWr14t6tWrJxo2bKiJ/+zZs6Jjx45CqVRqPpMjR44IIYTIy8sTPXr0EFZWVuLjjz8WcXFx4osvvhD169cXLVq0EI8ePdIcy8XFRTg5OYkmTZqIDRs2iPj4eHHs2LES38fQ0FBhZWUlcnNzi92srKxEaGiopv7Dhw/FSy+9JOzt7cWSJUvEgQMHxLJly4RCoRBdunQR+fn5Wp/FqlWrRGxsrDh48KBYunSpsLe31/oNLO116+O9L+t5V/Adb9SokZg3b57Yv3+/iIyMFCYmJiIwMLDE94+qNyYk4v9+LBcuXKi1f/To0cLc3FzrSwtAKBQK8c8//2jVLfgyOjs7i8ePH2v2379/X9ja2oquXbtq9hX8B5OUlCRyc3PFgwcPRGxsrFAqleK1114Tubm5FXodBQnJG2+8obX/m2++EQA0PxxCPPvhByB+/vnnIu24uLgImUwmUlJStPZ369ZN1KlTRzx8+FAIIUR+fr4YMWKEMDIyEgCETCYTHh4eYuLEiSI1NVVnvObm5qJ9+/Zlem2ZmZnCwsKiyGu7fv26kMvlIjg4WLMvNDS0SGKXm5sr6tWrJwCI33//XbP/7t27wtjYWEyaNEmzr+Dz6devn9axfvvtNwFAzJ07V+tYxSUkjo6O4v79+5p9KpVKGBkZiejo6BJf49OnT0VOTo5o1qyZmDhxomZ/cnKyACA2btyoVT8vL084OzsLLy8vkZeXp9n/4MED4eDgIDp06KDZV3COf/TRR0WOqyupyM/PF7m5uSIhIUEAEH/88UeZn1u4nkqlErm5uSIzM1Ns3bpVWFhYiIYNG2q+MyWdl9OnTxcAxNGjR7X2jxo1SshkMnHhwgUhxP99D1u3bq31nnz66acCgOjdu7fW88PCwgQATTJ9/vx5AUCMHj1aq97Ro0cFABEREaHZ9+abbxb57IUQ4quvvir2D4uCz3HlypWafS4uLsLY2FgTvy4F53Zp2/MJSXR0tDAyMiryx8+3334rAIh9+/YVe5y8vDyRm5srvvzyS2FsbKz1e1fS667se19YaeddwfuwbNkyrefMmzdPABCHDx8utk2q3jhk85zevXtrPW7VqhWePHlSpFuxS5cuJU467d+/P8zNzTWPra2t0atXL/zyyy/Iy8vTqtu+fXuYmprC2toaPXr0gI2NDb777juYmFRuak9xrwP4v27xAjY2NujSpUuxbbRs2RKtW7fW2hccHIz79+/j999/B/BsaGL16tW4evUqVq5ciaFDhyI3NxdLly5Fy5YtkZCQUKnX8bwjR47g8ePHRVYQNGzYEF26dCkyLCaTyfDGG29oHpuYmODFF1+Ek5MT2rRpo9lva2sLBweHIu8NAAwaNEjrcYcOHeDi4oL4+Hid8b7++uuwtrbWPHZ0dCxynKdPnyIqKgotWrSAmZkZTExMYGZmhkuXLhUZhirOhQsXcOvWLYSEhGgNj73wwgt46623kJSUVKT7+q233tLZLvBs5VNwcDCUSiWMjY1hamoKX19fAChTbCVRKpUwNTWFjY0N3n33Xbz88suIjY3V+s4Ud14ePHgQLVq0wKuvvqq1f8iQIRBC4ODBg1r733jjDa33xMPDAwDw5ptvatUr2H/9+nUA0Hy2hc+zV199FR4eHmUafv3hhx9Qt25d9OrVC0+fPtVsL730EpRKJQ4dOqRVv1WrVnBzc9PZbgELCwskJycXuxWee/LDDz/A09MTL730klYs3bt3h0wm04rl5MmT6N27N+zs7DSf+eDBg5GXl4eLFy+WOb6KvvdA+c+7wt/R4OBgACjTd5SqH05qfY6dnZ3WY7lcDuDZJLPnOTk5ldiGUqksdl9OTg6ys7O15kp8+eWX8PDwwIMHD/D1119jzZo1GDhwIH788cfKvAyDvg7g2dyR57m4uGDUqFGax9988w0GDhyIDz/8EMeOHSvxGI0aNUJqamqJ5c8rOGZxMTs7OyMuLk5rn6WlpdZ/cgBgZmYGW1vbIs83MzPDkydPiuwv6T0o/PqLU/gzAJ59Ds9/BpMmTcKKFSswbdo0+Pr6wsbGBkZGRnj//feLfFbF0fWe5OfnIzMzU2tCaGmfeYHs7Gx07twZ5ubmmDt3Ltzc3GBpaYm0tDT079+/TLGV5MCBA1AoFDA1NUWDBg2KfZ+Ki/Hu3bvFLq92dnbWlD+v8OdsZmZW6v6Cz1/Xe1pc4lrY7du3ce/ePU3bhRW+vEBZPpPnGRkZaeabFVdWOJbLly/D1NS01FiuX7+Ozp07w93dHcuWLUPjxo1hbm6OY8eOYcyYMeX6zCv63pf3vDMxMSly/pT0G0U1AxOSCihteaJKpSp2n5mZWZHrB3h4eGh+WF5//XXk5eXhiy++wLfffov//Oc/+g26GBV5HUDx/9k+LygoCNHR0Thz5kyp9bp3747PP/8cSUlJmsmNJSk4Znp6epGyW7duaVZu6FNJ78GLL76ol/a3bt2KwYMHIyoqSmv/33//jbp16+p8vq73xMjIqEhPXlmW1h48eBC3bt3CoUOHNH+dAtDL9UJat26t87MqLkY7O7sSXycAvX3+z7+nhVcelfU8s7e3h52dHWJjY4stf77nDCjbZ1JR9vb2sLCwwIYNG0osB4Ddu3fj4cOH2LVrF1xcXDTlhrr2TXHKe949ffoUd+/e1fo9KutvFFVPHLLRs127dmn9tf3gwQN8//336Ny5M4yNjUt97sKFC2FjY4OPPvpI55JZQzt79iz++OMPrX3bt2+HtbU1Xn75ZQDF/0cIPPtLJy0tTfPXa0kmTpwIKysrjB49ushqJuDZkuqCZb8+Pj6wsLDA1q1btercuHEDBw8ehL+/f5lfW1lt27ZN63FiYiKuXbsGPz8/vbQvk8k0vVcF9u7dW+RicCX1cLm7u6N+/frYvn07hBCa/Q8fPsTOnTs1K28qEtfzxy2wZs2acrelL/7+/jh37pxmuLDAl19+CZlMhtdff10vxykYKip8niUnJ+P8+fNa51nhHq8CgYGBuHv3LvLy8tC2bdsim7u7u15iLYvAwEBcuXIFdnZ2xcZS0OtU3GcuhMC6deuKtFnS666sipx3hb+j27dvBwC9fUeparGHRM+MjY3RrVs3TJo0Cfn5+ViwYAHu379fpqV8NjY2CA8Px9SpU7F9+3a8++67AJ5dOXTo0KHYuHGjwa/CWMDZ2Rm9e/dGZGQknJycsHXrVsTFxWHBggWa/+TmzZuH3377De+88w5eeuklWFhYIDU1FcuXL8fdu3exaNGiUo/h6uqKHTt2aJ5fcGE0ADh37hw2bNgAIQT69euHunXrYtasWYiIiMDgwYMxcOBA3L17Fx9//DHMzc0xe/Zsvb8Hx48fx/vvv4+3334baWlpmDFjBurXr4/Ro0frpf3AwEBs2rQJzZs3R6tWrXDixAksWrSoyF/mTZs2hYWFBbZt2wYPDw+88MILcHZ2hrOzMxYuXIhBgwYhMDAQI0aMgFqtxqJFi3Dv3j3NMufy6tChA2xsbDBy5EjMnj0bpqam2LZtW5EEtSpNnDgRX375Jd58803MmTMHLi4u2Lt3L1auXIlRo0aVaw5Gadzd3fHBBx/g888/h5GREXr27Im//voLs2bNQsOGDTFx4kRNXS8vL+zatQurVq2Ct7e3ZihlwIAB2LZtG9544w1MmDABr776KkxNTXHjxg3Ex8ejT58+6Nevn17i1SUsLAw7d+7Ea6+9hokTJ6JVq1bIz8/H9evXsX//fkyePBnt2rVDt27dYGZmhoEDB2Lq1Kl48uQJVq1ahczMzCJtlvS6K6u8552ZmRkWL16M7OxsvPLKK0hMTMTcuXPRs2dPdOrUqdLxUNVjQqJnY8eOxZMnTzB+/HhkZGSgZcuW2Lt3b4kXfSps3LhxWL58OebMmYOBAwfC2NgY2dnZAMo/1lwZL730EoYOHYrZs2fj0qVLcHZ2xpIlS7R+kENCQgAAO3bswKJFi5CVlQVbW1t4e3tj37596Nmzp87jBAYG4vTp01i8eDFWr16NtLQ0GBkZwdXVFT169MC4ceM0dcPDw+Hg4IDPPvsMX3/9teYaB1FRUWjWrJne34P169djy5YtGDBgANRqNV5//XUsW7as2HkoFbFs2TKYmpoiOjoa2dnZePnll7Fr1y7MnDlTq56lpSU2bNiAjz/+GAEBAcjNzcXs2bMRGRmJ4OBgWFlZITo6Gu+88w6MjY3Rvn17xMfHo0OHDhWKy87ODnv37sXkyZPx7rvvwsrKCn369MHXX3+t6R2ravXq1UNiYiLCw8MRHh6O+/fvo0mTJli4cCEmTZqk12OtWrUKTZs2xfr167FixQooFAr06NED0dHRWkMBEyZMwNmzZxEREYGsrCyIZ6sWYWxsjD179mDZsmXYsmULoqOjYWJiggYNGsDX1xdeXl56jbc0VlZW+PXXXzF//nysXbsWqampsLCwQKNGjdC1a1dND0nz5s2xc+dOzJw5E/3794ednR2Cg4MxadKkIt/jkl53ZZX3vDM1NcUPP/yA8ePHY+7cubCwsMDw4cN1/iFE1ZdM6ONMIvz1119wdXXFokWLMGXKFL22HRQUhNTUVCQnJ+u13ZI0btwYnp6eRS7kRURUHQwZMgTffvut5o81qh3YQ1LNCSFw6NChImPaREREtQkTkmpOJpMVuQ4KERFRbcMhGyIiIpIcl/0SERGR5JiQEBERkeSYkBAREZHkmJAQERGR5LjKhoiIyMDyVfq5mrCRsux3Xq5p2ENCREREkmMPCRERkYHlQz83TK3NvQhMSIiIiAwsT+gnIanN/2nX5tdGRERULeSD1yDVpTb3/hAREVENwR4SIiIiA9PXHJLajAkJERGRgeXxtnE6cciGiIiIJMceEiIiIgPjpFbdmJAQEREZWB4TEp04ZENERESSYw8JERGRgXHIRjf2kBARERlYnhB62cqjcePGkMlkRbYxY8YAAIQQiIyMhLOzMywsLODn54ezZ89qtaFWqzFu3DjY29vDysoKvXv3xo0bN7TqZGZmIiQkBAqFAgqFAiEhIbh371653yMmJERERLVQcnIy0tPTNVtcXBwA4O233wYALFy4EEuWLMHy5cuRnJwMpVKJbt264cGDB5o2wsLCEBMTgx07duDw4cPIzs5GYGAg8vLyNHWCg4ORkpKC2NhYxMbGIiUlBSEhIeWOVyYEF0cTEREZ0q2bznppx7n+rQo/NywsDD/88AMuXbr0rC1nZ4SFhWHatGkAnvWGODo6YsGCBRgxYgSysrJQr149bNmyBe+88w4A4NatW2jYsCH27duH7t274/z582jRogWSkpLQrl07AEBSUhJ8fHzw559/wt3dvczxsYeEiIjIwPIg9LKp1Wrcv39fa1Or1TqPn5OTg61bt2LYsGGQyWRITU2FSqVCQECApo5cLoevry8SExMBACdOnEBubq5WHWdnZ3h6emrqHDlyBAqFQpOMAED79u2hUCg0dcqKCQkREZGB5Qn9bNHR0Zq5GgVbdHS0zuPv3r0b9+7dw5AhQwAAKpUKAODo6KhVz9HRUVOmUqlgZmYGGxubUus4ODgUOZ6Dg4OmTllxlQ0REVENER4ejkmTJmntk8vlOp+3fv169OzZE87O2kNHMplM67EQosi+wgrXKa5+WdopjAkJERGRgenr1npyubxMCcjzrl27hgMHDmDXrl2afUqlEsCzHg4nJyfN/oyMDE2viVKpRE5ODjIzM7V6STIyMtChQwdNndu3bxc55p07d4r0vujCIRsiIiIDy4NML1tFbNy4EQ4ODnjzzTc1+1xdXaFUKjUrb4Bn80wSEhI0yYa3tzdMTU216qSnp+PMmTOaOj4+PsjKysKxY8c0dY4ePYqsrCxNnbKqtT0k3YzeljqEMonL/x8AoN9vYySOpGxiOq4AANy/1UjiSMqujvN1AIA6vYnEkZSN3OkqAOCp6kWJIykbE+VlADUv3nyVm8SRlI2R8iIAxmsoBfHWVvn5+di4cSNCQ0NhYvJ//+XLZDKEhYUhKioKzZo1Q7NmzRAVFQVLS0sEBwcDABQKBd577z1MnjwZdnZ2sLW1xZQpU+Dl5YWuXbsCADw8PNCjRw8MHz4ca9asAQB88MEHCAwMLNcKG6AWJyRERETVRb5EF9g4cOAArl+/jmHDhhUpmzp1Kh4/fozRo0cjMzMT7dq1w/79+2Ftba2ps3TpUpiYmCAoKAiPHz+Gv78/Nm3aBGNjY02dbdu2Yfz48ZrVOL1798by5cvLHWutvQ4Je0gMgz0khsceEsNiD4lh1dR4De1cWn29tNOi4U29tFMdcQ4JERERSY5DNkRERAZW0Qmp/yZMSIiIiAwsXzAh0YVDNkRERCQ59pAQEREZGIdsdGNCQkREZGB5HJDQqVokJHl5efj7778hk8lgZ2entb6ZiIiopuMcEt0kTdliYmLQsWNHWFpawtnZGU5OTrC0tETHjh2xe/duKUMjIiKiKiRZQrJmzRoMGDAArVq1wtdff43Dhw/j119/xddff41WrVphwIABWLdunVThERER6Y2U97KpKSQbslm0aBFWrlyJ9957r0hZ37598corr2DevHkYPny4BNERERHpT57gHBJdJHuHbt68iU6dOpVY3qFDB9y6dasKIyIiIiKpSJaQtGzZEmvXri2xfN26dWjZsmUVRkRERGQY+TDSy1abSTZks3jxYrz55puIjY1FQEAAHB0dIZPJoFKpEBcXh2vXrmHfvn1ShUdERKQ3tX3+hz5IlpD4+vrizJkzWLVqFZKSkqBSqQAASqUSgYGBGDlyJBo3bixVeERERFSFJL0OSePGjbFgwYJKtaFWq6FWq7X2yeXySrVJRESkT5zUqluNf4eio6OhUCi0tujoaKnDIiIi0siHTC9bbVZtE5LQ0FB06dJFZ73w8HBkZWVpbeHh4VUQIREREelLtbh0fHGcnZ1hZKQ7X5LL5RyiISKiao33stGt2iYkHHYhIqLagnNIdJM0Iblx4wZWrVqFxMREqFQqyGQyODo6okOHDhg1ahQaNGggZXhERER6UduvIaIPkr1Dhw8fhoeHB2JiYtC6dWsMHjwY7777Llq3bo3du3ejRYsW+O2336QKj4iIiKqQZD0kEydOxPvvv4+lS5eWWB4WFobk5OQqjoyIiEi/8kTtXiGjD5L1kJw5cwYjR44ssXzEiBE4c+ZMFUZERERkGHkw0stWm0n26pycnJCYmFhi+ZEjR+Dk5FSFEREREZFUJBuymTJlCkaOHIkTJ06gW7duRe5l88UXX+DTTz+VKjwiIiK9yecqG50kS0hGjx4NOzs7LF26FGvWrEFeXh4AwNjYGN7e3vjyyy8RFBQkVXhERER6U9uHW/RB0mW/77zzDt555x3k5ubi77//BgDY29vD1NRUyrCIiIioilWLC6OZmppyvggREdVaXGWjW7VISIiIiGozXhhNN75DREREJDn2kBARERkY72WjGxMSIiIiA8sH55DowoSEiIjIwNhDoptMCCGkDoKIiKg2W3PBVy/tjHBP0Es71RF7SIiIiAyMF0bTrdYmJP1+GyN1CGUS03EFAODOrfoSR1I29ZxvAgCeql6UOJKyM1FeBgDkq9wkjqRsjJQXATBeQ2G8hlVT4zW0fF6HRCembERERCS5WttDQkREVF1wyEY3JiREREQGxrv96sZ3iIiIiCTHHhIiIiIDy+OF0XRiQkJERGRgHLLRje8QERERSU7SHpKcnByYmZlpHl+5cgWff/45Ll26BCcnJ4waNQre3t4SRkhERFR5HLLRTdIeEgsLC2RkZAAAUlJS0KpVKyQkJKB+/fo4deoUOnTogGPHjkkZIhERUaXlCyO9bLWZpD0kz99GZ9asWXjjjTfwzTffQCZ7lkkOGzYMs2fPxo8//ihViERERJXGm+vpVm0mtaakpGDHjh2aZAQAJkyYgO7du0sYFREREVUFSRMSmUymSUCMjY1Rp04drfI6deogKytLitCIiIj0Jp9zSHSSfMjGzc0NMpkM2dnZOH36NLy8vDTlly5dglKplDBCIiKiyuOQjW6SJiQbN27Uety0aVOtx0lJSejXr19VhkREREQSkDRlCw0N1dratWunVf7RRx9hyZIlEkVHRESkH/lCppetvG7evIl3330XdnZ2sLS0xEsvvYQTJ05oyoUQiIyMhLOzMywsLODn54ezZ89qtaFWqzFu3DjY29vDysoKvXv3xo0bN7TqZGZmIiQkBAqFAgqFAiEhIbh37165YmUfEhERkYHlwUgvW3lkZmaiY8eOMDU1xY8//ohz585h8eLFqFu3rqbOwoULsWTJEixfvhzJyclQKpXo1q0bHjx4oKkTFhaGmJgY7NixA4cPH0Z2djYCAwORl5enqRMcHIyUlBTExsYiNjYWKSkpCAkJKVe8kg7ZeHl5ISgoCEOGDEHDhg2lDIWIiKhWWbBgARo2bKg1PaJx48aafwsh8Omnn2LGjBno378/AGDz5s1wdHTE9u3bMWLECGRlZWH9+vXYsmULunbtCgDYunUrGjZsiAMHDqB79+44f/48YmNjkZSUpBnpWLduHXx8fHDhwgW4u7uXKV5Je0jOnj2LZcuWwdXVFT169MDOnTvx9OnTcrWhVqtx//59rU2tVhsoYiIiovKTYshmz549aNu2Ld5++204ODigTZs2WLdunaY8NTUVKpUKAQEBmn1yuRy+vr5ITEwEAJw4cQK5ubladZydneHp6ampc+TIESgUCq1pF+3bt4dCodDUKQvJh2xOnTqFb7/9FmZmZhgwYACcnZ0xZcoUnD9/vkzPj46O1oxZFWzR0dEGjpqIiKjs8mGkl608f4RfvXoVq1atQrNmzfDTTz9h5MiRGD9+PL788ksAgEqlAgA4OjpqPc/R0VFTplKpYGZmBhsbm1LrODg4FDm+g4ODpk5ZSJ6QmJiYoG/fvtizZw/S0tIwceJE7NmzB56enujQoQM2bNhQ6vPDw8ORlZWltYWHh1dR9ERERFWnPH+E5+fn4+WXX0ZUVBTatGmDESNGYPjw4Vi1apVWvecvSAo8G8opvK+wwnWKq1+Wdp4naUJSOFClUonw8HBcvHgRP//8M5o2bYrx48eX2oZcLkedOnW0NrlcbsiwiYiIyiVPyPSyleePcCcnJ7Ro0UJrn4eHB65fvw4Amut8Fe7FyMjI0PSaKJVK5OTkIDMzs9Q6t2/fLnL8O3fuFOl9KY2kCcnz97IpzM/PD1u2bMGtW7eqMCIiIiL909cckvL8Ed6xY0dcuHBBa9/Fixfh4uICAHB1dYVSqURcXJymPCcnBwkJCejQoQMAwNvbG6amplp10tPTcebMGU0dHx8fZGVlad0M9+jRo8jKytLUKQtJV9mEhobCwsKi1DqFLydPRERU00hxp96JEyeiQ4cOiIqKQlBQEI4dO4a1a9di7dq1AJ6NUoSFhSEqKgrNmjVDs2bNEBUVBUtLSwQHBwMAFAoF3nvvPUyePBl2dnawtbXFlClT4OXlpVl14+HhgR49emD48OFYs2YNAOCDDz5AYGBgmVfYANXsSq1ERESkH6+88gpiYmIQHh6OOXPmwNXVFZ9++ikGDRqkqTN16lQ8fvwYo0ePRmZmJtq1a4f9+/fD2tpaU2fp0qUwMTFBUFAQHj9+DH9/f2zatAnGxsaaOtu2bcP48eM1q3F69+6N5cuXlytemSht3KQG6/fbGKlDKJOYjisAAHdu1Zc4krKp53wTAPBU9aLEkZSdifIyACBf5SZxJGVjpLwIgPEaCuM1rJoar6GN/v1dvbSz8uWtemmnOpJ8lU1pQkND0aVLF6nDICIiqhSpLh1fk0g6ZKOLs7MzjIyqdc5EREREelCtExJe4IyIiGoDKSa11jSSv0Pnz5/Hxo0b8eeffwIA/vzzT4waNQrDhg3DwYMHJY6OiIio8vIh08tWm0naQxIbG4s+ffrghRdewKNHjxATE4PBgwejdevWEEKge/fu+OmnnziPhIiIqJaTtIdkzpw5+PDDD3H37l1s3LgRwcHBGD58OOLi4nDgwAFMnToV8+fPlzJEIiKiStPXlVprM8nv9jtkyBAAQFBQEB48eIC33npLUz5w4ECcOnVKouiIiIj0I18Y6WWrzarNqzMyMoK5uTnq1q2r2WdtbY2srCzpgiIiIqIqIWlC0rhxY1y+fFnz+MiRI2jUqJHmcVpaGpycnKQIjYiISG94HRLdJJ3UOmrUKOTl5Wkee3p6apX/+OOPnNBKREQ1Xm1fIaMPkiYkI0eOLLV83rx5VRQJERGR4dT23g19qLX3siEiIqouBiZ9oJd2vmq/Vi/tVEfV+kqtREREtUFtXyGjD7U2Ibl/q5HuStVAHefrAGrO3XNr2p1zgZp791HGaxiM17BqaryGxiEb3ZiyERERkeRqbQ8JERFRdcFVNroxISEiIjIwDtnoxiEbIiIikhx7SIiIiAyMPSS6MSEhIiIyMCYkunHIhoiIiCTHHhIiIiIDYw+JbkxIiIiIDIzLfnVjQkJERGRg7CHRjXNIiIiISHKSJiQXL17E8zcbPnz4MPr27YuWLVuia9eu+O677ySMjoiISD/yhUwvW20maULi4eGBO3fuAAAOHToEX19f5OfnY9CgQahbty769++Pn376ScoQiYiIKo0JiW6SziF5vndk7ty5GDlyJFasWKHZFx4ejqioKHTv3l2K8IiIiKiKVJs5JOfOncPgwYO19oWEhODs2bMSRURERKQf7CHRTfJVNg8ePIC5uTksLCwgl8u1yszMzPD48WOJIiMiItIPUcuTCX2QPCFxc3MD8Gz45sSJE3jppZc0ZWfPnkX9+vUlioyIiIiqiqQJSXx8vNZjJycnrcd//fUXhg8fXmobarUaarVaa1/hnhYiIiIp8cJoukmakPj6+pZaPmHCBJ1tREdH4+OPP9baN3v2bEz6oFKhERER6U1tn/+hD5IP2VRWeHg4Jk2apLVPLpdDfXeDRBERERFReVXrhCQ0NBRpaWk4ePBgiXXkcnmxQzTqYuoSERFJgZNadavWCUn9+vVhZFRtViYTERFVCIdsdKvWCUlUVJTUIRAREVUae0h0k7T7Ydy4cfj111+lDIGIiIiqAUkTkhUrVsDPzw9ubm5YsGABVCqVlOEQEREZBK/UqpvkEzT279+PN954A5988gkaNWqEPn364IcffkB+fr7UoREREemFEPrZajPJExIvLy98+umnuHXrFrZu3Qq1Wo2+ffuiYcOGmDFjBi5fvix1iERERGRgkickBUxNTREUFITY2FhcvXoVw4cPx7Zt2+Du7i51aERERJWSD5lettqs2iQkz2vUqBEiIyORmpqK2NhYqcMhIiKqFCFketlqM0kTEhcXFxgbG5dYLpPJ0K1btyqMiIiIiKQg6XVIUlNTpTw8ERFRlajtK2T0oVpfGI2IiKg2qO0rZPShWs4hISIion8X9pAQEREZWG2fkKoPTEiIiIgMjAmJbkxIiIiIDIyTWnWTCcGpNkRERIbU8rtIvbRzto9+2qmOOKmViIjIwKS4l01kZCRkMpnWplQqn4tJIDIyEs7OzrCwsICfnx/Onj2r1YZarca4ceNgb28PKysr9O7dGzdu3NCqk5mZiZCQECgUCigUCoSEhODevXvlfo9q7ZCNOr2J1CGUidzpKgAgX+UmcSRlY6S8CKDmxAvUvJgZr2ExXsOqqfEamlRzSFq2bIkDBw5oHj9/MdKFCxdiyZIl2LRpE9zc3DB37lx069YNFy5cgLW1NQAgLCwM33//PXbs2AE7OztMnjwZgYGBOHHihKat4OBg3LhxQ3Nl9Q8++AAhISH4/vvvyxVrrU1IiIiI/u1MTEy0ekUKCCHw6aefYsaMGejfvz8AYPPmzXB0dMT27dsxYsQIZGVlYf369diyZQu6du0KANi6dSsaNmyIAwcOoHv37jh//jxiY2ORlJSEdu3aAQDWrVsHHx8fXLhwoVz3o+OQDRERkYHp6142arUa9+/f19rUanWJx7106RKcnZ3h6uqKAQMG4OrVZ73yqampUKlUCAgI0NSVy+Xw9fVFYmIiAODEiRPIzc3VquPs7AxPT09NnSNHjkChUGiSEQBo3749FAqFpk5ZVauEJDMzE59++inGjBmDuXPnIi0tTeqQiIiIKk3oaYuOjtbM1SjYoqOjiz1mu3bt8OWXX+Knn37CunXroFKp0KFDB9y9excqlQoA4OjoqPUcR0dHTZlKpYKZmRlsbGxKrePg4FDk2A4ODpo6ZSXpkI2zszNOnz4NOzs7pKamokOHDgAALy8v7NmzB5988gmSkpLQvHlzKcMkIiKqFsLDwzFp0iStfXK5vNi6PXv21Pzby8sLPj4+aNq0KTZv3oz27dsDeHYT2+cJIYrsK6xwneLql6WdwiTtIVGpVMjLywMAREREoHnz5rhy5Qr279+Py5cvo3Pnzpg1a5aUIRIREVWavoZs5HI56tSpo7WVlJAUZmVlBS8vL1y6dEkzr6RwL0ZGRoam10SpVCInJweZmZml1rl9+3aRY925c6dI74su1WbI5ujRo5g1axYsLS0BPMv4Zs6ciaSkJIkjIyIiqiR9jdlUglqtxvnz5+Hk5ARXV1colUrExcVpynNycpCQkKAZrfD29oapqalWnfT0dJw5c0ZTx8fHB1lZWTh27JimztGjR5GVlaWpU1aSr7Ip6NJRq9XFjmXduXNHirCIiIj0Roplv1OmTEGvXr3QqFEjZGRkYO7cubh//z5CQ0Mhk8kQFhaGqKgoNGvWDM2aNUNUVBQsLS0RHBwMAFAoFHjvvfcwefJk2NnZwdbWFlOmTIGXl5dm1Y2Hhwd69OiB4cOHY82aNQCeLfsNDAws1woboBokJP7+/jAxMcH9+/dx8eJFtGzZUlN2/fp12NvbSxgdERFRzXTjxg0MHDgQf//9N+rVq4f27dsjKSkJLi4uAICpU6fi8ePHGD16NDIzM9GuXTvs379fcw0SAFi6dClMTEwQFBSEx48fw9/fH5s2bdK6nsm2bdswfvx4zWqc3r17Y/ny5eWOV9KEZPbs2VqPC4ZrCnz//ffo3LlzVYZERESkd1LcpGXHjh2llstkMkRGRiIyMrLEOubm5vj888/x+eefl1jH1tYWW7durWiYGtUqISls0aJFVRQJERGR4fBuv7pVm0mtRERE9O8leUKSnp6OrVu3Yt++fcjJydEqe/jwIebMmSNRZERERHoiZPrZajFJE5Lk5GS0aNECY8aMwX/+8x94enpq3WkwOzsbH3/8sYQREhERVZ4Ud/utaSRNSCIiItC/f39kZmbi9u3b6NatG3x9fXHy5EkpwyIiIqIqJumk1hMnTmDFihUwMjKCtbU1VqxYARcXF/j7++Onn35Co0aNpAyPiIhIP2p574Y+SH4dkidPnmg9njp1KoyMjBAQEIANGzZIFBUREZH+cJWNbpImJAW3MG7VqpXW/ilTpkAIgYEDB0oUGREREVUlSeeQDB48GL/99luxZR9++CHmzJmjc9hGrVbj/v37WptarTZEuERERBVTDe5lU91JmpC8//772LJlS4nlU6dORWpqaqltREdHQ6FQaG3R0dH6DpWIiKjC9HW339pM8jkklRUeHo5JkyZp7ZPL5cA/X0oUERERUSG1vHdDH6p1QhIREQGVSlXq5Fa5XP4sASmEgzZEREQ1R7VOSG7evIm0tDSpwyAiIqqk2j3cog/VOiHZvHmz1CEQERFVHodsdJL8XjZERERE1TohuX37Nm+uR0RENR+X/epUrRMSlUrFm+sREVHNx7v96iTpHJJTp06VWn7hwoUqioSIiIikJGlC8tJLL0Emk0EUc0/lgv0yWe3OCImIqPYr5r85KkTShMTOzg4LFiyAv79/seVnz55Fr169qjgqIiIiPWNCopOkCYm3tzdu3boFFxeXYsvv3btXbO8JERER1S6SJiQjRozAw4cPSyxv1KgRNm7cWIURERERGUAtn5CqD5ImJP369Su13MbGBqGhoVUUDRERkWHI2NmvU7Ve9puWloZhw4ZJHQYREVHl8DokOlXrhOSff/7h5eOJiIj+BfQ6ZPPkyRMsX74cU6ZMKVP9PXv2lFp+9epVfYRFREQkLc4h0ancCcnff/+No0ePwtTUFP7+/jA2NkZubi5WrlyJ6OhoPH36tMwJSd++fUu8DkkBXoeEiIhqvFo+3KIPMlGOdbWJiYl48803kZWVBZlMhrZt22Ljxo3o27cv8vPzERYWhmHDhsHS0rJM7dWvXx8rVqxA3759iy1PSUmBt7c38vLyyhoiERFRtdN49Sd6aeevkWX7g78mKtccklmzZqF79+44deoUJkyYgOTkZAQGBmLmzJm4dOkSxo4dW+ZkBHh2HZLff/+9xHJdvSdEREQ1Aie16lSuHhJ7e3skJCSgZcuWePToEaytrbFjxw68/fbbFTr4r7/+iocPH6JHjx7Flj98+BDHjx+Hr69vudt+qnqxQjFVNRPlZQBAvspN4kjKxkh5EUDNiReoeTEzXsNivIZVU+M1tMYr9dRDMrr29pCUaw7JP//8g3r16gEALC0tYWlpiTZt2lT44J07dy613MrKqkLJCBEREdUs5UpIZDIZHjx4AHNzc82N7x49eoT79+9r1atTp45egyQiIqrRuMpGp3IlJEIIuLm5aT1+voekIEnhJFQiIqL/wyu16lauhCQ+Pt5QcRAREdG/WLkSEs7nICIiqgD2kOhU4Su15uXlISYmBufPn4dMJoOHhwf69OkDExNJ79dHRERENVCFsoczZ86gT58+UKlUcHd3BwBcvHgR9erVw549e+Dl5VXmtpKTk/Hpp58iMTERKpUKMpkMjo6O6NChAyZOnIi2bdtWJEQiIqJqg3NIdKtQQvL++++jZcuWOH78OGxsbAAAmZmZGDJkCD744AMcOXKkTO3s3r0bQUFB8Pf3x4QJE+Do6AghBDIyMrB//3507NgR33zzDfr06VORMImIiKiGqFBC8scff2glIwBgY2ODefPm4ZVXXilzOzNnzsScOXMwffr0ImVhYWFYsGABIiIimJAQEVHNxmW/OpXr0vEF3N3dcfv27SL7MzIy8OKLZb9C6uXLl9G/f/8Sy/v27YsrV65UJEQiIqLqg5eO16lCCUlUVBTGjx+Pb7/9Fjdu3MCNGzfw7bffano17t+/r9lK07RpU+zevbvE8u+++w5NmjSpSIhERERUg1RoyCYwMBAAEBQUBJnsWTdUwS1xevXqpXms6yJpc+bMwYABA5CQkICAgAA4OjpCJpNBpVIhLi4O+/fvx44dOyoSIhERUfVRy3s39KFCCYm+LpD21ltv4ZdffsGyZcuwZMkSqFQqAIBSqYSPjw8SEhLg4+Ojl2MRERFJhatsdCt3QvL06VMcOnQIw4YNQ8OGDSsdgI+PD5MOIiKif7lyzyExMTHBJ598wvvVEBERlRUntepUoUmt/v7+OHTokJ5DKSoiIgLDhg0z+HGIiIgMigmJThWaQ9KzZ0+Eh4fjzJkz8Pb2hpWVlVZ579699RLczZs3kZaWppe2iIiIqPqqUEIyatQoAMCSJUuKlOlaWVMemzdv1llHrVZDrVZr7ZPL5TDWSwRERESVx0mtulVoyCY/P7/ErarnlkRHR0OhUGht0dHRVRoDERFRqYRMP1stVuFb8/7888/4+eefkZGRgfz8fM1+mUyG9evXV6jN3Nxc7N27F5cuXYKTkxP69etXZDiosPDwcEyaNElrn1wuBzK3VigGIiIivWMPiU4VSkg+/vhjzJkzB23btoWTk5Pm4mjl1aFDB+zbtw9169bFnTt34O/vjwsXLsDFxQVpaWmYMWMGEhMTUb9+/RLbkMvlzxKQQp5WKCIiIiKSQoWGbFavXo1Nmzbh6NGj2L17N2JiYrS2skpKSkJOTg4AYMaMGTA2Nsa1a9dw8eJF3LhxAw0aNMBHH31UkRCJiIiqDZnQz1YZ0dHRkMlkCAsL0+wTQiAyMhLOzs6wsLCAn58fzp49q/U8tVqNcePGwd7eHlZWVujduzdu3LihVSczMxMhISGaqRMhISG4d+9eueKrUEKSk5ODDh06VOSpJUpISMDcuXOhVCoBAHZ2dpg3bx4OHjyo1+MQERFVOYmX/SYnJ2Pt2rVo1aqV1v6FCxdiyZIlWL58OZKTk6FUKtGtWzc8ePBAUycsLAwxMTHYsWMHDh8+jOzsbAQGBmrNGQ0ODkZKSgpiY2MRGxuLlJQUhISElCvGCiUk77//PrZv316RpxZRMNxz7949uLq6apW5uroiPT1dL8chIiL6N8rOzsagQYOwbt062NjYaPYLIfDpp59ixowZ6N+/Pzw9PbF582Y8evRI8398VlYW1q9fj8WLF6Nr165o06YNtm7ditOnT+PAgQMAgPPnzyM2NhZffPGF5urr69atww8//IALFy6UOc4yzyF5fuJofn4+1q5diwMHDqBVq1YwNTXVqlvccuCSDBkyBHK5HLm5ubh27RpatGihKUtPT0fdunXL3BYREVF1pK9lvyVd6qK4uZQFxowZgzfffBNdu3bF3LlzNftTU1OhUqkQEBCg1Zavry8SExMxYsQInDhxArm5uVp1nJ2d4enpicTERHTv3h1HjhyBQqFAu3btNHXat28PhUKBxMREuLu7l+m1lTkhOXnypNbjl156CQBw5swZrf3lmeAaGhqq+XefPn2QnZ2tVb5z507NcYiIiGosPSUk0dHR+Pjjj7X2zZ49G5GRkcXW37FjB37//XckJycXKSu4oa2jo6PWfkdHR1y7dk1Tx8zMTKtnpaBOwfNVKhUcHByKtO/g4KCpUxZlTkj0dYff523cuLHU8sjISBgb8xJnREREQCmXuihGWloaJkyYgP3798Pc3LzENgt3JAghdHYuFK5TXP2ytPO8Cs0hqSr//PMPRo8eLXUYRERElaOnSa1yuRx16tTR2kpKSE6cOIGMjAx4e3vDxMQEJiYmSEhIwGeffQYTExNNz0jhXoyMjAxNmVKpRE5ODjIzM0utc/v27SLHv3PnTpHel9JU+4SkLJePJyIiqs6kWPbr7++P06dPIyUlRbO1bdsWgwYNQkpKCpo0aQKlUom4uDjNc3JycpCQkKBZSevt7Q1TU1OtOunp6Thz5oymjo+PD7KysnDs2DFNnaNHjyIrK6tcK3IrfKVWfdizZ0+p5VevXq2iSIiIiGoXa2treHp6au2zsrKCnZ2dZn9YWBiioqLQrFkzNGvWDFFRUbC0tERwcDAAQKFQ4L333sPkyZNhZ2cHW1tbTJkyBV5eXujatSsAwMPDAz169MDw4cOxZs0aAMAHH3yAwMDAMk9oBSROSPr27QuZTAYhSk77KnoVWCIiIird1KlT8fjxY4wePRqZmZlo164d9u/fD2tra02dpUuXwsTEBEFBQXj8+DH8/f2xadMmrTme27Ztw/jx4zWrcXr37o3ly5eXKxaZKC0bMLD69etjxYoV6Nu3b7HlKSkp8Pb2rtAN+56qXqxkdFXDRHkZAJCvcpM4krIxUl4EUHPiBWpezIzXsBivYdXUeA3Nfc5SvbRz4aOJemmnOpJ0Dom3tzd+//33Est19Z4QERHVBNXh0vHVnaRDNh9++CEePnxYYvmLL75okOXGREREVL1ImpB07ty51HIrKyv4+vpWUTREREQGUst7N/RB0oSEiIjoX4EJiU6STmolIiL6N2g+Wz+TWv/8uPZOamUPCRERkYHV9gmp+lBrExIu+zWMmrakD6h5MTNew2K8hlVT4zU4JiQ6VetLxxMREdG/Q63tISEiIqouOGSjW7VKSDIzM7F582ZcunQJTk5OCA0NRcOGDaUOi4iIqHKYkOgk6ZCNs7Mz7t69CwBITU1FixYtsGDBAly6dAlr1qyBl5cX/vzzTylDJCIioiogaUKiUqk096mJiIhA8+bNceXKFezfvx+XL19G586dMWvWLClDJCIiqjyhp60WqzZDNkePHsUXX3wBS0tLAIBcLsfMmTPxn//8R+LIiIiIKodzSHSTPCGRyWQAALVaDUdHR60yR0dH3LlzR4qwiIiI9IcJiU6SJyT+/v4wMTHB/fv3cfHiRbRs2VJTdv36ddjb20sYHREREVUFSROS2bNnaz0uGK4p8P333+u8AR8REVG1xx4SnapVQlLYokWLqigSIiIiw+EcEt14pVYiIiKSnKQJSa9evbBlyxY8fvxYyjCIiIgMi8t+dZI0Idm7dy+GDRsGJycnjBo1CidOnJAyHCIiIoOQCf1stZnkQzZ//PEHIiMj8dtvv+HVV19F69atsXz5cmRmZkodGhEREVURyRMSe3t7hIWF4dSpUzhy5Ajat2+PmTNnon79+ggODsbBgwelDpGIiKhyOGSjk+QJyfNeffVVrFmzBunp6Vi5ciXS0tLQrVs3qcMiIiKqHCYkOlWrhKSAhYUFhgwZgl9//ZU31yMiIvoXkPQ6JL6+vjAzMyu1TrNmzUotV6vVUKvVWvvkcjmMKx0dERGRfsikDqAGkLSHJD4+HnXr1q1UG9HR0VAoFFpbdHS0fgIkIiLSBw7Z6CT5vWwqKzw8HJMmTdLaJ5fLgcytEkVERESkrbYv2dWHap2QREREQKVSYcOGDSXWkcvlzxKQQp4aMjAiIiLSq2qdkNy8eRNpaWlSh0FERFQ57CHRqVonJJs3b5Y6BCIiospjQqJTtVz2S0RERP8ukick33//PWbPno0jR44AAA4ePIg33ngDPXr0wNq1ayWOjoiIqPJ4LxvdJE1IVq9ejf79+2Pv3r3o0aMHtm3bhr59+6J+/fpo3LgxwsLCsGzZMilDJCIiqjwu+9VJ0jkkn332GVauXInhw4cjPj4eb7zxBhYvXozRo0cDANq3b4+FCxdiwoQJUoZJREREBiZpD8lff/2F7t27AwBef/115OXl4bXXXtOU+/n54dq1a1KFR0REpBccstFN0oTEzs5Ok3DcunULT58+xfXr1zXl165dg62trVThERER6QeHbHSSdMimT58+eO+99xAaGoo9e/Zg8ODBmDx5MoyMjCCTyfDhhx8iICBAyhCJiIioCkiakCxYsABqtRo7duxAp06d8Nlnn2HZsmXo06cPcnNz4evry/vSEBFRjVfbh1v0QdKExMrKCuvWrdPaN2XKFIwdOxa5ubmwtraWKDIiIiI9YkKik+TXISmOubk5rK2tkZaWhmHDhkkdDhERUeVwDolO1TIhKfDPP//w8vFERET/ApIO2ezZs6fU8qtXr1ZRJERERIbDOSS6SZqQ9O3bFzKZDEKU/EnJZLIqjIiIiMgAmJDoJBOlZQMGVr9+faxYsQJ9+/YttjwlJQXe3t7Iy8ur2sCIiIj0yHv4Ur20c2LdRL20Ux1JOofE29sbv//+e4nlunpPiIiIagKZEHrZajNJh2w+/PBDPHz4sMTyF198EfHx8RVqO1/lVtGwqpSR8iIAxmtINS1mxmtYjNewamq8Ble7cwm9kDQh6dy5c6nlVlZW8PX1raJoiIiISCqSJiRERET/BlxloxsTEiIiIkNjQqJTtb4wGhEREf07MCEhIiIyMJnQz1Yeq1atQqtWrVCnTh3UqVMHPj4++PHHHzXlQghERkbC2dkZFhYW8PPzw9mzZ7XaUKvVGDduHOzt7WFlZYXevXvjxo0bWnUyMzMREhIChUIBhUKBkJAQ3Lt3r9zvkaQJSU5OjtbjK1euICwsDG+++Sbef/99nDhxQqLIiIiI9EiCe9k0aNAA8+fPx/Hjx3H8+HF06dIFffr00SQdCxcuxJIlS7B8+XIkJydDqVSiW7duePDggaaNsLAwxMTEYMeOHTh8+DCys7MRGBiodX2w4OBgpKSkIDY2FrGxsUhJSUFISEi53yJJExILCwtkZGQAeHYRtFatWiEhIQH169fHqVOn0KFDBxw7dkzKEImIiCpNih6SXr164Y033oCbmxvc3Nwwb948vPDCC0hKSoIQAp9++ilmzJiB/v37w9PTE5s3b8ajR4+wfft2AEBWVhbWr1+PxYsXo2vXrmjTpg22bt2K06dP48CBAwCA8+fPIzY2Fl988QV8fHzg4+ODdevW4YcffsCFCxfKFa+kCcnzFz2bNWsW3njjDfz+++9Yu3Ytjh07hkGDBmH27NkSRkhERFR9qNVq3L9/X2tTq9U6n5eXl4cdO3bg4cOH8PHxQWpqKlQqFQICAjR15HI5fH19kZiYCAA4ceIEcnNzteo4OzvD09NTU+fIkSNQKBRo166dpk779u2hUCg0dcqq2swhSUlJQVhYmNa9ayZMmICTJ09KGBUREZEe6GnIJjo6WjNXo2CLjo4u8bCnT5/GCy+8ALlcjpEjRyImJgYtWrSASqUCADg6OmrVd3R01JSpVCqYmZnBxsam1DoODg5Fjuvg4KCpU1aSLvuVyWSaBMTY2Bh16tTRKq9Tpw6ysrKkCI2IiEhv9HUdkvDwcEyaNElrn1wuL7G+u7s7UlJScO/ePezcuROhoaFISEj4v7gK3cBWCKHzpraF6xRXvyztFCb5kI2bmxtsbW1x69YtnD59Wqv80qVLUCqVEkVHRERUvcjlcs2qmYKttITEzMwML774Itq2bYvo6Gi0bt0ay5Yt0/zfWrgXIyMjQ9NrolQqkZOTg8zMzFLr3L59u8hx79y5U6T3RRdJe0g2btyo9bhp06Zaj5OSktCvX7+qDImIiEj/qsmN8YQQUKvVcHV1hVKpRFxcHNq0aQPg2crXhIQELFiwAMCzG+CampoiLi4OQUFBAID09HScOXMGCxcuBAD4+PggKysLx44dw6uvvgoAOHr0KLKystChQ4dyxSZpQhIaGlpq+UcffVRFkRARERmOFJeOj4iIQM+ePdGwYUM8ePAAO3bswKFDhxAbGwuZTIawsDBERUWhWbNmaNasGaKiomBpaYng4GAAgEKhwHvvvYfJkyfDzs4Otra2mDJlCry8vNC1a1cAgIeHB3r06IHhw4djzZo1AIAPPvgAgYGBcHd3L1e8vHQ8ERFRLXT79m2EhIQgPT0dCoUCrVq1QmxsLLp16wYAmDp1Kh4/fozRo0cjMzMT7dq1w/79+2Ftba1pY+nSpTAxMUFQUBAeP34Mf39/bNq0CcbGxpo627Ztw/jx4zWrcXr37o3ly5eXO16ZENWkH6kYERERUKlU2LBhQ7mfW9Nufc14Daemxcx4DYvxGlZNjdfQfAYu1ks7R76arJd2qqNq3UNy8+ZNpKWlSR0GERFRpcjypY6g+qvWCcnmzZt11lGr1UUuCiOXy2FqqKCIiIhI76rNhdEqqrwXiSEiIqpyEtzLpqapFgnJjRs3kJ2dXWR/bm4ufvnll1KfGx4ejqysLK0tPDzcUKESERGVmxT3sqlpJE1I0tPT8eqrr8LFxQV169ZFaGioVmLyzz//4PXXXy+1jfJeJIaIiKjKCaGfrRaTNCGZPn06jI2NcfToUcTGxuLcuXPw8/PTuipcNV4ERERERHoi6aTWAwcOICYmBm3btgUAdO7cGe+88w66dOmCn3/+GUDx18gnIiKqSWr7cIs+SNpDkpWVpXUXQblcjm+//RaNGzfG66+/joyMDAmjIyIi0hNOatVJ0oSkSZMmOHXqlNY+ExMT/O9//0OTJk0QGBgoUWRERERUlSRNSHr27Im1a9cW2V+QlLz00ktVHxQREZGecZWNbpLOIZk3bx4ePXpUbJmJiQl27dqFGzduVHFUREREesYFGjpJ2kNiYmKCOnXqlFhubGwMFxeXKoyIiIiIpCD5hdEeP36Mw4cP49y5c0XKnjx5gi+//FKCqIiIiPSHQza6SZqQXLx4ER4eHnjttdfg5eUFPz8/pKena8qzsrIwdOhQCSMkIiLSA66y0UnShGTatGnw8vJCRkYGLly4gDp16qBjx464fv26lGERERFRFZN0UmtiYiIOHDgAe3t72NvbY8+ePRgzZgw6d+6M+Ph4WFlZSRkeERGRXtT24RZ9kDQhefz4MUxMtENYsWIFjIyM4Ovri+3bt0sUGRERkR7lMyPRRdKEpHnz5jh+/Dg8PDy09n/++ecQQqB3794SRUZERKRHzEd0knQOSb9+/fDVV18VW7Z8+XIMHDiQN9cjIiL6F5A0IQkPD8e+fftKLF+5ciXy8/OrMCIiIiL947Jf3SQdsiEiIvpXYG+/TjLBMREiIiKD8uuxQC/tHIqdppd2qiP2kBARERlYbR9u0Ydam5Dkq9ykDqFMjJQXATBeQ6ppMTNew2K8hlVT4zU4JiQ6SX4vGyIiIqJq1UOSmZmJzZs349KlS3ByckJoaCgaNmwodVhERESVIuN0TZ0k7SFxdnbG3bt3AQCpqalo0aIFFixYgEuXLmHNmjXw8vLCn3/+KWWIRERElZevp60WkzQhUalUyMvLAwBERESgefPmuHLlCvbv34/Lly+jc+fOmDVrlpQhEhERURWoNkM2R48exRdffAFLS0sAgFwux8yZM/Gf//xH4siIiIgqh0M2ukmekMhkMgCAWq2Go6OjVpmjoyPu3LkjRVhERET6w3xEJ8kTEn9/f5iYmOD+/fu4ePEiWrZsqSm7fv067O3tJYyOiIhID9hDopOkCcns2bO1HhcM1xT4/vvv0blz56oMiYiIiCRQrRKSwhYtWlRFkRARERkOr9Sqm+RDNkRERLUeh2x0kvxKrenp6di6dSv27duHnJwcrbKHDx9izpw5EkVGREREVUXShCQ5ORktWrTAmDFj8J///Aeenp44e/aspjw7Oxsff/yxhBESERFVnixfP1ttJmlCEhERgf79+yMzMxO3b99Gt27d4Ovri5MnT0oZFhERkX4JoZ+tFpN0DsmJEyewYsUKGBkZwdraGitWrICLiwv8/f3x008/oVGjRlKGR0RERFVE8kmtT5480Xo8depUGBkZISAgABs2bJAoKiIiIj2q3Z0beiFpQuLp6YnExES0atVKa/+UKVMghMDAgQMlioyIiEh/eOl43SSdQzJ48GD89ttvxZZ9+OGHmDNnjs5hG7Vajfv372ttarXaEOESERGRgUiakLz//vvYsmVLieVTp05FampqqW1ER0dDoVBobdHR0foOlYiIqOI4qVUnyeeQVFZ4eDgmTZqktU8ulwOZ2yWKiIiIqJBavmRXH6p1QhIREQGVSlXq5Fa5XP4sASmEnz0REVUXnEOiW7VOSG7evIm0tDSpwyAiIiIDq9YJyebNm6UOgYiIqPLYQ6JTtU5IiIiIagUmJDpJusrmxo0b+PvvvzWPf/31VwwaNAidO3fGu+++iyNHjkgYHREREVUVSROSoKAgJCcnAwC+++47+Pn5ITs7Gx07dsSjR4/g6+uLH374QcoQiYiIKi9fT1stJumQzZkzZ+Dh4QHg2fVEoqKiMG3aNE358uXL8dFHHyEwMFCqEImIiCqNq2x0k7SHxMjICPfv3wcApKamomfPnlrlPXv2xIULF6QIjYiIiKqQpAmJr68vvvrqKwBAmzZtcOjQIa3y+Ph41K9fX4LIiIiI9EiCK7VGR0fjlVdegbW1NRwcHNC3b98if+QLIRAZGQlnZ2dYWFjAz88PZ8+e1aqjVqsxbtw42Nvbw8rKCr1798aNGze06mRmZiIkJERzxfSQkBDcu3evXPFKmpDMnz8f69atQ2hoKDp16oQZM2YgJCQEUVFRCA0NxdixYxERESFliERERJUnQUKSkJCAMWPGICkpCXFxcXj69CkCAgLw8OFDTZ2FCxdiyZIlWL58OZKTk6FUKtGtWzc8ePBAUycsLAwxMTHYsWMHDh8+jOzsbAQGBiIvL09TJzg4GCkpKYiNjUVsbCxSUlIQEhJSrnhlQkg7sHXlyhXMnDkTe/fuRXZ2NgDAxMQEr7zyCj788EP07du3Qu3mq9z0GKXhGCkvAmC8hlTTYma8hsV4DaumxmtoPVrN1Es7safmVvi5d+7cgYODAxISEvDaa69BCAFnZ2eEhYVp5m+q1Wo4OjpiwYIFGDFiBLKyslCvXj1s2bIF77zzDgDg1q1baNiwIfbt24fu3bvj/PnzaNGiBZKSktCuXTsAQFJSEnx8fPDnn3/C3d29TPFJfh2Spk2b4quvvoIQAhkZGcjPz4e9vT1MTU2lDo2IiEg/9PS3v1qtLnJH+5JuoVJYVlYWAMDW1hbAs7mbKpUKAQEBWm35+voiMTERI0aMwIkTJ5Cbm6tVx9nZGZ6enkhMTET37t1x5MgRKBQKTTICAO3bt4dCoUBiYmKZExJJh2yeJ5PJ4OjoCCcnJyYjRERUu+hp2W9F73AvhMCkSZPQqVMneHp6AgBUKhUAwNHRUauuo6OjpkylUsHMzAw2Njal1nFwcChyTAcHB02dspC8h+Tx48c4ceIEbG1t0aJFC62yJ0+e4JtvvsHgwYMlio6IiKjy9LXst8Q73OswduxYnDp1CocPHy4am0ym9VgIUWRfYYXrFFe/LO08T9IekosXL8LDwwOvvfYavLy84Ofnh/T0dE15VlYWhg4dKmGERERE1YdcLkedOnW0Nl0Jybhx47Bnzx7Ex8ejQYMGmv1KpRIAivRiZGRkaHpNlEolcnJykJmZWWqd27dvFznunTt3ivS+lEbShGTatGnw8vJCRkYGLly4gDp16qBjx464fv26lGERERHplwSrbIQQGDt2LHbt2oWDBw/C1dVVq9zV1RVKpRJxcXGafTk5OUhISECHDh0AAN7e3jA1NdWqk56ejjNnzmjq+Pj4ICsrC8eOHdPUOXr0KLKysjR1ykLSIZvExEQcOHAA9vb2sLe3x549ezBmzBh07twZ8fHxsLKykjI8IiIi/civ+gWtY8aMwfbt2/Hdd9/B2tpa0xOiUChgYWEBmUyGsLAwREVFoVmzZmjWrBmioqJgaWmJ4OBgTd333nsPkydPhp2dHWxtbTFlyhR4eXmha9euAAAPDw/06NEDw4cPx5o1awAAH3zwAQIDA8s8oRWQOCF5/PgxTEy0Q1ixYgWMjIzg6+uL7du3V7jtqlrKpS+M1/BqWsyM17AYr2HVtHhro1WrVgEA/Pz8tPZv3LgRQ4YMAQBMnToVjx8/xujRo5GZmYl27dph//79sLa21tRfunQpTExMEBQUhMePH8Pf3x+bNm2CsbGxps62bdswfvx4zWqc3r17Y/ny5eWKV9LrkLz66qsYN25csRdPGTt2LLZt24b79+9rXXyFiIiopunpNk13pTL48eICvbRTHUnaQ9KvXz989dVXxSYky5cvR35+PlavXl2htmvaRXkYr+HUtJgZr2ExXsOqqfEaHG+up5Okk1rDw8Oxb9++EstXrlyJ/Pxafr9lIiIikv46JERERLUee0h0YkJCRERkaBKssqlpqs2l44mIiOjfiz0kREREhiY4H1IXJiRERESGxjkkOkk+ZJOcnIxBgwbB1dUVFhYWsLS0hKurKwYNGoTjx49LHR4REVHl5Qv9bLWYpD0ku3fvRlBQEPz9/TFhwgQ4OjpCCIGMjAzs378fHTt2xDfffIM+ffpIGSYREREZmKQJycyZMzFnzhxMnz69SFlYWBgWLFiAiIgIJiRERFSzcchGJ0mHbC5fvoz+/fuXWN63b19cuXKlCiMiIiIyAAnu9lvTSJqQNG3aFLt37y6x/LvvvkOTJk2qLiAiIiKShKRDNnPmzMGAAQOQkJCAgIAAODo6QiaTQaVSIS4uDvv378eOHTukDJGIiKjyannvhj5ImpC89dZb+OWXX7Bs2TIsWbIEKpUKAKBUKuHj44OEhAT4+PhIGSIREVHl8b5sOkl+HRIfHx8mHURERP9ykickREREtR6HbHSS/MJopYmIiMCwYcOkDoOIiKhyuMpGp2rdQ3Lz5k2kpaWVWketVkOtVmvtk8vlMDVkYERERKRX1bqHZPPmzTh48GCpdaKjo6FQKLS26OjoKoqQiIioDHjpeJ2qdQ9JWYSHh2PSpEla++RyOZC5XaKIiIiItAne7VcnyROShw8fYvv27UhMTIRKpYJMJoOjoyM6duyIgQMHwsrKqtTny+XyZwlIIfzoiYio2qjlvRv6IOmQzblz5+Dm5oapU6ciMzMTjRo1QoMGDZCZmYkPP/wQ7u7uOHfunJQhEhERURWQtIdkzJgxeO2117B582aYmZlpleXk5GDIkCEYM2YM4uPjJYqQiIhID2r5Chl9kDQhOXr0KI4fP14kGQEAMzMzRERE4NVXX5UgMiIiIj3ilVp1knTIxsbGBpcuXSqx/PLly7CxsanCiIiIiEgKkvaQDB8+HKGhoZg5cya6detW5OZ6UVFRCAsLkzJEIiKiyuOQjU6SJiSRkZGwsLDAkiVLMHXqVMhkMgCAEAJKpRLTp0/H1KlTpQyRiIio0gSHbHSSfNnvtGnTMG3aNKSmpmrd7dfV1VXiyIiIiKiqSH6l1vPnz2Pjxo3IycmBj48PbGxssHDhQgwbNkznVVqJiIhqBN7LRidJe0hiY2PRp08fvPDCC3j06BFiYmIwePBgtG7dGkIIdO/eHT/99BO6dOkiZZhERESVwwuj6SRpD8mcOXPw4Ycf4u7du9i4cSOCg4MxfPhwxMXF4cCBA5g6dSrmz58vZYhERERUBSRNSM6ePYshQ4YAAIKCgvDgwQO89dZbmvKBAwfi1KlTEkVHRESkJyJfP1stJvmk1gJGRkYwNzdH3bp1Nfusra2RlZUlXVBERER6IDhko5OkPSSNGzfG5cuXNY+PHDmCRo0aaR6npaXByclJitCIiIj0hz0kOknaQzJq1Cjk5eVpHnt6emqV//jjj5zQSkRE9C8gaUIycuTIUsvnzZtXRZEQEREZDodsdKs2c0iIiIhqrVo+3KIPMiFq+ZVWiIiIJNbN6G29tBOX/z+9tFMtCSqTJ0+eiNmzZ4snT55IHUqZ1LR4hah5MTNew2K8hsV4qbphD0kZ3b9/HwqFAllZWahTp47U4ehU0+IFal7MjNewGK9hMV6qbiS/lw0RERERExIiIiKSHBMSIiIikhwTkjKSy+WYPXs25HK51KGUSU2LF6h5MTNew2K8hsV4qbrhpFYiIiKSHHtIiIiISHJMSIiIiEhyTEiIiIhIckxIiIiISHJMSJ6zcuVKuLq6wtzcHN7e3vj1119LrZ+QkABvb2+Ym5ujSZMmWL16tUHiWrVqFVq1aoU6deqgTp068PHxwY8//lhi/SFDhkAmkxXZWrZsqamzadOmYus8efLEIK+hcePGxR5vzJgxxdY/dOhQsfX//PPPSsfyyy+/oFevXnB2doZMJsPu3bu1ynft2oXu3bvD3t4eMpkMKSkpOttct24dOnfuDBsbG9jY2KBr1644duyYVp3IyMgir0epVBrkNRR3DrRv315nuzt37kSLFi0gl8vRokULxMTE6D227OxsjB07Fg0aNICFhQU8PDywatWqUtv08/Mr9nx48803NXX09f5GR0fjlVdegbW1NRwcHNC3b19cuHBBU56bm4tp06bBy8sLVlZWcHZ2xuDBg3Hr1q1S29XXd05XfAXOnz+P3r17Q6FQwNraGu3bt8f169dLbLcqz2Fdv2lCCERGRsLZ2RkWFhbw8/PD2bNndbarj/OXpMOE5P/7+uuvERYWhhkzZuDkyZPo3LkzevbsWeIXODU1FW+88QY6d+6MkydPIiIiAuPHj8fOnTv1HluDBg0wf/58HD9+HMePH0eXLl3Qp0+fEr+gy5YtQ3p6umZLS0uDra0t3n5b++ZOderU0aqXnp4Oc3NzvccPAMnJyVrHiYuLA4AiMRV24cIFrec1a9as0rE8fPgQrVu3xvLly0ss79ixI+bPn1/mNg8dOoSBAwciPj4eR44cQaNGjRAQEICbN29q1WvZsqXW6zl9+rRBXgMA9OjRQ+tY+/btK7XNI0eO4J133kFISAj++OMPhISEICgoCEePHtVrbBMnTkRsbCy2bt2K8+fPY+LEiRg3bhy+++67EtvctWuX1ms5c+YMjI2Ni5w/+nh/ExISMGbMGCQlJSEuLg5Pnz5FQEAAHj58CAB49OgRfv/9d8yaNQu///47du3ahYsXL6J3794629bHd05XfABw5coVdOrUCc2bN8ehQ4fwxx9/YNasWaUeqyrPYV2/aQsXLsSSJUuwfPlyJCcnQ6lUolu3bnjw4EGJberr/CUJSXonnWrk1VdfFSNHjtTa17x5czF9+vRi60+dOlU0b95ca9+IESNE+/btDRbj82xsbMQXX3xRproxMTFCJpOJv/76S7Nv48aNQqFQGCg63SZMmCCaNm0q8vPziy2Pj48XAERmZqZB4wAgYmJiii1LTU0VAMTJkyfL3e7Tp0+FtbW12Lx5s2bf7NmzRevWrSsWaCmKew2hoaGiT58+5WonKChI9OjRQ2tf9+7dxYABA/QaW8uWLcWcOXO09r388sti5syZZW536dKlwtraWmRnZ2v2Ger9zcjIEABEQkJCiXWOHTsmAIhr166VWMdQ37ni4nvnnXfEu+++W6l2q/IcFuL/ftPy8/OFUqkU8+fP15Q9efJEKBQKsXr16hKfb4jzl6oWe0gA5OTk4MSJEwgICNDaHxAQgMTExGKfc+TIkSL1u3fvjuPHjyM3N9dgsebl5WHHjh14+PAhfHx8yvSc9evXo2vXrnBxcdHan52dDRcXFzRo0ACBgYE4efKkIUIuIicnB1u3bsWwYcMgk8lKrdumTRs4OTnB398f8fHxVRKfPjx69Ai5ubmwtbXV2n/p0iU4OzvD1dUVAwYMwNWrVw0Ww6FDh+Dg4AA3NzcMHz4cGRkZpdYv6Zwu6TtQUZ06dcKePXtw8+ZNCCEQHx+Pixcvonv37mVuY/369RgwYACsrKy09hvi/c3KygKAIp9l4ToymQx169YttS1DfOcKx5efn4+9e/fCzc0N3bt3h4ODA9q1a1dk6EyXqjqHC/+mpaamQqVSaZ2Lcrkcvr6+pZ6LVXX+kuEwIQHw999/Iy8vD46Ojlr7HR0doVKpin2OSqUqtv7Tp0/x999/6z3G06dP44UXXoBcLsfIkSMRExODFi1a6Hxeeno6fvzxR7z//vta+5s3b45NmzZhz549+Oqrr2Bubo6OHTvi0qVLeo+9sN27d+PevXsYMmRIiXWcnJywdu1a7Ny5E7t27YK7uzv8/f3xyy+/GDw+fZg+fTrq16+Prl27ava1a9cOX375JX766SesW7cOKpUKHTp0wN27d/V+/J49e2Lbtm04ePAgFi9ejOTkZHTp0gVqtbrE55R0Tpf0Haiozz77DC1atECDBg1gZmaGHj16YOXKlejUqVOZnn/s2DGcOXOmyDltiPdXCIFJkyahU6dO8PT0LLbOkydPMH36dAQHB5d6F1pDfOeKiy8jIwPZ2dmYP38+evTogf3796Nfv37o378/EhISyty2oc/hkn7TCs638p6LVXX+kgFJ3ENTLdy8eVMAEImJiVr7586dK9zd3Yt9TrNmzURUVJTWvsOHDwsAIj09Xe8xqtVqcenSJZGcnCymT58u7O3txdmzZ3U+LyoqStjZ2Qm1Wl1qvby8PNG6dWsxbtw4fYVcooCAABEYGFju5wUGBopevXrpNRYYYMhmwYIFwsbGRvzxxx+l1svOzhaOjo5i8eLF5Wq/sNJeQ4Fbt24JU1NTsXPnzhLrmJqaiu3bt2vt27p1q5DL5XqNbdGiRcLNzU3s2bNH/PHHH+Lzzz8XL7zwgoiLiytTmx988IHw9PTUWU8f7+/o0aOFi4uLSEtLK7Y8JydH9OnTR7Rp00ZkZWWVq219fOeKi6/g92zgwIFadXv16lXm4YuqOIdL+k377bffBABx69Ytrfrvv/++6N69e4ntGeL8paplIl0qVH3Y29vD2Ni4SCadkZFRJOMuoFQqi61vYmICOzs7vcdoZmaGF198EQDQtm1bJCcnY9myZVizZk2JzxFCYMOGDQgJCYGZmVmp7RsZGeGVV14xeA/JtWvXcODAAezatavcz23fvj22bt1qgKj055NPPkFUVBQOHDiAVq1alVrXysoKXl5eVdIr5eTkBBcXl1KPVdI5XdJ3oCIeP36MiIgIxMTEaFbItGrVCikpKfjkk0+0/hovzqNHj7Bjxw7MmTNH57Eq+/6OGzcOe/bswS+//IIGDRoUKc/NzUVQUBBSU1Nx8ODBUntHilPZ71xJ8dnb28PExKRID6qHhwcOHz6ss92qOodL+k2bNm0agGc9Hk5OTpr6us7Fqjh/ybA4ZINnXwxvb2/Nyo8CcXFx6NChQ7HP8fHxKVJ///79aNu2LUxNTQ0WawEhRKnd78Cz2fiXL1/Ge++9V6b2UlJStH4ADGHjxo1wcHDQWq5ZVidPnjR4fJWxaNEi/Pe//0VsbCzatm2rs75arcb58+er5DXdvXsXaWlppR6rpHO6pO9AReTm5iI3NxdGRto/PcbGxsjPz9f5/G+++QZqtRrvvvuuzroVfX+FEBg7dix27dqFgwcPwtXVtUidgmTk0qVLOHDgQIX+CKnod05XfGZmZnjllVeKLAW+ePFikXlkhUl5Dhf8prm6ukKpVGqdizk5OUhISCj1XKyK85cMTMLemWplx44dwtTUVKxfv16cO3dOhIWFCSsrK83KlOnTp4uQkBBN/atXrwpLS0sxceJEce7cObF+/Xphamoqvv32W73HFh4eLn755ReRmpoqTp06JSIiIoSRkZHYv39/sbEVePfdd0W7du2KbTMyMlLExsaKK1euiJMnT4qhQ4cKExMTcfToUb3HXyAvL080atRITJs2rUhZ4dewdOlSERMTIy5evCjOnDkjpk+fLgCUOuRQVg8ePBAnT54UJ0+eFADEkiVLxMmTJzUrJO7evStOnjwp9u7dKwCIHTt2iJMnT2oNxYWEhGitwFqwYIEwMzMT3377rUhPT9dsDx480NSZPHmyOHTokLh69apISkoSgYGBwtraWmv1kz5ew4MHD8TkyZNFYmKiSE1NFfHx8cLHx0fUr19f3L9/v8TX8NtvvwljY2Mxf/58cf78eTF//nxhYmIikpKS9Pr++vr6ipYtW4r4+Hhx9epVsXHjRmFubi5WrlxZYmwFOnXqJN55551ij6uv93fUqFFCoVCIQ4cOaX2Wjx49EkIIkZubK3r37i0aNGggUlJStOo8PzRa+DXo6zunKz4hhNi1a5cwNTUVa9euFZcuXRKff/65MDY2Fr/++muJ8VXlOazrN23+/PlCoVCIXbt2idOnT4uBAwcKJyenKjl/STpMSJ6zYsUK4eLiIszMzMTLL7+stYwuNDRU+Pr6atU/dOiQaNOmjTAzMxONGzcWq1atMkhcw4YN08RVr1494e/vr/nilhTbvXv3hIWFhVi7dm2xbYaFhYlGjRpp2gwICCgyh0bffvrpJwFAXLhwoUhZ4dewYMEC0bRpU2Fubi5sbGxEp06dxN69e/USR8GS4sJbaGioEOLZ8sziymfPnq1pw9fXV1NfCCFcXFx0Puedd94RTk5OwtTUVDg7O4v+/fuXaR5QeV/Do0ePREBAgKhXr54wNTUVjRo1EqGhoeL69etabRR+DUII8b///U+4u7sLU1NT0bx58wolgLre3/T0dDFkyBDh7OwszM3Nhbu7u1i8eLHWEvDiYrtw4YIAoHXuP09f729xsQMQGzduFEL839yi4rb4+PgSX4O+vnO64iuwfv168eKLLwpzc3PRunVrsXv3bq1yKc9hXb9p+fn5Yvbs2UKpVAq5XC5ee+01cfr06VLjF0I/5y9JRyaEEHrscCEiIiIqN84hISIiIskxISEiIiLJMSEhIiIiyTEhISIiIskxISEiIiLJMSEhIiIiyTEhISIiIskxISGqYfz8/BAWFiZ1GEREesWEhIg01qxZg9atW8PKygp169ZFmzZtsGDBAqnDIqJ/Ad7tl6iWy83NLdMNH9evX49Jkybhs88+g6+vL9RqNU6dOoVz585VQZRE9G/HHhKiGig/Px9Tp06Fra0tlEolIiMjNWUymQyrV69Gnz59YGVlhblz5+LQoUOQyWTYu3cvWrduDXNzc7Rr1w6nT5/WPO/7779HUFAQ3nvvPbz44oto2bIlBg4ciP/+978SvEIi+rdhQkJUA23evBlWVlY4evQoFi5ciDlz5mjden327Nno06cPTp8+jWHDhmn2f/jhh/jkk0+QnJwMBwcH9O7dG7m5uQAApVKJpKQkXLt2rcpfDxERExKiGqhVq1aYPXs2mjVrhsGDB6Nt27b4+eefNeXBwcEYNmwYmjRpAhcXF83+2bNno1u3bvDy8sLmzZtx+/ZtxMTEaMrq1q2Lxo0bw93dHUOGDME333yD/Pz8Kn99RPTvw4SEqAZq1aqV1mMnJydkZGRoHrdt27bY5/n4+Gj+bWtrC3d3d5w/f17TxpEjR3D69GmMHz8eubm5CA0NRY8ePZiUEJHBMSEhqoEKT1KVyWRaSYOVlVWZ25LJZFqPPT09MWbMGGzbtg1xcXGIi4tDQkJC5QImItKBCQnRv0hSUpLm35mZmbh48SKaN29eYv0WLVoAAB4+fGjw2Ijo343Lfon+RebMmQM7Ozs4OjpixowZsLe3R9++fQEAo0aNgrOzM7p06YIGDRogPT0dc+fORb169bSGeoiIDIE9JET/IvPnz8eECRPg7e2N9PR07NmzB2ZmZgCArl27IikpCW+//Tbc3Nzw1ltvwdzcHD///DPs7OwkjpyIajuZEEJIHQQRGdahQ4fw+uuvIzMzE3Xr1pU6HCKiIthDQkRERJJjQkJERESS45ANERERSY49JERERCQ5JiREREQkOSYkREREJDkmJERERCQ5JiREREQkOSYkREREJDkmJERERCQ5JiREREQkOSYkREREJLn/BwyF9RxEKU51AAAAAElFTkSuQmCC", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "if(make_heatmap):\n", " #aggregator data frame\n", - " GFP_max = pd.DataFrame(columns = ['hrpS_conc', 'hrpR_conc', 'GFP_max'])\n", + " GFP_data = []\n", " #Different initial values of R and S\n", " conc_hrpR = np.linspace(0, 30, 9)\n", " conc_hrpS = np.linspace(0, 30, 9)\n", @@ -710,12 +838,14 @@ " x0[\"protein_hrpR\"] = conc_R\n", " for conc_S in conc_hrpS:\n", " x0[\"protein_hrpS\"] = conc_S #Change my initial condition dictionary\n", - " Re1 = CRN_extract_1.simulate_with_bioscrape(timepoints, initial_condition_dict = x0)\n", + " Re1 = CRN_extract_1.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0)\n", " #now we are simulating over and over again, but only taking the final protein_GFP value\n", " if Re1 is not None:\n", - " GFP_max = GFP_max.append({'hrpS_conc':conc_S,\n", - " 'hrpR_conc':conc_R,\n", - " 'GFP_max': Re1[\"protein_GFP\"].values[-1]}, ignore_index=True)\n", + " GFP_data.append( \n", + " {'hrpS_conc':conc_S, 'hrpR_conc':conc_R,\n", + " 'GFP_max': Re1[\"protein_GFP\"].values[-1]})\n", + " GFP_max = pd.DataFrame(GFP_data, columns = ['hrpS_conc', 'hrpR_conc', 'GFP_max'])\n", + " \n", " #now, you make a 2d plot with all the data\n", " if GFP_max is not None:\n", " data = pd.pivot_table(data = GFP_max, index = 'hrpS_conc',\n", @@ -762,36 +892,35 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mixture.add_component: DNAassembly: mydna rna_mydna\n", - "mixture.update_species: DNAassembly: mydna rna_mydna\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\integrate\\odepack.py:248: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", - " warnings.warn(warning_msg, ODEintWarning)\n", - "odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500...odeint failed with mxstep=500..." + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/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", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -831,7 +960,7 @@ "\n", "if(make_heatmap):\n", " #aggregator data frame\n", - " GFP_max = pd.DataFrame(columns = ['hrpS_conc', 'hrpR_conc', 'GFP_max'])\n", + " GFP_data = []\n", " #Different initial values of R and S\n", " conc_hrpR = np.linspace(0, 30, 9)\n", " conc_hrpS = np.linspace(0, 30, 9)\n", @@ -841,11 +970,12 @@ " x0[\"protein_hrpR\"] = conc_R \n", " for conc_S in conc_hrpS:\n", " x0[\"protein_hrpS\"] = conc_S #Change my initial condition dictionary\n", - " Re1 = CRN_extract_1.simulate_with_bioscrape(timepoints, initial_condition_dict = x0)\n", + " Re1 = CRN_extract_1.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0)\n", " #now we are simulating over and over again, but only taking the final protein_GFP value\n", - " GFP_max = GFP_max.append({'hrpS_conc':conc_S,\n", + " GFP_data.append({'hrpS_conc':conc_S,\n", " 'hrpR_conc':conc_R,\n", - " 'GFP_max': Re1[\"protein_GFP\"].values[-1]}, ignore_index=True)\n", + " 'GFP_max': Re1[\"protein_GFP\"].values[-1]})\n", + " GFP_max = pd.DataFrame(GFP_data, columns = ['hrpS_conc', 'hrpR_conc', 'GFP_max'])\n", "\n", " #now, you make a 2d plot with all the data\n", " data = pd.pivot_table(data = GFP_max, index = 'hrpS_conc',\n", @@ -875,9 +1005,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/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", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n", + " result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "newparam = {\n", " (None, 'phrpL_hrpR', 'cooperativity'): 1.0, \n", @@ -909,7 +1066,7 @@ "\n", "if(make_heatmap):\n", " #aggregator data frame\n", - " GFP_max = pd.DataFrame(columns = ['hrpS_conc', 'hrpR_conc', 'GFP_max'])\n", + " GFP_data = []\n", " #Different initial values of R and S\n", " conc_hrpR = np.linspace(0,30, 9)\n", " conc_hrpS = np.linspace(0, 30, 9)\n", @@ -917,11 +1074,12 @@ " x0[\"protein_hrpR\"] = conc_R \n", " for conc_S in conc_hrpS:\n", " x0[\"protein_hrpS\"] = conc_S #Change my initial condition dictionary\n", - " Re1 = CRN_extract_1.simulate_with_bioscrape(timepoints, initial_condition_dict = x0)\n", + " Re1 = CRN_extract_1.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0)\n", " #now we are simulating over and over again, but only taking the final protein_GFP value\n", - " GFP_max = GFP_max.append({'hrpS_conc':conc_S,\n", + " GFP_data.append({'hrpS_conc':conc_S,\n", " 'hrpR_conc':conc_R,\n", - " 'GFP_max': Re1[\"protein_GFP\"].values[-1]}, ignore_index=True)\n", + " 'GFP_max': Re1[\"protein_GFP\"].values[-1]})\n", + " GFP_max = pd.DataFrame(GFP_data, columns = ['hrpS_conc', 'hrpR_conc', 'GFP_max'])\n", "\n", " #now, you make a 2d plot with all the data\n", " data = pd.pivot_table(data = GFP_max, index = 'hrpS_conc',\n", @@ -948,7 +1106,7 @@ "metadata": { "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -962,9 +1120,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.12.4" } }, "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "nbformat_minor": 4 +} diff --git a/examples/Specialized Tutorials/5. TxTl Toolbox.ipynb b/examples/Specialized Tutorials/5. TxTl Toolbox.ipynb index c54372d0..2ceca1b9 100644 --- a/examples/Specialized Tutorials/5. TxTl Toolbox.ipynb +++ b/examples/Specialized Tutorials/5. TxTl Toolbox.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -68,20 +68,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/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", + "/Users/murray/miniconda3/envs/python3.12-biocrnpyler/lib/python3.12/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -103,7 +103,6 @@ " timepoints = np.arange(0, maxtime, 100)\n", " R = CRN.simulate_with_bioscrape_via_sbml(timepoints)\n", " if R is not None:\n", - " %matplotlib inline\n", " import pylab as plt\n", " plt.plot(timepoints, R[str(E.ntps.get_species())], label = E.ntps.get_species())\n", " plt.plot(timepoints, R[str(E.amino_acids.get_species())], label = E.amino_acids.get_species())\n", @@ -124,17 +123,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -249,16 +240,22 @@ "]\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/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": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -274,7 +271,6 @@ " timepoints = np.arange(0, maxtime, 100)\n", " R = CRN.simulate_with_bioscrape_via_sbml(timepoints)\n", " if R is not None:\n", - " %matplotlib inline\n", " plt.subplot(121)\n", " plt.plot(timepoints, R[str(E.ntps.get_species())], label = E.ntps.get_species())\n", " plt.plot(timepoints, R[str(E.amino_acids.get_species())], label = E.amino_acids.get_species())\n", @@ -301,7 +297,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -315,7 +311,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.12.4" } }, "nbformat": 4,